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7e2b63fb-f797-459e-b0c5-40ac77dd6490	self-supervised-rubik-s-cube-solver	2106.03157	null	https://arxiv.org/abs/2106.03157v5	https://arxiv.org/pdf/2106.03157v5.pdf	Self-Supervision is All You Need for Solving Rubik's Cube	Existing combinatorial search methods are often complex and require some level of expertise. This work introduces a simple and efficient deep learning method for solving combinatorial problems with a predefined goal, represented by Rubik's Cube. We demonstrate that, for such problems, training a deep neural network on random scrambles branching from the goal state is sufficient to achieve near-optimal solutions. When tested on Rubik's Cube, 15 Puzzle, and 7$\times$7 Lights Out, our method outperformed the previous state-of-the-art method DeepCubeA, improving the trade-off between solution optimality and computational cost, despite significantly less training data. Furthermore, we investigate the scaling law of our Rubik's Cube solver with respect to model size and training data volume.	['Kyo Takano']	2021-06-06	self-supervision-is-all-you-need-for-solving	https://openreview.net/forum?id=9HmtMeHmyR4	https://openreview.net/pdf?id=9HmtMeHmyR4	null	['rubik-s-cube']	['graphs']	[-1.66410103e-01 7.25962594e-02 -1.78310052e-01 -2.27635857e-02 -8.87867868e-01 -8.71837199e-01 9.98985171e-02 1.62744673e-03 -3.90319705e-01 9.59254980e-01 -1.84154481e-01 -5.60503542e-01 -5.87809503e-01 -9.14004624e-01 -9.11710739e-01 -6.38577342e-01 -3.12801659e-01 9.39930081e-01 -1.49909034e-01 -3.30780596e-01 4.93344903e-01 3.02604914e-01 -1.02589953e+00 2.40910038e-01 7.55851209e-01 1.26991117e+00 2.01199695e-01 6.11866593e-01 -1.21606983e-01 5.62786222e-01 -8.12486470e-01 -2.96867192e-01 7.18390286e-01 -2.66651124e-01 -1.10355306e+00 -2.86038697e-01 3.59642714e-01 -2.08371654e-01 -3.71562272e-01 7.64869153e-01 4.31776255e-01 1.21632420e-01 2.38731608e-01 -1.25919008e+00 -5.04032433e-01 8.42148006e-01 -5.58321238e-01 2.29949787e-01 6.23951927e-02 6.05502188e-01 1.22979128e+00 -2.48385713e-01 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7363e14f-5cde-4a08-9024-5ac69b085101	measuring-systematic-generalization-in-neural	2009.14786	null	https://arxiv.org/abs/2009.14786v2	https://arxiv.org/pdf/2009.14786v2.pdf	Measuring Systematic Generalization in Neural Proof Generation with Transformers	We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded in the form of natural language. We investigate their systematic generalization abilities on a logical reasoning task in natural language, which involves reasoning over relationships between entities grounded in first-order logical proofs. Specifically, we perform soft theorem-proving by leveraging TLMs to generate natural language proofs. We test the generated proofs for logical consistency, along with the accuracy of the final inference. We observe length-generalization issues when evaluated on longer-than-trained sequences. However, we observe TLMs improve their generalization performance after being exposed to longer, exhaustive proofs. In addition, we discover that TLMs are able to generalize better using backward-chaining proofs compared to their forward-chaining counterparts, while they find it easier to generate forward chaining proofs. We observe that models that are not trained to generate proofs are better at generalizing to problems based on longer proofs. This suggests that Transformers have efficient internal reasoning strategies that are harder to interpret. These results highlight the systematic generalization behavior of TLMs in the context of logical reasoning, and we believe this work motivates deeper inspection of their underlying reasoning strategies.	['Christopher Pal', 'Koustuv Sinha', 'Siva Reddy', 'Nicolas Gontier']	2020-09-30	null	http://proceedings.neurips.cc/paper/2020/hash/fc84ad56f9f547eb89c72b9bac209312-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/fc84ad56f9f547eb89c72b9bac209312-Paper.pdf	neurips-2020-12	['systematic-generalization']	['reasoning']	[ 3.36734951e-01 8.50238621e-01 -2.69672945e-02 -2.66563654e-01 -6.51407063e-01 -9.87268806e-01 7.58096814e-01 2.85363317e-01 5.74867539e-02 7.50659525e-01 8.45999345e-02 -1.28703475e+00 -2.29591876e-01 -1.25990605e+00 -1.22228467e+00 2.33095493e-02 -3.20493430e-01 3.12991291e-01 3.77104372e-01 -3.07458341e-01 1.46087050e-01 1.33630484e-01 -1.28230810e+00 7.19493270e-01 1.10172021e+00 2.79106677e-01 -9.96547639e-02 8.24306548e-01 -1.80312544e-01 1.59305966e+00 -3.37942839e-01 -9.51802313e-01 -7.71083385e-02 -4.95155782e-01 -1.36505377e+00 -4.46475923e-01 6.57185972e-01 -4.06539470e-01 -1.10916365e-02 1.08885920e+00 -5.54474115e-01 -2.39881918e-01 5.48380494e-01 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0085425d-a30c-4113-8a40-fa374b8cb7c2	adaptive-control-flow-in-transformers	null	null	https://openreview.net/forum?id=KBQP4A_J1K	https://openreview.net/pdf?id=KBQP4A_J1K	Adaptive Control Flow in Transformers Improves Systematic Generalization	Despite successes across a broad range of applications, Transformers have limited capability in systematic generalization. The situation is especially frustrating in the case of algorithmic tasks, where they often fail to find intuitive solutions that can be simply expressed in terms of attention patterns. In the end, it is often all about routing the right information to the right node/operation at the right time in the grid represented by Transformer columns. To facilitate the learning of useful control flow, we propose two modifications to the Transformer architecture, copy gate and geometric attention. Our novel Transformer Control Flow (TCF) achieves 100% length generalization accuracy on the classic compositional table lookup task, as well as near-perfect accuracy on the simple arithmetic task and a new variant of ListOps testing for computational depth generalization. TCF's attention and gating patterns tend to be interpretable.	['Jürgen Schmidhuber', 'Kazuki Irie', 'Róbert Csordás']	2021-09-29	null	null	null	iclr-2022-4	['systematic-generalization']	['reasoning']	[ 3.14234942e-01 2.52091903e-02 -2.72203922e-01 -2.55328715e-01 -4.78484750e-01 -7.98175514e-01 3.56880784e-01 5.03618300e-01 -4.64178342e-03 7.92984903e-01 3.38855177e-01 -1.00743842e+00 -3.04223746e-01 -9.49515104e-01 -5.18545806e-01 -3.89790475e-01 -1.04930699e-01 6.58669889e-01 2.52335191e-01 -5.26300550e-01 6.55628204e-01 5.71952939e-01 -1.31697953e+00 4.94608015e-01 8.14656615e-01 1.12045562e+00 1.29500866e-01 4.23915029e-01 -3.80370110e-01 9.70112801e-01 -4.70478088e-01 -6.34556055e-01 1.46084070e-01 -3.49501699e-01 -1.09337556e+00 -3.75977367e-01 4.95099396e-01 -5.98057285e-02 -7.00933412e-02 8.28582406e-01 1.46785706e-01 1.75032064e-01 6.01528227e-01 -1.23122215e+00 -7.15492308e-01 9.69914019e-01 -4.67182040e-01 5.76204002e-01 3.68130028e-01 1.82531670e-01 1.47862864e+00 -5.99209964e-01 5.36755860e-01 1.35997367e+00 6.37363553e-01 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-7.29612947e-01 -7.24408507e-01 -3.94708484e-01 2.69745290e-01 4.33682829e-01 -7.29027569e-01 -1.43297434e-01 -3.09760720e-01]	[9.494231224060059, 7.2097907066345215]
6f72120d-0bdc-4d66-8e05-70881b9ab6a0	a-single-channel-sleep-spindle-detector-based	null	null	https://doi.org/10.1016/j.jneumeth.2017.12.023	https://www.deepdyve.com/lp/elsevier/a-single-channel-sleep-spindle-detector-based-on-multivariate-grFKFTd9gR#bsSignUpModal	A single channel sleep-spindle detector based on multivariate classification of EEG epochs: MUSSDET.	BACKGROUND: Studies on sleep-spindles are typically based on visual-marks performed by experts, however this process is time consuming and presents a low inter-expert agreement, causing the data to be limited in quantity and prone to bias. An automatic detector would tackle these issues by generating large amounts of objectively marked data. NEW METHOD: Our goal was to develop a sensitive, precise and robust sleep-spindle detection method. Emphasis has been placed on achieving a consistent performance across heterogeneous recordings and without the need for further parameter fine tuning. The developed detector runs on a single channel and is based on multivariate classification using a support vector machine. Scalp-electroencephalogram recordings were segmented into epochs which were then characterized by a selection of relevant and non-redundant features. The training and validation data came from the Medical Center-University of Freiburg, the test data consisted of 27 records coming from 2 public databases. RESULTS: Using a sample based assessment, 53% sensitivity, 37% precision and 96% specificity was achieved on the DREAMS database. On the MASS database, 77% sensitivity, 46% precision and 96% specificity was achieved. The developed detector performed favorably when compared to previous detectors. The classification of normalized EEG epochs in a multidimensional space, as well as the use of a validation set, allowed to objectively define a single detection threshold for all databases and participants. CONCLUSIONS: The use of the developed tool will allow increasing the data-size and statistical significance of research studies on the role of sleep-spindles.	['Matthias Dümpelmann', 'Andreas Schulze-Bonhage', 'DanielLachner-Piza', 'Thomas Stieglitz', 'Nino Epitashvili', 'Julia Jacobs']	2018-03-01	null	null	null	journal-of-neuroscience-methods-volume-297	['spindle-detection']	['medical']	[ 1.08090788e-01 -1.61329851e-01 3.29119153e-02 -4.36342329e-01 -6.43923879e-01 -4.26636279e-01 1.79155916e-01 6.31627321e-01 -8.38207543e-01 1.01859546e+00 -6.23850338e-02 1.54083863e-01 -4.95852649e-01 -3.95794928e-01 -7.63811693e-02 -6.89114451e-01 -3.64899725e-01 4.24407959e-01 2.75988400e-01 7.05560818e-02 3.53214025e-01 4.80410725e-01 -1.71040082e+00 1.64399654e-01 9.47645366e-01 9.66320276e-01 6.00190938e-01 2.32873842e-01 1.12498626e-01 2.45663635e-02 -1.03710294e+00 8.84823054e-02 3.06024365e-02 -4.64365065e-01 -3.09792787e-01 -7.28876516e-02 -9.84617472e-02 2.32653007e-01 4.79302377e-01 9.11773860e-01 8.25412214e-01 -1.28977066e-02 6.21172071e-01 -1.07521296e+00 -1.39380589e-01 2.38184541e-01 -2.67383039e-01 5.99372745e-01 5.36413550e-01 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75749e2e-bbab-4c5f-b044-71cedfa6b472	convolutional-hough-matching-networks	2103.16831	null	https://arxiv.org/abs/2103.16831v1	https://arxiv.org/pdf/2103.16831v1.pdf	Convolutional Hough Matching Networks	Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM). The method distributes similarities of candidate matches over a geometric transformation space and evaluate them in a convolutional manner. We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters. To validate the effect, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling. Our method sets a new state of the art on standard benchmarks for semantic visual correspondence, proving its strong robustness to challenging intra-class variations.	['Minsu Cho', 'Juhong Min']	2021-03-31	null	http://openaccess.thecvf.com//content/CVPR2021/html/Min_Convolutional_Hough_Matching_Networks_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Min_Convolutional_Hough_Matching_Networks_CVPR_2021_paper.pdf	cvpr-2021-1	['geometric-matching']	['computer-vision']	[ 9.42654908e-02 1.15333237e-01 -1.76414028e-01 -5.39380431e-01 -7.56929338e-01 -6.76204503e-01 9.17758226e-01 -1.03052862e-01 -4.02360857e-01 -9.09450799e-02 2.26604044e-01 -1.20502383e-01 -8.72061029e-02 -8.99765670e-01 -1.28748989e+00 -2.15835407e-01 -2.19109841e-02 6.18030310e-01 3.09164166e-01 -3.53685319e-01 2.79038727e-01 8.87352824e-01 -1.38203371e+00 3.13197941e-01 5.60870945e-01 8.88702512e-01 -3.38783354e-01 4.33078170e-01 1.38438657e-01 4.37195480e-01 -9.12806690e-02 -6.78331852e-01 8.03272009e-01 -2.43089467e-01 -1.07855308e+00 -3.82967070e-02 1.42194164e+00 -3.29372764e-01 -5.40515125e-01 8.13632548e-01 4.67887700e-01 1.77400395e-01 6.45137072e-01 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85a9a330-aeaa-4937-83a9-afd2dc4e6ae7	robust-method-for-finding-sparse-solutions-to	1701.00573	null	http://arxiv.org/abs/1701.00573v3	http://arxiv.org/pdf/1701.00573v3.pdf	Robust method for finding sparse solutions to linear inverse problems using an L2 regularization	We analyzed the performance of a biologically inspired algorithm called the Corrected Projections Algorithm (CPA) when a sparseness constraint is required to unambiguously reconstruct an observed signal using atoms from an overcomplete dictionary. By changing the geometry of the estimation problem, CPA gives an analytical expression for a binary variable that indicates the presence or absence of a dictionary atom using an L2 regularizer. The regularized solution can be implemented using an efficient real-time Kalman-filter type of algorithm. The smoother L2 regularization of CPA makes it very robust to noise, and CPA outperforms other methods in identifying known atoms in the presence of strong novel atoms in the signal.	['Gonzalo H Otazu']	2017-01-03	null	null	null	null	['l2-regularization']	['methodology']	[ 5.42773187e-01 1.52105793e-01 5.63759990e-02 -1.16241083e-01 -3.03754866e-01 -3.93525273e-01 5.50626099e-01 -2.29270235e-01 -4.86745507e-01 9.12907779e-01 3.30088407e-01 1.05664104e-01 -6.78076074e-02 -4.03532267e-01 -7.33543992e-01 -1.07846344e+00 -8.92546549e-02 5.41191399e-01 -5.00373580e-02 9.79598835e-02 2.44288340e-01 7.78374255e-01 -1.21636248e+00 -6.42827079e-02 1.45524904e-01 5.72033882e-01 4.64185089e-01 5.33578396e-01 1.95107996e-01 4.24134433e-01 -4.21419919e-01 1.93897814e-01 5.86689830e-01 -8.00814688e-01 -3.84616822e-01 1.24288172e-01 2.03309327e-01 -4.14104238e-02 -3.47733349e-01 1.11913824e+00 4.56007004e-01 1.22159757e-02 6.49106741e-01 -6.16366982e-01 -1.89075664e-01 5.48180938e-01 -4.09442753e-01 2.03885272e-01 4.56176460e-01 -2.69089878e-01 7.74819732e-01 -1.19193888e+00 9.14756715e-01 1.21271050e+00 7.70920396e-01 3.82597417e-01 -1.58219981e+00 -4.14344341e-01 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8e8b723a-cd99-41e6-aeff-8784415ecf79	complementary-learning-of-aspect-terms-for	null	null	https://aclanthology.org/2022.lrec-1.760	https://aclanthology.org/2022.lrec-1.760.pdf	Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis	Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards a given aspect term in a sentence on the fine-grained level, which usually requires a good understanding of contextual information, especially appropriately distinguishing of a given aspect and its contexts, to achieve good performance. However, most existing ABSA models pay limited attention to the modeling of the given aspect terms and thus result in inferior results when a sentence contains multiple aspect terms with contradictory sentiment polarities. In this paper, we propose to improve ABSA by complementary learning of aspect terms, which serves as a supportive auxiliary task to enhance ABSA by explicitly recovering the aspect terms from each input sentence so as to better understand aspects and their contexts. Particularly, a discriminator is also introduced to further improve the learning process by appropriately balancing the impact of aspect recovery to sentiment prediction. Experimental results on five widely used English benchmark datasets for ABSA demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on all datasets.	['Yan Song', 'Fei Xia', 'Yuanhe Tian', 'Han Qin']	null	null	null	null	lrec-2022-6	['aspect-based-sentiment-analysis']	['natural-language-processing']	[ 4.22119677e-01 -1.76077202e-01 -4.61215645e-01 -7.92068124e-01 -9.01217937e-01 -5.94911516e-01 7.41399348e-01 5.09418547e-01 -1.19103692e-01 4.03103054e-01 4.76484120e-01 -3.25359374e-01 -7.15797534e-03 -8.77487779e-01 -5.46890914e-01 -7.86039114e-01 4.91867691e-01 2.69547164e-01 -1.67733997e-01 -5.58238447e-01 4.08401161e-01 -8.33388045e-02 -1.37829411e+00 5.15857160e-01 1.01037109e+00 1.18962228e+00 -4.12683785e-02 1.56363696e-01 -5.25253952e-01 7.21013963e-01 -6.03151143e-01 -7.47506261e-01 -2.96697728e-02 -4.07863021e-01 -7.30491936e-01 3.03869694e-01 1.13025963e-01 3.21953207e-01 4.56644207e-01 9.35899377e-01 2.11178124e-01 -1.29631266e-01 6.85261607e-01 -9.85294342e-01 -4.64269072e-01 5.96304715e-01 -7.24933088e-01 3.47574353e-02 3.84880364e-01 -4.25671749e-02 1.79962289e+00 -8.92517924e-01 1.19857356e-01 9.59794879e-01 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-1.86915010e-01 -2.08044127e-01 -9.34265435e-01 -5.49625754e-01 5.91282487e-01 4.84269112e-01 -3.45790774e-01 -5.02207458e-01 -5.72508052e-02]	[11.468877792358398, 6.648705005645752]
be036f8c-0f70-411a-9634-043462cdfa03	cooperative-multi-cell-massive-access-with	2304.09727	null	https://arxiv.org/abs/2304.09727v1	https://arxiv.org/pdf/2304.09727v1.pdf	Cooperative Multi-Cell Massive Access with Temporally Correlated Activity	This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped.	['Yunfeng Guan', 'Fan Xu', 'Xiaojun Yuan', 'Meixia Tao', 'Weifeng Zhu']	2023-04-19	null	null	null	null	['activity-detection', 'bayesian-inference']	['computer-vision', 'methodology']	[ 1.68085158e-01 -1.29208043e-01 -3.12003613e-01 2.41525531e-01 -8.18806410e-01 -2.07305729e-01 2.67746389e-01 -1.05310582e-01 -1.40192106e-01 1.08289671e+00 8.90024602e-02 -4.16920334e-01 -6.46566153e-01 -7.68380702e-01 -2.48748869e-01 -1.37557149e+00 -5.74667037e-01 2.91253984e-01 -7.53121823e-02 1.71294987e-01 -2.22876847e-01 5.01749158e-01 -5.07442474e-01 -4.78054345e-01 8.40312243e-01 1.48118639e+00 3.00632238e-01 7.23888099e-01 2.01636747e-01 7.43748367e-01 -5.57074785e-01 -6.20021895e-02 2.57007688e-01 -5.50301075e-01 -5.33555597e-02 5.59949636e-01 -8.12211871e-01 -2.80582815e-01 -6.22904062e-01 6.07487381e-01 8.04757953e-01 5.08644134e-02 6.54764652e-01 -1.36176288e+00 -6.15545101e-02 3.68826956e-01 -9.38511312e-01 4.44780797e-01 2.78150409e-01 -3.16715449e-01 7.97287285e-01 -1.04948688e+00 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ac666625-2940-4dad-a85e-6eead4a65178	scalable-and-accurate-self-supervised	2304.0208	null	https://arxiv.org/abs/2304.02080v1	https://arxiv.org/pdf/2304.02080v1.pdf	Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data	Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multimodal neural networks. However, existing large-scale video-text datasets and mining techniques suffer from several limitations, such as the scarcity of aligned data, the lack of diversity in the data, and the difficulty of collecting aligned data. Currently popular video-text data mining approach via automatic speech recognition (ASR) used in HowTo100M provides low-quality captions that often do not refer to the video content. Other mining approaches do not provide proper language descriptions (video tags) and are biased toward short clips (alt text). In this work, we show how recent advances in image captioning allow us to pre-train high-quality video models without any parallel video-text data. We pre-train several video captioning models that are based on an OPT language model and a TimeSformer visual backbone. We fine-tune these networks on several video captioning datasets. First, we demonstrate that image captioning pseudolabels work better for pre-training than the existing HowTo100M ASR captions. Second, we show that pre-training on both images and videos produces a significantly better network (+4 CIDER on MSR-VTT) than pre-training on a single modality. Our methods are complementary to the existing pre-training or data mining approaches and can be used in a variety of settings. Given the efficacy of the pseudolabeling method, we are planning to publicly release the generated captions.	['Wael Hamza', 'Anna Rumshisky', 'Shalini Ghosh', 'David Chan', 'Stephen Rawls', 'Vladislav Lialin']	2023-04-04	null	null	null	null	['video-captioning']	['computer-vision']	[ 6.12481475e-01 8.80565196e-02 -5.96802473e-01 -5.72786331e-01 -1.18904042e+00 -5.69275200e-01 8.03213179e-01 -2.00005889e-01 -5.73266447e-01 6.37164831e-01 4.87090319e-01 -3.61511141e-01 3.10119063e-01 -3.68318483e-02 -1.18625391e+00 -2.57166624e-01 5.17145284e-02 7.82141745e-01 2.30510533e-01 -2.55866438e-01 -1.11480527e-01 -1.49542585e-01 -1.70328379e+00 9.58911955e-01 3.21006089e-01 9.81506646e-01 4.69853461e-01 8.27534318e-01 -1.81067541e-01 9.64106560e-01 -4.67867792e-01 -4.83472705e-01 2.85078853e-01 -5.00231385e-01 -8.15690279e-01 4.84332025e-01 1.02175641e+00 -4.74572003e-01 -6.17946386e-01 7.39570796e-01 2.88778454e-01 -5.80649190e-02 5.12020707e-01 -1.66790271e+00 -9.64795589e-01 7.73970246e-01 -4.37686056e-01 4.11397070e-02 5.01195729e-01 -1.07147172e-02 9.05124545e-01 -8.62528443e-01 9.35411751e-01 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c42da289-cb8d-442b-8ca4-608cbd3ddb89	exploiting-local-geometry-for-feature-and	2103.15226	null	https://arxiv.org/abs/2103.15226v1	https://arxiv.org/pdf/2103.15226v1.pdf	Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks	We propose simple yet effective improvements in point representations and local neighborhood graph construction within the general framework of graph neural networks (GNNs) for 3D point cloud processing. As a first contribution, we propose to augment the vertex representations with important local geometric information of the points, followed by nonlinear projection using a MLP. As a second contribution, we propose to improve the graph construction for GNNs for 3D point clouds. The existing methods work with a k-nn based approach for constructing the local neighborhood graph. We argue that it might lead to reduction in coverage in case of dense sampling by sensors in some regions of the scene. The proposed methods aims to counter such problems and improve coverage in such cases. As the traditional GNNs were designed to work with general graphs, where vertices may have no geometric interpretations, we see both our proposals as augmenting the general graphs to incorporate the geometric nature of 3D point clouds. While being simple, we demonstrate with multiple challenging benchmarks, with relatively clean CAD models, as well as with real world noisy scans, that the proposed method achieves state of the art results on benchmarks for 3D classification (ModelNet40) , part segmentation (ShapeNet) and semantic segmentation (Stanford 3D Indoor Scenes Dataset). We also show that the proposed network achieves faster training convergence, i.e. ~40% less epochs for classification. The project details are available at https://siddharthsrivastava.github.io/publication/geomgcnn/	['Gaurav Sharma', 'Siddharth Srivastava']	2021-03-28	null	null	null	null	['3d-classification']	['computer-vision']	[ 9.85926986e-02 4.19947416e-01 4.83869091e-02 -3.64292115e-01 -5.02149522e-01 -5.11407912e-01 5.52863896e-01 2.09615201e-01 -2.13535413e-01 3.10179055e-01 -1.13275141e-01 -5.22982955e-01 -1.62684381e-01 -1.11011863e+00 -1.28602922e+00 -4.60974902e-01 -1.48313314e-01 7.44369984e-01 3.74491185e-01 -1.20642208e-01 2.53320843e-01 1.21580398e+00 -1.49540257e+00 -6.99593499e-02 5.45240402e-01 1.08390558e+00 2.19810709e-01 5.56948066e-01 -4.18276489e-01 2.40363106e-01 -4.07976300e-01 -1.90307330e-02 7.02046692e-01 3.52344632e-01 -7.78065085e-01 2.43258253e-01 7.30728328e-01 -4.04810011e-02 -2.95590281e-01 1.01672637e+00 4.09424484e-01 2.12344393e-01 6.19386375e-01 -1.21599805e+00 -6.30023360e-01 4.49380904e-01 -5.91738880e-01 -3.46192457e-02 9.75015089e-02 -7.27340812e-03 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48ba4bfa-57b0-4c9a-9ba5-570344ababd3	on-the-importance-of-karaka-framework-in	2204.04347	null	https://arxiv.org/abs/2204.04347v1	https://arxiv.org/pdf/2204.04347v1.pdf	On the Importance of Karaka Framework in Multi-modal Grounding	Computational Paninian Grammar model helps in decoding a natural language expression as a series of modifier-modified relations and therefore facilitates in identifying dependency relations closer to language (context) semantics compared to the usual Stanford dependency relations. However, the importance of this CPG dependency scheme has not been studied in the context of multi-modal vision and language applications. At IIIT Hyderabad, we plan to perform a novel study to explore the potential advantages and disadvantages of CPG framework in a vision-language navigation task setting, a popular and challenging multi-modal grounding task.	['Radhika Mamidi', 'Sai Kiran Gorthi']	2022-04-09	null	null	null	null	['vision-language-navigation']	['computer-vision']	[ 2.35470489e-01 2.85208374e-01 -2.09357068e-02 -4.92504239e-01 -6.06205225e-01 -5.79152226e-01 8.39847028e-01 4.03457969e-01 -7.00906217e-01 6.73479140e-01 5.58929920e-01 -6.07858658e-01 -3.73095512e-01 -6.26010358e-01 -3.54774028e-01 -5.79863966e-01 -2.79876113e-01 7.51940250e-01 4.58890855e-01 -5.93427837e-01 2.47600213e-01 3.95209879e-01 -1.49736285e+00 1.76499411e-01 4.85341758e-01 2.85489857e-01 7.05122471e-01 7.22578645e-01 8.87681693e-02 8.80316138e-01 -3.30297410e-01 2.99223233e-03 5.79526499e-02 -2.24630252e-01 -1.28203082e+00 -2.80383050e-01 4.84276474e-01 1.12998419e-01 7.94935375e-02 9.55225229e-01 3.28993976e-01 4.18042898e-01 4.60007995e-01 -9.17732596e-01 -5.00602126e-01 6.36573553e-01 -4.35203224e-01 5.97122550e-01 8.79911602e-01 -3.59833091e-01 1.32286811e+00 -3.42002302e-01 8.56985271e-01 1.30256581e+00 3.99763972e-01 3.04796457e-01 -8.06382537e-01 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-3.43387514e-01 3.15743148e-01 9.65739936e-02 -7.75348425e-01 1.29586503e-01 -5.00660762e-02]	[10.345497131347656, 9.520176887512207]
7d946fec-7a54-4c1f-8bc4-9c1a3075976c	fair-yet-asymptotically-equal-collaborative	2306.05764	null	https://arxiv.org/abs/2306.05764v1	https://arxiv.org/pdf/2306.05764v1.pdf	Fair yet Asymptotically Equal Collaborative Learning	In collaborative learning with streaming data, nodes (e.g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data. For the more resourceful nodes to be willing to share their model updates, they need to be fairly incentivized. This paper explores an incentive design that guarantees fairness so that nodes receive rewards commensurate to their contributions. Our approach leverages an explore-then-exploit formulation to estimate the nodes' contributions (i.e., exploration) for realizing our theoretically guaranteed fair incentives (i.e., exploitation). However, we observe a "rich get richer" phenomenon arising from the existing approaches to guarantee fairness and it discourages the participation of the less resourceful nodes. To remedy this, we additionally preserve asymptotic equality, i.e., less resourceful nodes achieve equal performance eventually to the more resourceful/"rich" nodes. We empirically demonstrate in two settings with real-world streaming data: federated online incremental learning and federated reinforcement learning, that our proposed approach outperforms existing baselines in fairness and learning performance while remaining competitive in preserving equality.	['Bryan Kian Hsiang Low', 'Chuan-Sheng Foo', 'See-Kiong Ng', 'Xinyi Xu', 'Xiaoqiang Lin']	2023-06-09	null	null	null	null	['incremental-learning']	['methodology']	[-3.56836557e-01 5.28215945e-01 -6.88559890e-01 -5.07075846e-01 -6.90998554e-01 -4.90020305e-01 2.25762084e-01 4.71985906e-01 -6.17945135e-01 8.40623677e-01 1.86327636e-01 -1.06888629e-01 -1.73144281e-01 -8.52300823e-01 -5.30222833e-01 -7.54923999e-01 -4.89650697e-01 4.40391243e-01 -3.32091063e-01 1.70980424e-01 1.93234280e-01 2.41826832e-01 -1.23113859e+00 -2.00279474e-01 9.02271390e-01 1.15530443e+00 -1.69579104e-01 6.78284168e-01 -2.18038619e-01 1.49042022e+00 -5.35510778e-01 -5.92744887e-01 8.18395495e-01 -3.32621396e-01 -8.16437006e-01 -3.13586555e-02 2.00806469e-01 -8.75578463e-01 -2.67188549e-01 1.11007488e+00 3.08597535e-01 2.02104077e-01 1.46617040e-01 -1.75748217e+00 -3.70735705e-01 1.27230513e+00 -8.00659359e-01 2.10396841e-01 -1.85381606e-01 1.69025853e-01 1.12835765e+00 -3.53347778e-01 4.38137591e-01 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-3.52027982e-01 -1.61101198e+00 -4.99465853e-01 -8.78818512e-01 4.29557979e-01 3.54076028e-01 1.27451792e-02 -7.75163472e-01 -2.92754471e-01]	[5.855634689331055, 6.327753067016602]
