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34cbf272-0d79-412e-a858-d13e1d7325ff	craspell-a-contextual-typo-robust-approach-to	null	null	https://aclanthology.org/2022.findings-acl.237	https://aclanthology.org/2022.findings-acl.237.pdf	CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction	Recently, Bert-based models have dominated the research of Chinese spelling correction (CSC). These methods have two limitations: (1) they have poor performance on multi-typo texts. In such texts, the context of each typo contains at least one misspelled character, which brings noise information. Such noisy context leads to the declining performance on multi-typo texts. (2) they tend to overcorrect valid expressions to more frequent expressions due to the masked token recovering task of Bert. We attempt to address these limitations in this paper. To make our model robust to contextual noise brought by typos, our approach first constructs a noisy context for each training sample. Then the correction model is forced to yield similar outputs based on the noisy and original contexts. Moreover, to address the overcorrection problem, copy mechanism is incorporated to encourage our model to prefer to choose the input character when the miscorrected and input character are both valid according to the given context. Experiments are conducted on widely used benchmarks. Our model achieves superior performance against state-of-the-art methods by a remarkable gain.	['Shengli Sun', 'TingHao Yu', 'Huihui Cai', 'Tao Yang', 'Tianchi Yue', 'Shengkang Song', 'Shulin Liu']	null	null	null	null	findings-acl-2022-5	['spelling-correction']	['natural-language-processing']	[ 4.56540614e-01 -4.83336926e-01 -3.22485358e-01 -2.21011087e-01 -7.82743216e-01 -3.38233054e-01 2.74959683e-01 2.48659417e-01 -6.18790925e-01 9.79562104e-01 3.30177009e-01 -2.97627151e-01 5.45787513e-01 -6.51605546e-01 -5.05053878e-01 -7.19120502e-01 6.05373442e-01 1.35138288e-01 4.34853852e-01 -3.16208035e-01 8.24050605e-01 -5.79261370e-02 -1.48573065e+00 6.22781754e-01 1.44044363e+00 4.04243499e-01 4.46399361e-01 4.47909802e-01 -6.81112051e-01 7.17828751e-01 -8.22982848e-01 -3.56841832e-01 -4.02254015e-02 -6.18369460e-01 -7.26820171e-01 -1.46473378e-01 3.07898045e-01 -4.68998775e-03 -5.72523549e-02 1.65925872e+00 4.58591133e-01 2.56035868e-02 4.96741921e-01 -8.17248762e-01 -9.07207251e-01 9.28456724e-01 -6.32916987e-01 2.65839458e-01 3.03422391e-01 -1.02362312e-01 1.02789390e+00 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02d26e63-afb2-4316-873b-1026f33bebec	protein-language-models-and-structure	2211.16742	null	https://arxiv.org/abs/2211.16742v1	https://arxiv.org/pdf/2211.16742v1.pdf	Protein Language Models and Structure Prediction: Connection and Progression	The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have proved the power of language models (LMs) in processing the protein sequence databases, which inherit the advantages of attention networks and capture useful information in learning representations for proteins. The past two years have witnessed remarkable success in tertiary protein structure prediction (PSP), including evolution-based and single-sequence-based PSP. It seems that instead of using energy-based models and sampling procedures, protein language model (pLM)-based pipelines have emerged as mainstream paradigms in PSP. Despite the fruitful progress, the PSP community needs a systematic and up-to-date survey to help bridge the gap between LMs in the natural language processing (NLP) and PSP domains and introduce their methodologies, advancements and practical applications. To this end, in this paper, we first introduce the similarities between protein and human languages that allow LMs extended to pLMs, and applied to protein databases. Then, we systematically review recent advances in LMs and pLMs from the perspectives of network architectures, pre-training strategies, applications, and commonly-used protein databases. Next, different types of methods for PSP are discussed, particularly how the pLM-based architectures function in the process of protein folding. Finally, we identify challenges faced by the PSP community and foresee promising research directions along with the advances of pLMs. This survey aims to be a hands-on guide for researchers to understand PSP methods, develop pLMs and tackle challenging problems in this field for practical purposes.	['Stan Z. Li', 'Yongjie Xu', 'Yufei Huang', 'Cheng Tan', 'Jiangbin Zheng', 'Jun Xia', 'Bozhen Hu']	2022-11-30	null	null	null	null	['protein-language-model', 'protein-folding']	['medical', 'natural-language-processing']	[ 5.16022682e-01 -9.60289389e-02 -3.44020873e-01 -3.29043627e-01 -3.77521217e-01 -4.15931165e-01 1.08444333e-01 3.82537901e-01 -4.95696485e-01 1.05407941e+00 -3.44360396e-02 -4.71613228e-01 1.13824094e-02 -4.88892704e-01 -8.35437894e-01 -9.99800861e-01 -1.45006612e-01 4.34910238e-01 4.73249778e-02 -2.92099774e-01 1.99424535e-01 9.43561912e-01 -1.27103007e+00 5.34350872e-01 9.45639014e-01 5.40464997e-01 8.10054302e-01 4.30757523e-01 -6.46276355e-01 8.01886261e-01 -3.05244237e-01 -3.81097049e-01 -1.91546246e-01 -5.02321601e-01 -1.05443442e+00 -3.11053753e-01 -1.74669921e-01 4.16639954e-01 -1.72324553e-01 7.55787730e-01 5.77416897e-01 3.25448997e-02 6.01162136e-01 -8.36734414e-01 -1.03104270e+00 8.89477730e-02 -2.31147647e-01 5.28772362e-02 4.49727833e-01 4.62990910e-01 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8.06079090e-01 2.17184812e-01 -4.35216650e-02 -7.36357689e-01 -6.76963270e-01 -1.01038861e+00 -2.20650196e-01 1.15455464e-01 3.82594913e-02 -4.44627285e-01 1.54580548e-01 3.49083185e-01]	[4.687937259674072, 5.64888858795166]
7d32ce9b-de71-4eab-b42e-34df199df918	investigating-active-learning-sampling	null	null	https://aclanthology.org/2022.lrec-1.490	https://aclanthology.org/2022.lrec-1.490.pdf	Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification	Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate, even more so if they deal with domain specific language. In this work, we aim to build classifiers on these datasets using Active Learning in order to reduce the labeling effort. We outline the challenges when dealing with extreme multi-label settings and show the limitations of existing Active Learning strategies by focusing on their effectiveness as well as efficiency in terms of computational cost. In addition, we present five multi-label datasets which were compiled from hierarchical classification tasks to serve as benchmarks in the context of extreme multi-label classification for future experiments. Finally, we provide insight into multi-class, multi-label evaluation and present an improved classifier architecture on top of pre-trained transformer language models.	['Jasmina Bogojeska', 'Jonas Kuhn', 'Katsiaryna Mirylenka', 'Lukas Wertz']	null	null	null	null	lrec-2022-6	['multi-label-text-classification', 'extreme-multi-label-classification', 'multi-label-text-classification']	['methodology', 'methodology', 'natural-language-processing']	[ 5.89065671e-01 2.87877202e-01 -4.46897805e-01 -7.83761322e-01 -1.36746728e+00 -8.41844499e-01 6.24751449e-01 5.50521076e-01 -5.54578722e-01 8.37872624e-01 -7.72451013e-02 -2.75593579e-01 -5.31269722e-02 -6.09515846e-01 -2.16416478e-01 -6.48420036e-01 2.91718900e-01 9.43727911e-01 1.19511485e-01 -5.84795885e-02 5.14775589e-02 1.98951796e-01 -1.66873300e+00 8.70633245e-01 6.76069379e-01 8.26142848e-01 -7.89442062e-02 4.36826169e-01 -3.33275825e-01 1.37376308e+00 -4.95230258e-01 -8.36846650e-01 -3.14663574e-02 -1.26401797e-01 -1.24639261e+00 2.12607831e-01 6.62456632e-01 2.32712239e-01 4.63547230e-01 9.29692209e-01 5.34629703e-01 1.05495550e-01 8.21048141e-01 -1.37518251e+00 -3.11599076e-01 9.31068599e-01 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ca9741ab-1019-4244-a53e-1a08bf0637f9	actc-active-threshold-calibration-for-cold	2305.06395	null	https://arxiv.org/abs/2305.06395v2	https://arxiv.org/pdf/2305.06395v2.pdf	ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion	Self-supervised knowledge-graph completion (KGC) relies on estimating a scoring model over (entity, relation, entity)-tuples, for example, by embedding an initial knowledge graph. Prediction quality can be improved by calibrating the scoring model, typically by adjusting the prediction thresholds using manually annotated examples. In this paper, we attempt for the first time cold-start calibration for KGC, where no annotated examples exist initially for calibration, and only a limited number of tuples can be selected for annotation. Our new method ACTC finds good per-relation thresholds efficiently based on a limited set of annotated tuples. Additionally to a few annotated tuples, ACTC also leverages unlabeled tuples by estimating their correctness with Logistic Regression or Gaussian Process classifiers. We also experiment with different methods for selecting candidate tuples for annotation: density-based and random selection. Experiments with five scoring models and an oracle annotator show an improvement of 7% points when using ACTC in the challenging setting with an annotation budget of only 10 tuples, and an average improvement of 4% points over different budgets.	['Benjamin Roth', 'Anastasiia Sedova']	2023-05-10	null	null	null	null	['knowledge-graph-completion']	['knowledge-base']	[-4.50517163e-02 7.28863060e-01 -5.40245414e-01 -7.07046211e-01 -1.33614659e+00 -8.64180386e-01 2.07288146e-01 8.81270945e-01 -3.81869495e-01 7.72617698e-01 4.39132228e-02 1.14787305e-02 -6.40693977e-02 -8.03057373e-01 -7.92132080e-01 -2.60081798e-01 -3.00442666e-01 1.56096649e+00 4.96094763e-01 2.23309502e-01 -2.43279710e-01 6.38025552e-02 -1.21118450e+00 4.18321282e-01 7.35690236e-01 1.01571250e+00 -3.95339042e-01 7.02778518e-01 -1.97725490e-01 6.15221798e-01 -6.62756205e-01 -1.11642694e+00 3.26752096e-01 -2.06016898e-01 -8.91853452e-01 -1.31838145e-02 1.68655843e-01 2.71494184e-02 2.25528046e-01 8.53504062e-01 2.59118199e-01 -1.74668252e-01 5.97310126e-01 -1.30305457e+00 -2.83114016e-01 1.09376967e+00 -1.70672700e-01 -4.60758060e-01 4.31682169e-01 -7.08436817e-02 1.51515448e+00 -9.86396134e-01 6.80745840e-01 1.12828183e+00 7.37938643e-01 2.47644484e-01 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526d06c8-87d7-4330-93ac-e0fe1280a114	semantic-segmentation-enhanced-transformer	2301.11022	null	https://arxiv.org/abs/2301.11022v1	https://arxiv.org/pdf/2301.11022v1.pdf	Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction	Saliency Prediction aims to predict the attention distribution of human eyes given an RGB image. Most of the recent state-of-the-art methods are based on deep image feature representations from traditional CNNs. However, the traditional convolution could not capture the global features of the image well due to its small kernel size. Besides, the high-level factors which closely correlate to human visual perception, e.g., objects, color, light, etc., are not considered. Inspired by these, we propose a Transformer-based method with semantic segmentation as another learning objective. More global cues of the image could be captured by Transformer. In addition, simultaneously learning the object segmentation simulates the human visual perception, which we would verify in our investigation of human gaze control in cognitive science. We build an extra decoder for the subtask and the multiple tasks share the same Transformer encoder, forcing it to learn from multiple feature spaces. We find in practice simply adding the subtask might confuse the main task learning, hence Multi-task Attention Module is proposed to deal with the feature interaction between the multiple learning targets. Our method achieves competitive performance compared to other state-of-the-art methods.	['Shuo Zhang']	2023-01-26	null	null	null	null	['saliency-prediction']	['computer-vision']	[ 2.66215175e-01 8.84601027e-02 2.65579879e-01 -3.26421529e-01 -1.39403805e-01 -1.53310508e-01 3.04266036e-01 -1.37037620e-01 -3.55011731e-01 5.18846095e-01 4.83318232e-02 -4.77656946e-02 -1.25844590e-02 -5.61498046e-01 -9.57175910e-01 -8.28229249e-01 6.79528892e-01 -1.57868154e-02 7.21166670e-01 -8.74342024e-02 5.10792792e-01 4.95650992e-02 -1.82510555e+00 2.92282104e-01 1.03976059e+00 1.26072240e+00 9.28566158e-01 3.16221178e-01 -5.64509630e-02 7.59073913e-01 -3.75278234e-01 -1.70043111e-01 -4.35873633e-03 -5.96171558e-01 -7.00076461e-01 1.03167571e-01 3.72320354e-01 -9.18685570e-02 -2.88928926e-01 1.29418230e+00 3.99593413e-01 1.58091724e-01 5.21811903e-01 -1.25471604e+00 -9.74971354e-01 1.30008399e-01 -6.84585512e-01 3.09261113e-01 1.35567248e-01 2.19364852e-01 8.15710843e-01 -8.47164214e-01 2.58355886e-01 1.19414771e+00 1.79416567e-01 4.84232754e-01 -8.65201950e-01 -5.86066067e-01 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35e8f3d0-74c9-4f43-843d-fdfd285ede75	propagation-map-reconstruction-via	2207.13473	null	https://arxiv.org/abs/2207.13473v2	https://arxiv.org/pdf/2207.13473v2.pdf	Propagation Map Reconstruction via Interpolation Assisted Matrix Completion	Constructing a propagation map from a set of scattered measurements finds important applications in many areas, such as localization, spectrum monitoring and management. Classical interpolation-type methods have poor performance in regions with very sparse measurements. Recent advance in matrix completion has the potential to reconstruct a propagation map from sparse measurements, but the spatial resolution is limited. This paper proposes to integrate interpolation with matrix completion to exploit both the spatial correlation and the potential low rank structure of the propagation map. The proposed method first enriches matrix observations using interpolation, and develops the statistics of the interpolation error based on a local polynomial regression model. Then, two uncertainty-aware matrix completion algorithms are developed to exploit the interpolation error statistics. It is numerically demonstrated that the proposed method outperforms Kriging and other state-of-the-art schemes, and reduces the mean squared error (MSE) of propagation map reconstruction by 10%-50% for a medium to large number of measurements.	['Junting Chen', 'Hao Sun']	2022-07-27	null	null	null	null	['matrix-completion']	['methodology']	[ 1.67818740e-01 -3.82871360e-01 2.91591614e-01 -2.02453658e-01 -1.12335098e+00 -3.05583298e-01 3.95975292e-01 2.69484341e-01 -2.22248644e-01 1.09531033e+00 1.99010774e-01 -2.37910658e-01 -6.95441604e-01 -8.01711857e-01 -5.48203588e-01 -9.26494122e-01 -5.05777359e-01 8.75494108e-02 -2.04546824e-02 -2.34501973e-01 2.17905700e-01 4.15397108e-01 -9.28817809e-01 -2.71895796e-01 1.09386468e+00 1.14764094e+00 2.19092086e-01 3.67901593e-01 3.04982185e-01 7.10293889e-01 -3.57550293e-01 9.32928845e-02 1.56805411e-01 -1.69106603e-01 -2.39288330e-01 -1.83485180e-01 -3.17977630e-02 -3.32313091e-01 -4.64847863e-01 1.07826185e+00 4.35008705e-01 1.32745892e-01 4.46368515e-01 -8.43394637e-01 -3.83379728e-01 5.38649380e-01 -7.05689609e-01 -4.24266346e-02 5.00849068e-01 -5.66821694e-01 4.38900948e-01 -1.29029822e+00 2.02945486e-01 6.91294014e-01 1.20405865e+00 -4.99030799e-01 -1.57929921e+00 -6.78934813e-01 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9a538159-e720-45cc-b947-72110db0f66b	mf-pam-accurate-pitch-estimation-through	2306.09640	null	https://arxiv.org/abs/2306.09640v1	https://arxiv.org/pdf/2306.09640v1.pdf	MF-PAM: Accurate Pitch Estimation through Periodicity Analysis and Multi-level Feature Fusion	We introduce Multi-level feature Fusion-based Periodicity Analysis Model (MF-PAM), a novel deep learning-based pitch estimation model that accurately estimates pitch trajectory in noisy and reverberant acoustic environments. Our model leverages the periodic characteristics of audio signals and involves two key steps: extracting pitch periodicity using periodic non-periodic convolution (PNP-Conv) blocks and estimating pitch by aggregating multi-level features using a modified bi-directional feature pyramid network (BiFPN). We evaluate our model on speech and music datasets and achieve superior pitch estimation performance compared to state-of-the-art baselines while using fewer model parameters. Our model achieves 99.20 % accuracy in pitch estimation on a clean musical dataset. Overall, our proposed model provides a promising solution for accurate pitch estimation in challenging acoustic environments and has potential applications in audio signal processing.	['Hong-Goo Kang', 'Soo-Whan Chung', 'Doyeon Kim', 'Woo-Jin Chung']	2023-06-16	null	null	null	null	['audio-signal-processing']	['audio']	[-2.50959426e-01 -5.41179538e-01 2.84413427e-01 3.05079874e-02 -1.34207678e+00 -5.66882789e-01 -5.22977673e-03 -2.50883214e-02 -2.32632756e-01 1.75942898e-01 7.07971573e-01 3.34184766e-01 -2.17929319e-01 -4.65289682e-01 -5.15128136e-01 -6.55592561e-01 -7.99510717e-01 -2.73582339e-01 -1.61014512e-01 -1.55607119e-01 -1.41486228e-02 2.53066152e-01 -1.66451919e+00 3.55201364e-01 2.30030999e-01 1.15198457e+00 1.32939786e-01 1.33136797e+00 4.72170174e-01 5.96542299e-01 -9.07089055e-01 -1.41417563e-01 1.48930088e-01 -3.03483009e-01 -5.69176435e-01 -5.60337067e-01 3.98910612e-01 -3.69946301e-01 -2.26792872e-01 9.33511317e-01 1.17151916e+00 4.23046380e-01 2.71959186e-01 -5.11931896e-01 -1.98179260e-01 1.22733402e+00 -5.06115556e-01 4.88599837e-01 4.99528497e-01 8.42865556e-02 1.48468614e+00 -1.10090327e+00 -2.35015512e-01 1.07574940e+00 1.40584147e+00 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-1.29676402e+00 -9.42000091e-01 -1.82447627e-01 9.66725171e-01 -4.10173178e-01 -3.49363774e-01 1.47271916e-01]	[15.586923599243164, 5.65280818939209]
d8c2052c-8824-4107-9f72-4ed7f1f7da4b	synthetic-tumors-make-ai-segment-tumors	2210.14845	null	https://arxiv.org/abs/2210.14845v1	https://arxiv.org/pdf/2210.14845v1.pdf	Synthetic Tumors Make AI Segment Tumors Better	We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and texture, which even medical professionals can confuse with real tumors; (2) effective for AI model training, which can perform liver tumor segmentation similarly to a model trained on real tumors - this result is unprecedented because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to the model trained on real tumors. This result also implies that manual efforts for developing per-voxel annotation of tumors (which took years to create) can be considerably reduced for training AI models in the future. Moreover, our synthetic tumors have the potential to improve the success rate of small tumor detection by automatically generating enormous examples of small (or tiny) synthetic tumors.	['Zongwei Zhou', 'Alan Yuille', 'Jie-Neng Chen', 'Shuwen Sun', 'Yixiong Chen', 'Junfei Xiao', 'Qixin Hu']	2022-10-26	null	null	null	null	['tumor-segmentation']	['computer-vision']	[ 3.09874892e-01 1.01604295e+00 8.59559327e-02 -1.42341316e-01 -8.45753074e-01 -2.38447100e-01 6.46963298e-01 1.14672408e-01 -2.82736029e-02 8.88868511e-01 2.29115188e-02 -4.19776917e-01 3.89629632e-01 -9.34546351e-01 -5.01931906e-01 -7.14173019e-01 -3.89410816e-02 1.06991696e+00 3.59882802e-01 1.86057631e-02 -2.29116827e-01 5.54559708e-01 -9.67857361e-01 9.25610214e-02 1.14163029e+00 6.52886927e-01 1.62526697e-01 5.37110984e-01 -5.14471829e-02 7.16772437e-01 -5.35774052e-01 -3.27595383e-01 2.05209374e-01 -5.75023413e-01 -7.70909071e-01 4.78993654e-01 3.85278076e-01 -2.18006089e-01 -8.70907307e-02 9.00936127e-01 1.24459706e-01 -5.79993188e-01 9.36427534e-01 -1.02117193e+00 -5.12321770e-01 7.30177760e-01 -4.31742072e-01 -2.89988041e-01 1.03064455e-01 4.59113985e-01 3.72963667e-01 -6.15612984e-01 8.96584809e-01 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e89249e7-7748-4530-857a-baaa939ac0fc	multi-type-conversational-question-answer	2210.12979	null	https://arxiv.org/abs/2210.12979v1	https://arxiv.org/pdf/2210.12979v1.pdf	Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions	Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to the problem of data scarcity. In this paper, we introduce a novel method to synthesize data for CQA with various question types, including open-ended, closed-ended, and unanswerable questions. We design a different generation flow for each question type and effectively combine them in a single, shared framework. Moreover, we devise a hierarchical answerability classification (hierarchical AC) module that improves quality of the synthetic data while acquiring unanswerable questions. Manual inspections show that synthetic data generated with our framework have characteristics very similar to those of human-generated conversations. Across four domains, CQA systems trained on our synthetic data indeed show good performance close to the systems trained on human-annotated data.	['Gary Geunbae Lee', 'Yunsu Kim', 'Seonjeong Hwang']	2022-10-24	null	null	null	null	['question-answer-generation']	['natural-language-processing']	[ 7.52562359e-02 6.20296001e-01 6.59703374e-01 -5.58468223e-01 -1.31041372e+00 -9.77911830e-01 6.37578130e-01 8.44872184e-03 5.14058210e-03 8.94733250e-01 6.76823080e-01 -4.79488611e-01 1.46355838e-01 -8.74394298e-01 -3.35563123e-01 -4.21759225e-02 5.05217135e-01 8.33795249e-01 2.53434092e-01 -7.31833398e-01 1.56912103e-01 -2.91878134e-01 -1.58884132e+00 8.99796665e-01 1.61709273e+00 6.49342537e-01 1.50239691e-01 1.15867972e+00 -6.45648599e-01 1.26601386e+00 -1.15008318e+00 -8.33612323e-01 -5.64455166e-02 -9.70131457e-01 -1.59491277e+00 1.22583240e-01 4.46081996e-01 -3.91127199e-01 1.23181287e-02 4.82652366e-01 4.76123780e-01 1.41299203e-01 4.21517581e-01 -1.50985062e+00 -6.36789441e-01 5.85856080e-01 4.52289820e-01 -2.49642819e-01 1.39098465e+00 4.08027917e-01 1.29128599e+00 -7.23442614e-01 7.73659289e-01 1.36409795e+00 5.50134540e-01 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6f4ce30b-db76-4d4f-b32f-20bd5f2cfcf3	on-the-robustness-of-reading-comprehension-1	null	null	https://openreview.net/forum?id=lXczoncSyt0	https://openreview.net/pdf?id=lXczoncSyt0	On the Robustness of Reading Comprehension Models to Entity Renaming	We study the robustness of machine reading comprehension (MRC) models to entity renaming---do models make more wrong predictions when answer entities have different names? Such failures imply that models overly rely on entity information to answer questions, and thus may generalize poorly when facts about the world change or questions are asked about novel entities. To systematically audit this issue, we present a general and scalable pipeline to replace entity names with names from a variety of sources, ranging from common English names to names from other languages to arbitrary strings. Across five datasets and three pretrained model architectures, MRC models consistently perform worse when entities are renamed, with particularly large accuracy drops on datasets constructed via distant supervision. We also find large differences between models: SpanBERT, which is pretrained with span-level masking, is more robust than RoBERTa, despite having similar accuracy on unperturbed test data. We further experiment with different masking strategies as the continual pretraining objective and find that entity-based masking can improve the robustness of MRC models.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['continual-pretraining']	['methodology']	[ 2.77147353e-01 4.77552861e-01 -3.76453176e-02 -5.74835062e-01 -8.90886188e-01 -9.43532646e-01 5.52186906e-01 2.98917621e-01 -7.03823507e-01 9.64661896e-01 6.69350982e-01 -5.75637519e-01 -6.02038726e-02 -9.66734827e-01 -1.03607762e+00 6.39107898e-02 1.21344313e-01 6.51361763e-01 4.03853357e-01 -5.92129469e-01 1.68961525e-01 3.35226096e-02 -1.29062986e+00 4.07395363e-01 1.12622404e+00 1.81327909e-01 -2.17014953e-01 6.37643397e-01 -2.58560348e-02 1.09821677e+00 -8.51866663e-01 -8.22767496e-01 9.34255868e-02 -2.86045104e-01 -1.48247004e+00 -6.88052595e-01 9.15716469e-01 -2.55098611e-01 -4.95447755e-01 7.55540252e-01 4.73790675e-01 2.37083107e-01 5.08045316e-01 -8.34852219e-01 -1.35455024e+00 1.10555625e+00 -1.27672579e-03 7.39777029e-01 9.14320588e-01 3.00661296e-01 1.42031395e+00 -8.06291580e-01 9.57214773e-01 1.26545000e+00 1.04985619e+00 5.62113643e-01 -1.46772778e+00 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-1.18708658e+00 -1.19858151e-02 7.78975427e-01 5.69034815e-01 -5.18857777e-01 -6.77513838e-01 -1.30586877e-01]	[10.955958366394043, 8.145346641540527]
0d5677c2-65f2-4ce1-9475-2dc679235873	progressive-seed-generation-auto-encoder-for-1	2112.05213	null	https://arxiv.org/abs/2112.05213v1	https://arxiv.org/pdf/2112.05213v1.pdf	Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning	With the development of 3D scanning technologies, 3D vision tasks have become a popular research area. Owing to the large amount of data acquired by sensors, unsupervised learning is essential for understanding and utilizing point clouds without an expensive annotation process. In this paper, we propose a novel framework and an effective auto-encoder architecture named "PSG-Net" for reconstruction-based learning of point clouds. Unlike existing studies that used fixed or random 2D points, our framework generates input-dependent point-wise features for the latent point set. PSG-Net uses the encoded input to produce point-wise features through the seed generation module and extracts richer features in multiple stages with gradually increasing resolution by applying the seed feature propagation module progressively. We prove the effectiveness of PSG-Net experimentally; PSG-Net shows state-of-the-art performances in point cloud reconstruction and unsupervised classification, and achieves comparable performance to counterpart methods in supervised completion.	['Junmo Kim', 'Haeil Lee', 'Doyeon Kim', 'Pyunghwan Ahn', 'JuYoung Yang']	2021-12-09	progressive-seed-generation-auto-encoder-for	http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Progressive_Seed_Generation_Auto-Encoder_for_Unsupervised_Point_Cloud_Learning_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Progressive_Seed_Generation_Auto-Encoder_for_Unsupervised_Point_Cloud_Learning_ICCV_2021_paper.pdf	iccv-2021-1	['point-cloud-reconstruction', '3d-point-cloud-linear-classification']	['computer-vision', 'computer-vision']	[ 1.40255511e-01 4.72478904e-02 -9.63929072e-02 -5.48131943e-01 -8.35241854e-01 -3.47213596e-01 7.61226296e-01 5.73337600e-02 -1.83885783e-01 1.60330951e-01 2.54950151e-02 -7.08253756e-02 3.24616209e-02 -8.51522744e-01 -1.12185025e+00 -2.72707045e-01 -1.89465404e-01 7.35764682e-01 2.70146340e-01 4.29430492e-02 2.67654836e-01 9.68675196e-01 -1.84005237e+00 3.72442119e-02 7.62363911e-01 1.01637423e+00 6.54228091e-01 3.59718680e-01 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ef07be92-0ec0-49cc-9842-e0803ec5a247	efficient-neural-architecture-search-for-end	2011.05649	null	https://arxiv.org/abs/2011.05649v1	https://arxiv.org/pdf/2011.05649v1.pdf	Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients	Neural Architecture Search (NAS), the process of automating architecture engineering, is an appealing next step to advancing end-to-end Automatic Speech Recognition (ASR), replacing expert-designed networks with learned, task-specific architectures. In contrast to early computational-demanding NAS methods, recent gradient-based NAS methods, e.g., DARTS (Differentiable ARchiTecture Search), SNAS (Stochastic NAS) and ProxylessNAS, significantly improve the NAS efficiency. In this paper, we make two contributions. First, we rigorously develop an efficient NAS method via Straight-Through (ST) gradients, called ST-NAS. Basically, ST-NAS uses the loss from SNAS but uses ST to back-propagate gradients through discrete variables to optimize the loss, which is not revealed in ProxylessNAS. Using ST gradients to support sub-graph sampling is a core element to achieve efficient NAS beyond DARTS and SNAS. Second, we successfully apply ST-NAS to end-to-end ASR. Experiments over the widely benchmarked 80-hour WSJ and 300-hour Switchboard datasets show that the ST-NAS induced architectures significantly outperform the human-designed architecture across the two datasets. Strengths of ST-NAS such as architecture transferability and low computation cost in memory and time are also reported.	['Zhijian Ou', 'Keyu An', 'Huahuan Zheng']	2020-11-11	null	null	null	null	['graph-sampling']	['graphs']	[ 1.20091148e-01 1.79982074e-02 1.45602554e-01 -3.93027484e-01 -1.02855432e+00 -5.00028789e-01 3.18218499e-01 -4.71545935e-01 -3.35059524e-01 3.88464004e-01 2.22040251e-01 -6.99962735e-01 -2.53351361e-01 -3.48673224e-01 -8.08602810e-01 -3.48260581e-01 -1.92073390e-01 4.59415704e-01 -1.12838387e-01 -3.55519801e-01 -7.10949898e-02 6.18158042e-01 -1.16181660e+00 1.20355792e-01 7.35256135e-01 1.16555166e+00 2.35161856e-01 8.79148722e-01 -2.50394791e-01 6.90588117e-01 -4.50303465e-01 -5.09006977e-01 3.91034335e-01 -4.67426538e-01 -6.65414751e-01 -9.49682221e-02 6.39536560e-01 -1.34005457e-01 -6.08128846e-01 8.68094206e-01 7.54804254e-01 5.84097683e-01 2.74367183e-01 -1.14835858e+00 -7.84394562e-01 9.49583769e-01 -3.92384231e-01 2.96162575e-01 -1.84741184e-01 4.38386321e-01 1.52963626e+00 -1.51846051e+00 1.84312180e-01 1.42986679e+00 8.58689845e-01 8.63876700e-01 -1.10641849e+00 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1.13420822e-02 5.24392664e-01 -3.45902890e-01 -5.44255257e-01 -2.40869537e-01]	[8.731154441833496, 3.3932509422302246]
