paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8cb62944-5092-4acf-a3de-bf3747a42cbc | context-aware-frame-semantic-role-labeling | null | null | https://aclanthology.org/Q15-1032 | https://aclanthology.org/Q15-1032.pdf | Context-aware Frame-Semantic Role Labeling | Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-l... | ['Michael Roth', 'Mirella Lapata'] | 2015-01-01 | null | null | null | tacl-2015-1 | ['scene-generation', 'stock-price-prediction'] | ['computer-vision', 'time-series'] | [ 6.01451159e-01 5.61262786e-01 -3.96606326e-01 -7.04569697e-01
-6.75284386e-01 -4.38718945e-01 1.20063674e+00 8.18396628e-01
-3.08057338e-01 1.04846740e+00 9.53114271e-01 -1.95137635e-01
2.33892743e-02 -6.74623013e-01 -4.88656878e-01 -2.06104249e-01
1.88817278e-01 3.61030847e-01 6.31247759e-01 -6.49597228... | [10.281678199768066, 9.182944297790527] |
1ff16031-728b-4a8f-8ec0-a0c86c2cb492 | one-class-knowledge-distillation-for-face | 2205.03792 | null | https://arxiv.org/abs/2205.03792v1 | https://arxiv.org/pdf/2205.03792v1.pdf | One-Class Knowledge Distillation for Face Presentation Attack Detection | Face presentation attack detection (PAD) has been extensively studied by research communities to enhance the security of face recognition systems. Although existing methods have achieved good performance on testing data with similar distribution as the training data, their performance degrades severely in application s... | ['Alex C. Kot', 'Yongjian Hu', 'Kwok-Yan Lam', 'Haoliang Li', 'Rizhao Cai', 'Zhi Li'] | 2022-05-08 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.19970161e-01 -3.02429438e-01 -1.25719100e-01 -5.08464754e-01
-6.78185821e-01 -6.92850947e-01 4.94463563e-01 -2.11194873e-01
-2.24428579e-01 5.48109233e-01 -3.01066190e-01 -5.45627363e-02
1.50792524e-01 -8.50444019e-01 -4.48229492e-01 -9.72652793e-01
-2.50771046e-02 5.54190934e-01 4.45060849e-01 -1.63023576... | [13.086736679077148, 1.2065646648406982] |
868c3dfc-5edf-4f6c-b291-4a1f46814daf | summarizing-videos-using-concentrated | null | null | https://dl.acm.org/doi/10.1145/3512527.3531404 | https://www.iti.gr/~bmezaris/publications/icmr2022_preprint.pdf | Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames | In this work, we describe a new method for unsupervised video summarization. To overcome limitations of existing unsupervised video summarization approaches, that relate to the unstable training of Generator-Discriminator architectures, the use of RNNs for modeling long-range frames' dependencies and the ability to par... | ['Ioannis Patras', 'Vasileios Mezaris', 'Georgios Balaouras', 'Evlampios Apostolidis'] | 2022-06-29 | null | null | null | acm-icmr-2022-6 | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 3.11623245e-01 4.02376801e-01 -8.38439167e-02 -7.43714496e-02
-6.40492320e-01 -2.67894715e-01 6.81929708e-01 3.27606469e-01
-5.53671837e-01 7.30328679e-01 7.59313881e-01 1.18273355e-01
-1.70948237e-01 -4.75499153e-01 -7.29190528e-01 -7.65618920e-01
-4.18312699e-02 3.85976791e-01 2.31319949e-01 -3.03935766... | [10.406689643859863, 0.4185032248497009] |
5072c0f9-719e-439c-ae35-598dabfd88f9 | extending-phrase-grounding-with-pronouns-in | 2210.12658 | null | https://arxiv.org/abs/2210.12658v1 | https://arxiv.org/pdf/2210.12658v1.pdf | Extending Phrase Grounding with Pronouns in Visual Dialogues | Conventional phrase grounding aims to localize noun phrases mentioned in a given caption to their corresponding image regions, which has achieved great success recently. Apparently, sole noun phrase grounding is not enough for cross-modal visual language understanding. Here we extend the task by considering pronouns as... | ['Min Zhang', 'Meishan Zhang', 'Xin Zhang', 'Panzhong Lu'] | 2022-10-23 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.33856609e-01 6.83345973e-01 -2.97804207e-01 -3.44860762e-01
-8.32401395e-01 -6.22608960e-01 7.79462874e-01 1.58406004e-01
-2.55838424e-01 5.77724814e-01 5.60569286e-01 -1.26468509e-01
3.78057063e-01 -8.45605552e-01 -9.78012085e-01 -4.98980552e-01
9.73656029e-02 6.73748732e-01 5.83630681e-01 -4.89713430... | [10.590408325195312, 1.586643099784851] |
9fd46629-9e46-4478-921e-5a686689557c | graph-convolutional-module-for-temporal | 2112.00302 | null | https://arxiv.org/abs/2112.00302v1 | https://arxiv.org/pdf/2112.00302v1.pdf | Graph Convolutional Module for Temporal Action Localization in Videos | Temporal action localization has long been researched in computer vision. Existing state-of-the-art action localization methods divide each video into multiple action units (i.e., proposals in two-stage methods and segments in one-stage methods) and then perform action recognition/regression on each of them individuall... | ['Chuang Gan', 'Junzhou Huang', 'Peilin Zhao', 'Yu Rong', 'Mingkui Tan', 'Wenbing Huang', 'Runhao Zeng'] | 2021-12-01 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 3.02929908e-01 -1.62962882e-03 -6.13292158e-01 -7.39122159e-04
-2.24714428e-01 -4.38748747e-01 6.36782944e-01 8.87265131e-02
-2.13653401e-01 4.18349743e-01 4.98416483e-01 -5.32109756e-04
-4.67675664e-02 -7.64133394e-01 -5.63840270e-01 -7.79221237e-01
-1.60804406e-01 -2.76823658e-02 8.33185792e-01 -7.16822669... | [8.420783042907715, 0.6570942401885986] |
9ce1c165-97d6-4d5a-a664-e61df092ed09 | ns-hunter-bert-cloze-based-semantic-denoising | null | null | https://aclanthology.org/2021.ccl-1.99 | https://aclanthology.org/2021.ccl-1.99.pdf | NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification | “Distant supervision can generate large-scale relation classification data quickly and economi-cally. However a great number of noise sentences are introduced which can not express their labeled relations. By means of pre-trained language model BERT’s powerful function in this paper we propose a BERT-based semantic den... | ['Zhang Yifei', 'Feng Shi', 'Wang Daling', 'Shen Tielin'] | null | null | null | null | ccl-2021-8 | ['relation-classification'] | ['natural-language-processing'] | [ 7.58449435e-02 5.48579514e-01 -1.60401151e-01 -7.29906976e-01
-1.07342708e+00 -3.78061265e-01 6.45022333e-01 2.46835917e-01
-4.30394918e-01 8.03333044e-01 5.32748640e-01 -1.03688814e-01
-2.64810652e-01 -9.92725790e-01 -5.58490217e-01 -8.29161286e-01
9.54842120e-02 5.71620941e-01 2.62132466e-01 -8.50174248... | [9.337960243225098, 8.65870475769043] |
05e26082-271c-4013-9890-46ef2d756620 | knowledge-based-analysis-for-mortality | 1902.07687 | null | https://arxiv.org/abs/1902.07687v2 | https://arxiv.org/pdf/1902.07687v2.pdf | Knowledge-based Analysis for Mortality Prediction from CT Images | Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality. Low-Dose CT (LCDT) scans have led to significant improvements in the accuracy of lung cancer diagnosis and thus the reduction of cancer deaths. Ho... | ['Pingkun Yan', 'Mannudeep K. Kalra', 'Ge Wang', 'Hengtao Guo', 'Uwe Kruger'] | 2019-02-20 | null | null | null | null | ['lung-cancer-diagnosis', 'clinical-knowledge'] | ['medical', 'miscellaneous'] | [-2.41649583e-01 -1.78462341e-01 -4.33042169e-01 -1.34102046e-01
-9.20541286e-01 -1.03122100e-01 4.03903127e-01 4.10851181e-01
-3.40324700e-01 7.62581646e-01 3.36255521e-01 -4.53199714e-01
-2.34402403e-01 -1.14037657e+00 -1.83203310e-01 -8.23681831e-01
-4.46710102e-02 6.41059756e-01 3.13895255e-01 2.80343384... | [15.350183486938477, -2.2725727558135986] |
16a85575-a18e-4b5c-bb81-e3560b7110fd | treeqn-and-atreec-differentiable-tree | 1710.11417 | null | http://arxiv.org/abs/1710.11417v2 | http://arxiv.org/pdf/1710.11417v2.pdf | TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning | Combining deep model-free reinforcement learning with on-line planning is a
promising approach to building on the successes of deep RL. On-line planning
with look-ahead trees has proven successful in environments where transition
models are known a priori. However, in complex environments where transition
models need t... | ['Tim Rocktäschel', 'Gregory Farquhar', 'Shimon Whiteson', 'Maximilian Igl'] | 2017-10-31 | treeqn-and-atreec-differentiable-tree-1 | https://openreview.net/forum?id=H1dh6Ax0Z | https://openreview.net/pdf?id=H1dh6Ax0Z | iclr-2018-1 | ['value-prediction'] | ['computer-code'] | [ 1.39850587e-01 5.40074825e-01 -5.59425354e-01 -3.03302437e-01
-1.11018240e+00 -6.27276540e-01 5.69985449e-01 -1.11525930e-01
-7.26130903e-01 1.01550758e+00 1.92433402e-01 -5.64546525e-01
-2.10416257e-01 -7.46497810e-01 -8.99424970e-01 -5.65866232e-01
-4.50774521e-01 8.50189626e-01 1.23127192e-01 -3.34231913... | [4.096310138702393, 1.672236680984497] |
855f82ca-97db-430c-bb44-ec67eca79970 | speech-separation-with-large-scale-self | 2211.05172 | null | https://arxiv.org/abs/2211.05172v2 | https://arxiv.org/pdf/2211.05172v2.pdf | Speech separation with large-scale self-supervised learning | Self-supervised learning (SSL) methods such as WavLM have shown promising speech separation (SS) results in small-scale simulation-based experiments. In this work, we extend the exploration of the SSL-based SS by massively scaling up both the pre-training data (more than 300K hours) and fine-tuning data (10K hours). We... | ['Sefik Emre Eskimez', 'Sunit Sivasankaran', 'Jinyu Li', 'Takuya Yoshioka', 'Xiaofei Wang', 'Yu Wu', 'Jian Wu', 'Naoyuki Kanda', 'Zhuo Chen'] | 2022-11-09 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 1.53549120e-01 3.26413989e-01 1.99364468e-01 -6.20212078e-01
-1.51210201e+00 -3.61229032e-01 5.00869751e-01 1.46832690e-01
-6.10660315e-01 4.56538200e-01 4.51663613e-01 -6.07212365e-01
-8.75730291e-02 7.61819631e-02 -5.60519993e-01 -7.24860191e-01
-5.45494221e-02 4.77459073e-01 7.33658597e-02 -3.91160063... | [14.656167984008789, 6.292220592498779] |
64e209d6-5605-48ae-bcab-cc0cd4517a78 | a-constructive-gan-based-approach-to-exact | 2206.06116 | null | https://arxiv.org/abs/2206.06116v1 | https://arxiv.org/pdf/2206.06116v1.pdf | A Constructive GAN-based Approach to Exact Estimate Treatment Effect without Matching | Matching has become the mainstream in counterfactual inference, with which selection bias between sample groups can be significantly eliminated. However in practice, when estimating average treatment effect on the treated (ATT) via matching, no matter which method, the trade-off between estimation accuracy and informat... | ['Kerry Papps', 'Boyang You'] | 2022-06-13 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 2.72938251e-01 1.96416304e-01 -7.03436017e-01 -1.78964213e-01
-1.00453734e+00 -3.70730400e-01 5.90328276e-01 -2.40054354e-01
-1.73670843e-01 1.41190434e+00 2.58014739e-01 -4.15510416e-01
-9.55854207e-02 -1.12025130e+00 -7.25291729e-01 -9.33963358e-01
1.69177830e-01 4.22626585e-01 -5.90834737e-01 3.37474078... | [8.095526695251465, 5.423703670501709] |
df174195-02a4-45d7-98ec-73573f7ac75e | a-new-knowledge-distillation-network-for | 2209.00519 | null | https://arxiv.org/abs/2209.00519v1 | https://arxiv.org/pdf/2209.00519v1.pdf | A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect Detection | Surface defect detection is one of the most essential processes for industrial quality inspection. Deep learning-based surface defect detection methods have shown great potential. However, the well-performed models usually require large training data and can only detect defects that appeared in the training stage. When... | ['Yiping Gao', 'Xinyu Li', 'Liang Gao', 'Chen Sun'] | 2022-09-01 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.65208125e-01 1.62317068e-03 2.87815064e-01 -3.55272919e-01
-6.42151713e-01 3.75483632e-01 2.43296355e-01 1.68625906e-01
-2.57331967e-01 4.63121951e-01 -3.03972632e-01 1.87984869e-01
-4.64143306e-01 -1.03600383e+00 -6.55757844e-01 -8.52981329e-01
3.08431357e-01 2.44759515e-01 6.21691048e-01 -1.27130151... | [7.494106769561768, 2.0797042846679688] |
4458a7c5-038b-42b9-8ba4-f371f605154b | the-dependence-of-machine-learning-on | 1703.08251 | null | http://arxiv.org/abs/1703.08251v1 | http://arxiv.org/pdf/1703.08251v1.pdf | The Dependence of Machine Learning on Electronic Medical Record Quality | There is growing interest in applying machine learning methods to Electronic
Medical Records (EMR). Across different institutions, however, EMR quality can
vary widely. This work investigated the impact of this disparity on the
performance of three advanced machine learning algorithms: logistic regression,
multilayer p... | ['David Ledbetter', 'Randall Wetzel', 'Melissa Aczon', 'Long Ho'] | 2017-03-23 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 2.23417073e-01 -3.38678479e-01 -1.01187967e-01 -1.81053832e-01
-8.31037343e-01 -5.48793733e-01 -1.24444678e-01 7.56812751e-01
-8.02953005e-01 7.77383626e-01 4.66290385e-01 -1.03087807e+00
-6.18152618e-01 -5.75899780e-01 -4.77495164e-01 -3.34122747e-01
-6.98299929e-02 7.86747634e-01 -5.68705201e-01 4.27914113... | [7.9877543449401855, 6.196274757385254] |
31d19ef4-5206-4a93-95b8-d443002df0a6 | current-shortcomings-of-machine-translation | null | null | https://aclanthology.org/2022.clib-1.20 | https://aclanthology.org/2022.clib-1.20.pdf | Current Shortcomings of Machine Translation in Spanish and Bulgarian Vis-à-vis English | In late 2016, Google Translate (GT), widely considered a machine translation leader, replaced its statistical machine translation (SMT) functions with a neural machine translation (NMT) model for many large languages, including Spanish, with other languages following thereafter. Whereas the capabilities of GT had previ... | ['Travis Sorenson'] | null | null | null | null | clib-2022-9 | ['nmt'] | ['computer-code'] | [ 4.18585062e-01 2.34204784e-01 -4.33784902e-01 -2.33670294e-01
-1.26516044e+00 -1.03338909e+00 1.04710555e+00 5.19193672e-02
-3.77767980e-01 1.08771336e+00 2.58702308e-01 -1.33709300e+00
2.79302299e-01 -3.53052497e-01 -6.18829072e-01 -2.68583864e-01
4.54397559e-01 9.70160723e-01 -3.48825812e-01 -3.46287757... | [11.500082969665527, 10.3412446975708] |
bafd7588-fd32-49f4-8487-2ca1953f0e08 | self-supervised-matting-specific-portrait | 2208.06601 | null | https://arxiv.org/abs/2208.06601v1 | https://arxiv.org/pdf/2208.06601v1.pdf | Self-supervised Matting-specific Portrait Enhancement and Generation | We resolve the ill-posed alpha matting problem from a completely different perspective. Given an input portrait image, instead of estimating the corresponding alpha matte, we focus on the other end, to subtly enhance this input so that the alpha matte can be easily estimated by any existing matting models. This is acco... | ['Shengfeng He', 'Yangyang Xu Zeyang Zhou'] | 2022-08-13 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 7.40586162e-01 3.60439360e-01 -1.72202364e-01 -1.57733709e-01
-6.54112220e-01 -8.78873169e-01 5.72778881e-01 -6.07763350e-01
1.90017194e-01 6.51866078e-01 2.91309774e-01 -1.17408700e-01
1.93128303e-01 -8.29161882e-01 -8.83732855e-01 -8.47319663e-01
5.48006415e-01 4.79773521e-01 -3.99032414e-01 -2.08553106... | [11.733833312988281, -0.33132123947143555] |
3bff30b9-106f-4ec8-8aa4-92f86105dd4b | boosting-monocular-depth-estimation-with-1 | 2202.01470 | null | https://arxiv.org/abs/2202.01470v4 | https://arxiv.org/pdf/2202.01470v4.pdf | Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth | Existing monocular depth estimation methods have achieved excellent robustness in diverse scenes, but they can only retrieve affine-invariant depth, up to an unknown scale and shift. However, in some video-based scenarios such as video depth estimation and 3D scene reconstruction from a video, the unknown scale and shi... | ['Feng Wu', 'Chunhua Shen', 'Feng Zhao', 'Kai Cheng', 'Hao Chen', 'Wei Yin', 'Guangkai Xu'] | 2022-02-03 | null | null | null | null | ['3d-scene-reconstruction', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [ 1.32053196e-01 -2.90313214e-01 -1.37593180e-01 -4.02982354e-01
-9.04261708e-01 -3.67834449e-01 3.40430379e-01 -3.42948556e-01
-2.21032292e-01 4.28617954e-01 3.70541930e-01 1.75170392e-01
4.03746247e-01 -7.85140038e-01 -9.48963404e-01 -7.04908729e-01
3.72848660e-01 3.52193445e-01 7.28319466e-01 -4.12378274... | [8.734017372131348, -2.6544530391693115] |
6459e11a-0aa5-4fee-a26a-83527f7f3d73 | focal-onset-seizure-prediction-using | 1805.11576 | null | http://arxiv.org/abs/1805.11576v1 | http://arxiv.org/pdf/1805.11576v1.pdf | Focal onset seizure prediction using convolutional networks | Objective: This work investigates the hypothesis that focal seizures can be
predicted using scalp electroencephalogram (EEG) data. Our first aim is to
learn features that distinguish between the interictal and preictal regions.