43266b3e-54eb-4036-839f-e8d6d9cf9855	semantic-filtering	null	null	http://openaccess.thecvf.com/content_cvpr_2016/html/Yang_Semantic_Filtering_CVPR_2016_paper.html	http://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Semantic_Filtering_CVPR_2016_paper.pdf	Semantic Filtering	Edge-preserving image operations aim at smoothing an image without blurring the edges. Many excellent edge-preserving filtering techniques have been proposed recently to reduce the computational complexity or/and separate different scale structures. They normally adopt a user-selected scale measurement to control the detail/texture smoothing. However, natural photos contain objects of different sizes which cannot be described by a single scale measurement. On the other hand, edge/contour detection/analysis is closely related to edge-preserving filtering and has achieved significant progress recently. Nevertheless, most of the state-of-the-art filtering techniques ignore the success in this area. Inspired by the fact that learning-based edge detectors/classifiers significantly outperform traditional manually-designed detectors, this paper proposes a learning-based edge-preserving filtering technique. It synergistically combines the efficiency of the recursive filter and the effectiveness of the recent edge detector for scale-aware edge-preserving filtering. Unlike previous filtering methods, the propose filter can efficiently extract subjectively-meaningful structures from natural scenes containing multiple-scale objects.	['Qingxiong Yang']	2016-06-01	null	null	null	cvpr-2016-6	['contour-detection']	['computer-vision']	[ 3.65409553e-01 -3.88513774e-01 -4.16917950e-02 -2.55638599e-01 -4.61373001e-01 -2.20938146e-01 3.82624090e-01 3.24698806e-01 -4.42846864e-01 4.35664654e-01 1.01776943e-01 3.87408473e-02 -1.94265500e-01 -8.56689870e-01 -3.04365069e-01 -8.31307709e-01 2.37872172e-02 -3.84211063e-01 1.01140749e+00 -9.38520879e-02 5.32576859e-01 6.00354075e-01 -1.64697504e+00 1.93200395e-01 1.03630626e+00 1.04972327e+00 2.81978488e-01 5.71991622e-01 -3.21259052e-01 2.74408221e-01 -2.97729284e-01 -7.51407146e-02 4.04556483e-01 -5.24043620e-01 -2.02222809e-01 4.10485327e-01 6.50776088e-01 -2.16798395e-01 -2.43409947e-01 1.56476438e+00 5.06226659e-01 1.58749279e-02 3.65507275e-01 -8.24347198e-01 -6.72468781e-01 6.50272295e-02 -8.10661077e-01 2.56171972e-01 7.92223737e-02 -3.41581494e-01 5.44634163e-01 -8.60508502e-01 4.72504854e-01 1.07122028e+00 7.01428413e-01 1.72438100e-02 -1.14553845e+00 -4.89197582e-01 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e64376aa-9f9f-4e88-968c-61874cac5d76	insurance-question-answering-via-single-turn	null	null	https://aclanthology.org/2022.cai-1.5	https://aclanthology.org/2022.cai-1.5.pdf	Insurance Question Answering via Single-turn Dialogue Modeling	With great success in single-turn question answering (QA), conversational QA is currently receiving considerable attention. Several studies have been conducted on this topic from different perspectives. However, building a real-world conversational system remains a challenge. This study introduces our ongoing project, which uses Korean QA data to develop a dialogue system in the insurance domain. The goal is to construct a system that provides informative responses to general insurance questions. We present the current results of single-turn QA. A unique aspect of our approach is that we borrow the concepts of intent detection and slot filling from task-oriented dialogue systems. We present details of the data construction process and the experimental results on both learning tasks.	['Seung-Hwan Cho', 'Young-Min Kim', 'Seon-Ok Na']	null	null	null	null	cai-coling-2022-10	['intent-detection', 'slot-filling', 'task-oriented-dialogue-systems']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 1.61973476e-01 7.81298101e-01 1.34792209e-01 -8.62964809e-01 -1.48392880e+00 -4.17120278e-01 5.50760269e-01 -2.75229812e-02 -1.61270946e-01 1.16747808e+00 7.79372633e-01 -6.67230129e-01 1.26539245e-01 -7.74496615e-01 7.87583739e-02 -2.53814757e-01 1.99796975e-01 1.02219355e+00 4.60427523e-01 -1.07251060e+00 2.51691461e-01 -1.39859408e-01 -1.05604410e+00 8.16014588e-01 1.20950997e+00 5.90519547e-01 1.56263396e-01 1.06135094e+00 -7.00781405e-01 1.31534576e+00 -8.88157964e-01 -6.56592965e-01 -2.16517210e-01 -8.77940476e-01 -2.02756000e+00 1.38889387e-01 2.00761929e-01 -2.51964360e-01 1.04667798e-01 5.29587209e-01 7.03435779e-01 1.94050461e-01 3.21901947e-01 -1.30017483e+00 -4.00065064e-01 5.56387842e-01 5.09736061e-01 -2.94395126e-02 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03058fcf-1969-452f-856c-ed033070f3db	detection-algorithm-of-defects-on	null	null	http://www.elsevier.com/locate/ijpvp	http://www.elsevier.com/locate/ijpvp	Detection algorithm of defects on polyethylene gas pipe using image recognition	Aiming at the defects in the polyethylene (PE) gas pipeline, a defect detection algorithm based on image recognition is proposed. Firstly, the collected image is preliminarily screened to obtain the image with defects. Gamma correction algorithm is used to enhance the image, and then dual filtering is used to eliminate the interference of noise on image recognition. Then the defect edge is extracted by the improved traditional Sobel edge detection algorithm. Image defects are segmented by adaptive threshold method, and defect contours are effectively extracted through an open operation. Finally, the defect feature parameters are extracted and inputted into an optimized support vector machine for defect recognition. The experimental results show that the detection algorithm in this paper can effectively improve the observability of the image. Through the trained support vector machine, it can effectively identify the open joint, crack, and deformation defects. The recognition rate of pipeline image defects reached 96.3%.	['*', 'Jun-Qiang Wang b', 'Ya-Nan Sun a', '', 'Hui-Qing Lan a', 'Cong Li a']	2021-01-01	null	null	null	international-journal-of-pressure-vessels-and	['edge-detection', 'defect-detection']	['computer-vision', 'computer-vision']	[ 1.98756784e-01 -4.00254130e-01 3.10306042e-01 1.29776418e-01 1.42669389e-02 6.92184921e-03 -4.01105851e-01 6.96538761e-02 -4.68285799e-01 -1.14279166e-01 -3.16759795e-01 -6.47456720e-02 2.07251925e-02 -1.07311189e+00 3.23130167e-03 -8.57723832e-01 9.53887627e-02 -8.21768641e-02 8.33431661e-01 -2.96700113e-02 8.73678088e-01 6.40234828e-01 -1.31535983e+00 1.73472330e-01 1.06162679e+00 1.02100587e+00 5.16852677e-01 4.14416552e-01 -1.75657049e-01 5.77416480e-01 -6.98211432e-01 2.96995938e-01 3.53458822e-02 -2.43315354e-01 -3.72996062e-01 7.40103006e-01 -9.67995644e-01 -7.40976989e-01 -5.08378148e-01 1.34045351e+00 4.09534872e-01 -1.13663405e-01 7.94582307e-01 -8.50394666e-01 -4.49091941e-01 -1.30461931e-01 -9.45871234e-01 3.65786582e-01 -5.34520522e-02 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2bc3f017-a635-472a-9ac4-ad7ea3d693ba	exploiting-redundancy-separable-group-1	2110.13059	null	https://arxiv.org/abs/2110.13059v2	https://arxiv.org/pdf/2110.13059v2.pdf	Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups	Group convolutional neural networks (G-CNNs) have been shown to increase parameter efficiency and model accuracy by incorporating geometric inductive biases. In this work, we investigate the properties of representations learned by regular G-CNNs, and show considerable parameter redundancy in group convolution kernels. This finding motivates further weight-tying by sharing convolution kernels over subgroups. To this end, we introduce convolution kernels that are separable over the subgroup and channel dimensions. In order to obtain equivariance to arbitrary affine Lie groups we provide a continuous parameterisation of separable convolution kernels. We evaluate our approach across several vision datasets, and show that our weight sharing leads to improved performance and computational efficiency. In many settings, separable G-CNNs outperform their non-separable counterpart, while only using a fraction of their training time. In addition, thanks to the increase in computational efficiency, we are able to implement G-CNNs equivariant to the $\mathrm{Sim(2)}$ group; the group of dilations, rotations and translations. $\mathrm{Sim(2)}$-equivariance further improves performance on all tasks considered.	['Erik J. Bekkers', 'David W. Romero', 'David M. Knigge']	2021-10-25	exploiting-redundancy-separable-group	https://openreview.net/forum?id=WnOLO1f50MH	https://openreview.net/pdf?id=WnOLO1f50MH	null	['rotated-mnist']	['computer-vision']	[ 2.55783141e-01 3.44712317e-01 1.67881802e-01 -4.08000767e-01 -1.86967477e-01 -7.97459960e-01 7.08113968e-01 -3.68885249e-02 -8.09305549e-01 3.32637578e-01 2.23310858e-01 -4.75947201e-01 -3.77730668e-01 -9.00181949e-01 -1.03115714e+00 -7.69164979e-01 -4.59496528e-01 -4.89394413e-03 -7.33321486e-03 -3.27977389e-01 3.63652781e-02 8.51482809e-01 -1.33500695e+00 2.30928093e-01 6.00589991e-01 9.49339032e-01 -1.61268041e-01 7.27157176e-01 2.69809306e-01 4.69103247e-01 8.50827694e-02 -5.57144761e-01 6.97692096e-01 -8.45315158e-02 -9.01245892e-01 1.50090586e-02 9.51383948e-01 -2.37602368e-01 -4.91609305e-01 9.15129185e-01 4.73186284e-01 3.02585244e-01 7.51800418e-01 -1.05591106e+00 -8.55505347e-01 7.28679001e-01 -9.72453132e-02 7.88030401e-02 -4.16827559e-01 -3.14881979e-03 1.27585554e+00 -8.75674427e-01 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db0210c6-1ca9-4b49-b38d-e5c229f30144	a-generalized-reinforcement-learning	2007.00463	null	https://arxiv.org/abs/2007.00463v1	https://arxiv.org/pdf/2007.00463v1.pdf	A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing	We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size. The focus is on producing decisions that can be physically implemented by a robotic loading arm, a laboratory prototype used for testing the concept. The problem considered in this paper is novel in two ways. First, unlike the traditional 3D bin packing problem, we assume that the entire set of objects to be packed is not known a priori. Instead, a fixed number of upcoming objects is visible to the loading system, and they must be loaded in the order of arrival. Second, the goal is not to move objects from one point to another via a feasible path, but to find a location and orientation for each object that maximises the overall packing efficiency of the bin(s). Finally, the learnt model is designed to work with problem instances of arbitrary size without retraining. Simulation results show that the RL-based method outperforms state-of-the-art online bin packing heuristics in terms of empirical competitive ratio and volume efficiency.	['Harshad Khadilkar', 'Ansuma Basumatary', 'Siddharth Nayak', 'Richa Verma', 'Rajesh Sinha', 'Harsh Vardhan Singh', 'Swagat Kumar', 'Aniruddha Singhal']	2020-07-01	null	null	null	null	['3d-bin-packing']	['miscellaneous']	[-3.78771126e-01 2.19632924e-01 -2.59359509e-01 -1.48391753e-01 -1.60622165e-01 -6.66187108e-01 -5.09594142e-01 5.04089355e-01 -2.28504568e-01 8.93038869e-01 -4.33281004e-01 -5.60844064e-01 -7.16369689e-01 -1.18267655e+00 -1.24876070e+00 -7.91281939e-01 -7.49141097e-01 1.41753113e+00 1.57393143e-01 -3.59874934e-01 1.73000842e-02 1.02053523e+00 -1.38656676e+00 4.21299636e-02 3.43995869e-01 1.26661599e+00 6.37590885e-01 7.02858806e-01 1.23908212e-02 1.32815018e-01 -7.58885324e-01 -1.43181290e-02 8.89998853e-01 -1.55616000e-01 -9.45243537e-01 6.45710111e-01 -3.24046612e-01 -7.75170326e-01 -4.48588401e-01 6.05647981e-01 4.14117366e-01 4.46156114e-01 1.97590739e-01 -1.36294663e+00 -4.53134507e-01 7.75134087e-01 -5.67061484e-01 3.47155094e-01 1.27742782e-01 2.01848745e-01 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899c2195-4bfa-4da7-a397-6ea7fcd5ebe4	logits-are-predictive-of-network-type	2211.02272	null	https://arxiv.org/abs/2211.02272v1	https://arxiv.org/pdf/2211.02272v1.pdf	Logits are predictive of network type	We show that it is possible to predict which deep network has generated a given logit vector with accuracy well above chance. We utilize a number of networks on a dataset, initialized with random weights or pretrained weights, as well as fine-tuned networks. A classifier is then trained on the logit vectors of the trained set of this dataset to map the logit vector to the network index that has generated it. The classifier is then evaluated on the test set of the dataset. Results are better with randomly initialized networks, but also generalize to pretrained networks as well as fine-tuned ones. Classification accuracy is higher using unnormalized logits than normalized ones. We find that there is little transfer when applying a classifier to the same networks but with different sets of weights. In addition to help better understand deep networks and the way they encode uncertainty, we anticipate our finding to be useful in some applications (e.g. tailoring an adversarial attack for a certain type of network). Code is available at https://github.com/aliborji/logits.	['Ali Borji']	2022-11-04	null	null	null	null	['type']	['speech']	[ 1.94752708e-01 2.85572231e-01 -3.04310709e-01 -6.30928457e-01 -3.61750901e-01 -1.12860608e+00 6.56715393e-01 -8.07003155e-02 -6.75174654e-01 8.79701793e-01 1.79049537e-01 -5.45230567e-01 -2.81416357e-01 -1.26931584e+00 -8.64143789e-01 -5.94102502e-01 -3.46389204e-01 5.62467217e-01 2.14191034e-01 -2.43398726e-01 2.78024822e-01 7.20910013e-01 -9.16495621e-01 2.58809477e-01 1.35864481e-01 1.21602499e+00 -4.04462695e-01 7.51295328e-01 3.43410730e-01 6.49689078e-01 -7.99910963e-01 -9.23590422e-01 5.59607923e-01 -1.78419307e-01 -9.48812664e-01 -6.36689782e-01 2.00448513e-01 -1.99686334e-01 -6.51847482e-01 1.11811113e+00 2.55693018e-01 1.09497987e-01 8.87405038e-01 -1.46260130e+00 -4.25610185e-01 1.20204461e+00 -2.66680092e-01 2.64555126e-01 -7.78059587e-02 2.86345810e-01 1.00974226e+00 -2.09479094e-01 3.94559145e-01 1.33806360e+00 6.41974330e-01 6.92725420e-01 -1.27629077e+00 -1.04040110e+00 -1.85590144e-02 5.54972924e-02 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28975547-6754-4dd7-bc97-bf3fe2b502f8	curiosity-in-hindsight	2211.10515	null	https://arxiv.org/abs/2211.10515v1	https://arxiv.org/pdf/2211.10515v1.pdf	Curiosity in hindsight	Consider the exploration in sparse-reward or reward-free environments, such as Montezuma's Revenge. The curiosity-driven paradigm dictates an intuitive technique: At each step, the agent is rewarded for how much the realized outcome differs from their predicted outcome. However, using predictive error as intrinsic motivation is prone to fail in stochastic environments, as the agent may become hopelessly drawn to high-entropy areas of the state-action space, such as a noisy TV. Therefore it is important to distinguish between aspects of world dynamics that are inherently predictable and aspects that are inherently unpredictable: The former should constitute a source of intrinsic reward, whereas the latter should not. In this work, we study a natural solution derived from structural causal models of the world: Our key idea is to learn representations of the future that capture precisely the unpredictable aspects of each outcome -- not any more, not any less -- which we use as additional input for predictions, such that intrinsic rewards do vanish in the limit. First, we propose incorporating such hindsight representations into the agent's model to disentangle "noise" from "novelty", yielding Curiosity in Hindsight: a simple and scalable generalization of curiosity that is robust to all types of stochasticity. Second, we implement this framework as a drop-in modification of any prediction-based exploration bonus, and instantiate it for the recently introduced BYOL-Explore algorithm as a prime example, resulting in the noise-robust "BYOL-Hindsight". Third, we illustrate its behavior under various stochasticities in a grid world, and find improvements over BYOL-Explore in hard-exploration Atari games with sticky actions. Importantly, we show SOTA results in exploring Montezuma with sticky actions, while preserving performance in the non-sticky setting.	['Michal Valko', 'Rémi Munos', 'Thomas Mesnard', 'Florent Altché', 'Corentin Tallec', 'Daniel Jarrett']	2022-11-18	null	null	null	null	['montezumas-revenge', 'atari-games']	['playing-games', 'playing-games']	[-5.99952564e-02 4.80048090e-01 -1.37251943e-01 2.53388342e-02 -4.86705959e-01 -6.32722735e-01 6.96461678e-01 4.06189300e-02 -5.52204013e-01 1.08400953e+00 3.31433892e-01 -4.03116256e-01 -5.47279179e-01 -1.00901401e+00 -8.69347155e-01 -1.04349339e+00 -5.04355669e-01 5.91154814e-01 -8.16766843e-02 -6.51410401e-01 1.23129040e-01 1.70865908e-01 -1.36097932e+00 -3.03271145e-01 6.80393338e-01 7.73049653e-01 2.93805897e-01 6.00612879e-01 3.53434741e-01 1.05567944e+00 -3.27389896e-01 -1.80128515e-01 5.27898252e-01 -4.92139071e-01 -6.73967123e-01 -2.18799546e-01 -5.46865582e-01 -2.58164495e-01 -3.16910118e-01 1.13779557e+00 9.20259878e-02 1.84293315e-01 5.13589501e-01 -1.00923800e+00 -5.96724510e-01 9.90809858e-01 -3.96427095e-01 1.54159129e-01 2.30341837e-01 6.85877144e-01 1.16161966e+00 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dc00cde6-6146-4887-9920-9a874711d22d	hierarchical-classification-at-multiple	2210.10929	null	https://arxiv.org/abs/2210.10929v2	https://arxiv.org/pdf/2210.10929v2.pdf	Hierarchical classification at multiple operating points	Many classification problems consider classes that form a hierarchy. Classifiers that are aware of this hierarchy may be able to make confident predictions at a coarse level despite being uncertain at the fine-grained level. While it is generally possible to vary the granularity of predictions using a threshold at inference time, most contemporary work considers only leaf-node prediction, and almost no prior work has compared methods at multiple operating points. We present an efficient algorithm to produce operating characteristic curves for any method that assigns a score to every class in the hierarchy. Applying this technique to evaluate existing methods reveals that top-down classifiers are dominated by a naive flat softmax classifier across the entire operating range. We further propose two novel loss functions and show that a soft variant of the structured hinge loss is able to significantly outperform the flat baseline. Finally, we investigate the poor accuracy of top-down classifiers and demonstrate that they perform relatively well on unseen classes. Code is available online at https://github.com/jvlmdr/hiercls.	['Jack Valmadre']	2022-10-19	null	null	null	null	['classification']	['methodology']	[ 2.85548091e-01 3.80723327e-01 -6.67138934e-01 -8.49211633e-01 -1.30460405e+00 -7.43907034e-01 4.88521367e-01 5.37347019e-01 -2.17044055e-01 8.22021246e-01 -5.14766499e-02 -4.48762745e-01 -7.50253871e-02 -6.53864324e-01 -6.56717181e-01 -7.32856333e-01 2.68142670e-02 6.26297891e-01 6.26710176e-01 8.33343193e-02 2.53896922e-01 3.49163562e-01 -1.66684210e+00 8.23238969e-01 5.17173111e-01 1.28590059e+00 -4.02394682e-01 7.34252512e-01 2.67538935e-01 7.21768022e-01 -5.74065268e-01 -7.06668437e-01 2.29596794e-01 5.36317676e-02 -9.01331484e-01 -8.92517194e-02 9.09922361e-01 -2.50100076e-01 9.86870825e-02 8.58462691e-01 2.86897480e-01 -1.23055167e-01 8.50056529e-01 -1.44994175e+00 -2.62087971e-01 9.53890443e-01 -3.78640145e-01 8.45780522e-02 1.95365101e-01 -3.79170269e-01 1.54173505e+00 -8.01828444e-01 3.73396367e-01 1.08259225e+00 1.03618395e+00 2.72272497e-01 -1.58494329e+00 -5.73740542e-01 4.99798506e-01 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b8a105a9-3dc2-4898-ab41-4df869f293ea	no-dba-no-regret-multi-armed-bandits-for	2108.1013	null	https://arxiv.org/abs/2108.10130v1	https://arxiv.org/pdf/2108.10130v1.pdf	No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees	Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures. Despite significant progress, a majority of today's commercial solutions are highly manual, requiring offline invocation by database administrators (DBAs) who are expected to identify and supply representative training workloads. Even the latest advancements like query stores provide only limited support for dynamic environments. This status quo is untenable: identifying representative static workloads is no longer realistic; and physical design tools remain susceptible to the query optimiser's cost misestimates. Furthermore, modern application environments such as hybrid transactional and analytical processing (HTAP) systems render analytical modelling next to impossible. We propose a self-driving approach to online index selection that eschews the DBA and query optimiser, and instead learns the benefits of viable structures through strategic exploration and direct performance observation. We view the problem as one of sequential decision making under uncertainty, specifically within the bandit learning setting. Multi-armed bandits balance exploration and exploitation to provably guarantee average performance that converges to policies that are optimal with perfect hindsight. Our comprehensive empirical evaluation against a state-of-the-art commercial tuning tool demonstrates up to 75% speed-up on shifting and ad-hoc workloads and up to 28% speed-up on static workloads in analytical processing environments. In HTAP environments, our solution provides up to 59% speed-up on shifting and 51% speed-up on static workloads. Furthermore, our bandit framework outperforms deep reinforcement learning (RL) in terms of convergence speed and performance volatility (providing up to 58% speed-up).	['Renata Borovica-Gajic', 'Benjamin I. P. Rubinstein', 'Bastian Oetomo', 'R. Malinga Perera']	2021-08-23	null	null	null	null	['decision-making-under-uncertainty', 'decision-making-under-uncertainty']	['medical', 'reasoning']	[-3.26282829e-01 -1.38292342e-01 -7.31640518e-01 -2.37874821e-01 -1.15472507e+00 -4.86588836e-01 1.70599937e-01 1.69321612e-01 -4.87462789e-01 7.26131141e-01 -5.99489361e-02 -8.49131942e-01 -3.61155301e-01 -7.52540231e-01 -9.44760621e-01 -5.77036202e-01 -4.03148144e-01 1.18928170e+00 6.89512938e-02 -3.49730015e-01 2.14422241e-01 5.77504575e-01 -1.60076666e+00 3.15468222e-01 5.04182696e-01 1.35423350e+00 -5.68974391e-02 8.95201743e-01 -3.40772897e-01 6.49212897e-01 -6.21633947e-01 -1.21826351e-01 5.91167748e-01 1.04481526e-01 -5.53522170e-01 -2.10858613e-01 8.11179355e-02 -7.25010157e-01 -3.08534622e-01 5.37431479e-01 5.80981970e-01 -3.33403856e-01 -3.33494809e-03 -1.13166916e+00 -2.39432782e-01 9.98673916e-01 -9.97533262e-01 4.94118452e-01 -1.37111306e-01 5.36985755e-01 1.14062607e+00 -2.75767177e-01 3.43083680e-01 1.03133595e+00 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eb38fdd0-57a0-4b46-bb9f-7137cdd9ce7b	vector-summaries-of-persistence-diagrams-for	2306.06257	null	https://arxiv.org/abs/2306.06257v1	https://arxiv.org/pdf/2306.06257v1.pdf	Vector Summaries of Persistence Diagrams for Permutation-based Hypothesis Testing	Over the past decade, the techniques of topological data analysis (TDA) have grown into prominence to describe the shape of data. In recent years, there has been increasing interest in developing statistical methods and in particular hypothesis testing procedures for TDA. Under the statistical perspective, persistence diagrams -- the central multi-scale topological descriptors of data provided by TDA -- are viewed as random observations sampled from some population or process. In this context, one of the earliest works on hypothesis testing focuses on the two-group permutation-based approach where the associated loss function is defined in terms of within-group pairwise bottleneck or Wasserstein distances between persistence diagrams (Robinson and Turner, 2017). However, in situations where persistence diagrams are large in size and number, the permutation test in question gets computationally more costly to apply. To address this limitation, we instead consider pairwise distances between vectorized functional summaries of persistence diagrams for the loss function. In the present work, we explore the utility of the Betti function in this regard, which is one of the simplest function summaries of persistence diagrams. We introduce an alternative vectorization method for the Betti function based on integration and prove stability results with respect to the Wasserstein distance. Moreover, we propose a new shuffling technique of group labels to increase the power of the test. Through several experimental studies, on both synthetic and real data, we show that the vectorized Betti function leads to competitive results compared to the baseline method involving the Wasserstein distances for the permutation test.	['Hasani Pathirana', 'Umar Islambekov']	2023-06-09	null	null	null	null	['topological-data-analysis']	['graphs']	[ 2.93429166e-01 -2.96150357e-01 -1.16082141e-02 -1.34127706e-01 -3.52097154e-01 -6.83125734e-01 7.64723420e-01 7.17970371e-01 -4.72174287e-01 8.25610816e-01 -1.46726593e-01 -5.00719547e-01 -7.57830977e-01 -9.74872053e-01 -6.44224465e-01 -9.85619247e-01 -4.65750396e-01 2.84290701e-01 3.50881696e-01 -1.07256249e-01 5.58966100e-01 4.97217506e-01 -1.63700557e+00 -3.87646109e-01 1.09797430e+00 7.13060439e-01 4.06855857e-03 4.65773851e-01 5.30429743e-02 -1.43547609e-01 -4.95986551e-01 -1.93493426e-01 2.13082626e-01 -5.03732145e-01 -5.89695632e-01 -3.35854918e-01 4.46034610e-01 2.84812033e-01 -4.24241386e-02 1.04673123e+00 4.85664725e-01 2.38629267e-01 8.50623310e-01 -1.38949323e+00 -2.72314548e-01 3.17684829e-01 -5.50183654e-01 2.09320962e-01 1.61535427e-01 -2.53980774e-02 1.17828405e+00 -7.79221594e-01 7.05892444e-01 1.25269079e+00 7.10013568e-01 -1.84462428e-01 -1.84552085e+00 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dcc26f3d-f0f5-4a4f-9758-65e859c68d4a	multiple-human-association-between-top-and	1907.11458	null	https://arxiv.org/abs/1907.11458v1	https://arxiv.org/pdf/1907.11458v1.pdf	Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions	Video surveillance can be significantly enhanced by using both top-view data, e.g., those from drone-mounted cameras in the air, and horizontal-view data, e.g., those from wearable cameras on the ground. Collaborative analysis of different-view data can facilitate various kinds of applications, such as human tracking, person identification, and human activity recognition. However, for such collaborative analysis, the first step is to associate people, referred to as subjects in this paper, across these two views. This is a very challenging problem due to large human-appearance difference between top and horizontal views. In this paper, we present a new approach to address this problem by exploring and matching the subjects' spatial distributions between the two views. More specifically, on the top-view image, we model and match subjects' relative positions to the horizontal-view camera in both views and define a matching cost to decide the actual location of horizontal-view camera and its view angle in the top-view image. We collect a new dataset consisting of top-view and horizontal-view image pairs for performance evaluation and the experimental results show the effectiveness of the proposed method.	['Xiao-Yu Zhang', 'Jiewen Zhao', 'Song Wang', 'Chenxing Gong', 'Ruize Han', 'Liang Wan', 'Yujun Zhang', 'Wei Feng']	2019-07-26	null	null	null	null	['person-identification']	['computer-vision']	[-0.02515598 -0.5281435 0.06368653 -0.34733537 -0.23010959 -0.7258218 0.25571704 -0.19925818 -0.30478132 0.20464288 0.24529117 0.07373627 -0.04288439 -0.68571615 -0.43329588 -0.7283027 0.48120698 -0.06745765 0.4532851 0.22247536 -0.01189242 0.26910937 -1.4884403 0.23437843 0.34235522 0.9008955 0.08596814 0.61027986 0.500559 0.2357333 -0.5261886 -0.44710657 0.47233355 -0.26572067 -0.01083916 0.7309679 0.7298691 -0.5194663 -0.22682792 1.0923765 0.35495338 0.2103889 0.04232046 -1.3565998 -0.14706747 -0.33392215 -0.97437876 0.35964474 0.9085507 -0.04462031 0.45389757 -0.7663237 0.40621135 1.2034338 0.6028415 0.37203762 -0.7362614 -0.60681474 0.5968742 0.22236724 -1.3481251 -0.35663655 0.8537125 -0.7323663 0.05216994 0.27834916 0.9436134 0.9301553 0.0144107 0.5148789 0.9358103 -0.12042673 0.038693 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16309345-4dc2-44b7-b4ad-837c0d411324	real-time-limited-view-ct-inpainting-and	2101.07594	null	https://arxiv.org/abs/2101.07594v1	https://arxiv.org/pdf/2101.07594v1.pdf	Real-Time Limited-View CT Inpainting and Reconstruction with Dual Domain Based on Spatial Information	Low-dose Computed Tomography is a common issue in reality. Current reduction, sparse sampling and limited-view scanning can all cause it. Between them, limited-view CT is general in the industry due to inevitable mechanical and physical limitation. However, limited-view CT can cause serious imaging problem on account of its massive information loss. Thus, we should effectively utilize the scant prior information to perform completion. It is an undeniable fact that CT imaging slices are extremely dense, which leads to high continuity between successive images. We realized that fully exploit the spatial correlation between consecutive frames can significantly improve restoration results in video inpainting. Inspired by this, we propose a deep learning-based three-stage algorithm that hoist limited-view CT imaging quality based on spatial information. In stage one, to better utilize prior information in the Radon domain, we design an adversarial autoencoder to complement the Radon data. In the second stage, a model is built to perform inpainting based on spatial continuity in the image domain. At this point, we have roughly restored the imaging, while its texture still needs to be finely repaired. Hence, we propose a model to accurately restore the image in stage three, and finally achieve an ideal inpainting result. In addition, we adopt FBP instead of SART-TV to make our algorithm more suitable for real-time use. In the experiment, we restore and reconstruct the Radon data that has been cut the rear one-third part, they achieve PSNR of 40.209, SSIM of 0.943, while precisely present the texture.	['Hongwen Yang', 'Yitong Liu', 'Chang Sun', 'Ken Deng']	2021-01-19	null	null	null	null	['video-inpainting']	['computer-vision']	[ 1.15935199e-01 -1.49532646e-01 4.50971089e-02 -1.33578897e-01 -4.76174444e-01 -2.89479308e-02 5.29244170e-02 -4.28087801e-01 -2.33285010e-01 6.40202641e-01 4.98631865e-01 3.26457387e-03 -2.28665695e-02 -1.03539133e+00 -6.95224583e-01 -8.44701469e-01 3.53489608e-01 -3.30612832e-03 2.57281840e-01 -1.41195819e-01 -5.79452030e-02 3.91889453e-01 -9.46596324e-01 4.11131978e-01 8.45063269e-01 9.54298556e-01 5.60407996e-01 1.14822268e-01 -1.10278111e-02 9.00313973e-01 -1.98718101e-01 -1.63902625e-01 3.83766353e-01 -5.74365199e-01 -5.09376228e-01 2.67201781e-01 -1.22435711e-01 -1.23218060e+00 -9.01557028e-01 1.12052548e+00 5.32629371e-01 -7.37053305e-02 3.54849041e-01 -6.76786840e-01 -4.77021962e-01 3.75716090e-01 -9.34941232e-01 9.22715142e-02 3.64427805e-01 2.05737680e-01 3.28227937e-01 -7.67518103e-01 5.74689865e-01 1.03296196e+00 5.89990616e-01 4.61102635e-01 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30415018-c82a-4ccd-b5e4-947e029a48e9	holistically-attracted-wireframe-parsing-from	2210.12971	null	https://arxiv.org/abs/2210.12971v1	https://arxiv.org/pdf/2210.12971v1.pdf	Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning	This paper presents Holistically-Attracted Wireframe Parsing (HAWP) for 2D images using both fully supervised and self-supervised learning paradigms. At the core is a parsimonious representation that encodes a line segment using a closed-form 4D geometric vector, which enables lifting line segments in wireframe to an end-to-end trainable holistic attraction field that has built-in geometry-awareness, context-awareness and robustness. The proposed HAWP consists of three components: generating line segment and end-point proposal, binding line segment and end-point, and end-point-decoupled lines-of-interest verification. For self-supervised learning, a simulation-to-reality pipeline is exploited in which a HAWP is first trained using synthetic data and then used to ``annotate" wireframes in real images with Homographic Adaptation. With the self-supervised annotations, a HAWP model for real images is trained from scratch. In experiments, the proposed HAWP achieves state-of-the-art performance in both the Wireframe dataset and the YorkUrban dataset in fully-supervised learning. It also demonstrates a significantly better repeatability score than prior arts with much more efficient training in self-supervised learning. Furthermore, the self-supervised HAWP shows great potential for general wireframe parsing without onerous wireframe labels.	