a271ed53-892b-4f8c-b6a0-935ecda3f69b	a-wrong-answer-or-a-wrong-question-an	2010.06835	null	https://arxiv.org/abs/2010.06835v2	https://arxiv.org/pdf/2010.06835v2.pdf	A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering	The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area. In this paper, we show that question rewriting (QR) of the conversational context allows to shed more light on this phenomenon and also use it to evaluate robustness of different answer selection approaches. We introduce a simple framework that enables an automated analysis of the conversational question answering (QA) performance using question rewrites, and present the results of this analysis on the TREC CAsT and QuAC (CANARD) datasets. Our experiments uncover sensitivity to question formulation of the popular state-of-the-art models for reading comprehension and passage ranking. Our results demonstrate that the reading comprehension model is insensitive to question formulation, while the passage ranking changes dramatically with a little variation in the input question. The benefit of QR is that it allows us to pinpoint and group such cases automatically. We show how to use this methodology to verify whether QA models are really learning the task or just finding shortcuts in the dataset, and better understand the frequent types of error they make.	['Raviteja Anantha', 'Zhucheng Tu', 'Shayne Longpre', 'Svitlana Vakulenko']	2020-10-13	null	https://aclanthology.org/2020.scai-1.2	https://aclanthology.org/2020.scai-1.2.pdf	emnlp-scai-2020-11	['passage-ranking', 'question-rewriting', 'answer-selection']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 3.28263015e-01 2.10278541e-01 2.06602842e-01 -4.29456532e-01 -1.10193288e+00 -1.00296366e+00 8.50603759e-01 6.36123776e-01 -4.90399271e-01 6.19936228e-01 5.29222488e-01 -7.61076033e-01 -4.88944590e-01 -6.92462862e-01 -7.14829683e-01 -2.93368459e-01 1.60264954e-01 5.50854385e-01 8.00176382e-01 -8.86382520e-01 7.05760717e-01 -6.75978586e-02 -1.85229576e+00 7.62573481e-01 1.23686290e+00 5.47192335e-01 2.18402132e-01 1.25707138e+00 -3.45825821e-01 1.19410312e+00 -8.04614007e-01 -6.52543783e-01 -1.74273536e-01 -7.14774132e-01 -1.60090482e+00 -4.05863971e-01 7.99485624e-01 3.05745024e-02 1.07981309e-01 8.16386640e-01 2.95613497e-01 7.73572922e-02 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e98ceff4-e291-4e96-bca1-4424d71ce2f0	inductive-mutual-information-estimation-a	2102.13182	null	https://arxiv.org/abs/2102.13182v3	https://arxiv.org/pdf/2102.13182v3.pdf	MIND: Inductive Mutual Information Estimation, A Convex Maximum-Entropy Copula Approach	We propose a novel estimator of the mutual information between two ordinal vectors $x$ and $y$. Our approach is inductive (as opposed to deductive) in that it depends on the data generating distribution solely through some nonparametric properties revealing associations in the data, and does not require having enough data to fully characterize the true joint distributions $P_{x, y}$. Specifically, our approach consists of (i) noting that $I\left(y; x\right) = I\left(u_y; u_x\right)$ where $u_y$ and $u_x$ are the copula-uniform dual representations of $y$ and $x$ (i.e. their images under the probability integral transform), and (ii) estimating the copula entropies $h\left(u_y\right)$, $h\left(u_x\right)$ and $h\left(u_y, u_x\right)$ by solving a maximum-entropy problem over the space of copula densities under a constraint of the type $\alpha_m = E\left[\phi_m(u_y, u_x)\right]$. We prove that, so long as the constraint is feasible, this problem admits a unique solution, it is in the exponential family, and it can be learned by solving a convex optimization problem. The resulting estimator, which we denote MIND, is marginal-invariant, always non-negative, unbounded for any sample size $n$, consistent, has MSE rate $O(1/n)$, and is more data-efficient than competing approaches. Beyond mutual information estimation, we illustrate that our approach may be used to mitigate mode collapse in GANs by maximizing the entropy of the copula of fake samples, a model we refer to as Copula Entropy Regularized GAN (CER-GAN).	['Yves-Laurent Kom Samo']	2021-02-25	null	null	null	null	['mutual-information-estimation']	['methodology']	[ 1.93090156e-01 2.83935845e-01 -3.36846918e-01 -9.84582528e-02 -1.05993831e+00 -7.26476073e-01 1.24156095e-01 -3.22200209e-01 -3.91975164e-01 1.12456572e+00 -2.87762225e-01 -3.58254075e-01 -4.45362866e-01 -1.12271917e+00 -9.87631381e-01 -9.01652753e-01 -4.20547485e-01 3.37571323e-01 -4.90390867e-01 2.18150258e-01 -1.85638852e-02 -6.82894737e-02 -1.51823914e+00 -5.88847816e-01 1.01743865e+00 1.22285891e+00 -9.44819897e-02 7.41730630e-01 2.27107465e-01 5.92644930e-01 -5.12335122e-01 -7.55346119e-01 2.64675021e-01 -8.34183514e-01 -5.73015988e-01 -2.02993855e-01 -1.50448263e-01 -3.39235693e-01 3.53823267e-02 1.48315012e+00 2.80047655e-02 -7.74457455e-02 1.16695487e+00 -1.44090962e+00 -7.33369887e-01 7.60339379e-01 -1.04355836e+00 -2.74748117e-01 3.13379079e-01 -1.33178726e-01 1.37017560e+00 -3.63253087e-01 5.15167713e-01 8.12224030e-01 7.53653109e-01 4.96826530e-01 -1.64849889e+00 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b84ade75-d69f-4b6e-99cc-76515175c0b8	barfed-byzantine-attack-resistant-federated	2111.04550	null	https://arxiv.org/abs/2111.04550v2	https://arxiv.org/pdf/2111.04550v2.pdf	ARFED: Attack-Resistant Federated averaging based on outlier elimination	In federated learning, each participant trains its local model with its own data and a global model is formed at a trusted server by aggregating model updates coming from these participants. Since the server has no effect and visibility on the training procedure of the participants to ensure privacy, the global model becomes vulnerable to attacks such as data poisoning and model poisoning. Although many defense algorithms have recently been proposed to address these attacks, they often make strong assumptions that do not agree with the nature of federated learning, such as assuming Non-IID datasets. Moreover, they mostly lack comprehensive experimental analyses. In this work, we propose a defense algorithm called ARFED that does not make any assumptions about data distribution, update similarity of participants, or the ratio of the malicious participants. ARFED mainly considers the outlier status of participant updates for each layer of the model architecture based on the distance to the global model. Hence, the participants that do not have any outlier layer are involved in model aggregation. We have performed extensive experiments on diverse scenarios and shown that the proposed approach provides a robust defense against different attacks. To test the defense capability of the ARFED in different conditions, we considered label flipping, Byzantine, and partial knowledge attacks for both IID and Non-IID settings in our experimental evaluations. Moreover, we proposed a new attack, called organized partial knowledge attack, where malicious participants use their training statistics collaboratively to define a common poisoned model. We have shown that organized partial knowledge attacks are more effective than independent attacks.	['Altan Kocyigit', 'Gorkem Polat', 'Ece Isik-Polat']	2021-11-08	null	null	null	null	['model-posioning']	['adversarial']	[-4.57915753e-01 -2.82053620e-01 -1.49553902e-02 -3.01351666e-01 -4.52547401e-01 -9.94508624e-01 5.59158385e-01 4.57788140e-01 -4.69858825e-01 5.74313521e-01 -3.29418689e-01 -2.98397094e-01 -8.20576996e-02 -8.78969669e-01 -7.34200299e-01 -9.82430995e-01 -1.77772760e-01 6.49161279e-01 3.36738348e-01 1.18282884e-01 2.47392785e-02 6.18412137e-01 -1.01394594e+00 1.82873100e-01 6.05621397e-01 9.25761819e-01 -7.79514849e-01 3.32862616e-01 -5.05049638e-02 7.76160300e-01 -1.10538530e+00 -5.82625568e-01 6.95037425e-01 -2.90711045e-01 -4.10042167e-01 -2.76229650e-01 1.62446231e-01 -5.29669762e-01 -3.44725221e-01 1.22415626e+00 4.14792657e-01 -2.79500335e-01 3.96642685e-01 -1.90327716e+00 -1.49445355e-01 9.04613733e-01 -4.52499092e-01 1.89371239e-02 7.35321417e-02 3.36616904e-01 5.15434861e-01 -2.86047488e-01 3.50619316e-01 1.01210415e+00 6.94493055e-01 6.53578341e-01 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3f528bd6-9c68-4832-9f94-75e9c27091c1	learning-to-separate-object-sounds-by	1804.01665	null	http://arxiv.org/abs/1804.01665v2	http://arxiv.org/pdf/1804.01665v2.pdf	Learning to Separate Object Sounds by Watching Unlabeled Video	Perceiving a scene most fully requires all the senses. Yet modeling how objects look and sound is challenging: most natural scenes and events contain multiple objects, and the audio track mixes all the sound sources together. We propose to learn audio-visual object models from unlabeled video, then exploit the visual context to perform audio source separation in novel videos. Our approach relies on a deep multi-instance multi-label learning framework to disentangle the audio frequency bases that map to individual visual objects, even without observing/hearing those objects in isolation. We show how the recovered disentangled bases can be used to guide audio source separation to obtain better-separated, object-level sounds. Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video. We obtain state-of-the-art results on visually-aided audio source separation and audio denoising. Our video results: http://vision.cs.utexas.edu/projects/separating_object_sounds/	['Kristen Grauman', 'Ruohan Gao', 'Rogerio Feris']	2018-04-05	learning-to-separate-object-sounds-by-1	http://openaccess.thecvf.com/content_ECCV_2018/html/Ruohan_Gao_Learning_to_Separate_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Ruohan_Gao_Learning_to_Separate_ECCV_2018_paper.pdf	eccv-2018-9	['audio-denoising', 'audio-source-separation']	['audio', 'audio']	[ 3.54065746e-01 -5.36976635e-01 9.98838320e-02 -3.55372019e-02 -1.62061191e+00 -9.11688149e-01 4.58386727e-03 -1.44607261e-01 4.71826605e-02 3.25712591e-01 5.17834842e-01 4.48811382e-01 -1.54034168e-01 -1.00483536e-03 -9.28516865e-01 -9.42140400e-01 -2.30555937e-01 -6.35343865e-02 5.87390326e-02 2.14454725e-01 4.77487557e-02 1.75745726e-01 -1.98455644e+00 9.43640053e-01 3.40564102e-01 9.75956202e-01 2.58498937e-01 1.48095441e+00 1.58289805e-01 1.03812003e+00 -4.17481124e-01 1.05954096e-01 2.96243578e-01 -4.35157835e-01 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ce20841e-2fb9-42fd-b620-cd45152e825a	tttflow-unsupervised-test-time-training-with	2210.11389	null	https://arxiv.org/abs/2210.11389v1	https://arxiv.org/pdf/2210.11389v1.pdf	TTTFlow: Unsupervised Test-Time Training with Normalizing Flow	A major problem of deep neural networks for image classification is their vulnerability to domain changes at test-time. Recent methods have proposed to address this problem with test-time training (TTT), where a two-branch model is trained to learn a main classification task and also a self-supervised task used to perform test-time adaptation. However, these techniques require defining a proxy task specific to the target application. To tackle this limitation, we propose TTTFlow: a Y-shaped architecture using an unsupervised head based on Normalizing Flows to learn the normal distribution of latent features and detect domain shifts in test examples. At inference, keeping the unsupervised head fixed, we adapt the model to domain-shifted examples by maximizing the log likelihood of the Normalizing Flow. Our results show that our method can significantly improve the accuracy with respect to previous works.	['Christian Desrosiers', 'Ismail Ben Ayed', 'Milad Cheraghalikhani', 'Mehrdad Noori', 'Gustavo A. Vargas Hakim', 'David Osowiechi']	2022-10-20	null	null	null	null	['source-free-domain-adaptation']	['computer-vision']	[ 3.0208811e-01 -1.5865959e-02 -4.0950471e-01 -7.3278284e-01 -3.9191499e-01 -5.2303374e-01 5.6479239e-01 -2.3236895e-01 -4.8162940e-01 6.8489403e-01 -4.1078755e-01 -4.7208610e-01 -1.6696896e-01 -8.2596338e-01 -8.1867319e-01 -6.6809690e-01 1.2657082e-02 5.7217747e-01 5.4839909e-01 2.1778971e-01 3.0043268e-01 5.4801780e-01 -1.4264866e+00 4.3859020e-01 1.0648825e+00 1.2398789e+00 -9.3050517e-02 5.8758682e-01 -1.8489049e-01 6.3886380e-01 -9.4776100e-01 -3.6539990e-01 5.3577352e-01 -6.1156183e-01 -6.2227261e-01 2.4688038e-01 5.3295165e-01 -2.8511462e-01 -9.7850993e-02 1.0920480e+00 4.0205118e-01 1.4328845e-01 1.0083667e+00 -1.4488416e+00 -5.0474042e-01 3.2306689e-01 -3.6440054e-01 3.2049444e-01 -1.7446722e-01 2.4152383e-01 4.7896123e-01 -7.0621598e-01 4.7301909e-01 1.0191976e+00 5.5214781e-01 6.6122752e-01 -1.2966654e+00 -7.7646315e-01 2.2814430e-01 5.5352569e-01 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f3f10883-20c0-422c-9dcb-e27ff0d75209	paxion-patching-action-knowledge-in-video	2305.10683	null	https://arxiv.org/abs/2305.10683v3	https://arxiv.org/pdf/2305.10683v3.pdf	Paxion: Patching Action Knowledge in Video-Language Foundation Models	Action knowledge involves the understanding of textual, visual, and temporal aspects of actions. We introduce the Action Dynamics Benchmark (ActionBench) containing two carefully designed probing tasks: Action Antonym and Video Reversal, which targets multimodal alignment capabilities and temporal understanding skills of the model, respectively. Despite recent video-language models' (VidLM) impressive performance on various benchmark tasks, our diagnostic tasks reveal their surprising deficiency (near-random performance) in action knowledge, suggesting that current models rely on object recognition abilities as a shortcut for action understanding. To remedy this, we propose a novel framework, Paxion, along with a new Discriminative Video Dynamics Modeling (DVDM) objective. The Paxion framework utilizes a Knowledge Patcher network to encode new action knowledge and a Knowledge Fuser component to integrate the Patcher into frozen VidLMs without compromising their existing capabilities. Due to limitations of the widely-used Video-Text Contrastive (VTC) loss for learning action knowledge, we introduce the DVDM objective to train the Knowledge Patcher. DVDM forces the model to encode the correlation between the action text and the correct ordering of video frames. Our extensive analyses show that Paxion and DVDM together effectively fill the gap in action knowledge understanding (~50% to 80%), while maintaining or improving performance on a wide spectrum of both object- and action-centric downstream tasks.	['Heng Ji', 'Mohit Bansal', 'Zineng Tang', 'Jaemin Cho', 'Genglin Liu', 'Sha Li', 'Ansel Blume', 'Zhenhailong Wang']	2023-05-18	null	null	null	null	['action-understanding', 'object-recognition']	['computer-vision', 'computer-vision']	[ 1.97716281e-01 -1.23976909e-01 -6.43217146e-01 -1.07393317e-01 -5.46250582e-01 -7.94906437e-01 9.49234307e-01 -2.89554209e-01 -4.41991508e-01 1.77590370e-01 5.26013672e-01 -3.12721372e-01 -1.74218029e-01 -1.57629192e-01 -9.22384918e-01 -4.33102459e-01 7.95443580e-02 3.29146236e-01 3.73785377e-01 -5.30734695e-02 2.43509606e-01 2.15729311e-01 -1.47697401e+00 9.21491742e-01 6.52643144e-01 9.00949776e-01 2.17922717e-01 8.05280268e-01 -2.56481525e-02 1.50712442e+00 -1.68334052e-01 -4.94844496e-01 3.34043860e-01 -3.73682618e-01 -1.20556855e+00 5.35574257e-02 6.96458101e-01 -7.29911387e-01 -8.01328719e-01 7.07527578e-01 1.38653114e-01 4.22691524e-01 4.91287947e-01 -1.45387709e+00 -7.87271261e-01 7.19316602e-01 -5.30572832e-01 7.34394550e-01 5.83420396e-01 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b678d23d-7464-4df8-97b4-6000b48d80c1	vision-language-transformers-a-survey	2307.03254	null	https://arxiv.org/abs/2307.03254v1	https://arxiv.org/pdf/2307.03254v1.pdf	Vision Language Transformers: A Survey	Vision language tasks, such as answering questions about or generating captions that describe an image, are difficult tasks for computers to perform. A relatively recent body of research has adapted the pretrained transformer architecture introduced in \citet{vaswani2017attention} to vision language modeling. Transformer models have greatly improved performance and versatility over previous vision language models. They do so by pretraining models on a large generic datasets and transferring their learning to new tasks with minor changes in architecture and parameter values. This type of transfer learning has become the standard modeling practice in both natural language processing and computer vision. Vision language transformers offer the promise of producing similar advancements in tasks which require both vision and language. In this paper, we provide a broad synthesis of the currently available research on vision language transformer models and offer some analysis of their strengths, limitations and some open questions that remain.	['Casey Kennington', 'Clayton Fields']	2023-07-06	null	null	null	null	['transfer-learning']	['miscellaneous']	[ 3.96001995e-01 1.64943337e-01 -4.98404726e-02 -6.60944045e-01 -6.14749134e-01 -5.31345069e-01 1.07200468e+00 -3.51737201e-01 -5.19785166e-01 4.94847536e-01 2.11562335e-01 -5.58820248e-01 4.14236516e-01 -7.34288096e-01 -7.75006235e-01 -3.86368901e-01 4.84645665e-01 5.87065101e-01 3.22161376e-01 -1.49261564e-01 1.21015109e-01 2.66092181e-01 -1.52104688e+00 5.62979400e-01 5.09889424e-01 8.02935123e-01 5.76684773e-01 7.19168007e-01 -4.93521094e-01 1.25604606e+00 -2.86885500e-01 -5.70648372e-01 -9.32671409e-03 -3.22960764e-01 -1.08478999e+00 2.65771598e-01 1.06164896e+00 -4.38467354e-01 -5.58996022e-01 9.84231412e-01 3.18824291e-01 -9.93637666e-02 6.60062790e-01 -1.13168955e+00 -1.46473265e+00 4.08015907e-01 -3.53176206e-01 2.68567175e-01 2.07998291e-01 3.22352380e-01 1.02907848e+00 -1.13692296e+00 3.43480468e-01 1.32022500e+00 6.61902547e-01 1.05264115e+00 -1.16653442e+00 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-9.39166129e-01 4.89678346e-02 2.25414142e-01 5.46912968e-01 -4.04609799e-01 5.33306673e-02 6.75479949e-01]	[10.676665306091309, 1.6563628911972046]
5d05e279-4ca3-4043-88a7-7469b72161ae	on-multitask-loss-function-for-audio-event	2009.05527	null	https://arxiv.org/abs/2009.05527v1	https://arxiv.org/pdf/2009.05527v1.pdf	On Multitask Loss Function for Audio Event Detection and Localization	Audio event localization and detection (SELD) have been commonly tackled using multitask models. Such a model usually consists of a multi-label event classification branch with sigmoid cross-entropy loss for event activity detection and a regression branch with mean squared error loss for direction-of-arrival estimation. In this work, we propose a multitask regression model, in which both (multi-label) event detection and localization are formulated as regression problems and use the mean squared error loss homogeneously for model training. We show that the common combination of heterogeneous loss functions causes the network to underfit the data whereas the homogeneous mean squared error loss leads to better convergence and performance. Experiments on the development and validation sets of the DCASE 2020 SELD task demonstrate that the proposed system also outperforms the DCASE 2020 SELD baseline across all the detection and localization metrics, reducing the overall SELD error (the combined metric) by approximately 10% absolute.	['Alfred Mertins', 'Philipp Koch', 'Ngoc Q. K. Duong', 'Huy Phan', 'Lam Pham', 'Ian McLoughlin']	2020-09-11	null	null	null	null	['direction-of-arrival-estimation']	['audio']	[ 1.11347526e-01 4.27479148e-02 5.45533225e-02 -3.36091101e-01 -1.64362931e+00 -2.74399549e-01 4.16965514e-01 4.28091437e-01 -7.03085661e-01 6.81355476e-01 -1.33534297e-01 1.43973202e-01 -2.36667469e-02 -2.91908175e-01 -7.06865132e-01 -7.39253759e-01 -2.33935878e-01 3.46447043e-02 4.39539850e-01 3.04631561e-01 -2.05786437e-01 4.18022424e-02 -1.27686357e+00 3.70595187e-01 5.56819797e-01 1.47015691e+00 4.96940687e-03 5.74467599e-01 4.85497296e-01 1.04358196e+00 -8.60295594e-01 -3.35700691e-01 -1.81239203e-01 -1.94701985e-01 -5.94362319e-01 -2.88669407e-01 2.10883766e-01 6.28526285e-02 -9.57890302e-02 6.50772154e-01 1.01105618e+00 -3.06368386e-03 7.25037694e-01 -1.49059737e+00 3.25756580e-01 3.95317435e-01 -5.72587430e-01 4.44839597e-01 2.77298540e-01 -4.32451785e-01 1.11706412e+00 -1.13871527e+00 1.35859236e-01 8.98010552e-01 1.20791018e+00 -1.06700391e-01 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ebad0b72-fb1a-43bb-96a2-1bec3de8734e	fair-contrastive-learning-for-facial	2203.16209	null	https://arxiv.org/abs/2203.16209v1	https://arxiv.org/pdf/2203.16209v1.pdf	Fair Contrastive Learning for Facial Attribute Classification	Learning visual representation of high quality is essential for image classification. Recently, a series of contrastive representation learning methods have achieved preeminent success. Particularly, SupCon outperformed the dominant methods based on cross-entropy loss in representation learning. However, we notice that there could be potential ethical risks in supervised contrastive learning. In this paper, we for the first time analyze unfairness caused by supervised contrastive learning and propose a new Fair Supervised Contrastive Loss (FSCL) for fair visual representation learning. Inheriting the philosophy of supervised contrastive learning, it encourages representation of the same class to be closer to each other than that of different classes, while ensuring fairness by penalizing the inclusion of sensitive attribute information in representation. In addition, we introduce a group-wise normalization to diminish the disparities of intra-group compactness and inter-class separability between demographic groups that arouse unfair classification. Through extensive experiments on CelebA and UTK Face, we validate that the proposed method significantly outperforms SupCon and existing state-of-the-art methods in terms of the trade-off between top-1 accuracy and fairness. Moreover, our method is robust to the intensity of data bias and effectively works in incomplete supervised settings. Our code is available at https://github.com/sungho-CoolG/FSCL.	['Hyeran Byun', 'Dohyung Kim', 'Sunhee Hwang', 'Pilhyeon Lee', 'Jewook Lee', 'Sungho Park']	2022-03-30	null	http://openaccess.thecvf.com//content/CVPR2022/html/Park_Fair_Contrastive_Learning_for_Facial_Attribute_Classification_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Park_Fair_Contrastive_Learning_for_Facial_Attribute_Classification_CVPR_2022_paper.pdf	cvpr-2022-1	['facial-attribute-classification']	['computer-vision']	[ 2.79730678e-01 1.06896318e-01 -4.74756479e-01 -5.83901644e-01 -6.74996316e-01 -1.73989236e-01 4.87559497e-01 3.36638510e-01 -3.70368600e-01 7.49189913e-01 3.26642722e-01 -5.10309115e-02 -3.10284585e-01 -5.78387976e-01 -4.10104871e-01 -8.18089306e-01 1.15378618e-01 -1.19959176e-01 -5.15733302e-01 8.66272748e-02 3.44258070e-01 4.92008597e-01 -1.64000905e+00 2.23950997e-01 1.03532016e+00 1.28842485e+00 -3.33770305e-01 -1.25153229e-01 1.38201445e-01 1.01508570e+00 -4.83342439e-01 -9.11105096e-01 4.03781205e-01 -4.97180760e-01 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698c876e-f284-4126-914b-f3f8d3181884	when-automatic-voice-disguise-meets-automatic	2009.06863	null	https://arxiv.org/abs/2009.06863v1	https://arxiv.org/pdf/2009.06863v1.pdf	When Automatic Voice Disguise Meets Automatic Speaker Verification	The technique of transforming voices in order to hide the real identity of a speaker is called voice disguise, among which automatic voice disguise (AVD) by modifying the spectral and temporal characteristics of voices with miscellaneous algorithms are easily conducted with softwares accessible to the public. AVD has posed great threat to both human listening and automatic speaker verification (ASV). In this paper, we have found that ASV is not only a victim of AVD but could be a tool to beat some simple types of AVD. Firstly, three types of AVD, pitch scaling, vocal tract length normalization (VTLN) and voice conversion (VC), are introduced as representative methods. State-of-the-art ASV methods are subsequently utilized to objectively evaluate the impact of AVD on ASV by equal error rates (EER). Moreover, an approach to restore disguised voice to its original version is proposed by minimizing a function of ASV scores w.r.t. restoration parameters. Experiments are then conducted on disguised voices from Voxceleb, a dataset recorded in real-world noisy scenario. The results have shown that, for the voice disguise by pitch scaling, the proposed approach obtains an EER around 7% comparing to the 30% EER of a recently proposed baseline using the ratio of fundamental frequencies. The proposed approach generalizes well to restore the disguise with nonlinear frequency warping in VTLN by reducing its EER from 34.3% to 18.5%. However, it is difficult to restore the source speakers in VC by our approach, where more complex forms of restoration functions or other paralinguistic cues might be necessary to restore the nonlinear transform in VC. Finally, contrastive visualization on ASV features with and without restoration illustrate the role of the proposed approach in an intuitive way.	['Meng Sun', 'Jiakang Li', 'Xiongwei Zhang', 'Thomas Fang Zheng', 'Linlin Zheng']	2020-09-15	null	null	null	null	['miscellaneous']	['miscellaneous']	[ 2.00482920e-01 1.05297461e-01 5.53561330e-01 1.28315702e-01 -7.01106369e-01 -9.28520620e-01 6.69090271e-01 -1.17804430e-01 -3.21970791e-01 5.88494539e-01 4.89424884e-01 -2.72322595e-01 -1.89056560e-01 -4.09158289e-01 -3.96741003e-01 -9.34723616e-01 2.29897238e-02 -2.03115553e-01 4.04795952e-04 -3.85282755e-01 2.50513226e-01 8.05368841e-01 -1.80220032e+00 -1.70074388e-01 6.68438911e-01 6.32909238e-01 5.87293580e-02 8.18839908e-01 -4.30488177e-02 -8.02174285e-02 -1.25227654e+00 -3.09706062e-01 3.14187557e-01 -4.63464290e-01 -4.23631549e-01 1.28828054e-02 3.90263826e-01 1.03535928e-01 8.90461057e-02 1.21123016e+00 9.10295308e-01 3.19097757e-01 7.24434257e-01 -1.07015193e+00 -4.04389560e-01 5.22628188e-01 -2.74575561e-01 2.04922140e-01 6.79124951e-01 2.36474991e-01 7.04196215e-01 -8.32201242e-01 3.83564591e-01 1.42467177e+00 6.94050550e-01 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8dcf04ae-b4ec-4a20-9bc2-0dd01345b85e	cross-spectral-face-completion-for-nir-vis	1902.03565	null	http://arxiv.org/abs/1902.03565v1	http://arxiv.org/pdf/1902.03565v1.pdf	Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition	Near infrared-visible (NIR-VIS) heterogeneous face recognition refers to the process of matching NIR to VIS face images. Current heterogeneous methods try to extend VIS face recognition methods to the NIR spectrum by synthesizing VIS images from NIR images. However, due to self-occlusion and sensing gap, NIR face images lose some visible lighting contents so that they are always incomplete compared to VIS face images. This paper models high resolution heterogeneous face synthesis as a complementary combination of two components, a texture inpainting component and pose correction component. The inpainting component synthesizes and inpaints VIS image textures from NIR image textures. The correction component maps any pose in NIR images to a frontal pose in VIS images, resulting in paired NIR and VIS textures. A warping procedure is developed to integrate the two components into an end-to-end deep network. A fine-grained discriminator and a wavelet-based discriminator are designed to supervise intra-class variance and visual quality respectively. One UV loss, two adversarial losses and one pixel loss are imposed to ensure synthesis results. We demonstrate that by attaching the correction component, we can simplify heterogeneous face synthesis from one-to-many unpaired image translation to one-to-one paired image translation, and minimize spectral and pose discrepancy during heterogeneous recognition. Extensive experimental results show that our network not only generates high-resolution VIS face images and but also facilitates the accuracy improvement of heterogeneous face recognition.	['Tieniu Tan', 'Zhenan Sun', 'Jie Cao', 'Ran He', 'Lingxiao Song']	2019-02-10	null	null	null	null	['heterogeneous-face-recognition', 'facial-inpainting']	['computer-vision', 'computer-vision']	[ 7.48698294e-01 3.15608270e-02 2.03608111e-01 -5.45919359e-01 -9.58138227e-01 -3.37878138e-01 3.30446094e-01 -1.03521073e+00 1.10398360e-01 7.13858664e-01 8.97584017e-03 2.01911315e-01 1.14146844e-01 -9.13219392e-01 -9.30254161e-01 -1.20612407e+00 6.36408806e-01 1.84667066e-01 -5.19847214e-01 -1.95936963e-01 -2.15180397e-01 9.22922432e-01 -1.83567476e+00 5.50098598e-01 6.76577032e-01 1.29700339e+00 -2.27147415e-02 4.13230121e-01 -1.62393704e-01 6.04758561e-01 -5.39877474e-01 -5.26735961e-01 8.81926835e-01 -8.09163213e-01 -5.36284782e-02 2.35049307e-01 8.90553415e-01 -5.49865961e-01 -4.97714370e-01 1.15029359e+00 1.02435219e+00 3.74181978e-02 5.60202181e-01 -1.21175766e+00 -1.07017887e+00 2.52173543e-01 -8.94837976e-01 -3.43935460e-01 4.25515443e-01 2.19805032e-01 1.42094254e-01 -9.59076762e-01 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0ee8861e-d89e-4bca-923c-4c3c50d2c73d	a-provably-correct-and-robust-algorithm-for	1906.06899	null	https://arxiv.org/abs/1906.06899v4	https://arxiv.org/pdf/1906.06899v4.pdf	A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization	In this paper, we propose a provably correct algorithm for convolutive nonnegative matrix factorization (CNMF) under separability assumptions. CNMF is a convolutive variant of nonnegative matrix factorization (NMF), which functions as an NMF with additional sequential structure. This model is useful in a number of applications, such as audio source separation and neural sequence identification. While a number of heuristic algorithms have been proposed to solve CNMF, to the best of our knowledge no provably correct algorithms have been developed. We present an algorithm that takes advantage of the NMF model underlying CNMF and exploits existing algorithms for separable NMF to provably find a solution under certain conditions. Our approach guarantees the solution in low noise settings, and runs in polynomial time. We illustrate its effectiveness on synthetic datasets, and on a singing bird audio sequence.	['Nicolas Gillis', 'Anthony Degleris']	2019-06-17	null	null	null	null	['audio-source-separation']	['audio']	[ 5.88011980e-01 -1.88404784e-01 4.81192907e-03 1.69569324e-03 -7.05188274e-01 -1.03301013e+00 1.18497364e-01 -7.38089979e-02 -2.67510772e-01 5.89783669e-01 6.79149851e-02 -7.92206824e-01 -5.72496891e-01 -2.81655908e-01 -6.16388142e-01 -7.32452691e-01 -4.10275757e-01 3.91423941e-01 -1.37265101e-01 -2.24128544e-01 1.36217311e-01 3.60248774e-01 -1.50929606e+00 3.56427372e-01 6.76956296e-01 5.69558799e-01 3.80905241e-01 1.29868901e+00 8.09064060e-02 6.41974688e-01 -4.80668724e-01 -3.56930852e-01 6.29509091e-01 -5.07382929e-01 -1.02046478e+00 2.89270133e-01 3.75852734e-01 1.92319289e-01 -7.24376142e-02 1.25166547e+00 5.03465533e-01 3.28993618e-01 2.61471272e-01 -1.67145491e+00 -5.57272792e-01 7.86663115e-01 -5.30720949e-01 3.84834886e-01 4.59958971e-01 -3.12996000e-01 1.33137488e+00 -7.88119853e-01 2.22167566e-01 1.39382994e+00 7.51376092e-01 6.15029991e-01 -1.63123977e+00 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1.94923624e-01 3.61692131e-01 -2.63997078e-01 -5.40508687e-01 2.79210269e-01]	[7.20707893371582, 4.536574840545654]
