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
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
166dea06-e99e-4441-ad79-ad6a5aa659d7 | deep-hdr-hallucination-for-inverse-tone | 2106.09486 | null | https://arxiv.org/abs/2106.09486v1 | https://arxiv.org/pdf/2106.09486v1.pdf | Deep HDR Hallucination for Inverse Tone Mapping | Inverse Tone Mapping (ITM) methods attempt to reconstruct High Dynamic Range (HDR) information from Low Dynamic Range (LDR) image content. The dynamic range of well-exposed areas must be expanded and any missing information due to over/under-exposure must be recovered (hallucinated). The majority of methods focus on th... | ['Kurt Debattista', 'Thomas Bashford-Rogers', 'Demetris Marnerides'] | 2021-06-17 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 5.04027605e-01 3.49132895e-01 7.42170541e-03 -2.52631638e-04
-6.23476923e-01 -1.45298868e-01 6.72326446e-01 -6.18630946e-01
-7.30910525e-02 1.03422165e+00 4.19024140e-01 2.00743705e-01
7.83049539e-02 -1.14003384e+00 -6.95983410e-01 -6.92880154e-01
2.54301220e-01 5.10765135e-01 3.49196374e-01 -8.00397098... | [11.034914016723633, -2.0511703491210938] |
4bdaa8b0-5558-4b2f-820e-c60f756b6a03 | neural-network-based-general-method-for | 1906.10935 | null | https://arxiv.org/abs/1906.10935v2 | https://arxiv.org/pdf/1906.10935v2.pdf | Solving Statistical Mechanics on Sparse Graphs with Feedback Set Variational Autoregressive Networks | We propose a method for solving statistical mechanics problems defined on sparse graphs. It extracts a small Feedback Vertex Set (FVS) from the sparse graph, converting the sparse system to a much smaller system with many-body and dense interactions with an effective energy on every configuration of the FVS, then learn... | ['Hai-Jun Zhou', 'Feng Pan', 'Pan Zhang', 'Pengfei Zhou'] | 2019-06-26 | null | null | null | null | ['feedback-vertex-set-fvs'] | ['graphs'] | [ 1.03620857e-01 5.15460372e-01 -4.66810875e-02 -1.39607593e-01
-5.85213125e-01 -1.95180625e-01 7.84220099e-01 -2.14690939e-01
-6.67158291e-02 1.04300737e+00 3.65223765e-01 -1.40941087e-02
4.09158766e-02 -1.16558254e+00 -1.10301745e+00 -1.19902956e+00
-3.68792653e-01 1.41539419e+00 1.46752849e-01 -2.55099505... | [5.729547500610352, 4.834125518798828] |
9c1bfdaa-aab1-4a4a-b120-1f05f8443fd7 | value-prediction-network | 1707.03497 | null | http://arxiv.org/abs/1707.03497v2 | http://arxiv.org/pdf/1707.03497v2.pdf | Value Prediction Network | This paper proposes a novel deep reinforcement learning (RL) architecture,
called Value Prediction Network (VPN), which integrates model-free and
model-based RL methods into a single neural network. In contrast to typical
model-based RL methods, VPN learns a dynamics model whose abstract states are
trained to make opti... | ['Satinder Singh', 'Junhyuk Oh', 'Honglak Lee'] | 2017-07-11 | value-prediction-network-1 | http://papers.nips.cc/paper/7192-value-prediction-network | http://papers.nips.cc/paper/7192-value-prediction-network.pdf | neurips-2017-12 | ['value-prediction'] | ['computer-code'] | [-2.89044768e-01 3.52649003e-01 -8.30187082e-01 -2.61275887e-01
-7.13538110e-01 -3.89502406e-01 9.61939156e-01 -1.35183945e-01
-7.35605597e-01 1.29463494e+00 4.75616932e-01 -4.49537188e-01
-1.38885632e-01 -1.05753350e+00 -6.92204237e-01 -5.50857842e-01
-5.08570015e-01 8.51502419e-01 1.79870725e-01 -7.01814830... | [4.0968546867370605, 1.681186318397522] |