['Philip H. S. Torr', 'Liangpei Zhang', 'Gui-Song Xia', 'Fu-Dong Wang', 'Song Bai', 'Tianfu Wu', 'Nan Xue']	2022-10-24	null	null	null	null	['wireframe-parsing']	['computer-vision']	[-3.38937379e-02 4.16117638e-01 -2.41634220e-01 -5.90368390e-01 -1.35433614e+00 -5.79057813e-01 3.84441376e-01 -8.06871951e-02 9.94269177e-02 2.15730280e-01 -4.20196772e-01 -4.32548046e-01 6.39907196e-02 -6.91228092e-01 -1.28493011e+00 -3.17403585e-01 -6.86670467e-02 4.86337125e-01 5.94539523e-01 -4.86581951e-01 8.81324038e-02 6.90136731e-01 -1.25405240e+00 -2.58016344e-02 4.92030978e-01 1.34350359e+00 1.69314191e-01 5.20314038e-01 1.09279849e-01 2.90558308e-01 8.84642527e-02 -6.48967206e-01 8.57212067e-01 1.25580505e-01 -5.68352699e-01 3.40396672e-01 9.67508376e-01 -2.93840259e-01 -3.72047096e-01 5.92244267e-01 4.30160969e-01 -3.03021133e-01 4.06149268e-01 -1.02595544e+00 -2.21958719e-02 3.04984272e-01 -9.12113130e-01 -4.00467694e-01 4.80920941e-01 1.95942953e-01 1.13268256e+00 -1.05066872e+00 8.06834996e-01 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ef408b98-f79f-46ba-a6d1-e5ead5dc2544	dxslam-a-robust-and-efficient-visual-slam	2008.05416	null	https://arxiv.org/abs/2008.05416v1	https://arxiv.org/pdf/2008.05416v1.pdf	DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features	A robust and efficient Simultaneous Localization and Mapping (SLAM) system is essential for robot autonomy. For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments. This paper shows that feature extraction with deep convolutional neural networks (CNNs) can be seamlessly incorporated into a modern SLAM framework. The proposed SLAM system utilizes a state-of-the-art CNN to detect keypoints in each image frame, and to give not only keypoint descriptors, but also a global descriptor of the whole image. These local and global features are then used by different SLAM modules, resulting in much more robustness against environmental changes and viewpoint changes compared with using hand-crafted features. We also train a visual vocabulary of local features with a Bag of Words (BoW) method. Based on the local features, global features, and the vocabulary, a highly reliable loop closure detection method is built. Experimental results show that all the proposed modules significantly outperforms the baseline, and the full system achieves much lower trajectory errors and much higher correct rates on all evaluated data. Furthermore, by optimizing the CNN with Intel OpenVINO toolkit and utilizing the Fast BoW library, the system benefits greatly from the SIMD (single-instruction-multiple-data) techniques in modern CPUs. The full system can run in real-time without any GPU or other accelerators. The code is public at https://github.com/ivipsourcecode/dxslam.	['Xuesong Shi', 'Dongjiang Li', 'Qi Wei', 'Fangshi Wang', 'Wei Yang', 'Fei Qiao', 'Shenghui Liu', 'Qiwei Long']	2020-08-12	null	null	null	null	['loop-closure-detection']	['computer-vision']	[-4.29108322e-01 -6.19025648e-01 -2.76168346e-01 -4.34646070e-01 -5.86073458e-01 -3.26256633e-01 6.19762003e-01 1.77429363e-01 -7.95195937e-01 4.99561816e-01 -1.79194078e-01 -3.46766025e-01 1.43639967e-01 -9.46500003e-01 -8.46739769e-01 -6.53372526e-01 -2.07261056e-01 3.25291276e-01 4.63383079e-01 -4.29525942e-01 2.96782464e-01 7.71335661e-01 -1.79048264e+00 -4.43815619e-01 6.06581330e-01 1.22938085e+00 5.35457194e-01 5.08007944e-01 -3.52716893e-02 5.39407313e-01 -2.66179681e-01 2.87559628e-01 3.62432003e-01 1.00950830e-01 -3.37916851e-01 -3.53868514e-01 4.85402137e-01 -3.76508862e-01 -5.94206452e-01 1.07998419e+00 4.74436581e-01 2.40244597e-01 1.76565975e-01 -1.35894620e+00 -2.75987566e-01 -1.66323200e-01 -4.64876890e-01 -1.88251600e-01 3.58121783e-01 2.56765157e-01 8.10194552e-01 -1.13204360e+00 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f1b43937-f45e-4eb6-90d1-8c8fc9522c53	marta-gans-unsupervised-representation	1612.08879	null	http://arxiv.org/abs/1612.08879v3	http://arxiv.org/pdf/1612.08879v3.pdf	MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification	With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.	['Kun fu', 'Daoyu Lin', 'Guangluan Xu', 'Xian Sun', 'Yang Wang']	2016-12-28	null	null	null	null	['remote-sensing-image-classification']	['miscellaneous']	[ 3.95171106e-01 -1.40045524e-01 1.97778210e-01 -6.05841517e-01 -6.71146333e-01 -3.64541918e-01 5.60528457e-01 -3.57894242e-01 -2.36945957e-01 9.14534092e-01 -2.26553306e-01 -2.88758278e-01 -1.26492092e-02 -1.52904689e+00 -6.84362411e-01 -1.01751292e+00 -2.36800462e-02 -5.95874563e-02 -3.19828868e-01 -1.85679883e-01 -1.69027656e-01 7.00797498e-01 -1.66071165e+00 7.48341456e-02 1.13466227e+00 1.00355339e+00 2.48585910e-01 3.05625111e-01 -2.60251880e-01 5.60080409e-01 -6.55559719e-01 -9.36817750e-02 6.01132512e-01 -7.29961872e-01 -4.63105619e-01 2.88615972e-01 7.89664686e-02 -4.47069436e-01 -3.73811841e-01 1.25111210e+00 5.50117254e-01 2.48644844e-01 7.69237757e-01 -1.19467378e+00 -8.90117943e-01 4.62834865e-01 -5.33415556e-01 3.47969197e-02 -3.44119996e-01 1.92569554e-01 6.67844355e-01 -7.59860218e-01 3.83918405e-01 1.14452636e+00 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98e998c8-7db8-413d-b356-3e529b7d7178	one-thing-one-click-self-training-for-weakly	2303.14727	null	https://arxiv.org/abs/2303.14727v1	https://arxiv.org/pdf/2303.14727v1.pdf	One Thing One Click++: Self-Training for Weakly Supervised 3D Scene Understanding	3D scene understanding, e.g., point cloud semantic and instance segmentation, often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to prepare. While some recent methods propose to train a 3D network with small percentages of point labels, we take the approach to an extreme and propose ``One Thing One Click,'' meaning that the annotator only needs to label one point per object. To leverage these extremely sparse labels in network training, we design a novel self-training approach, in which we iteratively conduct the training and label propagation, facilitated by a graph propagation module. Also, we adopt a relation network to generate the per-category prototype to enhance the pseudo label quality and guide the iterative training. Besides, our model can be compatible to 3D instance segmentation equipped with a point-clustering strategy. Experimental results on both ScanNet-v2 and S3DIS show that our self-training approach, with extremely-sparse annotations, outperforms all existing weakly supervised methods for 3D semantic and instance segmentation by a large margin, and our results are also comparable to those of the fully supervised counterparts. Codes and models are available at https://github.com/liuzhengzhe/One-Thing-One-Click.	['Chi-Wing Fu', 'Xiaojuan Qi', 'Zhengzhe Liu']	2023-03-26	null	null	null	null	['3d-instance-segmentation-1', 'pseudo-label']	['computer-vision', 'miscellaneous']	[ 7.49121159e-02 5.38580894e-01 -3.32935810e-01 -8.15313756e-01 -7.33678401e-01 -6.18394554e-01 4.57285255e-01 1.56094238e-01 -2.21623436e-01 2.60083079e-01 -2.99210697e-01 -3.77518058e-01 -7.81007186e-02 -8.51875067e-01 -1.05042315e+00 -5.19140840e-01 1.00679711e-01 9.76184726e-01 5.94023645e-01 1.07276201e-01 8.99606794e-02 5.24479389e-01 -1.45107651e+00 -2.55279869e-01 9.99541283e-01 1.11160815e+00 3.19938928e-01 5.44066019e-02 -5.92624784e-01 4.59609717e-01 -9.40266699e-02 -3.26431662e-01 4.27979022e-01 5.42589910e-02 -1.02576804e+00 5.19283056e-01 3.55641425e-01 -2.34071791e-01 -4.29183580e-02 1.09461010e+00 3.09212148e-01 1.24329738e-01 6.06889129e-01 -1.25731754e+00 -3.57949167e-01 5.43953657e-01 -8.37832451e-01 -4.17252570e-01 1.59164704e-02 1.91947997e-01 9.73887920e-01 -9.66337144e-01 4.59821284e-01 9.98090923e-01 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a3a5cbf5-c961-44b8-8056-2c9604e03ceb	predictive-process-monitoring-methods-which	1804.02422	null	http://arxiv.org/abs/1804.02422v1	http://arxiv.org/pdf/1804.02422v1.pdf	Predictive Process Monitoring Methods: Which One Suits Me Best?	Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques.	['Chiara Ghidini', 'Chiara Di Francescomarino', 'Fredrik Milani', 'Fabrizio Maria Maggi']	2018-04-06	null	null	null	null	['predictive-process-monitoring']	['time-series']	[ 6.46580279e-01 2.70629287e-01 -3.51454943e-01 -1.89465374e-01 1.24560809e-02 -3.46263140e-01 6.50511742e-01 7.59728014e-01 8.89600962e-02 2.69890666e-01 6.40361086e-02 -4.82050031e-01 -8.01329315e-01 -8.62290502e-01 2.26630792e-01 -4.41686690e-01 9.14213136e-02 7.75828063e-01 -9.35182068e-03 1.48872837e-01 9.00460362e-01 7.83169150e-01 -1.59772897e+00 2.60764539e-01 5.45220137e-01 9.61875319e-01 9.55865309e-02 3.74438077e-01 -3.37025374e-01 1.33677745e+00 -2.31019363e-01 -2.33593658e-01 1.05654977e-01 -4.91641104e-01 -8.48171592e-01 3.48364502e-01 -3.72114509e-01 1.82788849e-01 1.60674259e-01 5.36142528e-01 -1.27625972e-01 1.98797047e-01 4.24036652e-01 -1.03597534e+00 -3.64962101e-01 2.83148050e-01 -2.51394123e-01 4.67373401e-01 6.14042997e-01 2.70946652e-01 8.08914244e-01 -4.17930752e-01 5.39712489e-01 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fa9272dc-d10f-48b3-a89b-735e14edafcc	transfusion-cross-view-fusion-with	2110.09554	null	https://arxiv.org/abs/2110.09554v3	https://arxiv.org/pdf/2110.09554v3.pdf	TransFusion: Cross-view Fusion with Transformer for 3D Human Pose Estimation	Estimating the 2D human poses in each view is typically the first step in calibrated multi-view 3D pose estimation. But the performance of 2D pose detectors suffers from challenging situations such as occlusions and oblique viewing angles. To address these challenges, previous works derive point-to-point correspondences between different views from epipolar geometry and utilize the correspondences to merge prediction heatmaps or feature representations. Instead of post-prediction merge/calibration, here we introduce a transformer framework for multi-view 3D pose estimation, aiming at directly improving individual 2D predictors by integrating information from different views. Inspired by previous multi-modal transformers, we design a unified transformer architecture, named TransFusion, to fuse cues from both current views and neighboring views. Moreover, we propose the concept of epipolar field to encode 3D positional information into the transformer model. The 3D position encoding guided by the epipolar field provides an efficient way of encoding correspondences between pixels of different views. Experiments on Human 3.6M and Ski-Pose show that our method is more efficient and has consistent improvements compared to other fusion methods. Specifically, we achieve 25.8 mm MPJPE on Human 3.6M with only 5M parameters on 256 x 256 resolution.	['Xiaohui Xie', 'Shih-Yao Lin', 'Yusheng Xie', 'Xiangyi Yan', 'Hao Tang', 'Xingwei Liu', 'Zhe Wang', 'Deying Kong', 'Liangjian Chen', 'Haoyu Ma']	2021-10-18	null	null	null	null	['3d-pose-estimation']	['computer-vision']	[-1.65735736e-01 -1.40593514e-01 -9.32807773e-02 -3.73108208e-01 -7.30390608e-01 -3.13673139e-01 2.28375897e-01 -2.49718681e-01 -3.50700133e-02 2.91372567e-01 5.55045009e-01 4.30262297e-01 1.21858492e-01 -4.67704415e-01 -7.34913170e-01 -3.33513469e-01 3.47952634e-01 5.50680876e-01 4.36927617e-01 -2.59724438e-01 7.90276453e-02 3.39368194e-01 -1.38957214e+00 3.02462131e-01 4.72072303e-01 9.23741281e-01 1.46925002e-01 4.37602699e-01 4.83027369e-01 2.51929462e-01 -3.76944661e-01 -4.01655972e-01 4.97786462e-01 -1.20734058e-01 -3.67733479e-01 5.36279976e-01 8.01079750e-01 -4.92369026e-01 -3.65697563e-01 7.61090279e-01 7.67474115e-01 -1.44510373e-01 3.00109029e-01 -1.10552001e+00 -1.01897426e-01 -9.13041004e-04 -1.03830266e+00 -1.42303593e-02 1.05239439e+00 -7.21978098e-02 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f2b1c09c-43d1-4f25-9feb-54348870b810	robot-to-human-object-handover-using-vision	2210.15085	null	https://arxiv.org/abs/2210.15085v1	https://arxiv.org/pdf/2210.15085v1.pdf	Robot to Human Object Handover using Vision and Joint Torque Sensor Modalities	We present a robot-to-human object handover algorithm and implement it on a 7-DOF arm equipped with a 3-finger mechanical hand. The system performs a fully autonomous and robust object handover to a human receiver in real-time. Our algorithm relies on two complementary sensor modalities: joint torque sensors on the arm and an eye-in-hand RGB-D camera for sensor feedback. Our approach is entirely implicit, i.e., there is no explicit communication between the robot and the human receiver. Information obtained via the aforementioned sensor modalities is used as inputs to their related deep neural networks. While the torque sensor network detects the human receiver's "intention" such as: pull, hold, or bump, the vision sensor network detects if the receiver's fingers have wrapped around the object. Networks' outputs are then fused, based on which a decision is made to either release the object or not. Despite substantive challenges in sensor feedback synchronization, object, and human hand detection, our system achieves robust robot-to-human handover with 98\% accuracy in our preliminary real experiments using human receivers.	['Mehran Mehrandezh', 'Kamal Gupta', 'Behnam Moradi', 'Mohammadhadi Mohandes']	2022-10-27	null	null	null	null	['hand-detection']	['computer-vision']	[ 1.12779036e-01 5.14190257e-01 -8.04461539e-02 -4.59873937e-02 -2.67719060e-01 -4.54457551e-01 -1.07024215e-01 -4.74122763e-01 -5.47939539e-01 2.45299250e-01 -3.88062209e-01 1.40647858e-01 7.50117935e-03 -2.24978164e-01 -7.97733784e-01 -6.83613896e-01 1.49457008e-01 7.38432169e-01 4.79029953e-01 -1.85176045e-01 1.93280622e-01 7.71221995e-01 -1.46301651e+00 -4.48131524e-02 -3.59187990e-01 1.40370679e+00 4.52395797e-01 1.09071779e+00 7.28785038e-01 9.48101461e-01 -6.22212768e-01 2.54328132e-01 5.57317674e-01 1.86085794e-02 -6.85327590e-01 1.84068084e-01 -2.82222569e-01 -9.46829438e-01 -6.32826090e-01 5.49227178e-01 1.00541258e+00 -1.67672455e-01 5.40897727e-01 -1.54005206e+00 -1.42677844e-01 3.91121447e-01 -6.77207887e-01 -3.24840456e-01 8.14381003e-01 5.86350143e-01 4.79754627e-01 -8.02874565e-01 7.34586179e-01 1.23958647e+00 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-9.36843395e-01 -1.18132746e+00 -5.65197468e-01 -8.15550983e-02 7.36902714e-01 -8.58108163e-01 -1.54774964e-01 -3.29282731e-02]	[6.0098700523376465, -0.8924053907394409]
0bed69a3-3d54-474b-a97f-134d8544d105	offline-rl-without-off-policy-evaluation	2106.08909	null	https://arxiv.org/abs/2106.08909v3	https://arxiv.org/pdf/2106.08909v3.pdf	Offline RL Without Off-Policy Evaluation	Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-critic approach involving off-policy evaluation. In this paper we show that simply doing one step of constrained/regularized policy improvement using an on-policy Q estimate of the behavior policy performs surprisingly well. This one-step algorithm beats the previously reported results of iterative algorithms on a large portion of the D4RL benchmark. The one-step baseline achieves this strong performance while being notably simpler and more robust to hyperparameters than previously proposed iterative algorithms. We argue that the relatively poor performance of iterative approaches is a result of the high variance inherent in doing off-policy evaluation and magnified by the repeated optimization of policies against those estimates. In addition, we hypothesize that the strong performance of the one-step algorithm is due to a combination of favorable structure in the environment and behavior policy.	['Joan Bruna', 'Rajesh Ranganath', 'William F. Whitney', 'David Brandfonbrener']	2021-06-16	null	http://proceedings.neurips.cc/paper/2021/hash/274a10ffa06e434f2a94df765cac6bf4-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/274a10ffa06e434f2a94df765cac6bf4-Paper.pdf	neurips-2021-12	['d4rl']	['robots']	[-4.59569693e-02 1.85248315e-01 -5.90722382e-01 -1.32997096e-01 -8.89997482e-01 -7.27709293e-01 9.59722936e-01 1.68087348e-01 -8.89991522e-01 1.11889386e+00 5.52461267e-01 -5.29997289e-01 -1.99548304e-01 -1.76188484e-01 -8.01346719e-01 -4.78798777e-01 -1.31174773e-01 5.37099898e-01 1.37656316e-01 -4.44600433e-01 5.73563576e-01 4.04407501e-01 -1.29895663e+00 -3.06979090e-01 5.30976415e-01 5.58895350e-01 -8.18709210e-02 8.03517759e-01 1.77414581e-01 1.05370116e+00 -6.74974978e-01 5.06533384e-02 3.97799879e-01 -6.75164044e-01 -5.93596756e-01 2.00075001e-01 3.94229479e-02 -8.49766254e-01 -5.09182736e-03 8.91156793e-01 5.51519275e-01 4.75962669e-01 2.81276077e-01 -9.48476076e-01 -1.67553291e-01 5.01793087e-01 -4.81111914e-01 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3e62a614-efbe-492a-b110-84d9e21ede14	encoding-explanatory-knowledge-for-zero-shot	2105.05737	null	https://arxiv.org/abs/2105.05737v2	https://arxiv.org/pdf/2105.05737v2.pdf	Encoding Explanatory Knowledge for Zero-shot Science Question Answering	This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms. We demonstrate that N-XKT is able to improve accuracy and generalization on science Question Answering (QA). Specifically, by leveraging facts from background explanatory knowledge corpora, the N-XKT model shows a clear improvement on zero-shot QA. Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset, enabling faster convergence and more accurate results. A systematic analysis is conducted to quantitatively analyze the performance of the N-XKT model and the impact of different categories of knowledge on the zero-shot generalization task.	['Andre Freitas', 'Donal Landers', 'Marco Valentino', 'Zili Zhou']	2021-05-12	null	https://aclanthology.org/2021.iwcs-1.5	https://aclanthology.org/2021.iwcs-1.5.pdf	iwcs-acl-2021-6	['science-question-answering']	['miscellaneous']	[ 4.00756955e-01 6.40265048e-01 -1.90679953e-01 -6.63952351e-01 -1.10544682e+00 -5.07019460e-01 7.78478503e-01 2.05051765e-01 -1.33375600e-01 9.83808875e-01 5.01985788e-01 -4.55586702e-01 -6.96120203e-01 -1.11746430e+00 -1.04566002e+00 -3.33941758e-01 1.58099294e-01 7.61459351e-01 1.37746558e-01 -4.27912652e-01 3.34597260e-01 -1.37396753e-01 -1.51518893e+00 7.86982179e-01 1.55614185e+00 9.25260544e-01 9.26721692e-02 4.72952694e-01 -3.10297847e-01 1.05645609e+00 -7.76549280e-01 -6.99761331e-01 -2.71176398e-01 -6.76306665e-01 -1.49257958e+00 -6.71777964e-01 8.08946192e-01 -6.81773806e-03 -3.34661841e-01 7.87328243e-01 3.31507355e-01 5.30665934e-01 8.85762811e-01 -8.47640753e-01 -1.57993126e+00 9.71348822e-01 2.40930438e-01 5.46160221e-01 3.56189042e-01 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dda005d8-7327-41db-8863-fdd1ddbaba39	face-hallucination-using-cascaded-super	1805.10938	null	http://arxiv.org/abs/1805.10938v2	http://arxiv.org/pdf/1805.10938v2.pdf	Face hallucination using cascaded super-resolution and identity priors	In this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors. We approach the problem with convolutional neural networks (CNNs) and propose a novel (deep) face hallucination model that incorporates identity priors into the learning procedure. The model consists of two main parts: i) a cascaded super-resolution network that upscales the low-resolution images, and ii) an ensemble of face recognition models that act as identity priors for the super-resolution network during training. Different from competing super-resolution approaches that typically rely on a single model for upscaling (even with large magnification factors), our network uses a cascade of multiple SR models that progressively upscale the low-resolution images using steps of $2\times$. This characteristic allows us to apply supervision signals (target appearances) at different resolutions and incorporate identity constraints at multiple-scales. Our model is able to upscale (very) low-resolution images captured in unconstrained conditions and produce visually convincing results. We rigorously evaluate the proposed model on a large datasets of facial images and report superior performance compared to the state-of-the-art.	['Vitomir Štruc', 'Simon Dobrišek', 'Walter J. Scheirer', 'Klemen Grm']	2018-05-28	null	null	null	null	['face-hallucination']	['computer-vision']	[ 6.12985075e-01 5.34023225e-01 2.41528496e-01 -4.62569565e-01 -7.90277362e-01 -1.77640021e-01 7.99875975e-01 -6.80906296e-01 -3.06240797e-01 7.99723625e-01 2.13368222e-01 2.75752038e-01 1.04020566e-01 -8.90633702e-01 -9.80054379e-01 -5.62106907e-01 2.45812938e-01 1.93110719e-01 1.79023013e-01 -4.98965234e-01 -2.21537668e-02 9.02100623e-01 -1.96164894e+00 5.96132755e-01 6.62114739e-01 7.94771373e-01 2.11327642e-01 5.44687808e-01 1.85792163e-01 7.86014259e-01 -5.01448572e-01 -4.54634875e-01 4.03093129e-01 -5.25457203e-01 -7.40074337e-01 3.36166829e-01 9.37849700e-01 -5.14970958e-01 -1.69434965e-01 1.28911936e+00 4.30516481e-01 3.64875013e-04 3.30290914e-01 -6.80844367e-01 -1.31933737e+00 4.19235349e-01 -8.80470157e-01 2.42678836e-01 4.04068440e-01 -2.10480154e-01 5.77322960e-01 -1.35046971e+00 8.62463713e-01 1.70360935e+00 6.45570099e-01 9.03590322e-01 -1.74365282e+00 -6.86521769e-01 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4.07626361e-01 6.67719126e-01 -5.43115020e-01 -4.69780594e-01 3.24463516e-01]	[12.777303695678711, -0.1315096914768219]
45f9feaa-48a6-420c-90f2-77b30cee4c1a	knowledge-aided-consistency-for-weakly	1803.03879	null	http://arxiv.org/abs/1803.03879v1	http://arxiv.org/pdf/1803.03879v1.pdf	Knowledge Aided Consistency for Weakly Supervised Phrase Grounding	Given a natural language query, a phrase grounding system aims to localize mentioned objects in an image. In weakly supervised scenario, mapping between image regions (i.e., proposals) and language is not available in the training set. Previous methods address this deficiency by training a grounding system via learning to reconstruct language information contained in input queries from predicted proposals. However, the optimization is solely guided by the reconstruction loss from the language modality, and ignores rich visual information contained in proposals and useful cues from external knowledge. In this paper, we explore the consistency contained in both visual and language modalities, and leverage complementary external knowledge to facilitate weakly supervised grounding. We propose a novel Knowledge Aided Consistency Network (KAC Net) which is optimized by reconstructing input query and proposal's information. To leverage complementary knowledge contained in the visual features, we introduce a Knowledge Based Pooling (KBP) gate to focus on query-related proposals. Experiments show that KAC Net provides a significant improvement on two popular datasets.	['Jiyang Gao', 'Ram Nevatia', 'Kan Chen']	2018-03-11	knowledge-aided-consistency-for-weakly-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Knowledge_Aided_Consistency_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Knowledge_Aided_Consistency_CVPR_2018_paper.pdf	cvpr-2018-6	['phrase-grounding']	['natural-language-processing']	[-7.76223540e-02 4.51866299e-01 -5.63119411e-01 -6.25781715e-01 -9.33541119e-01 -4.67981249e-01 5.85427284e-01 2.06308842e-01 -4.20361698e-01 5.45052290e-01 3.19055408e-01 1.54604316e-01 3.42112273e-01 -8.11412454e-01 -1.09300709e+00 -3.90182525e-01 2.79915839e-01 3.00577015e-01 5.75100362e-01 -7.68076777e-02 6.05960786e-02 1.27569675e-01 -1.23258877e+00 8.40226233e-01 4.75059837e-01 1.07177472e+00 7.45929003e-01 3.66975330e-02 -5.58781207e-01 1.25153542e+00 -1.41224265e-01 -4.27812606e-01 1.51276454e-01 -4.70195144e-01 -1.00488448e+00 9.75658521e-02 5.69085479e-01 -3.97944093e-01 -3.80882829e-01 1.24469244e+00 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77a67d56-3e28-403a-83f6-910b6db96cf1	self-supervised-leaf-segmentation-under	2203.15943	null	https://arxiv.org/abs/2203.15943v1	https://arxiv.org/pdf/2203.15943v1.pdf	Self-Supervised Leaf Segmentation under Complex Lighting Conditions	As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the semantically similar pixels by iteratively referring to the self-contained information, allowing the pixels of the same semantic object to be jointly considered by the color-based leaf segmentation algorithm for identifying the leaf regions. Additionally, we propose to use a self-supervised color correction model for images taken under complex illumination conditions. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework in achieving effective and generalizable leaf segmentation.	['Adam Guskic', 'Todd Mcclellan', 'Lawrence Webb', 'Egan Doeven', 'Michael Vernon', 'Yongjian Hu', 'Ligang He', 'Richard Jiang', 'Abbas Kouzani', 'Scott Adams', 'Chang-Tsun Li', 'Xufeng Lin']	2022-03-29	null	null	null	null	['plant-phenotyping']	['computer-vision']	[ 6.15613341e-01 -1.43539503e-01 -2.66386837e-01 -5.58519304e-01 -3.52529794e-01 -7.86315858e-01 1.18888475e-01 4.87768114e-01 9.32722390e-02 4.33207214e-01 -6.34621739e-01 -3.02148968e-01 -2.12845638e-01 -9.33953226e-01 -2.66920477e-01 -8.82867634e-01 3.44574451e-01 4.90633309e-01 3.88815135e-01 2.08001360e-01 1.17326766e-01 6.42915487e-01 -1.66702080e+00 -1.09149620e-01 1.33907652e+00 1.04035127e+00 7.73992777e-01 5.80516219e-01 -7.97591984e-01 4.25581306e-01 -3.09811592e-01 3.77821624e-01 2.41738155e-01 -7.54071653e-01 -7.41946757e-01 9.80644643e-01 3.60893965e-01 1.49971306e-01 4.71404880e-01 1.29067338e+00 1.64837748e-01 -2.24531487e-01 4.56444651e-01 -1.14009309e+00 -2.91754842e-01 4.31918770e-01 -7.49394834e-01 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bbe79537-a204-4d9b-998f-b2cac2bdb1f1	a-nir-to-vis-face-recognition-via-part	2102.00689	null	https://arxiv.org/abs/2102.00689v1	https://arxiv.org/pdf/2102.00689v1.pdf	A NIR-to-VIS face recognition via part adaptive and relation attention module	In the face recognition application scenario, we need to process facial images captured in various conditions, such as at night by near-infrared (NIR) surveillance cameras. The illumination difference between NIR and visible-light (VIS) causes a domain gap between facial images, and the variations in pose and emotion also make facial matching more difficult. Heterogeneous face recognition (HFR) has difficulties in domain discrepancy, and many studies have focused on extracting domain-invariant features, such as facial part relational information. However, when pose variation occurs, the facial component position changes, and a different part relation is extracted. In this paper, we propose a part relation attention module that crops facial parts obtained through a semantic mask and performs relational modeling using each of these representative features. Furthermore, we suggest component adaptive triplet loss function using adaptive weights for each part to reduce the intra-class identity regardless of the domain as well as pose. Finally, our method exhibits a performance improvement in the CASIA NIR-VIS 2.0 and achieves superior result in the BUAA-VisNir with large pose and emotion variations.	['Sangyoun Lee', 'MyeongAh Cho', 'Rushuang Xu']	2021-02-01	null	null	null	null	['heterogeneous-face-recognition']	['computer-vision']	[ 1.65917486e-01 -4.08440351e-01 -1.37204984e-02 -8.16851437e-01 -5.90264082e-01 -4.07537729e-01 1.85928941e-01 -8.28391254e-01 4.92565744e-02 3.94710958e-01 1.02224901e-01 4.54109669e-01 -4.70120274e-02 -3.98792505e-01 -4.71816599e-01 -1.02657044e+00 6.41140580e-01 2.93833077e-01 -3.26477945e-01 -4.10328925e-01 -2.93453038e-01 9.69681621e-01 -1.84847653e+00 5.26242197e-01 5.09336174e-01 1.48893428e+00 -5.18388376e-02 -9.92989466e-02 -2.03601643e-01 4.00570601e-01 -6.13815427e-01 -7.95308888e-01 7.65794694e-01 -3.54624659e-01 -4.75076109e-01 6.44030988e-01 1.01518691e+00 -3.38717639e-01 -6.23217940e-01 1.24094725e+00 8.61735106e-01 1.18458197e-01 4.95150715e-01 -1.62191260e+00 -5.86051226e-01 -5.32694571e-02 -1.00109768e+00 -8.27088282e-02 3.68453175e-01 1.04707561e-01 3.52157146e-01 -1.10434461e+00 5.26711643e-01 1.63948202e+00 5.71894765e-01 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62661211-f258-4f0e-9500-d442ceb90887	multi-step-reasoning-over-unstructured-text	2104.05883	null	https://arxiv.org/abs/2104.05883v1	https://arxiv.org/pdf/2104.05883v1.pdf	Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval	Complex question answering often requires finding a reasoning chain that consists of multiple evidence pieces. Current approaches incorporate the strengths of structured knowledge and unstructured text, assuming text corpora is semi-structured. Building on dense retrieval methods, we propose a new multi-step retrieval approach (BeamDR) that iteratively forms an evidence chain through beam search in dense representations. When evaluated on multi-hop question answering, BeamDR is competitive to state-of-the-art systems, without using any semi-structured information. Through query composition in dense space, BeamDR captures the implicit relationships between evidence in the reasoning chain. The code is available at https://github.com/ henryzhao5852/BeamDR.	