The second aim is to define a prediction horizon in which the prediction is as
accurate and ... | ['Bülent Yener', 'Madeline Fields', 'Lara Marcuse', 'Kalina Swann', 'Haidar Khan'] | 2018-05-29 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 2.31970161e-01 -4.34730761e-02 1.11877047e-01 -5.03169894e-01
-7.71149218e-01 -3.82267863e-01 5.17619491e-01 2.29054362e-01
-3.38540167e-01 8.27609718e-01 1.60609081e-01 9.71504599e-02
-6.62427723e-01 -3.83952022e-01 -2.57722050e-01 -7.52096236e-01
-9.85117912e-01 1.88920557e-01 -1.09769545e-01 1.79802820... | [13.227317810058594, 3.5239193439483643] |
c6eced68-964a-4837-86c3-f166dea238a0 | lipkey-a-large-scale-news-dataset-for-absent | null | null | https://aclanthology.org/2022.coling-1.303 | https://aclanthology.org/2022.coling-1.303.pdf | LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization | Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has released the datasets of keyphrases and summarization separately, in this work, we introduce LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, an... | ['Jey Han Lau', 'Timothy Baldwin', 'Fajri Koto'] | null | null | null | null | coling-2022-10 | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.04943168e-01 2.31420442e-01 -6.27868772e-01 -4.33078073e-02
-1.35438812e+00 -8.44376743e-01 1.16359723e+00 8.50545585e-01
-3.60832453e-01 1.14614892e+00 1.61739886e+00 -3.20265442e-01
-1.56408310e-01 -3.53478223e-01 -8.02339435e-01 -1.39694571e-01
1.56479865e-01 1.97857529e-01 1.38053373e-02 -2.41736576... | [12.526122093200684, 9.489278793334961] |
17988190-112e-4cfa-9640-ed151315e4fe | robust-kernelized-multi-view-self | 1709.05083 | null | http://arxiv.org/abs/1709.05083v1 | http://arxiv.org/pdf/1709.05083v1.pdf | Robust Kernelized Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization | Most recently, tensor-SVD is implemented on multi-view self-representation
clustering and has achieved the promising results in many real-world
applications such as face clustering, scene clustering and generic object
clustering. However, tensor-SVD based multi-view self-representation clustering
is proposed originally... | ['Yanyun Qu', 'Jinyan Liu', 'Yuan Xie', 'Wensheng Zhang'] | 2017-09-15 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-2.84067392e-01 -4.48907971e-01 -9.94966924e-02 -2.57647932e-01
-7.10761428e-01 -5.15836477e-01 2.46815071e-01 -2.08892867e-01
-2.00836249e-02 3.36800143e-02 1.48832932e-01 1.35220841e-01
-4.52434093e-01 -5.04470706e-01 -3.09510708e-01 -1.15493226e+00
2.13726997e-01 5.27724564e-01 9.80799124e-02 6.55037612... | [8.068828582763672, 4.505044460296631] |
5093ae0b-86d0-4fdc-ac49-d2904f5f11ea | boundary-aware-supervoxel-level-iteratively | 2303.10692 | null | https://arxiv.org/abs/2303.10692v1 | https://arxiv.org/pdf/2303.10692v1.pdf | Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning | Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints. While iterative in nature, most existing interactive segmentation methods tend to ignore the dynamics of successive interactions and take each interaction i... | ['Ya zhang', 'Yanfeng Wang', 'Xiaoyun Zhang', 'Bo Jin', 'Xiangfeng Wang', 'Qisen Xu', 'Chaofan Ma'] | 2023-03-19 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 1.25442043e-01 3.00088137e-01 -2.54721701e-01 -2.82013595e-01
-5.19317031e-01 -3.11960280e-01 3.01837891e-01 2.48465300e-01
-6.68936670e-01 6.94575310e-01 -1.66676268e-01 -2.23493695e-01
-1.42268494e-01 -6.68892801e-01 -7.19666064e-01 -9.45120454e-01
-1.29897505e-01 4.66620713e-01 7.63169050e-01 1.15125820... | [9.528047561645508, 0.056760165840387344] |
dc38a52a-17d4-4233-8cf9-3e382999c89a | stecformer-spatio-temporal-encoding-cascaded | 2305.16370 | null | https://arxiv.org/abs/2305.16370v1 | https://arxiv.org/pdf/2305.16370v1.pdf | Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting | Multivariate long-term time series forecasting is of great application across many domains, such as energy consumption and weather forecasting. With the development of transformer-based methods, the performance of multivariate long-term time series forecasting has been significantly improved, however, the study of spat... | ['Long Yu', 'Wenxiao Jia', 'Yi Wei', 'Zheng Sun'] | 2023-05-25 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 1.52781457e-01 -5.60186505e-01 -1.47107154e-01 -2.93643028e-01
-4.74974394e-01 -3.37005556e-01 6.92557275e-01 1.39131904e-01
1.97829217e-01 5.41562676e-01 2.70781338e-01 -4.44405138e-01
-4.15330797e-01 -1.06139481e+00 -4.94131267e-01 -8.95323098e-01
-5.28195143e-01 1.15012281e-01 5.66762805e-01 -4.05835122... | [6.891205310821533, 2.899203300476074] |
90715363-00ff-49b7-b028-38b181e1a88b | probabilistic-program-induction-for-intuitive | null | null | https://openreview.net/forum?id=HJMsiiRctX | https://openreview.net/pdf?id=HJMsiiRctX | Probabilistic Program Induction for Intuitive Physics Game Play | Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the physics of objects. We propose a framework for bots to deploy similar tools for interacting with intuitive physics environments. The framework employs a physics simulation in a probabilistic way to infer about m... | ['Fahad Alhasoun'] | 2018-09-27 | null | null | null | null | ['program-induction'] | ['computer-code'] | [-3.38763177e-01 7.04595670e-02 4.79125887e-01 5.95416725e-02
-1.02322541e-01 -8.23202789e-01 8.75014961e-01 -1.44791469e-01
-4.03782517e-01 7.55105495e-01 -1.21723056e-01 -6.02589846e-01
-2.09865957e-01 -1.41145766e+00 -8.34823132e-01 -3.34548444e-01
-1.63243353e-01 1.07237458e+00 8.37349474e-01 -5.01460493... | [4.001101016998291, 1.2993706464767456] |
6c2d8852-8109-47f3-aab2-c10029f219ea | accelerated-componentwise-gradient-boosting | 2110.03513 | null | https://arxiv.org/abs/2110.03513v2 | https://arxiv.org/pdf/2110.03513v2.pdf | Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization | Componentwise boosting (CWB), also known as model-based boosting, is a variant of gradient boosting that builds on additive models as base learners to ensure interpretability. CWB is thus often used in research areas where models are employed as tools to explain relationships in data. One downside of CWB is its computa... | ['David Rügamer', 'Bernd Bischl', 'Daniel Schalk'] | 2021-10-07 | null | null | null | null | ['additive-models'] | ['methodology'] | [-1.95929110e-01 -2.77495980e-01 -3.76440853e-01 -6.22122109e-01
-7.48844087e-01 -6.21810481e-02 6.51482463e-01 5.59556782e-01
-3.45992386e-01 1.10707235e+00 -1.25867739e-01 -7.90617466e-01
-2.00696319e-01 -8.47267747e-01 -6.63369060e-01 -5.77223003e-01
-2.97135442e-01 1.67264104e-01 3.52515340e-01 -5.18902898... | [8.39678955078125, 4.359677791595459] |
69e83ce3-4f29-4917-9996-25369d6beeaf | treating-motion-as-option-to-reduce-motion | 2209.03138 | null | https://arxiv.org/abs/2209.03138v5 | https://arxiv.org/pdf/2209.03138v5.pdf | Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation | Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level. In unsupervised VOS, most state-of-the-art methods leverage motion cues obtained from optical flow maps in addition to appearance cues to exploit the property that salient objects usually have dis... | ['Sangyoun Lee', 'Donghyeong Kim', 'Chaewon Park', 'Seunghoon Lee', 'Minhyeok Lee', 'Suhwan Cho'] | 2022-09-04 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 3.79103035e-01 -2.08643183e-01 -6.34625793e-01 -1.45193741e-01
-3.22380692e-01 -4.36045080e-01 4.01106387e-01 -1.07618406e-01
-4.79931742e-01 5.56350470e-01 1.68191373e-01 -4.69042175e-02
1.04845077e-01 -3.39105695e-01 -7.60623753e-01 -6.62812889e-01
-1.22884296e-01 -2.26479620e-01 8.46865416e-01 5.46819381... | [9.139840126037598, -0.24853579699993134] |
ec918eec-15f4-46cf-b68f-30d16d0bddc0 | one-class-meta-learning-towards-generalizable | 2109.06859 | null | https://arxiv.org/abs/2109.06859v1 | https://arxiv.org/pdf/2109.06859v1.pdf | One-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification | Real-world classification tasks are frequently required to work in an open-set setting. This is especially challenging for few-shot learning problems due to the small sample size for each known category, which prevents existing open-set methods from working effectively; however, most multiclass few-shot methods are lim... | ['Matthew Turk', 'Jedrzej Kozerawski'] | 2021-09-14 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 5.29371679e-01 -1.50110554e-02 -5.31064749e-01 -4.40358013e-01
-1.09139884e+00 -1.80466831e-01 5.94902992e-01 9.76965800e-02
-4.40366477e-01 8.41965973e-01 -3.23595881e-01 1.26007259e-01
-2.69241214e-01 -8.16906214e-01 -6.22173965e-01 -7.47594059e-01
-1.04872948e-02 3.92799020e-01 6.07143641e-01 -2.19847918... | [9.990998268127441, 2.9966847896575928] |
a0810d74-425f-49fa-a3b7-013eb823c670 | 190602392 | 1906.02392 | null | https://arxiv.org/abs/1906.02392v1 | https://arxiv.org/pdf/1906.02392v1.pdf | Generative Model-Based Ischemic Stroke Lesion Segmentation | CT perfusion (CTP) has been used to triage ischemic stroke patients in the early stage, because of its speed, availability, and lack of contraindications. Perfusion parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) and time of peak (Tmax) could also be computed from CT... | ['Tao Song'] | 2019-06-06 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 6.56636953e-02 -3.07857066e-01 -1.85635000e-01 -3.99568886e-01
-9.35706913e-01 -6.36225700e-01 3.24902385e-01 -2.87933480e-02
-6.50406301e-01 8.80014718e-01 2.59159029e-01 -4.81753707e-01
-1.47876078e-02 -6.95408642e-01 -1.64315403e-01 -9.31044936e-01
-1.96243718e-01 7.17658460e-01 3.58851194e-01 3.75599474... | [14.303686141967773, -2.0722150802612305] |
4b2c9b53-b244-4295-82ef-87d468534bf4 | automatic-aortic-valve-pathology-detection | 2304.05885 | null | https://arxiv.org/abs/2304.05885v2 | https://arxiv.org/pdf/2304.05885v2.pdf | Automatic Aortic Valve Pathology Detection from 3-Chamber Cine MRI with Spatio-Temporal Attention Maps | The assessment of aortic valve pathology using magnetic resonance imaging (MRI) typically relies on blood velocity estimates acquired using phase contrast (PC) MRI. However, abnormalities in blood flow through the aortic valve often manifest by the dephasing of blood signal in gated balanced steady-state free precessio... | ['M. Varela', 'A. A. Bharath', 'G. Cole', 'N. Peters', 'N. Linton', 'J. Howard', 'S. Zaman', 'C. Galazis', 'K. Vimalesvaran', 'Y. On'] | 2023-04-12 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-1.61057862e-03 3.16825975e-03 -1.40863676e-02 -1.43026903e-01
-4.29456443e-01 -8.25574279e-01 1.90109953e-01 -6.55112183e-03
-2.65891403e-01 6.92405641e-01 1.93945140e-01 -8.67467344e-01
-4.69485223e-01 -3.10907722e-01 1.51028158e-02 -6.22647643e-01
-9.12300467e-01 1.13640404e+00 3.32370073e-01 2.61680961... | [14.10211181640625, -2.4657020568847656] |
2036baf2-9b8a-4026-823d-f01075200994 | a-spatio-temporal-spot-forecasting-framework | 2003.13977 | null | https://arxiv.org/abs/2003.13977v2 | https://arxiv.org/pdf/2003.13977v2.pdf | A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction | Spatio-temporal forecasting is an open research field whose interest is growing exponentially. In this work we focus on creating a complex deep neural framework for spatio-temporal traffic forecasting with comparatively very good performance and that shows to be adaptable over several spatio-temporal conditions while r... | ['José L. Aznarte', 'Rodrigo de Medrano'] | 2020-03-31 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-2.00361311e-01 -3.49719584e-01 -1.64476350e-01 -5.80804169e-01
2.91264076e-02 -2.23119721e-01 1.02622688e+00 -9.28723812e-02
-2.24847823e-01 6.63317263e-01 1.85283646e-01 -8.58455896e-01
-4.39322412e-01 -6.64686739e-01 -6.23813152e-01 -4.70240593e-01
-5.22461593e-01 4.79199946e-01 6.81882262e-01 -6.64444745... | [6.640868186950684, 2.4112589359283447] |
cf752237-0f4b-4e6c-9528-ae9159b3014c | controlvideo-adding-conditional-control-for | 2305.17098 | null | https://arxiv.org/abs/2305.17098v1 | https://arxiv.org/pdf/2305.17098v1.pdf | ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing | In this paper, we present ControlVideo, a novel method for text-driven video editing. Leveraging the capabilities of text-to-image diffusion models and ControlNet, ControlVideo aims to enhance the fidelity and temporal consistency of videos that align with a given text while preserving the structure of the source video... | ['Jun Zhu', 'Chongxuan Li', 'Fan Bao', 'Rongzhen Wang', 'Min Zhao'] | 2023-05-26 | null | null | null | null | ['text-to-video-editing'] | ['computer-vision'] | [ 1.11776315e-01 -2.34391838e-01 -4.25044626e-01 -1.24979265e-01
-5.07322729e-01 -8.02323580e-01 7.90673375e-01 -2.27942199e-01
-2.66438276e-01 4.03805286e-01 6.53796673e-01 -1.33168042e-01
1.07235566e-01 -3.95881563e-01 -6.29143476e-01 -3.41397524e-01
-5.19382879e-02 -2.82940399e-02 3.86608601e-01 -1.87612832... | [10.942914009094238, -0.6106119155883789] |
23641db4-c0b2-4a14-be58-742cab8aa5df | color-aware-deep-temporal-backdrop-duplex | 2306.02954 | null | https://arxiv.org/abs/2306.02954v1 | https://arxiv.org/pdf/2306.02954v1.pdf | Color-aware Deep Temporal Backdrop Duplex Matting System | Deep learning-based alpha matting showed tremendous improvements in recent years, yet, feature film production studios still rely on classical chroma keying including costly post-production steps. This perceived discrepancy can be explained by some missing links necessary for production which are currently not adequate... | ['Bodo Rosenhahn', 'Hendrik Hachmann'] | 2023-06-05 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 4.61699247e-01 -3.96476120e-01 1.71703085e-01 -1.49897024e-01
