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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ecc7a9a9-2c74-438c-9099-373a4c17328f | sequential-neural-networks-for-noetic-end-to | 2003.02126 | null | https://arxiv.org/abs/2003.02126v1 | https://arxiv.org/pdf/2003.02126v1.pdf | Sequential Neural Networks for Noetic End-to-End Response Selection | The noetic end-to-end response selection challenge as one track in the 7th Dialog System Technology Challenges (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for... | ['Wen Wang', 'Qian Chen'] | 2020-03-03 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-3.22128274e-02 1.57379642e-01 -1.42250508e-01 -9.30031478e-01
-1.04805136e+00 -5.79479814e-01 7.58098483e-01 1.79328531e-01
-4.86635268e-01 7.55045056e-01 6.66040838e-01 -3.71932566e-01
2.27076504e-02 -2.60622501e-01 3.20568353e-01 -9.96288434e-02
1.77727640e-01 1.18165600e+00 5.89978814e-01 -1.29666603... | [12.711976051330566, 7.887463092803955] |
7bf63a7f-d47b-4bcd-9687-d4c8d6fb62ab | textattack-a-framework-for-adversarial | 2005.05909 | null | https://arxiv.org/abs/2005.05909v4 | https://arxiv.org/pdf/2005.05909v4.pdf | TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP | While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, dat... | ['Eli Lifland', 'Yanjun Qi', 'John X. Morris', 'Jake Grigsby', 'Jin Yong Yoo', 'Di Jin'] | 2020-04-29 | null | https://aclanthology.org/2020.emnlp-demos.16 | https://aclanthology.org/2020.emnlp-demos.16.pdf | emnlp-2020-11 | ['adversarial-text'] | ['adversarial'] | [-1.33320883e-01 2.68157005e-01 -3.51545811e-01 -5.50052524e-01
-9.21482325e-01 -1.40838718e+00 7.08966076e-01 1.14646144e-01
-3.26410383e-02 5.53535104e-01 1.74502805e-01 -8.21563482e-01
3.46779883e-01 -9.17805314e-01 -8.05963278e-01 -2.87179470e-01
1.12442479e-01 6.77173793e-01 -5.45758195e-02 -2.79626966... | [6.023875713348389, 8.096360206604004] |
13fa0700-046a-4440-8249-493eea3f9b32 | adversarial-disentanglement-of-speaker | 2012.04454 | null | https://arxiv.org/abs/2012.04454v3 | https://arxiv.org/pdf/2012.04454v3.pdf | Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation | In speech technologies, speaker's voice representation is used in many applications such as speech recognition, voice conversion, speech synthesis and, obviously, user authentication. Modern vocal representations of the speaker are based on neural embeddings. In addition to the targeted information, these representatio... | ['Andreas Nautsch', 'Jean-François Bonastre', 'Titouan Parcollet', 'Driss Matrouf', 'Mohammad Mohammadamini', 'Paul-Gauthier Noé'] | 2020-12-08 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 3.24145645e-01 3.42511922e-01 -1.40864491e-01 -5.19044876e-01
-4.22126591e-01 -6.66190863e-01 6.87870800e-01 3.76499385e-01
-4.83168542e-01 5.56720197e-01 3.74402076e-01 -3.85349602e-01
4.15596403e-02 -6.79693878e-01 -4.94210124e-01 -8.62061262e-01
-1.08979858e-01 1.50699854e-01 -2.38545671e-01 1.03774041... | [14.023775100708008, 5.879519462585449] |
e28026e0-52e2-4e75-8ee6-5c2cce1b05a2 | desta-a-framework-for-safe-reinforcement-1 | 2110.14468 | null | https://arxiv.org/abs/2110.14468v3 | https://arxiv.org/pdf/2110.14468v3.pdf | DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention | Reinforcement learning (RL) involves performing exploratory actions in an unknown system. This can place a learning agent in dangerous and potentially catastrophic system states. Current approaches for tackling safe learning in RL simultaneously trade-off safe exploration and task fulfillment. In this paper, we introdu... | ['Changmin Yu', 'Xiuling Zhang', 'Yaqi Sun', 'Usman Islam', 'Jun Wang', 'Ziyan Wang', 'Yaodong Yang', 'Aivar Sootla', 'Joel Jennings', 'David Mguni'] | 2021-10-27 | desta-a-framework-for-safe-reinforcement | https://openreview.net/forum?id=ht61oVsaya | https://openreview.net/pdf?id=ht61oVsaya | null | ['safe-exploration'] | ['robots'] | [ 2.64315784e-01 8.34923446e-01 -2.33434498e-01 1.26781896e-01
-7.77944386e-01 -7.05753505e-01 5.46390891e-01 -9.59305018e-02
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-4.47791159e-01 -7.10833013e-01 -7.87688375e-01 -1.07806265e+00
-6.58915579e-01 3.97886425e-01 7.74004590e-03 -2.97964990... | [4.510750770568848, 2.092385768890381] |
934f78ff-4dc4-435e-be35-cddab87c11c9 | multilingual-corpora-with-coreferential | null | null | https://aclanthology.org/L14-1701 | https://aclanthology.org/L14-1701.pdf | Multilingual corpora with coreferential annotation of person entities | This paper presents three corpora with coreferential annotation of person entities for Portuguese, Galician and Spanish. They contain coreference links between several types of pronouns (including elliptical, possessive, indefinite, demonstrative, relative and personal clitic and non-clitic pronouns) and nominal phrase... | ['Pablo Gamallo', 'Marcos Garcia'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['open-information-extraction'] | ['natural-language-processing'] | [-3.73689175e-01 4.60723013e-01 -1.71043769e-01 -1.56072333e-01
-5.24038792e-01 -1.16594076e+00 9.26568210e-01 5.91577530e-01
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6.87783584e-02 1.17119312e+00 3.20813864e-01 -6.49866879... | [9.339009284973145, 9.55928897857666] |
0b11a1b3-6840-4e8e-8995-ae2e64e7a185 | category-level-pose-retrieval-with | 2208.06195 | null | https://arxiv.org/abs/2208.06195v3 | https://arxiv.org/pdf/2208.06195v3.pdf | Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation | Pose estimation is usually tackled as either a bin classification or a regression problem. In both cases, the idea is to directly predict the pose of an object. This is a non-trivial task due to appearance variations between similar poses and similarities between dissimilar poses. Instead, we follow the key idea that c... | ['Tinne Tuytelaars', 'Punarjay Chakravarty', 'Sushruth Nagesh', 'Cédric Picron', 'Shubham Shrivastava', 'Georgios Kouros'] | 2022-08-12 | null | null | null | null | ['pose-retrieval'] | ['computer-vision'] | [ 1.00892812e-01 -3.47581357e-01 9.53067765e-02 -4.93692309e-01
-1.24827087e+00 -7.58913159e-01 7.73601353e-01 4.77007702e-02
-5.02334893e-01 3.08969706e-01 -1.62900105e-01 8.99266526e-02
1.49251848e-01 -4.22327727e-01 -8.11875105e-01 -7.01789796e-01
9.26906317e-02 7.51807690e-01 3.94235611e-01 -7.02268332... | [7.830636978149414, -2.603100299835205] |
750ae449-9600-4224-8605-f3eec7497af9 | adversarial-de-confounding-in-individualised | 2210.10530 | null | https://arxiv.org/abs/2210.10530v3 | https://arxiv.org/pdf/2210.10530v3.pdf | Adversarial De-confounding in Individualised Treatment Effects Estimation | Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In obs... | ['David A. Clifton', 'Tingting Zhu', 'Anshul Thakur', 'Marzia Hoque Tania', 'Soheila Molaei', 'Vinod Kumar Chauhan'] | 2022-10-19 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.66653591e-01 1.79253951e-01 -1.13110888e+00 -2.33519703e-01
-6.75080538e-01 -2.43328854e-01 5.47836781e-01 -3.15198377e-02
-3.69217068e-01 1.34198141e+00 6.49247766e-01 -5.75597525e-01
-6.13851130e-01 -7.84194767e-01 -6.99349821e-01 -8.80869448e-01
-4.10706103e-01 3.70533884e-01 -5.74069381e-01 2.25967705... | [8.042593955993652, 5.388043403625488] |
8a406eaf-c131-4072-9e3f-59e822e1c8d7 | synthesis-of-realistic-ecg-using-generative | 1909.09150 | null | https://arxiv.org/abs/1909.09150v1 | https://arxiv.org/pdf/1909.09150v1.pdf | Synthesis of Realistic ECG using Generative Adversarial Networks | Access to medical data is highly restricted due to its sensitive nature, preventing communities from using this data for research or clinical training. Common methods of de-identification implemented to enable the sharing of data are sometimes inadequate to protect the individuals contained in the data. For our researc... | ['Anne Marie Delaney', 'Eoin Brophy', 'Tomas E. Ward'] | 2019-09-19 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 6.07901394e-01 5.67898750e-01 4.39634055e-01 -3.14180225e-01
-1.01101291e+00 -8.99112403e-01 3.46263558e-01 3.10010258e-02
-1.53685868e-01 1.11662138e+00 -1.45539343e-02 -2.40513399e-01
8.82610828e-02 -8.63931417e-01 -6.98020995e-01 -6.57298803e-01
-3.14180791e-01 2.01788485e-01 -5.06341279e-01 6.95009157... | [14.304494857788086, 3.0460662841796875] |
f8cdca36-aad2-431c-997a-608b71b1bb4f | ai-outperformed-every-dermatologist-improved | 2003.02597 | null | https://arxiv.org/abs/2003.02597v2 | https://arxiv.org/pdf/2003.02597v2.pdf | AI outperformed every dermatologist: Improved dermoscopic melanoma diagnosis through customizing batch logic and loss function in an optimized Deep CNN architecture | Melanoma, one of most dangerous types of skin cancer, re-sults in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent research has used artificial intelligence to classify melanoma and nevus and to compare the assessment of these algorithms to that of dermatologist... | ['Antoine Doucet', 'Dung Van Hoang', 'Cong Tri Pham', 'Mai Chi Luong'] | 2020-03-05 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 1.85181394e-01 1.22932464e-01 -4.50693816e-01 -1.01368301e-01
-4.28648770e-01 -1.60546497e-01 2.89379507e-01 4.69907165e-01
-9.30429161e-01 8.41986060e-01 -4.62371618e-01 -3.94923240e-01
-4.57503229e-01 -9.33322787e-01 -1.43020555e-01 -8.41138482e-01
1.04740009e-01 9.41731557e-02 -2.92967185e-02 -7.12629482... | [15.636204719543457, -2.987696409225464] |
11070a0c-ecd3-4c8b-a8b3-1af98d89af47 | limit-bert-linguistics-informed-multi-task | null | null | https://aclanthology.org/2020.findings-emnlp.399 | https://aclanthology.org/2020.findings-emnlp.399.pdf | LIMIT-BERT : Linguistics Informed Multi-Task BERT | In this paper, we present Linguistics Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistics tasks by Multi-Task Learning. LIMIT-BERT includes five key linguistics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semanti... | ['Shuailiang Zhang', 'Hai Zhao', 'Zhuosheng Zhang', 'Junru Zhou'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['semantic-role-labeling'] | ['natural-language-processing'] | [-1.86673969e-01 3.32682222e-01 -3.04721326e-01 -7.11641431e-01
-1.17019093e+00 -4.54836756e-01 2.50658900e-01 3.11327815e-01
-6.42431021e-01 6.35122538e-01 5.21843851e-01 -3.44326317e-01
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5.29546402e-02 7.00286865e-01 3.87572020e-01 -5.09388626... | [10.441217422485352, 9.438273429870605] |
dd0a07c6-3069-4ee4-919a-887097f048b0 | vaxformer-antigenicity-controlled-transformer | 2305.11194 | null | https://arxiv.org/abs/2305.11194v1 | https://arxiv.org/pdf/2305.11194v1.pdf | Vaxformer: Antigenicity-controlled Transformer for Vaccine Design Against SARS-CoV-2 | The SARS-CoV-2 pandemic has emphasised the importance of developing a universal vaccine that can protect against current and future variants of the virus. The present study proposes a novel conditional protein Language Model architecture, called Vaxformer, which is designed to produce natural-looking antigenicity-contr... | ['Javier Antonio Alfaro', 'Diego A. Oyarzún', 'Ajitha Rajan', 'Achille Fraisse', 'Michał Kobiela', 'Aryo Pradipta Gema'] | 2023-05-18 | null | null | null | null | ['protein-language-model'] | ['medical'] | [ 1.58387423e-01 -3.25290263e-01 7.88621828e-02 -3.36214811e-01
-7.10347116e-01 -6.38336897e-01 2.49397010e-01 2.09926665e-01
-1.90287888e-01 1.04437089e+00 1.46655366e-01 -8.12503934e-01
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-1.70862213e-01 9.14898217e-01 -2.88328439e-01 -4.96473163... | [4.902958869934082, 5.439722537994385] |
0fe29de9-6e46-4196-8179-44dcb18e0db9 | openood-v1-5-enhanced-benchmark-for-out-of | 2306.09301 | null | https://arxiv.org/abs/2306.09301v2 | https://arxiv.org/pdf/2306.09301v2.pdf | OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection | Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems. Despite the emergence of an increasing number of OOD detection methods, the evaluation inconsistencies present challenges for tracking the progress in this field. OpenOOD v1 initiated the unification of the OOD... | ['Hai Li', 'Yiran Chen', 'Ziwei Liu', 'Yixuan Li', 'Wayne Zhang', 'Kaiyang Zhou', 'Xuefeng Du', 'Yiyou Sun', 'Haoran Zhang', 'Yueqian Lin', 'Haoqi Wang', 'Pengyun Wang', 'Jingkang Yang', 'Jingyang Zhang'] | 2023-06-15 | null | null | null | null | ['out-of-distribution-detection'] | ['computer-vision'] | [-2.38989487e-01 2.03385353e-01 -4.22972262e-01 -2.27183446e-01
-4.58368838e-01 -7.46540904e-01 8.65638673e-01 4.72596377e-01
3.31858806e-02 7.77667537e-02 1.42579526e-01 -1.86883047e-01
-2.00649232e-01 -7.32008636e-01 -1.29058942e-01 -1.87252492e-01
-4.59082693e-01 4.88287538e-01 5.15656114e-01 -5.31180799... | [9.231376647949219, 3.0950820446014404] |