['Hal Daumé III', 'Jordan Boyd-Graber', 'Chenyan Xiong', 'Chen Zhao']	2021-04-13	null	https://aclanthology.org/2021.naacl-main.368	https://aclanthology.org/2021.naacl-main.368.pdf	naacl-2021-4	['multi-hop-question-answering']	['knowledge-base']	[-2.68147826e-01 1.58831239e-01 -4.86486197e-01 -1.51369095e-01 -1.47424495e+00 -4.84479606e-01 6.53126001e-01 4.74181086e-01 -2.97705352e-01 6.63143814e-01 7.44754851e-01 -2.99429774e-01 -7.60123551e-01 -1.02011824e+00 -6.77822590e-01 -5.76811358e-02 2.51426399e-01 1.14427054e+00 5.64154088e-01 -4.09574062e-01 3.23632181e-01 1.40828237e-01 -1.21213806e+00 6.61672533e-01 8.31055403e-01 8.90938163e-01 8.87254924e-02 7.25659668e-01 -6.34595335e-01 1.47123861e+00 -3.39951605e-01 -6.65283620e-01 7.72906765e-02 -3.37058812e-01 -1.31315184e+00 -4.13298905e-01 8.03663135e-01 -5.28878808e-01 -8.49912286e-01 1.00996149e+00 3.40715349e-01 3.57009768e-01 3.51665050e-01 -5.98349512e-01 -1.05127990e+00 8.37379515e-01 -1.58275023e-01 6.71829939e-01 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b6eafe01-8a47-4018-9812-22d5a77a15de	colibridoc-an-eye-in-hand-autonomous-trocar	2111.15373	null	https://arxiv.org/abs/2111.15373v1	https://arxiv.org/pdf/2111.15373v1.pdf	ColibriDoc: An Eye-in-Hand Autonomous Trocar Docking System	Retinal surgery is a complex medical procedure that requires exceptional expertise and dexterity. For this purpose, several robotic platforms are currently being developed to enable or improve the outcome of microsurgical tasks. Since the control of such robots is often designed for navigation inside the eye in proximity to the retina, successful trocar docking and inserting the instrument into the eye represents an additional cognitive effort, and is, therefore, one of the open challenges in robotic retinal surgery. For this purpose, we present a platform for autonomous trocar docking that combines computer vision and a robotic setup. Inspired by the Cuban Colibri (hummingbird) aligning its beak to a flower using only vision, we mount a camera onto the endeffector of a robotic system. By estimating the position and pose of the trocar, the robot is able to autonomously align and navigate the instrument towards the Trocar's Entry Point (TEP) and finally perform the insertion. Our experiments show that the proposed method is able to accurately estimate the position and pose of the trocar and achieve repeatable autonomous docking. The aim of this work is to reduce the complexity of robotic setup preparation prior to the surgical task and therefore, increase the intuitiveness of the system integration into the clinical workflow.	['M. Ali Nasseri', 'Nassir Navab', 'Iulian Iordachita', 'Peter Gehlbach', 'Kai Huang', 'Benjamin Busam', 'Junjie Yang', 'Michael Sommersperger', 'Shervin Dehghani']	2021-11-30	null	null	null	null	['medical-procedure']	['medical']	[-3.75217229e-01 3.76844108e-01 3.36352617e-01 2.78964072e-01 2.04149097e-01 -7.68314540e-01 1.54168054e-01 1.18502909e-02 -8.58511806e-01 1.60647571e-01 -4.09681231e-01 -4.04924303e-01 -1.85018703e-01 -8.71066451e-02 -7.50448287e-01 -6.25887156e-01 3.11831415e-01 4.90607589e-01 -1.71576943e-02 -1.87890634e-01 4.58896399e-01 1.00475264e+00 -1.55736125e+00 -7.36072779e-01 9.33467865e-01 8.26770246e-01 1.04567182e+00 4.15118754e-01 3.09445202e-01 3.14722091e-01 -2.06940651e-01 -3.12964201e-01 3.92342687e-01 -2.37338081e-01 -5.20829976e-01 1.30131558e-01 -1.34681582e-01 -1.94383502e-01 9.70057845e-02 8.63062561e-01 3.54261726e-01 -2.10120872e-01 5.44375181e-01 -6.10637903e-01 3.88269663e-01 3.20750922e-01 -2.90505171e-01 -5.10812461e-01 2.05170810e-01 -3.76723222e-02 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950a53f2-579b-4134-9bb4-7851b655405b	adversarial-estimators	2204.10495	null	https://arxiv.org/abs/2204.10495v3	https://arxiv.org/pdf/2204.10495v3.pdf	Adversarial Estimators	We develop an asymptotic theory of adversarial estimators ('A-estimators'). They generalize maximum-likelihood-type estimators ('M-estimators') as their average objective is maximized by some parameters and minimized by others. This class subsumes the continuous-updating Generalized Method of Moments, Generative Adversarial Networks and more recent proposals in machine learning and econometrics. In these examples, researchers state which aspects of the problem may in principle be used for estimation, and an adversary learns how to emphasize them optimally. We derive the convergence rates of A-estimators under pointwise and partial identification, and the normality of functionals of their parameters. Unknown functions may be approximated via sieves such as deep neural networks, for which we provide simplified low-level conditions. As a corollary, we obtain the normality of neural-net M-estimators, overcoming technical issues previously identified by the literature. Our theory yields novel results about a variety of A-estimators, providing intuition and formal justification for their success in recent applications.	['Jonas Metzger']	2022-04-22	null	null	null	null	['econometrics']	['miscellaneous']	[ 1.38175458e-01 5.27529001e-01 -2.34032795e-01 -4.21116471e-01 -1.08208632e+00 -8.10864151e-01 6.81335151e-01 -5.37233710e-01 -5.57108164e-01 1.10251296e+00 -1.71370864e-01 -3.24503869e-01 -3.75459492e-01 -7.43346691e-01 -1.13413990e+00 -1.08423078e+00 -1.82267636e-01 1.88136771e-01 -4.18445557e-01 9.51542426e-03 2.70771027e-01 6.09663785e-01 -7.71923423e-01 -4.71700251e-01 7.79070854e-01 1.01362765e+00 -3.94174397e-01 8.19269538e-01 1.95719555e-01 6.75188780e-01 -6.93950295e-01 -1.05146098e+00 5.02527654e-01 -4.55933779e-01 -4.09903258e-01 -3.27777974e-02 4.79546338e-01 -7.01908112e-01 -4.78149951e-01 1.64414394e+00 4.49396610e-01 7.80334175e-02 1.28233027e+00 -1.57272637e+00 -7.44282544e-01 7.72970378e-01 -4.20167953e-01 2.37141680e-02 -2.57192224e-01 -1.08803533e-01 7.48255193e-01 -5.86547196e-01 1.61425903e-01 1.40225339e+00 1.26891673e+00 8.71677935e-01 -1.23860157e+00 -8.21291089e-01 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7ffacd6b-2f9d-4333-a00d-eefa21c5fdcc	information-theoretic-inducing-point	2206.02437	null	https://arxiv.org/abs/2206.02437v2	https://arxiv.org/pdf/2206.02437v2.pdf	Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation	Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly parallelised. By using representative subsets of the available data to build approximate posteriors, sparse models dramatically reduce the computational costs of surrogate modelling by relying on a small set of pseudo-observations, the so-called inducing points, in lieu of the full data set. However, current approaches to design inducing points are not appropriate within BO loops as they seek to reduce global uncertainty in the objective function. Thus, the high-fidelity modelling of promising and data-dense regions required for precise optimisation is sacrificed and computational resources are instead wasted on modelling areas of the space already known to be sub-optimal. Inspired by entropy-based BO methods, we propose a novel inducing point design that uses a principled information-theoretic criterion to select inducing points. By choosing inducing points to maximally reduce both global uncertainty and uncertainty in the maximum value of the objective function, we build surrogate models able to support high-precision high-throughput BO.	['Victor Picheny', 'Sebastian W. Ober', 'Henry B. Moss']	2022-06-06	null	null	null	null	['bayesian-optimisation']	['methodology']	[ 2.33187690e-01 2.57014275e-01 9.92875248e-02 -2.38085285e-01 -1.29966891e+00 -3.07668984e-01 7.31162548e-01 5.09636641e-01 -6.17014885e-01 8.23173523e-01 3.16267282e-01 1.51932746e-01 -7.20047712e-01 -8.58002961e-01 -6.91212356e-01 -9.42990661e-01 -1.21011153e-01 9.84278917e-01 1.01603754e-01 2.20139831e-01 3.92751515e-01 4.77866769e-01 -1.44687474e+00 -3.86067033e-02 8.63296390e-01 1.05461621e+00 3.85443985e-01 5.26990891e-01 2.59616554e-01 1.18855730e-01 -4.27698046e-01 -7.45123103e-02 3.38200957e-01 -4.15988743e-01 -3.75214458e-01 -2.39320457e-01 -2.65465379e-01 9.35816020e-02 1.00218914e-01 1.00574148e+00 7.65623033e-01 5.57185411e-01 9.20384407e-01 -5.97703993e-01 2.64811337e-01 3.63774359e-01 -3.87659341e-01 4.29494977e-02 8.18487033e-02 4.55117226e-01 8.43517900e-01 -6.76328123e-01 4.34064686e-01 1.20998490e+00 7.02887833e-01 2.14105517e-01 -1.87934303e+00 -6.28585890e-02 -8.47088322e-02 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52cdda15-81de-40e1-871c-d77994647c02	behm-gan-bandwidth-extension-of-historical	2204.06478	null	https://arxiv.org/abs/2204.06478v2	https://arxiv.org/pdf/2204.06478v2.pdf	BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks	Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes BEHM-GAN, a model based on generative adversarial networks, as a practical solution to this problem. The proposed method works with the complex spectrogram representation of audio and, thanks to a dedicated regularization strategy, can effectively extend the bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is designed to be applied as a second step after denoising the recording to suppress any additive disturbances, such as clicks and background noise. We train and evaluate the method using solo piano classical music. The proposed method outperforms the compared baselines in both objective and subjective experiments. The results of a formal blind listening test show that BEHM-GAN significantly increases the perceptual sound quality in early-20th-century gramophone recordings. For several items, there is a substantial improvement in the mean opinion score after enhancing historical recordings with the proposed bandwidth-extension algorithm. This study represents a relevant step toward data-driven music restoration in real-world scenarios.	['Vesa Välimäki', 'Eloi Moliner']	2022-04-13	null	null	null	null	['bandwidth-extension', 'bandwidth-extension']	['audio', 'speech']	[ 3.97924840e-01 -2.09715605e-01 3.77518564e-01 1.78587973e-01 -1.26769018e+00 -5.86419463e-01 2.20769554e-01 -3.47979546e-01 -2.71744728e-01 8.24818254e-01 4.56478775e-01 -4.07267660e-02 -3.45687807e-01 -5.77974796e-01 -6.12652481e-01 -9.06428695e-01 -9.51566081e-03 -1.49244666e-01 -4.03058566e-02 -4.37180758e-01 8.99219289e-02 2.46569708e-01 -1.61824942e+00 2.35615402e-01 9.14046228e-01 8.80636156e-01 2.03228310e-01 7.93210685e-01 3.77970874e-01 4.55238760e-01 -1.15009069e+00 -5.71908772e-01 4.50363755e-01 -8.63631725e-01 -2.67755687e-01 -1.16528973e-01 2.73616940e-01 -2.10336134e-01 -2.01421365e-01 1.22089589e+00 1.09497213e+00 3.86181235e-01 3.88629556e-01 -8.63133609e-01 -4.07643855e-01 1.02659321e+00 -2.59519786e-01 1.59224644e-01 2.65743375e-01 2.57153548e-02 9.13506985e-01 -6.91245735e-01 1.01207942e-01 8.73054087e-01 8.98643911e-01 3.30710143e-01 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188a72e4-be34-4083-a58e-6b81e1e27b48	towards-boosting-the-accuracy-of-non-latin	2201.03185	null	https://arxiv.org/abs/2201.03185v1	https://arxiv.org/pdf/2201.03185v1.pdf	Towards Boosting the Accuracy of Non-Latin Scene Text Recognition	Scene-text recognition is remarkably better in Latin languages than the non-Latin languages due to several factors like multiple fonts, simplistic vocabulary statistics, updated data generation tools, and writing systems. This paper examines the possible reasons for low accuracy by comparing English datasets with non-Latin languages. We compare various features like the size (width and height) of the word images and word length statistics. Over the last decade, generating synthetic datasets with powerful deep learning techniques has tremendously improved scene-text recognition. Several controlled experiments are performed on English, by varying the number of (i) fonts to create the synthetic data and (ii) created word images. We discover that these factors are critical for the scene-text recognition systems. The English synthetic datasets utilize over 1400 fonts while Arabic and other non-Latin datasets utilize less than 100 fonts for data generation. Since some of these languages are a part of different regions, we garner additional fonts through a region-based search to improve the scene-text recognition models in Arabic and Devanagari. We improve the Word Recognition Rates (WRRs) on Arabic MLT-17 and MLT-19 datasets by 24.54% and 2.32% compared to previous works or baselines. We achieve WRR gains of 7.88% and 3.72% for IIIT-ILST and MLT-19 Devanagari datasets.	['C. V. Jawahar', 'Rohit Saluja', 'Sanjana Gunna']	2022-01-10	null	null	null	null	['scene-text-recognition']	['computer-vision']	[-1.43762492e-02 -7.38332570e-01 3.68187092e-02 -4.50573802e-01 -5.49362302e-01 -6.96045101e-01 9.17463720e-01 -2.13187769e-01 -6.92728043e-01 5.52743673e-01 1.79952919e-01 -5.00815511e-01 4.44923133e-01 -8.29450905e-01 -4.46029335e-01 -6.11106455e-01 2.96556443e-01 5.10606706e-01 1.29595175e-01 -5.94125092e-01 6.43032491e-01 5.31421363e-01 -1.28068793e+00 6.01924598e-01 1.09148264e+00 6.61803961e-01 2.03590930e-01 9.48248327e-01 -6.67923510e-01 5.52760005e-01 -1.07991326e+00 -4.38397050e-01 5.09004712e-01 -4.06217009e-01 -2.98367560e-01 4.50136334e-01 7.53103852e-01 -3.04665297e-01 -4.43865299e-01 7.90085077e-01 6.12946808e-01 -1.27522051e-01 1.01208878e+00 -9.39292908e-01 -1.17581916e+00 7.23854661e-01 -1.01114333e+00 -4.20838334e-02 3.13619316e-01 2.16960311e-01 5.04658937e-01 -1.40651369e+00 4.75276947e-01 1.50465715e+00 5.41067243e-01 2.37485856e-01 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-6.33167505e-01 3.07344258e-01 -2.81151205e-01 -1.73942044e-01 -2.31819049e-01 4.22994614e-01]	[11.861541748046875, 2.4576847553253174]
df88a3dc-be57-4282-beff-db648e6895b6	deeply-supervised-multimodal-attentional	1902.05829	null	http://arxiv.org/abs/1902.05829v1	http://arxiv.org/pdf/1902.05829v1.pdf	Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection	Detecting visual relationships, i.e. <Subject, Predicate, Object> triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch. We introduce a new deeply supervised two-branch architecture, the Multimodal Attentional Translation Embeddings, where the visual features of each branch are driven by a multimodal attentional mechanism that exploits spatio-linguistic similarities in a low-dimensional space. We present a variety of experiments comparing against all related approaches in the literature, as well as by re-implementing and fine-tuning several of them. Results on the commonly employed VRD dataset [1] show that the proposed method clearly outperforms all others, while we also justify our claims both quantitatively and qualitatively.	['Athanasia Zlatintsi', 'Nikolaos Gkanatsios', 'Vassilis Pitsikalis', 'Petros Maragos', 'Petros Koutras']	2019-02-15	null	null	null	null	['visual-relationship-detection']	['computer-vision']	[ 2.42477745e-01 -4.61067399e-03 -3.72460455e-01 -4.58003163e-01 -5.55868387e-01 -7.35948682e-01 9.78654265e-01 4.22644913e-01 -3.83368134e-01 3.52616757e-01 5.34797370e-01 -3.55673820e-01 -2.99257785e-01 -3.17592531e-01 -6.25284374e-01 -6.12207472e-01 -1.72017723e-01 4.50475186e-01 2.57138431e-01 -1.58664972e-01 5.57287037e-01 6.32701814e-01 -1.72740674e+00 4.88879532e-01 4.18898970e-01 1.05429530e+00 5.11150956e-02 5.50547540e-01 -1.61468729e-01 7.28640914e-01 -8.54816735e-02 -2.42904022e-01 -1.83107927e-02 5.23694903e-02 -1.00791931e+00 1.49153784e-01 8.17676663e-01 8.59550238e-02 -4.77918535e-01 8.26327324e-01 2.87629277e-01 3.47070217e-01 9.23574269e-01 -1.38938570e+00 -1.31012917e+00 1.17421411e-01 -8.11644793e-01 5.57657599e-01 5.96922398e-01 -1.24713153e-01 1.46483326e+00 -1.28253567e+00 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5413e7c4-c60b-48ae-b2ae-59b155358e2f	transductive-multi-class-and-multi-label-zero	1503.07884	null	http://arxiv.org/abs/1503.07884v1	http://arxiv.org/pdf/1503.07884v1.pdf	Transductive Multi-class and Multi-label Zero-shot Learning	Recently, zero-shot learning (ZSL) has received increasing interest. The key idea underpinning existing ZSL approaches is to exploit knowledge transfer via an intermediate-level semantic representation which is assumed to be shared between the auxiliary and target datasets, and is used to bridge between these domains for knowledge transfer. The semantic representation used in existing approaches varies from visual attributes to semantic word vectors and semantic relatedness. However, the overall pipeline is similar: a projection mapping low-level features to the semantic representation is learned from the auxiliary dataset by either classification or regression models and applied directly to map each instance into the same semantic representation space where a zero-shot classifier is used to recognise the unseen target class instances with a single known 'prototype' of each target class. In this paper we discuss two related lines of work improving the conventional approach: exploiting transductive learning ZSL, and generalising ZSL to the multi-label case.	['Shaogang Gong', 'Tao Xiang', 'Yanwei Fu', 'Yongxin Yang', 'Timothy M. Hospedales']	2015-03-26	null	null	null	null	['multi-label-zero-shot-learning']	['computer-vision']	[ 8.18851888e-01 4.41445380e-01 -6.17553949e-01 -5.34217238e-01 -8.12797844e-01 -5.70249915e-01 9.71309900e-01 4.39409554e-01 -2.31086120e-01 6.74320936e-01 2.28401870e-01 1.43039301e-01 -3.12877536e-01 -1.10250592e+00 -5.42189062e-01 -7.17577577e-01 3.25460970e-01 6.31566226e-01 5.02951801e-01 -2.05402613e-01 1.79660842e-01 1.07323460e-01 -1.78441620e+00 6.25536144e-01 2.38190711e-01 1.00469494e+00 5.23432717e-02 5.38778782e-01 -7.06106782e-01 9.46109295e-01 -3.31801027e-01 -3.32211584e-01 1.30355671e-01 -5.72801054e-01 -1.34597290e+00 -1.64396986e-02 3.27111483e-01 4.61761624e-01 1.10591017e-01 1.09077573e+00 2.05343738e-01 5.38730443e-01 1.03370714e+00 -1.66882241e+00 -9.01916981e-01 1.46373034e-01 -1.92175806e-01 -9.69131738e-02 5.24347484e-01 -3.24436128e-01 1.12099445e+00 -8.53573442e-01 9.57268059e-01 1.37490261e+00 6.32066965e-01 8.43900800e-01 -1.50081086e+00 -4.08312976e-01 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7.61939526e-01 1.36718988e-01 -1.06836426e+00 -1.89918116e-01 3.28911126e-01]	[10.01850414276123, 2.463548421859741]
34c93ab9-9e8d-4603-9d1a-8af7db03ad31	segment-and-complete-defending-object	2112.04532	null	https://arxiv.org/abs/2112.04532v2	https://arxiv.org/pdf/2112.04532v2.pdf	Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection	Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of-the-art object detectors. Developing reliable defenses for object detectors against patch attacks is critical but severely understudied. In this paper, we propose Segment and Complete defense (SAC), a general framework for defending object detectors against patch attacks through detection and removal of adversarial patches. We first train a patch segmenter that outputs patch masks which provide pixel-level localization of adversarial patches. We then propose a self adversarial training algorithm to robustify the patch segmenter. In addition, we design a robust shape completion algorithm, which is guaranteed to remove the entire patch from the images if the outputs of the patch segmenter are within a certain Hamming distance of the ground-truth patch masks. Our experiments on COCO and xView datasets demonstrate that SAC achieves superior robustness even under strong adaptive attacks with no reduction in performance on clean images, and generalizes well to unseen patch shapes, attack budgets, and unseen attack methods. Furthermore, we present the APRICOT-Mask dataset, which augments the APRICOT dataset with pixel-level annotations of adversarial patches. We show SAC can significantly reduce the targeted attack success rate of physical patch attacks. Our code is available at https://github.com/joellliu/SegmentAndComplete.	['Soheil Feizi', 'Rama Chellappa', 'Chun Pong Lau', 'Alexander Levine', 'Jiang Liu']	2021-12-08	null	http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Segment_and_Complete_Defending_Object_Detectors_Against_Adversarial_Patch_Attacks_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Segment_and_Complete_Defending_Object_Detectors_Against_Adversarial_Patch_Attacks_CVPR_2022_paper.pdf	cvpr-2022-1	['real-world-adversarial-attack', 'adversarial-attack-detection', 'adversarial-attack-detection']	['adversarial', 'computer-vision', 'knowledge-base']	[ 3.74834865e-01 -1.63202733e-01 5.98527491e-02 1.44796610e-01 -1.01939297e+00 -1.40098429e+00 4.38660443e-01 -6.95659518e-02 -5.32619096e-02 1.62911072e-01 -5.01062632e-01 -4.98501867e-01 3.00183326e-01 -8.10773492e-01 -1.16121829e+00 -8.59827399e-01 -9.24476236e-02 4.57036234e-02 6.94511712e-01 -1.82733938e-01 2.60234594e-01 8.65834475e-01 -8.96325946e-01 3.41546983e-01 2.77477503e-01 1.14222944e+00 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65c5d3bc-79a9-4e66-8c2b-52f18c859e8b	bubblenet-a-disperse-recurrent-structure-to	null	null	https://ieeexplore.ieee.org/document/9190769	https://ieeexplore.ieee.org/document/9190769	Bubblenet: A Disperse Recurrent Structure To Recognize Activities	This paper presents an approach to perform human activity recognition in videos through the employment of a deep recurrent network, taking as inputs appearance and optical flow information. Our method proposes a novel architecture named BubbleNET, which is based on a recurrent layer dispersed into several modules (referred to as bubbles) along with an attention mechanism based on squeeze-and-excitation strategy, responsible to modulate each bubble contribution. Thereby, we intend to gather information from fundamentally correlated segments of the input data, creating a signature of components that characterize each activity. Our experiments, conducted on widely employed activity recognition datasets, support the existence of these signatures, evidenced by maps of bubble activations for every class of the datasets. To compare the approach to literature methods, mean accuracy is taken into account, for which BubbleNET obtained 97.62%, 91.70% and 82.60% on UCF-101, YUP++ and HMDB-51 datasets, respectively, being placed among state-of-the-art methods.	['William R. Schwartz', 'Victor H. C. Melo', 'Igor L. O. Bastos']	2020-10-30	null	null	null	null	['activity-recognition-in-videos']	['computer-vision']	[ 1.87137470e-01 1.59621481e-02 -1.69938520e-01 1.72117263e-01 1.31095927e-02 -2.46982440e-01 9.24948335e-01 -7.47421607e-02 -3.96433324e-01 8.45263422e-01 4.34183806e-01 1.54478148e-01 -2.89945453e-01 -5.72305977e-01 -6.34672999e-01 -9.16225553e-01 -4.74246383e-01 -1.11771591e-01 5.69378063e-02 8.61397944e-03 4.96434778e-01 4.88989323e-01 -1.75378180e+00 4.10266072e-01 5.87647378e-01 1.06302094e+00 7.44768884e-03 5.88524580e-01 2.55551822e-02 1.42799449e+00 -7.77393818e-01 -5.00225276e-02 -2.05938457e-04 -7.82322109e-01 -6.68447018e-01 1.74838006e-01 1.48478314e-01 3.65503468e-02 -4.59972143e-01 5.19476831e-01 3.12210947e-01 3.77516240e-01 9.13546562e-01 -8.72064769e-01 -5.65274477e-01 5.00899315e-01 -5.01908481e-01 7.98893094e-01 6.57564044e-01 2.21305132e-01 9.52019691e-01 -8.84066880e-01 1.12434769e+00 6.77179813e-01 2.45283976e-01 3.53204131e-01 -9.97730732e-01 -2.74933338e-01 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02b2a4f3-9b3e-49bd-b481-231887dcc234	towards-a-taxonomy-for-the-use-of-synthetic	2212.02622	null	https://arxiv.org/abs/2212.02622v1	https://arxiv.org/pdf/2212.02622v1.pdf	Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics	The proliferation of deep learning techniques led to a wide range of advanced analytics applications in important business areas such as predictive maintenance or product recommendation. However, as the effectiveness of advanced analytics naturally depends on the availability of sufficient data, an organization's ability to exploit the benefits might be restricted by limited data or likewise data access. These challenges could force organizations to spend substantial amounts of money on data, accept constrained analytics capacities, or even turn into a showstopper for analytics projects. Against this backdrop, recent advances in deep learning to generate synthetic data may help to overcome these barriers. Despite its great potential, however, synthetic data are rarely employed. Therefore, we present a taxonomy highlighting the various facets of deploying synthetic data for advanced analytics systems. Furthermore, we identify typical application scenarios for synthetic data to assess the current state of adoption and thereby unveil missed opportunities to pave the way for further research.	['Frédéric Thiesse', 'Giacomo Welsch', 'Peter Kowalczyk']	2022-12-05	null	null	null	null	['product-recommendation']	['miscellaneous']	[-3.68113182e-02 3.33283663e-01 -2.74568588e-01 -3.21545303e-01 -3.22275251e-01 -4.20330048e-01 6.10327780e-01 5.47409952e-01 -3.55776511e-02 4.39675450e-01 -2.57273652e-02 -7.81883895e-01 -2.54509091e-01 -9.91658092e-01 -3.81647855e-01 -3.12088341e-01 3.43716778e-02 5.25751472e-01 -2.96546787e-01 -5.01614392e-01 3.76798064e-01 7.20199823e-01 -1.63949251e+00 2.00481459e-01 7.04977632e-01 1.22926700e+00 -5.57533465e-02 2.76021421e-01 -3.18316698e-01 1.04375362e+00 -7.60447800e-01 -5.22920966e-01 3.61667097e-01 4.35790643e-02 -4.19863164e-01 2.66216369e-03 4.37803939e-02 -4.86616373e-01 9.44145322e-02 5.02899051e-01 2.77123630e-01 -5.18161058e-02 1.09844498e-01 -1.43352938e+00 -3.95997584e-01 2.87526548e-01 -4.79420990e-01 3.64800483e-01 1.19988978e-01 4.74445254e-01 9.21345115e-01 -7.59035170e-01 4.56282228e-01 7.52434134e-01 5.82626104e-01 8.85657966e-02 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2c6a21b4-7950-4fb7-b197-7144aa12801f	statistical-mechanical-analysis-of-adaptive	2110.06517	null	https://arxiv.org/abs/2110.06517v5	https://arxiv.org/pdf/2110.06517v5.pdf	Statistical-mechanical analysis of adaptive filter with clipping saturation-type nonlinearity	In most practical adaptive signal processing systems, e.g., active noise control, active vibration control, and acoustic echo cancellation, substantial nonlinearities that cannot be neglected exist. In this paper, we analyze the behaviors of an adaptive system in which the output of the adaptive filter has the clipping saturation-type nonlinearity by a statistical-mechanical method. We discuss the dynamical and steady-state behaviors of the adaptive system by asymptotic analysis, steady-state analysis, and numerical calculation. As a result, it has become clear that the saturation value has the critical point at which the system's mean-square stability and instability switch. The obtained theory well explains the strange behaviors around the critical point observed in the computer simulation. Finally, the exact value of the critical point is also derived.	['Seiji Miyoshi']	2021-10-13	null	null	null	null	['acoustic-echo-cancellation', 'acoustic-echo-cancellation']	['medical', 'speech']	[ 1.76211879e-01 7.89644644e-02 3.52206975e-01 2.31564164e-01 -1.62707344e-01 -3.11019391e-01 1.24844879e-01 -1.23793729e-01 -3.39536548e-01 5.94793737e-01 -2.40948558e-01 -1.90796301e-01 -4.83876258e-01 -3.49838406e-01 -4.08370405e-01 -1.32528853e+00 -1.27478540e-01 -1.92264557e-01 4.47193831e-01 -5.53413570e-01 1.74117416e-01 2.99312145e-01 -1.49500978e+00 -3.89047533e-01 9.60098147e-01 1.07563889e+00 -4.20307973e-03 7.75096118e-01 7.41620660e-02 3.78322899e-02 -7.34382987e-01 3.55969787e-01 1.65108055e-01 -6.92341864e-01 -8.52926746e-02 -1.27457380e-01 1.00070052e-01 -8.98978785e-02 -4.06009793e-01 1.39460480e+00 6.31434679e-01 4.30319816e-01 7.83859789e-01 -9.33537126e-01 -2.26227835e-01 3.49173576e-01 -3.90561134e-01 2.90030032e-01 1.41091421e-01 3.03485692e-01 3.80113184e-01 -8.30604970e-01 2.70730317e-01 1.03279173e+00 8.23156297e-01 6.58320248e-01 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adf78187-2150-4fc1-8ef7-bc9adb00cac2	dual-branch-residual-network-for-lung-nodule	1905.08413	null	https://arxiv.org/abs/1905.08413v1	https://arxiv.org/pdf/1905.08413v1.pdf	Dual-branch residual network for lung nodule segmentation	An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: 1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; 2) we combine the features of the intensity and the convolution neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with an average dice score of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison showed that our average dice score was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist.	['Chih-Cheng Hung', 'Xiangyang Xu', 'Renchao Jin', 'Hong Liu', 'Haichao Cao', 'Enmin Song', 'Jianguo Lu', 'Guangzhi Ma']	2019-05-21	null	null	null	null	['lung-nodule-segmentation']	['medical']	[ 1.76701471e-02 -1.75734222e-01 -1.45658469e-02 -1.17590651e-01 -4.48348761e-01 -2.18617722e-01 1.76385820e-01 -1.39315501e-01 -6.01329744e-01 3.55332494e-01 6.24370761e-02 -1.69090420e-01 -9.08803344e-02 -8.06795835e-01 -1.77626386e-01 -9.56188500e-01 8.87755230e-02 2.32101217e-01 8.47368121e-01 2.26656601e-01 1.92435849e-02 8.01445067e-01 -1.00524473e+00 3.61093462e-01 8.26086760e-01 1.21048832e+00 4.55800444e-01 3.92313868e-01 -1.34495109e-01 7.58156061e-01 -1.92794651e-01 9.92379263e-02 4.02318686e-01 -5.30386209e-01 -7.34578967e-01 2.83420205e-01 4.04282287e-02 -4.65806842e-01 -4.48690891e-01 9.50555742e-01 4.32965189e-01 -3.95342968e-02 7.52123296e-01 -6.17235303e-01 -2.93483764e-01 2.96008199e-01 -7.87427187e-01 4.26139325e-01 -4.86240506e-01 1.93856567e-01 6.79118931e-01 -7.04586983e-01 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8.82965565e-01 -4.57751632e-01 -5.81799090e-01 -9.40710008e-01 -6.57575130e-01 3.60739142e-01 -2.30647484e-03 -3.80010217e-01 -4.28959906e-01 -2.73458868e-01]	[15.253607749938965, -2.1207754611968994]
