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values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 ... | ['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 -3.30780596... | [5.093490123748779, 2.973680257797241] |
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 relationsh... | ['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 -3.07458341... | [9.125873565673828, 7.170050144195557] |
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, ... | ['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 -5.26300550... | [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 ... | ['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 7.05560818... | [13.34634017944336, 3.3220536708831787] |
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 Convolu... | ['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 -3.53685319... | [8.398602485656738, -2.1695516109466553] |
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 analyti... | ['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 9.79598835... | [11.736742973327637, -2.3550865650177] |
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 performanc... | ['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 -5.58238447... | [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 burd... | ['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 1.71294987... | [6.201169013977051, 1.4114385843276978] |
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 ... | ['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 -2.55866438... | [10.653780937194824, 1.0653997659683228] |
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... | ['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 -1.20642208... | [7.968259811401367, -3.4242911338806152] |
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 de... | ['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 -5.93427837... | [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 incenti... | ['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 1.70980424... | [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 d... | ['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 -9.38520879... | [11.032933235168457, -2.5041913986206055] |
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, ... | ['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 -1.07251060... | [12.405561447143555, 7.9784650802612305] |
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 elimi... | ['**', '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 -2.96700113... | [7.463508605957031, 1.6188957691192627] |
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.... | ['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 -3.27977389... | [8.88386058807373, 2.4516842365264893] |
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 c... | ['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 -3.59874934... | [4.962265491485596, 2.6797008514404297] |
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 trai... | ['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 -2.43398726... | [5.755390644073486, 7.719440937042236] |
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 moti... | ['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 -6.51410401... | [4.032714366912842, 2.0001862049102783] |
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 infe... | ['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 8.33343193... | [8.632132530212402, 4.102550983428955] |
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... | ['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 -3.49730015... | [4.743631362915039, 2.764132261276245] |
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 ... | ['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 -1.07256249... | [7.4491496086120605, 4.205277919769287] |
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, ... | ['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 0.0... | [7.241637706756592, -1.0076324939727783] |
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 o... | ['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 -1.41195819... | [13.497773170471191, -2.5339066982269287] |
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 e... | ['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 -4.86581951... | [8.100237846374512, -1.960958480834961] |
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 co... | ['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 -4.29525942... | [7.479434490203857, -2.087754249572754] |
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 c... | ['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 -1.85679883... | [9.944907188415527, -1.46554696559906] |
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 ... | ['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 1.07276201... | [8.020421028137207, -3.0976004600524902] |
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 o... | ['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 1.48872837... | [8.622678756713867, 5.994503021240234] |
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 correspondence... | ['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 -2.59724438... | [7.071608543395996, -1.016973853111267] |
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... | ['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 -1.85176045... | [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... | ['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 -4.44600433... | [4.174993515014648, 2.103184700012207] |
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 lever... | ['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 -4.27912652... | [10.58615493774414, 8.001258850097656] |
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 learnin... | ['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 -4.98965234... | [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... | ['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 -7.68076777... | [10.645530700683594, 1.4357026815414429] |
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... | ['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 2.08001360... | [9.148274421691895, -1.5240271091461182] |
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 a... | ['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 -4.10328925... | [13.157588005065918, 0.4976847171783447] |
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 ... | ['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 -4.09574062... | [11.137929916381836, 7.849193096160889] |
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 proximi... | ['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 -1.87890634... | [13.728578567504883, -3.040841579437256] |
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 Advers... | ['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 9.51542426... | [7.17929220199585, 3.9253768920898438] |
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 com... | ['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 2.20139831... | [6.313209056854248, 3.8056294918060303] |
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 adve... | ['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 -4.37180758... | [15.429186820983887, 5.901686191558838] |
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-L... | ['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 -5.94125092... | [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 Embed... | ['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 -1.58664972... | [10.602974891662598, 1.6589117050170898] |
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 f... | ['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 -2.05402613... | [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... | ['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 -1.82733938... | [5.56706428527832, 7.91452169418335] |
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 (refe... | ['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 8.61397944... | [8.180147171020508, 0.2565425932407379] |
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 abili... | ['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 -5.01614392... | [8.959161758422852, 6.270346164703369] |
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... | ['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 -5.53413570... | [5.69908332824707, 2.876546621322632] |
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. ... | ['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 2.26656601... | [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... | ['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 1.74481466... | [10.302032470703125, 3.129917860031128] |