dcefd4b7-b06c-4f7a-af0e-6b2f29a8c2c6	time-aware-multiway-adaptive-fusion-network	2302.12529	null	https://arxiv.org/abs/2302.12529v2	https://arxiv.org/pdf/2302.12529v2.pdf	Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering	Knowledge graphs (KGs) have received increasing attention due to its wide applications on natural language processing. However, its use case on temporal question answering (QA) has not been well-explored. Most of existing methods are developed based on pre-trained language models, which might not be capable to learn \emph{temporal-specific} presentations of entities in terms of temporal KGQA task. To alleviate this problem, we propose a novel \textbf{T}ime-aware \textbf{M}ultiway \textbf{A}daptive (\textbf{TMA}) fusion network. Inspired by the step-by-step reasoning behavior of humans. For each given question, TMA first extracts the relevant concepts from the KG, and then feeds them into a multiway adaptive module to produce a \emph{temporal-specific} representation of the question. This representation can be incorporated with the pre-trained KG embedding to generate the final prediction. Empirical results verify that the proposed model achieves better performance than the state-of-the-art models in the benchmark dataset. Notably, the Hits@1 and Hits@10 results of TMA on the CronQuestions dataset's complex questions are absolutely improved by 24\% and 10\% compared to the best-performing baseline. Furthermore, we also show that TMA employing an adaptive fusion mechanism can provide interpretability by analyzing the proportion of information in question representations.	['Rui Jiang', 'Wei Wu', 'Sirui Wang', 'Fang Fang', 'Di Liang', 'Yonghao Liu']	2023-02-24	null	null	null	null	['graph-question-answering']	['graphs']	[ 8.91861245e-02 3.51032168e-01 -1.09558497e-02 -5.43971419e-01 -8.55874240e-01 -6.53794527e-01 6.85158014e-01 4.14474398e-01 -5.29986024e-01 5.49188375e-01 3.72328579e-01 -5.17000139e-01 -5.26609778e-01 -9.34972584e-01 -7.28740513e-01 -4.89225954e-01 1.96290299e-01 4.69629854e-01 4.74115103e-01 -6.18442595e-01 4.97606024e-02 -5.12473891e-03 -1.42554224e+00 5.83696604e-01 1.22737670e+00 1.21207619e+00 5.52224480e-02 4.34116662e-01 -4.10571665e-01 1.01602232e+00 -5.51966071e-01 -8.32812667e-01 -1.99271664e-01 -4.09871548e-01 -1.20185781e+00 -3.72205675e-01 2.76325017e-01 -9.75349173e-02 -2.97150373e-01 7.81880677e-01 2.65925974e-01 4.06612873e-01 7.30672121e-01 -9.19953406e-01 -9.47187245e-01 8.62025678e-01 -2.28897408e-01 4.48734909e-01 4.86523718e-01 -1.03496119e-01 1.49765468e+00 -8.59460950e-01 5.60701191e-01 1.36645174e+00 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218d77ff-bec6-4bdb-b5dc-5ecee3a38c3a	representation-learning-on-heterostructures	2201.06972	null	https://arxiv.org/abs/2201.06972v1	https://arxiv.org/pdf/2201.06972v1.pdf	Representation Learning on Heterostructures via Heterogeneous Anonymous Walks	Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks while the related study on heterogeneous networks is still a void. In this paper, we try to take the first step for representation learning on heterostructures, which is very challenging due to their highly diverse combinations of node types and underlying structures. To effectively distinguish diverse heterostructures, we firstly propose a theoretically guaranteed technique called heterogeneous anonymous walk (HAW) and its variant coarse HAW (CHAW). Then, we devise the heterogeneous anonymous walk embedding (HAWE) and its variant coarse HAWE in a data-driven manner to circumvent using an extremely large number of possible walks and train embeddings by predicting occurring walks in the neighborhood of each node. Finally, we design and apply extensive and illustrative experiments on synthetic and real-world networks to build a benchmark on heterostructure learning and evaluate the effectiveness of our methods. The results demonstrate our methods achieve outstanding performance compared with both homogeneous and heterogeneous classic methods, and can be applied on large-scale networks.	['Wenjun Wang', 'Danyang Shi', 'Mengyu Jia', 'Wang Zhang', 'Ting Pan', 'Pengfei Jiao', 'Xuan Guo']	2022-01-18	null	null	null	null	['network-embedding']	['methodology']	[-2.90437024e-02 6.15077280e-02 -1.64722234e-01 -1.14943601e-01 -2.81141222e-01 -5.16481578e-01 7.00093210e-01 1.45937562e-01 -3.07151824e-02 8.83487582e-01 2.46768668e-01 -4.78249580e-01 -3.36439461e-01 -1.34125662e+00 -4.17974412e-01 -1.13258934e+00 -1.09625928e-01 4.71593529e-01 6.15771532e-01 -3.07578683e-01 1.12855434e-01 4.09424692e-01 -9.83748317e-01 -2.96126544e-01 7.76729047e-01 6.77360713e-01 -7.25216419e-02 3.52420002e-01 -1.36139721e-01 4.37164515e-01 -3.19406390e-01 -6.50859892e-01 2.97319517e-02 -4.46994722e-01 -5.84503293e-01 -5.26929051e-02 2.14774329e-02 1.09078638e-01 -8.29332411e-01 9.19330239e-01 6.73737347e-01 -2.95478329e-02 1.00385737e+00 -1.36625862e+00 -9.72388804e-01 9.44699109e-01 -4.16386485e-01 -4.43964964e-03 -5.44149466e-02 -7.63393119e-02 1.35417914e+00 -7.52683342e-01 7.92580843e-01 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ac4b1dc8-d403-4c46-8b25-9ad03234157c	entsum-a-data-set-for-entity-centric	null	null	https://openreview.net/forum?id=1llL_tYlV54	https://openreview.net/pdf?id=1llL_tYlV54	EntSUM: A Data Set for Entity-Centric Extractive Summarization	Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document. We introduce a human-annotated data set EntSUM for controllable summarization with a focus on named entities as the aspects to control. We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries. We propose extensions to state-of-the-art summarization approaches that achieve substantially better results on our data set. Our analysis and results show the challenging nature of this task and of the proposed data set.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['extractive-summarization']	['natural-language-processing']	[ 2.25250497e-01 8.31466496e-01 -5.61563849e-01 -4.16319340e-01 -1.31500590e+00 -9.13918734e-01 7.36494124e-01 7.97821939e-01 -1.82744250e-01 1.06817114e+00 1.32242846e+00 1.29872724e-01 -5.03944978e-02 -5.49223721e-01 -3.37160826e-01 -6.10562451e-02 1.15979932e-01 8.01773787e-01 4.67528962e-02 -3.49249154e-01 6.73726022e-01 1.72175854e-01 -1.34651804e+00 5.38435340e-01 1.57789063e+00 3.10460687e-01 3.92234419e-03 9.67518568e-01 -2.28370160e-01 5.20320654e-01 -1.29412460e+00 -5.01875997e-01 -2.56480336e-01 -4.46213067e-01 -9.31673586e-01 3.26726198e-01 7.81839490e-01 -2.37924978e-01 -4.94951233e-02 8.25136542e-01 8.27773392e-01 3.50609154e-01 9.78723943e-01 -1.10121202e+00 -6.12507463e-01 1.19110215e+00 -2.43719622e-01 5.27826846e-02 9.69167829e-01 -1.28012985e-01 1.49804974e+00 -6.55538976e-01 9.71322179e-01 1.14305246e+00 3.04222018e-01 5.74569583e-01 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aabbc17d-7868-4d7e-ad6f-9997e05e7ebc	neuralodf-learning-omnidirectional-distance	2206.05837	null	https://arxiv.org/abs/2206.05837v3	https://arxiv.org/pdf/2206.05837v3.pdf	NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation	In visual computing, 3D geometry is represented in many different forms including meshes, point clouds, voxel grids, level sets, and depth images. Each representation is suited for different tasks thus making the transformation of one representation into another (forward map) an important and common problem. We propose Omnidirectional Distance Fields (ODFs), a new 3D shape representation that encodes geometry by storing the depth to the object's surface from any 3D position in any viewing direction. Since rays are the fundamental unit of an ODF, it can be used to easily transform to and from common 3D representations like meshes or point clouds. Different from level set methods that are limited to representing closed surfaces, ODFs are unsigned and can thus model open surfaces (e.g., garments). We demonstrate that ODFs can be effectively learned with a neural network (NeuralODF) despite the inherent discontinuities at occlusion boundaries. We also introduce efficient forward mapping algorithms for transforming ODFs to and from common 3D representations. Specifically, we introduce an efficient Jumping Cubes algorithm for generating meshes from ODFs. Experiments demonstrate that NeuralODF can learn to capture high-quality shape by overfitting to a single object, and also learn to generalize on common shape categories.	['Srinath Sridhar', 'Rao Fu', 'Shivam Duggal', 'Cheng-You Lu', 'Trevor Houchens']	2022-06-12	null	null	null	null	['3d-shape-representation']	['computer-vision']	[ 1.20850772e-01 2.72412986e-01 1.42115414e-01 -4.03350234e-01 -3.52402747e-01 -6.89346969e-01 6.67225718e-01 1.48966968e-01 2.15604782e-01 3.92705262e-01 -5.54057434e-02 -2.91675985e-01 -9.24250484e-02 -1.51133144e+00 -1.22373748e+00 -2.58456230e-01 -2.78830767e-01 8.76360655e-01 2.66494840e-01 -1.47715867e-01 1.32789657e-01 1.26623929e+00 -1.76717234e+00 2.80011952e-01 7.09676564e-01 1.13633227e+00 6.54582381e-02 5.31245828e-01 -5.62719166e-01 8.25138316e-02 -4.16269541e-01 -1.54102799e-02 4.17923927e-01 2.43756130e-01 -6.52666926e-01 8.33588392e-02 7.62347698e-01 -2.48535037e-01 -1.00013867e-01 8.78673911e-01 1.98181838e-01 1.04652286e-01 1.06445527e+00 -1.40637243e+00 -9.80890989e-01 -1.56493813e-01 -3.32729638e-01 -5.47650874e-01 5.19559979e-01 -3.42802346e-01 5.21600604e-01 -1.33333445e+00 1.01860833e+00 1.54989171e+00 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24bb83f0-434c-4771-b08f-fc96052fa893	are-nlp-models-really-able-to-solve-simple	2103.07191	null	https://arxiv.org/abs/2103.07191v2	https://arxiv.org/pdf/2103.07191v2.pdf	Are NLP Models really able to Solve Simple Math Word Problems?	The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered "solved" with the bulk of research attention moving to more complex MWPs. In this paper, we restrict our attention to English MWPs taught in grades four and lower. We provide strong evidence that the existing MWP solvers rely on shallow heuristics to achieve high performance on the benchmark datasets. To this end, we show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs. Similarly, models that treat MWPs as bag-of-words can also achieve surprisingly high accuracy. Further, we introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over examples sampled from existing datasets. The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs.	['Navin Goyal', 'Satwik Bhattamishra', 'Arkil Patel']	2021-03-12	null	https://aclanthology.org/2021.naacl-main.168	https://aclanthology.org/2021.naacl-main.168.pdf	naacl-2021-4	['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving']	['knowledge-base', 'reasoning', 'time-series']	[-1.40567645e-01 4.03843701e-01 -2.57016033e-01 -2.25654200e-01 -1.19117856e+00 -1.03075898e+00 1.44551396e-01 4.66548741e-01 -3.25144649e-01 9.00077581e-01 1.56344771e-01 -7.34664261e-01 -3.45529795e-01 -1.27640545e+00 -1.05980134e+00 -2.15068027e-01 1.62909821e-01 8.62528622e-01 1.44314513e-01 -5.07558525e-01 2.29381695e-01 3.13501984e-01 -1.39085770e+00 6.77726209e-01 1.08965039e+00 5.27566075e-01 7.85737187e-02 6.71933651e-01 -5.55721819e-01 9.16611731e-01 -6.76898420e-01 -8.27476442e-01 2.27945119e-01 7.98290223e-02 -1.41878235e+00 -5.55870354e-01 1.10145950e+00 -1.11947440e-01 -3.16131324e-01 7.96936452e-01 3.11919957e-01 2.43183166e-01 3.83943230e-01 -1.13457906e+00 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987f43f2-fea8-46c2-ada0-4eda074ecd94	pathologist-level-classification-of	1901.11489	null	http://arxiv.org/abs/1901.11489v1	http://arxiv.org/pdf/1901.11489v1.pdf	Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks	Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide.	['Yevgeniy A. Linnik', 'Saeed Hassanpour', 'Louis J. Vaickus', 'Jason W. Wei', 'Naofumi Tomita', 'Laura J. Tafe']	2019-01-31	null	null	null	null	['lung-cancer-diagnosis']	['medical']	[-5.05335517e-02 9.24249366e-02 -4.95711893e-01 -6.40239492e-02 -1.26920390e+00 -1.02911413e+00 1.31201446e-01 5.11433601e-01 -5.79808950e-01 5.16408980e-01 7.27283955e-02 -8.53366613e-01 1.49566501e-01 -7.98689604e-01 -3.16515654e-01 -9.63033557e-01 2.70503014e-01 6.05601907e-01 2.96353728e-01 4.20234561e-01 -1.99951351e-01 7.44924307e-01 -7.16933191e-01 7.99170673e-01 6.07474566e-01 9.21701074e-01 3.12486768e-01 1.22087252e+00 -3.88775282e-02 7.49057174e-01 -3.10544878e-01 -1.31522849e-01 9.09296572e-02 -7.69235119e-02 -8.26557577e-01 -6.01831302e-02 6.23323560e-01 -4.92469907e-01 -3.27806979e-01 9.24752116e-01 3.47176701e-01 -4.34500217e-01 9.75100875e-01 -6.28592491e-01 -4.40327406e-01 3.19424033e-01 -5.21940053e-01 5.55200458e-01 -4.55524117e-01 4.17189002e-01 1.22941077e+00 -8.74951780e-01 5.01641870e-01 2.00348988e-01 9.16578472e-01 4.07907814e-01 -9.48602796e-01 -6.60701334e-01 -4.19497639e-01 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1.90707564e-01 5.31980872e-01 -1.11103989e-01 -7.06356943e-01 1.11173488e-01]	[15.178449630737305, -2.974123001098633]
fb4d7b56-8d5b-4fd9-ba94-dc37c7ad51c5	integrating-local-material-recognition-with	1604.01345	null	http://arxiv.org/abs/1604.01345v4	http://arxiv.org/pdf/1604.01345v4.pdf	Integrating Local Material Recognition with Large-Scale Perceptual Attribute Discovery	Material attributes have been shown to provide a discriminative intermediate representation for recognizing materials, especially for the challenging task of recognition from local material appearance (i.e., regardless of object and scene context). In the past, however, material attributes have been recognized separately preceding category recognition. In contrast, neuroscience studies on material perception and computer vision research on object and place recognition have shown that attributes are produced as a by-product during the category recognition process. Does the same hold true for material attribute and category recognition? In this paper, we introduce a novel material category recognition network architecture to show that perceptual attributes can, in fact, be automatically discovered inside a local material recognition framework. The novel material-attribute-category convolutional neural network (MAC-CNN) produces perceptual material attributes from the intermediate pooling layers of an end-to-end trained category recognition network using an auxiliary loss function that encodes human material perception. To train this model, we introduce a novel large-scale database of local material appearance organized under a canonical material category taxonomy and careful image patch extraction that avoids unwanted object and scene context. We show that the discovered attributes correspond well with semantically-meaningful visual material traits via Boolean algebra, and enable recognition of previously unseen material categories given only a few examples. These results have strong implications in how perceptually meaningful attributes can be learned in other recognition tasks.	['Ko Nishino', 'Gabriel Schwartz']	2016-04-05	null	null	null	null	['material-recognition']	['computer-vision']	[ 8.44298959e-01 8.31356198e-02 4.59435135e-02 -6.92004621e-01 -2.77584702e-01 -7.02969491e-01 8.79854441e-01 3.89818877e-01 -2.98439264e-01 3.71657133e-01 -6.53165728e-02 7.53784403e-02 -3.71933341e-01 -9.43720996e-01 -1.14830494e+00 -9.74324524e-01 1.43460918e-03 3.07516307e-01 2.18247101e-01 -8.74111895e-03 5.82995534e-01 8.51151109e-01 -1.99938071e+00 8.33219826e-01 3.14095736e-01 1.76490331e+00 3.54791790e-01 5.67447007e-01 -2.94431925e-01 6.98825061e-01 -4.76508409e-01 -3.28524917e-01 6.29401922e-01 -1.64550781e-01 -1.06677961e+00 3.54973763e-01 1.18820965e+00 -1.62681237e-01 -6.74962103e-02 9.44907486e-01 2.10410729e-01 2.82932729e-01 1.18028402e+00 -1.05239117e+00 -1.37697887e+00 2.30317488e-01 -7.76675418e-02 -8.04809183e-02 3.53343934e-01 1.85684115e-01 1.22189069e+00 -9.35825527e-01 6.57062709e-01 1.34387326e+00 4.90534693e-01 6.56053126e-01 -1.61377585e+00 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1.19892550e+00 3.71712148e-01 -9.69079077e-01 -3.70569706e-01 4.42226261e-01]	[10.190380096435547, -0.016658896580338478]
b29868d2-0bd4-4743-bf91-97c59b172c82	galaxy-morphology-prediction-using-capsule	1809.08377	null	http://arxiv.org/abs/1809.08377v1	http://arxiv.org/pdf/1809.08377v1.pdf	Galaxy morphology prediction using capsule networks	Understanding morphological types of galaxies is a key parameter for studying their formation and evolution. Neural networks that have been used previously for galaxy morphology classification have some disadvantages, such as not being invariant under rotation. In this work, we studied the performance of Capsule Network, a recently introduced neural network architecture that is rotationally invariant and spatially aware, on the task of galaxy morphology classification. We designed two evaluation scenarios based on the answers from the question tree in the Galaxy Zoo project. In the first scenario, we used Capsule Network for regression and predicted probabilities for all of the questions. In the second scenario, we chose the answer to the first morphology question that had the highest user agreement as the class of the object and trained a Capsule Network classifier, where we also reconstructed galaxy images. We achieved promising results in both of these scenarios. Automated approaches such as the one introduced here will greatly decrease the workload of astronomers and will play a critical role in the upcoming large sky surveys.	['Yadi Zhou', 'Razvan Bunescu', 'Ryan Chornock', 'Reza Katebi']	2018-09-22	null	null	null	null	['morphology-classification']	['computer-vision']	[-4.01824355e-01 -6.30404986e-03 4.75767493e-01 -3.47883880e-01 -1.12373218e-01 -8.91119957e-01 7.07069099e-01 4.42904048e-02 -4.54360425e-01 4.04755741e-01 1.43103808e-01 -5.09689629e-01 -5.00008821e-01 -1.02875960e+00 -3.55750740e-01 -8.76548588e-01 -6.23388477e-02 8.22046578e-01 7.58443117e-01 8.53828490e-02 3.65397632e-01 8.15067351e-01 -1.60151923e+00 3.48541409e-01 4.75763679e-01 1.07911217e+00 5.47720015e-01 7.79581130e-01 3.05163674e-02 8.77637267e-01 -5.58776915e-01 -3.53609622e-01 1.89350843e-01 -2.33594626e-01 -7.59798646e-01 -6.34147450e-02 3.56586695e-01 3.18526626e-01 -3.63931775e-01 8.72103572e-01 4.33915347e-01 3.10459435e-01 6.15755320e-01 -7.66621172e-01 -2.94014275e-01 3.00844759e-01 1.78922802e-01 2.92005002e-01 -6.29175231e-02 1.27198353e-01 7.99911678e-01 -7.48246729e-01 8.66434038e-01 9.83449817e-01 6.46499157e-01 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cd795aaf-f144-4163-9474-7f2fff3e7526	plot-writing-from-pre-trained-language-models-1	2206.03021	null	https://arxiv.org/abs/2206.03021v1	https://arxiv.org/pdf/2206.03021v1.pdf	Plot Writing From Pre-Trained Language Models	Pre-trained language models (PLMs) fail to generate long-form narrative text because they do not consider global structure. As a result, the generated texts are often incohesive, repetitive, or lack content. Recent work in story generation reintroduced explicit content planning in the form of prompts, keywords, or semantic frames. Trained on large parallel corpora, these models can generate more logical event sequences and thus more contentful stories. However, these intermediate representations are often not in natural language and cannot be utilized by PLMs without fine-tuning. We propose generating story plots using off-the-shelf PLMs while maintaining the benefit of content planning to generate cohesive and contentful stories. Our proposed method, ScratchPlot, first prompts a PLM to compose a content plan. Then, we generate the story's body and ending conditioned on the content plan. Furthermore, we take a generate-and-rank approach by using additional PLMs to rank the generated (story, ending) pairs. We benchmark our method with various baselines and achieved superior results in both human and automatic evaluation.	['Dittaya Wanvarie', 'Vishakha Kadam', 'Yiping Jin']	2022-06-07	null	null	null	null	['story-generation']	['natural-language-processing']	[ 3.31702411e-01 4.47203517e-01 -1.91842452e-01 -2.12892294e-01 -9.92875993e-01 -8.17719281e-01 1.24157441e+00 2.00043097e-01 5.11649773e-02 9.76254523e-01 1.10745454e+00 -1.80001166e-02 1.30412042e-01 -9.99192595e-01 -6.92499876e-01 -2.05064178e-01 4.58252549e-01 6.13113046e-01 3.09505612e-01 -3.21508110e-01 3.70750666e-01 -1.35682495e-02 -1.29736567e+00 9.14026618e-01 9.34557617e-01 2.73444384e-01 4.59860623e-01 7.03804731e-01 -5.73106706e-01 1.43560588e+00 -9.26068246e-01 -3.66721094e-01 -2.01874599e-01 -1.06670535e+00 -8.99512470e-01 1.68295965e-01 1.45525292e-01 -4.52745557e-01 -3.05761844e-01 4.35212761e-01 3.81992877e-01 3.40930283e-01 8.27106953e-01 -1.05611050e+00 -4.35639977e-01 1.49552453e+00 -3.98896694e-01 -2.31259599e-01 9.60563123e-01 1.29892737e-01 1.17992115e+00 -8.54761302e-01 1.19667804e+00 1.33145106e+00 4.95400608e-01 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-2.16686636e-01 -1.31238937e+00 3.09237000e-02 4.68199313e-01 -1.13429174e-01 -4.05551970e-01 1.87461346e-01 3.82802308e-01]	[11.683140754699707, 8.848231315612793]
6fdbb6a2-7bac-4bcb-9baf-2be84a25e7f2	thermal-to-visible-synthesis-of-face-images	1803.07599	null	http://arxiv.org/abs/1803.07599v1	http://arxiv.org/pdf/1803.07599v1.pdf	Thermal to Visible Synthesis of Face Images using Multiple Regions	Synthesis of visible spectrum faces from thermal facial imagery is a promising approach for heterogeneous face recognition; enabling existing face recognition software trained on visible imagery to be leveraged, and allowing human analysts to verify cross-spectrum matches more effectively. We propose a new synthesis method to enhance the discriminative quality of synthesized visible face imagery by leveraging both global (e.g., entire face) and local regions (e.g., eyes, nose, and mouth). Here, each region provides (1) an independent representation for the corresponding area, and (2) additional regularization terms, which impact the overall quality of synthesized images. We analyze the effects of using multiple regions to synthesize a visible face image from a thermal face. We demonstrate that our approach improves cross-spectrum verification rates over recently published synthesis approaches. Moreover, using our synthesized imagery, we report the results on facial landmark detection-commonly used for image registration-which is a critical part of the face recognition process.	['Nathaniel J. Short', 'Benjamin S. Riggan', 'Shuowen Hu']	2018-03-20	null	null	null	null	['heterogeneous-face-recognition']	['computer-vision']	[ 5.83874345e-01 -8.57035890e-02 -8.20305869e-02 -4.18414980e-01 -9.33214664e-01 -6.65832996e-01 5.63331664e-01 -5.35749853e-01 1.53165519e-01 1.98641837e-01 1.78302839e-01 4.90818396e-02 2.52181023e-01 -7.55916059e-01 -6.24157906e-01 -1.03245556e+00 2.49897555e-01 -9.88322049e-02 -3.89226139e-01 -8.63036513e-02 -2.72517069e-03 1.10174310e+00 -1.85043037e+00 1.84889928e-01 5.87675452e-01 1.37429500e+00 -2.66383797e-01 1.29540041e-01 3.07045639e-01 2.38325000e-01 -3.44630539e-01 -3.96989346e-01 7.04733253e-01 -4.79747862e-01 -1.78306475e-01 4.82983410e-01 1.10996437e+00 -4.59858239e-01 -1.21326253e-01 1.10547185e+00 2.77132124e-01 -5.27191954e-03 7.73622870e-01 -1.26122987e+00 -6.93228126e-01 1.33445039e-01 -9.14542675e-01 -2.28694066e-01 5.09866774e-01 2.88073868e-01 7.01302052e-01 -1.01766086e+00 6.32280111e-01 1.15434813e+00 6.49313271e-01 4.70047086e-01 -1.25728703e+00 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-9.09328043e-01 3.03629726e-01 4.75686669e-01 -8.25425625e-01 -4.23829049e-01 2.57274151e-01]	[12.993993759155273, 0.3300498127937317]
bc75e268-55d5-460d-b1e9-d521042f3f7c	template-matching-with-white-balance	2208.02035	null	https://arxiv.org/abs/2208.02035v1	https://arxiv.org/pdf/2208.02035v1.pdf	Template matching with white balance adjustment under multiple illuminants	In this paper, we propose a novel template matching method with a white balancing adjustment, called N-white balancing, which was proposed for multi-illuminant scenes. To reduce the influence of lighting effects, N-white balancing is applied to images for multi-illumination color constancy, and then a template matching method is carried out by using adjusted images. In experiments, the effectiveness of the proposed method is demonstrated to be effective in object detection tasks under various illumination conditions.	['Hitoshi Kiya', 'Yuma Kinoshita', 'Teruaki Akazawa']	2022-08-03	null	null	null	null	['template-matching', 'color-constancy']	['computer-vision', 'computer-vision']	[ 6.28416002e-01 -8.96128953e-01 5.71405776e-02 -2.11387858e-01 1.43291980e-01 -1.56026423e-01 2.81623751e-01 -6.57335877e-01 -3.95579487e-01 5.25136411e-01 1.08580813e-02 -1.34882346e-01 5.31764030e-02 -5.96998930e-01 -1.91753313e-01 -8.15087259e-01 8.22644532e-01 -4.25908804e-01 3.52850556e-01 -1.02741949e-01 5.39358377e-01 6.49927020e-01 -1.69704866e+00 -7.44430944e-02 8.50459754e-01 8.58190775e-01 3.36821705e-01 5.78177750e-01 -2.95480102e-01 2.76651233e-01 -7.56397545e-01 -4.15501684e-01 7.95565903e-01 -6.22167110e-01 1.79126382e-01 7.10030735e-01 7.19529927e-01 -1.56986654e-01 -2.24283695e-01 1.27315748e+00 7.41174996e-01 4.42209244e-01 4.19446081e-01 -1.16252315e+00 -5.08575737e-01 -2.06079885e-01 -1.25662076e+00 3.61947775e-01 3.15744430e-01 7.29594305e-02 4.04705524e-01 -1.06092703e+00 2.58265406e-01 1.34977853e+00 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cd98a465-a6bb-4c2b-a70d-c492d2e8e7cf	language-informed-transfer-learning-for	2301.05318	null	https://arxiv.org/abs/2301.05318v1	https://arxiv.org/pdf/2301.05318v1.pdf	Language-Informed Transfer Learning for Embodied Household Activities	For service robots to become general-purpose in everyday household environments, they need not only a large library of primitive skills, but also the ability to quickly learn novel tasks specified by users. Fine-tuning neural networks on a variety of downstream tasks has been successful in many vision and language domains, but research is still limited on transfer learning between diverse long-horizon tasks. We propose that, compared to reinforcement learning for a new household activity from scratch, home robots can benefit from transferring the value and policy networks trained for similar tasks. We evaluate this idea in the BEHAVIOR simulation benchmark which includes a large number of household activities and a set of action primitives. For easy mapping between state spaces of different tasks, we provide a text-based representation and leverage language models to produce a common embedding space. The results show that the selection of similar source activities can be informed by the semantic similarity of state and goal descriptions with the target task. We further analyze the results and discuss ways to overcome the problem of catastrophic forgetting.	['Gaurav Sukhatme', 'Govind Thattai', 'Qiaozi Gao', 'Yuqian Jiang']	2023-01-12	null	null	null	null	['semantic-textual-similarity']	['natural-language-processing']	[ 3.51949781e-02 4.41771783e-02 -2.23874021e-03 -4.48925555e-01 -4.66148287e-01 -4.83213454e-01 7.72368789e-01 -4.18904386e-02 -6.01481259e-01 9.45817411e-01 5.72494566e-01 4.38557044e-02 -5.59608489e-02 -5.49445987e-01 -7.64257669e-01 -6.79403603e-01 -4.10421520e-01 6.69769764e-01 1.63338915e-01 -4.32727486e-01 -7.53813758e-02 2.68565118e-01 -1.40931726e+00 1.84732974e-01 5.71077406e-01 6.08395219e-01 7.93390572e-01 4.11264569e-01 2.39111006e-01 9.70813334e-01 -4.15555656e-01 -2.26941630e-02 2.84791559e-01 -3.37811738e-01 -9.00619745e-01 1.82085767e-01 -1.21739253e-01 -6.87170029e-01 -6.52495265e-01 7.56991029e-01 6.42138720e-01 7.10362315e-01 8.21371913e-01 -1.39819264e+00 -8.21797192e-01 7.27679253e-01 8.14524144e-02 5.20511493e-02 4.87634182e-01 7.72628784e-01 8.05173755e-01 -4.15407598e-01 6.24375701e-01 1.36013746e+00 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-1.13336647e+00 -1.04164970e+00 -6.59051895e-01 8.31328452e-01 3.13746810e-01 -9.99182045e-01 -5.06469682e-02 1.96004406e-01]	[4.3838090896606445, 1.0798568725585938]