7352a3d0-1abd-4952-bb61-c47e48cf3dfb	exploiting-inter-sample-affinity-for	2207.0928	null	https://arxiv.org/abs/2207.09280v4	https://arxiv.org/pdf/2207.09280v4.pdf	Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptation	Universal domain adaptation (UDA) aims to transfer the knowledge of common classes from the source domain to the target domain without any prior knowledge on the label set, which requires distinguishing in the target domain the unknown samples from the known ones. Recent methods usually focused on categorizing a target sample into one of the source classes rather than distinguishing known and unknown samples, which ignores the inter-sample affinity between known and unknown samples and may lead to suboptimal performance. Aiming at this issue, we propose a novel UDA framework where such inter-sample affinity is exploited. Specifically, we introduce a knowability-based labeling scheme which can be divided into two steps: 1) Knowability-guided detection of known and unknown samples based on the intrinsic structure of the neighborhoods of samples, where we leverage the first singular vectors of the affinity matrices to obtain the knowability of every target sample. 2) Label refinement based on neighborhood consistency to relabel the target samples, where we refine the labels of each target sample based on its neighborhood consistency of predictions. Then, auxiliary losses based on the two steps are used to reduce the inter-sample affinity between the unknown and the known target samples. Finally, experiments on four public datasets demonstrate that our method significantly outperforms existing state-of-the-art methods.	['Paul L. Rosin', 'Hongliang Li', 'Wei zhang', 'Ran Song', 'Lin Zhang', 'Yifan Wang']	2022-07-19	null	null	null	null	['universal-domain-adaptation']	['computer-vision']	[ 1.64226592e-01 -3.14890482e-02 -5.14622152e-01 -6.08391821e-01 -8.51257086e-01 -6.42614424e-01 4.26951617e-01 2.54755259e-01 -1.31011620e-01 6.52140796e-01 3.06839477e-02 1.88269794e-01 -2.64673591e-01 -9.73379970e-01 -7.12095976e-01 -9.49841976e-01 5.23216426e-01 5.84658027e-01 2.36673653e-01 1.74481466e-01 -6.85904846e-02 1.63364142e-01 -1.32808089e+00 1.94717765e-01 1.09435654e+00 1.30573404e+00 1.50727361e-01 -1.48013353e-01 -8.71952251e-02 5.58913291e-01 -2.56107330e-01 -2.09102392e-01 3.40907365e-01 -6.75838172e-01 -6.94970071e-01 4.08515841e-01 3.00465584e-01 -9.18805450e-02 -7.99848512e-02 1.35784626e+00 1.77733660e-01 1.66238293e-01 8.67120862e-01 -1.25052238e+00 -8.65489006e-01 4.77221221e-01 -5.63344240e-01 -1.24071397e-01 6.08445983e-03 -1.23174451e-01 1.12726355e+00 -8.96821499e-01 4.60322857e-01 1.18676853e+00 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e50add78-779e-4259-82ba-f4dda96ecc61	temporal-relational-hypergraph-tri-attention	2107.14033	null	https://arxiv.org/abs/2107.14033v2	https://arxiv.org/pdf/2107.14033v2.pdf	Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction	Predicting the future price trends of stocks is a challenging yet intriguing problem given its critical role to help investors make profitable decisions. In this paper, we present a collaborative temporal-relational modeling framework for end-to-end stock trend prediction. The temporal dynamics of stocks is firstly captured with an attention-based recurrent neural network. Then, different from existing studies relying on the pairwise correlations between stocks, we argue that stocks are naturally connected as a collective group, and introduce the hypergraph structures to jointly characterize the stock group-wise relationships of industry-belonging and fund-holding. A novel hypergraph tri-attention network (HGTAN) is proposed to augment the hypergraph convolutional networks with a hierarchical organization of intra-hyperedge, inter-hyperedge, and inter-hypergraph attention modules. In this manner, HGTAN adaptively determines the importance of nodes, hyperedges, and hypergraphs during the information propagation among stocks, so that the potential synergies between stock movements can be fully exploited. Extensive experiments on real-world data demonstrate the effectiveness of our approach. Also, the results of investment simulation show that our approach can achieve a more desirable risk-adjusted return. The data and codes of our work have been released at https://github.com/lixiaojieff/HGTAN.	['Yilong Yin', 'Meng Wang', 'Xiushan Nie', 'Chunyun Zhang', 'Juan Du', 'Xiaojie Li', 'Chaoran Cui']	2021-07-22	null	null	null	null	['stock-trend-prediction']	['time-series']	[-9.34105337e-01 -1.58601869e-02 -5.61950326e-01 -9.03108791e-02 1.00900404e-01 -6.59168422e-01 4.78960931e-01 -7.65599012e-02 2.08286002e-01 3.33017290e-01 7.21897542e-01 -4.26710039e-01 -5.38024127e-01 -1.23370755e+00 -7.01121986e-01 -5.10607123e-01 -5.92335641e-01 1.46666095e-01 3.78690898e-01 -4.61729705e-01 4.25502390e-01 1.75275788e-01 -9.80700254e-01 -1.98667310e-02 6.24479413e-01 1.13879943e+00 -5.22422269e-02 1.24565497e-01 -3.96915644e-01 1.28681934e+00 -1.96077332e-01 -8.76991510e-01 5.79881489e-01 -7.52980635e-02 -3.01368684e-01 1.56661794e-01 -9.80199352e-02 -3.55407536e-01 -8.95723164e-01 1.15431774e+00 6.17975220e-02 -1.24040328e-01 4.17528182e-01 -1.22833848e+00 -1.09094369e+00 1.39703918e+00 -9.01963770e-01 6.45709395e-01 -3.91365856e-01 1.10238530e-01 1.63743305e+00 -6.21768057e-01 3.96412283e-01 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c7a8fd93-46d4-4fce-a241-1c4e8043a7d3	group-anomaly-detection-using-flexible-genre	null	null	http://papers.nips.cc/paper/4299-group-anomaly-detection-using-flexible-genre-models	http://papers.nips.cc/paper/4299-group-anomaly-detection-using-flexible-genre-models.pdf	Group Anomaly Detection using Flexible Genre Models	An important task in exploring and analyzing real-world data sets is to detect unusual and interesting phenomena. In this paper, we study the group anomaly detection problem. Unlike traditional anomaly detection research that focuses on data points, our goal is to discover anomalous aggregated behaviors of groups of points. For this purpose, we propose the Flexible Genre Model (FGM). FGM is designed to characterize data groups at both the point level and the group level so as to detect various types of group anomalies. We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data, and show that it is superior to existing approaches in detecting group anomalies.	['Barnabás Póczos', 'Liang Xiong', 'Jeff G. Schneider']	2011-12-01	null	null	null	neurips-2011-12	['group-anomaly-detection']	['methodology']	[-7.06534758e-02 -5.85033715e-01 3.31779540e-01 -1.66807607e-01 -3.81783843e-02 -4.41188246e-01 5.95776498e-01 8.81010771e-01 1.47193030e-01 3.20517004e-01 -6.37065694e-02 -3.85921091e-01 -4.05564368e-01 -7.09562480e-01 -1.77779362e-01 -6.60733700e-01 -9.23874080e-01 1.19874157e-01 4.87151533e-01 -2.07919493e-01 7.00713873e-01 9.20207560e-01 -1.68457198e+00 1.18650101e-01 1.05208194e+00 1.07047760e+00 -3.10419858e-01 5.82242131e-01 -3.48379999e-01 6.78742707e-01 -9.58241463e-01 2.06527844e-01 3.29537809e-01 -2.85417289e-01 -5.08243144e-01 5.60175955e-01 2.47094601e-01 -1.28131941e-01 1.02010936e-01 1.09636068e+00 1.90714970e-01 4.92992073e-01 6.95509851e-01 -1.78980339e+00 -1.39110103e-01 -1.46860080e-02 -9.04609680e-01 9.93975937e-01 3.07368100e-01 -7.75710260e-03 8.51438522e-01 -6.67725682e-01 5.95113598e-02 1.20244896e+00 3.55049163e-01 2.10831657e-01 -1.08719146e+00 -4.33127522e-01 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7d4496eb-13b7-4769-b151-555430b65db1	squibs-arc-eager-parsing-with-the-tree	null	null	https://aclanthology.org/J14-2002	https://aclanthology.org/J14-2002.pdf	Squibs: Arc-Eager Parsing with the Tree Constraint	null	["Daniel Fern{\\'a}ndez-Gonz{\\'a}lez", 'Joakim Nivre']	2014-06-01	null	null	null	cl-2014-6	['transition-based-dependency-parsing']	['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 -8.59008506e-02 -2.09758401e-01 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77278626-9140-46da-b9b4-a2b475622976	leveraging-open-information-extraction-for	2305.14163	null	https://arxiv.org/abs/2305.14163v1	https://arxiv.org/pdf/2305.14163v1.pdf	Leveraging Open Information Extraction for Improving Few-Shot Trigger Detection Domain Transfer	Event detection is a crucial information extraction task in many domains, such as Wikipedia or news. The task typically relies on trigger detection (TD) -- identifying token spans in the text that evoke specific events. While the notion of triggers should ideally be universal across domains, domain transfer for TD from high- to low-resource domains results in significant performance drops. We address the problem of negative transfer for TD by coupling triggers between domains using subject-object relations obtained from a rule-based open information extraction (OIE) system. We demonstrate that relations injected through multi-task training can act as mediators between triggers in different domains, enhancing zero- and few-shot TD domain transfer and reducing negative transfer, in particular when transferring from a high-resource source Wikipedia domain to a low-resource target news domain. Additionally, we combine the extracted relations with masked language modeling on the target domain and obtain further TD performance gains. Finally, we demonstrate that the results are robust to the choice of the OIE system.	['Jan Šnajder', 'Goran Glavaš', 'Kiril Gashteovski', 'David Dukić']	2023-05-23	null	null	null	null	['open-information-extraction']	['natural-language-processing']	[ 2.31679827e-01 3.51635098e-01 -1.99446887e-01 -2.58609444e-01 -1.15839481e+00 -8.72264028e-01 9.52070951e-01 5.37293673e-01 -7.22954929e-01 9.93543267e-01 3.67937624e-01 3.74316685e-02 6.21389924e-03 -1.01746941e+00 -8.45914364e-01 -2.69006509e-02 -4.18295681e-01 5.99825323e-01 6.74542069e-01 -4.16965604e-01 -2.39403367e-01 2.05014393e-01 -1.26652479e+00 7.34196007e-01 8.33872855e-01 8.36953163e-01 -5.10049239e-02 1.49531171e-01 -4.32414591e-01 9.73624885e-01 -7.51965523e-01 -4.24660146e-01 1.80033103e-01 -1.43737927e-01 -1.05117929e+00 -4.38795865e-01 -7.55368620e-02 1.31982595e-01 -2.35184446e-01 7.64961839e-01 4.38606679e-01 1.76132947e-01 7.40850866e-01 -1.28853893e+00 -3.49229068e-01 1.00081861e+00 -1.94751605e-01 5.75809300e-01 4.61985409e-01 -1.63311198e-01 1.12757730e+00 -1.08088982e+00 1.18877506e+00 1.04584348e+00 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1.74867496e-01 -2.78771818e-01 -3.40377390e-01 -9.88733232e-01 -4.24522251e-01 6.01018429e-01 1.23942316e-01 -8.26649904e-01 -3.61635629e-03 3.07968467e-01]	[9.253031730651855, 9.189151763916016]
2a26f4c3-1c4f-4fd4-9ef4-e09b2f03b8c1	where-does-trust-break-down-a-quantitative	2009.14701	null	https://arxiv.org/abs/2009.14701v1	https://arxiv.org/pdf/2009.14701v1.pdf	Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities	The advances and successes in deep learning in recent years have led to considerable efforts and investments into its widespread ubiquitous adoption for a wide variety of applications, ranging from personal assistants and intelligent navigation to search and product recommendation in e-commerce. With this tremendous rise in deep learning adoption comes questions about the trustworthiness of the deep neural networks that power these applications. Motivated to answer such questions, there has been a very recent interest in trust quantification. In this work, we introduce the concept of trust matrix, a novel trust quantification strategy that leverages the recently introduced question-answer trust metric by Wong et al. to provide deeper, more detailed insights into where trust breaks down for a given deep neural network given a set of questions. More specifically, a trust matrix defines the expected question-answer trust for a given actor-oracle answer scenario, allowing one to quickly spot areas of low trust that needs to be addressed to improve the trustworthiness of a deep neural network. The proposed trust matrix is simple to calculate, humanly interpretable, and to the best of the authors' knowledge is the first to study trust at the actor-oracle answer level. We further extend the concept of trust densities with the notion of conditional trust densities. We experimentally leverage trust matrices to study several well-known deep neural network architectures for image recognition, and further study the trust density and conditional trust densities for an interesting actor-oracle answer scenario. The results illustrate that trust matrices, along with conditional trust densities, can be useful tools in addition to the existing suite of trust quantification metrics for guiding practitioners and regulators in creating and certifying deep learning solutions for trusted operation.	['Andrew Hryniowski', 'Alexander Wong', 'Xiao Yu Wang']	2020-09-30	null	null	null	null	['product-recommendation']	['miscellaneous']	[-4.82626170e-01 2.96031743e-01 -4.57631312e-02 -9.42061007e-01 -3.08894426e-01 -6.14233017e-01 4.87370521e-01 2.93296456e-01 -4.94301170e-01 3.35805237e-01 -6.73942268e-02 -6.93290830e-01 -3.27571929e-01 -5.40964663e-01 -7.82013595e-01 -3.13923419e-01 -1.29840508e-01 8.12775567e-02 4.03074212e-02 -1.33081362e-01 1.37088612e-01 3.28115553e-01 -1.22215319e+00 4.33848053e-02 8.74119639e-01 1.60653746e+00 -4.74785447e-01 4.26818103e-01 4.01664078e-01 1.31285238e+00 -2.72137135e-01 -1.27752519e+00 2.66845226e-01 -1.01107508e-01 -9.55290437e-01 -4.98291016e-01 3.29731315e-01 -7.09840178e-01 -2.01385915e-01 1.17951715e+00 1.03283249e-01 -3.85379978e-02 2.74409950e-01 -1.70307815e+00 -1.06579828e+00 7.79665947e-01 -5.34850769e-02 2.35042080e-01 1.16109580e-01 -1.96674485e-02 1.67999136e+00 -8.48817885e-01 8.51266682e-02 9.20554399e-01 9.19787228e-01 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-7.16499209e-01 -6.75088882e-01 4.94873412e-02 4.12985891e-01 -5.45366347e-01 -6.26185775e-01 -5.45799315e-01]	[9.186840057373047, 3.7873659133911133]
312e04ba-58f2-4f5d-9584-f7352de4cf37	empirical-study-of-text-augmentation-on	2009.12319	null	https://arxiv.org/abs/2009.12319v2	https://arxiv.org/pdf/2009.12319v2.pdf	Empirical Study of Text Augmentation on Social Media Text in Vietnamese	In the text classification problem, the imbalance of labels in datasets affect the performance of the text-classification models. Practically, the data about user comments on social networking sites not altogether appeared - the administrators often only allow positive comments and hide negative comments. Thus, when collecting the data about user comments on the social network, the data is usually skewed about one label, which leads the dataset to become imbalanced and deteriorate the model's ability. The data augmentation techniques are applied to solve the imbalance problem between classes of the dataset, increasing the prediction model's accuracy. In this paper, we performed augmentation techniques on the VLSP2019 Hate Speech Detection on Vietnamese social texts and the UIT - VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis. The result of augmentation increases by about 1.5% in the F1-macro score on both corpora.	['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Son T. Luu']	2020-09-25	null	null	null	null	['text-augmentation']	['natural-language-processing']	[-1.15302205e-01 3.22082996e-01 -3.30024719e-01 -4.54649121e-01 -3.26653451e-01 -4.44169164e-01 1.73052356e-01 6.94141924e-01 -4.28346008e-01 7.56288946e-01 1.98007971e-01 -3.92361253e-01 4.96864319e-01 -5.31599462e-01 -1.97315276e-01 -5.26092350e-01 5.60415745e-01 1.93632454e-01 9.33324099e-02 -4.17746931e-01 2.85291463e-01 2.11444870e-02 -1.49930632e+00 6.05600178e-01 1.10663331e+00 7.44410396e-01 -2.91898727e-01 4.97930259e-01 -4.36467439e-01 1.12521422e+00 -1.07714057e+00 -8.83432388e-01 -1.28311262e-01 -3.55580449e-01 -7.45489478e-01 1.80297762e-01 2.64968842e-01 2.39276495e-02 -1.43473387e-01 1.24781585e+00 3.84727448e-01 -7.19537213e-03 7.60292947e-01 -1.58837259e+00 -5.93446374e-01 1.03916395e+00 -8.74315500e-01 3.54400426e-01 3.30474079e-02 -3.36624742e-01 1.10188186e+00 -7.87846386e-01 6.09658003e-01 9.70298231e-01 4.82881308e-01 6.58101201e-01 -9.83538926e-01 -9.68546748e-01 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6.57636106e-01 3.74268383e-01 -3.19717437e-01 -8.01562011e-01 -6.59836084e-02]	[8.914281845092773, 10.369072914123535]
40a7b546-40b9-4030-b139-4fa4e13a0c6c	fall-e-a-foley-sound-synthesis-model-and	2306.09807	null	https://arxiv.org/abs/2306.09807v1	https://arxiv.org/pdf/2306.09807v1.pdf	FALL-E: A Foley Sound Synthesis Model and Strategies	This paper introduces FALL-E, a foley synthesis system and its training/inference strategies. The FALL-E model employs a cascaded approach comprising low-resolution spectrogram generation, spectrogram super-resolution, and a vocoder. We trained every sound-related model from scratch using our extensive datasets, and utilized a pre-trained language model. We conditioned the model with dataset-specific texts, enabling it to learn sound quality and recording environment based on text input. Moreover, we leveraged external language models to improve text descriptions of our datasets and performed prompt engineering for quality, coherence, and diversity. FALL-E was evaluated by an objective measure as well as listening tests in the DCASE 2023 challenge Task 7. The submission achieved the second place on average, while achieving the best score for diversity, second place for audio quality, and third place for class fitness.	['Ben Sangbae Chon', 'Kyungyun Lee', 'Hyeongi Moon', 'Sangshin Oh', 'Minsung Kang']	2023-06-16	null	null	null	null	['super-resolution', 'prompt-engineering']	['computer-vision', 'natural-language-processing']	[ 1.76931292e-01 -5.77345118e-02 5.99841550e-02 -2.60580719e-01 -1.49511445e+00 -5.72879136e-01 4.66179788e-01 -2.19151136e-02 -1.33657902e-01 5.20117402e-01 7.77740061e-01 -1.01530962e-01 -1.08817011e-01 -4.78475600e-01 -6.55154228e-01 -3.87645304e-01 -8.56961682e-02 1.43468738e-01 -3.91304074e-03 -1.56465873e-01 2.83322185e-01 3.34402844e-02 -1.76665270e+00 5.26434422e-01 8.72662723e-01 1.18714094e+00 3.49380106e-01 1.44240737e+00 2.23537788e-01 9.91565585e-01 -8.17132473e-01 -2.96727866e-01 4.84181158e-02 -7.07843900e-01 -5.04410386e-01 -2.09689111e-01 5.65133870e-01 -2.23844245e-01 -2.73869693e-01 6.24332547e-01 1.03033328e+00 4.02877778e-01 5.43874085e-01 -8.75849366e-01 -3.45026433e-01 1.23459888e+00 2.54269987e-01 7.67748654e-02 6.45446420e-01 4.14534152e-01 1.40269470e+00 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8562cf0a-7aca-4930-83d4-31f93b8d5742	ems-efficient-and-effective-massively	2205.15744	null	https://arxiv.org/abs/2205.15744v1	https://arxiv.org/pdf/2205.15744v1.pdf	EMS: Efficient and Effective Massively Multilingual Sentence Representation Learning	Massively multilingual sentence representation models, e.g., LASER, SBERT-distill, and LaBSE, help significantly improve cross-lingual downstream tasks. However, multiple training procedures, the use of a large amount of data, or inefficient model architectures result in heavy computation to train a new model according to our preferred languages and domains. To resolve this issue, we introduce efficient and effective massively multilingual sentence representation learning (EMS), using cross-lingual sentence reconstruction (XTR) and sentence-level contrastive learning as training objectives. Compared with related studies, the proposed model can be efficiently trained using significantly fewer parallel sentences and GPU computation resources without depending on large-scale pre-trained models. Empirical results show that the proposed model significantly yields better or comparable results with regard to bi-text mining, zero-shot cross-lingual genre classification, and sentiment classification. Ablative analyses demonstrate the effectiveness of each component of the proposed model. We release the codes for model training and the EMS pre-trained model, which supports 62 languages (https://github.com/Mao-KU/EMS).	['Sadao Kurohashi', 'Chenhui Chu', 'Zhuoyuan Mao']	2022-05-31	null	null	null	null	['genre-classification']	['computer-vision']	[-1.00399099e-01 -5.72595716e-01 -3.88672799e-01 -4.68327910e-01 -1.45570064e+00 -4.60117429e-01 4.11697835e-01 2.20813423e-01 -5.68086565e-01 6.97318137e-01 2.59040564e-01 -5.07033527e-01 2.39137664e-01 -5.36398530e-01 -6.37426376e-01 -3.92813146e-01 3.12376350e-01 2.83479899e-01 -3.70920569e-01 -4.32721823e-01 3.61580789e-01 -4.20907438e-02 -1.50758529e+00 6.92428350e-01 1.12847877e+00 8.33875895e-01 5.52804589e-01 5.84960163e-01 -6.10510945e-01 8.57521594e-01 -4.17029619e-01 -8.65988314e-01 -2.01201476e-02 -2.43454620e-01 -7.21842825e-01 -9.34119746e-02 3.27470243e-01 1.07775636e-01 2.03536540e-01 1.04715633e+00 5.96633255e-01 6.38422072e-02 4.49857205e-01 -5.84923506e-01 -6.12524986e-01 9.73013163e-01 -8.07089984e-01 1.37403101e-01 3.92086476e-01 -2.99315959e-01 1.05162215e+00 -1.34854078e+00 6.14387810e-01 1.20299280e+00 8.93960595e-01 2.82220989e-01 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6f1c590a-2218-4e9d-ba4f-71b697f631ea	remove-noise-and-keep-truth-a-noisy-channel	null	null	https://openreview.net/forum?id=B_hhBeNshop	https://openreview.net/pdf?id=B_hhBeNshop	Remove Noise and Keep Truth: A Noisy Channel Model for Semantic Role Labeling	Semantic role labeling usually models structures using sequences, trees, or graphs. Past works focused on researching novel modeling methods and neural structures and integrating more features. In this paper, we re-examined the noise in neural semantic role labeling models, a problem that has been long-ignored. By proposing a noisy channel model structure, we effectively eliminate the noise in the labeling flow and thus improve performance. Without relying on additional features, our proposed novel model significantly outperforms a strong baseline on multiple popular semantic role labeling benchmarks, which demonstrates the effectiveness and robustness of our proposed model.	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f1bef88e-b22e-4d41-8ba3-984052155a8c	riemannian-information-gradient-methods-for	2011.02806	null	https://arxiv.org/abs/2011.02806v1	https://arxiv.org/pdf/2011.02806v1.pdf	Riemannian information gradient methods for the parameter estimation of ECD: Some applications in image processing	Elliptically-contoured distributions (ECD) play a significant role, in computer vision, image processing, radar, and biomedical signal processing. Maximum likelihood. estimation (MLE) of ECD leads to a system of non-linear equations, most-often addressed using fixed-point (FP) methods. Unfortunately, the computation time required for these methods is unacceptably long, for large-scale or high-dimensional datasets. To overcome this difficulty, the present work introduces a Riemannian optimisation method, the information stochastic gradient (ISG). The ISG is an online (recursive) method, which achieves the same performance as MLE, for large-scale datasets, while requiring modest memory and time resources. To develop the ISG method, the Riemannian information gradient is derived taking into account the product manifold associated to the underlying parameter space of the ECD. From this information gradient definition, we define also, the information deterministic gradient (IDG), an offline (batch) method, which is an alternative, for moderate-sized datasets. The present work formulates these two methods, and demonstrates their performance through numerical simulations. Two applications, to image re-colorization, and to texture classification, are also worked out.	['Yannick Berthoumieu', 'Salem Said', 'Jialun Zhou']	2020-11-05	null	null	null	null	['texture-classification']	['computer-vision']	[ 1.10672072e-01 -1.24808915e-01 2.10143983e-01 -1.13337584e-01 -6.59361959e-01 -3.54622602e-01 5.27810454e-01 -8.25205892e-02 -5.39049268e-01 5.33833027e-01 -3.17759782e-01 -3.45051140e-01 -3.85125786e-01 -3.87459487e-01 -2.74626046e-01 -8.52112293e-01 -3.20993215e-01 1.93812698e-02 -1.20172866e-01 1.78657234e-01 4.65972185e-01 7.77136087e-01 -1.17845762e+00 -5.67201972e-01 9.33806181e-01 9.89027381e-01 2.34174788e-01 6.64066136e-01 -1.39692441e-01 1.48685634e-01 -1.76753536e-01 -4.59858209e-01 2.79380649e-01 -3.72419506e-01 -5.51810265e-01 5.21243632e-01 2.33524442e-01 6.82606846e-02 -1.57304481e-03 1.22011447e+00 4.51304644e-01 3.42023879e-01 1.02094305e+00 -1.13111651e+00 -3.37299198e-01 -2.32288599e-01 -6.33351803e-01 7.18850195e-02 -3.47264707e-02 -3.62753540e-01 5.53165138e-01 -1.37614226e+00 4.43034351e-01 1.07614625e+00 6.04614258e-01 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0c1193d8-d357-4c1c-9d7d-560b0cbde17d	compressing-audio-cnns-with-graph-centrality	2305.03391	null	https://arxiv.org/abs/2305.03391v1	https://arxiv.org/pdf/2305.03391v1.pdf	Compressing audio CNNs with graph centrality based filter pruning	Convolutional neural networks (CNNs) are commonplace in high-performing solutions to many real-world problems, such as audio classification. CNNs have many parameters and filters, with some having a larger impact on the performance than others. This means that networks may contain many unnecessary filters, increasing a CNN's computation and memory requirements while providing limited performance benefits. To make CNNs more efficient, we propose a pruning framework that eliminates filters with the highest "commonality". We measure this commonality using the graph-theoretic concept of "centrality". We hypothesise that a filter with a high centrality should be eliminated as it represents commonality and can be replaced by other filters without affecting the performance of a network much. An experimental evaluation of the proposed framework is performed on acoustic scene classification and audio tagging. On the DCASE 2021 Task 1A baseline network, our proposed method reduces computations per inference by 71\% with 50\% fewer parameters at less than a two percentage point drop in accuracy compared to the original network. For large-scale CNNs such as PANNs designed for audio tagging, our method reduces 24\% computations per inference with 41\% fewer parameters at a slight improvement in performance.	['Mark D. Plumbley', 'Arshdeep Singh', 'James A King']	2023-05-05	null	null	null	null	['audio-tagging', 'acoustic-scene-classification', 'audio-classification', 'scene-classification']	['audio', 'audio', 'audio', 'computer-vision']	[ 8.12264234e-02 2.36928627e-01 3.05066437e-01 -4.63109553e-01 -3.45234364e-01 -3.61428857e-01 -7.39902258e-02 4.17348146e-01 -8.82246912e-01 3.88716757e-01 1.28058374e-01 -3.96519423e-01 -1.58282697e-01 -9.88042593e-01 -6.59050286e-01 -5.30345678e-01 -2.16872796e-01 -6.21277504e-02 5.67882478e-01 -1.25894602e-02 -1.38186157e-01 2.75314659e-01 -1.47185040e+00 3.22583199e-01 2.84849823e-01 1.31644595e+00 1.25137970e-01 5.56207597e-01 8.76024440e-02 5.85333526e-01 -8.22290123e-01 -7.54679143e-01 2.99788803e-01 -9.23756063e-02 -6.75474942e-01 -4.70402837e-01 7.03272879e-01 -3.06344479e-02 -4.41827595e-01 1.29474425e+00 7.21896291e-01 1.50928959e-01 3.39908451e-01 -1.11203825e+00 2.17950493e-01 1.15953887e+00 -3.05987716e-01 3.08342487e-01 -2.52781153e-01 -1.56833857e-01 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dcf05b31-ad61-4b3a-8368-569937177864	joint-diacritization-lemmatization	1910.02267	null	https://arxiv.org/abs/1910.02267v1	https://arxiv.org/pdf/1910.02267v1.pdf	Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging	Semitic languages can be highly ambiguous, having several interpretations of the same surface forms, and morphologically rich, having many morphemes that realize several morphological features. This is further exacerbated for dialectal content, which is more prone to noise and lacks a standard orthography. The morphological features can be lexicalized, like lemmas and diacritized forms, or non-lexicalized, like gender, number, and part-of-speech tags, among others. Joint modeling of the lexicalized and non-lexicalized features can identify more intricate morphological patterns, which provide better context modeling, and further disambiguate ambiguous lexical choices. However, the different modeling granularity can make joint modeling more difficult. Our approach models the different features jointly, whether lexicalized (on the character-level), where we also model surface form normalization, or non-lexicalized (on the word-level). We use Arabic as a test case, and achieve state-of-the-art results for Modern Standard Arabic, with 20% relative error reduction, and Egyptian Arabic (a dialectal variant of Arabic), with 11% reduction.	['Nizar Habash', 'Nasser Zalmout']	2019-10-05	joint-diacritization-lemmatization-1	https://aclanthology.org/2020.acl-main.736	https://aclanthology.org/2020.acl-main.736.pdf	acl-2020-6	['morphological-tagging']	['natural-language-processing']	[-1.60509631e-01 -3.87760878e-01 -1.43724352e-01 -3.85352314e-01 -4.92872953e-01 -1.01112711e+00 4.88854200e-01 4.96915877e-01 -6.33693933e-01 5.31073570e-01 2.79327899e-01 -4.84406024e-01 1.03998259e-01 -1.12492454e+00 -1.72477141e-01 -7.32456923e-01 1.18517369e-01 4.45305854e-01 5.67066111e-02 -7.18598485e-01 2.01459393e-01 3.06353927e-01 -1.47608185e+00 4.10989076e-01 1.22250283e+00 6.05345845e-01 1.07975453e-01 -2.99258940e-02 -5.35497606e-01 4.20495905e-02 -6.69967532e-01 -8.36147726e-01 9.47172344e-02 -1.35287181e-01 -5.52210212e-01 7.41195232e-02 5.17127752e-01 2.56937027e-01 2.49873832e-01 1.45424473e+00 1.63278863e-01 -9.40849185e-02 8.66726577e-01 -5.49315095e-01 -8.60293567e-01 8.28269720e-01 -6.84753299e-01 -6.47506416e-02 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b5034025-2071-47ce-ad25-08118622df08	towards-the-evaluation-of-simultaneous-speech	2103.08364	null	https://arxiv.org/abs/2103.08364v2	https://arxiv.org/pdf/2103.08364v2.pdf	Towards the evaluation of automatic simultaneous speech translation from a communicative perspective	In recent years, automatic speech-to-speech and speech-to-text translation has gained momentum thanks to advances in artificial intelligence, especially in the domains of speech recognition and machine translation. The quality of such applications is commonly tested with automatic metrics, such as BLEU, primarily with the goal of assessing improvements of releases or in the context of evaluation campaigns. However, little is known about how the output of such systems is perceived by end users or how they compare to human performances in similar communicative tasks. In this paper, we present the results of an experiment aimed at evaluating the quality of a real-time speech translation engine by comparing it to the performance of professional simultaneous interpreters. To do so, we adopt a framework developed for the assessment of human interpreters and use it to perform a manual evaluation on both human and machine performances. In our sample, we found better performance for the human interpreters in terms of intelligibility, while the machine performs slightly better in terms of informativeness. The limitations of the study and the possible enhancements of the chosen framework are discussed. Despite its intrinsic limitations, the use of this framework represents a first step towards a user-centric and communication-oriented methodology for evaluating real-time automatic speech translation.	