-5.84338605e-01 -5.17963886e-01 4.23833072e-01 -1.58442110e-01
-1.15275458e-01 4.27345306e-01 -3.62500966e-01 -1.38223723e-01
6.63773045e-02 -5.67187667e-01 -1.24728739e+00 -7.51538277e-01
1.70028284e-01 3.47556978e-01 4.98444200e-01 -1.07122459... | [10.884498596191406, -1.163111925125122] |
108c8412-d7be-4771-b8b3-2c65a12bf53d | multispanqa-a-dataset-for-multi-span-question | null | null | https://aclanthology.org/2022.naacl-main.90 | https://aclanthology.org/2022.naacl-main.90.pdf | MultiSpanQA: A Dataset for Multi-Span Question Answering | Most existing reading comprehension datasets focus on single-span answers, which can be extracted as a single contiguous span from a given text passage. Multi-span questions, i.e., questions whose answer is a series of multiple discontiguous spans in the text, are common real life but are less studied. In this paper, w... | ['Timothy Baldwin', 'Maria Vasardani', 'Martin Tomko', 'Haonan Li'] | null | null | null | null | naacl-2022-7 | ['natural-questions'] | ['miscellaneous'] | [ 1.26724929e-01 2.58885533e-01 -2.83518154e-02 -5.63970268e-01
-1.51795828e+00 -1.11863446e+00 4.55061793e-01 4.22295332e-01
-3.85201007e-01 9.07841146e-01 7.28765547e-01 -4.69179839e-01
-2.29675516e-01 -6.18319929e-01 -7.43480682e-01 5.62290363e-02
4.93687689e-01 5.05088449e-01 8.87419045e-01 -7.01322019... | [11.381495475769043, 8.063383102416992] |
985cef4a-371a-4b2c-8c79-fc97555be7ef | better-automatic-evaluation-of-open-domain | 1904.10635 | null | http://arxiv.org/abs/1904.10635v1 | http://arxiv.org/pdf/1904.10635v1.pdf | Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings | Despite advances in open-domain dialogue systems, automatic evaluation of
such systems is still a challenging problem. Traditional reference-based
metrics such as BLEU are ineffective because there could be many valid
responses for a given context that share no common words with reference
responses. A recent work propo... | ['Johnny Tian-Zheng Wei', 'Nanyun Peng', 'Aram Galstyan', 'Sarik Ghazarian'] | 2019-04-24 | better-automatic-evaluation-of-open-domain-1 | https://aclanthology.org/W19-2310 | https://aclanthology.org/W19-2310.pdf | ws-2019-6 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-1.02651663e-01 -4.51800413e-02 4.27134410e-02 -6.29840374e-01
-1.00095022e+00 -6.63407922e-01 8.74284923e-01 4.75834072e-01
-8.36991847e-01 9.73379195e-01 7.77975976e-01 -3.28282714e-02
-2.13027745e-01 -6.78409398e-01 3.12563062e-01 -2.34274924e-01
4.06548887e-01 7.07243323e-01 4.22032744e-01 -7.82924950... | [12.650997161865234, 8.16390323638916] |
1018698e-f9cd-42a0-a22f-d5cf271ca5b7 | dual-gated-fusion-with-prefix-tuning-for | 2306.11020 | null | https://arxiv.org/abs/2306.11020v1 | https://arxiv.org/pdf/2306.11020v1.pdf | Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction | Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues. Rich visual content is valuable for the MMRE task, but existing works cannot well model finer associations among different modalities, failing to capture the truly helpful visual information ... | ['JianXin Li', 'Shiyao Cui', 'Xutan Peng', 'Cheng Ji', 'Shu Guo', 'Qian Li'] | 2023-06-19 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 8.80910307e-02 -2.13983506e-02 -1.31614983e-01 -1.20789029e-01
-7.82658756e-01 -3.34317386e-01 8.74810934e-01 2.77416706e-01
-2.99495876e-01 5.27600646e-01 5.58722496e-01 1.19337350e-01
-4.97111958e-03 -7.41351724e-01 -5.15424371e-01 -7.35473275e-01
2.68619031e-01 2.56581336e-01 2.58831859e-01 -7.93770924... | [10.657267570495605, 1.3702291250228882] |
e0ed0b26-b334-4a94-8b1c-2293267fdeb6 | wider-closer-mixture-of-short-channel | 2212.03506 | null | https://arxiv.org/abs/2212.03506v1 | https://arxiv.org/pdf/2212.03506v1.pdf | WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition | Zero-shot cross-lingual named entity recognition (NER) aims at transferring knowledge from annotated and rich-resource data in source languages to unlabeled and lean-resource data in target languages. Existing mainstream methods based on the teacher-student distillation framework ignore the rich and complementary infor... | ['Cong Liu', 'Zhigang Chen', 'Quan Liu', 'Wu Guo', 'Zhen-Hua Ling', 'Jia-Chen Gu', 'Beiduo Chen', 'Jun-Yu Ma'] | 2022-12-07 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-1.54119760e-01 -5.02692945e-02 -3.33035856e-01 -6.65885329e-01
-8.03419292e-01 -6.04954183e-01 6.44581974e-01 -1.83591582e-02
-8.78306568e-01 7.16173232e-01 2.70254582e-01 -2.17318729e-01
3.44025850e-01 -6.66417599e-01 -5.28727293e-01 -5.04290521e-01
3.40455741e-01 3.57479632e-01 3.87814701e-01 -4.69822019... | [9.919981956481934, 9.640593528747559] |
e2fc265b-3411-4066-8bfc-b53686ecfbf0 | accurate-open-set-recognition-for-memory | 2212.08817 | null | https://arxiv.org/abs/2212.08817v1 | https://arxiv.org/pdf/2212.08817v1.pdf | Accurate Open-set Recognition for Memory Workload | How can we accurately identify new memory workloads while classifying known memory workloads? Verifying DRAM (Dynamic Random Access Memory) using various workloads is an important task to guarantee the quality of DRAM. A crucial component in the process is open-set recognition which aims to detect new workloads not see... | ['U Kang', 'Suhyun Chae', 'Jongmin Park', 'Jeeyong Lee', 'Vladimir Egay', 'Sooyeon Shim', 'Jun-Gi Jang'] | 2022-12-17 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 7.49732740e-03 -7.46032715e-01 -7.32784629e-01 -1.47867680e-01
-1.03691721e+00 -7.82686949e-01 3.66682500e-01 3.07828873e-01
-7.47313574e-02 6.65572166e-01 3.22842263e-02 -4.94550467e-01
1.63616836e-01 -8.63479912e-01 -6.91140890e-01 -7.10432708e-01
8.45600441e-02 8.37166846e-01 8.48763585e-01 -6.71201665... | [7.31020975112915, 7.551514148712158] |
5444abe6-b134-490d-9121-76f0ea202ce5 | mixture-of-prompt-experts-for-generalizable | 2305.14628 | null | https://arxiv.org/abs/2305.14628v1 | https://arxiv.org/pdf/2305.14628v1.pdf | Mixture of Prompt Experts for Generalizable and Interpretable Question Answering | One of the ultimate quests of question answering (QA) is to deploy a system that can answer any type of question from the users, and refrain from answering when it does not know the answer. While recent advancements in scaling large language models (LLMs) brought significant improvements on various QA datasets, it rema... | ['Jordan Boyd-Graber', 'Luke Zettlemoyer', 'Chen Zhao', 'Weijia Shi', 'Chenglei Si'] | 2023-05-24 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 5.92720089e-03 6.06703699e-01 8.47179815e-03 -4.97362763e-01
-1.10674703e+00 -1.06190431e+00 3.34603459e-01 2.34446153e-01
-2.62221754e-01 4.92801249e-01 3.96391273e-01 -8.73357296e-01
-4.89739716e-01 -6.64200723e-01 -3.87477517e-01 1.13052391e-01
6.58196568e-01 8.75664115e-01 5.23564041e-01 -6.81204855... | [11.063708305358887, 7.990869998931885] |
d4ba8e9b-9ecb-4254-9f81-0aaf5c725ea1 | parformer-transformer-based-multi-task | 2304.07230 | null | https://arxiv.org/abs/2304.07230v1 | https://arxiv.org/pdf/2304.07230v1.pdf | PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition | Pedestrian attribute recognition (PAR) has received increasing attention because of its wide application in video surveillance and pedestrian analysis. Extracting robust feature representation is one of the key challenges in this task. The existing methods mainly use the convolutional neural network (CNN) as the backbo... | ['Hanzi Wang', 'Yang Lu', 'Yukang Zhang', 'Xinwen Fan'] | 2023-04-14 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-3.04189622e-02 -5.79015017e-01 -9.11136866e-02 -7.42458403e-01
-5.82365394e-01 -1.51931599e-01 5.63898146e-01 -8.55012685e-02
-4.53939438e-01 4.69537526e-01 2.94651479e-01 2.01211333e-01
1.79243043e-01 -9.57130015e-01 -6.20988965e-01 -9.93169367e-01
3.18007559e-01 -7.23644271e-02 3.67712945e-01 -1.93644345... | [14.376360893249512, 0.9653447270393372] |
b1216be2-43bd-4ea5-8155-0e591359edaa | efficient-sampling-in-pomdps-with-lipschitz | 2106.04206 | null | https://arxiv.org/abs/2106.04206v1 | https://arxiv.org/pdf/2106.04206v1.pdf | Efficient Sampling in POMDPs with Lipschitz Bandits for Motion Planning in Continuous Spaces | Decision making under uncertainty can be framed as a partially observable Markov decision process (POMDP). Finding exact solutions of POMDPs is generally computationally intractable, but the solution can be approximated by sampling-based approaches. These sampling-based POMDP solvers rely on multi-armed bandit (MAB) he... | ['Martin Lauer', 'Felix Hauser', 'Ömer Şahin Taş'] | 2021-06-08 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 1.35949880e-01 5.46402276e-01 -7.07680166e-01 -2.92606831e-01
-1.06203580e+00 -6.35445476e-01 4.45598096e-01 5.63620590e-02
-5.22919893e-01 1.37313902e+00 5.29434919e-01 -6.61426067e-01
-5.85303664e-01 -9.66547847e-01 -5.90797484e-01 -6.12479925e-01
-9.74400714e-02 9.13771152e-01 1.37863234e-01 1.25528365... | [4.3071675300598145, 2.319322109222412] |
34d49ac2-c14a-4ebe-90f3-97709a0b49ea | a-transfer-learning-based-approach-for | 2111.00976 | null | https://arxiv.org/abs/2111.00976v2 | https://arxiv.org/pdf/2111.00976v2.pdf | A transfer learning based approach for pronunciation scoring | Phone-level pronunciation scoring is a challenging task, with performance far from that of human annotators. Standard systems generate a score for each phone in a phrase using models trained for automatic speech recognition (ASR) with native data only. Better performance has been shown when using systems that are train... | ['Luciana Ferrer', 'Cyntia Bonomi', 'Jazmin Vidal', 'Marcelo Sancinetti'] | 2021-11-01 | null | null | null | null | ['phone-level-pronunciation-scoring'] | ['speech'] | [ 1.00030221e-01 1.08830921e-01 6.45942427e-03 -6.53342485e-01
-1.82886732e+00 -5.54104388e-01 3.18711817e-01 5.65805733e-02
-7.08524227e-01 8.03978860e-01 5.51833749e-01 -4.69119757e-01
3.58288169e-01 -1.67702392e-01 -5.14005959e-01 -1.12632848e-01
4.28014606e-01 9.61959302e-01 3.11375827e-01 -4.86198187... | [14.433908462524414, 6.752139091491699] |
255de3cb-114d-461c-8320-c2a5d2baa4fa | mama-edha-at-semeval-2017-task-8-stance | null | null | https://aclanthology.org/S17-2084 | https://aclanthology.org/S17-2084.pdf | Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules | For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classific... | ['Edward Tj{\\"o}rnhammar', "Marianela Garc{\\'\\i}a Lozano", 'Hanna Lilja', 'Maja Karasalo'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-1.47570670e-01 6.24314547e-01 -4.65597451e-01 -6.41117036e-01
-4.15087372e-01 -3.91442209e-01 9.60378349e-01 7.28562713e-01
-5.34116864e-01 9.86617744e-01 5.37098885e-01 -6.85915470e-01
2.98989952e-01 -9.12608624e-01 -3.96703660e-01 -1.70287073e-01
-9.65696648e-02 6.68960512e-01 3.05725019e-02 -9.03890967... | [8.290837287902832, 10.090057373046875] |
cc898207-3ec4-4d8b-afce-4aafb77d37d9 | identifying-source-speakers-for-voice | 2206.09103 | null | https://arxiv.org/abs/2206.09103v2 | https://arxiv.org/pdf/2206.09103v2.pdf | Identifying Source Speakers for Voice Conversion based Spoofing Attacks on Speaker Verification Systems | An automatic speaker verification system aims to verify the speaker identity of a speech signal. However, a voice conversion system could manipulate a person's speech signal to make it sound like another speaker's voice and deceive the speaker verification system. Most countermeasures for voice conversion-based spoofin... | ['Ming Li', 'Zexin Cai', 'Danwei Cai'] | 2022-06-18 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.52243245e-01 2.33044580e-01 -6.04710728e-02 -4.95552212e-01
-7.87080586e-01 -8.38843942e-01 3.54167461e-01 -5.03184736e-01
-1.31961271e-01 3.92470658e-01 3.67665052e-01 -7.68555224e-01
5.58296204e-01 -3.26256692e-01 -4.73639399e-01 -6.62446797e-01
2.15919271e-01 1.98366448e-01 -2.91993946e-01 -2.38115966... | [14.106064796447754, 5.900349140167236] |
d9afe96e-5b88-488e-a8b4-fcd4a064c9c4 | pseudolikelihood-reranking-with-masked | 1910.14659 | null | https://arxiv.org/abs/1910.14659v3 | https://arxiv.org/pdf/1910.14659v3.pdf | Masked Language Model Scoring | Pretrained masked language models (MLMs) require finetuning for most NLP tasks. Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are computed by masking tokens one by one. We show that PLLs outperform scores from autoregressive language models like GPT-2 in a variety of task... | ['Toan Q. Nguyen', 'Katrin Kirchhoff', 'Julian Salazar', 'Davis Liang'] | 2019-10-31 | masked-language-model-scoring | https://aclanthology.org/2020.acl-main.240 | https://aclanthology.org/2020.acl-main.240.pdf | acl-2020-6 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 1.28576130e-01 1.86662972e-01 -4.92632031e-01 -5.53234816e-01
-1.75228822e+00 -7.94271946e-01 7.13125944e-01 -9.77494717e-02
-5.99020958e-01 9.00035441e-01 4.32394296e-01 -8.30282807e-01
5.04167855e-01 -3.87821078e-01 -1.08106446e+00 -2.24804059e-01
2.58418739e-01 7.30671763e-01 -4.91374917e-02 -3.03190023... | [11.575088500976562, 10.126014709472656] |
37ac919d-0bd1-4767-bc16-63ae20a27305 | exploration-in-feature-space-for | 1710.02210 | null | http://arxiv.org/abs/1710.02210v1 | http://arxiv.org/pdf/1710.02210v1.pdf | Exploration in Feature Space for Reinforcement Learning | The infamous exploration-exploitation dilemma is one of the oldest and most
important problems in reinforcement learning (RL). Deliberate and effective
exploration is necessary for RL agents to succeed in most environments.