11b1d491-0836-4fef-bf34-04fffc294098 | analysis-of-critical-parameters-of-satellite | 1905.07476 | null | https://arxiv.org/abs/1905.07476v1 | https://arxiv.org/pdf/1905.07476v1.pdf | Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping | Although nowadays advanced dense image matching (DIM) algorithms are able to produce LiDAR (Light Detection And Ranging) comparable dense point clouds from satellite stereo images, the accuracy and completeness of such point clouds heavily depend on the geometric parameters of the satellite stereo images. The intersect... | ['Rongjun Qin'] | 2019-05-17 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 1.83961481e-01 -3.75775665e-01 2.59270612e-02 -3.15014064e-01
-3.98087919e-01 -6.32439375e-01 6.60189927e-01 1.75597459e-01
-4.87261415e-01 7.00635612e-01 -3.05062711e-01 -2.88272500e-01
-3.45600277e-01 -1.30525637e+00 -4.25843298e-01 -6.42145276e-01
3.67248803e-02 1.14966261e+00 4.68185753e-01 -6.33400440... | [8.414410591125488, -2.6249208450317383] |
7143477c-e879-46f8-bd05-1970edd0d0fe | evolutionary-verbalizer-search-for-prompt | 2306.10514 | null | https://arxiv.org/abs/2306.10514v1 | https://arxiv.org/pdf/2306.10514v1.pdf | Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification | Recent advances for few-shot text classification aim to wrap textual inputs with task-specific prompts to cloze questions. By processing them with a masked language model to predict the masked tokens and using a verbalizer that constructs the mapping between predicted words and target labels. This approach of using pre... | ['Hai-Lin Liu', 'Yutao Lai', 'Lei Chen', 'Tongtao Ling'] | 2023-06-18 | null | null | null | null | ['text-classification', 'few-shot-text-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.95424604e-01 -6.54071346e-02 -2.38711908e-01 -4.56343859e-01
-8.23187709e-01 -4.58771795e-01 5.20381927e-01 9.52377841e-02
-6.15291536e-01 5.64117789e-01 2.83790886e-01 -1.31779373e-01
-1.51670249e-02 -7.13654578e-01 -2.85008013e-01 -5.45796037e-01
6.31654441e-01 5.87803304e-01 7.84299453e-04 -4.05520022... | [10.703187942504883, 7.905996799468994] |
1354928c-69b3-48f6-be4f-b30412d8638f | exploiting-uncertainty-in-regression-forests | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Valentin_Exploiting_Uncertainty_in_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Valentin_Exploiting_Uncertainty_in_2015_CVPR_paper.pdf | Exploiting Uncertainty in Regression Forests for Accurate Camera Relocalization | Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure. In these methods, each tree associates one pixel with a point in the scene's 3D world coordinate frame. In previous work, these predictions were point estimates and the subsequent camera po... | ['Jamie Shotton', 'Julien Valentin', 'Philip H. S. Torr', 'Matthias Niessner', 'Shahram Izadi', 'Andrew Fitzgibbon'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['camera-relocalization'] | ['computer-vision'] | [ 7.17780665e-02 1.02315657e-01 -3.46179485e-01 -3.54394346e-01
-6.96314633e-01 -5.81708729e-01 5.65032363e-01 -1.90320641e-01
-6.01232827e-01 6.39088631e-01 -3.79845058e-03 -1.62739202e-01
2.64181376e-01 -5.80377400e-01 -9.84041035e-01 -6.58806324e-01
4.65963066e-01 9.37376976e-01 5.79200923e-01 3.66880715... | [7.773956298828125, -2.3706836700439453] |
bc4eda9f-3fe9-46bb-8f05-537a468be0e7 | machine-learning-based-detection-of-clickbait | 1710.01977 | null | http://arxiv.org/abs/1710.01977v1 | http://arxiv.org/pdf/1710.01977v1.pdf | Machine Learning Based Detection of Clickbait Posts in Social Media | Clickbait (headlines) make use of misleading titles that hide critical
information from or exaggerate the content on the landing target pages to
entice clicks. As clickbaits often use eye-catching wording to attract viewers,
target contents are often of low quality. Clickbaits are especially widespread
on social media ... | ['Thai Le', 'Xinyue Cao', 'Jason', 'Zhang'] | 2017-10-05 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-2.68796057e-01 -2.17625290e-01 -3.39099020e-01 -3.91988248e-01
-1.06717563e+00 -8.05763304e-01 5.92486620e-01 4.17169034e-01