['Bianca Prandi', 'claudio Fantinuoli']	2021-03-15	null	https://aclanthology.org/2021.iwslt-1.29	https://aclanthology.org/2021.iwslt-1.29.pdf	acl-iwslt-2021-8	['speech-to-text-translation']	['natural-language-processing']	[ 2.91635275e-01 3.75162840e-01 2.18900830e-01 -4.16861743e-01 -9.96575713e-01 -5.95180929e-01 1.00708461e+00 4.68167394e-01 -6.87490284e-01 5.87315142e-01 5.30830204e-01 -6.30786777e-01 2.56181322e-02 -4.01600391e-01 -3.15756738e-01 -3.76934230e-01 4.05078918e-01 7.73141086e-01 2.29393512e-01 -4.58782643e-01 2.05095649e-01 3.62672240e-01 -1.45642138e+00 3.76835197e-01 8.67800772e-01 3.73521149e-01 3.39026570e-01 6.43071771e-01 -7.92247429e-02 4.86372918e-01 -9.47015047e-01 -6.04571104e-01 -1.45935699e-01 -6.77042484e-01 -1.11637831e+00 1.70610741e-01 -8.87494683e-02 -1.28022492e-01 -1.33841354e-02 8.36599767e-01 6.42314196e-01 -3.80540751e-02 3.31582397e-01 -6.25096977e-01 -2.37045094e-01 6.73309147e-01 5.96957862e-01 1.26221403e-01 8.69567215e-01 3.58629078e-01 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9670cec3-127a-4eea-bf92-1d32ec5c2aa6	multimodal-automated-fact-checking-a-survey	2305.13507	null	https://arxiv.org/abs/2305.13507v2	https://arxiv.org/pdf/2305.13507v2.pdf	Multimodal Automated Fact-Checking: A Survey	Misinformation, i.e. factually incorrect information, is often conveyed in multiple modalities, e.g. an image accompanied by a caption. It is perceived as more credible by humans, and spreads faster and wider than its text-only counterparts. While an increasing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on textual misinformation. In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation. Furthermore, we discuss related terminological developed in different communities in the context of our framework. We focus on four modalities prevalent in real-world fact-checking: text, image, audio, and video. We survey benchmarks and models, and discuss limitations and promising directions for future research.	['Vlachos Andreas', 'Simperl Elena', 'Cocarascu Oana', 'Guo Zhijiang', 'Schlichtkrull Michael', 'Akhtar Mubashara']	2023-05-22	null	null	null	null	['misinformation']	['miscellaneous']	[ 3.47472370e-01 5.99384964e-01 -3.60916823e-01 -2.07713142e-01 -1.13919759e+00 -9.20597494e-01 1.25775087e+00 7.78176606e-01 -1.84156924e-01 8.04861546e-01 7.76210070e-01 -3.74853164e-01 2.37587288e-01 -2.99957365e-01 -8.99455786e-01 -1.38433680e-01 2.30878606e-01 8.12442005e-02 2.40676016e-01 -1.77493960e-01 7.41957486e-01 -1.67537600e-01 -1.61789358e+00 7.68201828e-01 6.12663150e-01 1.01653314e+00 -4.28579539e-01 4.11963582e-01 -3.89994472e-01 1.40744627e+00 -9.70925748e-01 -1.31204188e+00 -3.26799512e-01 -4.27851915e-01 -1.10045040e+00 5.30103683e-01 1.21172714e+00 -3.96234185e-01 -2.28397086e-01 1.54223037e+00 5.69801815e-02 -3.31645966e-01 5.95054984e-01 -1.57590938e+00 -9.18255746e-01 8.10644388e-01 -6.38026536e-01 1.16123587e-01 1.14980185e+00 -9.41353962e-02 6.74175918e-01 -9.37348008e-01 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da33874d-301d-4225-b5ac-cdbfa91bae79	learning-to-segment-object-candidates-via	1612.01057	null	http://arxiv.org/abs/1612.01057v4	http://arxiv.org/pdf/1612.01057v4.pdf	Learning to Segment Object Candidates via Recursive Neural Networks	To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images. In this paper, we present a simple yet effective approach for segmenting object proposals via a deep architecture of recursive neural networks (ReNNs), which hierarchically groups regions for detecting object candidates over scales. Unlike traditional methods that mainly adopt fixed similarity measures for merging regions or finding object proposals, our approach adaptively learns the region merging similarity and the objectness measure during the process of hierarchical region grouping. Specifically, guided by a structured loss, the ReNN model jointly optimizes the cross-region similarity metric with the region merging process as well as the objectness prediction. During inference of the object proposal generation, we introduce randomness into the greedy search to cope with the ambiguity of grouping regions. Extensive experiments on standard benchmarks, e.g., PASCAL VOC and ImageNet, suggest that our approach is capable of producing object proposals with high recall while well preserving the object boundaries and outperforms other existing methods in both accuracy and efficiency.	['Xian Wu', 'Liang Lin', 'Xiaonan Luo', 'Tianshui Chen', 'Nong Xiao']	2016-12-04	null	null	null	null	['object-proposal-generation']	['computer-vision']	[ 7.30579868e-02 -1.26446029e-02 -2.52802104e-01 -6.69717550e-01 -7.00031281e-01 -4.48218882e-01 4.70304251e-01 4.48318005e-01 -7.27258205e-01 2.00670660e-01 -3.14322323e-01 -1.23904213e-01 4.19349410e-02 -8.47688913e-01 -7.89084613e-01 -4.94960904e-01 -1.55627400e-01 5.12997210e-01 1.04246485e+00 9.62400287e-02 6.38852954e-01 8.17966938e-01 -1.70021021e+00 1.48823932e-01 8.56820703e-01 1.30327225e+00 4.07970965e-01 5.48965335e-01 -2.74181187e-01 5.35846949e-01 -6.18075550e-01 -3.31984997e-01 6.24448478e-01 -1.42200604e-01 -8.96637499e-01 3.15404952e-01 6.96586788e-01 -5.26403725e-01 -1.66272804e-01 1.14533246e+00 4.27207202e-01 3.05265695e-01 4.59784508e-01 -1.16425240e+00 -3.19297284e-01 7.51812458e-01 -7.27886260e-01 2.40193605e-01 -2.79664665e-01 2.27251947e-01 1.12369585e+00 -1.12340951e+00 5.25705040e-01 1.41812289e+00 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10ec8ae4-5833-412a-b16b-dcd7820e79d8	federated-prompting-and-chain-of-thought	2304.13911	null	https://arxiv.org/abs/2304.13911v2	https://arxiv.org/pdf/2304.13911v2.pdf	Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering	We investigate how to enhance answer precision in frequently asked questions posed by distributed users using cloud-based Large Language Models (LLMs). Our study focuses on a typical situations where users ask similar queries that involve identical mathematical reasoning steps and problem-solving procedures. Due to the unsatisfactory accuracy of LLMs' zero-shot prompting with standalone questions, we propose to improve the distributed synonymous questions using Self-Consistency (SC) and Chain-of-Thought (CoT) techniques. Specifically, we first retrieve synonymous questions from a crowd-sourced database and create a federated question pool. We call these federated synonymous questions with the same or different parameters SP-questions or DP-questions, respectively. We refer to our methods as Fed-SP-SC and Fed-DP-CoT, which can generate significantly more accurate answers for all user queries without requiring sophisticated model-tuning. Through extensive experiments, we demonstrate that our proposed methods can significantly enhance question accuracy by fully exploring the synonymous nature of the questions and the consistency of the answers.	['Chenyou Fan', 'Tianqi Pang', 'Xiangyang Liu']	2023-04-27	null	null	null	null	['mathematical-reasoning']	['natural-language-processing']	[-5.29313266e-01 -3.30886841e-01 2.43986666e-01 -3.56268436e-01 -1.10340178e+00 -8.05692554e-01 5.82168400e-01 1.88396052e-01 -3.54493320e-01 6.38573825e-01 2.32381701e-01 -4.56674039e-01 -3.56449693e-01 -7.24737942e-01 -3.50880831e-01 5.51441535e-02 7.03093827e-01 7.46176839e-01 6.69683099e-01 -5.17658949e-01 5.16888797e-01 1.70909315e-01 -1.61414790e+00 5.81093013e-01 1.65701544e+00 8.70529413e-01 2.68966734e-01 1.15407312e+00 -1.18469405e+00 1.59567618e+00 -8.35000157e-01 -9.63944554e-01 3.08371186e-02 -4.44879264e-01 -1.16101253e+00 -5.23789644e-01 4.85037118e-01 -3.70001584e-01 -8.64190832e-02 1.05024445e+00 5.00003874e-01 3.43227655e-01 2.36564890e-01 -1.31069767e+00 -1.24212098e+00 1.53678894e-01 5.46466261e-02 4.04781133e-01 9.82844710e-01 -1.26881222e-03 1.05221057e+00 -9.18109000e-01 4.14008617e-01 1.34279573e+00 5.65623879e-01 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10d008eb-47d6-4b3b-921a-28c9b83d7a73	you-can-t-count-on-luck-why-decision	2205.15967	null	https://arxiv.org/abs/2205.15967v2	https://arxiv.org/pdf/2205.15967v2.pdf	You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments	Recently, methods such as Decision Transformer that reduce reinforcement learning to a prediction task and solve it via supervised learning (RvS) have become popular due to their simplicity, robustness to hyperparameters, and strong overall performance on offline RL tasks. However, simply conditioning a probabilistic model on a desired return and taking the predicted action can fail dramatically in stochastic environments since trajectories that result in a return may have only achieved that return due to luck. In this work, we describe the limitations of RvS approaches in stochastic environments and propose a solution. Rather than simply conditioning on the return of a single trajectory as is standard practice, our proposed method, ESPER, learns to cluster trajectories and conditions on average cluster returns, which are independent from environment stochasticity. Doing so allows ESPER to achieve strong alignment between target return and expected performance in real environments. We demonstrate this in several challenging stochastic offline-RL tasks including the challenging puzzle game 2048, and Connect Four playing against a stochastic opponent. In all tested domains, ESPER achieves significantly better alignment between the target return and achieved return than simply conditioning on returns. ESPER also achieves higher maximum performance than even the value-based baselines.	['Jimmy Ba', 'Sheila Mcilraith', 'Keiran Paster']	2022-05-31	null	null	null	null	['2048']	['playing-games']	[-1.43366521e-02 -1.84493698e-02 -2.28597417e-01 -3.52559507e-01 -1.34421945e+00 -8.37048948e-01 5.10657847e-01 6.87426627e-02 -6.91987634e-01 1.02898479e+00 2.10466068e-02 -3.42842042e-01 -4.40596700e-01 -7.85982013e-01 -9.68080282e-01 -7.15234816e-01 -5.53474724e-01 7.94353664e-01 3.51325691e-01 -2.44344845e-01 2.25522369e-01 2.77751386e-02 -1.49993670e+00 3.14998567e-01 7.42013574e-01 8.22027385e-01 3.08530420e-01 9.70582187e-01 2.60390043e-01 1.33987761e+00 -8.38803828e-01 -2.17921391e-01 3.85433614e-01 -5.93214035e-01 -6.41089678e-01 -3.92686874e-01 -1.59178302e-01 -3.80277097e-01 -2.98607141e-01 5.11982024e-01 6.22014701e-01 5.22212327e-01 7.07193434e-01 -1.36376572e+00 -3.18422839e-02 9.90499794e-01 -4.88128662e-01 -1.22430995e-01 4.44481134e-01 5.42403102e-01 1.27504385e+00 2.84747556e-02 4.52162862e-01 1.16331089e+00 6.22008145e-01 4.26238239e-01 -1.54508495e+00 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3433c348-b58f-457c-a887-aae30d4c4b69	discrete-constrained-regression-for-local	2207.09865	null	https://arxiv.org/abs/2207.09865v1	https://arxiv.org/pdf/2207.09865v1.pdf	Discrete-Constrained Regression for Local Counting Models	Local counts, or the number of objects in a local area, is a continuous value by nature. Yet recent state-of-the-art methods show that formulating counting as a classification task performs better than regression. Through a series of experiments on carefully controlled synthetic data, we show that this counter-intuitive result is caused by imprecise ground truth local counts. Factors such as biased dot annotations and incorrectly matched Gaussian kernels used to generate ground truth counts introduce deviations from the true local counts. Standard continuous regression is highly sensitive to these errors, explaining the performance gap between classification and regression. To mitigate the sensitivity, we loosen the regression formulation from a continuous scale to a discrete ordering and propose a novel discrete-constrained (DC) regression. Applied to crowd counting, DC-regression is more accurate than both classification and standard regression on three public benchmarks. A similar advantage also holds for the age estimation task, verifying the overall effectiveness of DC-regression.	['Angela Yao', 'Haipeng Xiong']	2022-07-20	null	null	null	null	['age-estimation', 'age-estimation']	['computer-vision', 'miscellaneous']	[ 9.52858627e-02 -2.08265767e-01 -1.40794516e-01 -4.97333765e-01 -9.24441516e-01 -3.11918378e-01 9.58125114e-01 4.85696435e-01 -8.50829661e-01 1.26012242e+00 2.80250460e-01 -1.42802715e-01 2.35350013e-01 -9.65219021e-01 -8.60933542e-01 -6.83753669e-01 2.26429641e-01 6.31661296e-01 4.13451672e-01 2.88842410e-01 4.44642544e-01 3.27062637e-01 -1.43259716e+00 -5.82493506e-02 9.46639478e-01 7.81580985e-01 -3.03634673e-01 7.48467863e-01 1.59061104e-02 1.10153711e+00 -1.06792009e+00 -6.44584894e-01 1.17044352e-01 -2.28588164e-01 -4.47660565e-01 -1.75131664e-01 8.94289315e-01 -4.35872823e-01 8.86080693e-03 1.03956628e+00 2.32731029e-01 -4.58257347e-02 1.10575199e+00 -1.41317368e+00 -8.21593761e-01 2.97398716e-01 -8.86322021e-01 3.15196186e-01 3.25548619e-01 2.95263063e-02 9.37704086e-01 -7.11522400e-01 2.72678256e-01 1.22825086e+00 1.13585985e+00 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4aeae272-9f96-4250-ba2d-e245bdca809e	detection-of-tool-based-edited-images-from	2204.09075	null	https://arxiv.org/abs/2204.09075v1	https://arxiv.org/pdf/2204.09075v1.pdf	Detection of Tool based Edited Images from Error Level Analysis and Convolutional Neural Network	Image Forgery is a problem of image forensics and its detection can be leveraged using Deep Learning. In this paper we present an approach for identification of authentic and tampered images done using image editing tools with Error Level Analysis and Convolutional Neural Network. The process is performed on CASIA ITDE v2 dataset and trained for 50 and 100 epochs respectively. The respective accuracies of the training and validation sets are represented using graphs.	['Ronald Laban', 'Raunak Joshi', 'Abhishek Gupta']	2022-04-19	null	null	null	null	['image-forensics']	['computer-vision']	[ 7.03231692e-02 -3.48313659e-01 4.07675385e-01 -2.48979956e-01 -4.59737659e-01 -6.46251619e-01 5.85828245e-01 1.11293189e-01 -5.00381410e-01 4.85835582e-01 -3.51896226e-01 -6.85891867e-01 2.14329794e-01 -6.14849865e-01 -6.99663043e-01 -4.08198565e-01 -3.04435611e-01 -1.69463679e-01 6.39262497e-02 5.44819869e-02 8.61297548e-01 8.37375879e-01 -1.05966353e+00 7.96129227e-01 2.59711087e-01 1.18251503e+00 -4.38125908e-01 1.35485530e+00 6.53655082e-02 1.36459601e+00 -1.19212604e+00 -8.76202524e-01 8.00546587e-01 3.61252539e-02 -8.17672551e-01 2.35331267e-01 6.38939142e-01 -6.20592058e-01 -8.64367843e-01 1.18461692e+00 5.13142705e-01 -2.50236809e-01 4.51068759e-01 -1.56023240e+00 -1.01165116e+00 2.34020501e-01 -7.40647316e-01 1.15742230e+00 2.70172930e-03 3.82795841e-01 -2.23660439e-01 -9.04509425e-01 4.28012967e-01 1.09933484e+00 1.07963979e+00 1.36969596e-01 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8.12402070e-01 5.15720963e-01 -4.66342807e-01 -3.57720613e-01 4.21306312e-01]	[12.440322875976562, 1.045306921005249]
3c780fb8-94b8-479e-a6b6-f32b1ef34740	information-extraction-in-domain-and-generic	2307.0013	null	https://arxiv.org/abs/2307.00130v1	https://arxiv.org/pdf/2307.00130v1.pdf	Information Extraction in Domain and Generic Documents: Findings from Heuristic-based and Data-driven Approaches	Information extraction (IE) plays very important role in natural language processing (NLP) and is fundamental to many NLP applications that used to extract structured information from unstructured text data. Heuristic-based searching and data-driven learning are two main stream implementation approaches. However, no much attention has been paid to document genre and length influence on IE tasks. To fill the gap, in this study, we investigated the accuracy and generalization abilities of heuristic-based searching and data-driven to perform two IE tasks: named entity recognition (NER) and semantic role labeling (SRL) on domain-specific and generic documents with different length. We posited two hypotheses: first, short documents may yield better accuracy results compared to long documents; second, generic documents may exhibit superior extraction outcomes relative to domain-dependent documents due to training document genre limitations. Our findings reveals that no single method demonstrated overwhelming performance in both tasks. For named entity extraction, data-driven approaches outperformed symbolic methods in terms of accuracy, particularly in short texts. In the case of semantic roles extraction, we observed that heuristic-based searching method and data-driven based model with syntax representation surpassed the performance of pure data-driven approach which only consider semantic information. Additionally, we discovered that different semantic roles exhibited varying accuracy levels with the same method. This study offers valuable insights for downstream text mining tasks, such as NER and SRL, when addressing various document features and genres.	['Carlo Lipizzi', 'Shiyu Yuan']	2023-06-30	null	null	null	null	['syntax-representation', 'semantic-role-labeling', 'named-entity-recognition-ner', 'cg']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 3.41101795e-01 3.57510388e-01 -6.13990366e-01 -3.04216921e-01 -8.24838579e-01 -7.90352225e-01 8.07368040e-01 6.75488591e-01 -7.38109171e-01 1.10389125e+00 6.70772851e-01 -3.85275245e-01 -8.14337909e-01 -8.01969349e-01 -3.14574689e-01 -3.56861442e-01 -2.42198054e-02 5.94278991e-01 1.06625259e-01 -2.62893677e-01 7.93779969e-01 5.47072232e-01 -1.68392706e+00 5.57652533e-01 7.10154533e-01 7.76373208e-01 2.00829461e-01 3.96468461e-01 -8.84316385e-01 9.80021894e-01 -7.04908252e-01 -1.12068512e-01 -8.89442563e-02 -7.30620325e-02 -1.09780514e+00 -1.76475137e-01 -2.84108341e-01 2.14922756e-01 5.78564182e-02 5.75155377e-01 4.67727870e-01 1.92309231e-01 6.85446858e-01 -1.17993414e+00 -3.68831784e-01 9.83479679e-01 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-2.85466820e-01 6.68857098e-01 7.27647781e-01 1.15612650e+00 4.84800488e-01 1.64782628e-01 3.12067688e-01 -6.31835833e-02 -5.88331580e-01 -1.09336483e+00 -5.94750345e-01 4.35646385e-01 -1.23310365e-01 -5.41788161e-01 -5.50909996e-01 1.71557426e-01]	[9.703054428100586, 8.828046798706055]
04d0f7e1-18d2-4299-8a3b-eece88880edc	sit3-code-summarization-with-structure	2012.1471	null	https://arxiv.org/abs/2012.14710v2	https://arxiv.org/pdf/2012.14710v2.pdf	Code Summarization with Structure-induced Transformer	Code summarization (CS) is becoming a promising area in recent language understanding, which aims to generate sensible human language automatically for programming language in the format of source code, serving in the most convenience of programmer developing. It is well known that programming languages are highly structured. Thus previous works attempt to apply structure-based traversal (SBT) or non-sequential models like Tree-LSTM and graph neural network (GNN) to learn structural program semantics. However, it is surprising that incorporating SBT into advanced encoder like Transformer instead of LSTM has been shown no performance gain, which lets GNN become the only rest means modeling such necessary structural clue in source code. To release such inconvenience, we propose structure-induced Transformer, which encodes sequential code inputs with multi-view structural clues in terms of a newly-proposed structure-induced self-attention mechanism. Extensive experiments show that our proposed structure-induced Transformer helps achieve new state-of-the-art results on benchmarks.	['Min Zhang', 'Hai Zhao', 'Hongqiu Wu']	2020-12-29	null	https://aclanthology.org/2021.findings-acl.93	https://aclanthology.org/2021.findings-acl.93.pdf	findings-acl-2021-8	['code-summarization']	['computer-code']	[ 3.37619275e-01 4.35584247e-01 -3.78369689e-01 -4.33845490e-01 -4.54313695e-01 -2.83412695e-01 4.26318467e-01 3.96364480e-01 1.75711855e-01 2.18172267e-01 6.24769866e-01 -7.75696874e-01 2.25425228e-01 -9.24348176e-01 -1.08297849e+00 -1.87119022e-01 -1.37543812e-01 -7.94291198e-02 1.18158653e-01 -4.27268714e-01 6.36662364e-01 -6.54153526e-02 -1.36023366e+00 7.74655521e-01 1.10818863e+00 4.24299031e-01 6.06796205e-01 5.42313397e-01 -7.69052148e-01 1.54414916e+00 -3.26976001e-01 -5.39167941e-01 -6.83706030e-02 -6.80181623e-01 -1.09930170e+00 -3.16316903e-01 4.09617722e-01 -1.27637982e-01 -2.44840384e-01 1.27296185e+00 1.01687521e-01 -1.33210108e-01 1.90553203e-01 -8.31984997e-01 -1.08992767e+00 1.39647901e+00 -8.95750046e-01 2.37450242e-01 4.67483610e-01 4.16300371e-02 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6a2199f3-cfde-47fe-b9b6-cd2046851761	hyper-rpca-joint-maximum-correntropy	2006.07795	null	https://arxiv.org/abs/2006.07795v1	https://arxiv.org/pdf/2006.07795v1.pdf	Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection	Moving object detection is critical for automated video analysis in many vision-related tasks, such as surveillance tracking, video compression coding, etc. Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporally varying (i.e., moving) foreground objects from the static background in video, assuming the background frames to be low-rank while the foreground to be spatially sparse. Classic RPCA imposes sparsity of the foreground component using l1-norm, and minimizes the modeling error via 2-norm. We show that such assumptions can be too restrictive in practice, which limits the effectiveness of the classic RPCA, especially when processing videos with dynamic background, camera jitter, camouflaged moving object, etc. In this paper, we propose a novel RPCA-based model, called Hyper RPCA, to detect moving objects on the fly. Different from classic RPCA, the proposed Hyper RPCA jointly applies the maximum correntropy criterion (MCC) for the modeling error, and Laplacian scale mixture (LSM) model for foreground objects. Extensive experiments have been conducted, and the results demonstrate that the proposed Hyper RPCA has competitive performance for foreground detection to the state-of-the-art algorithms on several well-known benchmark datasets.	['Yi-Fei PU', 'Bihan Wen', 'Zerui Shao', 'Yi Zhang', 'Jiliu Zhou']	2020-06-14	null	null	null	null	['moving-object-detection']	['computer-vision']	[ 3.13470244e-01 -6.07369423e-01 3.40625979e-02 3.04019541e-01 -4.72571880e-01 -2.51621485e-01 3.88219863e-01 -4.15730864e-01 -2.45136976e-01 3.98034483e-01 4.33469974e-02 -8.44824091e-02 -1.27670869e-01 -2.63690561e-01 -5.08302033e-01 -1.28631592e+00 -9.03023258e-02 -4.88850214e-02 7.66745925e-01 5.57388544e-01 2.21375465e-01 4.88731593e-01 -1.39891708e+00 6.31832108e-02 7.50751436e-01 9.31883812e-01 4.26272601e-01 7.88456142e-01 1.28971776e-02 1.13847375e+00 -5.88084280e-01 -1.25505522e-01 2.19195083e-01 -4.71395999e-01 -9.30337086e-02 5.44634521e-01 2.25814506e-01 -2.39666909e-01 -4.74144906e-01 1.46519005e+00 1.79378763e-01 3.19547266e-01 4.88214880e-01 -1.28880405e+00 -5.41964233e-01 2.26899013e-01 -1.21074104e+00 8.68556738e-01 -4.78891730e-02 3.74107808e-02 3.34740669e-01 -1.01752090e+00 4.37910169e-01 1.64744258e+00 3.93244565e-01 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50f7180d-13ff-4f9e-ae46-7ef87a83ed93	investigating-the-robustness-of-natural	2210.08548	null	https://arxiv.org/abs/2210.08548v1	https://arxiv.org/pdf/2210.08548v1.pdf	Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples	The aim of Logic2Text is to generate controllable and faithful texts conditioned on tables and logical forms, which not only requires a deep understanding of the tables and logical forms, but also warrants symbolic reasoning over the tables. State-of-the-art methods based on pre-trained models have achieved remarkable performance on the standard test dataset. However, we question whether these methods really learn how to perform logical reasoning, rather than just relying on the spurious correlations between the headers of the tables and operators of the logical form. To verify this hypothesis, we manually construct a set of counterfactual samples, which modify the original logical forms to generate counterfactual logical forms with rarely co-occurred table headers and logical operators. SOTA methods give much worse results on these counterfactual samples compared with the results on the original test dataset, which verifies our hypothesis. To deal with this problem, we firstly analyze this bias from a causal perspective, based on which we propose two approaches to reduce the model's reliance on the shortcut. The first one incorporates the hierarchical structure of the logical forms into the model. The second one exploits automatically generated counterfactual data for training. Automatic and manual experimental results on the original test dataset and the counterfactual dataset show that our method is effective to alleviate the spurious correlation. Our work points out the weakness of previous methods and takes a further step toward developing Logic2Text models with real logical reasoning ability.	['Fei Wu', 'Kun Kuang', 'Leilei Gan', 'Chengyuan Liu']	2022-10-16	null	null	null	null	['logical-reasoning']	['reasoning']	[ 2.12190345e-01 7.46067584e-01 -4.31587726e-01 -3.18705589e-01 -3.38046938e-01 -6.11168325e-01 8.99485767e-01 1.04849096e-02 6.75978512e-02 1.20459843e+00 3.71569425e-01 -9.39450443e-01 -1.92717254e-01 -1.36476564e+00 -1.04063022e+00 -1.37811124e-01 -2.94262823e-02 5.02598047e-01 2.36127138e-01 -3.92689645e-01 2.76267380e-01 1.41632855e-01 -1.58900237e+00 8.76246750e-01 1.18746018e+00 6.27372444e-01 -2.77851224e-01 2.95551181e-01 -3.92006963e-01 1.34885991e+00 -5.89491010e-01 -8.53166103e-01 1.53036028e-01 -7.03083992e-01 -1.00678957e+00 -3.52403492e-01 2.82473147e-01 -2.26956636e-01 -1.02457374e-01 1.20009458e+00 -3.90746973e-05 -3.20943028e-01 6.92716718e-01 -1.41187167e+00 -4.72825974e-01 1.55167985e+00 -1.75393447e-01 -1.79228261e-01 6.00975811e-01 2.19764531e-01 1.23290455e+00 -4.42895234e-01 6.73646986e-01 1.57805383e+00 7.45667279e-01 4.27278697e-01 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537fcecc-4185-47ee-bd64-916100f52050	a-pairing-enhancement-approach-for-aspect	2306.10042	null	https://arxiv.org/abs/2306.10042v1	https://arxiv.org/pdf/2306.10042v1.pdf	A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction	Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplet of an aspect term, an opinion term, and their corresponding sentiment polarity from the review texts. Due to the complexity of language and the existence of multiple aspect terms and opinion terms in a single sentence, current models often confuse the connections between an aspect term and the opinion term describing it. To address this issue, we propose a pairing enhancement approach for ASTE, which incorporates contrastive learning during the training stage to inject aspect-opinion pairing knowledge into the triplet extraction model. Experimental results demonstrate that our approach performs well on four ASTE datasets (i.e., 14lap, 14res, 15res and 16res) compared to several related classical and state-of-the-art triplet extraction methods. Moreover, ablation studies conduct an analysis and verify the advantage of contrastive learning over other pairing enhancement approaches.	['Xiabing Zhou', 'Gongzhen Hu', 'Mian Zhang', 'Fan Yang']	2023-06-11	null	null	null	null	['contrastive-learning', 'contrastive-learning', 'aspect-sentiment-triplet-extraction']	['computer-vision', 'methodology', 'natural-language-processing']	[ 1.07310727e-01 -5.38004227e-02 -4.04173970e-01 -4.73615110e-01 -8.64196420e-01 -7.25315630e-01 7.41722047e-01 4.22207475e-01 -2.43198693e-01 5.34310222e-01 2.26485312e-01 -3.40534687e-01 1.67622700e-01 -6.09901011e-01 -3.24274600e-01 -4.13368106e-01 2.19004735e-01 2.87872672e-01 -1.78799570e-01 -5.58166027e-01 3.92202079e-01 -1.37522370e-01 -1.23724151e+00 5.87195694e-01 9.14871752e-01 1.22210300e+00 -4.45797145e-01 1.79173768e-01 -5.35758615e-01 7.77096570e-01 -7.03073740e-01 -1.12129092e+00 2.07254320e-01 -3.27477932e-01 -6.09724104e-01 1.99264914e-01 3.51523370e-01 7.65747875e-02 2.86481857e-01 1.05182493e+00 2.56842434e-01 -3.39362830e-01 8.01281929e-01 -1.36985636e+00 -8.23732138e-01 8.98525298e-01 -1.07858372e+00 -5.95479645e-02 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4.94963914e-01 8.15153480e-01 1.37599993e+00 4.24476027e-01 6.07189536e-01 9.05547082e-01 -1.59133643e-01 -2.57004201e-01 -9.83356714e-01 -4.75696981e-01 3.68652672e-01 3.05113107e-01 -5.10733128e-01 -5.13305843e-01 -1.41964197e-01]	[11.488731384277344, 6.63883638381958]