8044280b-ec43-4962-8a3d-9eb9a18a9dec | conformal-prediction-intervals-with-temporal | 2205.12940 | null | https://arxiv.org/abs/2205.12940v3 | https://arxiv.org/pdf/2205.12940v3.pdf | Conformal Prediction Intervals with Temporal Dependence | Cross-sectional prediction is common in many domains such as healthcare, including forecasting tasks using electronic health records, where different patients form a cross-section. We focus on the task of constructing valid prediction intervals (PIs) in time series regression with a cross-section. A prediction interval... | ['Jimeng Sun', 'Shubhendu Trivedi', 'Zhen Lin'] | 2022-05-25 | null | null | null | null | ['prediction-intervals', 'time-series-regression'] | ['miscellaneous', 'time-series'] | [ 2.05940351e-01 5.24666570e-02 -7.71057308e-01 -5.11217356e-01
-9.04530466e-01 -4.95026797e-01 2.59080321e-01 5.33619106e-01
-1.37629151e-01 9.16823864e-01 3.20828855e-01 -8.08298469e-01
-8.40027273e-01 -8.21955323e-01 -9.46626663e-01 -4.64401275e-01
-6.81177974e-01 5.24527073e-01 -4.51161377e-02 1.69190675... | [7.814303398132324, 5.003946781158447] |
0701be8c-6fea-493f-9026-b4fa65a5e104 | causal-imitation-learning-under-temporally | 2202.01312 | null | https://arxiv.org/abs/2202.01312v1 | https://arxiv.org/pdf/2202.01312v1.pdf | Causal Imitation Learning under Temporally Correlated Noise | We develop algorithms for imitation learning from policy data that was corrupted by temporally correlated noise in expert actions. When noise affects multiple timesteps of recorded data, it can manifest as spurious correlations between states and actions that a learner might latch on to, leading to poor policy performa... | ['Zhiwei Steven Wu', 'J. Andrew Bagnell', 'Sanjiban Choudhury', 'Gokul Swamy'] | 2022-02-02 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-1.91335287e-02 1.02934606e-01 -9.83162969e-02 2.18458489e-01
-8.30618739e-01 -1.02501571e+00 7.72730768e-01 -1.94498330e-01
-6.44900143e-01 1.17640638e+00 -4.98994105e-02 -1.00451946e+00
-1.95881113e-01 -6.04641855e-01 -9.23599899e-01 -7.60700881e-01
-2.93838203e-01 5.41457772e-01 1.11603059e-01 -7.03722909... | [4.1044921875, 2.1130425930023193] |
c60d7608-251f-4bd9-84cf-58df8d4451c7 | universal-instance-perception-as-object | 2303.06674 | null | https://arxiv.org/abs/2303.06674v1 | https://arxiv.org/pdf/2303.06674v1.pdf | Universal Instance Perception as Object Discovery and Retrieval | All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent subtasks. In this work, we present a universal instance perception model of the next generation, t... | ['Huchuan Lu', 'Zehuan Yuan', 'Ping Luo', 'Dong Wang', 'Jiannan Wu', 'Yi Jiang', 'Bin Yan'] | 2023-03-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yan_Universal_Instance_Perception_As_Object_Discovery_and_Retrieval_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yan_Universal_Instance_Perception_As_Object_Discovery_and_Retrieval_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-discovery', 'referring-expression', 'visual-tracking', 'multiple-object-tracking', 'referring-expression-segmentation', 'video-instance-segmentation', 'referring-video-object-segmentation', 'visual-object-tracking', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.93212825e-01 4.32367437e-02 -5.58755457e-01 -6.02738678e-01
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-3.55386026e-02 -6.13611162e-01 -8.71651471e-01 -5.08709550e-01
2.66105741e-01 5.60517073e-01 4.44402933e-01 -8.14110041... | [10.166075706481934, 1.5883451700210571] |
54d7e811-c428-446b-9c34-bcce6c60e158 | remote-sensing-image-change-detection-towards | 2305.14722 | null | https://arxiv.org/abs/2305.14722v1 | https://arxiv.org/pdf/2305.14722v1.pdf | Remote Sensing Image Change Detection Towards Continuous Bitemporal Resolution Differences | Most contemporary supervised Remote Sensing (RS) image Change Detection (CD) approaches are customized for equal-resolution bitemporal images. Real-world applications raise the need for cross-resolution change detection, aka, CD based on bitemporal images with different spatial resolutions. Current cross-resolution met... | ['Zhenwei Shi', 'Zhengxia Zhou', 'Song Chen', 'Chenyao Zhou', 'Keyan Chen', 'Haotian Zhang', 'Hao Chen'] | 2023-05-24 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 6.98556244e-01 -3.28058839e-01 -2.16777310e-01 -4.96398062e-01
-1.10599029e+00 -4.19760227e-01 7.11143672e-01 -6.43636048e-01
-1.68808997e-01 8.08095932e-01 3.79640192e-01 -2.62093879e-02
-3.24966937e-01 -1.25346327e+00 -9.34295177e-01 -9.29085553e-01
-2.45936766e-01 -2.95389265e-01 1.47469983e-01 -4.23290163... | [10.738204956054688, -2.0406689643859863] |
21b56e9f-685a-4cb5-8f47-15238647ff4a | uncertainty-quantification-for-atlas-level | 2211.03793 | null | https://arxiv.org/abs/2211.03793v1 | https://arxiv.org/pdf/2211.03793v1.pdf | Uncertainty Quantification for Atlas-Level Cell Type Transfer | Single-cell reference atlases are large-scale, cell-level maps that capture cellular heterogeneity within an organ using single cell genomics. Given their size and cellular diversity, these atlases serve as high-quality training data for the transfer of cell type labels to new datasets. Such label transfer, however, mu... | ['Fabian Theis', 'Malte Luecken', 'Lisa Sikkema', 'Giovanni Palla', 'Leon Hetzel', 'Jan Engelmann'] | 2022-11-07 | null | null | null | null | ['type'] | ['speech'] | [ 1.72301427e-01 -3.17877382e-02 -1.67267933e-01 -3.76944035e-01
-1.36373520e+00 -1.11993098e+00 7.53221512e-01 7.33406007e-01
-2.77841240e-01 1.38482249e+00 -1.26848677e-02 1.06583335e-01
2.73515675e-02 -7.23524034e-01 -9.02485371e-01 -9.94036019e-01
1.92135751e-01 9.00455236e-01 2.08617762e-01 2.41691977... | [14.64203929901123, -3.0564961433410645] |
e4ea3c7c-eef8-458b-a8df-fbf3fdb59502 | dynamic-linear-transformer-for-3d-biomedical | 2206.00771 | null | https://arxiv.org/abs/2206.00771v2 | https://arxiv.org/pdf/2206.00771v2.pdf | Dynamic Linear Transformer for 3D Biomedical Image Segmentation | Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attention mechanism. However, most methods are still designed for 2D medical images while ignoring the essential 3D volume information. The main ch... | ['Ulas Bagci', 'Zheyuan Zhang'] | 2022-06-01 | null | null | null | null | ['3d-medical-imaging-segmentation', 'pancreas-segmentation'] | ['medical', 'medical'] | [ 3.04778852e-02 6.15759313e-01 -2.11318612e-01 -6.61477745e-01
-1.32804024e+00 -1.12979837e-01 9.32833850e-02 5.10662556e-01
-4.78607029e-01 4.77730423e-01 2.09058687e-01 -3.08568716e-01
4.48089391e-02 -5.89867234e-01 -9.47886527e-01 -6.94261611e-01
3.30150574e-02 7.03260660e-01 3.01141948e-01 2.34166637... | [14.57255744934082, -2.4575862884521484] |
159e5eb6-3873-46e5-83f0-148705f71564 | deep-learning-for-robust-motion-segmentation | 2102.10929 | null | https://arxiv.org/abs/2102.10929v1 | https://arxiv.org/pdf/2102.10929v1.pdf | Deep Learning for Robust Motion Segmentation with Non-Static Cameras | This work proposes a new end-to-end DCNN based approach for motion segmentation, especially for video sequences captured with such non-static cameras, called MOSNET. While other approaches focus on spatial or temporal context only, the proposed approach uses 3D convolutions as a key technology to factor in, spatio-temp... | ['Markus Bosch'] | 2021-02-22 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 6.51357025e-02 -2.03122348e-01 1.47688106e-01 -3.71002376e-01
-2.81866193e-01 -4.36935723e-01 6.33751690e-01 6.11157306e-02
-1.15166509e+00 4.28704977e-01 -1.36079133e-01 6.43853918e-02
-2.51711041e-01 -6.21089816e-01 -6.01751924e-01 -7.05295205e-01
-1.65835753e-01 1.16538763e-01 7.50359535e-01 -9.81402621... | [8.946513175964355, -0.712940514087677] |
a100fda1-305b-4ea1-9ba8-cdfd71f8e60f | cross-modal-retrieval-augmentation-for-multi-1 | 2104.08108 | null | https://arxiv.org/abs/2104.08108v1 | https://arxiv.org/pdf/2104.08108v1.pdf | Cross-Modal Retrieval Augmentation for Multi-Modal Classification | Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here, we explore the use of unstructured external knowledge sources of images and their corresponding captions for improving visual question answe... | ['Austin Reiter', 'Douwe Kiela', 'Ser-Nam Lim', 'Chris Stauffer', 'Natalia Neverova', 'Shir Gur'] | 2021-04-16 | cross-modal-retrieval-augmentation-for-multi | https://aclanthology.org/2021.findings-emnlp.11 | https://aclanthology.org/2021.findings-emnlp.11.pdf | findings-emnlp-2021-11 | ['multi-modal-classification'] | ['miscellaneous'] | [ 2.19966888e-01 1.12426095e-01 -3.28868866e-01 -4.10167098e-01
-1.83640385e+00 -9.45577800e-01 8.63964856e-01 3.84237431e-02
-4.87116247e-01 4.24049020e-01 7.62459934e-01 -2.43404999e-01
2.62503326e-01 -6.04182899e-01 -1.10685968e+00 -4.81580198e-01
3.30304623e-01 5.40497839e-01 2.84470022e-01 -2.51530975... | [10.905160903930664, 1.5714211463928223] |
e4178098-5175-477b-bf97-1144d7df48b6 | frame-interpolation-for-dynamic-scenes-with | 2209.13284 | null | https://arxiv.org/abs/2209.13284v2 | https://arxiv.org/pdf/2209.13284v2.pdf | Frame Interpolation for Dynamic Scenes with Implicit Flow Encoding | In this paper, we propose an algorithm to interpolate between a pair of images of a dynamic scene. While in the past years significant progress in frame interpolation has been made, current approaches are not able to handle images with brightness and illumination changes, which are common even when the images are captu... | ['Nima Khademi Kalantari', 'Avinash Paliwal', 'Pedro Figueirêdo'] | 2022-09-27 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 1.17528908e-01 -2.26594359e-01 4.22173366e-02 -3.60378355e-01
-1.75494403e-01 -4.86112744e-01 7.44555056e-01 -7.42753521e-02
-3.99736643e-01 8.42650771e-01 1.87367629e-02 -1.15897596e-01
1.23130456e-01 -8.10394168e-01 -8.09453428e-01 -3.41345966e-01
-6.58547506e-02 7.68352151e-02 4.48446423e-01 -5.09920716... | [10.664812088012695, -1.3347513675689697] |
47448e2d-97d5-4833-a882-892df7041ee6 | weighted-risk-minimization-deep-learning | 1812.03372 | null | https://arxiv.org/abs/1812.03372v3 | https://arxiv.org/pdf/1812.03372v3.pdf | What is the Effect of Importance Weighting in Deep Learning? | Importance-weighted risk minimization is a key ingredient in many machine learning algorithms for causal inference, domain adaptation, class imbalance, and off-policy reinforcement learning. While the effect of importance weighting is well-characterized for low-capacity misspecified models, little is known about how it... | ['Zachary C. Lipton', 'Jonathon Byrd'] | 2018-12-08 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 4.52753782e-01 3.81813288e-01 -5.41517496e-01 -5.52419841e-01
-5.08257747e-01 -4.28164274e-01 5.63807666e-01 2.88849890e-01
-6.85296893e-01 9.11795139e-01 6.48388743e-01 -5.42798042e-01
-6.12859845e-01 -5.70510149e-01 -9.22660649e-01 -8.41362596e-01
-1.59009427e-01 4.52752978e-01 1.46831572e-01 6.39634877... | [8.496439933776855, 4.077672958374023] |
37e1d529-6863-46b9-bc2d-c001525f105b | motion-segmentation-using-frequency-domain | 2004.08638 | null | https://arxiv.org/abs/2004.08638v1 | https://arxiv.org/pdf/2004.08638v1.pdf | Motion Segmentation using Frequency Domain Transformer Networks | Self-supervised prediction is a powerful mechanism to learn representations that capture the underlying structure of the data. Despite recent progress, the self-supervised video prediction task is still challenging. One of the critical factors that make the task hard is motion segmentation, which is segmenting individu... | ['Hafez Farazi', 'Sven Behnke'] | 2020-04-18 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 5.45475006e-01 1.59415916e-01 -5.27653098e-01 -5.30716240e-01
-3.10041994e-01 -2.27293164e-01 4.25677150e-01 -3.00882101e-01
1.47148535e-01 5.03910244e-01 3.82266343e-01 8.67854804e-02
3.58429849e-01 -5.24450123e-01 -9.94570673e-01 -6.33324325e-01
-4.36641015e-02 3.89131069e-01 7.90080130e-01 1.34868219... | [8.729410171508789, 0.22664016485214233] |
46083dd3-77b5-45f6-b803-d64971af0b49 | monitoring-model-deterioration-with | 2201.11676 | null | https://arxiv.org/abs/2201.11676v3 | https://arxiv.org/pdf/2201.11676v3.pdf | Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap | Monitoring machine learning models once they are deployed is challenging. It is even more challenging to decide when to retrain models in real-case scenarios when labeled data is beyond reach, and monitoring performance metrics becomes unfeasible. In this work, we use non-parametric bootstrapped uncertainty estimates a... | ['Dan Saattrup Nielsen', 'Carlos Mougan'] | 2022-01-27 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 3.27472165e-02 1.87458947e-01 -2.97139436e-02 -2.75891244e-01
-7.80410409e-01 -8.89064074e-01 5.23746669e-01 6.02550328e-01
-1.60098448e-01 1.02698350e+00 -3.20802718e-01 -6.60158157e-01
-4.02159393e-01 -5.94014704e-01 -9.60995734e-01 -6.40685201e-01
-3.27715665e-01 7.01494277e-01 3.19204390e-01 3.27515274... | [7.358674049377441, 3.7839975357055664] |
0732672d-166a-4fc3-b732-47d6a0442fe1 | a-neural-network-approach-to-missing-marker | 1803.02665 | null | http://arxiv.org/abs/1803.02665v4 | http://arxiv.org/pdf/1803.02665v4.pdf | A Neural Network Approach to Missing Marker Reconstruction in Human Motion Capture | Optical motion capture systems have become a widely used technology in
various fields, such as augmented reality, robotics, movie production, etc.