0a9fd608-c97f-40a8-a9d3-02d90167ae73	formal-covariate-benchmarking-to-bound	2306.10562	null	https://arxiv.org/abs/2306.10562v1	https://arxiv.org/pdf/2306.10562v1.pdf	Formal Covariate Benchmarking to Bound Omitted Variable Bias	Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking after residualizing the unobserved confounder on the set of observed covariates. In this paper, I explain the rationale and details of this procedure. I clarify some important details of the process of formal covariate benchmarking and highlight some of the difficulties of interpretation that researchers face in reasoning about the residualized part of unobserved confounders. I explain all the points with several empirical examples.	['Deepankar Basu']	2023-06-18	null	null	null	null	['benchmarking', 'benchmarking']	['miscellaneous', 'robots']	[ 3.07286590e-01 3.61565560e-01 -1.03188801e+00 -6.70638442e-01 -7.00752437e-01 -5.09086490e-01 4.26901132e-01 3.25543940e-01 -5.13078153e-01 9.95145619e-01 1.07575417e+00 -8.95836115e-01 -5.94331503e-01 -4.61267322e-01 -5.72623372e-01 -5.73605478e-01 -2.58825868e-02 1.02772087e-01 -5.19732714e-01 3.87163490e-01 2.21997216e-01 3.97418022e-01 -9.27801013e-01 -2.24773675e-01 8.11266959e-01 -2.71544550e-02 -3.17124993e-01 1.51348993e-01 2.66852081e-01 9.98748243e-01 -6.38968050e-01 -4.96826500e-01 -8.78145769e-02 -6.21581256e-01 -5.82988381e-01 -5.25201112e-02 1.36100709e-01 -4.82914686e-01 -9.97692719e-02 6.99483514e-01 4.93463010e-01 6.71500266e-02 1.16577101e+00 -1.46897244e+00 -4.07325685e-01 1.06643164e+00 -5.58299601e-01 3.91653925e-01 2.17861861e-01 2.82132655e-01 7.37629175e-01 -5.81068814e-01 6.25968754e-01 1.37685168e+00 6.39944732e-01 1.20039046e-01 -1.35135126e+00 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32d435b7-21e5-4a03-99b3-48eb76045819	a-methodology-based-on-trace-based-clustering	null	null	https://www.sciencedirect.com/science/article/pii/S0950705121007310	https://reader.elsevier.com/reader/sd/pii/S0950705121007310?token=AC1F6F1CB4E9FDFF110EA7B6F114EFA66366F1BB4D4640BE93020F90873D828C42D9DFC5AB1C530870F3AFC8EA0F4394&originRegion=eu-west-1&originCreation=20220120171012	A methodology based on Trace-based clustering for patient phenotyping	Background: The current situation of critical progression as regards the resistance of bacteria to antibiotics has led to the use of machine learning techniques in order to provide clinicians with new knowledge for decision making. One of the key aspects is precision medicine, which focuses on finding phenotypes of patients for whom treatments may be more effective or detecting high risk patients whose progress must be closely monitored. The identification of these phenotypes requires the application of a methodology whose results are consistent and interpretable, along with the control of the process by a clinical expert. Studies concerning machine learning phenotyping use conventional clustering or subgroup algorithms that require information to be obtained a priori. Methods: We propose a new unsupervised machine learning technique, denominated as Trace-based clustering, and a 5-step methodology in order to support clinicians when identifying patient phenotypes. The steps proposed are: (1) Extraction and transformation of data and analysis of clustering tendency, (2) Selection of clustering algorithm and parameters, (3) Automatic generation of candidate clusters, (4) Visual support for selection of candidate clusters, and (5) Evaluation by clinical experts. Experiments and Results: We undertake an antimicrobial resistance use case by employing the MIMIC-III open-access database for patients infected with the Methicillin-resistant Staphylococcus Aereus and Enterococcus Faecium treated with Vancomycin. The experiments were carried out using the Hopkins statistic in order to evaluate the clustering tendency of the data, the K-Means algorithm for clustering, and the Dice coefficient to measure the similarity of the clusters. Our experiments computed 370 potential patient sets (clusters) so as to obtain 19 candidate clusters for their final evaluation. We evaluated the final result with a classification model in order to ensure the consistency of the phenotypes obtained and we compared the result with a traditional clustering approach. We found a reduced set of consistent candidate clusters with a common phenotype (resistance and death), which were different from the other candidate clusters. An expert in the domain could add labels with clinical meaning to the reduced number of clusters. Conclusions: We show that the proposed methodology allows physicians to identify consistent patient phenotypes. Our experiments confirm that quality measures, and the visual analysis could help expert clinicians to control the knowledge discovery process and obtain interpretable results. Our approach provides a new perspective: that of finding patient sets using clustering techniques evaluated by overlapping clusters of the previous partitions. The method proposed is general and can be easily adapted to any other problem and any other clinical settings.	['Bernardo Canovas-Segura', 'Manuel Campos', 'Jose M. Juarez', 'Antonio Lopez-Martinez-Carrasco']	2021-11-28	null	null	null	knowledge-based-systems-2021-11	['patient-phenotyping', 'data-mining', 'data-mining']	['medical', 'methodology', 'natural-language-processing']	[ 3.21032673e-01 -3.38941664e-01 1.02987178e-01 -1.40672892e-01 -2.26012111e-01 -7.53013849e-01 2.51633406e-01 1.01350915e+00 -4.46288615e-01 6.06759846e-01 -2.75995165e-01 -6.04130566e-01 -8.85464549e-01 -6.15009010e-01 -1.09716147e-01 -1.05497718e+00 -3.46447557e-01 1.06040859e+00 1.93999588e-01 2.85024732e-01 4.51221973e-01 8.05705190e-01 -1.52893293e+00 5.85129559e-01 9.60451603e-01 4.68025327e-01 3.93601716e-01 8.23705018e-01 4.63422388e-02 6.81946993e-01 -5.83603442e-01 3.44901115e-01 1.16478354e-01 -6.29527092e-01 -6.71275139e-01 3.72392088e-01 -6.38098180e-01 2.81149060e-01 8.42004597e-01 7.38747776e-01 4.66771811e-01 3.60248089e-02 1.07037258e+00 -1.10276294e+00 -1.27296776e-01 2.31485292e-01 -3.49455863e-01 -2.06496596e-01 3.36351007e-01 3.00889552e-01 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c158ebc1-a551-45fe-a2e0-3fa58fdcffa0	episodic-memory-reader-learning-what-to	1903.06164	null	https://arxiv.org/abs/1903.06164v3	https://arxiv.org/pdf/1903.06164v3.pdf	Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data	We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, which is difficult to solve with existing QA methods due to their lack of scalability. To tackle this problem, we propose a novel end-to-end deep network model for reading comprehension, which we refer to as Episodic Memory Reader (EMR) that sequentially reads the input contexts into an external memory, while replacing memories that are less important for answering \emph{unseen} questions. Specifically, we train an RL agent to replace a memory entry when the memory is full, in order to maximize its QA accuracy at a future timepoint, while encoding the external memory using either the GRU or the Transformer architecture to learn representations that considers relative importance between the memory entries. We validate our model on a synthetic dataset (bAbI) as well as real-world large-scale textual QA (TriviaQA) and video QA (TVQA) datasets, on which it achieves significant improvements over rule-based memory scheduling policies or an RL-based baseline that independently learns the query-specific importance of each memory.	['Hyunwoo Jung', 'Sung Ju Hwang', 'Moonsu Han', 'Minki Kang']	2019-03-14	episodic-memory-reader-learning-what-to-1	https://aclanthology.org/P19-1434	https://aclanthology.org/P19-1434.pdf	acl-2019-7	['triviaqa']	['miscellaneous']	[ 4.68030572e-01 4.62499976e-01 2.46970162e-01 -4.52450901e-01 -1.29597890e+00 -5.20310104e-01 4.11422700e-01 2.39430413e-01 -5.84426582e-01 5.99694908e-01 5.46659231e-01 -5.92644334e-01 -1.74069509e-03 -9.54887211e-01 -1.37376094e+00 -4.32954222e-01 2.20790938e-01 9.90108907e-01 4.33642924e-01 -1.70733884e-01 9.42334309e-02 -2.42711380e-01 -1.51547408e+00 5.87281406e-01 7.88607955e-01 1.13617921e+00 7.45638967e-01 9.04372990e-01 -7.23669901e-02 1.59082973e+00 -6.64406657e-01 -3.57064903e-01 -1.80694491e-01 -5.72633445e-01 -1.50262320e+00 -2.26707742e-01 4.91505831e-01 -7.68087566e-01 -6.24285161e-01 5.53435981e-01 4.29114074e-01 5.18703580e-01 2.72975087e-01 -7.26482332e-01 -8.45586956e-01 6.77026331e-01 -1.04417451e-01 4.61930752e-01 4.84049022e-01 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d7ef86a8-3ba6-4d79-8da5-bdbdd6088daf	sketch-guided-scenery-image-outpainting	2006.09788	null	https://arxiv.org/abs/2006.09788v2	https://arxiv.org/pdf/2006.09788v2.pdf	Sketch-Guided Scenery Image Outpainting	The outpainting results produced by existing approaches are often too random to meet users' requirement. In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance. To this end, we propose an encoder-decoder based network to conduct sketch-guided outpainting, where two alignment modules are adopted to impose the generated content to be realistic and consistent with the provided sketches. First, we apply a holistic alignment module to make the synthesized part be similar to the real one from the global view. Second, we reversely produce the sketches from the synthesized part and encourage them be consistent with the ground-truth ones using a sketch alignment module. In this way, the learned generator will be imposed to pay more attention to fine details and be sensitive to the guiding sketches. To our knowledge, this work is the first attempt to explore the challenging yet meaningful conditional scenery image outpainting. We conduct extensive experiments on two collected benchmarks to qualitatively and quantitatively validate the effectiveness of our approach compared with the other state-of-the-art generative models.	['Yunchao Wei', 'Xueming Qian', 'Yi Yang', 'Yaxiong Wang', 'Li Zhu']	2020-06-17	null	null	null	null	['image-outpainting']	['computer-vision']	[ 4.08354729e-01 1.42451197e-01 4.75491174e-02 -4.07101065e-01 -5.60967803e-01 -4.60761487e-01 7.20515847e-01 -4.01113212e-01 1.35126829e-01 7.34970927e-01 2.61325121e-01 8.61979946e-02 3.53254706e-01 -8.86166930e-01 -1.04514802e+00 -4.63374019e-01 5.32353759e-01 2.91696817e-01 1.28483132e-01 -3.12659949e-01 2.95640171e-01 4.19724107e-01 -1.25808263e+00 3.33555579e-01 9.15920198e-01 7.50901699e-01 5.55772424e-01 4.32138205e-01 -2.06275314e-01 7.20443308e-01 -4.97650325e-01 -5.96207738e-01 4.33895558e-01 -7.64552712e-01 -4.69478995e-01 5.50800443e-01 6.02029979e-01 -7.02259719e-01 -3.44695091e-01 1.14535034e+00 3.81379306e-01 -2.81746895e-03 5.93918025e-01 -1.04611361e+00 -1.00641894e+00 5.19204974e-01 -8.24441016e-01 -4.20717895e-01 4.93309885e-01 4.60323453e-01 9.13386226e-01 -8.71121168e-01 8.60392869e-01 1.29733324e+00 1.69094056e-01 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67a70d65-7e6e-47a3-8be0-49d9e47069af	event-based-temporally-dense-optical-flow	2210.01244	null	https://arxiv.org/abs/2210.01244v1	https://arxiv.org/pdf/2210.01244v1.pdf	Event-based Temporally Dense Optical Flow Estimation with Sequential Neural Networks	Prior works on event-based optical flow estimation have investigated several gradient-based learning methods to train neural networks for predicting optical flow. However, they do not utilize the fast data rate of event data streams and rely on a spatio-temporal representation constructed from a collection of events over a fixed period of time (often between two grayscale frames). As a result, optical flow is only evaluated at a frequency much lower than the rate data is produced by an event-based camera, leading to a temporally sparse optical flow estimation. To predict temporally dense optical flow, we cast the problem as a sequential learning task and propose a training methodology to train sequential networks for continuous prediction on an event stream. We propose two types of networks: one focused on performance and another focused on compute efficiency. We first train long-short term memory networks (LSTMs) on the DSEC dataset and demonstrated 10x temporally dense optical flow estimation over existing flow estimation approaches. The additional benefit of having a memory to draw long temporal correlations back in time results in a 19.7% improvement in flow prediction accuracy of LSTMs over similar networks with no memory elements. We subsequently show that the inherent recurrence of spiking neural networks (SNNs) enables them to learn and estimate temporally dense optical flow with 31.8% lesser parameters than LSTM, but with a slightly increased error. This demonstrates potential for energy-efficient implementation of fast optical flow prediction using SNNs.	['Kaushik Roy', 'Chamika Mihiranga Liyanagedera', 'Wachirawit Ponghiran']	2022-10-03	null	null	null	null	['event-based-optical-flow']	['computer-vision']	[ 2.05759481e-01 -5.38501918e-01 6.51259497e-02 -8.51487592e-02 -2.30972528e-01 -4.09358531e-01 4.56620574e-01 -4.22960445e-02 -8.44368875e-01 1.12833405e+00 1.12420909e-01 -6.82253912e-02 2.12105904e-02 -8.91814232e-01 -8.45979869e-01 -5.34468293e-01 -5.46050310e-01 9.49782785e-03 5.93152463e-01 4.43664223e-01 4.72641557e-01 9.65603650e-01 -1.58491921e+00 4.56523359e-01 3.38813305e-01 1.26460230e+00 1.84425086e-01 1.23695028e+00 -1.91181824e-01 1.64655364e+00 -5.95763922e-01 1.95611492e-01 2.78996229e-01 -6.62352324e-01 -7.87964106e-01 -2.84803718e-01 7.95171499e-01 -8.70352387e-01 -9.08682168e-01 3.37174863e-01 4.83685166e-01 5.51911175e-01 2.38127187e-01 -1.22145844e+00 -1.25950009e-01 1.41176298e-01 -1.64578736e-01 1.04716563e+00 2.06959590e-01 5.26351273e-01 6.03031814e-01 -7.64256001e-01 1.08998346e+00 7.80554652e-01 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c4fafaf8-fbbc-4ac0-b3f7-8245272d2109	single-camera-3d-head-fitting-for-mixed	2109.02740	null	https://arxiv.org/abs/2109.02740v2	https://arxiv.org/pdf/2109.02740v2.pdf	Single-Camera 3D Head Fitting for Mixed Reality Clinical Applications	We address the problem of estimating the shape of a person's head, defined as the geometry of the complete head surface, from a video taken with a single moving camera, and determining the alignment of the fitted 3D head for all video frames, irrespective of the person's pose. 3D head reconstructions commonly tend to focus on perfecting the face reconstruction, leaving the scalp to a statistical approximation. Our goal is to reconstruct the head model of each person to enable future mixed reality applications. To do this, we recover a dense 3D reconstruction and camera information via structure-from-motion and multi-view stereo. These are then used in a new two-stage fitting process to recover the 3D head shape by iteratively fitting a 3D morphable model of the head with the dense reconstruction in canonical space and fitting it to each person's head, using both traditional facial landmarks and scalp features extracted from the head's segmentation mask. Our approach recovers consistent geometry for varying head shapes, from videos taken by different people, with different smartphones, and in a variety of environments from living rooms to outdoor spaces.	['Elena Bernardis', 'Philippos Mordohai', 'Kostas Daniilidis', 'Aylar Bayramova', 'Tejas Mane']	2021-09-06	null	null	null	null	['face-reconstruction']	['computer-vision']	[-1.23602979e-01 3.42903405e-01 3.52707773e-01 -4.78853434e-01 -8.22521567e-01 -4.96738493e-01 2.73335248e-01 -3.92330289e-01 -2.51086265e-01 3.07559907e-01 4.57741439e-01 5.85856974e-01 3.11400324e-01 -3.40936899e-01 -6.43946350e-01 -5.65788269e-01 3.27568442e-01 9.23033357e-01 5.86547405e-02 2.14347437e-01 1.23627037e-01 7.63328671e-01 -1.62670434e+00 -2.62435079e-01 8.42471197e-02 7.04337120e-01 5.50471023e-02 7.61773348e-01 3.08551818e-01 6.86610788e-02 -1.88286096e-01 -7.77316332e-01 3.20848584e-01 -2.05220371e-01 -6.10106707e-01 8.05935681e-01 7.45878398e-01 -7.31232285e-01 -3.91484708e-01 7.93282807e-01 5.31120241e-01 6.15999848e-03 5.24210513e-01 -8.72886419e-01 1.76350713e-01 -1.03846833e-01 -8.12722206e-01 -2.44969621e-01 1.09617591e+00 -3.73830974e-01 6.30364716e-01 -1.01128614e+00 8.98787498e-01 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667dac45-bba4-42f0-b5e3-f03b38944cc2	mist-multi-modal-iterative-spatial-temporal	2212.09522	null	https://arxiv.org/abs/2212.09522v1	https://arxiv.org/pdf/2212.09522v1.pdf	MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering	To build Video Question Answering (VideoQA) systems capable of assisting humans in daily activities, seeking answers from long-form videos with diverse and complex events is a must. Existing multi-modal VQA models achieve promising performance on images or short video clips, especially with the recent success of large-scale multi-modal pre-training. However, when extending these methods to long-form videos, new challenges arise. On the one hand, using a dense video sampling strategy is computationally prohibitive. On the other hand, methods relying on sparse sampling struggle in scenarios where multi-event and multi-granularity visual reasoning are required. In this work, we introduce a new model named Multi-modal Iterative Spatial-temporal Transformer (MIST) to better adapt pre-trained models for long-form VideoQA. Specifically, MIST decomposes traditional dense spatial-temporal self-attention into cascaded segment and region selection modules that adaptively select frames and image regions that are closely relevant to the question itself. Visual concepts at different granularities are then processed efficiently through an attention module. In addition, MIST iteratively conducts selection and attention over multiple layers to support reasoning over multiple events. The experimental results on four VideoQA datasets, including AGQA, NExT-QA, STAR, and Env-QA, show that MIST achieves state-of-the-art performance and is superior at computation efficiency and interpretability.	['Mike Zheng Shou', 'Yi Yang', 'Linchao Zhu', 'Lei Ji', 'Luowei Zhou', 'Difei Gao']	2022-12-19	null	http://openaccess.thecvf.com//content/CVPR2023/html/Gao_MIST_Multi-Modal_Iterative_Spatial-Temporal_Transformer_for_Long-Form_Video_Question_Answering_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_MIST_Multi-Modal_Iterative_Spatial-Temporal_Transformer_for_Long-Form_Video_Question_Answering_CVPR_2023_paper.pdf	cvpr-2023-1	['video-question-answering', 'visual-reasoning', 'visual-reasoning']	['computer-vision', 'computer-vision', 'reasoning']	[ 1.05371876e-02 -4.45258796e-01 -7.82459304e-02 -4.31356132e-01 -1.04473126e+00 -4.79469448e-01 3.11951905e-01 4.42026816e-02 -4.54062104e-01 4.68897223e-01 3.65004957e-01 -1.44243017e-01 2.91979369e-02 -6.57051206e-01 -7.66472995e-01 -3.97072792e-01 2.56776094e-01 4.70567912e-01 6.64197564e-01 -4.26412791e-01 1.08199000e-01 1.96160749e-01 -1.73449767e+00 1.13591206e+00 8.74579012e-01 1.20153522e+00 2.90396035e-01 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f42a2d14-f667-4b29-8a79-a2955daa6011	addressing-the-selection-bias-in-voice	2301.00646	null	https://arxiv.org/abs/2301.00646v1	https://arxiv.org/pdf/2301.00646v1.pdf	Addressing the Selection Bias in Voice Assistance: Training Voice Assistance Model in Python with Equal Data Selection	In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focused on Virtual Assistance in Natural Language Processing. Natural Language Processing is a form of AI that helps machines understand people and create feedback loops. This project will use deep learning to create a Voice Recognizer and use Commonvoice and data collected from the local community for model training using Google Colaboratory. After recognizing a command, the AI assistant will be able to perform the most suitable actions and then give a response. The motivation for this project comes from the race and gender bias that exists in many virtual assistants. The computer industry is primarily dominated by the male gender, and because of this, many of the products produced do not regard women. This bias has an impact on natural language processing. This project will be utilizing various open-source projects to implement machine learning algorithms and train the assistant algorithm to recognize different types of voices, accents, and dialects. Through this project, the goal to use voice data from underrepresented groups to build a voice assistant that can recognize voices regardless of gender, race, or accent. Increasing the representation of women in the computer industry is important for the future of the industry. By representing women in the initial study of voice assistants, it can be shown that females play a vital role in the development of this technology. In line with related work, this project will use first-hand data from the college population and middle-aged adults to train voice assistant to combat gender bias.	['Tauheed Khan Mohd', 'Estephanos Jebessa', 'Cameran Frank', 'Srijal Shrestha', 'Kashav Piya']	2022-12-20	null	null	null	null	['selection-bias']	['natural-language-processing']	[-3.12076628e-01 1.94238037e-01 -3.42959046e-01 -5.96835554e-01 -1.22996256e-01 -6.40354931e-01 4.93270487e-01 -2.45968416e-01 -4.93368685e-01 2.24739939e-01 7.14201510e-01 -6.99036181e-01 3.74712765e-01 -8.36751401e-01 3.90916504e-02 -4.95186225e-02 5.02908230e-01 8.09706151e-01 -4.01521891e-01 -5.27760148e-01 1.37757301e-01 7.06836939e-01 -1.40606165e+00 3.27872306e-01 6.11436069e-01 3.08655411e-01 -1.83560066e-02 7.66749322e-01 -6.48966074e-01 6.00360692e-01 -7.09535301e-01 -2.59072483e-01 1.35598496e-01 -2.80983239e-01 -9.25964415e-01 -3.17925662e-02 6.22275293e-01 -4.57919091e-01 -1.27312690e-01 7.93046296e-01 8.98570478e-01 2.58849233e-01 3.15507621e-01 -1.16597760e+00 -2.57403940e-01 7.77972698e-01 -2.18252793e-01 1.10445365e-01 5.37584364e-01 1.45798385e-01 9.29573298e-01 -7.58194029e-01 4.92563754e-01 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1391d123-bffd-4c6f-99d7-37c05b520521	brain-diffuser-natural-scene-reconstruction	2303.05334	null	https://arxiv.org/abs/2303.05334v2	https://arxiv.org/pdf/2303.05334v2.pdf	Natural scene reconstruction from fMRI signals using generative latent diffusion	In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have typically failed to reconstruct these properties together for complex scene images. Generative AI has recently made a leap forward with latent diffusion models capable of generating high-complexity images. Here, we investigate how to take advantage of this innovative technology for brain decoding. We present a two-stage scene reconstruction framework called ``Brain-Diffuser''. In the first stage, starting from fMRI signals, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Variational Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images. On the publicly available Natural Scenes Dataset benchmark, our method outperforms previous models both qualitatively and quantitatively. When applied to synthetic fMRI patterns generated from individual ROI (region-of-interest) masks, our trained model creates compelling ``ROI-optimal'' scenes consistent with neuroscientific knowledge. Thus, the proposed methodology can have an impact on both applied (e.g. brain-computer interface) and fundamental neuroscience.	['Rufin VanRullen', 'Furkan Ozcelik']	2023-03-09	null	null	null	null	['brain-decoding', 'brain-decoding']	['medical', 'miscellaneous']	[ 4.32557940e-01 8.71150196e-02 5.53981841e-01 -3.39764059e-01 -3.52229327e-01 -5.14438272e-01 1.03575754e+00 -1.33100422e-02 -4.37030226e-01 6.77779555e-01 3.05727839e-01 1.75531860e-02 -6.20906055e-02 -8.72306347e-01 -9.87495005e-01 -9.37571704e-01 7.61651620e-02 4.52343881e-01 1.79260716e-01 -8.03175271e-02 3.04120064e-01 5.52054822e-01 -1.93961203e+00 6.17064953e-01 7.24286795e-01 8.72447729e-01 8.89104366e-01 5.19147396e-01 -9.54902768e-02 6.33975327e-01 -1.36994526e-01 -2.53593326e-01 1.79126009e-01 -7.42472887e-01 -4.35428411e-01 1.75247744e-01 2.36759380e-01 -3.02947342e-01 -6.77616522e-02 1.30022502e+00 3.78318191e-01 -9.94275603e-03 1.10366404e+00 -6.66082144e-01 -8.51822078e-01 4.53322500e-01 -3.38111967e-01 1.48977116e-01 3.30981731e-01 4.38385069e-01 7.82234073e-01 -1.07531118e+00 1.10219455e+00 1.23293972e+00 1.23264827e-01 4.69759852e-01 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cc5b1db4-6113-4743-8f2b-467b8f1b0872	acpl-anti-curriculum-pseudo-labelling-forsemi	2111.12918	null	https://arxiv.org/abs/2111.12918v3	https://arxiv.org/pdf/2111.12918v3.pdf	ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification	Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosis) problems, and 2) handle imbalanced learning (because of the high variance in disease prevalence). One strategy to explore in SSL MIA is based on the pseudo labelling strategy, but it has a few shortcomings. Pseudo-labelling has in general lower accuracy than consistency learning, it is not specifically designed for both multi-class and multi-label problems, and it can be challenged by imbalanced learning. In this paper, unlike traditional methods that select confident pseudo label by threshold, we propose a new SSL algorithm, called anti-curriculum pseudo-labelling (ACPL), which introduces novel techniques to select informative unlabelled samples, improving training balance and allowing the model to work for both multi-label and multi-class problems, and to estimate pseudo labels by an accurate ensemble of classifiers (improving pseudo label accuracy). We run extensive experiments to evaluate ACPL on two public medical image classification benchmarks: Chest X-Ray14 for thorax disease multi-label classification and ISIC2018 for skin lesion multi-class classification. Our method outperforms previous SOTA SSL methods on both datasets	['Gustavo Carneiro', 'Vasileios Belagiannis', 'Yuyuan Liu', 'Yuanhong Chen', 'Yu Tian', 'Fengbei Liu']	2021-11-25	null	http://openaccess.thecvf.com//content/CVPR2022/html/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.pdf	cvpr-2022-1	['semi-supervised-medical-image-classification']	['medical']	[ 6.55118287e-01 1.54380187e-01 -8.94376338e-01 -4.72119331e-01 -1.35415113e+00 -2.21614882e-01 2.68824577e-01 4.90071952e-01 -2.83542335e-01 8.37006032e-01 -1.18563294e-01 -3.30486715e-01 -4.82711583e-01 -5.46304822e-01 -5.16475618e-01 -1.02072787e+00 3.69359404e-01 8.86128426e-01 1.58572823e-01 3.39685470e-01 -5.82530871e-02 2.16764569e-01 -1.61746323e+00 8.32551777e-01 9.62652147e-01 9.50589180e-01 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fe4abce4-b4c9-486f-9575-f51a34f1378c	at-which-level-should-we-extract-an-empirical	2004.02664	null	https://arxiv.org/abs/2004.02664v2	https://arxiv.org/pdf/2004.02664v2.pdf	At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization	Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence level. However, it is unclear whether performing extraction at sentence level is the best solution. In this work, we show that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising alternative. Specifically, we propose extracting sub-sentential units based on the constituency parsing tree. A neural extractive model which leverages the sub-sentential information and extracts them is presented. Extensive experiments and analyses show that extracting sub-sentential units performs competitively comparing to full sentence extraction under the evaluation of both automatic and human evaluations. Hopefully, our work could provide some inspiration of the basic extraction units in extractive summarization for future research.	['Furu Wei', 'Qingyu Zhou', 'Ming Zhou']	2020-04-06	null	https://aclanthology.org/2020.coling-main.492	https://aclanthology.org/2020.coling-main.492.pdf	coling-2020-8	['constituency-parsing', 'extractive-document-summarization']	['natural-language-processing', 'natural-language-processing']	[ 6.28348887e-01 6.09284818e-01 -3.66026342e-01 -2.63731658e-01 -1.09604871e+00 -7.09627926e-01 4.95884657e-01 5.84439695e-01 -6.48750722e-01 1.05853117e+00 1.18998599e+00 -2.66313404e-01 3.08927000e-01 -5.99693716e-01 -3.99450123e-01 -2.92483151e-01 -8.98049846e-02 2.80296151e-02 1.23033151e-02 -3.12613964e-01 7.40785420e-01 2.97739953e-01 -1.22932553e+00 7.99875498e-01 1.28677559e+00 4.24488932e-01 2.68291384e-01 9.26140726e-01 -4.29423213e-01 9.69895244e-01 -1.07568586e+00 -6.34580374e-01 5.76816313e-02 -7.30292201e-01 -1.26844549e+00 5.27132079e-02 4.69141632e-01 -5.74785590e-01 -4.39290069e-02 1.04134881e+00 5.56901395e-01 -7.61400685e-02 7.29613364e-01 -3.66018265e-01 -1.98531434e-01 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716daabc-1aed-4a6d-9b7b-50cf44c4b57a	cgi-stereo-accurate-and-real-time-stereo	2301.02789	null	https://arxiv.org/abs/2301.02789v2	https://arxiv.org/pdf/2301.02789v2.pdf	CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction	In this paper, we propose CGI-Stereo, a novel neural network architecture that can concurrently achieve real-time performance, competitive accuracy, and strong generalization ability. The core of our CGI-Stereo is a Context and Geometry Fusion (CGF) block which adaptively fuses context and geometry information for more effective cost aggregation and meanwhile provides feedback to feature learning to guide more effective contextual feature extraction. The proposed CGF can be easily embedded into many existing stereo matching networks, such as PSMNet, GwcNet and ACVNet. The resulting networks show a significant improvement in accuracy. Specially, the model which incorporates our CGF with ACVNet ranks $1^{st}$ on the KITTI 2012 and 2015 leaderboards among all the published methods. We further propose an informative and concise cost volume, named Attention Feature Volume (AFV), which exploits a correlation volume as attention weights to filter a feature volume. Based on CGF and AFV, the proposed CGI-Stereo outperforms all other published real-time methods on KITTI benchmarks and shows better generalization ability than other real-time methods. Code is available at https://github.com/gangweiX/CGI-Stereo.	['Xin Yang', 'Huan Zhou', 'Gangwei Xu']	2023-01-07	null	null	null	null	['stereo-matching-1']	['computer-vision']	[-6.65234774e-02 -4.08703655e-01 -6.80444390e-02 -5.46922505e-01 -5.90103805e-01 -2.07849309e-01 5.77452183e-01 -1.95985716e-02 -4.82565135e-01 4.46297169e-01 3.66093099e-01 -1.16322607e-01 -4.75905210e-01 -9.71849740e-01 -8.39072108e-01 -5.88249803e-01 1.04605183e-01 3.47211033e-01 3.12760174e-01 -3.47855359e-01 2.21168458e-01 5.10708869e-01 -1.82459021e+00 1.54210895e-01 1.10407996e+00 1.52362084e+00 4.16022837e-01 2.26591170e-01 4.71657142e-02 4.95189190e-01 -1.70416057e-01 -3.57397914e-01 5.08995891e-01 2.32189149e-01 -5.87327838e-01 -3.64049435e-01 1.03810239e+00 -4.65753108e-01 -7.69801199e-01 9.51844811e-01 7.54857957e-01 3.75128657e-01 4.77824330e-01 -1.15003407e+00 -5.50277710e-01 5.50914347e-01 -5.76819301e-01 3.70545626e-01 1.85260594e-01 2.67652780e-01 1.03177583e+00 -1.27092814e+00 5.53477347e-01 1.23557556e+00 6.94056451e-01 1.56694487e-01 -8.15233529e-01 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4.09055710e-01 1.28078043e-01 -1.02714264e+00 -1.14269562e-01 -6.49813950e-01]	[8.850839614868164, -2.213054656982422]