However, until very recently even very sophisticated RL algorithms employed
simple, undirected e... | ['Suraj Narayanan Sasikumar'] | 2017-10-05 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-3.12998533e-01 2.08929121e-01 -6.14423573e-01 -7.48048052e-02
-8.60681593e-01 -5.61181009e-01 5.84529638e-01 -4.89231385e-02
-1.14379048e+00 1.64854503e+00 -9.08795595e-02 -3.85871172e-01
-3.65223587e-01 -7.91799605e-01 -5.52152336e-01 -9.98791635e-01
-7.95581698e-01 1.00133991e+00 -2.61397362e-02 -4.94515121... | [3.900191307067871, 1.7203702926635742] |
c89a93fb-3dbb-43c4-8926-a2b7e4f55822 | boosting-semi-supervised-few-shot-object | 2303.05739 | null | https://arxiv.org/abs/2303.05739v2 | https://arxiv.org/pdf/2303.05739v2.pdf | Boosting Semi-Supervised Few-Shot Object Detection with SoftER Teacher | Few-shot object detection (FSOD) is an emerging problem aimed at detecting novel concepts from few exemplars. Existing approaches to FSOD assume abundant base labels to adapt to novel objects. This paper studies the task of semi-supervised FSOD by considering a realistic scenario in which both base and novel labels are... | ['Phi Vu Tran'] | 2023-03-10 | null | null | null | null | ['few-shot-object-detection', 'novel-concepts'] | ['computer-vision', 'reasoning'] | [ 1.98552832e-01 3.98535371e-01 -3.58239979e-01 -3.51122290e-01
-9.89484727e-01 -6.71926618e-01 8.41004908e-01 3.35501760e-01
-4.74898636e-01 6.40120029e-01 -7.15250662e-03 1.36600554e-01
-1.01938412e-01 -4.30509120e-01 -6.87496066e-01 -6.76584423e-01
-7.96301570e-03 5.07562876e-01 6.88163936e-01 -1.12600960... | [9.82412052154541, 2.5422592163085938] |
864a41e3-8dbc-4db4-a2a0-bd877ea63894 | coordinated-multi-agent-pathfinding-for | 2110.08802 | null | https://arxiv.org/abs/2110.08802v2 | https://arxiv.org/pdf/2110.08802v2.pdf | Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks | We address the problem of routing a team of drones and trucks over large-scale urban road networks. To conserve their limited flight energy, drones can use trucks as temporary modes of transit en route to their own destinations. Such coordination can yield significant savings in total vehicle distance traveled, i.e., t... | ['Marco Pavone', 'Mykel Kochenderfer', 'Kiril Solovey', 'Shushman Choudhury'] | 2021-10-17 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.43025705e-01 2.57249951e-01 -3.13478589e-01 5.09854406e-03
-4.66947109e-01 -1.10710657e+00 8.20983499e-02 1.58833191e-01
-7.17767656e-01 1.10720861e+00 -5.81453264e-01 -6.86956704e-01
-6.61026537e-01 -1.47291064e+00 -5.47612906e-01 -6.16388738e-01
-8.34040105e-01 1.18381679e+00 3.71459961e-01 -5.75808406... | [5.0030198097229, 1.8441797494888306] |
69706b87-af74-4c04-81d8-42a6c5a628e8 | polibertweet-a-pre-trained-language-model-for | null | null | https://aclanthology.org/2022.lrec-1.801 | https://aclanthology.org/2022.lrec-1.801.pdf | PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter | Transformer-based models have become the state-of-the-art for numerous natural language processing (NLP) tasks, especially for noisy data sets, including social media posts. For example, BERTweet, pre-trained RoBERTa on a large amount of Twitter data, has achieved state-of-the-art results on several Twitter NLP tasks. ... | ['Lisa Singh', 'Kornraphop Kawintiranon'] | null | null | null | null | lrec-2022-6 | ['stance-detection'] | ['natural-language-processing'] | [-1.37585491e-01 2.07722455e-01 -6.39070690e-01 -7.31531262e-01
-1.22879744e+00 -8.58121872e-01 1.33021057e+00 5.64992845e-01
-6.92493796e-01 7.89275408e-01 9.65781689e-01 -7.42307901e-01
2.70652831e-01 -1.01167393e+00 -6.59457862e-01 -1.97364256e-01
2.28299499e-01 7.74997354e-01 -1.31872147e-02 -9.98712778... | [9.015236854553223, 9.933893203735352] |
3c0f4f39-90e6-4461-928d-0020c49d6fc1 | compilation-based-solvers-for-multi-agent | 2104.11809 | null | https://arxiv.org/abs/2104.11809v1 | https://arxiv.org/pdf/2104.11809v1.pdf | Compilation-based Solvers for Multi-Agent Path Finding: a Survey, Discussion, and Future Opportunities | Multi-agent path finding (MAPF) attracts considerable attention in artificial intelligence community as well as in robotics, and other fields such as warehouse logistics. The task in the standard MAPF is to find paths through which agents can navigate from their starting positions to specified individual goal positions... | ['Pavel Surynek'] | 2021-04-23 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 7.74824247e-02 3.43915820e-01 -6.54623657e-02 -3.23881775e-01
-4.46487308e-01 -1.02238393e+00 4.28830802e-01 6.49226785e-01
-3.22993249e-01 1.39486432e+00 -2.47721210e-01 -3.58600557e-01
-8.49903286e-01 -1.22271812e+00 -8.22730243e-01 -4.84391332e-01
-5.21836400e-01 1.31812334e+00 3.42373759e-01 -5.90149462... | [4.928563594818115, 1.8168296813964844] |
ba036587-4668-4c07-946a-6a2b20ad098a | a-polynomial-time-iterative-algorithm-for | 2212.13677 | null | https://arxiv.org/abs/2212.13677v1 | https://arxiv.org/pdf/2212.13677v1.pdf | A polynomial time iterative algorithm for matching Gaussian matrices with non-vanishing correlation | Motivated by the problem of matching vertices in two correlated Erd\H{o}s-R\'enyi graphs, we study the problem of matching two correlated Gaussian Wigner matrices. We propose an iterative matching algorithm, which succeeds in polynomial time as long as the correlation between the two Gaussian matrices does not vanish. ... | ['Zhangsong Li', 'Jian Ding'] | 2022-12-28 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 3.66279483e-01 3.30187649e-01 -5.26966453e-02 1.26269341e-01
-8.11852694e-01 -5.35691857e-01 1.97038651e-01 -2.57687345e-02
-2.56295860e-01 2.66289055e-01 -3.07992637e-01 -5.09865463e-01
-5.66146374e-01 -9.38115358e-01 -4.54281747e-01 -7.21122444e-01
-6.35250509e-01 1.07426989e+00 3.14840227e-01 -3.30811203... | [6.843785762786865, 5.124528408050537] |
c0bae23a-ecf1-4bf3-be7b-eb035aafdff4 | robustscanner-dynamically-enhancing | 2007.07542 | null | https://arxiv.org/abs/2007.07542v2 | https://arxiv.org/pdf/2007.07542v2.pdf | RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition | The attention-based encoder-decoder framework has recently achieved impressive results for scene text recognition, and many variants have emerged with improvements in recognition quality. However, it performs poorly on contextless texts (e.g., random character sequences) which is unacceptable in most of real applicatio... | ['Hongbin Sun', 'Chenhao Lin', 'Wayne Zhang', 'Zhanghui Kuang', 'Xiaoyu Yue'] | 2020-07-15 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3160_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640137.pdf | eccv-2020-8 | ['irregular-text-recognition'] | ['computer-vision'] | [ 6.88252330e-01 -6.06118381e-01 -9.53188986e-02 -1.73541948e-01
-8.34778607e-01 -5.40253460e-01 6.24434233e-01 8.77805427e-02
-6.32521152e-01 4.06330198e-01 2.81323701e-01 -3.58935446e-01
2.25915492e-01 -5.56671321e-01 -6.02005780e-01 -1.12564731e+00
5.82650304e-01 1.40744746e-01 4.12244380e-01 -1.94828987... | [11.968368530273438, 2.2301025390625] |
bcf96a90-8aeb-4a24-9211-2f7b44e21335 | hiding-your-signals-a-security-analysis-of | 2207.04434 | null | https://arxiv.org/abs/2207.04434v1 | https://arxiv.org/pdf/2207.04434v1.pdf | Hiding Your Signals: A Security Analysis of PPG-based Biometric Authentication | Recently, physiological signal-based biometric systems have received wide attention. Unlike traditional biometric features, physiological signals can not be easily compromised (usually unobservable to human eyes). Photoplethysmography (PPG) signal is easy to measure, making it more attractive than many other physiologi... | ['Yang Xiang', 'Jun Zhang', 'Yonghang Tai', 'Lei Pan', 'Chao Chen', 'Lin Li'] | 2022-07-10 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 2.25856230e-01 -1.92619652e-01 2.43976146e-01 8.39749128e-02
-9.15385634e-02 -6.20203078e-01 6.82975426e-02 -2.72470295e-01
-3.96694630e-01 8.09688210e-01 -3.33293378e-01 -3.84740442e-01
2.39926875e-01 -6.20882332e-01 -2.42718160e-01 -1.12916481e+00
-2.97171772e-01 -6.78644180e-01 -2.60719031e-01 2.23910168... | [13.463593482971191, 2.0319459438323975] |
5cc3376d-8aca-4ee4-9b0e-09a59f3adef9 | ct-sgan-computed-tomography-synthesis-gan | 2110.09288 | null | https://arxiv.org/abs/2110.09288v2 | https://arxiv.org/pdf/2110.09288v2.pdf | CT-SGAN: Computed Tomography Synthesis GAN | Diversity in data is critical for the successful training of deep learning models. Leveraged by a recurrent generative adversarial network, we propose the CT-SGAN model that generates large-scale 3D synthetic CT-scan volumes ($\geq 224\times224\times224$) when trained on a small dataset of chest CT-scans. CT-SGAN offer... | ['Mohammad Havaei', 'Yiping Wang', 'Ahmad Pesaranghader'] | 2021-10-14 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.47070754e-01 6.27215922e-01 -2.07600300e-03 -2.76336908e-01
-1.47604775e+00 -4.76407170e-01 3.96238476e-01 -3.81275266e-01
-1.26606777e-01 7.58598626e-01 1.00995809e-01 -6.41766012e-01
-6.00754060e-02 -8.28502238e-01 -9.72106576e-01 -6.55623674e-01
-1.23104885e-01 7.83414721e-01 -5.67394122e-03 1.23285629... | [14.355653762817383, -1.949344277381897] |
34598a57-3dfa-4c56-8e0b-470b1b0c5b67 | a-transformer-based-approach-for-source-code | 2005.00653 | null | https://arxiv.org/abs/2005.00653v1 | https://arxiv.org/pdf/2005.00653v1.pdf | A Transformer-based Approach for Source Code Summarization | Generating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwise relationship between code tokens to capture their long-range dependencies is crucial. To learn code representation for summarization, we ... | ['Kai-Wei Chang', 'Baishakhi Ray', 'Wasi Uddin Ahmad', 'Saikat Chakraborty'] | 2020-05-01 | a-transformer-based-approach-for-source-code-1 | https://aclanthology.org/2020.acl-main.449 | https://aclanthology.org/2020.acl-main.449.pdf | acl-2020-6 | ['code-summarization'] | ['computer-code'] | [ 4.23002481e-01 4.39598888e-01 -5.76488137e-01 -3.51796627e-01
-1.28493142e+00 -6.02905393e-01 3.91629934e-01 6.30703390e-01
1.04777083e-01 6.15137160e-01 9.58206296e-01 -4.74462956e-01
6.67454973e-02 -3.63918781e-01 -9.87332761e-01 -2.19263777e-01
-3.32776636e-01 -1.10471003e-01 -8.15452449e-03 -1.76060811... | [7.590944766998291, 7.951099395751953] |
00f3adbf-d24f-40e9-b230-ea25e13dee5c | pu-mfa-point-cloud-up-sampling-via-multi | 2208.10968 | null | https://arxiv.org/abs/2208.10968v1 | https://arxiv.org/pdf/2208.10968v1.pdf | PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention | Recently, research using point clouds has been increasing with the development of 3D scanner technology. According to this trend, the demand for high-quality point clouds is increasing, but there is still a problem with the high cost of obtaining high-quality point clouds. Therefore, with the recent remarkable developm... | ['Sejoon Lim', 'Hyungjun Lee'] | 2022-08-22 | null | null | null | null | ['point-cloud-reconstruction', 'point-cloud-super-resolution'] | ['computer-vision', 'computer-vision'] | [-2.01738775e-01 -4.83986855e-01 1.07776858e-01 -2.11356029e-01