Such systems use a large number of cameras to triangulate the position of
optical markers.The marker positions are estimated with high accuracy. However,
especially when tra... | ['Hedvig Kjellström', 'Taras Kucherenko', 'Jonas Beskow'] | 2018-03-07 | null | null | null | null | ['missing-markers-reconstruction'] | ['computer-vision'] | [-4.08291705e-02 -3.66374642e-01 -1.40469864e-01 -4.10036631e-02
-4.16405767e-01 -4.05053020e-01 5.06585956e-01 -1.94114298e-01
-6.35733724e-01 7.71794021e-01 -1.51781753e-01 1.14892967e-01
2.44109437e-01 -5.88082910e-01 -9.39496577e-01 -5.49388409e-01
4.72316220e-02 4.54657048e-01 3.83382857e-01 1.76152319... | [7.498161315917969, -1.1025842428207397] |
191c9e22-08dd-4c5a-ab2a-9040863f38a3 | diva-deep-unfolded-network-from-quantum | 2301.00247 | null | https://arxiv.org/abs/2301.00247v1 | https://arxiv.org/pdf/2301.00247v1.pdf | DIVA: Deep Unfolded Network from Quantum Interactive Patches for Image Restoration | This paper presents a deep neural network called DIVA unfolding a baseline adaptive denoising algorithm (De-QuIP), relying on the theory of quantum many-body physics. Furthermore, it is shown that with very slight modifications, this network can be enhanced to solve more challenging image restoration tasks such as imag... | ['Denis Kouamé', 'Bertrand Georgeot', 'Adrian Basarab', 'Sayantan Dutta'] | 2022-12-31 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 4.26471174e-01 9.03461874e-02 2.54655808e-01 -8.94262642e-02
-5.97674251e-01 -1.85826659e-01 6.01624608e-01 -3.32891941e-03
-3.11719298e-01 8.06403100e-01 2.13931590e-01 1.29030775e-02
-3.26409608e-01 -6.83935881e-01 -9.74155545e-01 -1.22607386e+00
1.53241068e-01 2.46715844e-01 -4.39208671e-02 -6.10494137... | [11.74710464477539, -2.425461769104004] |
af966639-7259-4011-aab8-4abcdeb64274 | dexmv-imitation-learning-for-dexterous | 2108.05877 | null | https://arxiv.org/abs/2108.05877v5 | https://arxiv.org/pdf/2108.05877v5.pdf | DexMV: Imitation Learning for Dexterous Manipulation from Human Videos | While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation. In this paper, we propose a new platform and pipeline DexMV (Dexterous Manipulation from Videos) for imitation learning. We design a p... | ['Xiaolong Wang', 'Yang Fu', 'Ruihan Yang', 'Hanwen Jiang', 'Shaowei Liu', 'Yueh-Hua Wu', 'Yuzhe Qin'] | 2021-08-12 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [-2.38660812e-01 -1.29375979e-01 -1.56928986e-01 6.73952000e-03
-3.95130664e-01 -7.74234176e-01 5.01542687e-01 -9.52147722e-01
-4.16517526e-01 6.75023139e-01 -3.09946865e-01 -3.65642399e-01
2.05634329e-02 1.53403925e-02 -1.08639669e+00 -5.24521470e-01
-2.35518903e-01 8.57700288e-01 4.20858115e-01 -1.30699977... | [4.697535514831543, 0.646843671798706] |
6078181b-7d57-4ff2-932a-e29224f34b99 | hypergraph-neural-networks-for-hypergraph | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liao_Hypergraph_Neural_Networks_for_Hypergraph_Matching_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liao_Hypergraph_Neural_Networks_for_Hypergraph_Matching_ICCV_2021_paper.pdf | Hypergraph Neural Networks for Hypergraph Matching | Hypergraph matching is a useful tool to find feature correspondence by considering higher-order structural information. Recently, the employment of deep learning has made great progress in the matching of graphs, suggesting its potential for hypergraphs. Hence, in this paper, we present the first, to our best knowl... | ['Haibin Ling', 'Yong Xu', 'Xiaowei Liao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['hypergraph-matching'] | ['graphs'] | [ 3.23635072e-01 5.21192074e-01 -4.56934094e-01 -1.69917852e-01
-6.02763116e-01 -2.97986180e-01 4.16794449e-01 1.95527956e-01
-3.56187858e-02 3.28004092e-01 -6.13736436e-02 -4.05017823e-01
-5.25126994e-01 -1.16554439e+00 -6.55766129e-01 -5.21790266e-01
-1.52644619e-01 7.67544985e-01 -8.48717541e-02 -1.55186549... | [7.084822177886963, 6.3451385498046875] |
8a016444-d2b3-4322-92f5-af864f9019ec | chinese-named-entity-recognition-via-adaptive | null | null | https://aclanthology.org/2020.ccl-1.86 | https://aclanthology.org/2020.ccl-1.86.pdf | Chinese Named Entity Recognition via Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism | Named entity recognition (NER) aims to identify text spans that mention named entities and classify them into pre-defined categories. For Chinese NER task, most of the existing methods are character-based sequence labeling models and achieve great success. However, these methods usually ignore lexical knowledge, which ... | ['Jun Zhao', 'Kang Liu', 'Yubo Chen', 'Pengfei Cao'] | null | null | null | null | ccl-2020-10 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-2.19731495e-01 -6.39679432e-02 -2.84092903e-01 -3.16772074e-01
-6.13306642e-01 -5.54386020e-01 9.10318121e-02 2.13705033e-01
-7.97406197e-01 9.58939314e-01 3.03881019e-01 -2.96193928e-01
4.22880828e-01 -9.21634793e-01 -2.16743991e-01 -2.12683469e-01
2.48443082e-01 2.19697654e-01 6.71280742e-01 1.56610936... | [9.757789611816406, 9.723267555236816] |
3cb61058-a3db-4b9c-8261-93c150db471a | nmt5-is-parallel-data-still-relevant-for-pre | 2106.02171 | null | https://arxiv.org/abs/2106.02171v1 | https://arxiv.org/pdf/2106.02171v1.pdf | nmT5 -- Is parallel data still relevant for pre-training massively multilingual language models? | Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In this paper, we investigate the impact of incorporating parallel data into mT5 pre-training. We find that multi-tasking language modeling wit... | ['Linting Xue', 'Rami Al-Rfou', 'Melvin Johnson', 'Noah Constant', 'Aditya Siddhant', 'Mihir Kale'] | 2021-06-03 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [-2.10958764e-01 3.40582654e-02 -5.88839889e-01 -4.14498806e-01
-1.55687332e+00 -8.67991269e-01 6.87810779e-01 3.23776118e-02
-6.15079284e-01 8.90803337e-01 3.77038956e-01 -9.54038978e-01
2.53852993e-01 -1.70986623e-01 -8.35861981e-01 -1.47403508e-01
2.18825623e-01 7.99426377e-01 -1.92926794e-01 -4.07980293... | [11.334486961364746, 10.205280303955078] |
fd9a02fe-ce03-4a50-a145-3061cc0a43df | syngen-a-syntactic-plug-and-play-module-for | 2302.13032 | null | https://arxiv.org/abs/2302.13032v1 | https://arxiv.org/pdf/2302.13032v1.pdf | SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis | Aspect-based Sentiment Analysis (ABSA) is a sentiment analysis task at fine-grained level. Recently, generative frameworks have attracted increasing attention in ABSA due to their ability to unify subtasks and their continuity to upstream pre-training tasks. However, these generative models suffer from the neighboring ... | ['Yujiu Yang', 'Xingyu Bai', 'Jiayi Li', 'Taiqiang Wu', 'Chengze Yu'] | 2023-02-25 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-1.43914018e-02 2.61537611e-01 -9.40310583e-02 -6.47333443e-01
-7.51876891e-01 -5.30633271e-01 5.52488267e-01 -1.83765799e-01
1.38546675e-02 5.00517607e-01 5.50971329e-01 -3.09073180e-01
3.11125219e-02 -1.07329047e+00 -7.58958578e-01 -5.90487480e-01
4.54001546e-01 3.63726884e-01 8.87808576e-02 -5.20502388... | [11.473362922668457, 6.697690010070801] |
53791eda-f67c-4fba-bbda-6937c9a4dacd | tuple-oriented-compression-for-large-scale | 1702.06943 | null | http://arxiv.org/abs/1702.06943v3 | http://arxiv.org/pdf/1702.06943v3.pdf | Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent | Data compression is a popular technique for improving the efficiency of data
processing workloads such as SQL queries and more recently, machine learning
(ML) with classical batch gradient methods. But the efficacy of such ideas for
mini-batch stochastic gradient descent (MGD), arguably the workhorse algorithm
of moder... | ['Yijing Zeng', 'Fengan Li', 'Jeffrey F. Naughton', 'Xi Wu', 'Jignesh M. Patel', 'Lingjiao Chen', 'Arun Kumar'] | 2017-02-22 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 6.20480441e-02 -2.58067340e-01 -4.07558084e-01 -6.26357019e-01
-9.12787497e-01 -1.70901865e-01 4.13520306e-01 7.22722769e-01
-5.31729281e-01 5.30731082e-01 4.80728686e-01 -8.89285982e-01
-6.17779605e-02 -9.26529527e-01 -8.29426348e-01 -3.18520933e-01
-3.49577993e-01 7.88901806e-01 2.34131262e-01 -3.70167911... | [8.516111373901367, 3.4466450214385986] |
2014a509-4be2-4471-86d8-12d38d38271f | lane-graph-as-path-continuity-preserving-path | 2303.08815 | null | https://arxiv.org/abs/2303.08815v1 | https://arxiv.org/pdf/2303.08815v1.pdf | Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction | Online lane graph construction is a promising but challenging task in autonomous driving. Previous methods usually model the lane graph at the pixel or piece level, and recover the lane graph by pixel-wise or piece-wise connection, which breaks down the continuity of the lane. Human drivers focus on and drive along the... | ['Xinggang Wang', 'Chang Huang', 'Wenyu Liu', 'Qian Zhang', 'Tianheng Cheng', 'Bo Jiang', 'Shaoyu Chen', 'Bencheng Liao'] | 2023-03-15 | null | null | null | null | ['graph-construction', 'trajectory-planning'] | ['graphs', 'robots'] | [-1.65695533e-01 3.16006541e-01 -4.75826293e-01 -5.00613391e-01
-3.70751888e-01 -7.13177383e-01 4.15058374e-01 -1.54837351e-02
1.31854847e-01 4.41332757e-01 9.12136137e-02 -8.75858486e-01
-1.11838222e-01 -1.03182006e+00 -8.50192845e-01 -3.31580848e-01
-1.78579167e-02 2.18890697e-01 6.92347825e-01 -4.07653242... | [8.103949546813965, -1.5379945039749146] |
ed1cc550-4972-4ed3-9933-f437354b911a | og-sgg-ontology-guided-scene-graph-generation | 2202.10201 | null | https://arxiv.org/abs/2202.10201v3 | https://arxiv.org/pdf/2202.10201v3.pdf | OG-SGG: Ontology-Guided Scene Graph Generation. A Case Study in Transfer Learning for Telepresence Robotics | Scene graph generation from images is a task of great interest to applications such as robotics, because graphs are the main way to represent knowledge about the world and regulate human-robot interactions in tasks such as Visual Question Answering (VQA). Unfortunately, its corresponding area of machine learning is sti... | ['Luis Merino', 'Natalia Díaz-Rodríguez', 'Fernando Caballero', 'Fernando Amodeo'] | 2022-02-21 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 3.59990120e-01 8.57849419e-01 2.71500777e-02 -3.88871789e-01
5.72079141e-03 -3.85861456e-01 8.84987652e-01 4.73075986e-01
-3.28161865e-01 6.78236663e-01 8.83835405e-02 -3.89995694e-01
-3.42074603e-01 -1.11141455e+00 -7.37260699e-01 -1.91671252e-01
7.96466880e-03 8.80500019e-01 5.72175920e-01 -6.85563326... | [4.726789951324463, 0.860319197177887] |
6fb33d9d-8fd3-4d69-b155-deb6c46b1223 | effective-deep-learning-models-for-automatic | null | null | https://ieeexplore.ieee.org/document/9274427 | https://ieeexplore.ieee.org/document/9274427 | Effective Deep Learning Models for Automatic Diacritization of Arabic Text | While building a text-to-speech system for the Arabic language, we found that the system synthesized speeches with many pronunciation errors. The primary source of these errors is the lack of diacritics in modern standard Arabic writing. These diacritics are small strokes that appear above or below each letter to provi... | ['Ali Mustafa Qamar', 'Mokthar Ali Hasan Madhfar'] | 2020-11-01 | null | null | null | null | ['arabic-text-diacritization'] | ['natural-language-processing'] | [ 5.17874807e-02 1.51807457e-01 1.82114001e-02 -3.74689102e-01
-8.60350609e-01 -5.46985209e-01 6.58952713e-01 -7.10362270e-02
-3.73136550e-01 5.75422466e-01 4.89791244e-01 -7.89816797e-01
6.35448456e-01 -7.34365404e-01 -7.21617341e-01 -4.91739333e-01
3.51966232e-01 6.70901656e-01 1.76558346e-01 -8.35961819... | [10.888806343078613, 10.294568061828613] |
b1fdd6d6-efd2-4d2d-a950-c014f509fcee | plane-based-content-preserving-warps-for | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Zhou_Plane-Based_Content_Preserving_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Zhou_Plane-Based_Content_Preserving_2013_CVPR_paper.pdf | Plane-Based Content Preserving Warps for Video Stabilization | Recently, a new image deformation technique called content-preserving warping (CPW) has been successfully employed to produce the state-of-the-art video stabilization results in many challenging cases. The key insight of CPW is that the true image deformation due to viewpoint change can be well approximated by a carefu... | ['Zihan Zhou', 'Yi Ma', 'Hailin Jin'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['video-stabilization'] | ['computer-vision'] | [ 3.65668148e-01 8.84661302e-02 -7.36062899e-02 1.08911343e-01
-5.39102674e-01 -7.85580099e-01 3.98611009e-01 -2.17397362e-01
1.96042418e-01 5.96873820e-01 -5.59461154e-02 1.77205756e-01
1.77664638e-01 -6.39428616e-01 -1.03894484e+00 -9.56402123e-01
2.59476304e-01 2.93715984e-01 6.46674693e-01 -2.67394215... | [9.263564109802246, -2.2664644718170166] |
dddb6ec3-2481-4b51-b84e-ee51395c74d4 | what-makes-data-to-text-generation-hard-for | 2205.11505 | null | https://arxiv.org/abs/2205.11505v1 | https://arxiv.org/pdf/2205.11505v1.pdf | What Makes Data-to-Text Generation Hard for Pretrained Language Models? | Expressing natural language descriptions of structured facts or relations -- data-to-text generation (D2T) -- increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models(PLMs) perform remarkably well on this task after fine-tuning on a significant amount of tas... | ['Mark Dredze', 'Adrian Benton', 'Moniba Keymanesh'] | 2022-05-23 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 2.77950615e-01 5.55345178e-01 -1.72370553e-01 -3.71826917e-01
-9.33647037e-01 -5.15344739e-01 1.10371935e+00 3.67750406e-01
-4.62620944e-01 1.01143861e+00 6.00396097e-01 -1.60017744e-01
-1.54927656e-01 -8.97793829e-01 -7.48559535e-01 -1.12896644e-01
-6.42579570e-02 9.74516153e-01 4.83460307e-01 -5.06543577... | [10.766088485717773, 8.536148071289062] |
4c98969a-666e-4822-925c-ee99b3d6d784 | semantic-modeling-for-food-recommendation | 2105.01269 | null | https://arxiv.org/abs/2105.01269v1 | https://arxiv.org/pdf/2105.01269v1.pdf | Semantic Modeling for Food Recommendation Explanations | With the increased use of AI methods to provide recommendations in the health, specifically in the food dietary recommendation space, there is also an increased need for explainability of those recommendations. Such explanations would benefit users of recommendation systems by empowering them with justifications for fo... | ['Deborah L. McGuinness', 'Daniel Gruen', 'Shruthi Chari', 'Oshani Seneviratne', 'Ishita Padhiar'] | 2021-05-04 | null | null | null | null | ['food-recommendation', 'knowledge-base-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 8.94145668e-02 1.12562120e+00 -5.49357355e-01 -9.33754742e-01
2.05106005e-01 -4.87553507e-01 1.55602947e-01 8.95900369e-01
2.51734555e-01 1.87512681e-01 1.07041347e+00 -4.99796659e-01
-8.12162280e-01 -9.84676301e-01 -4.54379737e-01 1.93112284e-01
1.21762864e-01 4.75258887e-01 8.82341340e-03 -9.00714159... | [11.45789623260498, 4.564875602722168] |
123be388-0fe3-4ff7-b60e-bfe61a0e1e6f | adaptivepose-human-parts-as-adaptive-points | 2112.13635 | null | https://arxiv.org/abs/2112.13635v1 | https://arxiv.org/pdf/2112.13635v1.pdf | AdaptivePose: Human Parts as Adaptive Points | Multi-person pose estimation methods generally follow top-down and bottom-up paradigms, both of which can be considered as two-stage approaches thus leading to the high computation cost and low efficiency. Towards a compact and efficient pipeline for multi-person pose estimation task, in this paper, we propose to repre... | ['Mingshu He', 'Qian Zhang', 'Guoli Wang', 'Dongdong Yu', 'Xiaojuan Wang', 'Yabo Xiao'] | 2021-12-27 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-6.24615066e-02 -1.65431201e-02 1.26831636e-01 -3.36443305e-01