4e004a82-b606-4b7d-bbeb-3a862b390f7b	semi-supervised-haptic-material-recognition	1707.02796	null	http://arxiv.org/abs/1707.02796v2	http://arxiv.org/pdf/1707.02796v2.pdf	Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks	Material recognition enables robots to incorporate knowledge of material properties into their interactions with everyday objects. For example, material recognition opens up opportunities for clearer communication with a robot, such as "bring me the metal coffee mug", and recognizing plastic versus metal is crucial when using a microwave or oven. However, collecting labeled training data with a robot is often more difficult than unlabeled data. We present a semi-supervised learning approach for material recognition that uses generative adversarial networks (GANs) with haptic features such as force, temperature, and vibration. Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled. We explore how well this approach can recognize the material of new objects and we discuss challenges facing generalization. To motivate learning from unlabeled training data, we also compare results against several common supervised learning classifiers. In addition, we have released the dataset used for this work which consists of time-series haptic measurements from a robot that conducted thousands of interactions with 72 household objects.	['Sonia Chernova', 'Zackory Erickson', 'Charles C. Kemp']	2017-07-10	null	null	null	null	['material-recognition']	['computer-vision']	[ 5.59614301e-01 4.94356453e-01 2.60007381e-03 -4.18487132e-01 -7.49296129e-01 -6.08830452e-01 7.26167187e-02 -6.08137175e-02 -1.35795459e-01 8.15044999e-01 -2.09373757e-01 5.50022833e-02 1.27971604e-01 -9.48131025e-01 -1.54296458e+00 -6.71732247e-01 -1.18492462e-01 7.58245409e-01 -1.64893925e-01 -3.17428201e-01 8.85036364e-02 5.64954042e-01 -1.69288278e+00 3.61099809e-01 6.21873915e-01 1.30843866e+00 2.70882338e-01 6.51992798e-01 2.51243532e-01 7.21702039e-01 -6.64607286e-01 -1.09701648e-01 4.21307892e-01 1.97038516e-01 -1.02715564e+00 1.02591157e-01 1.69806257e-01 -5.01652896e-01 -9.69845429e-02 7.40086079e-01 2.00694472e-01 3.04253608e-01 1.14817894e+00 -1.48731136e+00 -7.13988960e-01 8.50360870e-01 -2.20367998e-01 -6.50413036e-01 6.89691007e-01 4.71701426e-03 5.20754874e-01 -5.95603943e-01 6.69070363e-01 1.19487500e+00 8.28717589e-01 7.64357626e-01 -1.17342615e+00 -6.78145647e-01 2.70557497e-02 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b034cb9d-c69e-4fcf-b5b8-d631e379dbc2	mitigating-frequency-bias-in-next-basket	2211.09072	null	https://arxiv.org/abs/2211.09072v1	https://arxiv.org/pdf/2211.09072v1.pdf	Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders	Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures the frequency of the user's interactions with the item. However, taking the PIF as an explicit feature incurs bias towards frequent items. Items that a user purchases frequently are assigned higher weights in the PIF-based recommender system and appear more frequently in the personalized recommendation list. As a result, the system will lose the fairness and balance between items that the user frequently purchases and items that the user never purchases. We refer to this systematic bias on personalized recommendation lists as frequency bias, which narrows users' browsing scope and reduces the system utility. We adopt causal inference theory to address this issue. Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram. In this paper, we propose a deconfounder model named FENDER (Frequency-aware Deconfounder for Next-basket Recommendation) to mitigate the frequency bias. With the deconfounder theory and the causal diagram we propose, FENDER decomposes PIF with a neural tensor layer to obtain substitute confounders for users and items. Then, FENDER performs unbiased recommendations considering the effect of these substitute confounders. Experimental results demonstrate that FENDER has derived diverse and fair results compared to ten baseline models on three datasets while achieving competitive performance. Further experiments illustrate how FENDER balances users' historical purchases and potential interests.	['Kannan Achan', 'Philip Yu', 'Stephen Guo', 'Kaushiki Nag', 'Luyi Ma', 'Zheng Liu', 'Xiaohan Li']	2022-11-16	null	null	null	null	['next-basket-recommendation']	['miscellaneous']	[-3.31637174e-01 -1.18570961e-01 -1.01530576e+00 -5.54135144e-01 1.77052617e-01 -1.97076678e-01 2.16264993e-01 -3.11899781e-01 -1.87800363e-01 4.93282706e-01 9.49983895e-01 -3.32045972e-01 -3.88661355e-01 -1.03570163e+00 -7.83968270e-01 -4.13826972e-01 -2.62263089e-01 1.74736008e-01 -1.05902441e-01 -2.98633665e-01 1.55668765e-01 -1.16113126e-01 -1.34603822e+00 5.58083355e-01 9.16437268e-01 8.37883353e-01 2.16628581e-01 -6.20335788e-02 5.67295961e-02 7.50561059e-01 -2.42205650e-01 -6.02030754e-01 4.00637746e-01 -1.68504909e-01 -4.44389343e-01 -3.09802800e-01 3.38168144e-01 -9.20394421e-01 -6.63775980e-01 9.10351038e-01 7.92542025e-02 4.00371134e-01 6.38819098e-01 -1.16026890e+00 -1.64002943e+00 1.39578223e+00 -6.88203633e-01 2.56577373e-01 2.93996334e-01 -2.76102215e-01 1.52121305e+00 -9.89372492e-01 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e5874343-5baa-456b-9c92-31cbd6f558fd	assessing-hidden-risks-of-llms-an-empirical	2305.10235	null	https://arxiv.org/abs/2305.10235v3	https://arxiv.org/pdf/2305.10235v3.pdf	Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility	The recent popularity of large language models (LLMs) has brought a significant impact to boundless fields, particularly through their open-ended ecosystem such as the APIs, open-sourced models, and plugins. However, with their widespread deployment, there is a general lack of research that thoroughly discusses and analyzes the potential risks concealed. In that case, we intend to conduct a preliminary but pioneering study covering the robustness, consistency, and credibility of LLMs systems. With most of the related literature in the era of LLM uncharted, we propose an automated workflow that copes with an upscaled number of queries/responses. Overall, we conduct over a million queries to the mainstream LLMs including ChatGPT, LLaMA, and OPT. Core to our workflow consists of a data primitive, followed by an automated interpreter that evaluates these LLMs under different adversarial metrical systems. As a result, we draw several, and perhaps unfortunate, conclusions that are quite uncommon from this trendy community. Briefly, they are: (i)-the minor but inevitable error occurrence in the user-generated query input may, by chance, cause the LLM to respond unexpectedly; (ii)-LLMs possess poor consistency when processing semantically similar query input. In addition, as a side finding, we find that ChatGPT is still capable to yield the correct answer even when the input is polluted at an extreme level. While this phenomenon demonstrates the powerful memorization of the LLMs, it raises serious concerns about using such data for LLM-involved evaluation in academic development. To deal with it, we propose a novel index associated with a dataset that roughly decides the feasibility of using such data for LLM-involved evaluation. Extensive empirical studies are tagged to support the aforementioned claims.	['Junbo Zhao', 'Haobo Wang', 'Gang Chen', 'Jie Fu', 'Sai Wu', 'Yifan Yanggong', 'Xuetao Ma', 'Yipeng chen', 'Tianyi Li', 'Mingfeng Ou', 'Wentao Ye']	2023-05-15	null	null	null	null	['memorization']	['natural-language-processing']	[-1.49493068e-01 -1.87495071e-02 5.42379282e-02 -8.40462521e-02 -1.09924853e+00 -1.09663785e+00 6.81211948e-01 1.99023902e-01 -4.08075362e-01 4.61165339e-01 -1.35912478e-01 -8.59674335e-01 -1.96096718e-01 -8.10916245e-01 -8.41526330e-01 -2.20854998e-01 -1.35180786e-01 2.96087861e-01 3.78509730e-01 -3.73157203e-01 4.73376036e-01 5.48806787e-01 -1.42028642e+00 1.42630741e-01 9.03496087e-01 1.02122116e+00 -2.37241477e-01 4.59700316e-01 -1.72860727e-01 9.35636163e-01 -9.69336629e-01 -1.27224135e+00 3.46654058e-01 2.22394511e-01 -1.05126417e+00 -5.64018071e-01 2.50174493e-01 -4.00969863e-01 1.29192054e-01 1.10577822e+00 7.09183455e-01 -3.18314552e-01 2.11399689e-01 -1.66253316e+00 -4.84585702e-01 8.40995669e-01 -3.97473574e-01 7.84849226e-02 7.08861113e-01 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c54f7bb3-4943-4ab3-9fe5-652994e74374	multi-channel-attention-selection-gan-with	1904.06807	null	http://arxiv.org/abs/1904.06807v2	http://arxiv.org/pdf/1904.06807v2.pdf	Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation	Cross-view image translation is challenging because it involves images with drastically different views and severe deformation. In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map. The proposed SelectionGAN explicitly utilizes the semantic information and consists of two stages. In the first stage, the condition image and the target semantic map are fed into a cycled semantic-guided generation network to produce initial coarse results. In the second stage, we refine the initial results by using a multi-channel attention selection mechanism. Moreover, uncertainty maps automatically learned from attentions are used to guide the pixel loss for better network optimization. Extensive experiments on Dayton, CVUSA and Ego2Top datasets show that our model is able to generate significantly better results than the state-of-the-art methods. The source code, data and trained models are available at https://github.com/Ha0Tang/SelectionGAN.	['Yan Yan', 'Hao Tang', 'Yanzhi Wang', 'Nicu Sebe', 'Jason J. Corso', 'Dan Xu']	2019-04-15	multi-channel-attention-selection-gan-with-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Tang_Multi-Channel_Attention_Selection_GAN_With_Cascaded_Semantic_Guidance_for_Cross-View_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_Multi-Channel_Attention_Selection_GAN_With_Cascaded_Semantic_Guidance_for_Cross-View_CVPR_2019_paper.pdf	cvpr-2019-6	['cross-view-image-to-image-translation', 'bird-view-synthesis']	['computer-vision', 'computer-vision']	[ 4.15011317e-01 2.34188080e-01 2.09385633e-01 -4.06528860e-01 -7.22517788e-01 -3.50175172e-01 6.61166251e-01 -5.42399645e-01 -1.20487370e-01 7.87774205e-01 1.40500382e-01 1.68616727e-01 2.39399582e-01 -8.81251812e-01 -9.42280650e-01 -7.52331495e-01 7.35776961e-01 4.85233337e-01 1.56768322e-01 -3.55870038e-01 3.02901655e-01 2.31041178e-01 -1.44860125e+00 3.43809366e-01 1.02428389e+00 8.82218122e-01 6.59079671e-01 4.03846383e-01 1.90504268e-01 5.53969026e-01 -1.33913949e-01 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1bafc0a1-0439-4ffa-b583-0aad9a4638db	federated-domain-generalization-a-survey	2306.01334	null	https://arxiv.org/abs/2306.01334v1	https://arxiv.org/pdf/2306.01334v1.pdf	Federated Domain Generalization: A Survey	Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge nodes. Consequently, it is imperative to develop models that can effectively generalize to unseen distributions where data is distributed across different domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG combines the strengths of federated learning (FL) and domain generalization (DG) techniques to enable multiple source domains to collaboratively learn a model capable of directly generalizing to unseen domains while preserving data privacy. However, generalizing the federated model under domain shifts is a technically challenging problem that has received scant attention in the research area so far. This paper presents the first survey of recent advances in this area. Initially, we discuss the development process from traditional machine learning to domain adaptation and domain generalization, leading to FDG as well as provide the corresponding formal definition. Then, we categorize recent methodologies into four classes: federated domain alignment, data manipulation, learning strategies, and aggregation optimization, and present suitable algorithms in detail for each category. Next, we introduce commonly used datasets, applications, evaluations, and benchmarks. Finally, we conclude this survey by providing some potential research topics for the future.	['Schahram Dustdar', 'Min Huang', 'Ilir Murturi', 'Praveen Kumar Donta', 'Rongfei Zeng', 'Xingwei Wang', 'Ying Li']	2023-06-02	null	null	null	null	['domain-generalization']	['methodology']	[ 2.52127767e-01 -2.68620133e-01 -5.45267642e-01 -7.39161789e-01 -4.54008609e-01 -8.87617052e-01 3.94735456e-01 3.36363286e-01 -1.42868504e-01 1.12095237e+00 -2.50883371e-01 -1.47116438e-01 -3.42762828e-01 -8.92167985e-01 -4.64625537e-01 -7.01070786e-01 -4.51279022e-02 6.55586123e-01 -2.06992462e-01 -1.20301545e-02 -1.16498873e-01 5.85449755e-01 -1.60109067e+00 2.92624056e-01 1.14455533e+00 1.29230547e+00 -2.14502975e-01 1.94957674e-01 -3.33010703e-01 3.07355493e-01 -9.35791254e-01 -6.94560349e-01 5.78311324e-01 -4.23194319e-01 -7.30196655e-01 -4.09554951e-02 4.47357029e-01 -1.88491598e-01 -1.53628945e-01 1.17603016e+00 5.43404579e-01 1.89272165e-01 5.82028329e-01 -1.83287692e+00 -8.28571141e-01 3.22400987e-01 -2.06670746e-01 8.64950493e-02 1.22023495e-02 -8.75967741e-02 6.07607543e-01 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874db3ac-d9d5-4a78-b6cf-3c54ee2dadc2	soft-landing-strategy-for-alleviating-the	2211.06023	null	https://arxiv.org/abs/2211.06023v1	https://arxiv.org/pdf/2211.06023v1.pdf	Soft-Landing Strategy for Alleviating the Task Discrepancy Problem in Temporal Action Localization Tasks	Temporal Action Localization (TAL) methods typically operate on top of feature sequences from a frozen snippet encoder that is pretrained with the Trimmed Action Classification (TAC) tasks, resulting in a task discrepancy problem. While existing TAL methods mitigate this issue either by retraining the encoder with a pretext task or by end-to-end fine-tuning, they commonly require an overload of high memory and computation. In this work, we introduce Soft-Landing (SoLa) strategy, an efficient yet effective framework to bridge the transferability gap between the pretrained encoder and the downstream tasks by incorporating a light-weight neural network, i.e., a SoLa module, on top of the frozen encoder. We also propose an unsupervised training scheme for the SoLa module; it learns with inter-frame Similarity Matching that uses the frame interval as its supervisory signal, eliminating the need for temporal annotations. Experimental evaluation on various benchmarks for downstream TAL tasks shows that our method effectively alleviates the task discrepancy problem with remarkable computational efficiency.	['Seon Joo Kim', 'Minsu Cho', 'Joungbin An', 'Hanjung Kim', 'Hyolim Kang']	2022-11-11	null	http://openaccess.thecvf.com//content/CVPR2023/html/Kang_Soft-Landing_Strategy_for_Alleviating_the_Task_Discrepancy_Problem_in_Temporal_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Kang_Soft-Landing_Strategy_for_Alleviating_the_Task_Discrepancy_Problem_in_Temporal_CVPR_2023_paper.pdf	cvpr-2023-1	['action-classification', 'action-localization']	['computer-vision', 'computer-vision']	[ 6.41686141e-01 -8.92579556e-03 -1.95490181e-01 -5.22028685e-01 -9.82140362e-01 -3.50882024e-01 5.84606647e-01 -1.92087665e-01 -6.48557305e-01 4.86688465e-01 1.97149292e-01 -1.74327776e-01 1.04196638e-01 -2.79470742e-01 -8.48978341e-01 -7.37193644e-01 4.76327632e-03 3.87254246e-02 7.39304602e-01 -1.83206141e-01 2.55990744e-01 -1.50094375e-01 -1.42609906e+00 6.43306613e-01 8.25691640e-01 1.39896441e+00 2.40251407e-01 3.27107102e-01 7.76366293e-02 1.26548374e+00 -5.53049207e-01 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a339d511-92f1-4f19-90d1-6f0b3bbd6724	on-reinforcement-learning-for-the-game-of	2212.11087	null	https://arxiv.org/abs/2212.11087v2	https://arxiv.org/pdf/2212.11087v2.pdf	On Reinforcement Learning for the Game of 2048	2048 is a single-player stochastic puzzle game. This intriguing and addictive game has been popular worldwide and has attracted researchers to develop game-playing programs. Due to its simplicity and complexity, 2048 has become an interesting and challenging platform for evaluating the effectiveness of machine learning methods. This dissertation conducts comprehensive research on reinforcement learning and computer game algorithms for 2048. First, this dissertation proposes optimistic temporal difference learning, which significantly improves the quality of learning by employing optimistic initialization to encourage exploration for 2048. Furthermore, based on this approach, a state-of-the-art program for 2048 is developed, which achieves the highest performance among all learning-based programs, namely an average score of 625377 points and a rate of 72% for reaching 32768-tiles. Second, this dissertation investigates several techniques related to 2048, including the n-tuple network ensemble learning, Monte Carlo tree search, and deep reinforcement learning. These techniques are promising for further improving the performance of the current state-of-the-art program. Finally, this dissertation discusses pedagogical applications related to 2048 by proposing course designs and summarizing the teaching experience. The proposed course designs adopt 2048-like games as materials for beginners to learn reinforcement learning and computer game algorithms. The courses have been successfully applied to graduate-level students and received well by student feedback.	['Hung Guei']	2022-12-21	null	null	null	null	['2048']	['playing-games']	[-3.89671147e-01 -2.04922870e-01 -1.89423770e-01 2.55195022e-01 -6.89566314e-01 -3.78958195e-01 -2.05354951e-03 7.53612816e-02 -4.09727395e-01 9.39564884e-01 -4.30732191e-01 -6.25284672e-01 -4.64170158e-01 -1.24251270e+00 -4.82550055e-01 -7.13332593e-01 -3.43919665e-01 3.95781517e-01 2.92422235e-01 -6.02178037e-01 7.70624995e-01 2.87294596e-01 -2.00561929e+00 2.92918712e-01 1.14019573e+00 7.75552630e-01 1.87729210e-01 7.50179470e-01 -1.51426390e-01 1.06230450e+00 -9.31437433e-01 -3.85373741e-01 3.84652577e-02 -6.21100426e-01 -7.72493780e-01 -5.80005229e-01 -2.62483865e-01 -2.07250178e-01 -2.26059914e-01 7.74800777e-01 8.80110860e-01 6.98455036e-01 3.66752863e-01 -1.38245130e+00 -3.19033027e-01 7.85228252e-01 -8.07217538e-01 2.64245898e-01 4.89827663e-01 1.81220412e-01 8.94145906e-01 -5.33079624e-01 2.55565077e-01 7.59471118e-01 6.51886404e-01 6.81740403e-01 -7.39823103e-01 -1.21028733e+00 -3.07730496e-01 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38de504a-e6b8-41dc-abf3-3f515902ace6	diagnostic-questions-the-neurips-2020	2007.12061	null	https://arxiv.org/abs/2007.12061v3	https://arxiv.org/pdf/2007.12061v3.pdf	Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge	Digital technologies are becoming increasingly prevalent in education, enabling personalized, high quality education resources to be accessible by students across the world. Importantly, among these resources are diagnostic questions: the answers that the students give to these questions reveal key information about the specific nature of misconceptions that the students may hold. Analyzing the massive quantities of data stemming from students' interactions with these diagnostic questions can help us more accurately understand the students' learning status and thus allow us to automate learning curriculum recommendations. In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers. These tasks closely mimic the goals of a real-world educational platform and are highly representative of the educational challenges faced today. We provide over 20 million examples of students' answers to mathematics questions from Eedi, a leading educational platform which thousands of students interact with daily around the globe. Participants to this competition have a chance to make a lasting, real-world impact on the quality of personalized education for millions of students across the world.	['José Miguel Hernández-Lobato', 'Evgeny Saveliev', 'Pashmina Cameron', 'Simon Woodhead', 'Richard E. Turner', 'Zichao Wang', 'Yordan Zaykov', 'Simon Peyton Jones', 'Cheng Zhang', 'Richard G. Baraniuk', 'Craig Barton', 'Angus Lamb']	2020-07-23	null	null	null	null	['misconceptions']	['miscellaneous']	[-2.88253397e-01 -5.13822958e-02 -3.26807708e-01 -2.56571263e-01 -7.46089756e-01 -1.05224931e+00 3.25564712e-01 9.21939075e-01 -2.27435634e-01 3.02389383e-01 4.81882691e-01 -9.05095756e-01 -5.61731040e-01 -1.20982015e+00 -6.62877262e-01 4.28998843e-03 2.50176311e-01 3.30710918e-01 6.33737743e-01 -5.99930882e-01 6.69944286e-01 4.28412467e-01 -2.26023340e+00 4.09351826e-01 1.22249794e+00 5.06828070e-01 1.13281518e-01 7.65801311e-01 -2.99066752e-01 1.01587474e+00 -7.25110829e-01 -4.33675796e-01 -3.73830169e-01 -3.98542225e-01 -1.17334950e+00 -2.90848821e-01 9.97092724e-01 -2.01411992e-01 -2.28174478e-01 1.06818211e+00 1.79564670e-01 7.83342481e-01 4.83743250e-02 -7.99216330e-01 -4.70342547e-01 5.34969866e-01 4.57689092e-02 5.92756569e-01 1.16367781e+00 -1.40244320e-01 1.05928814e+00 -4.16666478e-01 6.90949559e-01 9.42346871e-01 1.49704158e-01 4.23851937e-01 -9.49048936e-01 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3a8371ce-2f0c-4e88-b0fe-e80268ad8858	transition-forests-learning-discriminative	1607.02737	null	http://arxiv.org/abs/1607.02737v3	http://arxiv.org/pdf/1607.02737v3.pdf	Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection	A human action can be seen as transitions between one's body poses over time, where the transition depicts a temporal relation between two poses. Recognizing actions thus involves learning a classifier sensitive to these pose transitions as well as to static poses. In this paper, we introduce a novel method called transitions forests, an ensemble of decision trees that both learn to discriminate static poses and transitions between pairs of two independent frames. During training, node splitting is driven by alternating two criteria: the standard classification objective that maximizes the discrimination power in individual frames, and the proposed one in pairwise frame transitions. Growing the trees tends to group frames that have similar associated transitions and share same action label incorporating temporal information that was not available otherwise. Unlike conventional decision trees where the best split in a node is determined independently of other nodes, the transition forests try to find the best split of nodes jointly (within a layer) for incorporating distant node transitions. When inferring the class label of a new frame, it is passed down the trees and the prediction is made based on previous frame predictions and the current one in an efficient and online manner. We apply our method on varied skeleton action recognition and online detection datasets showing its suitability over several baselines and state-of-the-art approaches.	['Tae-Kyun Kim', 'Guillermo Garcia-Hernando']	2016-07-10	transition-forests-learning-discriminative-1	http://openaccess.thecvf.com/content_cvpr_2017/html/Garcia-Hernando_Transition_Forests_Learning_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Garcia-Hernando_Transition_Forests_Learning_CVPR_2017_paper.pdf	cvpr-2017-7	['spatio-temporal-action-localization']	['computer-vision']	[ 8.08172107e-01 2.52762586e-01 -5.38806736e-01 -3.82325441e-01 -5.23987114e-01 -2.61161089e-01 5.72904408e-01 2.29059830e-01 -3.61747950e-01 6.27336264e-01 1.65930569e-01 1.02756999e-01 -2.59339754e-02 -7.01242030e-01 -5.16998410e-01 -8.69870484e-01 -3.46694291e-01 7.36094415e-01 9.99531806e-01 1.02097325e-01 -3.39767197e-03 5.56532562e-01 -1.67258215e+00 7.82118380e-01 2.87654638e-01 1.03294420e+00 -6.28039092e-02 8.51811051e-01 1.06581718e-01 8.81477118e-01 -3.74359936e-01 -2.73731381e-01 3.72341812e-01 -7.27087438e-01 -1.11693096e+00 3.63173097e-01 5.38058162e-01 -2.25452766e-01 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1169e10e-8e3f-424a-943b-cf6dbe7b8d44	learning-collision-free-and-torque-limited	2103.03793	null	https://arxiv.org/abs/2103.03793v3	https://arxiv.org/pdf/2103.03793v3.pdf	Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors	This paper presents an approach for learning online generation of collision-free and torque-limited robot trajectories. In order to generate future motions, a neural network is periodically invoked. Based on the current kinematic state of the robot and the network output, a trajectory for the current time interval can be calculated. The main idea of our paper is to execute the computed motion only if a collision-free and torque-limited way to continue the trajectory is known. In practice, the motion computed for the current time interval is extended by a braking trajectory and simulated using a physics engine. If the simulated trajectory complies with all safety constraints, the computed motion is carried out. Otherwise, the braking trajectory calculated in the previous time interval serves as an alternative safe behavior. Given a task-specific reward function, the neural network is trained using reinforcement learning. The design of the action space used for reinforcement learning ensures that all computed trajectories comply with kinematic joint limits. For our evaluation, simulated humanoid robots and industrial robots are trained to reach as many randomly placed target points as possible. We show that our method reliably prevents collisions with static obstacles and collisions between the robot arms, while generating motions that respect both torque limits and kinematic joint limits. Experiments with a real robot demonstrate that safe trajectories can be generated in real-time.	['Torsten Kröger', 'Jonas C. Kiemel']	2021-03-05	null	null	null	null	['industrial-robots']	['robots']	[ 8.96878764e-02 4.99591947e-01 -3.66095304e-01 1.62725121e-01 -2.10956261e-01 -4.62453514e-01 3.08021218e-01 9.12023932e-02 -8.28840792e-01 1.05988014e+00 -5.69008827e-01 -4.39505577e-01 -4.10459161e-01 -1.00521958e+00 -8.73897314e-01 -8.75188231e-01 -3.85154992e-01 6.78844392e-01 3.03971380e-01 -3.17038596e-01 3.00625980e-01 9.82293546e-01 -1.36284089e+00 -3.10933501e-01 7.79212058e-01 6.15749598e-01 7.29146957e-01 6.77744567e-01 3.13238889e-01 5.35242140e-01 -3.86737436e-01 3.85445178e-01 4.39215302e-01 -4.16970253e-01 -8.31512690e-01 2.89208353e-01 -5.47810376e-01 -3.36646438e-01 -3.63588005e-01 8.75490129e-01 6.40098527e-02 8.96186829e-01 5.73582947e-01 -1.57927728e+00 1.93087891e-01 6.19538426e-01 5.80566470e-03 -4.22481060e-01 1.08282037e-01 5.13235569e-01 2.71922767e-01 -2.24055126e-01 9.16832149e-01 9.37592387e-01 2.68459320e-01 7.75793850e-01 -9.61801291e-01 -3.56911361e-01 6.85722604e-02 1.00314245e-02 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1.18555166e-01]	[4.7477569580078125, 1.529340147972107]
58906c85-914c-4673-a957-71d1b5783cda	clinicalbert-modeling-clinical-notes-and	1904.05342	null	https://arxiv.org/abs/1904.05342v3	https://arxiv.org/pdf/1904.05342v3.pdf	ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission	Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structured data, because notes are high-dimensional and sparse. This work develops and evaluates representations of clinical notes using bidirectional transformers (ClinicalBERT). ClinicalBERT uncovers high-quality relationships between medical concepts as judged by humans. ClinicalBert outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intensive care unit. Code and model parameters are available.	['Jaan Altosaar', 'Kexin Huang', 'Rajesh Ranganath']	2019-04-10	null	null	null	null	['readmission-prediction']	['medical']	[-2.68175930e-01 3.04884106e-01 -5.60815156e-01 -5.73011041e-01 -8.01533878e-01 -7.06452429e-01 1.79825753e-01 1.17048180e+00 1.40967160e-01 9.01726723e-01 1.39478230e+00 -4.67011660e-01 -6.32040739e-01 -7.45655775e-01 -5.49412705e-02 -1.91222265e-01 -7.49935091e-01 1.16464448e+00 -6.91588521e-01 1.44488022e-01 4.81323525e-02 4.42359656e-01 -6.33315325e-01 8.82783949e-01 5.87382257e-01 1.07264066e+00 -4.87491697e-01 6.75499082e-01 -1.57231823e-01 1.47728693e+00 -3.83451730e-01 -3.91691118e-01 1.27326488e-01 -5.54039359e-01 -5.06057560e-01 -1.82961762e-01 2.64110744e-01 -6.40310585e-01 -3.14753503e-01 4.26623613e-01 8.10124099e-01 -2.60999411e-01 8.60051870e-01 -9.82917190e-01 -1.03492856e+00 6.59841239e-01 2.69784421e-01 3.42699647e-01 5.59100032e-01 1.72824740e-01 1.13303161e+00 -1.04830825e+00 7.85349548e-01 9.37425792e-01 1.24201536e+00 2.51633316e-01 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b6436857-b94e-4de1-bdf4-68bf805348af	recipe-for-a-general-powerful-scalable-graph	2205.12454	null	https://arxiv.org/abs/2205.12454v4	https://arxiv.org/pdf/2205.12454v4.pdf	Recipe for a General, Powerful, Scalable Graph Transformer	We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack a common foundation about what constitutes a good positional or structural encoding, and what differentiates them. In this paper, we summarize the different types of encodings with a clearer definition and categorize them as being $\textit{local}$, $\textit{global}$ or $\textit{relative}$. The prior GTs are constrained to small graphs with a few hundred nodes, here we propose the first architecture with a complexity linear in the number of nodes and edges $O(N+E)$ by decoupling the local real-edge aggregation from the fully-connected Transformer. We argue that this decoupling does not negatively affect the expressivity, with our architecture being a universal function approximator on graphs. Our GPS recipe consists of choosing 3 main ingredients: (i) positional/structural encoding, (ii) local message-passing mechanism, and (iii) global attention mechanism. We provide a modular framework $\textit{GraphGPS}$ that supports multiple types of encodings and that provides efficiency and scalability both in small and large graphs. We test our architecture on 16 benchmarks and show highly competitive results in all of them, show-casing the empirical benefits gained by the modularity and the combination of different strategies.	['Dominique Beaini', 'Guy Wolf', 'Anh Tuan Luu', 'Vijay Prakash Dwivedi', 'Mikhail Galkin', 'Ladislav Rampášek']	2022-05-25	null	null	null	null	['graph-property-prediction', 'graph-regression']	['graphs', 'graphs']	[ 8.58426839e-02 3.74511093e-01 -2.54078776e-01 -9.79899168e-02 -4.59733188e-01 -6.15537941e-01 4.91054386e-01 4.13177639e-01 -1.71160743e-01 5.71768403e-01 -6.74786195e-02 -7.19932377e-01 -4.54102606e-01 -1.39088118e+00 -9.62058842e-01 -6.85832381e-01 -6.25707984e-01 6.27773285e-01 4.85007137e-01 -4.95663166e-01 -1.15745224e-01 3.20064753e-01 -1.47851276e+00 9.09419134e-02 7.23700166e-01 1.19926763e+00 4.04402912e-02 6.94994569e-01 -3.04983914e-01 1.05956721e+00 -4.10422415e-01 -8.30870509e-01 3.26914996e-01 -2.93863773e-01 -1.13172793e+00 -1.33662313e-01 3.26557040e-01 2.12752516e-03 -7.20678210e-01 1.00016236e+00 4.22907531e-01 -3.09962835e-02 3.75777721e-01 -1.36323512e+00 -6.75731599e-01 1.14867473e+00 -5.36116064e-01 2.61758626e-01 2.19702765e-01 1.35938272e-01 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75aa7bc6-0921-428e-a023-9a750f2ffe32	shortcomings-of-question-answering-based	2210.06748	null	https://arxiv.org/abs/2210.06748v2	https://arxiv.org/pdf/2210.06748v2.pdf	Shortcomings of Question Answering Based Factuality Frameworks for Error Localization	Despite recent progress in abstractive summarization, models often generate summaries with factual errors. Numerous approaches to detect these errors have been proposed, the most popular of which are question answering (QA)-based factuality metrics. These have been shown to work well at predicting summary-level factuality and have potential to localize errors within summaries, but this latter capability has not been systematically evaluated in past research. In this paper, we conduct the first such analysis and find that, contrary to our expectations, QA-based frameworks fail to correctly identify error spans in generated summaries and are outperformed by trivial exact match baselines. Our analysis reveals a major reason for such poor localization: questions generated by the QG module often inherit errors from non-factual summaries which are then propagated further into downstream modules. Moreover, even human-in-the-loop question generation cannot easily offset these problems. Our experiments conclusively show that there exist fundamental issues with localization using the QA framework which cannot be fixed solely by stronger QA and QG models.	['Greg Durrett', 'Tanya Goyal', 'Ryo Kamoi']	2022-10-13	null	null	null	null	['abstractive-text-summarization', 'question-generation']	['natural-language-processing', 'natural-language-processing']	[ 2.85681337e-01 5.66615939e-01 -2.50322241e-02 -1.47127226e-01 -1.60810149e+00 -8.02741766e-01 8.61961722e-01 7.26604044e-01 -1.48376495e-01 1.03404403e+00 8.44923794e-01 -4.67343241e-01 -2.72743553e-01 -5.83161354e-01 -7.64780164e-01 -5.07288612e-02 3.86177450e-01 5.68121552e-01 3.57297868e-01 -3.99741828e-01 9.39177096e-01 -1.84909403e-01 -1.56611407e+00 5.39099753e-01 1.49495304e+00 3.19172472e-01 -8.63816440e-02 9.36075449e-01 -4.41642433e-01 1.28380322e+00 -1.23486948e+00 -7.02675581e-01 -3.61068010e-01 -8.47489715e-01 -1.17614472e+00 -3.72352451e-01 9.14475441e-01 -4.06497598e-01 -5.51960245e-02 9.02302921e-01 5.79448223e-01 -2.67678201e-01 7.15727806e-01 -1.04427314e+00 -6.22798860e-01 8.33940625e-01 -9.69506279e-02 4.98787642e-01 8.54933739e-01 1.30974099e-01 1.56077552e+00 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23668df6-3157-411a-b836-4015bb1a2020	decentralized-stochastic-multi-player-multi	2212.06279	null	https://arxiv.org/abs/2212.06279v1	https://arxiv.org/pdf/2212.06279v1.pdf	Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits	Multi-player multi-armed bandit is an increasingly relevant decision-making problem, motivated by applications to cognitive radio systems. Most research for this problem focuses exclusively on the settings that players have \textit{full access} to all arms and receive no reward when pulling the same arm. Hence all players solve the same bandit problem with the goal of maximizing their cumulative reward. However, these settings neglect several important factors in many real-world applications, where players have \textit{limited access} to \textit{a dynamic local subset of arms} (i.e., an arm could sometimes be ``walking'' and not accessible to the player). To this end, this paper proposes a \textit{multi-player multi-armed walking bandits} model, aiming to address aforementioned modeling issues. The goal now is to maximize the reward, however, players can only pull arms from the local subset and only collect a full reward if no other players pull the same arm. We adopt Upper Confidence Bound (UCB) to deal with the exploration-exploitation tradeoff and employ distributed optimization techniques to properly handle collisions. By carefully integrating these two techniques, we propose a decentralized algorithm with near-optimal guarantee on the regret, and can be easily implemented to obtain competitive empirical performance.	['Jian Li', 'Guojun Xiong']	2022-12-12	null	null	null	null	['distributed-optimization']	['methodology']	[ 9.86989290e-02 3.31881762e-01 -7.66803682e-01 1.79165095e-01 -8.22792828e-01 -8.48082304e-01 -1.21180743e-01 -8.80533010e-02 -8.46621692e-01 1.18212748e+00 -3.46728593e-01 -7.29241610e-01 -8.50347698e-01 -1.12141418e+00 -5.26954770e-01 -1.08736050e+00 -1.27709642e-01 7.78495312e-01 2.08147056e-02 -1.86237484e-01 7.27481162e-03 3.16207081e-01 -8.96312892e-01 -2.66496599e-01 6.93962812e-01 1.46783876e+00 4.00493413e-01 5.44339955e-01 -9.48224738e-02 5.51258981e-01 -6.10062659e-01 -4.75967377e-01 6.05251908e-01 -4.17646140e-01 -5.88182569e-01 1.49662867e-01 -5.78986287e-01 -6.73040390e-01 -2.64326930e-01 9.19551253e-01 6.30928934e-01 3.38937014e-01 3.57067257e-01 -1.40380013e+00 -2.79982030e-01 1.06175101e+00 -1.49962270e+00 5.57856917e-01 -5.98265557e-03 -2.37798676e-01 1.20792615e+00 6.80397674e-02 1.44872636e-01 8.78863990e-01 -3.39632109e-02 4.00677443e-01 -9.86808121e-01 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-1.04165745e+00 -3.14459383e-01 3.14507186e-01 -7.99645901e-01 -1.14760116e-01 -8.17700624e-01]	[4.52764368057251, 3.2689225673675537]