-7.00392723e-01 9.17249978e-01 1.09722661e-02 -3.79777163e-01
1.05694316e-01 -5.53550243e-01 -6.95058763e-01 -1.41588911e-01
5.02622649e-02 1.02420449e-01 6.80938840e-01 -2.39844751... | [7.754286289215088, 9.787952423095703] |
efcb6643-4f90-4b7c-bdcd-9513b28ebd06 | sparse-needlets-for-lighting-estimation-with | 2106.13090 | null | https://arxiv.org/abs/2106.13090v1 | https://arxiv.org/pdf/2106.13090v1.pdf | Sparse Needlets for Lighting Estimation with Spherical Transport Loss | Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting. Existing approaches model lighting in either frequency domain or spatial domain which is insufficient to represent the complex lighting conditions in scenes and tends... | ['Ling Shao', 'Xuansong Xie', 'Feiying Ma', 'Shijian Lu', 'WenBo Hu', 'Changgong Zhang', 'Fangneng Zhan'] | 2021-06-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhan_Sparse_Needlets_for_Lighting_Estimation_With_Spherical_Transport_Loss_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhan_Sparse_Needlets_for_Lighting_Estimation_With_Spherical_Transport_Loss_ICCV_2021_paper.pdf | iccv-2021-1 | ['lighting-estimation'] | ['computer-vision'] | [ 2.69884437e-01 -6.94266081e-01 1.99294731e-01 -5.58834612e-01
-6.76571012e-01 -4.01140392e-01 3.83628845e-01 -5.08580267e-01
-3.27113010e-02 6.51781738e-01 4.61736433e-02 -6.38642460e-02
2.28043333e-01 -5.53387702e-01 -5.09591818e-01 -8.78244400e-01
4.06893194e-01 -1.04641177e-01 7.75963664e-02 -5.32659963... | [9.95829963684082, -2.886890411376953] |
497f0b14-bee9-415d-9553-2c9cd72a4e78 | universal-domain-adaptation-in-ordinal | 2106.11576 | null | https://arxiv.org/abs/2106.11576v2 | https://arxiv.org/pdf/2106.11576v2.pdf | Universal Domain Adaptation in Ordinal Regression | We address the problem of universal domain adaptation (UDA) in ordinal regression (OR), which attempts to solve classification problems in which labels are not independent, but follow a natural order. We show that the UDA techniques developed for classification and based on the clustering assumption, under-perform in O... | ['Boris Chidlovskii', 'Christian Wolf', 'Assem Sadek'] | 2021-06-22 | null | null | null | null | ['age-estimation', 'universal-domain-adaptation', 'age-estimation'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 5.68046749e-01 2.01043665e-01 -5.39465964e-01 -5.42665601e-01
-8.06956172e-01 -7.36221313e-01 8.10693920e-01 5.74257821e-02
-3.42431813e-01 8.97077739e-01 -8.27334076e-02 -1.95138454e-01
-3.90148491e-01 -6.09455526e-01 -6.55289710e-01 -9.06989813e-01
-9.83003229e-02 1.08426452e+00 -7.23373890e-02 7.75558203... | [10.339712142944336, 3.243985414505005] |
923e64bf-c776-4bfe-a6e0-550b45700105 | deep-understanding-based-multi-document | 2204.03494 | null | https://arxiv.org/abs/2204.03494v1 | https://arxiv.org/pdf/2204.03494v1.pdf | Deep Understanding based Multi-Document Machine Reading Comprehension | Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings. First, to understand the semantic meaning of words in the input question and documents from the perspective of each... | ['Bingchao Wang', 'Chunchao Liu', 'Jiaqi Wang', 'Huimin Wu', 'Shilei Liu', 'Yu Guo', 'Zhibo Wang', 'Bochao Li', 'Yongkang Liu', 'Feiliang Ren'] | 2022-02-25 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 2.00856254e-01 3.19792569e-01 -1.77316274e-02 -6.89551830e-01
-8.76273036e-01 -7.27148652e-01 6.38453424e-01 7.56596923e-01
-1.81455597e-01 1.60224408e-01 7.18552589e-01 -5.48010528e-01
-2.27738813e-01 -9.18189466e-01 -8.58654976e-01 -2.08727509e-01
4.65514004e-01 7.40425289e-01 4.56035584e-01 -5.02911448... | [11.226245880126953, 8.050590515136719] |