-7.00871766e-01 1.78909823e-01 4.31941688e-01 -2.56410092e-01
-1.08571783e-01 4.03489113e-01 -1.77813813e-01 -1.21829964e-01
-2.40954921e-01 -1.26105273e+00 -1.02387261e+00 -6.08188033e-01
1.29239932e-01 5.56231678e-01 7.92545304e-02 -2.58801699... | [8.122742652893066, -3.4821414947509766] |
15120d5d-4e8f-48c7-80e9-a11212624d93 | behaviour-diverse-automatic-penetration | 2202.10630 | null | https://arxiv.org/abs/2202.10630v1 | https://arxiv.org/pdf/2202.10630v1.pdf | Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach | Penetration Testing plays a critical role in evaluating the security of a target network by emulating real active adversaries. Deep Reinforcement Learning (RL) is seen as a promising solution to automating the process of penetration tests by reducing human effort and improving reliability. Existing RL solutions focus o... | ['Xin Liu', 'Yizhou Yang'] | 2022-02-22 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 2.01701775e-01 -3.64063382e-01 -2.64429241e-01 1.97817296e-01
-7.49092877e-01 -8.31326127e-01 2.24829182e-01 5.33192903e-02
-4.73404199e-01 9.34650123e-01 -4.81207013e-01 -6.28610671e-01
-3.86490732e-01 -1.01940739e+00 -4.92089689e-01 -1.03032553e+00
-4.07357752e-01 6.16318643e-01 2.84681499e-01 -2.46811405... | [3.7463486194610596, 2.3353400230407715] |
40a250cf-a5a8-4669-8202-d85d13e10998 | refind-relation-extraction-financial-dataset | 2305.18322 | null | https://arxiv.org/abs/2305.18322v1 | https://arxiv.org/pdf/2305.18322v1.pdf | REFinD: Relation Extraction Financial Dataset | A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment. However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general kno... | ['Sameena Shah', 'Toyin Aguda', 'Suchetha Siddagangappa', 'Dongsheng Wang', 'Joy Sain', 'Akshat Gupta', 'Charese Smiley', 'Simerjot Kaur'] | 2023-05-22 | null | null | null | null | ['general-knowledge', 'relation-extraction', 'information-retrieval'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [-3.33089679e-01 6.18214846e-01 -5.23211479e-01 -4.36791480e-01
-7.63030410e-01 -8.05970430e-01 9.62876856e-01 7.98824668e-01
-4.76444066e-01 1.11437714e+00 3.63903254e-01 -6.99567020e-01
-5.44116735e-01 -1.20013273e+00 -7.61165082e-01 2.37633929e-01
-3.60703409e-01 1.04723477e+00 1.02099344e-01 -6.88094676... | [9.434627532958984, 8.65766429901123] |
eb85a1f7-185f-4441-99b4-175a7e41327a | cross-domain-few-shot-classification-via | 2104.14385 | null | https://arxiv.org/abs/2104.14385v2 | https://arxiv.org/pdf/2104.14385v2.pdf | Cross-Domain Few-Shot Classification via Adversarial Task Augmentation | Few-shot classification aims to recognize unseen classes with few labeled samples from each class. Many meta-learning models for few-shot classification elaborately design various task-shared inductive bias (meta-knowledge) to solve such tasks, and achieve impressive performance. However, when there exists the domain s... | ['Zhi-Hong Deng', 'Haoqing Wang'] | 2021-04-29 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 2.26721212e-01 -1.89894915e-01 -3.75797212e-01 -3.11742336e-01
-7.19146132e-01 -1.89231798e-01 6.55706704e-01 -1.98966995e-01
-1.78266719e-01 8.36162031e-01 -6.23946115e-02 2.57738288e-02
4.70858254e-02 -9.62588966e-01 -7.06469774e-01 -7.06682682e-01
2.15203717e-01 3.20365250e-01 5.16780734e-01 -4.96253639... | [10.078750610351562, 3.0548694133758545] |
2d36748b-7d42-4aa4-a65d-b485e0a36492 | density-map-guided-object-detection-in-aerial | 2004.05520 | null | https://arxiv.org/abs/2004.05520v1 | https://arxiv.org/pdf/2004.05520v1.pdf | Density Map Guided Object Detection in Aerial Images | Object detection in high-resolution aerial images is a challenging task because of 1) the large variation in object size, and 2) non-uniform distribution of objects. A common solution is to divide the large aerial image into small (uniform) crops and then apply object detection on each small crop. In this paper, we inv... | ['Taojiannan Yang', 'Shanyue Guan', 'Chen Chen', 'Changlin Li', 'Sijie Zhu'] | 2020-04-12 | null | null | null | null | ['rgb-d-salient-object-detection', 'object-detection-in-aerial-images', 'image-cropping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.01091731e-01 -3.35937023e-01 4.50730920e-02 -3.65010649e-02
-6.55011535e-02 -7.53353655e-01 3.18345010e-01 -1.62751880e-03
-2.83216059e-01 5.50775230e-01 -2.80993491e-01 -3.33167352e-02
1.24556134e-02 -1.35075915e+00 -7.79992163e-01 -8.06572855e-01
-1.22410260e-01 2.07751274e-01 8.70657384e-01 1.15311071... | [8.723426818847656, -0.8080422878265381] |
2b45521b-f008-4750-93c0-bb361811e13e | towards-realistic-few-shot-relation | null | null | https://aclanthology.org/2021.emnlp-main.433 | https://aclanthology.org/2021.emnlp-main.433.pdf | Towards Realistic Few-Shot Relation Extraction | In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this task, leading to the impression that few-shot relation classification... | ['Adrian Benton', 'Sichao Wu', 'Sam Brody'] | null | null | null | null | emnlp-2021-11 | ['few-shot-relation-classification', 'relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 9.85504910e-02 6.96083307e-01 -6.83736205e-01 -2.62025952e-01
-5.53094208e-01 -2.21753404e-01 9.56695437e-01 7.06963003e-01
-2.97434866e-01 7.04076707e-01 3.81100714e-01 -1.85993135e-01
-4.28767443e-01 -9.83033657e-01 -9.14075524e-02 -2.82304853e-01
-6.17987402e-02 8.21959257e-01 3.41731578e-01 -6.63301289... | [9.302884101867676, 8.631239891052246] |
96a65992-9e49-431e-b868-2efa8af53a08 | sgd-with-adagrad-stepsizes-full-adaptivity | 2302.08783 | null | https://arxiv.org/abs/2302.08783v2 | https://arxiv.org/pdf/2302.08783v2.pdf | SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance | We study Stochastic Gradient Descent with AdaGrad stepsizes: a popular adaptive (self-tuning) method for first-order stochastic optimization. Despite being well studied, existing analyses of this method suffer from various shortcomings: they either assume some knowledge of the problem parameters, impose strong global L... | ['Tomer Koren', 'Amit Attia'] | 2023-02-17 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-4.00381237e-02 -1.87328950e-01 -1.41051307e-01 -3.15956533e-01
-1.33545578e+00 -6.32431209e-01 2.65966862e-01 -4.49719317e-02
-7.64731288e-01 1.01289225e+00 -3.20277177e-02 -3.75761002e-01
-3.84961128e-01 -4.37555104e-01 -7.74000466e-01 -1.17220211e+00
-3.76872048e-02 4.57061976e-01 2.09394097e-01 -2.71323085... | [6.911871433258057, 4.310386657714844] |
09396964-ac6e-4661-ae2e-96c78cdc1e88 | telescoping-density-ratio-estimation | 2006.12204 | null | https://arxiv.org/abs/2006.12204v2 | https://arxiv.org/pdf/2006.12204v2.pdf | Telescoping Density-Ratio Estimation | Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and generative modelling, with the number of use-cases continuing to proliferate. However, it suffers from a critical limitation: it fails to accu... | ['Michael U. Gutmann', 'Benjamin Rhodes', 'Kai Xu'] | 2020-06-22 | null | http://proceedings.neurips.cc/paper/2020/hash/33d3b157ddc0896addfb22fa2a519097-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/33d3b157ddc0896addfb22fa2a519097-Paper.pdf | neurips-2020-12 | ['density-ratio-estimation', 'mutual-information-estimation'] | ['methodology', 'methodology'] | [ 3.00050288e-01 -2.82966435e-01 -3.66947353e-01 -2.46354938e-01
-1.22860885e+00 -3.65583807e-01 1.06474292e+00 3.91174555e-02
-2.86944509e-01 1.02687144e+00 2.31679216e-01 -2.02247217e-01
-2.60656565e-01 -8.27343583e-01 -3.45468640e-01 -1.00386143e+00
-7.16289505e-02 7.76273847e-01 3.76628637e-02 1.04068309... | [7.287106990814209, 4.0165486335754395] |
15541347-a785-4437-96d8-59364c5cc40b | scale-prior-deformable-convolution-for | null | null | https://bmvc2022.mpi-inf.mpg.de/313/ | https://bmvc2022.mpi-inf.mpg.de/0313.pdf | Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting | Class-agnostic counting has recently emerged as a more practical counting task, which aims to predict the number and distribution of any exemplar objects, instead of counting specific categories like pedestrians or cars. However, recent methods are developed by designing suitable similarity matching rules between exemp... | ['Antoni B. Chan', 'Shuai Yi', 'Jun Hou', 'Shinan Liu', 'Lingbo Liu', 'Junyu Gao', 'Xinzhu Ma', 'Kunlin Yang', 'Wei Lin'] | 2022-12-21 | null | null | null | conference-2022-12 | ['object-counting'] | ['computer-vision'] | [-9.62845609e-02 -5.63762844e-01 5.28111123e-02 -6.80817604e-01
-5.35655916e-01 -4.48093802e-01 4.65977371e-01 3.20522904e-01
-8.54660869e-01 6.20210826e-01 -1.01500332e-01 2.09209263e-01
-3.55449319e-02 -1.08076227e+00 -6.94045961e-01 -6.13037825e-01
4.68882918e-01 5.42501926e-01 6.79032683e-01 5.86205348... | [8.840219497680664, 0.24187985062599182] |
ba763190-7d6e-46b4-a54b-5bcca2b91119 | sentence-encoding-for-dialogue-act | null | null | https://www.cambridge.org/core/journals/natural-language-engineering/article/sentence-encoding-for-dialogue-act-classification/2EF3DC8E57D1019960D18FDE685B1EBA | https://www.cambridge.org/core/journals/natural-language-engineering/article/sentence-encoding-for-dialogue-act-classification/2EF3DC8E57D1019960D18FDE685B1EBA | Sentence encoding for Dialogue Act classification | In this study, we investigate the process of generating single-sentence representations for the purpose of Dialogue Act (DA) classification, including several aspects of text pre-processing and input representation which are often overlooked or underreported within the literature, for example, the number of words to ke... | ['Jim Smith', 'Steve Battle', 'Nathan Duran'] | 2021-11-02 | null | null | null | natural-language-engineering-2021-11 | ['dialog-act-classification', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.55414140e-01 3.32002252e-01 -1.47036329e-01 -4.59593356e-01
-6.10699534e-01 -5.76189458e-01 1.02136922e+00 2.68888235e-01
-7.41620183e-01 9.43365812e-01 8.19485009e-01 -6.35921419e-01
1.00652657e-01 -7.12921202e-01 1.54779693e-02 -4.59081709e-01
1.92034781e-01 6.33536279e-01 -7.22306594e-02 -7.14142323... | [12.676212310791016, 7.8724822998046875] |
cf40a9b7-5bce-4e51-94ab-4ee5651f3afd | sleepnet-automated-sleep-staging-system-via | 1707.08262 | null | http://arxiv.org/abs/1707.08262v1 | http://arxiv.org/pdf/1707.08262v1.pdf | SLEEPNET: Automated Sleep Staging System via Deep Learning | Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect
50-70 million adults in the United States (Hillman et al., 2006). Overnight
polysomnography (PSG), including brain monitoring using electroencephalography
(EEG), is a central component of the diagnostic evaluation for sleep disorders.