847f0deb-b1b6-426a-ab64-2b41bbb2f41e	ddrel-a-new-dataset-for-interpersonal	2012.02553	null	https://arxiv.org/abs/2012.02553v1	https://arxiv.org/pdf/2012.02553v1.pdf	DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues	Interpersonal language style shifting in dialogues is an interesting and almost instinctive ability of human. Understanding interpersonal relationship from language content is also a crucial step toward further understanding dialogues. Previous work mainly focuses on relation extraction between named entities in texts. In this paper, we propose the task of relation classification of interlocutors based on their dialogues. We crawled movie scripts from IMSDb, and annotated the relation labels for each session according to 13 pre-defined relationships. The annotated dataset DDRel consists of 6300 dyadic dialogue sessions between 694 pair of speakers with 53,126 utterances in total. We also construct session-level and pair-level relation classification tasks with widely-accepted baselines. The experimental results show that this task is challenging for existing models and the dataset will be useful for future research.	['Kenny Q. Zhu', 'Hongru Huang', 'Qi Jia']	2020-12-04	null	null	null	null	['dialog-relation-extraction']	['natural-language-processing']	[-1.58398360e-01 5.49062729e-01 -2.72190273e-01 -9.02364433e-01 -3.54383081e-01 -7.37250805e-01 9.22423601e-01 1.55069694e-01 -2.59246171e-01 9.51544523e-01 6.51569664e-01 -1.55450657e-01 7.56509602e-02 -5.06922007e-01 -7.11831599e-02 -2.49887496e-01 -3.34193438e-01 8.20038199e-01 7.07079992e-02 -6.85476899e-01 8.77254903e-02 5.04299141e-02 -6.85577095e-01 6.58659339e-01 6.61520720e-01 7.50236332e-01 -3.18609953e-01 9.69440162e-01 -3.11951637e-01 1.48872864e+00 -9.39593792e-01 -1.24670410e+00 -2.43452549e-01 -8.50801051e-01 -1.77067292e+00 2.15849251e-01 4.92107794e-02 -3.20871055e-01 -4.95570809e-01 5.92674494e-01 3.60004991e-01 3.39892238e-01 7.24622250e-01 -1.11145425e+00 -3.94185275e-01 1.21623695e+00 -2.72525519e-01 4.69375789e-01 9.97916698e-01 -3.62490743e-01 1.33279419e+00 -3.33250880e-01 8.09253097e-01 1.58381081e+00 4.63816077e-01 6.51786447e-01 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5e5222b5-69b4-4185-ab0e-38293bba58fe	fast-conformer-with-linearly-scalable	2305.05084	null	https://arxiv.org/abs/2305.05084v4	https://arxiv.org/pdf/2305.05084v4.pdf	Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition	Conformer-based models have become the most dominant end-to-end architecture for speech processing tasks. In this work, we propose a carefully redesigned Conformer with a new down-sampling schema. The proposed model, named Fast Conformer, is 2.8x faster than original Conformer, while preserving state-of-the-art accuracy on Automatic Speech Recognition benchmarks. Also we replace the original Conformer global attention with limited context attention post-training to enable transcription of an hour-long audio. We further improve long-form speech transcription by adding a global token. Fast Conformer combined with a Transformer decoder also outperforms the original Conformer in accuracy and in speed for Speech Translation and Spoken Language Understanding.	['He Huang', 'Boris Ginsburg', 'Ankur Kumar', 'Oleksii Hrinchuk', 'Vahid Noroozi', 'Somshubra Majumdar', 'Samuel Kriman', 'Dima Rekesh']	2023-05-08	null	null	null	null	['spoken-language-understanding', 'spoken-language-understanding']	['natural-language-processing', 'speech']	[ 1.36519447e-01 1.17455691e-01 1.84121370e-01 -7.89501131e-01 -1.57026899e+00 -4.04203296e-01 6.55206025e-01 -4.51922156e-02 -5.37597179e-01 4.43441004e-01 7.15306997e-01 -6.09691143e-01 3.42189699e-01 -2.00130850e-01 -7.41179466e-01 -1.99043244e-01 4.17882085e-01 6.60849452e-01 1.55296609e-01 -2.43116692e-01 1.96759915e-03 4.49479446e-02 -9.66665626e-01 8.31984997e-01 7.46726871e-01 9.67605174e-01 4.18490767e-01 7.09492922e-01 -2.55906880e-01 3.05454463e-01 -6.66196942e-01 -3.31865698e-01 8.44254419e-02 -3.70941937e-01 -8.93153369e-01 -1.98037311e-01 6.41018927e-01 -3.22919846e-01 -4.08567339e-01 8.01009774e-01 9.24921751e-01 4.04472947e-01 1.69560835e-01 -7.55923212e-01 -8.13090920e-01 1.08648956e+00 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c96c2766-770e-4e47-b583-86a0ce625da4	patch-level-contrasting-without-patch	2306.13337	null	https://arxiv.org/abs/2306.13337v1	https://arxiv.org/pdf/2306.13337v1.pdf	Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning	We propose ADCLR: A ccurate and D ense Contrastive Representation Learning, a novel self-supervised learning framework for learning accurate and dense vision representation. To extract spatial-sensitive information, ADCLR introduces query patches for contrasting in addition with global contrasting. Compared with previous dense contrasting methods, ADCLR mainly enjoys three merits: i) achieving both global-discriminative and spatial-sensitive representation, ii) model-efficient (no extra parameters in addition to the global contrasting baseline), and iii) correspondence-free and thus simpler to implement. Our approach achieves new state-of-the-art performance for contrastive methods. On classification tasks, for ViT-S, ADCLR achieves 77.5% top-1 accuracy on ImageNet with linear probing, outperforming our baseline (DINO) without our devised techniques as plug-in, by 0.5%. For ViT-B, ADCLR achieves 79.8%, 84.0% accuracy on ImageNet by linear probing and finetune, outperforming iBOT by 0.3%, 0.2% accuracy. For dense tasks, on MS-COCO, ADCLR achieves significant improvements of 44.3% AP on object detection, 39.7% AP on instance segmentation, outperforming previous SOTA method SelfPatch by 2.2% and 1.2%, respectively. On ADE20K, ADCLR outperforms SelfPatch by 1.0% mIoU, 1.2% mAcc on the segme	['Junchi Yan', 'Rui Zhao', 'Feng Zhu', 'Shaofeng Zhang']	2023-06-23	null	null	null	null	['self-supervised-learning', 'instance-segmentation']	['computer-vision', 'computer-vision']	[ 1.11911647e-01 -3.69400643e-02 -1.15514845e-01 -1.31415725e-01 -1.22678339e+00 -6.56745553e-01 6.12111628e-01 8.39662366e-03 -7.38372087e-01 6.77945018e-01 -2.34230161e-01 -6.25142902e-02 1.35251045e-01 -7.09470212e-01 -9.22854245e-01 -6.42608523e-01 7.11356178e-02 4.46530402e-01 7.05528438e-01 -1.16609439e-01 2.30556667e-01 5.20948231e-01 -1.63300574e+00 2.85244912e-01 8.53860080e-01 1.43652010e+00 4.02143061e-01 7.60379016e-01 -7.60694873e-03 8.53330374e-01 -7.02097714e-01 -1.25509799e-01 3.38402599e-01 -1.55003607e-01 -8.00331831e-01 -1.67282417e-01 1.28260279e+00 -2.56076485e-01 -4.10331339e-01 1.02425897e+00 5.83316445e-01 -1.98997427e-02 8.62212360e-01 -9.53429699e-01 -7.08973706e-01 3.88974845e-01 -1.19013727e+00 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a9ea08d0-ad06-4dd0-8c8c-5f238648409f	categorizing-semantic-representations-for-1	2210.06709	null	https://arxiv.org/abs/2210.06709v1	https://arxiv.org/pdf/2210.06709v1.pdf	Categorizing Semantic Representations for Neural Machine Translation	Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks. However, they have recently been shown to suffer limitation in compositional generalization, failing to effectively learn the translation of atoms (e.g., words) and their semantic composition (e.g., modification) from seen compounds (e.g., phrases), and thus suffering from significantly weakened translation performance on unseen compounds during inference. We address this issue by introducing categorization to the source contextualized representations. The main idea is to enhance generalization by reducing sparsity and overfitting, which is achieved by finding prototypes of token representations over the training set and integrating their embeddings into the source encoding. Experiments on a dedicated MT dataset (i.e., CoGnition) show that our method reduces compositional generalization error rates by 24\% error reduction. In addition, our conceptually simple method gives consistently better results than the Transformer baseline on a range of general MT datasets.	['Yue Zhang', 'Jie zhou', 'Fandong Meng', 'Yafu Li', 'Yongjing Yin']	2022-10-13	categorizing-semantic-representations-for	https://aclanthology.org/2022.coling-1.464	https://aclanthology.org/2022.coling-1.464.pdf	coling-2022-10	['semantic-composition']	['natural-language-processing']	[ 7.54169345e-01 2.11852893e-01 -5.32429099e-01 -1.77051008e-01 -1.05994701e+00 -8.74198854e-01 9.17336047e-01 1.41172603e-01 -3.59556347e-01 8.15468848e-01 5.23136854e-01 -4.02910203e-01 4.35084313e-01 -6.96804047e-01 -1.20839703e+00 -6.85705841e-01 4.03296232e-01 6.21417999e-01 -4.51723374e-02 -2.09148780e-01 2.35077947e-01 1.67113259e-01 -1.36951840e+00 5.73504388e-01 9.66346800e-01 6.47649884e-01 2.83220232e-01 7.90955778e-03 -2.45197654e-01 3.62304837e-01 -5.06962895e-01 -6.47485495e-01 8.95700604e-02 -6.51064634e-01 -9.38517332e-01 -5.90359159e-02 7.39725471e-01 -1.28104687e-01 -1.65329203e-01 1.11861849e+00 3.36824030e-01 1.49342835e-01 8.85657847e-01 -7.40391910e-01 -1.23100102e+00 1.09174860e+00 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f057e62e-c6d9-4900-899a-265078ca38cf	an-unsupervised-deep-learning-framework-for	2103.06575	null	https://arxiv.org/abs/2103.06575v1	https://arxiv.org/pdf/2103.06575v1.pdf	An unsupervised deep learning framework for medical image denoising	Medical image acquisition is often intervented by unwanted noise that corrupts the information content. This paper introduces an unsupervised medical image denoising technique that learns noise characteristics from the available images and constructs denoised images. It comprises of two blocks of data processing, viz., patch-based dictionaries that indirectly learn the noise and residual learning (RL) that directly learns the noise. The model is generalized to account for both 2D and 3D images considering different medical imaging instruments. The images are considered one-by-one from the stack of MRI/CT images as well as the entire stack is considered, and decomposed into overlapping image/volume patches. These patches are given to the patch-based dictionary learning to learn noise characteristics via sparse representation while given to the RL part to directly learn the noise properties. K-singular value decomposition (K-SVD) algorithm for sparse representation is used for training patch-based dictionaries. On the other hand, residue in the patches is trained using the proposed deep residue network. Iterating on these two parts, an optimum noise characterization for each image/volume patch is captured and in turn it is subtracted from the available respective image/volume patch. The obtained denoised image/volume patches are finally assembled to a denoised image or 3D stack. We provide an analysis of the proposed approach with other approaches. Experiments on MRI/CT datasets are run on a GPU-based supercomputer and the comparative results show that the proposed algorithm preserves the critical information in the images as well as improves the visual quality of the images.	['S. K. Patra', 'Jignesh S. Bhatt', 'Swati Rai']	2021-03-11	null	null	null	null	['medical-image-denoising']	['computer-vision']	[ 5.96544325e-01 -2.61300672e-02 2.34357119e-01 -6.34343922e-02 -8.68727744e-01 -1.24775529e-01 8.38376805e-02 1.80838794e-01 -4.81280327e-01 5.16971827e-01 3.19682807e-01 8.07152018e-02 -3.05847734e-01 -8.63148987e-01 -6.40397668e-01 -1.29638219e+00 -2.36503303e-01 2.38882467e-01 1.36317149e-01 -1.84643358e-01 6.54357299e-02 4.70463157e-01 -1.51172280e+00 3.20317835e-01 6.32402956e-01 1.11145139e+00 4.19449091e-01 6.46674633e-01 8.01756233e-02 1.03318894e+00 -3.36412609e-01 3.91544402e-01 5.27365327e-01 -5.06460726e-01 -4.50531930e-01 4.59355921e-01 2.77235091e-01 -4.23566490e-01 -3.27889591e-01 1.22960937e+00 6.84101820e-01 2.73969769e-01 5.77534020e-01 -4.31031287e-01 -3.50949109e-01 2.77930111e-01 -5.92420936e-01 6.80203080e-01 -4.27421853e-02 1.03649877e-01 1.98903292e-01 -8.41894031e-01 6.78932428e-01 9.94872153e-01 6.63989127e-01 2.92495787e-01 -1.28110182e+00 -2.38218933e-01 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dcfd0fe3-31e7-4520-803e-0347560f115f	physics-informed-machine-learning-for-2	2211.03022	null	https://arxiv.org/abs/2211.03022v1	https://arxiv.org/pdf/2211.03022v1.pdf	Physics Informed Machine Learning for Chemistry Tabulation	Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve, the two sub-systems are typically (re)solved separately. Popular approaches such as the Flamelet Generated Manifolds (FGM) use a two-step strategy where the governing reaction kinetics are pre-computed and mapped to a low-dimensional manifold, characterized by a few reaction progress variables (model reduction) and the manifold is then ``looked-up'' during the runtime to estimate the high-dimensional system state by the flow system. While existing works have focused on these two steps independently, in this work we show that joint learning of the progress variables and the look--up model, can yield more accurate results. We build on the base formulation and implementation ChemTab to include the dynamically generated Themochemical State Variables (Lower Dimensional Dynamic Source Terms). We discuss the challenges in the implementation of this deep neural network architecture and experimentally demonstrate it's superior performance.	['Varun Chandola', 'Paul DesJardin', 'Dwyer Deighan', 'Amol Salunkhe']	2022-11-06	null	null	null	null	['physics-informed-machine-learning']	['graphs']	[-1.63376436e-01 -3.52207154e-01 1.92909911e-01 9.35836658e-02 -3.20044369e-01 -6.29368544e-01 8.92702520e-01 8.94833580e-02 -3.02786767e-01 7.58419394e-01 -2.58250028e-01 -5.46021640e-01 -3.17428820e-02 -7.46542871e-01 -5.68598568e-01 -1.15749073e+00 -2.73082316e-01 5.74061573e-01 -6.85501024e-02 -2.50276566e-01 -1.27791837e-01 7.55318582e-01 -1.38278174e+00 -9.86882076e-02 4.95281518e-01 9.65368867e-01 -3.17522466e-01 1.32388711e+00 -3.10169697e-01 1.20243478e+00 -6.28464669e-02 3.80499244e-01 5.99396586e-01 -6.88429594e-01 -7.40434051e-01 -7.75118619e-02 1.62235588e-01 2.23966618e-03 -4.81373936e-01 7.91991472e-01 2.15187043e-01 9.89722908e-01 8.23290288e-01 -9.94655490e-01 1.57555982e-01 3.09855759e-01 -3.53853673e-01 1.42174304e-01 -4.60954785e-01 4.65860993e-01 4.82887387e-01 -7.10206807e-01 5.87988555e-01 1.18543601e+00 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332aa3e4-1a12-4db2-b37c-9bd6a8422d5a	qopt-optimistic-value-function	2006.1201	null	https://arxiv.org/abs/2006.12010v2	https://arxiv.org/pdf/2006.12010v2.pdf	QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning	QTRAN is a multi-agent reinforcement learning (MARL) algorithm capable of learning the largest class of joint-action value functions up to date. However, despite its strong theoretical guarantee, it has shown poor empirical performance in complex environments, such as Starcraft Multi-Agent Challenge (SMAC). In this paper, we identify the performance bottleneck of QTRAN and propose a substantially improved version, coined QTRAN++. Our gains come from (i) stabilizing the training objective of QTRAN, (ii) removing the strict role separation between the action-value estimators of QTRAN, and (iii) introducing a multi-head mixing network for value transformation. Through extensive evaluation, we confirm that our diagnosis is correct, and QTRAN++ successfully bridges the gap between empirical performance and theoretical guarantee. In particular, QTRAN++ newly achieves state-of-the-art performance in the SMAC environment. The code will be released.	['Sung-Soo Ahn', 'Yung Yi', 'Roben Delos Reyes', 'Kyunghwan Son', 'Jinwoo Shin']	2020-06-22	qtran-improved-value-transformation-for	https://openreview.net/forum?id=TlS3LBoDj3Z	https://openreview.net/pdf?id=TlS3LBoDj3Z	null	['smac-1', 'smac']	['playing-games', 'playing-games']	[-2.70050377e-01 -4.65022251e-02 -4.81467247e-01 1.65239781e-01 -1.07197917e+00 -6.66848421e-01 7.04702139e-01 1.47350863e-01 -6.65633738e-01 1.21452808e+00 2.32706919e-01 -3.74870628e-01 -5.19838750e-01 -5.26902556e-01 -8.66041660e-01 -8.67305338e-01 -4.47130352e-01 7.58065343e-01 2.33313888e-01 -4.22678828e-01 1.37988925e-01 1.35500252e-01 -1.30023408e+00 -9.17839333e-02 6.61866903e-01 8.52782547e-01 -4.00190726e-02 8.02142739e-01 3.22615117e-01 1.33597279e+00 -5.20944595e-01 -4.30345088e-01 4.21596915e-01 -5.36925852e-01 -8.74973416e-01 -2.07001239e-01 -6.12355396e-03 -5.22197008e-01 -1.94589481e-01 8.02688599e-01 6.34878099e-01 2.81227827e-01 3.75553787e-01 -1.82148659e+00 -2.11445451e-01 1.08108878e+00 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396e67aa-cf3c-45cc-8c10-e792650b97e8	gan-leaks-a-taxonomy-of-membership-inference	1909.03935	null	https://arxiv.org/abs/1909.03935v3	https://arxiv.org/pdf/1909.03935v3.pdf	GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models	Deep learning has achieved overwhelming success, spanning from discriminative models to generative models. In particular, deep generative models have facilitated a new level of performance in a myriad of areas, ranging from media manipulation to sanitized dataset generation. Despite the great success, the potential risks of privacy breach caused by generative models have not been analyzed systematically. In this paper, we focus on membership inference attack against deep generative models that reveals information about the training data used for victim models. Specifically, we present the first taxonomy of membership inference attacks, encompassing not only existing attacks but also our novel ones. In addition, we propose the first generic attack model that can be instantiated in a large range of settings and is applicable to various kinds of deep generative models. Moreover, we provide a theoretically grounded attack calibration technique, which consistently boosts the attack performance in all cases, across different attack settings, data modalities, and training configurations. We complement the systematic analysis of attack performance by a comprehensive experimental study, that investigates the effectiveness of various attacks w.r.t. model type and training configurations, over three diverse application scenarios (i.e., images, medical data, and location data).	['Dingfan Chen', 'Yang Zhang', 'Ning Yu', 'Mario Fritz']	2019-09-09	null	null	null	null	['membership-inference-attack']	['computer-vision']	[ 2.92269260e-01 -9.18533280e-02 -2.07565892e-02 -1.76804483e-01 -8.35671723e-01 -8.75887334e-01 8.56259882e-01 -7.09353089e-02 -1.00558568e-02 4.51288074e-01 -4.10135806e-04 -5.18677115e-01 -3.71291488e-01 -1.04421842e+00 -6.64639950e-01 -6.68861926e-01 -1.43087476e-01 4.08764750e-01 -9.92930233e-02 -1.09820031e-02 1.04830913e-01 6.79440439e-01 -1.13544822e+00 -2.86161304e-02 3.63813460e-01 8.41787338e-01 -6.52574241e-01 5.33966959e-01 3.62922311e-01 5.03691792e-01 -1.06041896e+00 -1.13407457e+00 2.70122886e-01 -2.33627424e-01 -6.08461082e-01 -8.73306617e-02 3.81723821e-01 -6.18711710e-01 -5.95151484e-01 1.03985131e+00 9.05976236e-01 -3.30951124e-01 4.02208716e-01 -1.65474164e+00 -5.32457292e-01 8.11679006e-01 -4.25337404e-01 4.86873500e-02 2.35057384e-01 1.41185716e-01 7.88239181e-01 -5.14367402e-01 4.92691308e-01 1.20825529e+00 5.90671480e-01 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-8.19425881e-01 -6.50636077e-01 -5.64981937e-01 3.97184134e-01 8.53575096e-02 -7.19026685e-01 -3.61207128e-01 -2.44928181e-01]	[5.893445014953613, 7.337096691131592]
098a1dec-418c-49c0-9ee9-09d515ce5058	a-hierarchical-spectral-method-for-extreme	1511.0326	null	http://arxiv.org/abs/1511.03260v4	http://arxiv.org/pdf/1511.03260v4.pdf	A Hierarchical Spectral Method for Extreme Classification	Extreme classification problems are multiclass and multilabel classification problems where the number of outputs is so large that straightforward strategies are neither statistically nor computationally viable. One strategy for dealing with the computational burden is via a tree decomposition of the output space. While this typically leads to training and inference that scales sublinearly with the number of outputs, it also results in reduced statistical performance. In this work, we identify two shortcomings of tree decomposition methods, and describe two heuristic mitigations. We compose these with an eigenvalue technique for constructing the tree. The end result is a computationally efficient algorithm that provides good statistical performance on several extreme data sets.	['Paul Mineiro', 'Nikos Karampatziakis']	2015-11-10	null	null	null	null	['tree-decomposition']	['graphs']	[ 2.95337409e-01 4.19947598e-03 -7.51110837e-02 -6.09475493e-01 -9.81764674e-01 -8.53067815e-01 4.10213023e-01 1.32922620e-01 -3.74862969e-01 9.89863813e-01 -1.60189569e-01 -7.37711847e-01 -3.18481863e-01 -6.38608813e-01 -2.88576763e-02 -8.48061740e-01 5.55378608e-02 5.41801810e-01 -2.23908171e-01 -4.88587236e-03 1.91971183e-01 5.31058013e-01 -1.75471199e+00 3.36935014e-01 9.17072713e-01 1.04999328e+00 -4.47443217e-01 8.73291016e-01 -3.89447898e-01 5.96991837e-01 -4.74969655e-01 -4.47018504e-01 3.80723178e-01 -4.64720607e-01 -8.95117342e-01 2.47210622e-01 5.67775667e-01 2.11452901e-01 3.11998248e-01 1.15500307e+00 5.60336590e-01 1.37997851e-01 1.05467498e+00 -1.45941257e+00 -2.93622285e-01 6.00965142e-01 -7.85353780e-01 8.33037496e-02 3.95341098e-01 -4.35058415e-01 1.25237024e+00 -9.06212986e-01 3.77751678e-01 1.36254716e+00 9.14630115e-01 1.65498033e-01 -1.88491225e+00 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-9.17448103e-01 2.19450474e-01 1.31711900e-01 -6.15008116e-01 -6.10734224e-01 -1.61327362e-01]	[8.58407211303711, 4.313014984130859]
991eede5-e17a-4518-8270-8c6b5a437bf1	a-general-purpose-tagger-with-convolutional	1706.01723	null	http://arxiv.org/abs/1706.01723v1	http://arxiv.org/pdf/1706.01723v1.pdf	A General-Purpose Tagger with Convolutional Neural Networks	We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-the-art results in part-of-speech tagging, morphological tagging and supertagging. The CNN tagger is also robust against the out-of-vocabulary problem, it performs well on artificially unnormalized texts.	['Ngoc Thang Vu', 'Agnieszka Faleńska', 'Xiang Yu']	2017-06-06	a-general-purpose-tagger-with-convolutional-1	https://aclanthology.org/W17-4118	https://aclanthology.org/W17-4118.pdf	ws-2017-9	['morphological-tagging']	['natural-language-processing']	[-6.61519170e-02 1.13595515e-01 -3.93725306e-01 -4.51904446e-01 -1.05220044e+00 -8.77848446e-01 6.43960536e-01 3.70559990e-01 -8.28516066e-01 6.00317180e-01 6.70828342e-01 -3.89175117e-01 2.54194707e-01 -6.68945372e-01 -4.10548449e-01 -5.92610657e-01 -8.32965225e-02 7.09835291e-01 3.59168321e-01 -4.19153810e-01 -1.05279557e-01 2.57781446e-01 -1.00593567e+00 2.19387785e-01 3.16684961e-01 9.60182786e-01 3.97093110e-02 5.82427919e-01 -4.99096513e-01 5.74923813e-01 -5.64743996e-01 -5.53690195e-01 -2.70050187e-02 1.31711423e-01 -9.50071752e-01 -4.05760527e-01 4.26431566e-01 2.51360446e-01 -4.54061955e-01 1.16659510e+00 6.57833040e-01 2.22120881e-01 3.80495220e-01 -6.01479828e-01 -1.08244824e+00 1.20803535e+00 2.12425396e-01 3.92867863e-01 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6db4e576-d33b-40c4-9ca5-d501e98cfb81	fv-upatches-enhancing-universality-in-finger	2206.01061	null	https://arxiv.org/abs/2206.01061v1	https://arxiv.org/pdf/2206.01061v1.pdf	FV-UPatches: Enhancing Universality in Finger Vein Recognition	Many deep learning-based models have been introduced in finger vein recognition in recent years. These solutions, however, suffer from data dependency and are difficult to achieve model generalization. To address this problem, we are inspired by the idea of domain adaptation and propose a universal learning-based framework, which achieves generalization while training with limited data. To reduce differences between data distributions, a compressed U-Net is introduced as a domain mapper to map the raw region of interest image onto a target domain. The concentrated target domain is a unified feature space for the subsequent matching, in which a local descriptor model SOSNet is employed to embed patches into descriptors measuring the similarity of matching pairs. In the proposed framework, the domain mapper is an approximation to a specific extraction function thus the training is only a one-time effort with limited data. Moreover, the local descriptor model can be trained to be representative enough based on a public dataset of non-finger-vein images. The whole pipeline enables the framework to be well generalized, making it possible to enhance universality and helps to reduce costs of data collection, tuning and retraining. The comparable experimental results to state-of-the-art (SOTA) performance in five public datasets prove the effectiveness of the proposed framework. Furthermore, the framework shows application potential in other vein-based biometric recognition as well.	['Hakil Kim', 'Changlong Jin', 'Changwen Cao', 'Jiazhen Liu', 'Ziyan Chen']	2022-06-02	null	null	null	null	['finger-vein-recognition']	['computer-vision']	[ 1.73272446e-01 -2.46170610e-01 -2.48105943e-01 -5.12347341e-01 -4.85788256e-01 -3.56953114e-01 4.85572666e-01 8.32410902e-02 -3.88309300e-01 5.49592495e-01 -2.21456811e-01 3.07886988e-01 -3.09240282e-01 -1.12586343e+00 -4.42502290e-01 -7.81975806e-01 2.32015163e-01 4.72772181e-01 2.59207129e-01 -5.38616739e-02 1.75857171e-01 8.22556674e-01 -1.36792874e+00 1.52522683e-01 9.47288573e-01 1.26092947e+00 2.00624704e-01 6.07559644e-02 -2.89790511e-01 7.09882304e-02 -4.94107246e-01 -5.25038362e-01 4.75658447e-01 -3.81678700e-01 -4.76098925e-01 -3.52567881e-02 6.44790709e-01 -2.83789694e-01 -4.52419370e-01 1.00303435e+00 1.05765927e+00 7.28012025e-02 6.99272454e-01 -8.03967357e-01 -6.60990000e-01 -2.51953546e-02 -3.58555853e-01 -1.76340848e-01 1.28378659e-01 1.59586087e-01 7.85891175e-01 -6.85721636e-01 7.76075721e-01 1.09144807e+00 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-6.40967250e-01 -9.47362244e-01 -1.08626676e+00 -8.97772133e-01 7.62260199e-01 9.08396095e-02 -6.62635386e-01 6.75192550e-02 -2.53471851e-01]	[12.953622817993164, 1.076197862625122]
95c81426-b3b8-4ac8-a273-a46b582caf20	impact-of-information-flow-topology-on-safety	2203.15772	null	https://arxiv.org/abs/2203.15772v1	https://arxiv.org/pdf/2203.15772v1.pdf	Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Control	Cooperative driving, enabled by Vehicle-to-Everything (V2X) communication, is expected to significantly contribute to the transportation system's safety and efficiency. Cooperative Adaptive Cruise Control (CACC), a major cooperative driving application, has been the subject of many studies in recent years. The primary motivation behind using CACC is to reduce traffic congestion and improve traffic flow, traffic throughput, and highway capacity. Since the information flow between cooperative vehicles can significantly affect the dynamics of a platoon, the design and performance of control components are tightly dependent on the communication component performance. In addition, the choice of Information Flow Topology (IFT) can affect certain platoons properties such as stability and scalability. Although cooperative vehicles perception can be expanded to multiple predecessors information by using V2X communication, the communication technologies still suffer from scalability issues. Therefore, cooperative vehicles are required to predict each other's behavior to compensate for the effects of non-ideal communication. The notion of Model-Based Communication (MBC) was proposed to enhance cooperative vehicles perception under non-ideal communication by introducing a new flexible content structure for broadcasting joint vehicles dynamic/drivers behavior models. By utilizing a non-parametric (Bayesian) modeling scheme, i.e., Gaussian Process Regression (GPR), and the MBC concept, this paper develops a discrete hybrid stochastic model predictive control approach and examines the impact of communication losses and different information flow topologies on the performance and safety of the platoon. The results demonstrate an improvement in response time and safety using more vehicles information, validating the potential of cooperation to attenuate disturbances and improve traffic flow and safety.	['Yaser P. Fallah', 'Javad Mohammadpour Velni', 'Arash Raftari', 'Sahand Mosharafian', 'Mahdi Razzaghpour']	2022-03-29	null	null	null	null	['gpr', 'gpr']	['computer-vision', 'miscellaneous']	[-1.58597529e-01 1.93451747e-01 -3.44593227e-01 -3.09419930e-01 -2.46733129e-01 -3.63047421e-01 6.26978576e-01 1.81270987e-01 -3.41660380e-01 7.71557927e-01 -1.77087203e-01 -5.37459910e-01 -5.35570860e-01 -9.71839607e-01 -5.61904907e-01 -1.03825164e+00 -3.47350538e-01 2.74217784e-01 7.04216421e-01 -4.68508184e-01 4.03828062e-02 7.08721340e-01 -1.65455210e+00 -4.37511653e-01 9.34145391e-01 9.56537783e-01 3.65312397e-01 5.35104096e-01 -8.25188011e-02 5.07639349e-01 -4.75560606e-01 -1.79909781e-01 4.36052643e-02 -4.39847186e-02 1.29046753e-01 -1.68779939e-02 -6.39024913e-01 -2.24244464e-02 -2.86861062e-01 6.68893933e-01 2.18615830e-01 2.18751989e-02 5.62605441e-01 -2.06729913e+00 1.48828760e-01 2.96421230e-01 -5.24207771e-01 1.09903432e-01 -2.40566850e-01 3.58137220e-01 4.31696057e-01 -2.51105130e-01 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3b379185-e1c9-48a7-bae5-26b1f0b88057	evaluating-chatgpt-s-performance-for	2305.13276	null	https://arxiv.org/abs/2305.13276v2	https://arxiv.org/pdf/2305.13276v2.pdf	Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection	Hate speech is a severe issue that affects many online platforms. So far, several studies have been performed to develop robust hate speech detection systems. Large language models like ChatGPT have recently shown a great promise in performing several tasks, including hate speech detection. However, it is crucial to comprehend the limitations of these models to build robust hate speech detection systems. To bridge this gap, our study aims to evaluate the strengths and weaknesses of the ChatGPT model in detecting hate speech at a granular level across 11 languages. Our evaluation employs a series of functionality tests that reveals various intricate failures of the model which the aggregate metrics like macro F1 or accuracy are not able to unfold. In addition, we investigate the influence of complex emotions, such as the use of emojis in hate speech, on the performance of the ChatGPT model. Our analysis highlights the shortcomings of the generative models in detecting certain types of hate speech and highlighting the need for further research and improvements in the workings of these models.	