da7828ef-9b1f-4ac4-a0af-714b9627f400 | benchmark-data-and-evaluation-framework-for | 2205.11966 | null | https://arxiv.org/abs/2205.11966v2 | https://arxiv.org/pdf/2205.11966v2.pdf | Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy | The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-1... | ['Noam Slonim', 'Yoav Katz', 'Pooja Sangha', 'João Sedoc', 'Naor Bar-Zeev', 'Rose Weeks', 'Dan Lahav', 'Roni Friedman', 'Assaf Toledo', 'Shai Gretz'] | 2022-05-24 | null | null | null | null | ['intent-discovery'] | ['natural-language-processing'] | [-3.00567448e-02 5.73816262e-02 -2.86568075e-01 -2.27303147e-01
-6.83954656e-01 -1.09840679e+00 9.45329070e-01 4.46121365e-01
-4.33867872e-01 8.17975521e-01 8.79071712e-01 -6.33214593e-01
2.69390762e-01 -5.38560629e-01 -4.51904312e-02 -7.79008195e-02
-2.83586293e-01 8.43857765e-01 -8.11939463e-02 -7.34245300... | [8.452095031738281, 9.435580253601074] |
51f758c8-0b8e-4b73-bd42-c4c376235674 | autonomous-uav-exploration-of-dynamic | 2010.07429 | null | https://arxiv.org/abs/2010.07429v3 | https://arxiv.org/pdf/2010.07429v3.pdf | Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap | Autonomous exploration requires robots to generate informative trajectories iteratively. Although sampling-based methods are highly efficient in unmanned aerial vehicle exploration, many of these methods do not effectively utilize the sampled information from the previous planning iterations, leading to redundant compu... | ['Kenji Shimada', 'Di Deng', 'Zhefan Xu'] | 2020-10-14 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 9.47163329e-02 2.34961510e-01 -1.61711082e-01 -1.84012383e-01
-5.33654332e-01 -9.24751818e-01 2.55608350e-01 2.55872644e-02
-4.31066036e-01 1.04573536e+00 1.02480590e-01 -3.96564454e-01
-5.75626433e-01 -1.30313241e+00 -4.73788649e-01 -6.00472033e-01
-5.16401947e-01 7.73564935e-01 6.41272366e-01 -2.89060742... | [4.874289512634277, 1.3525300025939941] |
0ba56209-02ec-48fa-ad87-4c983f82dd42 | using-n-gram-and-word-network-features-for | null | null | https://aclanthology.org/W13-1732 | https://aclanthology.org/W13-1732.pdf | Using N-gram and Word Network Features for Native Language Identification | null | ['Shibamouli Lahiri', 'Rada Mihalcea'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['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... | [-7.384732246398926, 3.7719781398773193] |
4d894448-6bf5-46a6-80df-96f30c658ad1 | coupling-adversarial-learning-with-selective | 2207.08145 | null | https://arxiv.org/abs/2207.08145v1 | https://arxiv.org/pdf/2207.08145v1.pdf | Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation | In contrast to a standard closed-set domain adaptation task, partial domain adaptation setup caters to a realistic scenario by relaxing the identical label set assumption. The fact of source label set subsuming the target label set, however, introduces few additional obstacles as training on private source category sam... | ['Arunabha Sen', 'Hemanth Venkateswara', 'Sandipan Choudhuri'] | 2022-07-17 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.75809062e-01 3.21525455e-01 -4.61653769e-01 -5.88441849e-01
-1.12346196e+00 -8.91720235e-01 7.03010440e-01 7.31283501e-02
-4.38608408e-01 1.21851647e+00 -4.96292040e-02 1.78234708e-02
-2.76804358e-01 -6.33592069e-01 -5.53915441e-01 -1.02683485e+00
1.55850977e-01 5.05446553e-01 9.58128124e-02 5.29965125... | [10.35435962677002, 3.204477548599243] |
7a488fdc-d3bf-4ca8-ba0f-92e08755399d | avlen-audio-visual-language-embodied | 2210.07940 | null | https://arxiv.org/abs/2210.07940v1 | https://arxiv.org/pdf/2210.07940v1.pdf | AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments | Recent years have seen embodied visual navigation advance in two distinct directions: (i) in equipping the AI agent to follow natural language instructions, and (ii) in making the navigable world multimodal, e.g., audio-visual navigation. However, the real world is not only multimodal, but also often complex, and thus ... | ['Anoop Cherian', 'Amit K. Roy-Chowdhury', 'Sudipta Paul'] | 2022-10-14 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 4.38599326e-02 2.65070885e-01 3.05775583e-01 -1.04161121e-01