While PSG ... | ['M. Brandon Westover', 'Matt T. Bianchi', 'Joshua Kulas', 'Balaji Goparaju', 'Haoqi Sun', 'Siddharth Biswal', 'Jimeng Sun'] | 2017-07-26 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-7.88825825e-02 -1.18933260e-01 -7.66149983e-02 -4.81826782e-01
-4.42313373e-01 -4.85880613e-01 -1.49451479e-01 2.74113178e-01
-6.18545711e-01 8.67644131e-01 2.26059899e-01 -1.96345568e-01
4.48480807e-02 -1.48315892e-01 1.91721782e-01 -4.63084996e-01
-1.84797391e-01 6.50968552e-01 -1.74031660e-01 1.49866998... | [13.509778022766113, 3.483172655105591] |
70386c09-3106-46fe-992d-c20f1fca56c9 | spherical-regression-learning-viewpoints | 1904.05404 | null | http://arxiv.org/abs/1904.05404v1 | http://arxiv.org/pdf/1904.05404v1.pdf | Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres | Many computer vision challenges require continuous outputs, but tend to be
solved by discrete classification. The reason is classification's natural
containment within a probability $n$-simplex, as defined by the popular softmax
activation function. Regular regression lacks such a closed geometry, leading
to unstable t... | ['Shuai Liao', 'Efstratios Gavves', 'Cees G. M. Snoek'] | 2019-04-10 | spherical-regression-learning-viewpoints-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liao_Spherical_Regression_Learning_Viewpoints_Surface_Normals_and_3D_Rotations_on_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liao_Spherical_Regression_Learning_Viewpoints_Surface_Normals_and_3D_Rotations_on_CVPR_2019_paper.pdf | cvpr-2019-6 | ['surface-normals-estimation', '3d-rotation-estimation', 'viewpoint-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-7.46002942e-02 3.28592032e-01 3.07871327e-02 -4.37409520e-01
-8.15095723e-01 -4.64061767e-01 4.86200213e-01 -1.18117660e-01
-5.33272028e-01 4.63854671e-01 -1.92937464e-01 -2.01863483e-01
7.86276683e-02 -6.16495490e-01 -1.05199444e+00 -8.43488991e-01
-1.52604431e-01 1.57216355e-01 -1.55318633e-01 -1.34720281... | [7.939638137817383, 3.648153305053711] |
126def7a-c96f-439c-a710-72226dbe128d | multi-domain-aspect-extraction-using-support | null | null | https://aclanthology.org/O17-1029 | https://aclanthology.org/O17-1029.pdf | Multi-Domain Aspect Extraction Using Support Vector Machines | null | ['Yasas Senarath', 'Surangika Ranathunga', 'Nadheesh Jihan', 'Dulanjaya Tennekoon', 'Mithila Wickramarathne'] | 2017-11-01 | multi-domain-aspect-extraction-using-support-1 | https://aclanthology.org/O17-1029 | https://aclanthology.org/O17-1029.pdf | roclingijclclp-2017-11 | ['aspect-extraction'] | ['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... | [-7.213913917541504, 3.791389226913452] |
ce918a97-9992-44d5-92f9-6cbb0f3c0437 | gmm-based-synthetic-samples-for | 1712.04778 | null | http://arxiv.org/abs/1712.04778v1 | http://arxiv.org/pdf/1712.04778v1.pdf | GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data | The amount of training data that is required to train a classifier scales
with the dimensionality of the feature data. In hyperspectral remote sensing,
feature data can potentially become very high dimensional. However, the amount
of training data is oftentimes limited. Thus, one of the core challenges in
hyperspectral... | ['Christian Riess', 'AmirAbbas Davari', 'Erchan Aptoula', 'Berrin Yanikoglu', 'Andreas Maier'] | 2017-12-13 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 6.00679398e-01 -2.58106053e-01 7.67182782e-02 -4.53780562e-01
-6.98832393e-01 -6.42409444e-01 4.25347149e-01 -4.50266562e-02
-1.74123287e-01 9.63148355e-01 -3.90900016e-01 -7.68627971e-02
-3.43553632e-01 -1.00267768e+00 -4.55812156e-01 -1.05897176e+00
-2.69749667e-02 3.37467849e-01 -3.01021934e-01 -6.98868930... | [9.982376098632812, -1.8636692762374878] |
9c03a4f5-aeec-4242-a25f-955783b84d1c | a-comprehensive-survey-on-source-free-domain | 2302.11803 | null | https://arxiv.org/abs/2302.11803v1 | https://arxiv.org/pdf/2302.11803v1.pdf | A Comprehensive Survey on Source-free Domain Adaptation | Over the past decade, domain adaptation has become a widely studied branch of transfer learning that aims to improve performance on target domains by leveraging knowledge from the source domain. Conventional domain adaptation methods often assume access to both source and target domain data simultaneously, which may no... | ['Heng Tao Shen', 'Lei Zhu', 'Zhekai Du', 'Jingjing Li', 'Zhiqi Yu'] | 2023-02-23 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [-2.22492311e-03 -3.52426618e-01 -7.06009984e-01 -5.04683316e-01
-7.88555741e-01 -7.51586437e-01 5.22834718e-01 -6.05246760e-02
-4.02013063e-01 1.18256867e+00 1.86771210e-02 -3.12087417e-01
-3.41096856e-02 -7.23257959e-01 -4.18816745e-01 -6.55481875e-01
2.56176621e-01 3.69846135e-01 4.39055637e-02 -2.45257497... | [10.325813293457031, 3.1370983123779297] |
77671719-c911-48f8-8103-3202fdd1ab04 | metaportrait-identity-preserving-talking-head | 2212.08062 | null | https://arxiv.org/abs/2212.08062v3 | https://arxiv.org/pdf/2212.08062v3.pdf | MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation | In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects. First, as opposed to interpolating from sparse flow, we claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields. Second, inspired by face-swapping methods, we adap... | ['Fang Wen', 'Yong Wang', 'Qifeng Chen', 'Dong Chen', 'HsiangTao Wu', 'Bo Zhang', 'Pan Zhang', 'Chenyang Qi', 'BoWen Zhang'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_MetaPortrait_Identity-Preserving_Talking_Head_Generation_With_Fast_Personalized_Adaptation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_MetaPortrait_Identity-Preserving_Talking_Head_Generation_With_Fast_Personalized_Adaptation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['talking-head-generation', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 9.06196758e-02 1.27632126e-01 -4.04702984e-02 -3.72047901e-01
-7.69026756e-01 -4.08603996e-01 6.16222501e-01 -2.31427878e-01
-1.52693261e-04 8.11161578e-01 6.16229594e-01 3.31361964e-02
-1.66042060e-01 -7.54967153e-01 -5.71845591e-01 -6.09871924e-01
1.33082226e-01 2.12338179e-01 1.74846515e-01 -3.40153992... | [12.7874174118042, -0.4393283724784851] |
072f720e-574e-4b77-85c7-1c1f8b7f09ba | symmetric-saliency-based-adversarial-attack | 2210.16777 | null | https://arxiv.org/abs/2210.16777v1 | https://arxiv.org/pdf/2210.16777v1.pdf | Symmetric Saliency-based Adversarial Attack To Speaker Identification | Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge. To address this issue, in this paper, we propose a novel generation-network-based approach, called symmetric saliency-based encoder-decoder (SSED), to generate adversarial voice examp... | ['Kunde Yang', 'Wei-Qiang Zhang', 'Xiao-Lei Zhang', 'Xing Chen', 'Jiadi Yao'] | 2022-10-30 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.46222460e-01 2.57898867e-01 2.20818877e-01 -3.06290984e-01
-1.26355588e+00 -4.83035594e-01 5.18908679e-01 -3.15658391e-01
-1.13110267e-01 5.16042233e-01 2.25093648e-01 -3.62109959e-01
1.57212093e-01 -3.21744978e-01 -4.65862453e-01 -8.27584803e-01
8.97284374e-02 1.75157022e-02 2.16174707e-01 -3.11990947... | [14.016963958740234, 5.83963680267334] |
b74fffc9-2f86-48a2-b724-3fc97b5332a9 | noisy-neural-network-compression-for-analog | null | null | https://openreview.net/forum?id=APvrboUZS7w | https://openreview.net/pdf?id=APvrboUZS7w | Noisy Neural Network Compression for Analog Storage Devices | Efficient compression and storage of neural network (NN) parameters is critical for resource-constrained, downstream machine learning applications. Although several methods for NN compression have been developed, there has been considerably less work in the efficient storage of NN weights. While analog storage devices ... | ['Armin Alaghi', 'Tsachy Weissman', 'Stefano Ermon', 'H.-S. Philip Wong', 'Xin Zheng', 'Kristy Choi', 'Berivan Isik'] | 2020-10-19 | null | null | null | neurips-workshop-dl-ig-2020-12 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 7.01804578e-01 -1.67429283e-01 -1.74699739e-01 -3.76146048e-01
-2.03635454e-01 -2.83740044e-01 3.30353856e-01 3.06758583e-01
-8.09070349e-01 7.83310115e-01 -1.79880679e-01 -5.99442065e-01
-2.25542709e-01 -7.45009780e-01 -7.55576491e-01 -5.81083655e-01
1.30215913e-01 2.47575432e-01 4.63504732e-01 1.32428005... | [8.517892837524414, 2.9910378456115723] |
844ddd49-0710-4b6a-b78c-782a5fa87013 | cnnpred-cnn-based-stock-market-prediction | 1810.08923 | null | http://arxiv.org/abs/1810.08923v1 | http://arxiv.org/pdf/1810.08923v1.pdf | CNNPred: CNN-based stock market prediction using several data sources | Feature extraction from financial data is one of the most important problems
in market prediction domain for which many approaches have been suggested.
Among other modern tools, convolutional neural networks (CNN) have recently
been applied for automatic feature selection and market prediction. However, in
experiments ... | ['Saman Haratizadeh', 'Ehsan Hoseinzade'] | 2018-10-21 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-4.30911750e-01 -5.38722038e-01 5.87611087e-02 -3.01468611e-01
-2.35108644e-01 -5.50270081e-01 8.04893553e-01 3.21990132e-01
-4.63925481e-01 5.89051366e-01 1.66846558e-01 -3.44786346e-01
-3.11139613e-01 -1.07740581e+00 -2.03360230e-01 -1.79875150e-01
-2.84524202e-01 2.53585279e-01 2.40568921e-01 -5.56291997... | [4.4488396644592285, 4.245652198791504] |
40de009d-a6f8-4ee8-b826-2537a361abd0 | a-model-aggregation-approach-for-high | 2205.07525 | null | https://arxiv.org/abs/2205.07525v2 | https://arxiv.org/pdf/2205.07525v2.pdf | A model aggregation approach for high-dimensional large-scale optimization | Bayesian optimization (BO) has been widely used in machine learning and simulation optimization. With the increase in computational resources and storage capacities in these fields, high-dimensional and large-scale problems are becoming increasingly common. In this study, we propose a model aggregation method in the Ba... | ['Giulia Pedrielli', 'Szu Hui Ng', 'Ercong Zhang', 'Haowei Wang'] | 2022-05-16 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-2.65280843e-01 -3.54632944e-01 -1.75419703e-01 -1.14923887e-01
-8.01174879e-01 7.71519989e-02 3.01040471e-01 -1.52966663e-01
-3.71562630e-01 6.72931373e-01 2.07216874e-01 -5.26422672e-02
-6.65272653e-01 -6.12955391e-01 -3.78385305e-01 -9.96518433e-01
-3.24528962e-02 3.82698119e-01 -8.16162862e-03 1.99346334... | [6.982014179229736, 4.07515811920166] |
e6d528b2-dda4-4aef-888d-b7a8dbc0b0a5 | using-channel-state-information-for-physical | 2011.03573 | null | https://arxiv.org/abs/2011.03573v3 | https://arxiv.org/pdf/2011.03573v3.pdf | Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach | This letter proposes a deep learning approach to detect a change in the antenna orientation of transmitter or receiver as a physical tamper attack in OFDM systems using channel state information. We treat the physical tamper attack problem as a semi-supervised anomaly detection problem and utilize a deep convolutional ... | ['Andreas Springer', 'Núria Ballber Torres', 'Bernhard Etzlinger', 'Eshagh Dehmollaian'] | 2020-11-06 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 3.26940149e-01 -4.53694761e-02 3.27347606e-01 -6.04501031e-02
-3.72332275e-01 -7.90994540e-02 3.86251867e-01 2.89031923e-01
-4.11085784e-01 6.80918097e-01 -5.25285423e-01 -7.62701988e-01
5.46939895e-02 -9.23738658e-01 -7.77112544e-01 -1.01790380e+00
-7.90614843e-01 -4.52591121e-01 -1.65619537e-01 5.89699000... | [6.389654636383057, 1.5210025310516357] |
a92503e8-5595-404a-bc91-901f6c6bea9e | seer-language-instructed-video-prediction | 2303.14897 | null | https://arxiv.org/abs/2303.14897v2 | https://arxiv.org/pdf/2303.14897v2.pdf | Seer: Language Instructed Video Prediction with Latent Diffusion Models | Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning, i.e., predicting future video frames with a given language instruction and reference frame... | ['Yang Gao', 'Jiaming Song', 'Chuan Wen', 'Xianfan Gu'] | 2023-03-27 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.37814873e-01 2.11295970e-02 -1.97155863e-01 -3.10764700e-01
-4.65826541e-01 -7.16749653e-02 7.18288898e-01 -5.68951488e-01
-5.47588110e-01 5.29616535e-01 4.31961566e-01 -2.97863066e-01
1.92912906e-01 -5.80922484e-01 -1.23563576e+00 -6.86442256e-01
-4.43468876e-02 4.31586981e-01 4.87856388e-01 -2.36544251... | [10.645153045654297, -0.5414929986000061] |
624d1f76-3870-420e-a9e2-c975a01b5b55 | balancing-test-accuracy-and-security-in | 2305.18312 | null | https://arxiv.org/abs/2305.18312v1 | https://arxiv.org/pdf/2305.18312v1.pdf | Balancing Test Accuracy and Security in Computerized Adaptive Testing | Computerized adaptive testing (CAT) is a form of personalized testing that accurately measures students' knowledge levels while reducing test length. Bilevel optimization-based CAT (BOBCAT) is a recent framework that learns a data-driven question selection algorithm to effectively reduce test length and improve test ac... | ['Andrew S. Lan', 'Stephen Sireci', 'Aritra Ghosh', 'Wanyong Feng'] | 2023-05-18 | null | null | null | null | ['bilevel-optimization', 'question-selection'] | ['methodology', 'natural-language-processing'] | [ 2.80346647e-02 -6.00435920e-02 -4.15680230e-01 -6.48857653e-01
-1.01982963e+00 -9.32304084e-01 -2.03411192e-01 3.68810654e-01
-2.39291370e-01 9.74801183e-01 -2.44267836e-01 -7.86704600e-01
-6.28807485e-01 -9.86943066e-01 -5.56221068e-01 -1.71041608e-01
9.46794525e-02 5.46703696e-01 5.64581633e-01 -5.11889644... | [10.17065715789795, 7.323965072631836] |