-7.76046574e-01 -2.02783167e-01 3.57366502e-01 -3.41299586e-02
-7.11363077e-01 5.26060104e-01 6.84113875e-02 4.15046871e-01
-3.22273485e-02 -5.43322206e-01 -6.89520895e-01 -5.09982705e-01
3.61228213e-02 7.05228984e-01 3.13440025e-01 -3.33736598... | [7.14849328994751, -0.7889328598976135] |
56bc0d93-f518-48b0-a55e-35847b82349e | cross-modal-clinical-graph-transformer-for-1 | 2206.01988 | null | https://arxiv.org/abs/2206.01988v1 | https://arxiv.org/pdf/2206.01988v1.pdf | Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation | Automatic generation of ophthalmic reports using data-driven neural networks has great potential in clinical practice. When writing a report, ophthalmologists make inferences with prior clinical knowledge. This knowledge has been neglected in prior medical report generation methods. To endow models with the capability ... | ['Xiaojun Chang', 'Xiaodan Liang', 'Shirui Pan', 'Karin Verspoor', 'Wenjia Cai', 'Mingjie Li'] | 2022-06-04 | cross-modal-clinical-graph-transformer-for | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Cross-Modal_Clinical_Graph_Transformer_for_Ophthalmic_Report_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Cross-Modal_Clinical_Graph_Transformer_for_Ophthalmic_Report_Generation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['medical-report-generation', 'clinical-knowledge'] | ['medical', 'miscellaneous'] | [ 4.13668811e-01 5.39630592e-01 -3.99838001e-01 -4.12672430e-01
-8.08037579e-01 -2.41070479e-01 4.68415946e-01 8.53230283e-02
7.33701587e-02 9.49140370e-01 4.71503764e-01 -4.74077940e-01
-3.40878874e-01 -8.00531149e-01 -6.76376700e-01 -3.39402080e-01
2.75590539e-01 2.32194901e-01 -1.63567260e-01 -1.63188286... | [15.059718132019043, -1.3923909664154053] |
680fee36-dd0e-464e-8dd4-7ccd082807cd | an-ensemble-of-convolutional-neural-networks | 2007.07966 | null | https://arxiv.org/abs/2007.07966v2 | https://arxiv.org/pdf/2007.07966v2.pdf | An Ensemble of Convolutional Neural Networks for Audio Classification | In this paper, ensembles of classifiers that exploit several data augmentation techniques and four signal representations for training Convolutional Neural Networks (CNNs) for audio classification are presented and tested on three freely available audio classification datasets: i) bird calls, ii) cat sounds, and iii) t... | ['Michelangelo Paci', 'Gianluca Maguolo', 'Loris Nanni', 'Sheryl Brahnam'] | 2020-07-15 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 3.98088574e-01 -2.69709498e-01 4.80091959e-01 -2.17835858e-01
-4.74641502e-01 -3.76762122e-01 4.38590139e-01 4.05474007e-01
-8.33694637e-01 5.49660742e-01 8.13604817e-02 -2.49343708e-01
-2.05883607e-01 -5.15892267e-01 -4.16187346e-01 -6.13363445e-01
-4.86610174e-01 1.67735800e-01 3.52001250e-01 -5.72546959... | [15.242955207824707, 5.2586588859558105] |
687f00c3-e07d-4363-9655-a64a42c14fd3 | cross-lingual-visual-pre-training-for | 2101.10044 | null | https://arxiv.org/abs/2101.10044v2 | https://arxiv.org/pdf/2101.10044v2.pdf | Cross-lingual Visual Pre-training for Multimodal Machine Translation | Pre-trained language models have been shown to improve performance in many natural language tasks substantially. Although the early focus of such models was single language pre-training, recent advances have resulted in cross-lingual and visual pre-training methods. In this paper, we combine these two approaches to lea... | ['Lucia Specia', 'Aykut Erdem', 'Erkut Erdem', 'Pranava Madhyastha', 'Mustafa Sercan Amac', 'Menekse Kuyu', 'Ozan Caglayan'] | 2021-01-25 | null | https://aclanthology.org/2021.eacl-main.112 | https://aclanthology.org/2021.eacl-main.112.pdf | eacl-2021-2 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.30747038e-02 1.36455745e-01 -4.09982026e-01 -2.47048736e-01
-1.44317794e+00 -8.50952566e-01 1.15494001e+00 -1.91855356e-02
-2.69872665e-01 4.92758811e-01 4.37033892e-01 -5.44922709e-01
5.84071755e-01 -3.17535877e-01 -8.72732103e-01 -3.61471832e-01
1.58914149e-01 5.89460313e-01 9.68364417e-04 -2.79149473... | [11.358719825744629, 1.4969853162765503] |
bd503b63-f705-430e-a603-7056438d45e8 | attention-flows-analyzing-and-comparing | 2009.07053 | null | https://arxiv.org/abs/2009.07053v1 | https://arxiv.org/pdf/2009.07053v1.pdf | Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models | Advances in language modeling have led to the development of deep attention-based models that are performant across a wide variety of natural language processing (NLP) problems. These language models are typified by a pre-training process on large unlabeled text corpora and subsequently fine-tuned for specific tasks. A... | ['Matthew Berger', 'Joseph F DeRose', 'Jiayao Wang'] | 2020-09-03 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 7.57305399e-02 1.08268380e-01 8.00229907e-02 -2.89345324e-01
-2.42965564e-01 -6.56176329e-01 6.08404219e-01 7.08609045e-01
-3.57361227e-01 6.03022315e-02 5.93990862e-01 -7.03352928e-01
3.22830714e-02 -6.04850173e-01 -2.78923303e-01 -1.80508852e-01
1.72158495e-01 3.98813754e-01 8.91543031e-02 -3.34752649... | [10.117263793945312, 7.761754035949707] |
fe9cd5f8-a96b-4b4b-83da-f9bbdea1012e | focus-and-detect-a-small-object-detection | 2203.12976 | null | https://arxiv.org/abs/2203.12976v1 | https://arxiv.org/pdf/2203.12976v1.pdf | Focus-and-Detect: A Small Object Detection Framework for Aerial Images | Despite recent advances, object detection in aerial images is still a challenging task. Specific problems in aerial images makes the detection problem harder, such as small objects, densely packed objects, objects in different sizes and with different orientations. To address small object detection problem, we propose ... | ['Behçet Uğur Töreyin', 'İbrahim Batuhan Akkaya', 'Reyhan Kevser Keser', 'Onur Can Koyun'] | 2022-03-24 | null | null | null | null | ['object-detection-in-aerial-images', 'small-object-detection'] | ['computer-vision', 'computer-vision'] | [ 5.50578296e-01 -2.39500299e-01 1.24946401e-01 3.96204926e-02
1.83591004e-02 -6.24472797e-01 2.82327294e-01 2.01454043e-01
-3.77833217e-01 4.85639900e-01 -3.00422281e-01 -3.07058636e-02
-2.16783598e-01 -7.39428818e-01 -3.88430655e-01 -8.56419563e-01
2.20589037e-03 2.29497299e-01 9.94279027e-01 3.36391218... | [8.674727439880371, -0.7913186550140381] |
e457d162-bdc3-4e58-bbbb-af2af93d7ef9 | a-cnn-based-super-resolution-technique-for | 1906.10413 | null | https://arxiv.org/abs/1906.10413v1 | https://arxiv.org/pdf/1906.10413v1.pdf | A CNN-Based Super-Resolution Technique for Active Fire Detection on Sentinel-2 Data | Remote Sensing applications can benefit from a relatively fine spatial resolution multispectral (MS) images and a high revisit frequency ensured by the twin satellites Sentinel-2. Unfortunately, only four out of thirteen bands are provided at the highest resolution of 10 meters, and the others at 20 or 60 meters. For i... | ['Daniele Riccio', "Domenico Antonio Giuseppe Dell'Aglio", 'Massimiliano Gargiulo', 'Giuseppe Ruello', 'Antonio Iodice'] | 2019-06-25 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 4.81061071e-01 -4.77768987e-01 6.41266033e-02 -1.58799259e-04
-5.93173385e-01 -3.95340174e-01 7.81845689e-01 -7.96774626e-02
-7.65457034e-01 1.13757980e+00 -7.55493864e-02 -2.79844284e-01
-8.14116895e-01 -1.47055447e+00 -1.17502145e-01 -9.62106049e-01
-5.59916854e-01 -6.11092970e-02 4.93006743e-02 -8.50438416... | [9.752008438110352, -1.7273523807525635] |
179ac34b-d4c1-4ca9-8b59-c51969a467b2 | fugashi-a-tool-for-tokenizing-japanese-in | 2010.06858 | null | https://arxiv.org/abs/2010.06858v1 | https://arxiv.org/pdf/2010.06858v1.pdf | fugashi, a Tool for Tokenizing Japanese in Python | Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source... | ['Paul McCann'] | 2020-10-14 | null | https://aclanthology.org/2020.nlposs-1.7 | https://aclanthology.org/2020.nlposs-1.7.pdf | emnlp-nlposs-2020-11 | ['multilingual-nlp'] | ['natural-language-processing'] | [-6.34735107e-01 -1.47923797e-01 -2.22923070e-01 -2.46739507e-01
-9.79689777e-01 -9.92017686e-01 2.33780533e-01 1.76442079e-02
-8.00012648e-01 1.29865158e+00 4.20638204e-01 -5.54116726e-01
3.30449015e-01 -4.36074078e-01 -2.21981823e-01 -3.93538743e-01
3.01569104e-01 4.08978492e-01 1.73470914e-01 -2.00045660... | [10.341728210449219, 10.019989013671875] |
93312071-efa4-4059-ba83-77ab28ec59d7 | attresdu-net-medical-image-segmentation-using | 2306.14255 | null | https://arxiv.org/abs/2306.14255v1 | https://arxiv.org/pdf/2306.14255v1.pdf | AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net | Manually inspecting polyps from a colonoscopy for colorectal cancer or performing a biopsy on skin lesions for skin cancer are time-consuming, laborious, and complex procedures. Automatic medical image segmentation aims to expedite this diagnosis process. However, numerous challenges exist due to significant variations... | ['Md. Hasanul Kabir', 'Md. Bakhtiar Hasan', 'Fahim Shahriar Khan', 'Alif Ashrafee', 'Akib Mohammed Khan'] | 2023-06-25 | null | null | null | null | ['medical-image-segmentation'] | ['medical'] | [ 2.34228417e-01 3.23772877e-01 -1.67923272e-01 -2.70343244e-01
-5.30634403e-01 -5.20159602e-01 1.15642855e-02 5.34180403e-01
-5.65008938e-01 4.14619714e-01 1.11856669e-01 -6.25870407e-01
-7.33782426e-02 -7.05328405e-01 -5.43440461e-01 -6.97179615e-01
-1.49044633e-01 5.66423163e-02 4.18671280e-01 -8.95667239... | [14.61913776397705, -2.73463773727417] |
9c998118-272b-4912-a2b2-95def8335950 | feature-learning-for-stock-price-prediction | 2103.09106 | null | https://arxiv.org/abs/2103.09106v1 | https://arxiv.org/pdf/2103.09106v1.pdf | Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating | To reject the Efficient Market Hypothesis a set of 5 technical indicators and 23 fundamental indicators was identified to establish the possibility of generating excess returns on the stock market. Leveraging these data points and various classification machine learning models, trading data of the 505 equities on the U... | ['Matloob Khushi', 'Jaideep Singh'] | 2021-03-13 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-7.04551935e-01 3.03591460e-01 2.60448316e-03 -7.34519884e-02
-2.91586101e-01 -9.07846689e-01 8.37007940e-01 1.77225605e-01
-1.75948516e-01 7.37327576e-01 -1.94223821e-01 -5.97974777e-01
-5.41217923e-01 -1.05147362e+00 -5.79408765e-01 -4.77054089e-01
-4.46537673e-01 5.71909606e-01 3.36284071e-01 -4.84054297... | [4.564206600189209, 4.177685260772705] |
d6ea87da-ce26-44ea-8364-b2f6bea5ffa7 | cyberwalle-at-semeval-2020-task-11-an | 2008.09859 | null | https://arxiv.org/abs/2008.09859v1 | https://arxiv.org/pdf/2008.09859v1.pdf | CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection | This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our arc... | ['Verena Blaschke', 'Sam Tureski', 'Maxim Korniyenko'] | 2020-08-22 | null | https://aclanthology.org/2020.semeval-1.192 | https://aclanthology.org/2020.semeval-1.192.pdf | semeval-2020 | ['propaganda-technique-identification', 'propaganda-detection', 'propaganda-span-identification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.60534827e-02 1.59935161e-01 -2.07665831e-01 -1.34255186e-01
-1.01177216e+00 -6.68951094e-01 1.13414443e+00 3.42895836e-01
-9.38356221e-01 6.13701284e-01 6.84088171e-01 -5.60343623e-01
9.99230295e-02 -4.43669170e-01 -4.83890384e-01 -2.38435254e-01
-3.28948200e-02 3.77762526e-01 -2.34155264e-02 -2.73968399... | [8.470608711242676, 10.694857597351074] |
4aac3cf3-1d32-4898-8592-700842e5021d | document-structure-extraction-for-forms-using | 1911.12170 | null | https://arxiv.org/abs/1911.12170v2 | https://arxiv.org/pdf/1911.12170v2.pdf | Document Structure Extraction using Prior based High Resolution Hierarchical Semantic Segmentation | Structure extraction from document images has been a long-standing research topic due to its high impact on a wide range of practical applications. In this paper, we share our findings on employing a hierarchical semantic segmentation network for this task of structure extraction. We propose a prior based deep hierarch... | ['Milan Aggarwal', 'Mausoom Sarkar', 'Hiresh Gupta', 'Balaji Krishnamurthy', 'Arneh Jain'] | 2019-11-27 | document-structure-extraction-using-prior | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6393_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730647.pdf | eccv-2020-8 | ['table-detection'] | ['miscellaneous'] | [ 7.05624998e-01 2.04158053e-01 5.27308555e-03 -4.38358963e-01
-9.54088271e-01 -7.71617949e-01 6.24663472e-01 4.20514680e-02
-4.38089490e-01 4.17963535e-01 2.75635421e-01 -3.58162075e-01
-2.49440849e-01 -8.76778841e-01 -7.49068141e-01 -1.41431957e-01
2.90154010e-01 6.31623745e-01 5.91563106e-01 -2.39604171... | [11.689555168151855, 2.6828339099884033] |
d7756fc6-6fac-467e-ab06-57e1241dca15 | personalized-pricing-with-invalid | 2302.12670 | null | https://arxiv.org/abs/2302.12670v1 | https://arxiv.org/pdf/2302.12670v1.pdf | Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learning | Pricing based on individual customer characteristics is widely used to maximize sellers' revenues. This work studies offline personalized pricing under endogeneity using an instrumental variable approach. Standard instrumental variable methods in causal inference/econometrics either focus on a discrete treatment space ... | ['Lin Lin', 'Cong Shi', 'Zhengling Qi', 'Rui Miao'] | 2023-02-24 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-1.07937925e-01 -5.32417744e-02 -8.33072126e-01 -1.98559940e-01
-6.85905814e-01 -6.14553750e-01 -1.16705030e-01 -1.28294110e-01
-3.12617958e-01 1.02667284e+00 1.09682018e-02 -6.49641752e-01
-6.15014911e-01 -8.74006033e-01 -8.93792570e-01 -8.96865070e-01
-6.39212728e-02 3.41732562e-01 -9.21859384e-01 2.09881544... | [8.282209396362305, 5.112167835235596] |
540686f4-6f88-45a0-af5a-69cb079f48a5 | dsgn-deep-stereo-geometry-network-for-3d | 2001.03398 | null | https://arxiv.org/abs/2001.03398v3 | https://arxiv.org/pdf/2001.03398v3.pdf | DSGN: Deep Stereo Geometry Network for 3D Object Detection | Most state-of-the-art 3D object detectors heavily rely on LiDAR sensors because there is a large performance gap between image-based and LiDAR-based methods. It is caused by the way to form representation for the prediction in 3D scenarios. Our method, called Deep Stereo Geometry Network (DSGN), significantly reduces t... | ['Yilun Chen', 'Shu Liu', 'Xiaoyong Shen', 'Jiaya Jia'] | 2020-01-10 | dsgn-deep-stereo-geometry-network-for-3d-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_DSGN_Deep_Stereo_Geometry_Network_for_3D_Object_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_DSGN_Deep_Stereo_Geometry_Network_for_3D_Object_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['vehicle-pose-estimation', '3d-object-detection-from-stereo-images'] | ['computer-vision', 'computer-vision'] | [-1.73001096e-01 -5.49577586e-02 3.98595759e-04 -3.37213606e-01
-8.58819187e-01 -5.60541034e-01 5.34928977e-01 5.21224104e-02
-2.12213621e-01 -1.55832738e-01 -1.35667995e-01 -3.38914901e-01
3.29777628e-01 -8.87798488e-01 -8.90090406e-01 -9.63236690e-02
7.01964572e-02 9.42416012e-01 8.39837372e-01 1.06759496... | [7.771086692810059, -2.6630361080169678] |
70c7f60b-a114-495f-9a95-fac2ca78f95f | implicit-discourse-relation-classification | 1603.02776 | null | http://arxiv.org/abs/1603.02776v1 | http://arxiv.org/pdf/1603.02776v1.pdf | Implicit Discourse Relation Classification via Multi-Task Neural Networks | Without discourse connectives, classifying implicit discourse relations is a
challenging task and a bottleneck for building a practical discourse parser.