-1.01055145e+00 -9.48823988e-01 5.76412737e-01 7.04848170e-02
-7.33545661e-01 3.73562396e-01 2.08209768e-01 -4.97945100e-01
4.70367037e-02 -7.32897937e-01 -6.95406377e-01 -5.03278971e-01
-2.25448087e-01 6.62510037e-01 1.79012209e-01 -3.42762649... | [4.409423828125, 0.7278719544410706] |
6333ed10-6912-477f-93b1-13f7d46af40a | joint-open-knowledge-base-canonicalization-1 | 2212.01207 | null | https://arxiv.org/abs/2212.01207v1 | https://arxiv.org/pdf/2212.01207v1.pdf | Joint Open Knowledge Base Canonicalization and Linking | Open Information Extraction (OIE) methods extract a large number of OIE triples (noun phrase, relation phrase, noun phrase) from text, which compose large Open Knowledge Bases (OKBs). However, noun phrases (NPs) and relation phrases (RPs) in OKBs are not canonicalized and often appear in different paraphrased textual v... | ['Xiaojie Yuan', 'Zhenglu Yang', 'Jianyong Wang', 'Yuanfei Wang', 'Wei Shen', 'Yinan Liu'] | 2022-12-02 | joint-open-knowledge-base-canonicalization | https://dl.acm.org/doi/10.1145/3448016.3452776 | https://dl.acm.org/doi/10.1145/3448016.3452776 | proceedings-of-the-2021-international | ['open-information-extraction'] | ['natural-language-processing'] | [-2.56604642e-01 4.04886872e-01 -4.27669138e-01 -1.53726861e-01
-5.80914259e-01 -7.66224265e-01 4.55207467e-01 3.11693817e-01
-2.88756996e-01 1.13880110e+00 3.00603807e-01 -1.98908716e-01
-3.11863244e-01 -9.66796875e-01 -1.00047231e+00 -2.96049416e-01
1.64601132e-01 7.01192319e-01 4.81945783e-01 -5.29719830... | [9.071941375732422, 8.330933570861816] |
dcefbc3f-3827-4bac-9bb9-c3fe62dee960 | accurate-facial-image-parsing-at-real-time | null | null | https://ieeexplore.ieee.org/document/8682072 | https://ieeexplore.ieee.org/document/8682072 | Accurate facial image parsing at real-time speed | In this paper, we propose a design scheme for deep learning networks in the face parsing task with promising accuracy and real-time inference speed. By analyzing the differences between the general image parsing task and face parsing task, we first revisit the structure of traditional FCN and make improvements to adapt... | ['Hefei Ling', 'Yao Sun', 'Si Liu', 'Zhen Wei'] | 2019-04-09 | null | null | null | ieee-transactions-on-image-processing-2019-4 | ['face-parsing'] | ['computer-vision'] | [ 2.81020105e-01 3.69722009e-01 -3.77940059e-01 -1.06876051e+00
-3.77665490e-01 -1.63122952e-01 3.64437699e-01 -5.47196090e-01
-3.46216679e-01 6.19685829e-01 2.57613808e-01 -2.69371390e-01
-1.94741741e-01 -7.57601023e-01 -7.91449368e-01 -7.17812598e-01
-5.22478446e-02 -1.81856025e-02 -1.43015936e-01 1.70708627... | [13.453548431396484, 0.7058273553848267] |
4c32a54f-c373-47d2-884c-cb953a90b463 | dialogue-act-classification-in-domain | null | null | https://aclanthology.org/C16-1189 | https://aclanthology.org/C16-1189.pdf | Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network | In this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number of layers are the parameters that have the largest effect on the final system accuracy. Using the findings of these ex... | ['Nishitha la', 'Rodney Nielsen', 'Hamed Khanpour', 'Guntak'] | 2016-12-01 | dialogue-act-classification-in-domain-1 | https://aclanthology.org/C16-1189 | https://aclanthology.org/C16-1189.pdf | coling-2016-12 | ['dialogue-act-classification', 'dialogue-interpretation'] | ['natural-language-processing', 'natural-language-processing'] | [-2.43741080e-01 4.23996329e-01 -7.39070699e-02 -4.93069917e-01