['Animesh Mukherjee', 'Saurabh Kumar Pandey', 'Mithun Das']	2023-05-22	null	null	null	null	['hate-speech-detection']	['natural-language-processing']	[-2.97262371e-01 6.21189848e-02 3.67570758e-01 1.74548160e-02 -3.68336886e-01 -6.21001363e-01 7.87967801e-01 2.76838429e-02 7.02401623e-02 3.51798803e-01 4.25504148e-01 -3.68319958e-01 2.25695550e-01 -2.00219825e-01 -9.14613605e-02 -5.12323499e-01 1.96869582e-01 -1.02094173e-01 3.20989490e-01 -4.11081940e-01 6.64090276e-01 2.97314614e-01 -1.43025529e+00 2.71431208e-01 8.61012280e-01 1.68964207e-01 -1.89412132e-01 8.71175826e-01 -8.13522637e-02 1.41149604e+00 -1.47632813e+00 -8.10901940e-01 -2.70272136e-01 -4.78909254e-01 -5.54875374e-01 -3.85000184e-02 4.35486495e-01 -5.08628428e-01 -2.09857002e-01 8.83495271e-01 7.79427886e-01 -1.47135640e-02 5.32325625e-01 -1.17186654e+00 -1.00696778e+00 4.26234782e-01 -1.04214497e-01 5.90928197e-01 6.10406101e-01 3.33247125e-01 6.55207634e-01 -7.49397576e-01 5.80336988e-01 1.31813395e+00 7.52604485e-01 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eccf87dd-6c0e-4903-aad8-45eba3e1086d	detection-and-segmentation-of-pancreas-using	2302.06356	null	https://arxiv.org/abs/2302.06356v1	https://arxiv.org/pdf/2302.06356v1.pdf	Detection and Segmentation of Pancreas using Morphological Snakes and Deep Convolutional Neural Networks	Pancreatic cancer is one of the deadliest types of cancer, with 25% of the diagnosed patients surviving for only one year and 6% of them for five. Computed tomography (CT) screening trials have played a key role in improving early detection of pancreatic cancer, which has shown significant improvement in patient survival rates. However, advanced analysis of such images often requires manual segmentation of the pancreas, which is a time-consuming task. Moreover, pancreas presents high variability in shape, while occupying only a very small area of the entire abdominal CT scans, which increases the complexity of the problem. The rapid development of deep learning can contribute to offering robust algorithms that provide inexpensive, accurate, and user-independent segmentation results that can guide the domain experts. This dissertation addresses this task by investigating a two-step approach for pancreas segmentation, by assisting the task with a prior rough localization or detection of pancreas. This rough localization of the pancreas is provided by an estimated probability map and the detection task is achieved by using the YOLOv4 deep learning algorithm. The segmentation task is tackled by a modified U-Net model applied on cropped data, as well as by using a morphological active contours algorithm. For comparison, the U-Net model was also applied on the full CT images, which provide a coarse pancreas segmentation to serve as reference. Experimental results of the detection network on the National Institutes of Health (NIH) dataset and the pancreas tumour task dataset within the Medical Segmentation Decathlon show 50.67% mean Average Precision. The best segmentation network achieved good segmentation results on the NIH dataset, reaching 67.67% Dice score.	['Agapi Davradou']	2023-02-13	null	null	null	null	['pancreas-segmentation']	['medical']	[-1.07734829e-01 2.49066010e-01 -3.13676506e-01 -3.08889635e-02 -7.47866392e-01 -4.36954111e-01 1.34499609e-01 5.69459856e-01 -7.90918410e-01 4.27492827e-01 -8.00408497e-02 -3.14393103e-01 -4.96156327e-02 -7.99238980e-01 -3.93285722e-01 -1.21417809e+00 -3.58283728e-01 7.97675312e-01 2.16399074e-01 4.53276098e-01 -4.46972661e-02 6.59875453e-01 -5.92509210e-01 -4.06173728e-02 1.09414780e+00 9.68116641e-01 2.72330284e-01 4.82512176e-01 -3.11583281e-01 3.11123282e-01 -2.97766447e-01 -1.61834717e-01 3.12885493e-01 -4.20080304e-01 -3.71572435e-01 1.17422156e-01 -7.74263442e-02 -3.51656646e-01 -2.52224971e-02 1.17313731e+00 3.17820907e-01 -1.96257681e-01 8.36035371e-01 -8.31223249e-01 -1.11092694e-01 6.86520755e-01 -3.85518074e-01 -1.21880798e-02 1.00074060e-01 1.95656911e-01 3.60978097e-01 -3.72415781e-01 5.97731948e-01 6.85396254e-01 7.60535419e-01 4.20990139e-01 -1.12816322e+00 -5.68816602e-01 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f913c5de-1467-4d24-8fb0-545b81ee2857	fusing-posture-and-position-representations	null	null	https://ieeexplore.ieee.org/abstract/document/9665889	https://csdl-downloads.ieeecomputer.org/proceedings/3dv/2021/2688/00/268800a617.pdf?Expires=1668520480&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jc2RsLWRvd25sb2Fkcy5pZWVlY29tcHV0ZXIub3JnL3Byb2NlZWRpbmdzLzNkdi8yMDIxLzI2ODgvMDAvMjY4ODAwYTYxNy5wZGYiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Njg1MjA0ODB9fX1dfQ__&Signature=hKtkwPFg6FnUo8KW8uKcpdTPncRHS7F~B79lMXn8y4HGF0nzwCUgCodE61NuQtadR9~BS8N2WobbwIOx~hlEF-93MlxyTCPNHspYFBI1PTywTddC8yHHUvCwjwm5k3GctRwUjDIVia8Udz~6b27zOwIPMIo985yiRlcRgc~4yRn2qw4jpQzVO7skF65pX0oqdSXN~EgqMWcPTlxZ6wfOOWyCL5UtaoCfLEiT5MqGu4hdS67SXzJx7ynKDer5EjcHubx726UXsl6Pe6vaj6dCA3H-AEQF4kbYF~owuCii0OeBHxKKKI3NuoHMIoyXxSH~gq4xMMTO27vJYmb8ierPCQ__&Key-Pair-Id=K12PMWTCQBDMDT	Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition	Hand gesture recognition can benefit from directly processing 3D point cloud sequences, which carry rich geometric information and enable the learning of expressive spatio-temporal features. However, currently employed single-stream models cannot sufficiently capture multi-scale features that include both fine-grained local posture variations and global hand movements. We therefore propose a novel dual-stream model, which decouples the learning of local and global features. These are eventually fused in an LSTM for temporal modelling. To induce the global and local stream to capture complementary position and posture features, we propose the use of different 3D learning architectures in both streams. Specifically, state-of-the-art point cloud networks excel at capturing fine posture variations from raw point clouds in the local stream. To track hand movements in the global stream, we combine an encoding with residual basis point sets and a fully-connected DenseNet. We evaluate the method on the Shrec'17 and DHG dataset and report state-of-the-art results at a reduced computational cost. Source code is available at https://github.com/multimodallearning/hand-gesture-posture-position.	['Mattias P Heinrich', 'Alexander Bigalke']	2022-01-06	null	null	null	3dv-2022-1	['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition']	['computer-vision', 'computer-vision', 'computer-vision']	[-1.03900827e-01 -6.86050713e-01 -9.38592330e-02 -4.35023308e-01 -7.75042892e-01 -5.57668805e-01 7.82776058e-01 -1.34049550e-01 -5.16157866e-01 2.62476653e-01 7.68830776e-02 4.71735001e-02 -1.89344823e-01 -7.98871458e-01 -7.48368859e-01 -7.24121630e-01 -3.21457177e-01 6.39684439e-01 2.26959005e-01 -1.94195673e-01 4.05310206e-02 9.55764294e-01 -1.73460269e+00 4.64347541e-01 4.35149580e-01 1.08348382e+00 2.57161766e-01 8.72240603e-01 -4.08628374e-01 4.91065681e-01 -2.70601988e-01 3.42799677e-03 3.61695111e-01 -1.98587894e-01 -5.56703746e-01 2.58020479e-02 6.09371066e-01 -5.87794900e-01 -6.56575739e-01 6.43632829e-01 8.46895397e-01 2.20483407e-01 4.20356452e-01 -1.11295652e+00 -3.71620715e-01 1.49646431e-01 -5.05026340e-01 1.09546117e-01 4.64633137e-01 5.62614560e-01 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9d1cddcf-55e3-4517-b888-614b1baecbb1	effective-neural-topic-modeling-with	2306.04217	null	https://arxiv.org/abs/2306.04217v1	https://arxiv.org/pdf/2306.04217v1.pdf	Effective Neural Topic Modeling with Embedding Clustering Regularization	Topic models have been prevalent for decades with various applications. However, existing topic models commonly suffer from the notorious topic collapsing: discovered topics semantically collapse towards each other, leading to highly repetitive topics, insufficient topic discovery, and damaged model interpretability. In this paper, we propose a new neural topic model, Embedding Clustering Regularization Topic Model (ECRTM). Besides the existing reconstruction error, we propose a novel Embedding Clustering Regularization (ECR), which forces each topic embedding to be the center of a separately aggregated word embedding cluster in the semantic space. This enables each produced topic to contain distinct word semantics, which alleviates topic collapsing. Regularized by ECR, our ECRTM generates diverse and coherent topics together with high-quality topic distributions of documents. Extensive experiments on benchmark datasets demonstrate that ECRTM effectively addresses the topic collapsing issue and consistently surpasses state-of-the-art baselines in terms of topic quality, topic distributions of documents, and downstream classification tasks.	['Anh Tuan Luu', 'Thong Nguyen', 'Xinshuai Dong', 'Xiaobao Wu']	2023-06-07	null	null	null	null	['topic-models']	['natural-language-processing']	[-1.55049101e-01 2.93832690e-01 -3.00651759e-01 -3.62038970e-01 -8.59491706e-01 -2.71910459e-01 8.11969161e-01 1.71677604e-01 6.74618036e-02 5.19240797e-01 7.05316007e-01 -1.70938559e-02 -1.95705563e-01 -7.81800926e-01 -6.18645012e-01 -8.85953128e-01 8.69894996e-02 5.85758507e-01 3.04307789e-01 3.03913504e-01 2.53558695e-01 -4.46257353e-01 -1.46489918e+00 8.06911811e-02 1.21157396e+00 6.01889372e-01 5.26621163e-01 -6.78888410e-02 -4.69899327e-01 5.66832460e-02 -7.88472533e-01 -1.80482149e-01 -2.42360041e-01 1.38564361e-02 -3.85929465e-01 3.00105840e-01 1.29780814e-01 -1.37607753e-01 -1.75906554e-01 9.61749554e-01 1.23292506e-01 3.72081786e-01 1.04776359e+00 -1.53045738e+00 -8.44334006e-01 8.06562006e-01 -8.13662469e-01 -2.98526585e-01 -2.27955699e-01 -3.34025472e-01 1.34752500e+00 -1.10505486e+00 7.13688552e-01 1.53028953e+00 4.51895654e-01 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3efe4a83-74e4-425e-98e9-a1aba591592c	simplerecon-3d-reconstruction-without-3d	2208.14743	null	https://arxiv.org/abs/2208.14743v1	https://arxiv.org/pdf/2208.14743v1.pdf	SimpleRecon: 3D Reconstruction Without 3D Convolutions	Traditionally, 3D indoor scene reconstruction from posed images happens in two phases: per-image depth estimation, followed by depth merging and surface reconstruction. Recently, a family of methods have emerged that perform reconstruction directly in final 3D volumetric feature space. While these methods have shown impressive reconstruction results, they rely on expensive 3D convolutional layers, limiting their application in resource-constrained environments. In this work, we instead go back to the traditional route, and show how focusing on high quality multi-view depth prediction leads to highly accurate 3D reconstructions using simple off-the-shelf depth fusion. We propose a simple state-of-the-art multi-view depth estimator with two main contributions: 1) a carefully-designed 2D CNN which utilizes strong image priors alongside a plane-sweep feature volume and geometric losses, combined with 2) the integration of keyframe and geometric metadata into the cost volume which allows informed depth plane scoring. Our method achieves a significant lead over the current state-of-the-art for depth estimation and close or better for 3D reconstruction on ScanNet and 7-Scenes, yet still allows for online real-time low-memory reconstruction. Code, models and results are available at https://nianticlabs.github.io/simplerecon	['Clément Godard', 'Michael Firman', 'Victor Prisacariu', 'Jamie Watson', 'John Gibson', 'Mohamed Sayed']	2022-08-31	null	null	null	null	['indoor-scene-reconstruction']	['computer-vision']	[ 1.75742090e-01 7.44515145e-03 2.24650607e-01 -4.86412138e-01 -1.00454497e+00 -3.80412430e-01 5.68004251e-01 -8.06974806e-03 -3.73058915e-01 2.94568151e-01 2.25282148e-01 -1.73825875e-01 9.07034576e-02 -1.00332105e+00 -8.87536347e-01 -3.23719174e-01 7.43924081e-02 6.98894799e-01 4.60658163e-01 -6.09843209e-02 2.40803704e-01 6.33499742e-01 -1.62878895e+00 1.87545925e-01 6.53764009e-01 1.40989459e+00 2.34159753e-01 8.00295055e-01 -2.02229187e-01 6.54245794e-01 9.01925936e-02 -2.67457634e-01 5.41430891e-01 -6.78390712e-02 -5.69806218e-01 1.04263052e-01 7.27867842e-01 -1.06270432e+00 -4.10069138e-01 5.50361097e-01 7.19807446e-01 -8.24757814e-02 3.40112686e-01 -5.27366936e-01 1.58939719e-01 -2.52395332e-01 -5.21018326e-01 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4fe79e86-25c3-4199-8ec1-38c642af5625	a-framework-for-fully-autonomous-design-of	2304.07445	null	https://arxiv.org/abs/2304.07445v1	https://arxiv.org/pdf/2304.07445v1.pdf	A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps	In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints. For these reasons, we present an active learning process based on multiobjective black-box optimization with continuously updated machine learning models. This workflow is built on open-source technologies for real-time data streaming and modular multiobjective optimization software development. We demonstrate a proof of concept for this workflow through the autonomous operation of a continuous-flow chemistry laboratory, which identifies ideal manufacturing conditions for the electrolyte 2,2,2-trifluoroethyl methyl carbonate.	['Joseph A. Libera', 'Santanu Chaudhuri', 'Stefan M. Wild', 'Jakob R. Elias', 'Tyler H. Chang']	2023-04-15	null	null	null	null	['multiobjective-optimization']	['methodology']	[ 2.25220248e-01 -2.15715438e-01 -3.15547794e-01 -3.81932199e-01 -5.87481856e-01 -4.71132129e-01 -3.40209566e-02 8.45338106e-01 -3.25296432e-01 7.30223358e-01 -5.57065248e-01 -5.67316055e-01 -6.50953770e-01 -5.57000875e-01 -4.89086539e-01 -5.97891092e-01 -3.45515639e-01 9.82890248e-01 -3.53803486e-01 -1.75501615e-01 5.17340422e-01 7.36376643e-01 -1.88286448e+00 3.11150253e-01 8.45394015e-01 1.15027571e+00 3.22366178e-01 1.03992057e+00 -5.93384467e-02 4.86260802e-01 -5.01566470e-01 3.06676894e-01 4.19790804e-01 1.51066175e-02 -5.52669704e-01 3.59728150e-02 -2.62245417e-01 2.09433153e-01 6.15420401e-01 3.65017951e-01 6.72041059e-01 1.52672097e-01 2.92074442e-01 -1.47024238e+00 3.24010342e-01 3.68625313e-01 1.09874599e-01 8.94226581e-02 1.34594411e-01 8.65341604e-01 8.73025775e-01 -6.85157359e-01 1.84412226e-01 9.58759248e-01 2.09587917e-01 3.85090448e-02 -1.50019777e+00 -5.73356986e-01 2.30986893e-01 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4.35223021e-02]	[6.076563835144043, 3.656547784805298]
b330ea5b-8642-45d4-ba4f-1a16476a83af	arcade-a-rapid-continual-anomaly-detector	2008.04042	null	https://arxiv.org/abs/2008.04042v2	https://arxiv.org/pdf/2008.04042v2.pdf	ARCADe: A Rapid Continual Anomaly Detector	Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored. The present work addresses a learning scenario where a model has to incrementally learn a sequence of anomaly detection tasks, i.e. tasks from which only examples from the normal (majority) class are available for training. We define this novel learning problem of continual anomaly detection (CAD) and formulate it as a meta-learning problem. Moreover, we propose A Rapid Continual Anomaly Detector (ARCADe), an approach to train neural networks to be robust against the major challenges of this new learning problem, namely catastrophic forgetting and overfitting to the majority class. The results of our experiments on three datasets show that, in the CAD problem setting, ARCADe substantially outperforms baselines from the continual learning and anomaly detection literature. Finally, we provide deeper insights into the learning strategy yielded by the proposed meta-learning algorithm.	['Denis Krompaß', 'Ahmed Frikha', 'Volker Tresp']	2020-08-10	null	null	null	null	['continual-anomaly-detection', 'one-class-classifier']	['computer-vision', 'methodology']	[ 5.31460583e-01 1.14345051e-01 -4.31459025e-02 -2.82504737e-01 -7.06075966e-01 -3.62718910e-01 8.10832739e-01 5.54027796e-01 -4.38593358e-01 3.50474089e-01 -2.61454910e-01 -6.08751714e-01 -1.63210556e-01 -4.88572568e-01 -8.55880022e-01 -5.90787947e-01 -2.08507195e-01 4.68476713e-01 2.82075584e-01 -1.20906733e-01 3.38081717e-01 3.64988029e-01 -1.77704942e+00 4.10671055e-01 9.78632927e-01 1.04083443e+00 -5.71078837e-01 5.80846548e-01 -1.79539502e-01 8.83655965e-01 -4.91004258e-01 -3.51845473e-01 4.55780655e-01 -4.42511022e-01 -9.37548757e-01 2.86104411e-01 9.05910969e-01 -4.11455870e-01 -1.80569872e-01 9.88138258e-01 1.23733461e-01 2.42164969e-01 7.03161120e-01 -1.61643970e+00 -6.00210607e-01 4.31655288e-01 -7.42463112e-01 7.88797259e-01 4.12384607e-02 2.21593931e-01 9.86622512e-01 -1.16077614e+00 1.95025340e-01 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42e95654-d8a7-47be-9101-ddd4f44768a9	prioritized-level-replay-1	2010.03934	null	https://arxiv.org/abs/2010.03934v4	https://arxiv.org/pdf/2010.03934v4.pdf	Prioritized Level Replay	Environments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning. In this setting, each level is an algorithmically created environment instance with a unique configuration of its factors of variation. Training on a prespecified subset of levels allows for testing generalization to unseen levels. What can be learned from a level depends on the current policy, yet prior work defaults to uniform sampling of training levels independently of the policy. We introduce Prioritized Level Replay (PLR), a general framework for selectively sampling the next training level by prioritizing those with higher estimated learning potential when revisited in the future. We show TD-errors effectively estimate a level's future learning potential and, when used to guide the sampling procedure, induce an emergent curriculum of increasingly difficult levels. By adapting the sampling of training levels, PLR significantly improves sample efficiency and generalization on Procgen Benchmark--matching the previous state-of-the-art in test return--and readily combines with other methods. Combined with the previous leading method, PLR raises the state-of-the-art to over 76% improvement in test return relative to standard RL baselines.	['Edward Grefenstette', 'Tim Rocktäschel', 'Minqi Jiang']	2020-10-08	prioritized-level-replay	https://openreview.net/forum?id=NfZ6g2OmXEk	https://openreview.net/pdf?id=NfZ6g2OmXEk	null	['systematic-generalization']	['reasoning']	[ 3.61204684e-01 2.50630789e-02 -4.37016964e-01 -8.20531696e-02 -1.06761920e+00 -8.62112880e-01 5.45700848e-01 1.49685532e-01 -7.98791409e-01 1.12091982e+00 1.89868420e-01 -3.16514254e-01 -1.81972861e-01 -9.26678777e-01 -1.06490815e+00 -7.71587968e-01 -3.64494413e-01 7.34974086e-01 4.47594255e-01 -3.35374117e-01 2.65575975e-01 4.55873698e-01 -1.89933634e+00 5.26346803e-01 8.62318277e-01 6.62675619e-01 2.38012135e-01 6.69664919e-01 2.23258927e-01 8.13118458e-01 -8.08849931e-01 1.42051335e-02 5.69377184e-01 -6.98681533e-01 -7.55054653e-01 4.71849693e-03 5.68089843e-01 -4.70676064e-01 -1.10759445e-01 7.29177952e-01 5.87545812e-01 5.14296830e-01 4.23537612e-01 -8.85425866e-01 -2.90596396e-01 1.02246320e+00 -3.44014674e-01 4.26912248e-01 2.68771559e-01 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6.73718512e-01 1.31309018e-01 8.64810705e-01 6.86627984e-01 -9.20886278e-01 -1.14842010e+00 -7.21220136e-01 -3.65113467e-01 3.65759790e-01 5.99016547e-01 -8.07243824e-01 -3.37498873e-01 -3.36573958e-01]	[4.012687683105469, 1.6575359106063843]
1565b780-fb1a-4f35-b6a2-b7ec059dd74a	dithymoquinone-as-a-novel-inhibitor-for-3	1709.03813	null	http://arxiv.org/abs/1709.03813v1	http://arxiv.org/pdf/1709.03813v1.pdf	Dithymoquinone as a novel inhibitor for 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) to prevent renal failure	3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) is a major endogenous ligand found in the human serum albumin (HSA) of renal failure patients. It gets accumulated in the HSA and its concentration in sera of patients may reflect the chronicity of renal failure [1-4]. It is considered uremic toxin due to its damaging effect on the renal cells. The high concentrations of CMPF inhibit the binding of other ligands to HSA. Removal of CMPF is difficult through conventional hemodialysis due to its strong binding affinity. We hypothesized that the competitive inhibition may be helpful in removal of CMPF binding to HSA. A compound with higher HSA binding affinity than CMPF could be useful to prevent CMPF from binding so that CMPF could be excreted by the body through the urine. We studied an active compound dihydrothymoquinone/ dithymoquinone (DTQ) found in black cumin seed (Nigella sativa), which has higher binding affinity for HSA. Molecular docking simulations were performed to find the binding affinity of CMPF and DTQ with HSA. DTQ was found to have higher binding affinity possessing more interactions with the binding residues than the CMPF. We studied the binding pocket flexibility of CMPF and DTQ to analyze the binding abilities of both the compounds. We have also predicted the ADME properties for DTQ which shows higher lipophilicity, higher gastrointestinal (GI) absorption, and blood-brain barrier (BBB) permeability. We discovered that DTQ has potential to act as an inhibitor of CMPF and can be considered as a candidate for the formation of the therapeutic drug against CMPF.	[]	2017-07-23	null	null	null	null	['molecular-docking']	['medical']	[-3.93057257e-01 7.63327032e-02 1.15613881e-02 -1.13920785e-01 2.55517429e-03 -4.26469803e-01 5.83471917e-02 5.76007366e-01 -1.85961530e-01 1.27434349e+00 3.16073149e-01 -3.18290591e-01 2.84815252e-01 -9.08178568e-01 -4.67023760e-01 -8.58947515e-01 -4.34010774e-01 5.15501857e-01 2.76935428e-01 -1.84513032e-01 2.11977810e-01 1.01085949e+00 -8.76306295e-01 -4.36875932e-02 1.93917263e+00 3.66852134e-02 8.25080812e-01 1.21442333e-01 -5.38728237e-01 7.12621391e-01 -2.72962749e-01 3.36006321e-02 1.01607762e-01 -6.01166725e-01 -7.72517622e-01 -3.42171907e-01 -2.32631311e-01 -2.75055021e-01 1.56865224e-01 9.19672489e-01 4.58870679e-01 -1.20160304e-01 9.85882401e-01 -7.49614775e-01 -5.18801391e-01 2.89994121e-01 -6.79687500e-01 -8.87701362e-02 4.61693585e-01 -7.41078779e-02 2.63865381e-01 -9.86170113e-01 1.63202882e-01 1.26159370e+00 7.47555792e-02 5.93523681e-01 -9.19798493e-01 -5.07549405e-01 -4.74269539e-01 2.91253388e-01 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4eec7702-8acd-43bc-a14b-7bf296715d97	cup-curriculum-learning-based-prompt-tuning	2205.00498	null	https://arxiv.org/abs/2205.00498v2	https://arxiv.org/pdf/2205.00498v2.pdf	CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction	Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document. Most previous work focuses on learning the direct relations between arguments and the given trigger, while the implicit relations with long-range dependency are not well studied. Moreover, recent neural network based approaches rely on a large amount of labeled data for training, which is unavailable due to the high labelling cost. In this paper, we propose a Curriculum learning based Prompt tuning (CUP) approach, which resolves implicit EAE by four learning stages. The stages are defined according to the relations with the trigger node in a semantic graph, which well captures the long-range dependency between arguments and the trigger. In addition, we integrate a prompt-based encoder-decoder model to elicit related knowledge from pre-trained language models (PLMs) in each stage, where the prompt templates are adapted with the learning progress to enhance the reasoning for arguments. Experimental results on two well-known benchmark datasets show the great advantages of our proposed approach. In particular, we outperform the state-of-the-art models in both fully-supervised and low-data scenarios.	['Liang He', 'Jian Jin', 'Jie zhou', 'Qin Chen', 'Jiaju Lin']	2022-05-01	null	null	null	null	['implicit-relations']	['natural-language-processing']	[ 4.79255170e-01 5.70699751e-01 -3.70333701e-01 -6.19384825e-01 -5.77909648e-01 -4.45651144e-01 8.17255139e-01 6.51894629e-01 -6.73337758e-01 7.34699249e-01 5.18612564e-01 -3.31282735e-01 -3.39412451e-01 -9.13884461e-01 -8.26218009e-01 -5.17968059e-01 3.65602672e-01 6.71467781e-01 6.41863167e-01 -2.88605273e-01 2.06113085e-01 9.33607966e-02 -1.14271212e+00 6.72950566e-01 1.07145774e+00 7.72368431e-01 1.32625461e-01 1.35296538e-01 -5.54771245e-01 1.32105052e+00 -6.60211504e-01 -7.00242460e-01 -5.13809204e-01 -3.81504744e-01 -1.00862670e+00 -4.11632448e-01 -2.05910221e-01 -5.01761511e-02 -1.57126486e-01 9.04589355e-01 3.94559950e-01 -1.59349516e-01 5.50343215e-01 -1.02122700e+00 -4.09174800e-01 1.14032435e+00 -2.80198127e-01 3.18480909e-01 9.97481775e-03 -2.72202969e-01 1.33015430e+00 -7.01624274e-01 6.12641931e-01 1.48352790e+00 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954502bb-2c00-4fe9-acc6-7fbc1fbe3573	arapreg-an-as-rigid-as-possible	2108.09432	null	https://arxiv.org/abs/2108.09432v2	https://arxiv.org/pdf/2108.09432v2.pdf	ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators	This paper introduces an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated shapes. Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy. We show how to develop the unsupervised loss via a spectral decomposition of the Hessian of the ARAP energy. Our loss nicely decouples pose and shape variations through a robust norm. The loss admits simple closed-form expressions. It is easy to train and can be plugged into any standard generation models, e.g., variational auto-encoder (VAE) and auto-decoder (AD). Experimental results show that our approach outperforms existing shape generation approaches considerably on public benchmark datasets of various shape categories such as human, animal and bone.	['Chandrajit Bajaj', 'Junfeng Jiang', 'Zaiwei Zhang', 'Bo Sun', 'Xiangru Huang', 'QiXing Huang']	2021-08-21	null	http://openaccess.thecvf.com//content/ICCV2021/html/Huang_ARAPReg_An_As-Rigid-As_Possible_Regularization_Loss_for_Learning_Deformable_Shape_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Huang_ARAPReg_An_As-Rigid-As_Possible_Regularization_Loss_for_Learning_Deformable_Shape_ICCV_2021_paper.pdf	iccv-2021-1	['3d-shape-generation']	['computer-vision']	[ 9.28948596e-02 5.37218690e-01 2.55177300e-02 -3.80898505e-01 -9.44285214e-01 -6.74521327e-01 7.71900177e-01 -3.41364145e-01 9.86026973e-03 6.64559603e-01 3.97162467e-01 1.31143734e-01 1.49197608e-01 -9.26199973e-01 -1.25713086e+00 -9.90925789e-01 3.52301419e-01 7.26547718e-01 1.27398878e-01 -2.56199360e-01 -7.74196759e-02 6.11879945e-01 -1.01917446e+00 -3.63093950e-02 5.20132422e-01 6.55658066e-01 -3.45894322e-02 3.90601516e-01 3.39879185e-01 3.12461585e-01 -1.80370614e-01 -7.74033308e-01 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47f7fd44-c675-4edb-86b7-c2642483b17e	clickbait-detection-in-youtube-videos	2107.12791	null	https://arxiv.org/abs/2107.12791v1	https://arxiv.org/pdf/2107.12791v1.pdf	Clickbait Detection in YouTube Videos	YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with multiple state-of-the-art machine learning techniques using a variety of textual features.	['Mark Stamp', 'Fabio Di Troia', 'Ruchira Gothankar']	2021-07-26	null	null	null	null	['clickbait-detection']	['natural-language-processing']	[-1.28702521e-01 -3.19420666e-01 -7.40559101e-01 -1.58588648e-01 -6.48087800e-01 -8.25649619e-01 4.74791259e-01 1.86012924e-01 -3.18589687e-01 6.21556282e-01 3.78823102e-01 -1.75690353e-01 2.93956280e-01 -2.68072069e-01 -7.23932803e-01 -2.07837611e-01 -4.90146950e-02 -3.40759277e-01 2.42440671e-01 2.00688347e-01 6.90208972e-01 -9.93821472e-02 -1.46389091e+00 5.33715963e-01 2.43970156e-01 1.27853775e+00 -2.63929740e-02 5.96130192e-01 -1.81965195e-02 9.76959229e-01 -4.25024092e-01 -5.53660214e-01 3.54631692e-01 -2.08506450e-01 -1.59774363e-01 2.14682817e-01 1.16878951e+00 -8.00814986e-01 -9.00194228e-01 1.10112822e+00 -1.19119480e-01 -1.52235165e-01 1.60407603e-01 -1.51618576e+00 -7.13609576e-01 3.47521186e-01 -7.12122500e-01 6.98846102e-01 9.18212771e-01 2.68289328e-01 1.45741189e+00 -7.07683504e-01 7.46645868e-01 8.08774233e-01 2.88945466e-01 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ae3aca7f-8ab7-47b6-91b1-c3034d388a33	setvae-learning-hierarchical-composition-for	2103.15619	null	https://arxiv.org/abs/2103.15619v1	https://arxiv.org/pdf/2103.15619v1.pdf	SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data	Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks for ordinary sequential data to a set-structured data is nontrivial as it should be invariant to the permutation of its elements. In this paper, we propose SetVAE, a hierarchical variational autoencoder for sets. Motivated by recent progress in set encoding, we build SetVAE upon attentive modules that first partition the set and project the partition back to the original cardinality. Exploiting this module, our hierarchical VAE learns latent variables at multiple scales, capturing coarse-to-fine dependency of the set elements while achieving permutation invariance. We evaluate our model on point cloud generation task and achieve competitive performance to the prior arts with substantially smaller model capacity. We qualitatively demonstrate that our model generalizes to unseen set sizes and learns interesting subset relations without supervision. Our implementation is available at https://github.com/jw9730/setvae.	['Seunghoon Hong', 'Juho Lee', 'Jaehoon Yoo', 'Jinwoo Kim']	2021-03-29	null	http://openaccess.thecvf.com//content/CVPR2021/html/Kim_SetVAE_Learning_Hierarchical_Composition_for_Generative_Modeling_of_Set-Structured_Data_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Kim_SetVAE_Learning_Hierarchical_Composition_for_Generative_Modeling_of_Set-Structured_Data_CVPR_2021_paper.pdf	cvpr-2021-1	['point-cloud-generation']	['computer-vision']	[ 4.41771820e-02 2.57853985e-01 7.84919262e-02 -4.15323347e-01 -9.55410182e-01 -9.31800604e-01 5.54596841e-01 -8.14402699e-02 1.70421213e-01 6.54763639e-01 3.13253820e-01 7.60931447e-02 -2.14979067e-01 -1.17354059e+00 -1.23595333e+00 -6.06057465e-01 -1.63615242e-01 1.24414539e+00 1.48481335e-02 -5.83316535e-02 -1.38390929e-01 6.08348191e-01 -1.78470075e+00 5.44668734e-01 3.27143848e-01 7.65944898e-01 2.34701484e-01 7.98837125e-01 -3.73452008e-02 4.64684427e-01 -3.32240462e-01 -2.19693080e-01 5.22769630e-01 -1.11255333e-01 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