Previous research usually makes use of one kind of discourse framework such as
PDTB or RST to improve the classification performance on discourse relations.
Actually,... | ['Xiaodong Zhang', 'Yang Liu', 'Zhifang Sui', 'Sujian Li'] | 2016-03-09 | null | null | null | null | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 3.28918636e-01 8.29496562e-01 -6.30235493e-01 -3.45706195e-01
-7.79621601e-01 -3.99749458e-01 1.05752492e+00 1.81399584e-01
-5.25467619e-02 8.75976264e-01 7.91320384e-01 -7.04118371e-01
2.14437321e-01 -8.19283664e-01 -5.53841293e-01 -3.43431830e-01
9.38469023e-02 5.37702799e-01 3.86367440e-01 -6.18949413... | [10.813200950622559, 9.290236473083496] |
e42dbf7e-67e6-4486-85b2-162ab971904a | on-degeneracy-issues-in-multi-parametric | 2304.00435 | null | https://arxiv.org/abs/2304.00435v1 | https://arxiv.org/pdf/2304.00435v1.pdf | On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization | This paper focuses on two aspects of interest related to multi-parametric linear/quadratic programming (mpLP/QP). First, we study degeneracy issues of mpLP/QP. A novel approach to deal with degeneracies is proposed to find all critical regions containing the given parameter. Our method leverages properties of the multi... | ['Hongbin Sun', 'Hao liu', 'Ye Guo', 'Haitian Liu'] | 2023-04-02 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 2.80865222e-01 3.85906957e-02 -7.94877112e-01 7.14834630e-02
-1.19444883e+00 -7.79732406e-01 -2.91455626e-01 2.89035946e-01
1.88218147e-01 1.20517170e+00 -4.80869770e-01 -3.36434573e-01
-8.95201385e-01 -6.90696239e-01 -8.68636787e-01 -8.13246667e-01
-3.20141286e-01 6.17101967e-01 1.84278443e-01 -2.46398017... | [5.285336971282959, 3.0373449325561523] |
dbbf3274-96bf-4729-919b-c1172f306404 | gpteval-nlg-evaluation-using-gpt-4-with | 2303.16634 | null | https://arxiv.org/abs/2303.16634v3 | https://arxiv.org/pdf/2303.16634v3.pdf | G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment | The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE, have been shown to have relatively low correlation with human judgments, especially for tasks that require creativity and diversity. Recent studies ... | ['Chenguang Zhu', 'Ruochen Xu', 'Shuohang Wang', 'Yichong Xu', 'Dan Iter', 'Yang Liu'] | 2023-03-29 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 7.62736723e-02 6.68885708e-01 6.54956251e-02 -8.69581699e-02
-1.24003410e+00 -7.04688132e-01 9.04307604e-01 3.91017765e-01
-4.41921681e-01 9.63984907e-01 7.83514440e-01 -2.48372361e-01
9.52699110e-02 -6.87353373e-01 -3.08484435e-01 -1.26295686e-01
4.02490020e-01 7.88174331e-01 -1.99727178e-01 -4.03154939... | [11.976853370666504, 9.12154483795166] |
43f8f5e4-66ae-4840-934a-ebc813a3dfb8 | ctrlstruct-dialogue-structure-learning-for | 2303.01094 | null | https://arxiv.org/abs/2303.01094v1 | https://arxiv.org/pdf/2303.01094v1.pdf | CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation | Dialogue structure discovery is essential in dialogue generation. Well-structured topic flow can leverage background information and predict future topics to help generate controllable and explainable responses. However, most previous work focused on dialogue structure learning in task-oriented dialogue other than open... | ['Zhaochun Ren', 'Piji Li', 'Congchi Yin'] | 2023-03-02 | null | null | null | null | ['dialogue-generation', 'response-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.95311004e-01 1.00337493e+00 -1.12015940e-01 -5.83193541e-01
-8.06799412e-01 -5.24817586e-01 1.02855051e+00 1.63681477e-01
1.79945305e-01 1.06023371e+00 1.05255568e+00 -2.07856223e-01
3.01057458e-01 -9.10858512e-01 -1.68100595e-01 -4.73109573e-01
8.80902410e-02 8.52839530e-01 7.86652975e-03 -8.27213347... | [12.707254409790039, 8.066283226013184] |
e98b1b5f-52d7-4b4f-8574-0d6879bad931 | approximating-interactive-human-evaluation | 1906.09308 | null | https://arxiv.org/abs/1906.09308v2 | https://arxiv.org/pdf/1906.09308v2.pdf | Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems | Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; ... | ['Rosalind Picard', 'Natasha Jaques', 'Noah Jones', 'Judy Hanwen Shen', 'Craig Ferguson', 'Agata Lapedriza', 'Asma Ghandeharioun'] | 2019-06-21 | approximating-interactive-human-evaluation-1 | http://papers.nips.cc/paper/9519-approximating-interactive-human-evaluation-with-self-play-for-open-domain-dialog-systems | http://papers.nips.cc/paper/9519-approximating-interactive-human-evaluation-with-self-play-for-open-domain-dialog-systems.pdf | neurips-2019-12 | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-3.52932364e-01 5.00535905e-01 1.47284597e-01 -8.46793652e-01
-8.82522166e-01 -1.02922821e+00 1.20870328e+00 1.08938232e-01
-2.98991203e-01 9.52294767e-01 1.01031017e+00 -1.18667968e-01
6.47127395e-04 -5.95900655e-01 -1.72743574e-02 -8.20450410e-02
2.82837097e-02 1.20023429e+00 2.11104661e-01 -7.85395086... | [12.76809310913086, 8.027803421020508] |
abcbce6a-0bca-4176-b1ea-35be95dfc333 | an-improved-graph-model-for-chinese-spell | null | null | https://aclanthology.org/W14-6825 | https://aclanthology.org/W14-6825.pdf | An Improved Graph Model for Chinese Spell Checking | null | ['Zhongye Jia', 'Yang Xin', 'Yuzhu Wang', 'Hai Zhao'] | 2014-10-01 | null | null | null | ws-2014-10 | ['chinese-spell-checking'] | ['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... | [-7.467456817626953, 3.6108603477478027] |
77c1864b-b98e-4da3-91ac-546b141ccf97 | joint-optimization-of-multi-objective | 2105.14125 | null | https://arxiv.org/abs/2105.14125v1 | https://arxiv.org/pdf/2105.14125v1.pdf | Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm | Many engineering problems have multiple objectives, and the overall aim is to optimize a non-linear function of these objectives. In this paper, we formulate the problem of maximizing a non-linear concave function of multiple long-term objectives. A policy-gradient based model-free algorithm is proposed for the problem... | ['Vaneet Aggarwal', 'Mridul Agarwal', 'Qinbo Bai'] | 2021-05-28 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.55578256e-01 1.54280230e-01 -9.66353714e-02 -1.49157450e-01
-6.65671170e-01 -2.43436009e-01 3.04673985e-02 3.65527242e-01
-1.18243742e+00 1.31545103e+00 -6.63151324e-01 -4.04670298e-01
-5.13824582e-01 -6.77229583e-01 -5.92725098e-01 -8.63405466e-01
-4.00139719e-01 3.99742663e-01 1.26962855e-01 -1.55871391... | [4.277583122253418, 2.637983560562134] |
34978fb7-cad8-48a0-a893-a2fcbc12af76 | multi-objective-hyperparameter-optimization-1 | 2209.04340 | null | https://arxiv.org/abs/2209.04340v1 | https://arxiv.org/pdf/2209.04340v1.pdf | Multi-objective hyperparameter optimization with performance uncertainty | The performance of any Machine Learning (ML) algorithm is impacted by the choice of its hyperparameters. As training and evaluating a ML algorithm is usually expensive, the hyperparameter optimization (HPO) method needs to be computationally efficient to be useful in practice. Most of the existing approaches on multi-o... | ['Gonzalo Nápoles', 'Inneke Van Nieuwenhuyse', 'Alejandro Morales-Hernández'] | 2022-09-09 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.64558287e-02 -2.47196808e-01 1.53757175e-02 -7.32097402e-02
-1.07364452e+00 -9.00361165e-02 2.42024288e-01 4.14937526e-01
-2.54154652e-01 1.23306191e+00 -4.37079817e-01 5.23362719e-02
-8.76097441e-01 -7.84744084e-01 -4.38166887e-01 -1.28662097e+00
3.07350438e-02 1.08077121e+00 -3.89934108e-02 3.20114017... | [6.0888872146606445, 3.620873212814331] |
1f770eb8-8bb4-46ca-bf4f-70d771863452 | learnable-acoustic-frontends-in-bird-activity | 2210.00889 | null | https://arxiv.org/abs/2210.00889v1 | https://arxiv.org/pdf/2210.00889v1.pdf | Learnable Acoustic Frontends in Bird Activity Detection | Autonomous recording units and passive acoustic monitoring present minimally intrusive methods of collecting bioacoustics data. Combining this data with species agnostic bird activity detection systems enables the monitoring of activity levels of bird populations. Unfortunately, variability in ambient noise levels and ... | ['Naomi Harte', 'Mark Anderson'] | 2022-10-03 | null | null | null | null | ['bird-audio-detection', 'activity-detection'] | ['audio', 'computer-vision'] | [ 1.90282956e-01 -5.75048149e-01 3.72905701e-01 -2.64268249e-01
-1.05974603e+00 -9.41627681e-01 1.31982192e-01 1.36337131e-01
-9.64022517e-01 3.93055141e-01 3.52591932e-01 2.92035133e-01
-6.64676726e-02 -2.73905277e-01 -5.31061411e-01 -7.47719944e-01
-7.67034054e-01 -2.68930614e-01 2.16949105e-01 -1.51884884... | [15.210835456848145, 5.315835952758789] |
0c1bf3b0-9d4e-45e7-97b5-041c1b1cf035 | effect-of-source-language-on-amr-structure | null | null | https://aclanthology.org/2022.law-1.12 | https://aclanthology.org/2022.law-1.12.pdf | Effect of Source Language on AMR Structure | The Abstract Meaning Representation (AMR) annotation schema was originally designed for English. But the formalism has since been adapted for annotation in a variety of languages. Meanwhile, cross-lingual parsers have been developed to derive English AMR representations for sentences from other languages—implicitly ass... | ['Nathan Schneider', 'Yifu Mu', 'Wai Ching Leung', 'Shira Wein'] | null | null | null | null | lrec-law-2022-6 | ['amr-parsing'] | ['natural-language-processing'] | [ 2.48891443e-01 4.79807228e-01 -1.78814694e-01 -4.75612015e-01
-6.60112858e-01 -8.68073940e-01 6.43186510e-01 4.40305740e-01
-2.88725704e-01 3.81308317e-01 6.78684950e-01 -5.41860640e-01
2.70039827e-01 -7.86337793e-01 -2.97368228e-01 -1.44785717e-01
3.38544399e-01 3.61171216e-01 4.03269082e-02 -3.05578411... | [10.494686126708984, 9.459127426147461] |
ddd3ed40-bedf-4051-b042-2653117776fb | ginius-lt-edi-acl2022-aasha-transformers | null | null | https://aclanthology.org/2022.ltedi-1.43 | https://aclanthology.org/2022.ltedi-1.43.pdf | giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI | This paper describes team giniUs’ submission to the Hope Speech Detection for Equality, Diversity and Inclusion Shared Task organised by LT-EDI ACL 2022. We have fine-tuned the Roberta-large pre-trained model and extracted the last four decoder layers to build a classifier. Our best result on the leaderboard achieve a ... | ['Basavraj Chinagundi', 'Harshul Surana'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-4.27652866e-01 5.52972376e-01 -3.62920642e-01 -3.91041040e-01
-1.52110207e+00 -4.18706447e-01 6.66636586e-01 3.85310985e-02
-8.01628530e-01 9.78569567e-01 1.21943676e+00 -5.25896549e-01
1.88311911e-04 7.45138004e-02 -3.14316601e-01 -1.05307989e-01
-2.75915228e-02 5.47630548e-01 -1.08625285e-01 -3.23063344... | [9.461379051208496, 10.723828315734863] |
933591fa-41b8-402f-801e-27575ee5182f | the-dark-side-of-explanations-poisoning | 2305.00574 | null | https://arxiv.org/abs/2305.00574v1 | https://arxiv.org/pdf/2305.00574v1.pdf | The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples | Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial intelligence (XAI) approaches to provide human-understandable reasons why a specific item gets recommended to a given user. One such method is ... | ['Gabriele Tolomei', 'Yongfeng Zhang', 'Jia Wang', 'Fabrizio Silvestri', 'Ziheng Chen'] | 2023-04-30 | null | null | null | null | ['counterfactual-explanation', 'logical-reasoning'] | ['miscellaneous', 'reasoning'] | [ 1.91603541e-01 4.82540309e-01 -3.10023546e-01 -3.95068079e-01
-3.08541715e-01 -7.65955925e-01 6.84092700e-01 -4.07063682e-03
1.78149551e-01 6.67434931e-01 3.89739662e-01 -9.72579062e-01
-3.12840581e-01 -9.75846350e-01 -9.48434055e-01 -2.34625414e-01
4.98251952e-02 2.46154070e-01 -4.10346031e-01 -3.30800682... | [9.530865669250488, 5.704881191253662] |