-3.54726851e-01 -4.20106560e-01 5.71254432e-01 -1.24506213e-01
-8.05189252e-01 1.03338909e+00 5.23451626e-01 -6.71416461e-01
3.01826090e-01 -5.86478233e-01 -8.83495286e-02 -4.02681023e-01
-1.29736960e-01 5.66576719e-01 5.01693822e-02 -5.50237656... | [12.848832130432129, 7.848941326141357] |
7500af4c-68e6-499b-b527-b5d3160d2397 | es-net-an-efficient-stereo-matching-network | 2103.03922 | null | https://arxiv.org/abs/2103.03922v1 | https://arxiv.org/pdf/2103.03922v1.pdf | ES-Net: An Efficient Stereo Matching Network | Dense stereo matching with deep neural networks is of great interest to the research community. Existing stereo matching networks typically use slow and computationally expensive 3D convolutions to improve the performance, which is not friendly to real-world applications such as autonomous driving. In this paper, we pr... | ['Panqu Wang', 'Theodore B. Norris', 'Zhengyu Huang'] | 2021-03-05 | null | null | null | null | ['stereo-depth-estimation', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 5.47173843e-02 -1.57761395e-01 3.39978002e-02 -6.00445271e-01
-1.85221851e-01 -1.87747538e-01 4.98972088e-01 -3.26180965e-01
-6.84159994e-01 6.33166373e-01 1.86221376e-01 -2.87146121e-01
1.84088200e-01 -1.16493154e+00 -7.68185973e-01 -3.91857445e-01
1.85978517e-01 3.56390506e-01 7.80975997e-01 -3.25468719... | [8.79751205444336, -2.268230438232422] |
78e3007d-eb19-4967-96fc-92053821d56b | densely-connected-graph-convolutional-1 | 1908.05957 | null | https://arxiv.org/abs/1908.05957v2 | https://arxiv.org/pdf/1908.05957v2.pdf | Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning | We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallo... | ['Zhijiang Guo', 'Zhiyang Teng', 'Yan Zhang', 'Wei Lu'] | 2019-08-16 | densely-connected-graph-convolutional | https://aclanthology.org/Q19-1019 | https://aclanthology.org/Q19-1019.pdf | tacl-2019-3 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 6.92028105e-01 7.21922636e-01 -3.00482064e-01 -2.82077163e-01
-5.16012251e-01 -5.74703217e-01 9.31475878e-01 1.97433278e-01
1.01733811e-01 8.22581768e-01 4.95691031e-01 -7.85689592e-01
3.80780697e-01 -1.44563520e+00 -1.15336680e+00 -3.79605323e-01
4.07803208e-02 6.12124741e-01 -1.33735046e-01 -6.60423219... | [10.25270938873291, 8.322868347167969] |
99701d07-0ca5-47fa-b1cf-0e06d7636c9d | local-to-global-learning-for-iterative | null | null | https://aclanthology.org/2022.naacl-industry.13 | https://aclanthology.org/2022.naacl-industry.13.pdf | Local-to-global learning for iterative training of production SLU models on new features | In production SLU systems, new training data becomes available with time so that ML models need to be updated on a regular basis. Specifically, releasing new features adds new classes of data while the old data remains constant. However, retraining the full model each time from scratch is computationally expensive. To ... | ['Daniil Sorokin', 'Yulia Grishina'] | null | null | null | null | naacl-acl-2022-7 | ['intent-classification', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.86380363e-01 4.00245279e-01 -5.75869739e-01 -4.63602155e-01
-1.06044722e+00 -6.45625889e-01 1.11057244e-01 2.27218434e-01
-3.99966419e-01 9.49778676e-01 1.33159935e-01 -5.84828556e-01
2.61150718e-01 -5.18822491e-01 -5.67979097e-01 -2.22621724e-01
1.69683918e-01 6.77673280e-01 3.66745859e-01 -4.75853533... | [10.848596572875977, 8.42949390411377] |