d6933c70-3336-4c0b-8c1f-d91b837d15fe	text-annotation-graphs-annotating-complex	1711.00529	null	http://arxiv.org/abs/1711.00529v2	http://arxiv.org/pdf/1711.00529v2.pdf	Text Annotation Graphs: Annotating Complex Natural Language Phenomena	This paper introduces a new web-based software tool for annotating text, Text Annotation Graphs, or TAG. It provides functionality for representing complex relationships between words and word phrases that are not available in other software tools, including the ability to define and visualize relationships between the relationships themselves (semantic hypergraphs). Additionally, we include an approach to representing text annotations in which annotation subgraphs, or semantic summaries, are used to show relationships outside of the sequential context of the text itself. Users can use these subgraphs to quickly find similar structures within the current document or external annotated documents. Initially, TAG was developed to support information extraction tasks on a large database of biomedical articles. However, our software is flexible enough to support a wide range of annotation tasks for any domain. Examples are provided that showcase TAG's capabilities on morphological parsing and event extraction tasks. The TAG software is available at: https://github.com/ CreativeCodingLab/TextAnnotationGraphs.	['Marco A. Valenzuela-Escárcega', 'Gus Hahn-Powell', 'Mihai Surdeanu', 'Angus G. Forbes', 'Kristine Lee']	2017-11-01	text-annotation-graphs-annotating-complex-2	https://aclanthology.org/L18-1169	https://aclanthology.org/L18-1169.pdf	lrec-2018-5	['text-annotation']	['natural-language-processing']	[ 2.73476601e-01 4.95970905e-01 -1.55415446e-01 -4.13297921e-01 -6.49268687e-01 -9.61102188e-01 4.84239519e-01 1.07375562e+00 -1.40091255e-01 5.50782442e-01 6.70294762e-01 -6.10554755e-01 -1.67010292e-01 -7.04829931e-01 1.56715773e-02 -2.45655179e-01 1.10912714e-02 3.68996799e-01 3.90865743e-01 -6.15965463e-02 2.72892535e-01 2.95239121e-01 -1.23982728e+00 5.38104594e-01 6.14508629e-01 2.55084902e-01 3.97988647e-01 4.91738647e-01 -1.01088607e+00 4.21332181e-01 -7.92788029e-01 -2.95582771e-01 -3.93915057e-01 -5.39071918e-01 -1.02299833e+00 -1.44037008e-01 1.85424462e-01 2.36669943e-01 6.59763888e-02 1.08094227e+00 4.17963743e-01 -5.14582135e-02 2.61771411e-01 -1.16921437e+00 -5.04242361e-01 1.14314461e+00 -2.60593861e-01 4.64102179e-01 9.28463876e-01 -3.89827162e-01 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4fc2a287-54b9-4482-a897-91e9e3ad336d	repbin-constraint-based-graph-representation	2112.11696	null	https://arxiv.org/abs/2112.11696v1	https://arxiv.org/pdf/2112.11696v1.pdf	RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning	Mixed communities of organisms are found in many environments (from the human gut to marine ecosystems) and can have profound impact on human health and the environment. Metagenomics studies the genomic material of such communities through high-throughput sequencing that yields DNA subsequences for subsequent analysis. A fundamental problem in the standard workflow, called binning, is to discover clusters, of genomic subsequences, associated with the unknown constituent organisms. Inherent noise in the subsequences, various biological constraints that need to be imposed on them and the skewed cluster size distribution exacerbate the difficulty of this unsupervised learning problem. In this paper, we present a new formulation using a graph where the nodes are subsequences and edges represent homophily information. In addition, we model biological constraints providing heterophilous signal about nodes that cannot be clustered together. We solve the binning problem by developing new algorithms for (i) graph representation learning that preserves both homophily relations and heterophily constraints (ii) constraint-based graph clustering method that addresses the problems of skewed cluster size distribution. Extensive experiments, on real and synthetic datasets, demonstrate that our approach, called RepBin, outperforms a wide variety of competing methods. Our constraint-based graph representation learning and clustering methods, that may be useful in other domains as well, advance the state-of-the-art in both metagenomics binning and graph representation learning.	['Yu Lin', 'Vaibhav Rajan', 'Yujia Zhang', 'Vijini Mallawaarachchi', 'Hansheng Xue']	2021-12-22	null	null	null	null	['graph-clustering']	['graphs']	[ 5.43752015e-01 -2.91616231e-01 -5.41695841e-02 1.75846536e-02 1.26824275e-01 -8.90734494e-01 4.18931305e-01 8.09395730e-01 5.05499355e-02 7.27512240e-01 2.31959745e-01 -4.23265696e-01 -5.88216066e-01 -1.02327156e+00 -7.51738369e-01 -1.19594085e+00 -6.35151148e-01 7.76788116e-01 3.21668051e-02 2.82697976e-02 2.29433119e-01 2.71183550e-01 -1.42074609e+00 3.93593162e-01 8.11127484e-01 2.56797940e-01 3.56311589e-01 8.06344569e-01 -4.41799164e-01 2.74363995e-01 -4.00049418e-01 -7.30845556e-02 3.69024605e-01 -6.55372918e-01 -4.31741983e-01 1.44912049e-01 9.33410227e-02 6.24318361e-01 -1.24030538e-01 1.33553791e+00 3.21011871e-01 3.29962261e-02 8.81628215e-01 -1.42073941e+00 -4.09450620e-01 9.20747817e-01 -9.41427708e-01 1.88986778e-01 2.33607784e-01 1.10307336e-01 1.16758919e+00 -3.05629104e-01 9.81743097e-01 1.49284256e+00 7.82823741e-01 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60a351ad-bb39-492b-b3e2-5b76d9223857	evaluate-confidence-instead-of-perplexity-for	2208.11007	null	https://arxiv.org/abs/2208.11007v1	https://arxiv.org/pdf/2208.11007v1.pdf	Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoning	Commonsense reasoning is an appealing topic in natural language processing (NLP) as it plays a fundamental role in supporting the human-like actions of NLP systems. With large-scale language models as the backbone, unsupervised pre-training on numerous corpora shows the potential to capture commonsense knowledge. Current pre-trained language model (PLM)-based reasoning follows the traditional practice using perplexity metric. However, commonsense reasoning is more than existing probability evaluation, which is biased by word frequency. This paper reconsiders the nature of commonsense reasoning and proposes a novel commonsense reasoning metric, Non-Replacement Confidence (NRC). In detail, it works on PLMs according to the Replaced Token Detection (RTD) pre-training objective in ELECTRA, in which the corruption detection objective reflects the confidence on contextual integrity that is more relevant to commonsense reasoning than existing probability. Our proposed novel method boosts zero-shot performance on two commonsense reasoning benchmark datasets and further seven commonsense question-answering datasets. Our analysis shows that pre-endowed commonsense knowledge, especially for RTD-based PLMs, is essential in downstream reasoning.	['Hai Zhao', 'Zuchao Li', 'Letian Peng']	2022-08-23	null	null	null	null	['unsupervised-pre-training']	['methodology']	[ 5.26977301e-01 3.45141292e-01 -6.36419952e-02 -3.14178020e-01 -6.92406416e-01 -4.79918659e-01 9.98753369e-01 6.37191474e-01 -6.59068763e-01 7.38866568e-01 7.47022450e-01 -6.09051287e-01 -2.83822775e-01 -1.15511930e+00 -5.05632401e-01 -2.79514313e-01 5.17080307e-01 4.76874471e-01 5.14567912e-01 -8.74160230e-01 8.35491419e-01 8.08309093e-02 -1.38980758e+00 6.02115750e-01 1.39610648e+00 8.12935829e-01 3.54891658e-01 5.78024209e-01 -7.01961577e-01 1.64421189e+00 -7.52504528e-01 -9.48676229e-01 -3.40153217e-01 -4.89619851e-01 -1.34566283e+00 -7.58460820e-01 3.04112226e-01 1.15209410e-03 -1.00898534e-01 1.42791927e+00 3.76918495e-01 4.77825373e-01 7.17123270e-01 -1.21736562e+00 -1.21728921e+00 1.16419566e+00 1.05811939e-01 6.35823488e-01 7.47626007e-01 2.01537207e-01 1.48293257e+00 -6.01792157e-01 6.19608641e-01 1.49168992e+00 4.44524199e-01 5.29708683e-01 -8.56076419e-01 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-5.16427755e-01 6.48019671e-01 -1.77947953e-02 -5.81675351e-01 -5.24557114e-01 1.07939951e-01]	[10.074423789978027, 8.036636352539062]
89e7271f-50a0-4fc3-8a43-2e09c09a978f	a-unified-model-and-dimension-for-interactive	2306.06184	null	https://arxiv.org/abs/2306.06184v1	https://arxiv.org/pdf/2306.06184v1.pdf	A Unified Model and Dimension for Interactive Estimation	We study an abstract framework for interactive learning called interactive estimation in which the goal is to estimate a target from its "similarity'' to points queried by the learner. We introduce a combinatorial measure called dissimilarity dimension which largely captures learnability in our model. We present a simple, general, and broadly-applicable algorithm, for which we obtain both regret and PAC generalization bounds that are polynomial in the new dimension. We show that our framework subsumes and thereby unifies two classic learning models: statistical-query learning and structured bandits. We also delineate how the dissimilarity dimension is related to well-known parameters for both frameworks, in some cases yielding significantly improved analyses.	['Robert Schapire', 'Aldo Pacchiano', 'Miroslav Dudik', 'Nataly Brukhim']	2023-06-09	null	null	null	null	['generalization-bounds']	['methodology']	[ 4.69199270e-02 4.29955721e-01 -9.24566627e-01 -4.51895088e-01 -1.74610937e+00 -1.19723403e+00 3.90020490e-01 2.60299116e-01 -4.61901426e-01 1.00682855e+00 -1.63353290e-02 -2.48107567e-01 -8.13977420e-01 -4.82698768e-01 -1.15865731e+00 -7.30645716e-01 -3.97620380e-01 6.49679720e-01 1.11491732e-01 3.29784065e-01 3.86463970e-01 4.81354713e-01 -1.35787523e+00 3.14620733e-02 8.36882412e-01 1.30250823e+00 -2.60845035e-01 6.88351572e-01 -1.42554775e-01 3.50791514e-01 -1.16906911e-01 -7.93092191e-01 5.16482770e-01 -1.14530802e-01 -1.01687014e+00 -3.06577375e-03 5.36292613e-01 -2.75655955e-01 -2.39583805e-01 1.00100696e+00 2.42086291e-01 1.40376911e-01 8.23634744e-01 -1.18640900e+00 -4.42184567e-01 6.21284008e-01 -3.64288449e-01 2.35218927e-01 4.27913994e-01 -2.89096713e-01 1.68354011e+00 -6.14982605e-01 5.40509403e-01 1.24134064e+00 6.38530731e-01 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d780f657-3d1d-46f1-ac20-656d224a2bb6	answer-ranking-in-community-question	2212.01218	null	https://arxiv.org/abs/2212.01218v1	https://arxiv.org/pdf/2212.01218v1.pdf	Answer ranking in Community Question Answering: a deep learning approach	Community Question Answering is the field of computational linguistics that deals with problems derived from the questions and answers posted to websites such as Quora or Stack Overflow. Among some of these problems we find the issue of ranking the multiple answers posted in reply to each question by how informative they are in the attempt to solve the original question. This work tries to advance the state of the art on answer ranking for community Question Answering by proceeding with a deep learning approach. We started off by creating a large data set of questions and answers posted to the Stack Overflow website. We then leveraged the natural language processing capabilities of dense embeddings and LSTM networks to produce a prediction for the accepted answer attribute, and present the answers in a ranked form ordered by how likely they are to be marked as accepted by the question asker. We also produced a set of numerical features to assist with the answer ranking task. These numerical features were either extracted from metadata found in the Stack Overflow posts or derived from the questions and answers texts. We compared the performance of our deep learning models against a set of forest and boosted trees ensemble methods and found that our models could not improve the best baseline results. We speculate that this lack of performance improvement versus the baseline models may be caused by the large number of out of vocabulary words present in the programming code snippets found in the questions and answers text. We conclude that while a deep learning approach may be helpful in answer ranking problems new methods should be developed to assist with the large number of out of vocabulary words present in the programming code snippets	['Lucas Valentin']	2022-10-16	null	null	null	null	['community-question-answering', 'community-question-answering']	['miscellaneous', 'natural-language-processing']	[-1.74232602e-01 1.29880711e-01 2.44189441e-01 -4.65949416e-01 -1.01054657e+00 -6.59581125e-01 5.16491354e-01 6.59062803e-01 -5.57517588e-01 4.10133034e-01 8.83322418e-01 -7.29826331e-01 -4.10628140e-01 -9.77939785e-01 -4.66907382e-01 -1.65306240e-01 5.45030609e-02 6.08065546e-01 4.26706225e-01 -5.19516110e-01 7.61122465e-01 -1.07464351e-01 -1.64093149e+00 6.74682319e-01 8.75298560e-01 8.60179782e-01 3.38576794e-01 8.65780175e-01 -9.33997512e-01 1.35892737e+00 -6.02522850e-01 -4.77435619e-01 -6.09002896e-02 -3.17371488e-01 -1.28638184e+00 -3.97807568e-01 7.20089674e-01 -4.90668356e-01 -2.15530857e-01 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ac86d165-c341-4c41-8270-3a2bd759d40f	prompt-and-trait-relation-aware-cross-prompt	2305.16826	null	https://arxiv.org/abs/2305.16826v1	https://arxiv.org/pdf/2305.16826v1.pdf	Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring	Automated essay scoring (AES) aims to score essays written for a given prompt, which defines the writing topic. Most existing AES systems assume to grade essays of the same prompt as used in training and assign only a holistic score. However, such settings conflict with real-education situations; pre-graded essays for a particular prompt are lacking, and detailed trait scores of sub-rubrics are required. Thus, predicting various trait scores of unseen-prompt essays (called cross-prompt essay trait scoring) is a remaining challenge of AES. In this paper, we propose a robust model: prompt- and trait relation-aware cross-prompt essay trait scorer. We encode prompt-aware essay representation by essay-prompt attention and utilizing the topic-coherence feature extracted by the topic-modeling mechanism without access to labeled data; therefore, our model considers the prompt adherence of an essay, even in a cross-prompt setting. To facilitate multi-trait scoring, we design trait-similarity loss that encapsulates the correlations of traits. Experiments prove the efficacy of our model, showing state-of-the-art results for all prompts and traits. Significant improvements in low-resource-prompt and inferior traits further indicate our model's strength.	['Gary Geunbae Lee', 'Yunsu Kim', 'Heejin Do']	2023-05-26	null	null	null	null	['automated-essay-scoring']	['natural-language-processing']	[-4.48715920e-03 -3.29574496e-01 -3.93319517e-01 -5.87757528e-01 -1.06875551e+00 -7.56834745e-01 3.71582896e-01 3.22880566e-01 -6.95153251e-02 5.36935687e-01 4.54980761e-01 1.80937499e-01 -3.57503176e-01 -5.46757698e-01 -9.72855017e-02 -3.16471249e-01 7.36788273e-01 3.33953738e-01 -6.84967265e-02 -1.38413399e-01 6.82639360e-01 -1.91037282e-01 -1.27465498e+00 4.09776211e-01 1.22409296e+00 1.07205737e+00 2.99740314e-01 7.83196568e-01 -2.13014126e-01 9.22393322e-01 -9.49645281e-01 -6.95418596e-01 -4.44079712e-02 -6.05212867e-01 -8.30492437e-01 1.37733921e-01 8.71352136e-01 -3.41462225e-01 -2.57465005e-01 8.84565651e-01 3.67487490e-01 2.86742330e-01 9.27699387e-01 -1.01803493e+00 -1.05606544e+00 4.04829055e-01 -4.08564895e-01 3.74928005e-02 7.48731256e-01 1.57367185e-01 1.60506201e+00 -9.35874164e-01 1.62556525e-02 5.95492423e-01 6.68534815e-01 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6b45bab0-0465-4b00-831d-eabd405af930	vidas-video-depth-aware-saliency-network	2305.11729	null	https://arxiv.org/abs/2305.11729v1	https://arxiv.org/pdf/2305.11729v1.pdf	ViDaS Video Depth-aware Saliency Network	We introduce ViDaS, a two-stream, fully convolutional Video, Depth-Aware Saliency network to address the problem of attention modeling ``in-the-wild", via saliency prediction in videos. Contrary to existing visual saliency approaches using only RGB frames as input, our network employs also depth as an additional modality. The network consists of two visual streams, one for the RGB frames, and one for the depth frames. Both streams follow an encoder-decoder approach and are fused to obtain a final saliency map. The network is trained end-to-end and is evaluated in a variety of different databases with eye-tracking data, containing a wide range of video content. Although the publicly available datasets do not contain depth, we estimate it using three different state-of-the-art methods, to enable comparisons and a deeper insight. Our method outperforms in most cases state-of-the-art models and our RGB-only variant, which indicates that depth can be beneficial to accurately estimating saliency in videos displayed on a 2D screen. Depth has been widely used to assist salient object detection problems, where it has been proven to be very beneficial. Our problem though differs significantly from salient object detection, since it is not restricted to specific salient objects, but predicts human attention in a more general aspect. These two problems not only have different objectives, but also different ground truth data and evaluation metrics. To our best knowledge, this is the first competitive deep learning video saliency estimation approach that combines both RGB and Depth features to address the general problem of saliency estimation ``in-the-wild". The code will be publicly released.	['Petros Maragos', 'Petros Koutras', 'Antigoni Tsiami', 'Ioanna Diamanti']	2023-05-19	null	null	null	null	['saliency-prediction', 'salient-object-detection-1']	['computer-vision', 'computer-vision']	[ 4.33173448e-01 8.35841820e-02 -2.14135885e-01 -1.54705018e-01 -5.50708354e-01 -2.01736420e-01 4.91540372e-01 7.75198489e-02 -4.04264331e-01 5.48397422e-01 2.76081890e-01 1.06143720e-01 3.11434537e-01 -3.59523207e-01 -9.24948931e-01 -6.16210282e-01 2.81183776e-02 -5.54527231e-02 9.67457116e-01 -3.31079096e-01 3.82669568e-01 8.60719308e-02 -2.16278815e+00 3.62422109e-01 5.29177606e-01 1.40823746e+00 7.60110319e-01 6.43500686e-01 9.73571837e-02 1.16996920e+00 -2.30327874e-01 -3.15975338e-01 2.67659664e-01 -3.28658789e-01 -8.19768071e-01 9.59304422e-02 7.22981572e-01 -4.11367595e-01 -3.25454950e-01 1.03998280e+00 5.88496327e-01 2.55094897e-02 1.30066246e-01 -1.56053686e+00 -5.51704228e-01 2.44830593e-01 -5.99390209e-01 5.89739740e-01 5.78616202e-01 2.21615016e-01 1.06647909e+00 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0aa6af9c-5fdc-41b8-8977-11edb99088d8	ball-trajectory-inference-from-multi-agent	2306.08206	null	https://arxiv.org/abs/2306.08206v1	https://arxiv.org/pdf/2306.08206v1.pdf	Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM	As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight. However, one of the major challenges is the difficulty of automated acquisition of continuous movement data during sports matches. In particular, it is a conundrum to reliably track a tiny ball on a wide soccer pitch with obstacles such as occlusion and imitations. Tackling the problem, this paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. We combine Set Transformers to get permutation-invariant and equivariant representations of the multi-agent contexts with a hierarchical architecture that intermediately predicts the player ball possession to support the final trajectory inference. Also, we introduce the reality loss term and postprocessing to secure the estimated trajectories to be physically realistic. The experimental results show that our model provides natural and accurate trajectories as well as admissible player ball possession at the same time. Lastly, we suggest several practical applications of our framework including missing trajectory imputation, semi-automated pass annotation, automated zoom-in for match broadcasting, and calculating possession-wise running performance metrics.	['Sang-Ki Ko', 'Jinsung Yoon', 'Chang Jo Kim', 'Han-Jun Choi', 'Hyunsung Kim']	2023-06-14	null	null	null	null	['sports-analytics', 'imputation', 'imputation', 'imputation']	['computer-vision', 'computer-vision', 'miscellaneous', 'time-series']	[-1.89942315e-01 -1.52378917e-01 -1.84044018e-01 -9.93862078e-02 -7.61971414e-01 -5.81495643e-01 2.24825561e-01 1.36982530e-01 -4.67987984e-01 6.46139383e-01 2.66914338e-01 9.94398445e-02 -7.09819138e-01 -8.90514076e-01 -9.53835607e-01 -4.55563277e-01 -3.94914687e-01 9.44489896e-01 5.52837372e-01 -5.25008857e-01 2.17536047e-01 5.15686929e-01 -1.72612751e+00 7.52256066e-02 7.60760784e-01 6.87025428e-01 6.62251338e-02 7.54697919e-01 3.78401399e-01 8.89531255e-01 -4.83257890e-01 -8.47769797e-01 5.38564980e-01 -3.41664016e-01 -6.79526150e-01 6.37398809e-02 1.61657304e-01 -3.27323645e-01 -7.66171813e-01 7.81068325e-01 3.79370719e-01 5.80162346e-01 3.85898441e-01 -1.54094017e+00 -1.00516863e-01 7.12730587e-01 -5.17482996e-01 2.76832163e-01 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1.01239681e+00 1.41274005e-01 4.52905655e-01 7.64856637e-02 -8.91088188e-01 -6.68479681e-01 -7.84230709e-01 -8.27806115e-01 7.03442991e-01 -1.60519276e-02 -1.08163297e+00 1.20950617e-01 -1.73496306e-02]	[7.008459568023682, -0.02646215446293354]
7f10f894-90b4-44b9-9453-7fd08d5c7119	micro-net-a-unified-model-for-segmentation-of	1804.08145	null	http://arxiv.org/abs/1804.08145v2	http://arxiv.org/pdf/1804.08145v2.pdf	Micro-Net: A unified model for segmentation of various objects in microscopy images	Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms recent deep learning algorithms.	['Simon Graham', 'Michael Khan', 'Nasir M. Rajpoot', 'Muhammad Shaban', 'David Epstein', 'Stella Pelengaris', 'Shan E Ahmed Raza', 'Linda Cheung']	2018-04-22	null	null	null	null	['multi-tissue-nucleus-segmentation']	['medical']	[ 4.08917755e-01 -9.70464125e-02 3.37167263e-01 -6.63477361e-01 -5.26457310e-01 -6.43976212e-01 2.24788964e-01 2.61779577e-01 -1.19647217e+00 5.21405458e-01 -6.08027101e-01 -1.20625585e-01 2.70994306e-01 -5.34817517e-01 -6.39206111e-01 -1.03328526e+00 1.16811410e-01 3.70171636e-01 6.49633050e-01 2.77139783e-01 4.20134991e-01 1.18193781e+00 -1.07750881e+00 6.13657713e-01 3.01309347e-01 8.01761389e-01 6.49696350e-01 1.19630980e+00 -2.02566713e-01 5.35040736e-01 -5.96568406e-01 3.03042661e-02 1.34623230e-01 2.41396707e-02 -1.18516004e+00 2.11089849e-01 2.70957023e-01 -4.31627154e-01 -1.82598997e-02 1.08981693e+00 6.51259601e-01 -1.74305260e-01 6.11634851e-01 -5.62552512e-01 -5.97788095e-01 2.65481859e-01 -5.02420843e-01 9.15433764e-01 -3.74904424e-01 1.81037486e-01 4.21556979e-01 -9.20543611e-01 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3c4684f1-a853-41ec-b5c0-3171d3d9ddd0	mbt-a-memory-based-part-of-speech-tagger	cmp-lg/9607012	null	https://arxiv.org/abs/cmp-lg/9607012v1	https://arxiv.org/pdf/cmp-lg/9607012v1.pdf	MBT: A Memory-Based Part of Speech Tagger-Generator	We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning approaches are useful when a tagged corpus is available as an example of the desired output of the tagger. Based on such a corpus, the tagger-generator automatically builds a tagger which is able to tag new text the same way, diminishing development time for the construction of a tagger considerably. Memory-based tagging shares this advantage with other statistical or machine learning approaches. Additional advantages specific to a memory-based approach include (i) the relatively small tagged corpus size sufficient for training, (ii) incremental learning, (iii) explanation capabilities, (iv) flexible integration of information in case representations, (v) its non-parametric nature, (vi) reasonably good results on unknown words without morphological analysis, and (vii) fast learning and tagging. In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using {\em IGTree}, a tree-based formalism for indexing and searching huge case bases.} The use of IGTree has as additional advantage that optimal context size for disambiguation is dynamically computed.	['Steven Gillis', 'Peter Berck', 'Jakub Zavrel', 'Walter Daelemans']	1996-07-11	null	null	null	null	['morphological-analysis']	['natural-language-processing']	[ 2.22702906e-01 2.96306044e-01 -2.66260117e-01 -3.63103837e-01 -1.18282640e+00 -6.73510313e-01 6.36743307e-01 6.91892087e-01 -5.19505382e-01 1.07324409e+00 1.11001268e-01 -6.48533463e-01 -5.05105734e-01 -8.27047050e-01 -2.80940115e-01 -6.43754661e-01 -2.27310076e-01 1.02481687e+00 7.28697002e-01 -1.65942177e-01 4.38230366e-01 6.67244315e-01 -1.79850101e+00 1.88622087e-01 8.06597412e-01 9.34608102e-01 7.20303476e-01 4.50765431e-01 -8.77556562e-01 7.46379614e-01 -4.78981704e-01 -3.33916396e-01 -7.87425488e-02 -2.85449654e-01 -1.38711035e+00 1.34561405e-01 3.88222709e-02 2.70750195e-01 -9.74662453e-02 8.40801179e-01 2.19335690e-01 4.98478740e-01 5.23522675e-01 -7.61840105e-01 -2.87804872e-01 8.36337209e-01 1.01448298e-02 3.56512457e-01 5.14284670e-01 -4.61902440e-01 9.71188307e-01 -6.75861835e-01 6.72475278e-01 1.03788686e+00 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9199e6c2-eef8-41bf-903f-bc238056d27c	dsi-updating-transformer-memory-with-new	2212.09744	null	https://arxiv.org/abs/2212.09744v1	https://arxiv.org/pdf/2212.09744v1.pdf	DSI++: Updating Transformer Memory with New Documents	Differentiable Search Indices (DSIs) encode a corpus of documents in the parameters of a model and use the same model to map queries directly to relevant document identifiers. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents (+12\%). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting by a significant margin. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.	['Donald Metzler', 'Emma Strubell', 'Marc Najork', 'Jinfeng Rao', 'Vinh Q. Tran', 'Mostafa Dehghani', 'Yi Tay', 'Jai Gupta', 'Sanket Vaibhav Mehta']	2022-12-19	null	null	null	null	['natural-questions']	['miscellaneous']	[ 0.538778 -0.03259462 -0.12807411 -0.22552375 -1.286987 -0.81930363 0.6507446 0.2128698 -0.9233722 0.64680976 0.04342129 -0.30046952 -0.34060523 -0.633627 -1.1354699 -0.41051477 -0.085579 1.0498921 0.5556187 -0.35135955 0.47523004 0.32165718 -1.7447184 0.19345267 0.8253717 0.77185124 0.46853384 0.81586874 -0.36496693 0.520938 -0.968146 -0.2800578 0.2480575 -0.04503617 -1.0859647 -0.38308406 0.8434834 -0.305517 -0.6195209 0.73544806 0.49224067 0.37463182 0.5103632 -0.8989374 -0.7835027 0.8230556 -0.3784808 0.63610005 0.16052875 0.00935814 1.0523974 -1.0274776 0.61859816 1.2812777 0.48603326 0.56764305 -1.351194 -0.74870723 0.47442198 0.09086686 -1.4437973 -0.54528874 0.34478673 0.06757395 1.277658 0.60091895 0.22753651 1.0116557 -0.02486731 0.99233025 0.43869832 -0.43183988 0.07610208 0.01296232 0.6269523 0.5322225 0.30259782 -0.04605298 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13a9388f-3264-4cb6-bba2-2f43752b2a54	advanced-medical-image-representation-for	2305.15411	null	https://arxiv.org/abs/2305.15411v1	https://arxiv.org/pdf/2305.15411v1.pdf	Advanced Medical Image Representation for Efficient Processing and Transfer in Multisite Clouds	An important topic in medical research is the process of improving the images obtained from medical devices. As a consequence, there is also a need to improve medical image resolution and analysis. Another issue in this field is the large amount of stored medical data [16]. Human brain databases at medical institutes, for example, can accumulate tens of Terabytes of data per year. In this paper, we propose a novel medical image format representation based on multiple data structures that improve the information maintained in the medical images. The new representation keeps additional metadata information, such as the image class or tags for the objects found in the image. We defined our own ontology to help us classify the objects found in medical images using a multilayer neural network. As we generally deal with large data sets, we used the MapReduce paradigm in the Cloud environment to speed up the image processing. To optimize the transfer between Cloud nodes and to reduce the preprocessing time, we also propose a data compression method based on deduplication. We test our solution for image representation and efficient data transfer in a multisite cloud environment. Our proposed solution optimizes the data transfer with a time improvement of 27% on average.	