bbd0987b-0d84-47de-92f0-79a850f4955c | large-margin-mechanism-and-pseudo-query-set | 2005.09218 | null | https://arxiv.org/abs/2005.09218v1 | https://arxiv.org/pdf/2005.09218v1.pdf | Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning | In recent years, few-shot learning problems have received a lot of attention. While methods in most previous works were trained and tested on datasets in one single domain, cross-domain few-shot learning is a brand-new branch of few-shot learning problems, where models handle datasets in different domains between train... | ['Ping-Chia Huang', 'Yi-Rong Chen', 'Bing-Chen Tsai', 'Hsin-Ying Lee', 'Jia-Fong Yeh', 'Winston H. Hsu'] | 2020-05-19 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 3.86461854e-01 6.83268011e-02 -3.96148831e-01 -7.24699855e-01
-7.67923176e-01 2.75109746e-02 8.05148244e-01 -2.01532707e-01
-2.58850336e-01 7.51027286e-01 5.41659929e-02 4.46385175e-01
-3.96042109e-01 -9.77459490e-01 -5.02816379e-01 -3.90347242e-01
1.10004835e-01 7.20092773e-01 7.77361035e-01 -4.52193171... | [10.03745174407959, 3.055036783218384] |
26d8ef36-19db-408e-a85b-1da375252171 | towards-optimal-energy-management-strategy | 2305.12365 | null | https://arxiv.org/abs/2305.12365v1 | https://arxiv.org/pdf/2305.12365v1.pdf | Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning | In recent years, the development of Artificial Intelligence (AI) has shown tremendous potential in diverse areas. Among them, reinforcement learning (RL) has proven to be an effective solution for learning intelligent control strategies. As an inevitable trend for mitigating climate change, hybrid electric vehicles (HE... | ['Marco F. Huber', 'Christof Nitsche', 'Elisabeth Wedernikow', 'Xinyang Wu'] | 2023-05-21 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.34274232e-01 -3.17039788e-02 -5.34737229e-01 -1.53147563e-01
-1.46666959e-01 -2.81402111e-01 5.16944230e-01 1.13224760e-01
-3.19586903e-01 1.16493678e+00 -5.46189487e-01 -4.07865673e-01
-3.33520681e-01 -1.00824082e+00 -4.78885919e-01 -7.89020360e-01
-4.98492606e-02 3.98348302e-01 1.06697232e-01 -3.70604008... | [5.514489650726318, 2.2643649578094482] |
e41409fe-5921-4263-86a5-2faea849f6fe | generative-cooperative-net-for-image | 1705.02887 | null | http://arxiv.org/abs/1705.02887v3 | http://arxiv.org/pdf/1705.02887v3.pdf | Generative Cooperative Net for Image Generation and Data Augmentation | How to build a good model for image generation given an abstract concept is a
fundamental problem in computer vision. In this paper, we explore a generative
model for the task of generating unseen images with desired features. We
propose the Generative Cooperative Net (GCN) for image generation. The idea is
similar to ... | ['Tao Wan', 'Qiangeng Xu', 'Zengchang Qin'] | 2017-05-08 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [ 4.89943266e-01 7.18679488e-01 2.70230681e-01 -3.59939367e-01
-6.52372181e-01 -4.29275244e-01 1.17568564e+00 -8.63783717e-01
1.72522366e-02 1.04657042e+00 1.29079506e-01 6.81459764e-03
3.65351558e-01 -1.00775504e+00 -7.27909088e-01 -9.72104967e-01
1.73731208e-01 5.85378230e-01 -1.77728966e-01 -5.19592702... | [11.749236106872559, -0.226897731423378] |
2fc4efee-0262-4501-8b6e-f43c0a54f4cf | verifying-global-neural-network | 2306.12495 | null | https://arxiv.org/abs/2306.12495v1 | https://arxiv.org/pdf/2306.12495v1.pdf | Verifying Global Neural Network Specifications using Hyperproperties | Current approaches to neural network verification focus on specifications that target small regions around known input data points, such as local robustness. Thus, using these approaches, we can not obtain guarantees for inputs that are not close to known inputs. Yet, it is highly likely that a neural network will enco... | ['Stefan Leue', 'David Boetius'] | 2023-06-21 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 1.94539309e-01 4.66533929e-01 -6.16760075e-01 -4.01048183e-01
-4.69966561e-01 -8.93493354e-01 3.52610856e-01 3.04811716e-01
-2.16842238e-02 6.67698026e-01 -1.98380008e-01 -7.70248950e-01
-5.58660090e-01 -1.00338078e+00 -1.19307625e+00 -3.46981764e-01
-3.87535363e-01 -8.63407701e-02 4.65018362e-01 -7.06486106... | [6.141655445098877, 7.565188884735107] |
45a0a996-6cea-4f60-98f7-358813001a6f | building-chinese-affective-resources-in | null | null | https://aclanthology.org/N16-1066 | https://aclanthology.org/N16-1066.pdf | Building Chinese Affective Resources in Valence-Arousal Dimensions | null | ['Xue-jie Zhang', 'Lung-Hao Lee', 'Liang-Chih Yu', 'K. Robert Lai', 'Jun Hu', 'Shuai Hao', 'Yunchao He', 'Jin Wang'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['twitter-sentiment-analysis'] | ['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... | [-7.391371726989746, 3.774693489074707] |
8dcc72b1-22d3-4793-b849-3a2814e7e7b3 | spherical-view-synthesis-for-self-supervised | 1909.08112 | null | https://arxiv.org/abs/1909.08112v1 | https://arxiv.org/pdf/1909.08112v1.pdf | Spherical View Synthesis for Self-Supervised 360 Depth Estimation | Learning based approaches for depth perception are limited by the availability of clean training data. This has led to the utilization of view synthesis as an indirect objective for learning depth estimation using efficient data acquisition procedures. Nonetheless, most research focuses on pinhole based monocular visio... | ['Federico Alvarez', 'Antonis Karakottas', 'Dimitrios Zarpalas', 'Petros Daras', 'Nikolaos Zioulis'] | 2019-09-17 | null | null | null | null | ['3d-depth-estimation'] | ['computer-vision'] | [ 6.27994686e-02 2.06248879e-01 -2.53971875e-01 -4.10493642e-01
-4.40001220e-01 -6.39010847e-01 7.75048971e-01 -3.89872193e-01
-4.45467621e-01 7.28169262e-01 3.42006087e-01 -3.43809485e-01
7.34746456e-02 -6.50793076e-01 -6.64627492e-01 -6.07869387e-01
3.53797346e-01 3.82690020e-02 -1.23272367e-01 3.54889706... | [8.680229187011719, -2.4554920196533203] |
9aef4bf7-d320-4294-a25f-e47bb79e4a80 | sequence-to-sequence-data-augmentation-for | 1807.01554 | null | http://arxiv.org/abs/1807.01554v1 | http://arxiv.org/pdf/1807.01554v1.pdf | Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding | In this paper, we study the problem of data augmentation for language
understanding in task-oriented dialogue system. In contrast to previous work
which augments an utterance without considering its relation with other
utterances, we propose a sequence-to-sequence generation based data
augmentation framework that lever... | ['Yutai Hou', 'Ting Liu', 'Yijia Liu', 'Wanxiang Che'] | 2018-07-04 | sequence-to-sequence-data-augmentation-for-1 | https://aclanthology.org/C18-1105 | https://aclanthology.org/C18-1105.pdf | coling-2018-8 | ['text-augmentation'] | ['natural-language-processing'] | [ 5.43528855e-01 8.61567914e-01 8.08678493e-02 -8.51183474e-01
-7.89923370e-01 -6.60748482e-01 7.99578547e-01 2.07742956e-02
-4.21259731e-01 1.23269892e+00 8.22372437e-01 -1.89666748e-01
3.12575877e-01 -4.95255291e-01 -3.78169030e-01 -3.22583735e-01
3.07047516e-01 1.06460774e+00 7.28496686e-02 -1.10898602... | [12.741643905639648, 7.987917423248291] |
465dd8c5-5233-4030-9976-105da2018a67 | grammarshap-an-efficient-model-agnostic-and | null | null | https://aclanthology.org/2022.lnls-1.2 | https://aclanthology.org/2022.lnls-1.2.pdf | GrammarSHAP: An Efficient Model-Agnostic and Structure-Aware NLP Explainer | Interpreting NLP models is fundamental for their development as it can shed light on hidden properties and unexpected behaviors. However, while transformer architectures exploit contextual information to enhance their predictive capabilities, most of the available methods to explain such predictions only provide import... | ['Georg Groh', 'Fabio Raffagnato', 'Luca Mülln', 'Defne Demirtürk', 'Edoardo Mosca'] | null | null | null | null | lnls-acl-2022-5 | ['constituency-parsing'] | ['natural-language-processing'] | [ 3.26367348e-01 8.19006503e-01 -6.70925081e-01 -6.98544800e-01
-5.42070210e-01 -5.27122617e-01 7.08475053e-01 5.12804747e-01
2.97720850e-01 7.46409595e-01 7.28847563e-01 -6.47489786e-01
-4.66774367e-02 -7.46780515e-01 -6.75419807e-01 4.22154292e-02
2.07349196e-01 4.03922111e-01 2.95137316e-01 -3.40375721... | [9.557624816894531, 6.961639881134033] |
c5294764-eea7-4df2-add2-9d6960354a95 | query-structure-modeling-for-inductive | 2305.13585 | null | https://arxiv.org/abs/2305.13585v1 | https://arxiv.org/pdf/2305.13585v1.pdf | Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs | Logical reasoning over incomplete knowledge graphs to answer complex logical queries is a challenging task. With the emergence of new entities and relations in constantly evolving KGs, inductive logical reasoning over KGs has become a crucial problem. However, previous PLMs-based methods struggle to model the logical s... | ['Xuanjing Huang', 'Qi Zhang', 'Haijun Shan', 'Zhihao Fan', 'Meng Han', 'Zhongyu Wei', 'Siyuan Wang'] | 2023-05-23 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.10946439e-01 6.51011646e-01 -3.41506451e-01 -6.54815793e-01
-4.84371036e-01 -7.14707494e-01 3.11651260e-01 5.59704661e-01
2.28496715e-02 5.23933828e-01 3.35943878e-01 -7.12827981e-01
-1.59111053e-01 -1.54755962e+00 -1.35641158e+00 2.37198353e-01
-1.39995456e-01 6.65759683e-01 3.29677463e-01 -3.81774127... | [9.286738395690918, 7.5770111083984375] |
41f331ad-29db-418b-8e09-c03eaff76fa9 | not-all-voxels-are-equal-semantic-scene | 2112.12925 | null | https://arxiv.org/abs/2112.12925v2 | https://arxiv.org/pdf/2112.12925v2.pdf | Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspective | We revisit Semantic Scene Completion (SSC), a useful task to predict the semantic and occupancy representation of 3D scenes, in this paper. A number of methods for this task are always based on voxelized scene representations for keeping local scene structure. However, due to the existence of visible empty voxels, thes... | ['Jiaxiang Tang', 'Xiaokang Chen', 'Gang Zeng', 'Jingbo Wang'] | 2021-12-24 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 2.84140468e-01 6.84184358e-02 2.38688394e-01 -5.44568539e-01
-4.74508405e-01 -1.59762591e-01 5.57477891e-01 2.19859660e-01
-3.39369923e-01 2.73930132e-01 1.41015828e-01 1.18875057e-02
7.91474506e-02 -1.13652849e+00 -9.69088614e-01 -4.91751194e-01
9.00969654e-02 4.51153815e-01 7.23384917e-01 1.13473525... | [8.475542068481445, -2.95529842376709] |
9bf22516-9838-4228-bbc1-457e57dda973 | a-practical-framework-for-roi-detection-in | 2103.01584 | null | https://arxiv.org/abs/2103.01584v1 | https://arxiv.org/pdf/2103.01584v1.pdf | A Practical Framework for ROI Detection in Medical Images -- a case study for hip detection in anteroposterior pelvic radiographs | Purpose Automated detection of region of interest (ROI) is a critical step for many medical image applications such as heart ROIs detection in perfusion MRI images, lung boundary detection in chest X-rays, and femoral head detection in pelvic radiographs. Thus, we proposed a practical framework of ROIs detection in med... | ['Chien-Hung Liao', 'Shann-Ching Chen', 'Chih-Chi Chen', 'Feng-Yu Liu'] | 2021-03-02 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 1.73697978e-01 3.10301065e-01 -3.47117245e-01 -7.98763856e-02
-1.27645683e+00 -1.91400405e-02 1.63587496e-01 -5.75699797e-03
-6.96500719e-01 7.47624636e-01 2.25070357e-01 -6.48165792e-02
-2.39940539e-01 -8.24868739e-01 -6.46197081e-01 -5.20429254e-01
-3.65457058e-01 8.19339395e-01 8.91626596e-01 1.38189495... | [15.067214012145996, -2.2918591499328613] |
2fba2663-d453-4ce8-b0b3-8045c2ea96d1 | evaluating-the-role-of-language-typology-in | 2004.13939 | null | https://arxiv.org/abs/2004.13939v2 | https://arxiv.org/pdf/2004.13939v2.pdf | Evaluating Transformer-Based Multilingual Text Classification | As NLP tools become ubiquitous in today's technological landscape, they are increasingly applied to languages with a variety of typological structures. However, NLP research does not focus primarily on typological differences in its analysis of state-of-the-art language models. As a result, NLP tools perform unequally ... | ['William Yang Wang', 'Sharon Levy', 'Aesha Parekh', 'Sophie Groenwold', 'Samhita Honnavalli', 'Lily Ou', 'Diba Mirza'] | 2020-04-29 | null | null | null | null | ['multilingual-text-classification'] | ['miscellaneous'] | [-3.18818152e-01 -3.20679367e-01 -9.89187419e-01 -4.90918234e-02