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 cap... | ['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 -4.61729705... | [4.327296257019043, 4.328462600708008] |
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 po... | ['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 -2.07919493... | [7.430656909942627, 2.67018461227417] |
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 -6.93831444... | [-7.466744422912598, 3.6344053745269775] |
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... | ['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 -4.16965604... | [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 ri... | ['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 -1.33081362... | [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 co... | ['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 -4.17746931... | [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 ut... | ['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 -1.56465873... | [15.610273361206055, 5.812326908111572] |
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... | ['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 -4.32721823... | [11.053670883178711, 9.678942680358887] |
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 prop... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.33439159e-01 4.19515818e-01 -5.17176032e-01 -5.55034935e-01
-4.14195329e-01 -6.37755156e-01 6.69777393e-01 4.58694518e-01
-5.60000241e-01 7.84224033e-01 8.23006809e-01 -1.31909639e-01
-1.55986857e-03 -7.25748479e-01 -4.97015774e-01 -5.32930672e-01
3.34142148e-01 3.61977100e-01 5.10608137e-01 -3.25869828... | [10.300371170043945, 9.217884063720703] |
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 ti... | ['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 1.78657234... | [7.473206520080566, 4.054086208343506] |
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... | ['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 -1.25894602... | [8.544306755065918, 3.0470640659332275] |
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 morpholo... | ['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 -7.18598485... | [10.445398330688477, 10.140813827514648] |
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 ... | ['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 -4.58782643... | [14.226408004760742, 7.177973747253418] |
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... | ['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 -1.77493960... | [8.244187355041504, 10.242483139038086] |
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 ... | ['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 9.62400287... | [9.309515953063965, 0.6458185315132141] |
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... | ['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 -5.17658949... | [11.553430557250977, 7.961942195892334] |
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 m... | ['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 -2.44344845... | [4.078582286834717, 2.0783932209014893] |
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-intuitiv... | ['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 2.88842410... | [8.50195598602295, -0.22963018715381622] |
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... | ['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 5.44819869... | [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 mu... | ['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 -2.62893677... | [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 stru... | ['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 -4.27268714... | [7.572083950042725, 7.94228458404541] |
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) fore... | ['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 5.57388544... | [9.027249336242676, -0.8078721165657043] |
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 ... | ['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 -3.92689645... | [9.232489585876465, 7.286314964294434] |
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 t... | ['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 -5.58166027... | [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.... | ['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 -6.85476899... | [12.453529357910156, 8.03557300567627] |
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 accurac... | ['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 -2.43116692... | [14.541570663452148, 6.94865083694458] |
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 previo... | ['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 -1.16609439... | [9.545875549316406, 0.5828443765640259] |
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... | ['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 -2.09148780... | [11.37160587310791, 9.776836395263672] |
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.,... | ['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 -1.84643358... | [13.175834655761719, -2.5252389907836914] |
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 app... | ['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 -2.50276566... | [6.499324798583984, 3.424060344696045] |
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 pap... | ['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 -4.22678828... | [3.7405505180358887, 2.057838201522827] |
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 ris... | ['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 -1.09820031... | [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. Whil... | ['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 -4.88587236... | [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 taggi... | ['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 -4.19153810... | [10.247138023376465, 9.94664478302002] |
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 frame... | ['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 -5.38616739... | [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 ... | ['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 -4.68508184... | [5.551820755004883, 1.541089415550232] |
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 co... | ['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 -4.11081940... | [8.623188018798828, 10.522159576416016] |
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 surviv... | ['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 4.53276098... | [14.501784324645996, -2.7294483184814453] |
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=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jc2RsLWRvd25sb2Fkcy5pZWVlY29tcHV0ZXIub3JnL3Byb2NlZWRpbmdzLzNkdi8yMDIxLzI2ODgvMDAvMjY4ODAwYTYxNy5wZGYiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOj... | 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 ... | ['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 -1.94195673... | [6.724125862121582, -0.37909483909606934] |
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. I... | ['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 3.03913504... | [10.394563674926758, 6.933133125305176] |
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 im... | ['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 -6.09843209... | [8.750468254089355, -2.788994073867798] |
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 ... | ['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 -1.75501615... | [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... | ['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 -1.20906733... | [7.658567905426025, 2.3655812740325928] |
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 o... | ['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 -3.35374117... | [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 dam... | [] | 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 -1.84513032... | [4.680663108825684, 5.074392318725586] |
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 bas... | ['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 -2.88605273... | [9.985621452331543, 9.137858390808105] |
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 unsuperv... | ['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 -2.56199360... | [8.940932273864746, -3.571307897567749] |
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, ... | ['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 2.00688347... | [7.738247394561768, 9.746729850769043] |
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 pape... | ['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 -5.83316535... | [8.730478286743164, -3.5792646408081055] |
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