b032c3b2-7647-46d5-ac44-496842f6edb6 | social-navigation-with-human-empowerment | 2003.08158 | null | https://arxiv.org/abs/2003.08158v3 | https://arxiv.org/pdf/2003.08158v3.pdf | Social Navigation with Human Empowerment driven Deep Reinforcement Learning | Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in the future. Therefore, the next generation of mobile robots needs to be socially-compliant to be accepted by their human collaborators. How... | ['Herke van Hoof', 'Florian Mirus', 'Tessa van der Heiden'] | 2020-03-18 | null | null | null | null | ['social-navigation'] | ['robots'] | [-1.46551311e-01 9.17969704e-01 -3.42491157e-02 -1.63856372e-01
4.07973915e-01 -1.21291310e-01 6.00948751e-01 -5.51268905e-02
-9.87351894e-01 1.12295735e+00 6.52952418e-02 8.04913715e-02
-1.96372226e-01 -6.93321049e-01 -2.17914745e-01 -6.78417146e-01
-2.58575946e-01 6.05603158e-01 2.84974009e-01 -7.32402861... | [4.890636920928955, 0.9476108551025391] |
fa0aae62-808b-4ff6-aa42-71f2866794a9 | amnet-deep-atrous-multiscale-stereo-disparity | 1904.09099 | null | http://arxiv.org/abs/1904.09099v1 | http://arxiv.org/pdf/1904.09099v1.pdf | AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks | In this paper, a new deep learning architecture for stereo disparity
estimation is proposed. The proposed atrous multiscale network (AMNet) adopts
an efficient feature extractor with depthwise-separable convolutions and an
extended cost volume that deploys novel stereo matching costs on the deep
features. A stacked atr... | ['Mostafa El-Khamy', 'Jungwon Lee', 'Xianzhi Du'] | 2019-04-19 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 2.35194623e-01 -4.04852748e-01 1.67804524e-01 -5.28778553e-01
-6.16867065e-01 5.05765080e-02 4.25684184e-01 -2.12239340e-01
-6.91523850e-01 4.96346682e-01 1.76880956e-02 -1.12905659e-01
1.17267914e-01 -8.50996554e-01 -7.36843228e-01 -6.22883141e-01
1.25704274e-01 1.54968910e-02 6.02357626e-01 -2.05897436... | [8.84946346282959, -2.2826404571533203] |
78f3e86a-3736-4928-87af-0555ce390a83 | mrdet-a-multi-head-network-for-accurate | 2012.13135 | null | https://arxiv.org/abs/2012.13135v2 | https://arxiv.org/pdf/2012.13135v2.pdf | MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images | Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected. Many recently developed methods attempt to solve these issues by estimating an extra orientation parameter and placing dense anchors, which will result in high model comp... | ['Yunhong Wang', 'Di Huang', 'Guangshuai Gao', 'Qingjie Liu', 'Ran Qin'] | 2020-12-24 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-7.60845244e-02 -8.08193609e-02 7.37074837e-02 -2.67578125e-01
-4.92683887e-01 -4.11921650e-01 1.47531360e-01 -1.63274273e-01
-4.13573682e-01 3.77434701e-01 -2.07891598e-01 -5.69011085e-02
1.33349270e-01 -7.77704477e-01 -7.16151834e-01 -7.49061704e-01
-3.80745083e-02 3.94360423e-01 8.35261047e-01 -1.14039280... | [8.731467247009277, -0.7282775640487671] |
7c4923d3-099e-4eaf-b007-d955c9e0c1f1 | convolution-in-the-cloud-learning-deformable | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Convolution_in_the_Cloud_Learning_Deformable_Kernels_in_3D_Graph_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Convolution_in_the_Cloud_Learning_Deformable_Kernels_in_3D_Graph_CVPR_2020_paper.pdf | Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis | Point clouds are among the popular geometry representations for 3D vision applications. However, without regular structures like 2D images, processing and summarizing information over these unordered data points are very challenging. Although a number of previous works attempt to analyze point clouds and achieve promis... | [' Yu-Chiang Frank Wang', ' Sheng-Yu Huang', 'Zhi-Hao Lin'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-9.61102545e-02 -1.30544081e-01 1.02924012e-01 -3.22533429e-01
2.49994616e-03 -6.95530891e-01 5.62484324e-01 4.65906441e-01
-1.44308373e-01 -5.62175997e-02 -2.67108738e-01 -5.03346264e-01
-2.56176978e-01 -9.65246558e-01 -7.46959925e-01 -4.19354171e-01
-5.34661233e-01 1.87205121e-01 5.02620101e-01 -4.93490063... | [7.95543098449707, -3.685879945755005] |
62aed925-566a-45a6-8c54-ee131155973f | towards-open-domain-topic-classification-1 | 2306.17290 | null | https://arxiv.org/abs/2306.17290v1 | https://arxiv.org/pdf/2306.17290v1.pdf | Towards Open-Domain Topic Classification | We introduce an open-domain topic classification system that accepts user-defined taxonomy in real time. Users will be able to classify a text snippet with respect to any candidate labels they want, and get instant response from our web interface. To obtain such flexibility, we build the backend model in a zero-shot wa... | ['Dan Roth', 'Hongming Zhang', 'Yuqian Deng', 'Jinrui Yang', 'Hantian Ding'] | 2023-06-29 | towards-open-domain-topic-classification | https://aclanthology.org/2022.naacl-demo.10 | https://aclanthology.org/2022.naacl-demo.10.pdf | naacl-acl-2022-7 | ['classification-1'] | ['methodology'] | [ 1.18136287e-01 2.15476498e-01 -7.93217838e-01 -4.93509114e-01
-1.05897748e+00 -8.14576447e-01 6.27118528e-01 4.39796597e-01
-5.80965817e-01 4.66099441e-01 2.37654060e-01 -2.34861553e-01
1.54160425e-01 -8.35911334e-01 -2.58833319e-01 5.36502451e-02
1.58102855e-01 9.99378026e-01 4.45941061e-01 -2.64324397... | [10.795675277709961, 7.6708478927612305] |
7177c5cb-911d-472f-8f31-6bd26f7fdf6f | improving-the-inference-of-topic-models-via | 2301.12974 | null | https://arxiv.org/abs/2301.12974v1 | https://arxiv.org/pdf/2301.12974v1.pdf | Improving the Inference of Topic Models via Infinite Latent State Replications | In text mining, topic models are a type of probabilistic generative models for inferring latent semantic topics from text corpus. One of the most popular inference approaches to topic models is perhaps collapsed Gibbs sampling (CGS), which typically samples one single topic label for each observed document-word pair. I... | ['Gao Cong', 'Manoranjan Dash', 'Juan Felipe Carmona', 'Zhen Hai', 'Daniel Rugeles'] | 2023-01-25 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 1.39061198e-01 3.26796651e-01 -5.28169036e-01 -3.99722546e-01
-9.40539896e-01 -9.96571556e-02 1.11629784e+00 -8.91966745e-02
2.31910959e-01 8.35816860e-01 3.49068940e-01 -3.35342914e-01
5.00830077e-02 -1.00768244e+00 -3.50836515e-01 -6.23513877e-01
8.10604319e-02 1.00472403e+00 3.38398397e-01 3.48493636... | [10.346939086914062, 6.898801326751709] |
ce9025f0-bf25-4ee7-9e4d-e8859c378796 | lagrangian-motion-magnification-with-double | 2204.07636 | null | https://arxiv.org/abs/2204.07636v1 | https://arxiv.org/pdf/2204.07636v1.pdf | Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition | Motion magnification techniques aim at amplifying and hence revealing subtle motion in videos. There are basically two main approaches to reach this goal, namely via Eulerian or Lagrangian techniques. While the first one magnifies motion implicitly by operating directly on image pixels, the Lagrangian approach uses opt... | ['Daniel J. Strauss', 'Gabriele Steidl', 'Cosmas Heiss', 'Philipp Flotho'] | 2022-04-15 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 1.72627479e-01 6.69393167e-02 8.81405771e-02 1.18939400e-01
-2.76417136e-01 -5.17961383e-01 6.03077352e-01 -6.59832835e-01
-3.29429895e-01 7.81512082e-01 1.89303234e-01 1.65311724e-01
-6.50727749e-02 -6.84709251e-01 -7.36851692e-01 -8.62216413e-01
3.10527515e-02 1.55131638e-01 -7.51574486e-02 -3.48981649... | [10.655635833740234, -1.3170521259307861] |
99dfffe5-3103-4613-ad9a-ebe08e836eb6 | ml-leaks-model-and-data-independent | 1806.01246 | null | http://arxiv.org/abs/1806.01246v2 | http://arxiv.org/pdf/1806.01246v2.pdf | ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models | Machine learning (ML) has become a core component of many real-world
applications and training data is a key factor that drives current progress.
This huge success has led Internet companies to deploy machine learning as a
service (MLaaS). Recently, the first membership inference attack has shown that
extraction of inf... | ['Michael Backes', 'Ahmed Salem', 'Yang Zhang', 'Pascal Berrang', 'Mathias Humbert', 'Mario Fritz'] | 2018-06-04 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.87298733e-01 2.36997321e-01 -2.51445323e-01 -3.45683068e-01
-6.37848854e-01 -1.03976321e+00 8.05619478e-01 1.36720791e-01
-4.40233022e-01 8.23610008e-01 -5.09380400e-01 -7.87847817e-01
-2.76178539e-01 -9.05893087e-01 -8.40485513e-01 -8.68018627e-01
-2.42362887e-01 6.82027221e-01 4.12979066e-01 -1.45993665... | [5.822941780090332, 7.291155815124512] |
4c7944a5-e884-4124-944a-054a33c6734e | recovering-and-simulating-pedestrians-in-the | 2011.08106 | null | https://arxiv.org/abs/2011.08106v1 | https://arxiv.org/pdf/2011.08106v1.pdf | Recovering and Simulating Pedestrians in the Wild | Sensor simulation is a key component for testing the performance of self-driving vehicles and for data augmentation to better train perception systems. Typical approaches rely on artists to create both 3D assets and their animations to generate a new scenario. This, however, does not scale. In contrast, we propose to r... | ['Raquel Urtasun', 'Wei-Chiu Ma', 'Bin Yang', 'Ming Liang', 'Siva Manivasagam', 'Ze Yang'] | 2020-11-16 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 2.44758680e-01 3.19274038e-01 3.61469030e-01 -5.51106572e-01
-6.64382398e-01 -5.95077634e-01 7.81480014e-01 -5.32241538e-02
-5.95113516e-01 6.59380138e-01 -8.03268328e-02 -7.65368268e-02
4.76852864e-01 -9.31814432e-01 -1.07721233e+00 -2.30767056e-01
2.41836697e-01 9.98892665e-01 6.00693107e-01 -4.99636948... | [8.147149085998535, -2.6856837272644043] |
838cc422-263b-435e-ad0e-1d5fd587395b | straight-to-the-facts-learning-knowledge-base | 1809.01124 | null | http://arxiv.org/abs/1809.01124v1 | http://arxiv.org/pdf/1809.01124v1.pdf | Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering | Question answering is an important task for autonomous agents and virtual
assistants alike and was shown to support the disabled in efficiently
navigating an overwhelming environment. Many existing methods focus on
observation-based questions, ignoring our ability to seamlessly combine
observed content with general kno... | ['Medhini Narasimhan', 'Alexander G. Schwing'] | 2018-09-04 | straight-to-the-facts-learning-knowledge-base-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Medhini_Gulganjalli_Narasimhan_Straight_to_the_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Medhini_Gulganjalli_Narasimhan_Straight_to_the_ECCV_2018_paper.pdf | eccv-2018-9 | ['factual-visual-question-answering', 'misconceptions'] | ['computer-vision', 'miscellaneous'] | [-7.16917291e-02 3.66212249e-01 -1.15357563e-01 -4.28377837e-01
-5.68652749e-01 -7.80413508e-01 8.61043811e-01 5.66846728e-01
-4.70034271e-01 6.33906841e-01 4.05292779e-01 -6.67667925e-01
-4.30061281e-01 -7.65850544e-01 -6.41868711e-01 -8.41881149e-03
-1.11641183e-01 6.97162569e-01 5.05561471e-01 -7.55639017... | [4.386695861816406, 0.6639400124549866] |
48b3a044-06fd-4aa1-a9d2-6ea4cdab2952 | matching-latent-encoding-for-audio-text-based | 2306.05245 | null | https://arxiv.org/abs/2306.05245v1 | https://arxiv.org/pdf/2306.05245v1.pdf | Matching Latent Encoding for Audio-Text based Keyword Spotting | Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown high-quality results, but the key challenge of how to semantically align two embeddings for multi-word keywords of different sequence lengths remains largely unsolved. In this paper, we propose an audio-text-based end-to-end model architecture... | ['Devang Naik', 'Minsik Cho', 'Kumari Nishu'] | 2023-06-08 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.95383757e-01 -3.50901335e-01 -4.81901504e-03 -3.68592411e-01
-1.30553687e+00 -5.49304843e-01 2.13647485e-01 5.91280386e-02
-6.41770661e-01 -1.87632011e-03 4.12283152e-01 -2.21816346e-01
-3.96662168e-02 -1.00426294e-01 -4.73465115e-01 -7.73052156e-01
-2.04657335e-02 4.22162622e-01 2.10788801e-01 1.07355520... | [14.264851570129395, 6.3595452308654785] |
b9255eca-4f7f-4685-98be-5e14f38de010 | deep-learning-for-human-parsing-a-survey | 2301.12416 | null | https://arxiv.org/abs/2301.12416v1 | https://arxiv.org/pdf/2301.12416v1.pdf | Deep Learning for Human Parsing: A Survey | Human parsing is a key topic in image processing with many applications, such as surveillance analysis, human-robot interaction, person search, and clothing category classification, among many others. Recently, due to the success of deep learning in computer vision, there are a number of works aimed at developing human... | ['Zhen Lei', 'Ming Tang', 'Xiangyu Zhu', 'Xiaomei Zhang'] | 2023-01-29 | null | null | null | null | ['human-parsing', 'person-search'] | ['computer-vision', 'computer-vision'] | [ 3.96334141e-01 1.43610895e-01 -2.76311219e-01 -6.15760148e-01
-1.94351926e-01 -1.27435341e-01 2.13462487e-01 8.54316056e-02
-3.71350080e-01 4.79637831e-01 4.02166992e-01 2.14182928e-01
-1.24441959e-01 -1.00531363e+00 -6.11861885e-01 -5.75561523e-01
-3.50509137e-01 3.95635843e-01 4.33748096e-01 -2.95914561... | [8.375535011291504, -0.1361280232667923] |
6b7a98ce-6d29-4638-afc6-b96c0520023e | paste-a-tagging-free-decoding-framework-using | 2110.04794 | null | https://arxiv.org/abs/2110.04794v1 | https://arxiv.org/pdf/2110.04794v1.pdf | PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment. Existing research efforts are majorly tagging-based. Among the methods taking a s... | ['Pawan Goyal', 'Sourangshu Bhattacharya', 'Yash Butala', 'Tapas Nayak', 'Rajdeep Mukherjee'] | 2021-10-10 | null | https://aclanthology.org/2021.emnlp-main.731 | https://aclanthology.org/2021.emnlp-main.731.pdf | emnlp-2021-11 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 2.54603177e-01 1.36919737e-01 -2.29946852e-01 -5.17010093e-01