-2.77560383e-01 -9.48056042e-01 7.20312953e-01 4.50941861e-01
-7.14897394e-01 4.52961206e-01 7.35134602e-01 -1.16109014e+00
-2.65599161e-01 -6.03793085e-01 -1.99950129e-01 1.48398831e-01
4.22021985e-01 5.32500505e-01 -2.14397013e-01 -1.81804687... | [10.330680847167969, 9.927830696105957] |
e3c45c61-c47b-4049-a28e-df5aab3b9cc0 | tree-based-optimization-a-meta-algorithm-for | 1809.09284 | null | http://arxiv.org/abs/1809.09284v1 | http://arxiv.org/pdf/1809.09284v1.pdf | Tree-Based Optimization: A Meta-Algorithm for Metaheuristic Optimization | Designing search algorithms for finding global optima is one of the most
active research fields, recently. These algorithms consist of two main
categories, i.e., classic mathematical and metaheuristic algorithms. This
article proposes a meta-algorithm, Tree-Based Optimization (TBO), which uses
other heuristic optimizer... | ['Hoda Mohammadzade', 'Saeed Sharifian', 'Benyamin Ghojogh'] | 2018-09-25 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 1.41446874e-01 -3.52006137e-01 -2.38259360e-01 -3.94910611e-02
4.71571796e-02 -2.96800554e-01 -8.05819482e-02 1.34708226e-01
-3.46970320e-01 1.04954016e+00 -1.76245525e-01 -1.63517877e-01
-9.76720870e-01 -9.65551972e-01 -1.81553382e-02 -9.42162812e-01
2.11258885e-02 6.67559981e-01 1.64440498e-01 -3.35052639... | [5.746366500854492, 3.5353596210479736] |
27f053a0-a6ae-4cbd-a367-eb2f5b022c1e | multi-temporal-scene-classification-and-scene | 2006.02176 | null | https://arxiv.org/abs/2006.02176v1 | https://arxiv.org/pdf/2006.02176v1.pdf | Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion | Classifying multi-temporal scene land-use categories and detecting their semantic scene-level changes for imagery covering urban regions could straightly reflect the land-use transitions. Existing methods for scene change detection rarely focus on the temporal correlation of bi-temporal features, and are mainly evaluat... | ['Bo Du', 'Lixiang Ru', 'Chen Wu'] | 2020-06-03 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 2.89538622e-01 -7.28648305e-01 2.04171106e-01 -8.00661564e-01
-4.35824990e-01 -1.40319273e-01 7.39572227e-01 3.06361429e-02
-7.20285833e-01 3.89982790e-01 1.47405773e-01 -8.87054950e-02
-2.19030946e-01 -1.11293948e+00 -6.01196289e-01 -8.80983531e-01
-4.81526107e-01 -6.03320837e-01 3.25983465e-01 -2.36909777... | [9.697660446166992, -1.3029162883758545] |
f1644e74-deb3-4bc2-a03b-3fdd8a465c74 | ultrastereo-efficient-learning-based-matching | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Fanello_UltraStereo_Efficient_Learning-Based_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Fanello_UltraStereo_Efficient_Learning-Based_CVPR_2017_paper.pdf | UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems | Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision. We efficiently solve the specialized problem of stereo matching under active illumination using a new learning-based algorithm. This type of 'active' stereo i.e. stereo matching where scene texture is augmented by ... | ['Sean Ryan Fanello', 'Julien Valentin', 'Philip Davidson', 'Vladimir Tankovich', 'Shahram Izadi', 'Christoph Rhemann', 'Adarsh Kowdle'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['stereo-matching'] | ['computer-vision'] | [ 8.12301695e-01 6.44635707e-02 4.78587952e-03 -6.15093887e-01
-7.50012696e-01 -4.64011341e-01 5.70321560e-01 -1.71324238e-01
-6.01253331e-01 6.98217750e-01 5.72841801e-02 -6.55367374e-02
-2.17478380e-01 -6.58717573e-01 -7.26671755e-01 -1.09815574e+00
5.02757668e-01 6.16394937e-01 3.42004150e-01 -3.64050530... | [9.050328254699707, -2.525481700897217] |
636cbf95-2943-4a8d-a49b-fedf54fc00b9 | language-model-pre-training-for-hierarchical | 1901.09128 | null | http://arxiv.org/abs/1901.09128v1 | http://arxiv.org/pdf/1901.09128v1.pdf | Language Model Pre-training for Hierarchical Document Representations | Hierarchical neural architectures are often used to capture long-distance
dependencies and have been applied to many document-level tasks such as
summarization, document segmentation, and sentiment analysis. However,
effective usage of such a large context can be difficult to learn, especially
in the case where there i... | ['Kristina Toutanova', 'Ming-Wei Chang', 'Kenton Lee', 'Jacob Devlin'] | 2019-01-26 | language-model-pre-training-for-hierarchical-1 | https://openreview.net/forum?id=rygnfn0qF7 | https://openreview.net/pdf?id=rygnfn0qF7 | iclr-2019-5 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 6.07541263e-01 1.93900615e-01 -4.79473501e-01 -5.81708133e-01
-9.71078336e-01 -5.66029966e-01 4.75188494e-01 8.96998048e-01
-6.13461912e-01 6.81337714e-01 8.13910842e-01 -5.99104583e-01
1.97750956e-01 -6.03049457e-01 -5.26338220e-01 -3.22319269e-01
1.92865089e-01 2.18099177e-01 -5.74378222e-02 3.28035988... | [12.53037166595459, 9.478259086608887] |
8348836e-c8fe-4647-8735-433910096961 | accelerating-diffusion-models-for-inverse | 2305.16965 | null | https://arxiv.org/abs/2305.16965v1 | https://arxiv.org/pdf/2305.16965v1.pdf | Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling | Recently, diffusion models have demonstrated a remarkable ability to solve inverse problems in an unsupervised manner. Existing methods mainly focus on modifying the posterior sampling process while neglecting the potential of the forward process. In this work, we propose Shortcut Sampling for Diffusion (SSD), a novel ... | ['Yujiu Yang', 'Fei Yin', 'Jiayi Li', 'Haoze Sun', 'Gongye Liu'] | 2023-05-26 | null | null | null | null | ['colorization', 'deblurring'] | ['computer-vision', 'computer-vision'] | [ 3.58100206e-01 -9.33437124e-02 2.89040685e-01 -2.03032956e-01
-8.18136513e-01 -1.55185461e-01 6.48686707e-01 -6.89162076e-01
-3.65094602e-01 4.77844685e-01 2.92565465e-01 1.17238760e-01
-1.47964448e-01 -6.48705781e-01 -5.67361474e-01 -8.67779195e-01
6.10735118e-01 2.81989992e-01 4.48032051e-01 -1.05315998... | [11.418567657470703, -2.1658918857574463] |
82f4cf3a-e503-4576-a39a-712bbb399e13 | joint-entity-and-relation-extraction-based-on | null | null | https://aclanthology.org/2022.spnlp-1.2 | https://aclanthology.org/2022.spnlp-1.2.pdf | Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural Networks | This study introduces a novel approach to the joint extraction of entities and relations by stacking convolutional neural networks (CNNs) on pretrained language models. We adopt table representations to model the entities and relations, casting the entity and relation extraction as a table-labeling problem. Regarding e... | ['Naoaki Okazaki', 'Tatsuya Hiraoka', 'Youmi Ma'] | null | null | null | null | spnlp-acl-2022-5 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.48198143e-01 4.27914113e-01 -3.28496397e-01 -6.32375300e-01
-7.17415273e-01 -5.84752381e-01 6.53637648e-01 3.28278959e-01
-4.27286595e-01 8.42705667e-01 7.97893330e-02 -2.77802140e-01
2.69021332e-01 -1.18813026e+00 -1.10928237e+00 -2.31075436e-01
-2.59075135e-01 7.51181483e-01 1.09913625e-01 -1.30741537... | [9.512981414794922, 8.054248809814453] |
631f4696-7fd1-46a3-a695-a3b8396daad6 | deep-relevance-ranking-using-enhanced | 1809.01682 | null | http://arxiv.org/abs/1809.01682v2 | http://arxiv.org/pdf/1809.01682v2.pdf | Deep Relevance Ranking Using Enhanced Document-Query Interactions | We explore several new models for document relevance ranking, building upon
the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM,
which uses context-insensitive encodings of terms and query-document term
interactions, we inject rich context-sensitive encodings throughout our models,
inspired by PA... | ['Ion Androutsopoulos', 'Georgios-Ioannis Brokos', 'Ryan McDonald'] | 2018-09-05 | deep-relevance-ranking-using-enhanced-1 | https://aclanthology.org/D18-1211 | https://aclanthology.org/D18-1211.pdf | emnlp-2018-10 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 1.72570154e-01 -1.28650561e-01 -2.07860976e-01 -4.72422540e-01
-1.37520051e+00 -8.38894069e-01 1.27615535e+00 6.26792669e-01
-7.00192213e-01 4.67788607e-01 8.71820450e-01 -5.33475637e-01
-5.15011013e-01 -5.67959070e-01 -6.58485830e-01 5.43118939e-02
-2.31042400e-01 5.48769355e-01 5.63116670e-01 -7.32038200... | [11.513283729553223, 7.7100019454956055] |
ff279b30-c2a8-456f-850e-a723efdb4b70 | weakly-supervised-attentional-model-for-low | null | null | https://aclanthology.org/D19-6129 | https://aclanthology.org/D19-6129.pdf | Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval | We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages. Low resource languages often lack relevance annotations for CLIR, and when available the training data usually has limited coverage for possible queries. In this paper, we design a model which ... | ['Zhongqiang Huang', 'Damianos Karakos', 'Lingjun Zhao', 'Zhuolin Jiang', 'Rabih Zbib'] | 2019-11-01 | null | null | null | ws-2019-11 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.08476558e-01 7.25376457e-02 -6.32129848e-01 -3.80049318e-01
-2.00586033e+00 -6.24309421e-01 9.08332348e-01 2.97898561e-01
-9.27444041e-01 9.69346881e-01 5.13274550e-01 -3.14817876e-01
-2.72461064e-02 -3.82088900e-01 -9.81649995e-01 -1.97655812e-01
3.94827157e-01 1.16714597e+00 -4.61529009e-03 -5.43932796... | [11.422146797180176, 9.83120346069336] |
2b97490e-26c8-4bdd-950d-8105d8442bbd | multi-modal-multi-level-fusion-for-3d-single | 2305.06794 | null | https://arxiv.org/abs/2305.06794v1 | https://arxiv.org/pdf/2305.06794v1.pdf | Multi-modal Multi-level Fusion for 3D Single Object Tracking | 3D single object tracking plays a crucial role in computer vision. Mainstream methods mainly rely on point clouds to achieve geometry matching between target template and search area. However, textureless and incomplete point clouds make it difficult for single-modal trackers to distinguish objects with similar structu... | ['Zheng Fang', 'Zuoxu Gu', 'Yubo Cui', 'Zhiheng Li'] | 2023-05-11 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-2.32480720e-01 -6.55609190e-01 1.67514514e-02 -4.92090778e-03
-8.88799965e-01 -7.35961258e-01 8.73466134e-01 -9.76343974e-02
-1.24425255e-01 -1.47433728e-01 -2.28229612e-01 -3.86131257e-02
-1.77844077e-01 -6.92090571e-01 -6.54318750e-01 -7.44239748e-01
3.01913828e-01 4.98751849e-01 8.85212481e-01 -1.68877855... | [6.553342819213867, -2.32308030128479] |
0bb0c09c-660d-4b9e-8dd8-3183b6b499a5 | mocapact-a-multi-task-dataset-for-simulated | 2208.07363 | null | https://arxiv.org/abs/2208.07363v3 | https://arxiv.org/pdf/2208.07363v3.pdf | MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control | Simulated humanoids are an appealing research domain due to their physical capabilities. Nonetheless, they are also challenging to control, as a policy must drive an unstable, discontinuous, and high-dimensional physical system. One widely studied approach is to utilize motion capture (MoCap) data to teach the humanoid... | ['Matthew Hausknecht', 'Ching-An Cheng', 'Ricky Loynd', 'Felipe Vieira Frujeri', 'Andrey Kolobov', 'Nolan Wagener'] | 2022-08-15 | null | null | null | null | ['humanoid-control'] | ['robots'] | [-4.53201294e-01 4.50330041e-03 -1.38418943e-01 3.17965150e-01
-5.61779320e-01 -5.80733478e-01 6.45047724e-01 -3.92809391e-01
-6.36017084e-01 9.65701699e-01 1.95568234e-01 -2.51842439e-01
-9.96530429e-02 -6.26352072e-01 -1.08838308e+00 -8.05924773e-01
-3.40432346e-01 5.89474440e-01 4.46455657e-01 -5.95537066... | [4.741871356964111, 0.9454472661018372] |
fe3f49cc-040a-495b-8938-355fcfa59ed8 | the-tetrazole-analogue-of-the-auxin-indole-3 | 1808.08842 | null | http://arxiv.org/abs/1808.08842v1 | http://arxiv.org/pdf/1808.08842v1.pdf | The tetrazole analogue of the auxin indole-3-acetic acid binds preferentially to TIR1 and not AFB5 | Auxin is considered one of the cardinal hormones in plant growth and
development. It regulates a wide range of processes throughout the plant.