['Ciprian-Octavian Truică', 'Elena-Simona Apostol']	2023-04-29	null	null	null	null	['data-compression']	['time-series']	[ 1.83977619e-01 -2.12864250e-01 1.13024302e-01 -5.49329758e-01 -8.37200582e-02 4.34673904e-03 -1.36982556e-02 9.23761368e-01 -8.99180710e-01 4.63574767e-01 9.53397807e-03 -1.07083425e-01 -3.63011032e-01 -1.42676163e+00 -3.68792325e-01 -6.99217856e-01 1.23825550e-01 6.14739776e-01 3.36312592e-01 1.78596154e-02 4.34603572e-01 6.52358294e-01 -1.94837892e+00 6.64623857e-01 9.89359319e-01 1.10084236e+00 8.94433737e-01 3.98274690e-01 -5.76888800e-01 6.94410443e-01 -7.92256415e-01 -9.37047377e-02 2.34772295e-01 1.11824282e-01 -8.51987958e-01 2.45142467e-02 -1.11902907e-01 -3.70606750e-01 -2.27266923e-01 1.25212991e+00 6.99682176e-01 -2.77904749e-01 1.18775874e-01 -1.02792370e+00 -4.28429782e-01 4.04366523e-01 -4.51179504e-01 5.20879388e-01 -1.89943373e-01 -4.55721587e-01 -4.86877374e-02 -4.65228349e-01 8.05819809e-01 8.40803444e-01 1.88724309e-01 2.55634278e-01 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bd503126-1a51-4421-a55d-1d7268120f64	generalizing-unmasking-for-short-texts	null	null	https://aclanthology.org/N19-1068	https://aclanthology.org/N19-1068.pdf	Generalizing Unmasking for Short Texts	Authorship verification is the problem of inferring whether two texts were written by the same author. For this task, unmasking is one of the most robust approaches as of today with the major shortcoming of only being applicable to book-length texts. In this paper, we present a generalized unmasking approach which allows for authorship verification of texts as short as four printed pages with very high precision at an adjustable recall tradeoff. Our generalized approach therefore reduces the required material by orders of magnitude, making unmasking applicable to authorship cases of more practical proportions. The new approach is on par with other state-of-the-art techniques that are optimized for texts of this length: it achieves accuracies of 75{--}80{\%}, while also allowing for easy adjustment to forensic scenarios that require higher levels of confidence in the classification.	['Benno Stein', 'Matthias Hagen', 'Martin Potthast', 'Janek Bevendorff']	2019-06-01	null	null	null	naacl-2019-6	['authorship-verification']	['natural-language-processing']	[ 4.19055253e-01 1.32328272e-01 -1.95362940e-01 -1.43319845e-01 -8.49706531e-01 -9.89055276e-01 7.64736593e-01 4.52165246e-01 -5.71544170e-01 8.68218303e-01 -3.69929612e-01 -5.34326315e-01 -1.95888743e-01 -6.36643052e-01 -3.74533236e-01 -4.90515471e-01 4.08854336e-01 7.93514490e-01 3.91252249e-01 1.67118162e-01 8.26271534e-01 7.59835303e-01 -1.61593437e+00 2.02929005e-01 7.70592928e-01 6.00431323e-01 -1.57391369e-01 9.80588317e-01 -2.02563152e-01 5.03626287e-01 -9.60874975e-01 -1.09968376e+00 2.03388125e-01 -2.36143321e-01 -7.59053349e-01 -1.44407704e-01 9.26855624e-01 -4.01250035e-01 -2.55347520e-01 9.18415725e-01 2.64755130e-01 -2.77350128e-01 8.22675586e-01 -1.01972330e+00 -4.90250528e-01 7.78927088e-01 -6.88034117e-01 3.51826638e-01 5.86125672e-01 -2.85059303e-01 1.07846403e+00 -5.09840727e-01 4.32852328e-01 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4a8b92d5-d8f4-4127-860c-5b7169b457a7	learning-phase-mask-for-privacy-preserving	null	null	https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/7139_ECCV_2022_paper.php	https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136670497.pdf	Learning Phase Mask for Privacy-Preserving Passive Depth Estimation	With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others. However, severe concerns regarding the privacy implications of camera-based solutions currently limit the range of environments where cameras can be deployed. The key question we address in this paper is: Can cameras be enhanced with a scalable solution to preserve users’ privacy without degrading their machine intelligence capabilities? Our solution is a novel end-to-end adversarial learning pipeline in which a phase mask placed at the aperture plane of a camera is jointly optimized with respect to privacy and utility objectives. We conduct an extensive design space analysis of sensor configurations with respect to phase mask design, focus settings and resolution to determine operating points with desirable privacy-utility tradeoffs that are also amenable to sensor fabrication and real-world constraints. We demonstrate the first working prototype that enables passive depth estimation while inhibiting face identification.	['Francesco Pittaluga', 'Manmohan Chandraker', 'Ashok Veeraraghavan', 'Xiang Yu', 'Yi-Hsuan Tsai', 'Giovanni Milione', 'Zaid Tasneem']	2022-11-13	null	null	null	european-conference-on-computer-vision-eccv-1	['depth-estimation', 'autonomous-navigation', 'face-identification']	['computer-vision', 'computer-vision', 'computer-vision']	[ 5.51036537e-01 3.56995881e-01 6.09612800e-02 -4.75339264e-01 -6.07074916e-01 -1.12278008e+00 4.43771213e-01 -3.05568188e-01 -5.22763193e-01 4.10525501e-01 1.60930425e-01 -3.08206737e-01 4.48846593e-02 -2.32637882e-01 -4.86476809e-01 -5.04592478e-01 2.67329901e-01 -2.65530437e-01 -6.09518476e-02 2.66704082e-01 2.43014932e-01 6.82612300e-01 -1.46565390e+00 -9.12355781e-02 5.95889628e-01 1.34654546e+00 -2.52008319e-01 4.69249487e-01 6.85640931e-01 3.44041228e-01 -5.32075524e-01 -8.26160550e-01 7.59308398e-01 -7.74598122e-02 -8.40494856e-02 4.19379860e-01 7.12085783e-01 -7.64436722e-01 -2.70284355e-01 1.06658947e+00 6.13035858e-01 -2.72746444e-01 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6238a491-02c3-4ecc-aca6-ff13d1ee6db8	feature-disentanglement-learning-with	2212.09498	null	https://arxiv.org/abs/2212.09498v1	https://arxiv.org/pdf/2212.09498v1.pdf	Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification	In video person re-identification (Re-ID), the network must consistently extract features of the target person from successive frames. Existing methods tend to focus only on how to use temporal information, which often leads to networks being fooled by similar appearances and same backgrounds. In this paper, we propose a Disentanglement and Switching and Aggregation Network (DSANet), which segregates the features representing identity and features based on camera characteristics, and pays more attention to ID information. We also introduce an auxiliary task that utilizes a new pair of features created through switching and aggregation to increase the network's capability for various camera scenarios. Furthermore, we devise a Target Localization Module (TLM) that extracts robust features against a change in the position of the target according to the frame flow and a Frame Weight Generation (FWG) that reflects temporal information in the final representation. Various loss functions for disentanglement learning are designed so that each component of the network can cooperate while satisfactorily performing its own role. Quantitative and qualitative results from extensive experiments demonstrate the superiority of DSANet over state-of-the-art methods on three benchmark datasets.	['Sangyoun Lee', 'MyeongAh Cho', 'Minjung Kim']	2022-12-16	null	null	null	null	['person-re-identification']	['computer-vision']	[ 7.73376971e-02 -4.55632448e-01 4.87189293e-02 -3.35308015e-01 -1.76718026e-01 -4.03870791e-01 7.42900968e-01 -1.23024307e-01 -5.69329679e-01 5.50656617e-01 1.85949624e-01 4.21254039e-01 -1.86879169e-02 -5.30933559e-01 -2.23134488e-01 -7.00080633e-01 -6.06520399e-02 1.44206006e-02 2.79395670e-01 -1.62238255e-01 7.19901100e-02 4.93791521e-01 -1.44498038e+00 4.05330583e-02 7.59354413e-01 1.01241541e+00 -6.65251911e-02 4.86612350e-01 2.62668908e-01 7.70808876e-01 -7.75861979e-01 -8.26573968e-01 5.51765919e-01 -4.53946292e-01 -3.42602968e-01 2.31252193e-01 7.37447083e-01 -2.92643756e-01 -6.83068335e-01 1.28129292e+00 7.08432376e-01 2.77403951e-01 4.92598087e-01 -1.45219028e+00 -5.55481315e-01 3.46601725e-01 -6.65424407e-01 5.54465771e-01 5.40514708e-01 2.56750464e-01 8.21977317e-01 -6.23157918e-01 3.43611270e-01 1.48737681e+00 7.99720526e-01 7.25706935e-01 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-6.15650237e-01 6.05978131e-01 4.92350489e-01 -9.83420372e-01 -3.76547605e-01 1.30360603e-01]	[14.673478126525879, 0.9669617414474487]
50e71e4c-8016-450b-8139-3bb5ac2434b3	hierarchical-attention-networks-for-document	null	null	https://aclanthology.org/N16-1174	https://aclanthology.org/N16-1174.pdf	Hierarchical Attention Networks for Document Classification	null	['Xiaodong He', 'Chris Dyer', 'Zichao Yang', 'Alex Smola', 'Eduard Hovy', 'Diyi Yang']	2016-06-01	null	null	null	naacl-2016-6	['citation-intent-classification']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 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6755d77a-54fa-4ccd-b49f-1da90df85bad	submodular-maximization-through-barrier	2002.03523	null	https://arxiv.org/abs/2002.03523v1	https://arxiv.org/pdf/2002.03523v1.pdf	Submodular Maximization Through Barrier Functions	In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization. This connection not only improves the running time for constrained submodular maximization but also provides the state of the art guarantee. More precisely, for maximizing a monotone submodular function subject to the combination of a $k$-matchoid and $\ell$-knapsack constraint (for $\ell\leq k$), we propose a potential function that can be approximately minimized. Once we minimize the potential function up to an $\epsilon$ error it is guaranteed that we have found a feasible set with a $2(k+1+\epsilon)$-approximation factor which can indeed be further improved to $(k+1+\epsilon)$ by an enumeration technique. We extensively evaluate the performance of our proposed algorithm over several real-world applications, including a movie recommendation system, summarization tasks for YouTube videos, Twitter feeds and Yelp business locations, and a set cover problem.	['Ehsan Kazemi', 'Ashwinkumar Badanidiyuru', 'Amin Karbasi', 'Jan Vondrak']	2020-02-10	null	http://proceedings.neurips.cc/paper/2020/hash/061412e4a03c02f9902576ec55ebbe77-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/061412e4a03c02f9902576ec55ebbe77-Paper.pdf	neurips-2020-12	['movie-recommendation']	['miscellaneous']	[ 6.50871992e-02 4.52418268e-01 -5.67278266e-01 -2.02585056e-01 -8.65095377e-01 -7.08457470e-01 -3.55759799e-01 5.17227232e-01 -5.90871453e-01 9.86273408e-01 -3.49335335e-02 -1.36132315e-01 -6.46836281e-01 -7.45335937e-01 -9.18071926e-01 -6.08776629e-01 -3.57281595e-01 5.05693853e-01 -2.08528921e-01 -3.59526038e-01 5.74148417e-01 3.38241249e-01 -1.22741616e+00 -3.20237160e-01 8.78099382e-01 1.34150147e+00 2.95905650e-01 4.01619941e-01 -2.57309359e-02 3.91840547e-01 -3.25594425e-01 -3.12963128e-01 7.09961414e-01 -1.21882692e-01 -7.42517471e-01 4.25083190e-01 5.09440362e-01 -1.85656622e-01 -1.36175126e-01 1.22773361e+00 2.28351578e-01 3.46376240e-01 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31cfb9de-33a3-4cbd-9498-acb51bd21b46	improving-clinical-document-understanding-on	2012.04005	null	https://arxiv.org/abs/2012.04005v1	https://arxiv.org/pdf/2012.04005v1.pdf	Improving Clinical Document Understanding on COVID-19 Research with Spark NLP	Following the global COVID-19 pandemic, the number of scientific papers studying the virus has grown massively, leading to increased interest in automated literate review. We present a clinical text mining system that improves on previous efforts in three ways. First, it can recognize over 100 different entity types including social determinants of health, anatomy, risk factors, and adverse events in addition to other commonly used clinical and biomedical entities. Second, the text processing pipeline includes assertion status detection, to distinguish between clinical facts that are present, absent, conditional, or about someone other than the patient. Third, the deep learning models used are more accurate than previously available, leveraging an integrated pipeline of state-of-the-art pretrained named entity recognition models, and improving on the previous best performing benchmarks for assertion status detection. We illustrate extracting trends and insights, e.g. most frequent disorders and symptoms, and most common vital signs and EKG findings, from the COVID-19 Open Research Dataset (CORD-19). The system is built using the Spark NLP library which natively supports scaling to use distributed clusters, leveraging GPUs, configurable and reusable NLP pipelines, healthcare specific embeddings, and the ability to train models to support new entity types or human languages with no code changes.	['David Talby', 'Veysel Kocaman']	2020-12-07	null	null	null	null	['clinical-concept-extraction', 'clinical-assertion-status-detection']	['medical', 'natural-language-processing']	[-3.27903062e-01 -1.20360866e-01 -2.65630215e-01 -1.86159045e-01 -6.32710099e-01 -6.57049716e-01 3.42740715e-01 1.29291308e+00 -6.62053227e-01 7.42339373e-01 5.59883058e-01 -7.20420778e-01 -1.05735347e-01 -7.25987792e-01 -2.69015223e-01 -3.50164235e-01 -6.00588143e-01 6.58052981e-01 -1.99784741e-01 1.42308518e-01 -4.42879014e-02 6.22096300e-01 -1.07894695e+00 2.75882781e-01 6.08287513e-01 9.19771433e-01 -2.64530778e-01 9.22924221e-01 1.85051318e-02 8.88314068e-01 -5.87485909e-01 -3.62596452e-01 -1.02189735e-01 9.34939831e-02 -6.65216267e-01 -8.85745227e-01 1.18782453e-03 -3.91487718e-01 -1.53146178e-01 4.23760980e-01 9.48601365e-01 -3.94010544e-01 5.07711172e-01 -1.01178670e+00 -8.43614101e-01 3.54803503e-01 -1.97130635e-01 7.13147521e-01 3.94988775e-01 1.55948490e-01 8.52047324e-01 -5.92050970e-01 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1023d7a2-a11f-4142-a41f-8fe0304c6286	qa4qg-using-question-answering-to-constrain	2202.06538	null	https://arxiv.org/abs/2202.06538v1	https://arxiv.org/pdf/2202.06538v1.pdf	QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation	Multi-hop question generation (MQG) aims to generate complex questions which require reasoning over multiple pieces of information of the input passage. Most existing work on MQG has focused on exploring graph-based networks to equip the traditional Sequence-to-sequence framework with reasoning ability. However, these models do not take full advantage of the constraint between questions and answers. Furthermore, studies on multi-hop question answering (QA) suggest that Transformers can replace the graph structure for multi-hop reasoning. Therefore, in this work, we propose a novel framework, QA4QG, a QA-augmented BART-based framework for MQG. It augments the standard BART model with an additional multi-hop QA module to further constrain the generated question. Our results on the HotpotQA dataset show that QA4QG outperforms all state-of-the-art models, with an increase of 8 BLEU-4 and 8 ROUGE points compared to the best results previously reported. Our work suggests the advantage of introducing pre-trained language models and QA module for the MQG task.	['Pascale Fung', 'Peng Xu', 'Dan Su']	2022-02-14	null	null	null	null	['multi-hop-question-answering']	['knowledge-base']	[ 5.26950508e-03 8.01098943e-01 3.45148683e-01 -8.68739784e-02 -1.09975982e+00 -7.52057076e-01 7.65815377e-01 2.28647679e-01 -2.15145305e-01 9.12868202e-01 5.25040388e-01 -8.07736337e-01 -2.71571666e-01 -1.18245280e+00 -6.06321454e-01 2.01864481e-01 2.82668173e-01 7.94678867e-01 6.83928728e-01 -9.75665212e-01 3.99546683e-01 5.78074791e-02 -1.16459656e+00 7.46160567e-01 1.31814420e+00 4.71383631e-01 1.18412383e-01 1.10966372e+00 -8.38753521e-01 1.35321057e+00 -8.10647905e-01 -8.44032466e-01 -1.43494932e-02 -1.02777088e+00 -1.41353905e+00 -3.89276713e-01 5.28310418e-01 -2.74363637e-01 -4.63549346e-01 5.81459999e-01 5.95735192e-01 4.21443343e-01 4.80280757e-01 -1.27270317e+00 -1.06592810e+00 7.78717160e-01 9.22446474e-02 2.36782342e-01 1.06104124e+00 1.91332638e-01 1.37288630e+00 -6.17831826e-01 8.02643538e-01 1.25725460e+00 6.28476202e-01 8.15313339e-01 -7.57137537e-01 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-1.63858280e-01 2.78809488e-01 1.29289746e-01 2.69572274e-03 -1.50364712e-01 -2.51828700e-01]	[11.27752685546875, 8.148984909057617]
420ecd13-fb9c-4148-9e7c-231f82de9738	query-translation-for-cross-language	null	null	https://aclanthology.org/W16-3716	https://aclanthology.org/W16-3716.pdf	Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters	In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a difficult problem to solve. We propose a technique towards solving this problem with the help of multilingual word clusters obtained from multilingual word embeddings. We use word embeddings of the languages projected to a common vector space on which a community-detection algorithm is applied to find clusters such that words that represent the same concept from different languages fall in the same group. We utilize these multilingual word clusters to perform query translation for Cross-Language Information Retrieval for three languages - English, Hindi and Bengali. We have experimented with the FIRE 2012 and Wikipedia datasets and have shown improvements over several standard methods like dictionary-based method, a transliteration-based model and Google Translate.	['Paheli Bhattacharya', 'Sudeshna Sarkar', 'Pawan Goyal']	2016-12-01	null	null	null	ws-2016-12	['multilingual-word-embeddings']	['methodology']	[-3.90306592e-01 -6.15245581e-01 -2.75747567e-01 1.96626380e-01 -1.23180842e+00 -9.54738915e-01 8.91727448e-01 5.93875945e-01 -8.77429366e-01 5.52703440e-01 5.46379387e-01 -6.74898505e-01 -1.03976063e-01 -5.94361007e-01 -3.56736869e-01 -4.01540309e-01 2.68718898e-01 9.16106522e-01 1.57049611e-01 -4.84419644e-01 3.12905997e-01 2.03798562e-01 -7.92468488e-01 2.27657661e-01 8.88688505e-01 3.29595692e-02 2.59760857e-01 3.00623059e-01 -6.69573188e-01 1.51266471e-01 -3.50120544e-01 -3.55533630e-01 3.83905917e-01 -3.68716925e-01 -8.62727940e-01 -2.33714104e-01 2.40666330e-01 5.74109435e-01 -1.24879301e-01 1.09434056e+00 6.86152637e-01 -5.42168692e-03 7.88159728e-01 -1.00012946e+00 -9.55666244e-01 5.07778406e-01 -5.93980908e-01 2.71067977e-01 6.10501349e-01 -6.78190768e-01 1.21081614e+00 -1.43336189e+00 1.07762408e+00 1.29086888e+00 3.32247317e-01 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efe57c52-0e7c-434a-8b4b-4c06cda1f7fb	diffalign-few-shot-learning-using-diffusion	2212.05404	null	https://arxiv.org/abs/2212.05404v1	https://arxiv.org/pdf/2212.05404v1.pdf	DiffAlign : Few-shot learning using diffusion based synthesis and alignment	We address the problem of few-shot classification where the goal is to learn a classifier from a limited set of samples. While data-driven learning is shown to be effective in various applications, learning from less data still remains challenging. To address this challenge, existing approaches consider various data augmentation techniques for increasing the number of training samples. Pseudo-labeling is commonly used in a few-shot setup, where approximate labels are estimated for a large set of unlabeled images. We propose DiffAlign which focuses on generating images from class labels. Specifically, we leverage the recent success of the generative models (e.g., DALL-E and diffusion models) that can generate realistic images from texts. However, naive learning on synthetic images is not adequate due to the domain gap between real and synthetic images. Thus, we employ a maximum mean discrepancy (MMD) loss to align the synthetic images to the real images minimizing the domain gap. We evaluate our method on the standard few-shot classification benchmarks: CIFAR-FS, FC100, miniImageNet, tieredImageNet and a cross-domain few-shot classification benchmark: miniImageNet to CUB. The proposed approach significantly outperforms the stateof-the-art in both 5-shot and 1-shot setups on these benchmarks. Our approach is also shown to be effective in the zero-shot classification setup	['Rama Chellappa', 'Anirban Roy', 'Ketul Shah', 'Anshul Shah', 'Aniket Roy']	2022-12-11	null	null	null	null	['cross-domain-few-shot']	['computer-vision']	[ 4.25403118e-01 1.39790103e-02 -2.24359542e-01 -4.85651314e-01 -1.08868563e+00 -2.32639953e-01 9.43894744e-01 -2.36863568e-01 -3.93907517e-01 9.12738442e-01 -1.64686665e-02 1.63963526e-01 1.68585494e-01 -7.67391860e-01 -8.70835543e-01 -6.12214565e-01 5.06328642e-01 7.12576568e-01 2.77814716e-01 -2.41728246e-01 1.03529900e-01 1.19572934e-02 -1.74757922e+00 2.21786022e-01 8.28263760e-01 1.13040853e+00 1.26807228e-01 6.04458988e-01 -2.92550474e-01 1.01193166e+00 -6.90134346e-01 -3.27276975e-01 3.77983809e-01 -9.73515809e-01 -8.49321663e-01 6.92793548e-01 4.41430032e-01 -3.20799321e-01 -1.65658787e-01 1.12942398e+00 6.05774879e-01 4.42575902e-01 1.16044807e+00 -1.51673234e+00 -6.92151725e-01 3.45739901e-01 -7.00028777e-01 1.17401093e-01 6.07712544e-04 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cb16274e-cf59-44a9-9a12-8b33b32e046b	neural-event-extraction-from-movies	null	null	https://aclanthology.org/W18-1507	https://aclanthology.org/W18-1507.pdf	Neural Event Extraction from Movies Description	We present a novel approach for event extraction and abstraction from movie descriptions. Our event frame consists of {``}who{''}, {``}did what{''} {``}to whom{''}, {``}where{''}, and {``}when{''}. We formulate our problem using a recurrent neural network, enhanced with structural features extracted from syntactic parser, and trained using curriculum learning by progressively increasing the difficulty of the sentences. Our model serves as an intermediate step towards question answering systems, visual storytelling, and story completion tasks. We evaluate our approach on MovieQA dataset.	["Dejan Jovanovi{\\'c}", 'Alex Tozzo', 'Mohamed Amer']	2018-06-01	null	null	null	ws-2018-6	['visual-storytelling', 'story-completion']	['natural-language-processing', 'natural-language-processing']	[ 2.54549950e-01 4.59105283e-01 2.91089684e-01 -4.92812097e-01 -9.72453296e-01 -7.81600416e-01 6.84504628e-01 6.54378653e-01 -3.17191720e-01 9.27967727e-01 6.90036535e-01 -3.80154341e-01 -1.39139056e-01 -9.10941601e-01 -7.82351077e-01 -1.85973197e-01 -1.61704659e-01 6.10272229e-01 4.89316076e-01 -4.47474808e-01 1.85554683e-01 8.74846280e-02 -1.42001426e+00 1.00522447e+00 5.53655140e-02 9.09560740e-01 3.93862218e-01 9.65338469e-01 -1.72391206e-01 1.99259198e+00 -9.11103547e-01 -8.83994102e-01 -2.88825572e-01 -8.25071692e-01 -1.41678882e+00 1.55258998e-01 1.15236796e-01 -7.83270597e-01 -4.41088259e-01 7.48938441e-01 -1.45579074e-02 6.74573660e-01 7.09836721e-01 -9.41850007e-01 -7.56776214e-01 9.93856192e-01 -4.56041843e-02 5.13353109e-01 6.93874478e-01 3.16618904e-02 1.39799464e+00 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7da48562-98f0-4796-b9df-16f47251cb92	gauge-equivariant-neural-networks-for-2-1d-u	2211.03198	null	https://arxiv.org/abs/2211.03198v1	https://arxiv.org/pdf/2211.03198v1.pdf	Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation	Gauge Theory plays a crucial role in many areas in science, including high energy physics, condensed matter physics and quantum information science. In quantum simulations of lattice gauge theory, an important step is to construct a wave function that obeys gauge symmetry. In this paper, we have developed gauge equivariant neural network wave function techniques for simulating continuous-variable quantum lattice gauge theories in the Hamiltonian formulation. We have applied the gauge equivariant neural network approach to find the ground state of 2+1-dimensional lattice gauge theory with U(1) gauge group using variational Monte Carlo. We have benchmarked our approach against the state-of-the-art complex Gaussian wave functions, demonstrating improved performance in the strong coupling regime and comparable results in the weak coupling regime.	['Bryan K. Clark', 'James Stokes', 'Shunyue Yuan', 'Di Luo']	2022-11-06	null	null	null	null	['variational-monte-carlo']	['miscellaneous']	[ 2.11316556e-01 -2.71277338e-01 1.64019182e-01 -1.99920878e-01 -6.05237126e-01 -2.70074129e-01 6.45878375e-01 -2.86454111e-01 -6.21057332e-01 1.16926467e+00 5.93849532e-02 -4.52755213e-01 -3.16402256e-01 -1.44185877e+00 -4.53056037e-01 -1.26739216e+00 -1.35284543e-01 1.00546873e+00 -1.54193372e-01 -7.02664018e-01 3.84898573e-01 4.19664323e-01 -9.86070812e-01 -2.05327198e-01 4.07771468e-01 6.49978817e-01 -4.14525181e-01 5.71667850e-01 3.85547101e-01 -1.20866701e-01 2.68010885e-01 8.78143683e-02 3.53333682e-01 -6.72613025e-01 -8.02518964e-01 -6.43465161e-01 3.04658949e-01 1.89794168e-01 -7.41787374e-01 1.60194504e+00 5.57924986e-01 6.86299503e-01 8.61143231e-01 -5.21460056e-01 -7.12155581e-01 4.88848120e-01 -1.07048966e-01 9.02857929e-02 -1.40639290e-01 5.39670289e-01 1.41746092e+00 -4.53826010e-01 8.94117057e-01 1.03063500e+00 4.04847950e-01 7.86515892e-01 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ba570014-e143-4038-a6dd-fc3c925ea853	unsupervised-semantic-segmentation-of-3d	2304.08965	null	https://arxiv.org/abs/2304.08965v2	https://arxiv.org/pdf/2304.08965v2.pdf	Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering	Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled or weaker forms of annotations. In this paper, we take the first attempt for fully unsupervised semantic segmentation of point clouds, which aims to delineate semantically meaningful objects without any form of annotations. Previous works of unsupervised pipeline on 2D images fails in this task of point clouds, due to: 1) Clustering Ambiguity caused by limited magnitude of data and imbalanced class distribution; 2) Irregularity Ambiguity caused by the irregular sparsity of point cloud. Therefore, we propose a novel framework, PointDC, which is comprised of two steps that handle the aforementioned problems respectively: Cross-Modal Distillation (CMD) and Super-Voxel Clustering (SVC). In the first stage of CMD, multi-view visual features are back-projected to the 3D space and aggregated to a unified point feature to distill the training of the point representation. In the second stage of SVC, the point features are aggregated to super-voxels and then fed to the iterative clustering process for excavating semantic classes. PointDC yields a significant improvement over the prior state-of-the-art unsupervised methods, on both the ScanNet-v2 (+18.4 mIoU) and S3DIS (+11.5 mIoU) semantic segmentation benchmarks.	['Hongbin Xu', 'Zisheng Chen']	2023-04-18	null	null	null	null	['unsupervised-semantic-segmentation']	['computer-vision']	[ 1.84805438e-01 2.49458373e-01 1.33938223e-01 -3.79642546e-01 -8.95624697e-01 -5.72464943e-01 7.08574355e-01 3.74856651e-01 -2.05438063e-01 2.52600163e-01 -3.51074427e-01 -5.43539152e-02 -6.56835437e-02 -7.47672975e-01 -6.87805474e-01 -5.37729025e-01 1.14211924e-01 1.14424717e+00 7.58072674e-01 3.59652787e-02 3.85741651e-01 7.68280983e-01 -1.82981360e+00 3.19213979e-02 9.47954297e-01 9.87347841e-01 4.13359076e-01 2.50965267e-01 -8.04222405e-01 6.46168217e-02 -2.40023434e-01 7.78677613e-02 4.75451410e-01 -8.73527676e-02 -9.43129063e-01 4.57128763e-01 3.98166478e-01 -6.35630116e-02 4.34068680e-01 1.13749015e+00 2.48803586e-01 2.67550766e-01 8.94346178e-01 -1.24838781e+00 -8.56402218e-02 1.43542662e-01 -9.18303788e-01 -5.36253974e-02 -1.03588225e-02 -3.39702293e-02 8.79033685e-01 -1.32467365e+00 6.03198647e-01 1.20629430e+00 5.26313007e-01 3.75265241e-01 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dc35d9c3-826d-4934-9148-da3e9ed980ee	dr3-value-based-deep-reinforcement-learning-1	2112.04716	null	https://arxiv.org/abs/2112.04716v1	https://arxiv.org/pdf/2112.04716v1.pdf	DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization	Despite overparameterization, deep networks trained via supervised learning are easy to optimize and exhibit excellent generalization. One hypothesis to explain this is that overparameterized deep networks enjoy the benefits of implicit regularization induced by stochastic gradient descent, which favors parsimonious solutions that generalize well on test inputs. It is reasonable to surmise that deep reinforcement learning (RL) methods could also benefit from this effect. In this paper, we discuss how the implicit regularization effect of SGD seen in supervised learning could in fact be harmful in the offline deep RL setting, leading to poor generalization and degenerate feature representations. Our theoretical analysis shows that when existing models of implicit regularization are applied to temporal difference learning, the resulting derived regularizer favors degenerate solutions with excessive "aliasing", in stark contrast to the supervised learning case. We back up these findings empirically, showing that feature representations learned by a deep network value function trained via bootstrapping can indeed become degenerate, aliasing the representations for state-action pairs that appear on either side of the Bellman backup. To address this issue, we derive the form of this implicit regularizer and, inspired by this derivation, propose a simple and effective explicit regularizer, called DR3, that counteracts the undesirable effects of this implicit regularizer. When combined with existing offline RL methods, DR3 substantially improves performance and stability, alleviating unlearning in Atari 2600 games, D4RL domains and robotic manipulation from images.	['Sergey Levine', 'George Tucker', 'Aaron Courville', 'Tengyu Ma', 'Rishabh Agarwal', 'Aviral Kumar']	2021-12-09	dr3-value-based-deep-reinforcement-learning	https://openreview.net/forum?id=POvMvLi91f	https://openreview.net/pdf?id=POvMvLi91f	iclr-2022-4	['d4rl']	['robots']	[-1.78940073e-02 5.57033777e-01 -3.05695713e-01 -2.12720394e-01 -3.84599656e-01 -7.52902329e-01 3.73441011e-01 -3.66666585e-01 -6.03066683e-01 1.10312808e+00 -5.31681534e-03 -3.25152278e-01 -4.43595052e-01 -5.65139115e-01 -8.85260999e-01 -9.12695348e-01 -1.98106825e-01 2.16930509e-01 -5.19343801e-02 -4.34030682e-01 1.94260508e-01 6.45452082e-01 -1.42047942e+00 -1.92481771e-01 7.95525074e-01 7.50231087e-01 -3.60127874e-02 3.90280128e-01 2.90433437e-01 9.05748785e-01 -6.62526608e-01 -2.09952652e-01 5.76314390e-01 -4.84105259e-01 -7.55055904e-01 6.53576106e-02 1.65846705e-01 -5.92236519e-01 -3.81381661e-01 8.58624756e-01 5.50125003e-01 4.27974284e-01 6.61555409e-01 -1.21685231e+00 -4.83951509e-01 6.10514462e-01 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fb768012-c5bd-4d0f-aca8-1a4490082765	why-the-rich-get-richer-on-the-balancedness	2201.12697	null	https://arxiv.org/abs/2201.12697v2	https://arxiv.org/pdf/2201.12697v2.pdf	Why the Rich Get Richer? On the Balancedness of Random Partition Models	Random partition models are widely used in Bayesian methods for various clustering tasks, such as mixture models, topic models, and community detection problems. While the number of clusters induced by random partition models has been studied extensively, another important model property regarding the balancedness of partition has been largely neglected. We formulate a framework to define and theoretically study the balancedness of exchangeable random partition models, by analyzing how a model assigns probabilities to partitions with different levels of balancedness. We demonstrate that the "rich-get-richer" characteristic of many existing popular random partition models is an inevitable consequence of two common assumptions: product-form exchangeability and projectivity. We propose a principled way to compare the balancedness of random partition models, which gives a better understanding of what model works better and what doesn't for different applications. We also introduce the "rich-get-poorer" random partition models and illustrate their application to entity resolution tasks.	['Huiyan Sang', 'Changwoo J. Lee']	2022-01-30	null	null	null	null	['topic-models', 'entity-resolution']	['natural-language-processing', 'natural-language-processing']	[-3.43434513e-02 3.04833561e-01 -4.81894344e-01 -3.07330728e-01 -2.07811043e-01 -5.57206929e-01 7.76593864e-01 2.23438129e-01 -1.80091243e-02 7.79331744e-01 1.49082303e-01 -2.01530904e-01 -7.91655123e-01 -1.11165738e+00 -3.01387280e-01 -8.59741628e-01 -5.29602394e-02 1.08212423e+00 4.16884571e-01 1.25622556e-01 -9.89820436e-03 1.10442325e-01 -1.41234708e+00 7.02708811e-02 1.01942027e+00 2.42995590e-01 2.18352422e-01 3.01857084e-01 -1.33797780e-01 5.07212758e-01 -5.09924829e-01 -5.18743753e-01 9.41221416e-02 -3.71316284e-01 -1.03911364e+00 -2.72781942e-02 -2.22335890e-01 2.50496328e-01 2.84191221e-02 1.31802762e+00 1.59076303e-01 4.22253925e-03 1.31381643e+00 -1.54976654e+00 -3.42488408e-01 1.34305668e+00 -9.21691060e-01 5.07997423e-02 1.76090345e-01 -5.07488608e-01 1.21930981e+00 -3.87700647e-01 8.00909758e-01 1.43933618e+00 5.76322973e-01 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-2.36247346e-01 -1.20516860e+00 -1.11004233e+00 1.00839846e-01 -1.62572652e-01 -1.03065062e+00 -3.76241058e-01 2.64220755e-04]	[7.004859924316406, 5.2276291847229]