103aa8d1-5ed3-4796-8314-93a8a4440991 | simulation-based-counterfactual-causal | 2306.03354 | null | https://arxiv.org/abs/2306.03354v1 | https://arxiv.org/pdf/2306.03354v1.pdf | Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour | Being able to reason about how one's behaviour can affect the behaviour of others is a core skill required of intelligent driving agents. Despite this, the state of the art struggles to meet the need of agents to discover causal links between themselves and others. Observational approaches struggle because of the non-s... | ['Lars Kunze', 'Rhys Howard'] | 2023-06-06 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.06838208e-01 2.33173132e-01 -7.22072899e-01 -2.91902602e-01
-3.07863593e-01 -3.61638397e-01 1.30521929e+00 8.06870088e-02
-4.76061851e-01 1.18855047e+00 6.12379789e-01 -8.06385994e-01
-6.33244634e-01 -7.85693944e-01 -1.02560329e+00 -6.40129268e-01
-4.14319128e-01 3.82362038e-01 3.02350938e-01 -2.35813349... | [7.843807220458984, 5.303234100341797] |
d005d65f-f029-40b0-b848-92957582087f | the-cropandweed-dataset-a-multi-modal | null | null | https://openaccess.thecvf.com/content/WACV2023/html/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.html | https://openaccess.thecvf.com/content/WACV2023/papers/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.pdf | The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation | Precision Agriculture and especially the application of automated weed intervention represents an increasingly essential research area, as sustainability and efficiency considerations are becoming more and more relevant. While the potentials of Convolutional Neural Networks for detection, classification and segmentatio... | ['Verena Widhalm', 'Julia Simon', 'Gerardus Croonen', 'Andreas Trondl', 'Daniel Steininger'] | 2023-01-06 | null | null | null | winter-conference-on-applications-of-computer-2 | ['plant-phenotyping', 'fine-grained-image-classification', 'crop-classification'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 6.41694546e-01 -1.64757669e-01 -1.89875469e-01 -4.08107996e-01
-3.80421430e-01 -1.00150979e+00 3.10132623e-01 8.32566559e-01
-3.12119603e-01 5.78610182e-01 -3.63751262e-01 -6.19866848e-01
-4.51178700e-01 -9.11238372e-01 -6.59950972e-01 -7.29940832e-01
-4.76722330e-01 3.28894675e-01 2.73902714e-01 -4.13482994... | [9.126935958862305, -1.5576788187026978] |
48360c01-716d-4afb-b46b-9b56bf00e85b | equitability-analysis-of-the-maximal | 1301.6314 | null | http://arxiv.org/abs/1301.6314v2 | http://arxiv.org/pdf/1301.6314v2.pdf | Equitability Analysis of the Maximal Information Coefficient, with Comparisons | A measure of dependence is said to be equitable if it gives similar scores to
equally noisy relationships of different types. Equitability is important in
data exploration when the goal is to identify a relatively small set of
strongest associations within a dataset as opposed to finding as many non-zero
associations a... | ['Michael Mitzenmacher', 'Yakir Reshef', 'David Reshef', 'Pardis Sabeti'] | 2013-01-27 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 1.17260844e-01 -7.89518207e-02 -1.71254486e-01 -4.56440985e-01
-6.01919830e-01 -5.48855543e-01 4.13289398e-01 6.50545597e-01
-5.82244456e-01 1.05315042e+00 3.40870917e-01 -3.68550122e-01
-1.02969217e+00 -7.26243556e-01 -2.89366096e-01 -6.95279896e-01
-8.39292943e-01 6.73210263e-01 -4.74973992e-02 1.11034095... | [7.628860950469971, 4.672356128692627] |
9cf2db4f-8ea2-43b9-bd19-0a8ff83f490a | dynamic-semantic-graph-construction-and | 2105.11776 | null | https://arxiv.org/abs/2105.11776v1 | https://arxiv.org/pdf/2105.11776v1.pdf | Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering | Knowledge retrieval and reasoning are two key stages in multi-hop question answering (QA) at web scale. Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process. In this paper, we propose a new framework to exploit more valid facts wh... | ['Wai Lam', 'Deng Cai', 'Huihui Zhang', 'Weiwen Xu'] | 2021-05-25 | null | https://aclanthology.org/2021.findings-acl.90 | https://aclanthology.org/2021.findings-acl.90.pdf | findings-acl-2021-8 | ['multi-hop-question-answering', 'science-question-answering'] | ['knowledge-base', 'miscellaneous'] | [-0.14973214 0.75143665 -0.30149794 -0.27714568 -1.1452833 -0.771951