Synthetic auxins exploit the auxin-signalling pathway and are valuable as
herbicidal agrochemicals. Currently, despite a diversity of chemical scaffolds
all synthetic auxins hav... | [] | 2018-08-27 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 9.72914696e-01 1.56734928e-01 -5.18453360e-01 6.93614632e-02
-2.95914680e-01 -1.13460159e+00 6.24158919e-01 5.97707868e-01
-3.93296421e-01 1.15813982e+00 -5.12609445e-02 -9.47760224e-01
-2.06120938e-01 -5.78692555e-01 -5.92943847e-01 -1.12139547e+00
7.10431859e-02 1.65628359e-01 4.24090892e-01 -5.31853259... | [4.678316593170166, 5.125249862670898] |
70a3c145-76ca-48ae-811f-644d093d7b5a | learning-to-smiles | 1602.06289 | null | http://arxiv.org/abs/1602.06289v2 | http://arxiv.org/pdf/1602.06289v2.pdf | Learning to SMILE(S) | This paper shows how one can directly apply natural language processing (NLP)
methods to classification problems in cheminformatics. Connection between these
seemingly separate fields is shown by considering standard textual
representation of compound, SMILES. The problem of activity prediction against
a target protein... | ['Damian Leśniak', 'Stanisław Jastrzębski', 'Wojciech Marian Czarnecki'] | 2016-02-19 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 9.45867360e-01 6.38144672e-01 -4.69653904e-01 -2.33641893e-01
-4.01926488e-01 -6.79622948e-01 9.16863561e-01 1.05380774e+00
-3.06839705e-01 1.48249102e+00 4.39093232e-01 -9.66462076e-01
-2.57187486e-01 -6.54906809e-01 -4.61355656e-01 -7.59444773e-01
-8.27411637e-02 4.62079227e-01 -1.37611106e-01 -2.18655705... | [5.066187858581543, 5.899891376495361] |
af0da6ae-d729-497f-be52-7c4da71a0dee | semiconductor-defect-detection-by-hybrid-1 | 2208.03514 | null | https://arxiv.org/abs/2208.03514v1 | https://arxiv.org/pdf/2208.03514v1.pdf | Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning | With the rapid development of artificial intelligence and autonomous driving technology, the demand for semiconductors is projected to rise substantially. However, the massive expansion of semiconductor manufacturing and the development of new technology will bring many defect wafers. If these defect wafers have not be... | ['Min Sun', 'YuanFu Yang'] | 2022-08-06 | semiconductor-defect-detection-by-hybrid | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Semiconductor_Defect_Detection_by_Hybrid_Classical-Quantum_Deep_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Semiconductor_Defect_Detection_by_Hybrid_Classical-Quantum_Deep_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['defect-detection'] | ['computer-vision'] | [ 3.25896293e-01 1.00847455e-02 -3.38438782e-03 -2.94775724e-01
-7.04833329e-01 -4.01963472e-01 2.20604181e-01 3.43431056e-01
5.73683381e-02 4.06640738e-01 -6.26668990e-01 -5.35494804e-01
4.90596369e-02 -1.43315589e+00 -4.65661168e-01 -8.44437063e-01
4.52889711e-01 5.95153272e-01 8.42663366e-03 -4.48632002... | [5.554671287536621, 4.9677510261535645] |
dbffaf15-1d15-416e-877f-cbd32289f149 | question-answering-via-web-extracted-tables | 1903.07113 | null | http://arxiv.org/abs/1903.07113v2 | http://arxiv.org/pdf/1903.07113v2.pdf | Question Answering via Web Extracted Tables and Pipelined Models | In this paper, we describe a dataset and baseline result for a question
answering that utilizes web tables. It contains commonly asked questions on the
web and their corresponding answers found in tables on websites. Our dataset is
novel in that every question is paired with a table of a different signature.
In particu... | ['Anthony Tomasic', 'Bhavya Karki', 'Zihua Liu', 'Suhail Barot', 'Matthias Grabmair', 'Lucile Callebert', 'Nithin Haridas', 'Fan Hu'] | 2019-03-17 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 8.15573633e-02 6.86082959e-01 -2.08490863e-01 -8.00417960e-01
-1.50294578e+00 -1.16183829e+00 5.50245285e-01 8.67596447e-01
8.08214098e-02 5.35306990e-01 4.51659143e-01 -7.36563504e-01
-2.06076071e-01 -1.52769506e+00 -1.13476205e+00 2.64682233e-01
2.51732826e-01 7.99824357e-01 7.01002717e-01 -4.59545344... | [9.976139068603516, 7.890412330627441] |
1f3505f7-48e4-4760-9612-1aeeaf266b8a | enhancing-few-shot-text-to-sql-capabilities | 2305.12586 | null | https://arxiv.org/abs/2305.12586v1 | https://arxiv.org/pdf/2305.12586v1.pdf | Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies | In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context that has been supplemented with a few examples or task-specific instructions. In this paper, we aim to extend this method to question answerin... | ['Dragomir Radev', 'Arman Cohan', 'Ellen Zhang', 'Jaesung Tae', 'Narutatsu Ri', 'Weijin Zou', 'Yilun Zhao', 'Linyong Nan'] | 2023-05-21 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 1.62799746e-01 -2.37315655e-01 -2.92256087e-01 -6.00079656e-01
-1.12098348e+00 -6.43231034e-01 6.98984563e-01 5.05906463e-01
-6.26685381e-01 3.17819446e-01 2.76243627e-01 -7.94221997e-01
-1.34952545e-01 -5.98824263e-01 -8.77602875e-01 9.46574435e-02
1.07830197e-01 2.22341537e-01 5.98042130e-01 -3.61082315... | [10.42761516571045, 8.075784683227539] |
271aad28-ad27-4964-bc81-8349fe02af70 | designing-deep-networks-for-scene-recognition | 2303.07402 | null | https://arxiv.org/abs/2303.07402v1 | https://arxiv.org/pdf/2303.07402v1.pdf | Designing Deep Networks for Scene Recognition | Most deep learning backbones are evaluated on ImageNet. Using scenery images as an example, we conducted extensive experiments to demonstrate the widely accepted principles in network design may result in dramatic performance differences when the data is altered. Exploratory experiments are engaged to explain the under... | ['Xiaohui Yuan', 'Zhinan Qiao'] | 2023-03-13 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [-4.13199626e-02 1.43848360e-01 -1.14286445e-01 -5.01723468e-01
5.46545744e-01 -4.27129924e-01 2.94687331e-01 -3.95549119e-01
-6.86409771e-01 7.38053501e-01 -1.50270201e-02 -5.40352523e-01
-2.35296607e-01 -9.88222361e-01 -5.49360931e-01 -6.29809976e-01
-5.55944219e-02 -1.57535970e-01 4.93545532e-01 -2.40521759... | [9.081787109375, 2.2958738803863525] |
8265b408-1425-4735-a9cd-fdb0769da0e6 | designing-discontinuities | 2305.08559 | null | https://arxiv.org/abs/2305.08559v1 | https://arxiv.org/pdf/2305.08559v1.pdf | Designing Discontinuities | Discontinuities can be fairly arbitrary but also cause a significant impact on outcomes in social systems. Indeed, their arbitrariness is why they have been used to infer causal relationships among variables in numerous settings. Regression discontinuity from econometrics assumes the existence of a discontinuous variab... | ['Lav R. Varshney', 'Ting-Yi Wu', 'Suyoung Park', 'Ibtihal Ferwana'] | 2023-05-15 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 2.73637861e-01 5.94454110e-01 -8.53146493e-01 -2.40725100e-01
-6.05051756e-01 -1.44321369e-02 4.73901063e-01 5.03000498e-01
-4.43323165e-01 1.14916611e+00 5.02875566e-01 -6.66726947e-01
-6.69128895e-01 -1.03348887e+00 -1.02546358e+00 -5.95141888e-01
-4.93391484e-01 4.77763712e-01 -2.42817104e-01 -1.38178207... | [8.049784660339355, 5.301637649536133] |
98bd9bd0-f738-4549-a572-a9b7e4162c9f | a-horizon-detection-algorithm-for-maritime | 2110.13694 | null | https://arxiv.org/abs/2110.13694v3 | https://arxiv.org/pdf/2110.13694v3.pdf | A vectorized sea horizon edge filter for maritime video processing tasks | The horizon line is a fundamental semantic feature in several maritime video processing tasks, such as digital video stabilization, camera calibration, target tracking, and target distance estimation. Visible range Electro-Optical (EO) sensors capture richer information in the daytime, which often comes with challengin... | ['Astito Abdelali', 'Boulaala Mohammed', 'Yassir Zardoua'] | 2021-10-26 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 4.16157603e-01 -4.52693939e-01 5.29463515e-02 -1.66372329e-01
-3.29548597e-01 -8.55332732e-01 4.64524835e-01 6.86196983e-02
-6.57084346e-01 3.43415141e-01 -1.12311497e-01 -2.40535840e-01
-3.74040037e-01 -6.66859210e-01 -6.15267277e-01 -7.19259024e-01
-8.61308947e-02 -2.84856886e-01 6.40842974e-01 -2.47113943... | [7.800045490264893, -1.7048269510269165] |
c6d153bf-6010-4ab9-bb49-944b9dd00b23 | an-analysis-of-the-transfer-learning-of | 2011.02727 | null | https://arxiv.org/abs/2011.02727v2 | https://arxiv.org/pdf/2011.02727v2.pdf | An analysis of the transfer learning of convolutional neural networks for artistic images | Transfer learning from huge natural image datasets, fine-tuning of deep neural networks and the use of the corresponding pre-trained networks have become de facto the core of art analysis applications. Nevertheless, the effects of transfer learning are still poorly understood. In this paper, we first use techniques for... | ['Saïd Ladjal', 'Yann Gousseau', 'Nicolas Gonthier'] | 2020-11-05 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 2.76479006e-01 1.77561417e-01 2.83569068e-01 -2.92715639e-01
3.78660336e-02 -7.52096176e-01 9.06856298e-01 8.44442844e-02
-5.82195878e-01 6.91885233e-01 5.43935522e-02 2.13028282e-01
-1.47486389e-01 -8.96896183e-01 -9.39658821e-01 -8.21538210e-01
2.52690312e-04 3.85165930e-01 4.06074405e-01 -3.69538724... | [9.761373519897461, 2.381601572036743] |
5ce24361-1282-42c4-999d-78934a5689af | towards-robust-face-recognition-with | 2208.13600 | null | https://arxiv.org/abs/2208.13600v2 | https://arxiv.org/pdf/2208.13600v2.pdf | Towards Robust Face Recognition with Comprehensive Search | Data cleaning, architecture, and loss function design are important factors contributing to high-performance face recognition. Previously, the research community tries to improve the performance of each single aspect but failed to present a unified solution on the joint search of the optimal designs for all three aspec... | ['Hongsheng Li', 'Yu Liu', 'Guanglu Song', 'Manyuan Zhang'] | 2022-08-29 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-9.09459665e-02 -8.00799280e-02 -5.63962042e-01 -4.33854133e-01
-7.91707635e-01 -3.48389596e-01 2.46701270e-01 -4.09391075e-01
-1.74315840e-01 4.07691628e-01 9.07988399e-02 -2.46353358e-01
-5.87206423e-01 -4.89087820e-01 -7.12369919e-01 -8.06106806e-01
2.25060791e-01 2.44424626e-01 -3.16231936e-01 -2.25008521... | [9.343235969543457, 3.205422878265381] |
a9eddadf-a638-4dc2-8749-8ce820db0c23 | improved-attention-models-for-memory | 1910.01189 | null | http://arxiv.org/abs/1910.01189v7 | http://arxiv.org/pdf/1910.01189v7.pdf | Improved Attention Models for Memory Augmented Neural Network Adaptive Controllers | We introduced a {\it working memory} augmented adaptive controller in our
recent work. The controller uses attention to read from and write to the
working memory. Attention allows the controller to read specific information
that is relevant and update its working memory with information based on its
relevance. The retr... | [] | 2020-03-19 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.93711627e-01 4.30532634e-01 -2.69572139e-01 2.87981451e-01
-1.56798456e-02 -2.92429954e-01 1.89252958e-01 2.62615949e-01
-6.18667841e-01 9.60067630e-01 -3.73986699e-02 8.65887329e-02
-4.09433305e-01 -8.16394567e-01 -6.62399888e-01 -9.21207249e-01
3.92770112e-01 4.52107191e-01 9.23789978e-01 -2.71944791... | [4.658388614654541, 0.9706303477287292] |
e0d7d820-d568-429e-ba7b-caeba249eb61 | prediction-of-chronic-kidney-disease-a | null | null | https://ieeexplore.ieee.org/abstract/document/9333572 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9333572 | Prediction of Chronic Kidney Disease - A Machine Learning Perspective | Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is
required as soon as possible. Machine learning technique has become reliable for medical treatment. With
the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this
perspective, C... | ['Vadim Bolshev', 'Elżbieta Jasińska', 'Radomir Gono', 'Łukasz Jasiński', 'Michał Jasiński', 'Zbigniew Leonowicz', 'Tulika Chakrabarti', 'Gaurav Kumawat', 'Prasun Chakrabarti', 'Sandeep Chaurasia', 'Pankaj Chittora'] | 2021-01-22 | null | null | null | journal-2021-1 | ['disease-prediction'] | ['medical'] | [ 1.08658053e-01 -1.66205782e-02 -2.54766464e-01 -4.86443877e-01
-2.53487349e-01 6.53637620e-03 3.58306020e-01 7.84049094e-01
-4.86452401e-01 1.04747379e+00 5.02204814e-04 -2.63774127e-01
-7.38218188e-01 -8.69460881e-01 2.34710542e-03 -7.05214560e-01
-5.11580944e-01 6.38096392e-01 -3.15964192e-01 1.92669883... | [8.402148246765137, 4.829646110534668] |
fd0d88ba-2477-40b7-b3a3-1140123a80d1 | chip-channel-independence-based-pruning-for | 2110.13981 | null | https://arxiv.org/abs/2110.13981v3 | https://arxiv.org/pdf/2110.13981v3.pdf | CHIP: CHannel Independence-based Pruning for Compact Neural Networks | Filter pruning has been widely used for neural network compression because of its enabled practical acceleration. To date, most of the existing filter pruning works explore the importance of filters via using intra-channel information. In this paper, starting from an inter-channel perspective, we propose to perform eff... | ['Bo Yuan', 'Saman Zonouz', 'Huy Phan', 'Yi Xie', 'Miao Yin', 'Yang Sui'] | 2021-10-26 | null | http://proceedings.neurips.cc/paper/2021/hash/ce6babd060aa46c61a5777902cca78af-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ce6babd060aa46c61a5777902cca78af-Paper.pdf | neurips-2021-12 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 1.51530117e-01 -2.77076978e-02 3.00913379e-02 -1.98016807e-01
-2.41035059e-01 -1.56904891e-01 -6.91402555e-02 2.13869408e-01
-8.46649528e-01 1.02311623e+00 -2.65999436e-01 -3.20985883e-01
-2.61844993e-01 -1.01322937e+00 -8.62340510e-01 -4.69351679e-01
-3.01045239e-01 -2.58137375e-01 2.54572421e-01 -1.21479206... | [8.545400619506836, 3.007338762283325] |
60666bc6-ace6-4b76-b23c-0f70f3c99fc8 | multi-task-learning-and-adapted-knowledge | 2106.09790 | null | https://arxiv.org/abs/2106.09790v1 | https://arxiv.org/pdf/2106.09790v1.pdf | Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction | Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Co... | ['Smaranda Muresan', 'Yaser Al-Onaizan', 'Kasturi Bhattacharjee', 'Rishita Anubhai', 'Shuai Wang', 'Elsbeth Turcan'] | 2021-06-17 | null | https://aclanthology.org/2021.findings-acl.348 | https://aclanthology.org/2021.findings-acl.348.pdf | findings-acl-2021-8 | ['emotion-cause-extraction'] | ['natural-language-processing'] | [ 2.37380430e-01 -9.12649557e-02 -3.67737293e-01 -7.03413188e-01
-8.47734928e-01 -6.39144003e-01 4.84579355e-01 6.49309278e-01
-5.01587868e-01 6.10521197e-01 6.00787163e-01 1.16607226e-01
-7.60536194e-02 -4.22570854e-01 -3.43549341e-01 -4.06846732e-01
-1.77117751e-03 1.68149486e-01 -2.49859512e-01 -2.38654330... | [12.687495231628418, 6.2456374168396] |
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