01410ca4-be79-4484-be51-4cb0e7cb3371	multi-scale-distributed-representation-for	1811.12069	null	http://arxiv.org/abs/1811.12069v1	http://arxiv.org/pdf/1811.12069v1.pdf	Multi-Scale Distributed Representation for Deep Learning and its Application to b-Jet Tagging	Recently machine learning algorithms based on deep layered artificial neural networks (DNNs) have been applied to a wide variety of high energy physics problems such as jet tagging or event classification. We explore a simple but effective preprocessing step which transforms each real-valued observational quantity or input feature into a binary number with a fixed number of digits. Each binary digit represents the quantity or magnitude in different scales. We have shown that this approach improves the performance of DNNs significantly for some specific tasks without any further complication in feature engineering. We apply this multi-scale distributed binary representation to deep learning on b-jet tagging using daughter particles' momenta and vertex information.	['Jason Lee', 'Inkyu Park', 'Sangnam Park']	2018-11-29	null	null	null	null	['jet-tagging']	['graphs']	[-7.05698580e-02 -2.22757366e-02 -3.74279320e-01 -7.68064439e-01 -5.51589251e-01 -5.82899094e-01 7.34276175e-01 6.24156058e-01 -6.22291207e-01 8.45823884e-01 -7.62595050e-03 -6.68410003e-01 -2.66857147e-01 -1.20178974e+00 -7.63626158e-01 -7.41467416e-01 -1.81976229e-01 9.83224154e-01 4.40480351e-01 -1.39978006e-01 -1.56221360e-01 1.05116415e+00 -8.53197396e-01 3.83286506e-01 -1.80222347e-01 1.49745667e+00 -4.83424753e-01 9.79044020e-01 -4.56332453e-02 8.48916531e-01 -6.94238067e-01 -5.74688613e-01 5.37097394e-01 -4.71274137e-01 -6.02871180e-01 -8.86781335e-01 -7.88567662e-02 5.78620955e-02 -6.92478836e-01 1.26711071e+00 6.64431512e-01 4.01279539e-01 9.27118540e-01 -1.10655951e+00 -5.36174059e-01 7.33664691e-01 -2.42863089e-01 8.67128789e-01 -5.13659060e-01 -2.47036129e-01 8.55946898e-01 -4.76377159e-01 4.10762221e-01 1.21647727e+00 1.06837320e+00 -2.24073809e-02 -9.30256307e-01 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34fa3222-9cb8-4159-8825-106e556722af	non-contact-photoplethysmogram-and	1902.05194	null	http://arxiv.org/abs/1902.05194v1	http://arxiv.org/pdf/1902.05194v1.pdf	Non-contact photoplethysmogram and instantaneous heart rate estimation from infrared face video	Extracting the instantaneous heart rate (iHR) from face videos has been well studied in recent years. It is well known that changes in skin color due to blood flow can be captured using conventional cameras. One of the main limitations of methods that rely on this principle is the need of an illumination source. Moreover, they have to be able to operate under different light conditions. One way to avoid these constraints is using infrared cameras, allowing the monitoring of iHR under low light conditions. In this work, we present a simple, principled signal extraction method that recovers the iHR from infrared face videos. We tested the procedure on 7 participants, for whom we recorded an electrocardiogram simultaneously with their infrared face video. We checked that the recovered signal matched the ground truth iHR, showing that infrared is a promising alternative to conventional video imaging for heart rate monitoring, especially in low light conditions. Code is available at https://github.com/natalialmg/IR_iHR	['Hau-Tieng Wu', 'Natalia Martinez', 'Martin Bertran', 'Guillermo Sapiro']	2019-02-14	null	null	null	null	['heart-rate-estimation']	['medical']	[ 4.10802662e-01 -9.56116915e-02 -6.05005585e-02 -2.23690376e-01 -1.13269828e-01 -4.42743748e-01 5.55794276e-02 -1.60383761e-01 -5.27481496e-01 7.27157295e-01 -1.24495640e-01 1.54175773e-01 1.98404595e-01 -6.24470413e-01 -2.00869352e-01 -8.16721499e-01 -2.56886426e-02 -2.76993126e-01 -9.12315696e-02 2.08518282e-01 1.00964211e-01 6.14418268e-01 -1.68755364e+00 -1.25041053e-01 5.81190288e-01 9.61217701e-01 -5.27591705e-02 6.25252306e-01 1.81558087e-01 4.40797299e-01 -6.41902566e-01 -1.72676623e-01 3.24252069e-01 -7.78962135e-01 -2.80246496e-01 -5.80116324e-02 6.54874623e-01 -6.27934992e-01 -1.59631252e-01 9.35225487e-01 6.94142759e-01 -1.82048425e-01 2.74661869e-01 -9.53387499e-01 -5.34838997e-02 1.51177615e-01 -6.45842671e-01 2.00920835e-01 6.21193111e-01 1.50356933e-01 3.56841385e-01 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2672f267-8fe4-4adb-b8b3-97e2727c8d21	scatterformer-locally-invariant-scattering	2304.14919	null	https://arxiv.org/abs/2304.14919v1	https://arxiv.org/pdf/2304.14919v1.pdf	ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges	Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy. However, precise detection remains a considerable challenge due to subtle variabilities across subjects, channels and time points. Thus, capturing fine-grained, discriminative features of EEG patterns, which is associated with high-frequency textural information, is yet to be resolved. In this work, we propose Scattering Transformer (ScatterFormer), an invariant scattering transform-based hierarchical Transformer that specifically pays attention to subtle features. In particular, the disentangled frequency-aware attention (FAA) enables the Transformer to capture clinically informative high-frequency components, offering a novel clinical explainability based on visual encoding of multichannel EEG signals. Evaluations on two distinct tasks of epileptiform detection demonstrate the effectiveness our method. Our proposed model achieves median AUCROC and accuracy of 98.14%, 96.39% in patients with Rolandic epilepsy. On a neonatal seizure detection benchmark, it outperforms the state-of-the-art by 9% in terms of average AUCROC.	['Yuguo Yu', 'Tian Luo', 'Yi Wang', 'Jun Li', 'Ruizhe Zheng']	2023-04-26	null	null	null	null	['seizure-detection']	['medical']	[ 1.81874558e-01 -4.05940443e-01 5.21460593e-01 -2.34012887e-01 -1.06535423e+00 -5.59589028e-01 2.16179326e-01 6.40287250e-02 1.01097003e-01 7.03568161e-01 4.90245342e-01 1.16692953e-01 -7.41067350e-01 -1.54945478e-01 -3.11678201e-01 -1.09899366e+00 -6.84586883e-01 1.68319680e-02 -1.57748982e-01 1.02011129e-01 1.28776342e-01 3.12502623e-01 -1.24926400e+00 6.73518658e-01 1.13379741e+00 1.31028807e+00 2.39445522e-01 2.16662377e-01 4.17300224e-01 3.96411419e-01 -5.98615110e-01 1.15298115e-01 -1.51495606e-01 -5.43781400e-01 -2.79558897e-01 -2.60902017e-01 -1.20567679e-01 -1.54698370e-02 -3.73935074e-01 1.20250380e+00 7.95796692e-01 -5.53089619e-01 9.63582397e-01 -8.54362369e-01 -4.74182606e-01 5.97046316e-01 -8.38731229e-01 8.57181251e-01 5.05323946e-01 3.39058600e-02 7.31968224e-01 -1.08751738e+00 9.96545032e-02 4.66157705e-01 5.49935162e-01 2.04996973e-01 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-1.06591284e+00 7.92636201e-02 8.62134576e-01 -1.05058849e+00 -6.61024451e-01 -1.90608963e-01]	[13.192484855651855, 3.4984853267669678]
4af59bc1-e255-4e6b-96f4-a5084df534c6	multi-instrument-music-synthesis-with	2206.05408	null	https://arxiv.org/abs/2206.05408v3	https://arxiv.org/pdf/2206.05408v3.pdf	Multi-instrument Music Synthesis with Spectrogram Diffusion	An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural synthesizers have exhibited a tradeoff between domain-specific models that offer detailed control of only specific instruments, or raw waveform models that can train on any music but with minimal control and slow generation. In this work, we focus on a middle ground of neural synthesizers that can generate audio from MIDI sequences with arbitrary combinations of instruments in realtime. This enables training on a wide range of transcription datasets with a single model, which in turn offers note-level control of composition and instrumentation across a wide range of instruments. We use a simple two-stage process: MIDI to spectrograms with an encoder-decoder Transformer, then spectrograms to audio with a generative adversarial network (GAN) spectrogram inverter. We compare training the decoder as an autoregressive model and as a Denoising Diffusion Probabilistic Model (DDPM) and find that the DDPM approach is superior both qualitatively and as measured by audio reconstruction and Fr\'echet distance metrics. Given the interactivity and generality of this approach, we find this to be a promising first step towards interactive and expressive neural synthesis for arbitrary combinations of instruments and notes.	['Jesse Engel', 'Ethan Manilow', 'Josh Gardner', 'Neil Zeghidour', 'Adam Roberts', 'Ian Simon', 'Curtis Hawthorne']	2022-06-11	null	null	null	null	['music-generation', 'music-generation']	['audio', 'music']	[ 3.82198215e-01 1.39220744e-01 2.81380385e-01 1.00228479e-02 -1.01480055e+00 -1.20703590e+00 6.98974967e-01 -5.56640565e-01 1.80518776e-01 6.80178404e-01 4.12245542e-01 -5.79594038e-02 -2.57514089e-01 -8.24585795e-01 -6.88625515e-01 -6.45696044e-01 2.46357080e-02 5.82679391e-01 -7.84887187e-03 -3.74071985e-01 -1.30301371e-01 6.44650161e-01 -1.56537199e+00 3.54580313e-01 6.81412220e-01 8.75686407e-01 7.87367951e-03 1.24339521e+00 3.55161577e-02 8.37568998e-01 -1.01102126e+00 -4.76307273e-01 3.66493076e-01 -8.18561196e-01 -4.78424609e-01 -9.65340286e-02 2.82248557e-01 -1.89106300e-01 -3.12462986e-01 7.68179595e-01 9.21407640e-01 1.00926943e-01 8.03193390e-01 -9.30010557e-01 -6.91441298e-01 1.03545380e+00 -8.74223858e-02 -3.10128361e-01 4.65532690e-01 4.73843038e-01 1.11178458e+00 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af045ad4-5947-4e15-a169-18fcc8328eba	learning-algebraic-representation-for	null	null	https://openreview.net/forum?id=jQSBcVURlpW	https://openreview.net/pdf?id=jQSBcVURlpW	Learning Algebraic Representation for Abstract Spatial-Temporal Reasoning	Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate (Fodor et al., 1988; Fodor &McLaughlin, 1990) between connectionist and classicist, wherein the latter continually advocates an algebraic treatment in cognitive architectures. In this work, we follow the classicist's call and propose a hybrid approach to improve systematic generalization in reasoning. Specifically, we showcase a prototype with algebraic representations for the abstract spatial-temporal reasoning task of Raven’s Progressive Matrices (RPM) and present the ALgebra-Aware Neuro-Semi-Symbolic (ALANS$^2$) learner. The ALANS$^2$ learner is motivated by abstract algebra and the representation theory. It consists of a neural visual perception frontend and an algebraic abstract reasoning backend: the frontend summarizes the visual information from object-based representations, while the backend transforms it into an algebraic structure and induces the hidden operator on-the-fly. The induced operator is later executed to predict the answer's representation, and the choice most similar to the prediction is selected as the solution. Extensive experiments show that by incorporating an algebraic treatment, the ALANS$^2$ learner outperforms various pure connectionist models in domains requiring systematic generalization. We further show that the algebraic representation learned can be decoded by isomorphism and used to generate an answer.	['Song-Chun Zhu', 'Ying Nian Wu', 'Yixin Zhu', 'Baoxiong Jia', 'Sirui Xie', 'Chi Zhang']	2021-01-01	null	null	null	null	['abstract-algebra', 'systematic-generalization']	['reasoning', 'reasoning']	[ 2.49330640e-01 5.09314060e-01 1.05881512e-01 -1.99178919e-01 1.08641014e-01 -4.62181002e-01 7.13427663e-01 2.94075906e-01 -2.51130939e-01 2.21179649e-01 7.32695386e-02 -6.55941546e-01 -7.78278112e-01 -7.75479972e-01 -4.89023268e-01 -4.38944042e-01 -4.67793532e-02 7.49996781e-01 1.22736193e-01 -6.49620593e-01 7.54036725e-01 7.73151517e-01 -1.60305309e+00 5.89154065e-01 9.29079175e-01 9.37693417e-01 5.30154943e-01 5.62480211e-01 -3.55353892e-01 1.35279465e+00 -3.32473397e-01 -6.06481254e-01 2.78223127e-01 -9.02399957e-01 -1.14356446e+00 -2.75457829e-01 2.86520183e-01 -1.76280309e-02 -1.97829977e-01 9.69533741e-01 -6.68148100e-02 2.17775941e-01 8.59358370e-01 -1.19743538e+00 -1.15194833e+00 7.68779397e-01 -3.83392870e-01 6.56494856e-01 5.22051930e-01 2.38968149e-01 1.06879103e+00 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2.60058828e-02 6.02863431e-01 -1.04435451e-01 -8.32257688e-01 -6.16347969e-01 -7.48732269e-01 -6.30287468e-01 3.01464289e-01 2.54101437e-02 -7.63582349e-01 -3.79477829e-01 1.99177533e-01]	[10.6093111038208, 2.2774100303649902]
aeb2f239-6389-4080-9024-2382772364ce	on-adversarial-examples-and-stealth-attacks	2004.04479	null	https://arxiv.org/abs/2004.04479v1	https://arxiv.org/pdf/2004.04479v1.pdf	On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems	In this work we present a formal theoretical framework for assessing and analyzing two classes of malevolent action towards generic Artificial Intelligence (AI) systems. Our results apply to general multi-class classifiers that map from an input space into a decision space, including artificial neural networks used in deep learning applications. Two classes of attacks are considered. The first class involves adversarial examples and concerns the introduction of small perturbations of the input data that cause misclassification. The second class, introduced here for the first time and named stealth attacks, involves small perturbations to the AI system itself. Here the perturbed system produces whatever output is desired by the attacker on a specific small data set, perhaps even a single input, but performs as normal on a validation set (which is unknown to the attacker). We show that in both cases, i.e., in the case of an attack based on adversarial examples and in the case of a stealth attack, the dimensionality of the AI's decision-making space is a major contributor to the AI's susceptibility. For attacks based on adversarial examples, a second crucial parameter is the absence of local concentrations in the data probability distribution, a property known as Smeared Absolute Continuity. According to our findings, robustness to adversarial examples requires either (a) the data distributions in the AI's feature space to have concentrated probability density functions or (b) the dimensionality of the AI's decision variables to be sufficiently small. We also show how to construct stealth attacks on high-dimensional AI systems that are hard to spot unless the validation set is made exponentially large.	['Ivan Y. Tyukin', 'Desmond J. Higham', 'Alexander N. Gorban']	2020-04-09	null	null	null	null	['small-data']	['computer-vision']	[ 5.11153102e-01 4.15572643e-01 3.29445332e-01 1.55579671e-01 -8.69740173e-02 -1.01867366e+00 8.63035798e-01 2.50285268e-01 -4.12421674e-01 7.54324555e-01 -5.02439022e-01 -5.81116498e-01 -3.23858231e-01 -1.03727472e+00 -9.65227485e-01 -1.28844106e+00 -8.21128339e-02 5.47540247e-01 1.55973896e-01 -3.92166972e-01 1.45803422e-01 8.30921888e-01 -1.41876507e+00 -5.54309189e-02 6.01942062e-01 9.12139893e-01 -6.11064911e-01 8.98190260e-01 2.26223797e-01 6.74553812e-01 -1.14940226e+00 -4.94733095e-01 7.86346257e-01 -4.36520815e-01 -7.00102568e-01 -1.94244191e-01 2.36151025e-01 1.29727246e-02 -1.22062698e-01 1.54804146e+00 2.70507127e-01 -8.17518085e-02 1.05221081e+00 -1.69668508e+00 -4.58160728e-01 5.96486092e-01 -7.21538886e-02 8.35360307e-03 -4.01511341e-02 5.68164289e-01 4.69022304e-01 -1.35528728e-01 3.47933978e-01 1.16675186e+00 4.96672362e-01 8.49770844e-01 -1.29999042e+00 -7.38379955e-01 -5.78996055e-02 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9b0d4614-88df-468e-a568-678878f59dc8	introducing-mantis-a-novel-multi-domain	1912.04639	null	https://arxiv.org/abs/1912.04639v1	https://arxiv.org/pdf/1912.04639v1.pdf	Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset	Conversational search is an approach to information retrieval (IR), where users engage in a dialogue with an agent in order to satisfy their information needs. Previous conceptual work described properties and actions a good agent should exhibit. Unlike them, we present a novel conceptual model defined in terms of conversational goals, which enables us to reason about current research practices in conversational search. Based on the literature, we elicit how existing tasks and test collections from the fields of IR, natural language processing (NLP) and dialogue systems (DS) fit into this model. We describe a set of characteristics that an ideal conversational search dataset should have. Lastly, we introduce MANtIS (the code and dataset are available at https://guzpenha.github.io/MANtIS/), a large-scale dataset containing multi-domain and grounded information seeking dialogues that fulfill all of our dataset desiderata. We provide baseline results for the conversation response ranking and user intent prediction tasks.	['Claudia Hauff', 'Gustavo Penha', 'Alexandru Balan']	2019-12-10	null	null	null	null	['conversational-search']	['natural-language-processing']	[ 1.16526075e-01 5.28529942e-01 -5.73987961e-01 -4.22965050e-01 -9.14532244e-01 -7.06842005e-01 1.48675001e+00 1.28359511e-01 -2.33126059e-01 4.62343663e-01 1.07733285e+00 -4.78873193e-01 -4.70669001e-01 -3.06408167e-01 2.56199509e-01 -1.87672690e-01 7.54007772e-02 9.63372767e-01 -2.38455664e-02 -7.84720600e-01 8.28228593e-01 6.34554476e-02 -1.38895059e+00 6.37045324e-01 6.80773675e-01 8.23608696e-01 4.00466621e-01 7.38286674e-01 -2.14944378e-01 1.23268199e+00 -4.69787002e-01 -3.56118113e-01 -3.64762209e-02 -4.79589641e-01 -1.98318255e+00 -2.88706750e-01 -8.54771701e-04 -4.10907865e-01 -4.25458640e-01 4.87728775e-01 6.64851248e-01 4.82287854e-01 6.86649144e-01 -1.39007223e+00 -6.89886451e-01 6.49283946e-01 3.53449106e-01 6.52508065e-02 1.33643019e+00 2.19156682e-01 1.42552578e+00 -7.36150265e-01 9.05982554e-01 1.44496989e+00 1.64266244e-01 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c1cbdbab-8b6d-4fc9-949d-c45e354ee6d4	gating-revisited-deep-multi-layer-rnns-that-1	1911.11033	null	https://arxiv.org/abs/1911.11033v4	https://arxiv.org/pdf/1911.11033v4.pdf	Gating Revisited: Deep Multi-layer RNNs That Can Be Trained	We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM and GRU while being more robust against vanishing or exploding gradients. Stacking recurrent units into deep architectures suffers from two major limitations: (i) many recurrent cells (e.g., LSTMs) are costly in terms of parameters and computation resources; and (ii) deep RNNs are prone to vanishing or exploding gradients during training. We investigate the training of multi-layer RNNs and examine the magnitude of the gradients as they propagate through the network in the "vertical" direction. We show that, depending on the structure of the basic recurrent unit, the gradients are systematically attenuated or amplified. Based on our analysis we design a new type of gated cell that better preserves gradient magnitude. We validate our design on a large number of sequence modelling tasks and demonstrate that the proposed STAR cell allows to build and train deeper recurrent architectures, ultimately leading to improved performance while being computationally more efficient.	["Stefano D'Aronco", 'Mehmet Ozgur Turkoglu', 'Konrad Schindler', 'Jan Dirk Wegner']	2019-11-25	null	null	null	null	['sequential-image-classification', 'music-modeling']	['computer-vision', 'music']	[ 3.09491992e-01 -1.15020372e-01 1.60558715e-01 -6.94294348e-02 -1.82886526e-01 -4.61441338e-01 4.58623111e-01 -2.62214113e-02 -5.83949089e-01 7.28361309e-01 4.48902160e-01 -6.17757738e-01 4.59130853e-01 -6.45076215e-01 -8.79282713e-01 -7.37617254e-01 -1.48305506e-01 -9.84840095e-02 4.95842844e-01 -5.18175483e-01 2.94582397e-01 6.87256873e-01 -1.49014199e+00 3.47176820e-01 5.45216501e-01 7.96921194e-01 4.25628513e-01 7.84175456e-01 8.91714022e-02 1.39928091e+00 -5.81035495e-01 3.52866687e-02 -3.74276675e-02 -6.60678983e-01 -6.70598805e-01 -4.40301448e-01 1.67060941e-01 -1.56468749e-01 -2.33692482e-01 7.21525788e-01 6.93600178e-01 1.87479734e-01 2.72685796e-01 -4.63545889e-01 -4.51210409e-01 7.77598619e-01 -3.00882608e-01 4.02473271e-01 4.46820296e-02 -1.40486285e-01 1.02909935e+00 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b5ed4760-a4ed-402c-961a-8e8d28b30333	mass-segmentation-in-automated-3-d-breast	2109.08330	null	https://arxiv.org/abs/2109.08330v2	https://arxiv.org/pdf/2109.08330v2.pdf	Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net	Automated 3-D breast ultrasound (ABUS) is a newfound system for breast screening that has been proposed as a supplementary modality to mammography for breast cancer detection. While ABUS has better performance in dense breasts, reading ABUS images is exhausting and time-consuming. So, a computer-aided detection system is necessary for interpretation of these images. Mass segmentation plays a vital role in the computer-aided detection systems and it affects the overall performance. Mass segmentation is a challenging task because of the large variety in size, shape, and texture of masses. Moreover, an imbalanced dataset makes segmentation harder. A novel mass segmentation approach based on deep learning is introduced in this paper. The deep network that is used in this study for image segmentation is inspired by U-net, which has been used broadly for dense segmentation in recent years. The system's performance was determined using a dataset of 50 masses including 38 malign and 12 benign lesions. The proposed segmentation method attained a mean Dice of 0.82 which outperformed a two-stage supervised edge-based method with a mean Dice of 0.74 and an adaptive region growing method with a mean Dice of 0.65.	['Mohsen Soryani', 'Tao Tan', 'Ehsan Kozegar', 'Hamed Fayyaz']	2021-09-17	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[ 1.90268114e-01 3.20397049e-01 -3.02604347e-01 -3.89020264e-01 -4.65463310e-01 -5.83650582e-02 1.59756511e-01 4.63069171e-01 -5.24209559e-01 3.88660580e-01 -2.55754054e-01 -7.83470750e-01 1.59509510e-01 -9.00577188e-01 -4.23668593e-01 -7.49587774e-01 -1.76707119e-01 4.89995658e-01 5.57326436e-01 -5.25680324e-03 1.27041966e-01 5.18049121e-01 -9.72707868e-01 1.97044447e-01 9.49643373e-01 1.26289964e+00 4.23473716e-01 7.45391250e-01 -3.07266474e-01 3.74531329e-01 -1.38171256e-01 -5.83699286e-01 3.02176744e-01 -8.77020717e-01 -6.66211426e-01 3.14070672e-01 1.18445352e-01 -5.35799026e-01 -2.94572115e-01 1.11614418e+00 5.33498228e-01 -2.76940078e-01 7.65239656e-01 -7.57780969e-01 -2.07550138e-01 5.07734895e-01 -1.05192149e+00 5.39231479e-01 -2.20862925e-01 -1.57869577e-01 2.87016124e-01 -3.05633873e-01 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2ec0b3e0-f1c1-4001-b8c8-c3e825393a45	invariant-representation-driven-neural	2201.07199	null	https://arxiv.org/abs/2201.07199v5	https://arxiv.org/pdf/2201.07199v5.pdf	Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging	We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that, with a \emph{well-calibrated} and \emph{powerful enough feature extractor}, a well-trained \emph{mass-decorrelated} supervised Standard Model neural jet classifier can serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD background. Imposing \emph{data-augmented} mass-invariance (and thus decoupling the dominant factor) not only facilitates background estimation, but also induces more substructure-aware representation learning. We are able to reach excellent tagging efficiencies for all the test signals considered. In the best case, we reach a background rejection rate of 51 and a significance improvement factor of 3.6 at 50 \% signal acceptance, with the jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.	['Aaron Courville', 'Taoli Cheng']	2022-01-18	null	null	null	null	['jet-tagging']	['graphs']	[ 1.70104563e-01 -2.62035336e-02 -2.59821832e-01 -5.08982241e-01 -9.08131838e-01 -7.35958099e-01 8.82494569e-01 3.59955043e-01 -6.64062023e-01 4.45697933e-01 1.24370465e-02 -4.30964261e-01 -2.79972464e-01 -6.27538323e-01 -4.20379311e-01 -9.56135094e-01 5.56863435e-02 8.90023410e-01 5.23686588e-01 -2.81536460e-01 4.21166271e-02 8.49885523e-01 -1.01064312e+00 4.09320205e-01 3.58227491e-01 1.30991828e+00 -1.67679921e-01 8.14479709e-01 -1.82902053e-01 6.91636086e-01 -5.28235555e-01 -9.91204739e-01 4.92250800e-01 -6.84815347e-01 -2.67523408e-01 -8.12755346e-01 4.88532364e-01 1.37530312e-01 -6.71375394e-01 1.55368948e+00 8.16118360e-01 9.61611941e-02 7.47113705e-01 -7.47877955e-01 -5.06052911e-01 7.76156187e-01 -1.70269072e-01 9.45332766e-01 -5.42827725e-01 8.12228844e-02 1.15151048e+00 -9.39469695e-01 6.22347891e-01 1.23888493e+00 4.72978413e-01 3.85782301e-01 -1.60573053e+00 -1.09167278e+00 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5c6e2f0a-ab9d-43dc-892b-9b9ab5a053ed	an-efficient-provably-exact-algorithm-for-the	2306.12344	null	https://arxiv.org/abs/2306.12344v1	https://arxiv.org/pdf/2306.12344v1.pdf	An efficient, provably exact algorithm for the 0-1 loss linear classification problem	Algorithms for solving the linear classification problem have a long history, dating back at least to 1936 with linear discriminant analysis. For linearly separable data, many algorithms can obtain the exact solution to the corresponding 0-1 loss classification problem efficiently, but for data which is not linearly separable, it has been shown that this problem, in full generality, is NP-hard. Alternative approaches all involve approximations of some kind, including the use of surrogates for the 0-1 loss (for example, the hinge or logistic loss) or approximate combinatorial search, none of which can be guaranteed to solve the problem exactly. Finding efficient algorithms to obtain an exact i.e. globally optimal solution for the 0-1 loss linear classification problem with fixed dimension, remains an open problem. In research we report here, we detail the construction of a new algorithm, incremental cell enumeration (ICE), that can solve the 0-1 loss classification problem exactly in polynomial time. To our knowledge, this is the first, rigorously-proven polynomial time algorithm for this long-standing problem.	['Max A. Little', 'Xi He']	2023-06-21	null	null	null	null	['classification-1']	['methodology']	[ 1.70583069e-01 2.64079094e-01 -3.55890483e-01 -3.74262601e-01 -1.17260838e+00 -6.04463458e-01 -1.63359806e-01 4.01036143e-01 -3.56246382e-01 1.01197755e+00 -3.98708940e-01 -4.67358947e-01 -5.97153604e-01 -7.27132201e-01 -4.08545345e-01 -9.94094431e-01 -4.36025351e-01 1.09768653e+00 2.09397018e-01 2.77980324e-02 3.88473153e-01 7.23958194e-01 -1.43327904e+00 7.94363841e-02 6.93871915e-01 1.42882645e+00 -4.07279670e-01 8.42901647e-01 -1.54841151e-02 2.19134718e-01 -2.74862736e-01 -3.13757420e-01 6.80753350e-01 -4.91457403e-01 -1.17624283e+00 4.06323094e-03 5.31721711e-01 -2.60843605e-01 -1.56442285e-01 8.99428725e-01 4.10258025e-01 -3.79110724e-02 8.14301670e-01 -1.65103483e+00 -2.71648228e-01 2.23793313e-01 -5.32488942e-01 -5.49611151e-02 2.66024798e-01 -5.01655042e-01 1.29747307e+00 -7.20711172e-01 3.81400913e-01 1.07873023e+00 9.44733143e-01 4.80144113e-01 -1.79725718e+00 -3.11716229e-01 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eda768f7-c97b-42a7-93c3-40ba37f41688	padgan-a-generative-adversarial-network-for	2002.11304	null	https://arxiv.org/abs/2002.11304v5	https://arxiv.org/pdf/2002.11304v5.pdf	PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs	Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: 1) generated designs lack diversity and do not cover all areas of the design space, 2) it is difficult to explicitly improve the overall performance or quality of generated designs, and 3) existing models generally do not generate novel designs, outside the domain of the training data. In this paper, we simultaneously address these challenges by proposing a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and quality. With this new loss function, we develop a variant of the Generative Adversarial Network, named "Performance Augmented Diverse Generative Adversarial Network" or PaDGAN, which can generate novel high-quality designs with good coverage of the design space. Using three synthetic examples and one real-world airfoil design example, we demonstrate that PaDGAN can generate diverse and high-quality designs. In comparison to a vanilla Generative Adversarial Network, on average, it generates samples with a 28% higher mean quality score with larger diversity and without the mode collapse issue. Unlike typical generative models that usually generate new designs by interpolating within the boundary of training data, we show that PaDGAN expands the design space boundary outside the training data towards high-quality regions. The proposed method is broadly applicable to many tasks including design space exploration, design optimization, and creative solution recommendation.	['Wei Chen', 'Faez Ahmed']	2020-02-26	null	null	null	null	['design-synthesis']	['adversarial']	[ 7.37422053e-03 2.68084388e-02 -8.86199549e-02 1.37935698e-01 -5.32170475e-01 -7.62148082e-01 2.92994857e-01 -5.02565920e-01 4.35568511e-01 1.09734201e+00 1.76264390e-01 -2.66342342e-01 -3.61732900e-01 -1.18880594e+00 -8.09070289e-01 -6.56548500e-01 3.57694536e-01 5.30011714e-01 -3.64934295e-01 -4.14718896e-01 1.41485691e-01 4.52937752e-01 -1.39209914e+00 -6.61170390e-03 1.17269111e+00 7.66566038e-01 -1.13232136e-01 4.45079803e-01 1.14896946e-01 1.46953717e-01 -9.82999802e-01 -1.86221898e-01 3.74512881e-01 -7.05869257e-01 -3.63977909e-01 -6.57758638e-02 9.80737060e-02 -1.32929742e-01 -2.24828884e-01 7.08589137e-01 9.34540212e-01 1.10369831e-01 9.39461470e-01 -1.28309107e+00 -1.06930792e+00 6.10282362e-01 -1.60914391e-01 -5.95208347e-01 3.26793998e-01 4.90470290e-01 9.05994833e-01 -1.07983339e+00 6.31155193e-01 1.15013516e+00 7.25160897e-01 6.86616659e-01 -1.59404397e+00 -8.68843198e-01 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-5.41441441e-02 6.81105405e-02 -9.63876724e-01 -1.59053743e-01 -2.29238093e-01]	[5.810977935791016, 3.2923712730407715]

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