-9.33738232e-01 -9.12806928e-01 4.25283045e-01 3.85454327e-01
-5.43688163e-02 6.59198701e-01 5.24489224e-01 -4.50304717e-01
1.12350151e-01 -5.83540797e-01 -5.53610682e-01 -5.09552002e-01
9.39174891e-02 4.63051260e-01 1.92283571e-01 -3.78749967... | [11.524885177612305, 6.609610557556152] |
407ba94b-2d49-4619-ac8f-da8f692fd442 | increased-complexity-and-fitness-of | 2103.08406 | null | https://arxiv.org/abs/2103.08406v1 | https://arxiv.org/pdf/2103.08406v1.pdf | Increased Complexity and Fitness of Artificial Cells that Reproduce Using Spatially Distributed Asynchronous Parallel Processes | Replication time is among the most important components of a bacterial cell's reproductive fitness. Paradoxically, larger cells replicate in less time than smaller cells despite the fact that building a larger cell requires increased quantities of raw materials and energy. This feat is primarily accomplished by the mas... | ['Lance R. Williams'] | 2021-03-15 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.11095297e-01 5.27692437e-02 3.61483604e-01 4.78525609e-01
3.93400371e-01 -9.41999853e-01 6.91702485e-01 5.68173707e-01
-5.86223602e-01 8.64335179e-01 -3.04679930e-01 -5.06901205e-01
1.80161774e-01 -1.12242186e+00 -5.49992025e-01 -9.59115982e-01
2.05415502e-01 6.85630202e-01 3.57653856e-01 -2.19832599... | [5.6337761878967285, 4.2269673347473145] |
8ecb5fba-3fc7-4491-a458-e73ee83986d5 | teaching-probabilistic-logical-reasoning-to | 2305.13179 | null | https://arxiv.org/abs/2305.13179v1 | https://arxiv.org/pdf/2305.13179v1.pdf | Teaching Probabilistic Logical Reasoning to Transformers | Recent research on transformer-based language models investigates their reasoning ability over logical rules expressed in natural language text. However, their logic is not yet well-understood as we cannot explain the abstractions made by the models that help them in reasoning. These models are criticized for merely me... | ['Parisa Kordjamshidi', 'Kristen Brent Venable', 'Aliakbar Nafar'] | 2023-05-22 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.17648849e-02 1.09134746e+00 -1.55633226e-01 -8.37935865e-01
-7.89682865e-01 -5.10783672e-01 7.57619202e-01 2.10247457e-01
2.82657072e-02 9.21443224e-01 2.13770643e-01 -8.40574861e-01
-5.25156856e-01 -1.26287496e+00 -1.05191755e+00 -9.48911458e-02
1.44854203e-01 1.19012272e+00 7.37653077e-01 -3.60874712... | [9.237848281860352, 7.202805995941162] |
2abcfe8f-794d-4e61-b114-300ed279c727 | dyntypo-example-based-dynamic-text-effects | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Men_DynTypo_Example-Based_Dynamic_Text_Effects_Transfer_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Men_DynTypo_Example-Based_Dynamic_Text_Effects_Transfer_CVPR_2019_paper.pdf | DynTypo: Example-Based Dynamic Text Effects Transfer | In this paper, we present a novel approach for dynamic text effects transfer by using example-based texture synthesis. In contrast to previous works that require an input video of the target to provide motion guidance, we aim to animate a still image of the target text by transferring the desired dynamic effects from a... | [' Jianguo Xiao', ' Yingmin Tang', ' Zhouhui Lian', 'Yifang Men'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['text-effects-transfer'] | ['natural-language-processing'] | [ 6.82992637e-01 -1.37847379e-01 1.95383027e-01 -6.75836727e-02
-3.13917011e-01 -2.13993683e-01 8.37547362e-01 -3.84404004e-01
-2.54410580e-02 7.38467991e-01 4.00357306e-01 3.48035842e-01
-1.53875336e-01 -7.88996279e-01 -8.16384196e-01 -9.58034158e-01
4.23362218e-02 1.05230518e-01 6.47070587e-01 -3.92692327... | [10.922082901000977, -0.9018504023551941] |
372ab0c7-3ade-40da-81da-0f7903dfe159 | adaptive-least-mean-squares-estimation-of | 1602.05703 | null | http://arxiv.org/abs/1602.05703v3 | http://arxiv.org/pdf/1602.05703v3.pdf | Adaptive Least Mean Squares Estimation of Graph Signals | The aim of this paper is to propose a least mean squares (LMS) strategy for
adaptive estimation of signals defined over graphs. Assuming the graph signal
to be band-limited, over a known bandwidth, the method enables reconstruction,
with guaranteed performance in terms of mean-square error, and tracking from a
limited ... | ['Paolo Di Lorenzo', 'Stefania Sardellitti', 'Sergio Barbarossa', 'Paolo Banelli'] | 2016-02-18 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 5.70656121e-01 4.53710884e-01 -9.67218280e-02 3.27038050e-01
-3.07649434e-01 -4.07269478e-01 9.61779132e-02 1.65131435e-01
1.60742223e-01 8.29578936e-01 -2.39229277e-01 -3.81860673e-01
-8.17158878e-01 -7.71435440e-01 -6.23299599e-01 -6.26928031e-01
-6.44169927e-01 -9.27866697e-02 -4.57300767e-02 -4.33642380... | [6.537106990814209, 1.5154876708984375] |
5bc104b9-92a6-41d2-8039-f70aba28405d | neural-graph-matching-network-learning | 1911.11308 | null | https://arxiv.org/abs/1911.11308v3 | https://arxiv.org/pdf/1911.11308v3.pdf | Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching | Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's Quadratic Assignment Problem (QAP). This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is tr... | ['Junchi Yan', 'Xiaokang Yang', 'Runzhong Wang'] | 2019-11-26 | neural-graph-matching-network-learning-lawler | https://ieeexplore.ieee.org/document/9426408 | https://arxiv.org/pdf/1911.11308.pdf | null | ['hypergraph-matching'] | ['graphs'] | [ 2.18733639e-01 4.37010437e-01 -3.68285537e-01 -3.19529742e-01
-7.57698655e-01 -5.23892462e-01 1.63618997e-01 2.42124304e-01
-1.85179070e-01 5.39499760e-01 -2.84383446e-01 -3.10891569e-01
-4.30586576e-01 -1.07583344e+00 -9.68877435e-01 -5.02487957e-01
-2.13836148e-01 9.65312064e-01 1.15473099e-01 -2.53417522... | [7.145209312438965, 6.352992057800293] |
eb758fae-b455-4b1e-a34d-8c4621ba1e31 | large-scale-decipherment-for-out-of-domain | null | null | https://aclanthology.org/D12-1025 | https://aclanthology.org/D12-1025.pdf | Large Scale Decipherment for Out-of-Domain Machine Translation | null | ['Qing Dou', 'Kevin Knight'] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['decipherment'] | ['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... | [-7.37230920791626, 3.7303531169891357] |
90759603-41ff-4d2a-9be1-f75f7edb1c2e | modeling-local-geometric-structure-of-3d | 1811.07782 | null | http://arxiv.org/abs/1811.07782v1 | http://arxiv.org/pdf/1811.07782v1.pdf | Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN | Recent advances in deep convolutional neural networks (CNNs) have motivated
researchers to adapt CNNs to directly model points in 3D point clouds. Modeling
local structure has been proven to be important for the success of
convolutional architectures, and researchers exploited the modeling of local
point sets in the fe... | ['Ruichi Yu', 'Shiyi Lan', 'Larry S. Davis', 'Gang Yu'] | 2018-11-19 | modeling-local-geometric-structure-of-3d-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Lan_Modeling_Local_Geometric_Structure_of_3D_Point_Clouds_Using_Geo-CNN_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lan_Modeling_Local_Geometric_Structure_of_3D_Point_Clouds_Using_Geo-CNN_CVPR_2019_paper.pdf | cvpr-2019-6 | ['modeling-local-geometric-structure'] | ['miscellaneous'] | [-6.69994354e-01 -3.69143188e-01 1.50187999e-01 -4.09188062e-01
-4.87093143e-02 -4.77634370e-01 5.59419453e-01 3.88645828e-01
-4.02779043e-01 -1.83643326e-01 -1.14934206e-01 -2.34025523e-01
-2.19694600e-02 -1.15405011e+00 -8.91053081e-01 -1.28104493e-01
-2.95447826e-01 2.81294465e-01 2.15238392e-01 -2.33942255... | [7.882780075073242, -3.5904598236083984] |
6c4ccb2e-f2f4-4c86-9d88-2eafec661588 | data-driven-pronunciation-modeling-of-swiss | null | null | https://aclanthology.org/L18-1498 | https://aclanthology.org/L18-1498.pdf | Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition | null | ['Christoph Schmidt', 'Michael Stadtschnitzer'] | 2018-05-01 | data-driven-pronunciation-modeling-of-swiss-1 | https://aclanthology.org/L18-1498 | https://aclanthology.org/L18-1498.pdf | lrec-2018-5 | ['robust-speech-recognition'] | ['speech'] | [-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... | [-7.248467445373535, 3.649506092071533] |
e273e68a-458f-49c3-a7c2-337ab40a07f6 | what-do-they-capture-a-structural-analysis-of | 2202.06840 | null | https://arxiv.org/abs/2202.06840v1 | https://arxiv.org/pdf/2202.06840v1.pdf | What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code | Recently, many pre-trained language models for source code have been proposed to model the context of code and serve as a basis for downstream code intelligence tasks such as code completion, code search, and code summarization. These models leverage masked pre-training and Transformer and have achieved promising resul... | ['Hai Jin', 'Guandong Xu', 'Yulei Sui', 'Hongyu Zhang', 'Wei Zhao', 'Yao Wan'] | 2022-02-14 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 1.03561148e-01 4.60376233e-01 -4.52678680e-01 -3.50855380e-01
-5.02166033e-01 -7.34252334e-01 4.31507021e-01 5.66952705e-01
2.50488400e-01 -3.36275324e-02 8.67456019e-01 -1.07229877e+00
2.14306042e-01 -5.57687342e-01 -6.88613474e-01 -1.85998693e-01
-1.70437872e-01 -2.54934579e-01 -8.94580185e-02 -3.48842561... | [7.6208696365356445, 7.895417213439941] |
fe37c344-8ab9-41e5-9e9e-5c8c07447bb3 | question-generation-and-answering-for | null | null | https://aclanthology.org/2022.lrec-1.486 | https://aclanthology.org/2022.lrec-1.486.pdf | Question Generation and Answering for exploring Digital Humanities collections | This paper introduces the question answering paradigm as a way to explore digitized archive collections for Social Science studies. In particular, we are interested in evaluating largely studied question generation and question answering approaches on a new type of documents, as a step forward beyond traditional benchm... | ['Géraldine Damnati', 'Jérémy Auguste', 'Elie Antoine', 'Frederic Bechet'] | null | null | null | null | lrec-2022-6 | ['question-generation'] | ['natural-language-processing'] | [ 2.10688248e-01 7.48632371e-01 2.77106464e-01 -3.38226348e-01
-8.82619202e-01 -6.74642563e-01 1.28670025e+00 7.80011535e-01
-4.91649985e-01 8.95498753e-01 5.73588192e-01 -2.66935349e-01
-2.91554689e-01 -1.29712832e+00 -6.52014375e-01 -1.47446752e-01
4.42177057e-01 1.08401501e+00 4.29192066e-01 -9.46773648... | [11.534296035766602, 8.254302024841309] |
2ec3db4d-ca3d-4ec4-af83-918db14c2d14 | invariance-adapted-decomposition-and-lasso | 2210.07413 | null | https://arxiv.org/abs/2210.07413v1 | https://arxiv.org/pdf/2210.07413v1.pdf | Invariance-adapted decomposition and Lasso-type contrastive learning | Recent years have witnessed the effectiveness of contrastive learning in obtaining the representation of dataset that is useful in interpretation and downstream tasks. However, the mechanism that describes this effectiveness have not been thoroughly analyzed, and many studies have been conducted to investigate the data... | ['Kenji Fukumizu', 'Takeru Miyato', 'Masanori Koyama'] | 2022-10-13 | null | null | null | null | ['type'] | ['speech'] | [ 3.34882885e-01 2.38358006e-02 -3.86913240e-01 -1.95518151e-01
-5.71191072e-01 -8.31139743e-01 8.55915248e-01 7.99546838e-02
-2.19893772e-02 4.53246564e-01 3.95312577e-01 3.70870940e-02
-7.71584094e-01 -6.96949244e-01 -6.43735051e-01 -1.18051624e+00
-3.70047987e-01 2.62642741e-01 -1.72590837e-01 -2.24657193... | [7.932477951049805, 4.137331008911133] |
3d5b6c4f-90a5-43e9-90d3-2abf17ae011a | spectral-illumination-correction-achieving | null | null | https://ieeexplore.ieee.org/document/8642637 | https://pureadmin.qub.ac.uk/ws/portalfiles/portal/163783181/sic_converted.pdf | Spectral Illumination Correction: Achieving Relative Color Constancy Under the Spectral Domain | Achieving color constancy between and within images, i.e., minimizing the color difference between the same object imaged under nonuniform and varied illuminations is crucial for computer vision tasks such as colorimetric analysis and object recognition. Most current methods attempt to solve this by illumination correc... | ['Huiyu Zhou', 'Yunfeng Zhao', 'Karen Rafferty', 'Chris Elliott'] | 2018-12-06 | null | null | null | 2018-ieee-international-symposium-on-signal | ['color-constancy'] | ['computer-vision'] | [ 7.37953782e-01 -6.74574375e-01 3.64968061e-01 -3.41679305e-01
-2.14736000e-01 -8.09958160e-01 4.18604791e-01 -2.57348903e-02
-5.10442257e-01 6.32362485e-01 -3.70485276e-01 -5.90484813e-02
1.08795809e-02 -7.10471749e-01 -5.23938060e-01 -8.51164281e-01
7.15168834e-01 -1.49580553e-01 2.30741352e-01 5.57280630... | [10.488709449768066, -2.6058218479156494] |
6a133c7b-83a4-43ea-a4b7-c800d62c3665 | sam-iqa-can-segment-anything-boost-image | 2307.04455 | null | https://arxiv.org/abs/2307.04455v1 | https://arxiv.org/pdf/2307.04455v1.pdf | SAM-IQA: Can Segment Anything Boost Image Quality Assessment? | Image Quality Assessment (IQA) is a challenging task that requires training on massive datasets to achieve accurate predictions. However, due to the lack of IQA data, deep learning-based IQA methods typically rely on pre-trained networks trained on massive datasets as feature extractors to enhance their generalization ... | ['Shuaicheng Liu', 'Haoqiang Fan', 'Ting Jiang', 'Xinpeng Li'] | 2023-07-10 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 7.40620196e-02 -3.67683113e-01 -4.17939126e-02 -4.29740369e-01
-1.04209220e+00 -2.82616049e-01 5.86392105e-01 -1.10799246e-01
-2.80718237e-01 4.62904781e-01 3.09404284e-01 1.01866722e-01
-3.66143435e-01 -8.36965859e-01 -6.87611639e-01 -5.79519987e-01
-6.21705912e-02 -1.79035723e-01 1.37342408e-01 -2.35343538... | [11.793706893920898, -1.8068432807922363] |
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