0.40973285 0.6298919 0.06550033 0.9194366 0.48534396 -0.8120061
-0.89833635 -1.4470533 -1.1659086 0.10794199 -0.10998552 0.7947577
0.731428 -0.6952317 0.21023792 0.17912154 -1.3150518 0.67096126
1.3165418 1.0633367 -0.0919... | [10.468173027038574, 7.892541885375977] |
b888ee7e-9d75-4291-86a6-575e141809aa | animal-kingdom-a-large-and-diverse-dataset | 2204.08129 | null | https://arxiv.org/abs/2204.08129v2 | https://arxiv.org/pdf/2204.08129v2.pdf | Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding | Understanding animals' behaviors is significant for a wide range of applications. However, existing animal behavior datasets have limitations in multiple aspects, including limited numbers of animal classes, data samples and provided tasks, and also limited variations in environmental conditions and viewpoints. To addr... | ['Jun Liu', 'Si Yong Yeo', 'Yun Ni', 'Qichen Zheng', 'Kian Eng Ong', 'Xun Long Ng'] | 2022-04-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ng_Animal_Kingdom_A_Large_and_Diverse_Dataset_for_Animal_Behavior_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ng_Animal_Kingdom_A_Large_and_Diverse_Dataset_for_Animal_Behavior_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-grounding', 'animal-pose-estimation', 'animal-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.47762254e-01 -7.84712791e-01 -9.85144749e-02 -5.97245097e-01
-3.06724072e-01 -6.73681140e-01 2.04718083e-01 -2.06372932e-01
-4.60884243e-01 7.10671186e-01 7.04780892e-02 5.07675588e-01
2.70396173e-02 -4.52598363e-01 -8.13915014e-01 -8.39817524e-01
-4.90409255e-01 1.40505910e-01 5.16536176e-01 -1.62398573... | [7.72604513168335, -0.7765602469444275] |
82f1c5d2-5938-4662-96ea-e11305440ce8 | can-foundation-models-wrangle-your-data | 2205.09911 | null | https://arxiv.org/abs/2205.09911v2 | https://arxiv.org/pdf/2205.09911v2.pdf | Can Foundation Models Wrangle Your Data? | Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models continue to grow in size, innovations continue to push the boundaries of what these models can do on language and image tasks. This paper aims ... | ['Christopher Ré', 'Simran Arora', 'Laurel Orr', 'Ines Chami', 'Avanika Narayan'] | 2022-05-20 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-1.78388894e-01 1.45596504e-01 -1.22995839e-01 -7.77692795e-01
-8.82879674e-01 -5.61268926e-01 6.07798040e-01 1.18074335e-01
-3.89511466e-01 5.53363860e-01 2.15018049e-01 -4.61608320e-01
7.69567639e-02 -4.39142287e-01 -9.30851758e-01 -3.18204373e-01
-1.01946080e-02 5.39505601e-01 5.09081874e-03 -1.99912921... | [9.513955116271973, 8.45335578918457] |
bbd996ec-3426-46ea-a608-893729f7cdee | zo-grof-a-comprehensive-corpus-for-offensive | null | null | https://aclanthology.org/2022.woah-1.5 | https://aclanthology.org/2022.woah-1.5.pdf | “Zo Grof !”: A Comprehensive Corpus for Offensive and Abusive Language in Dutch | This paper presents a comprehensive corpus for the study of socially unacceptable language in Dutch. The corpus extends and revise an existing resource with more data and introduces a new annotation dimension for offensive language, making it a unique resource in the Dutch language panorama. Each language phenomenon (a... | ['Tommaso Caselli', 'Zhenja Gnezdilov', 'Robin Van Der Noord', 'Victor Zwart', 'Ward Ruitenbeek'] | null | null | null | null | naacl-woah-2022-7 | ['abusive-language'] | ['natural-language-processing'] | [ 9.03251581e-03 2.71185040e-01 -4.12688911e-01 -2.99205035e-01
-7.33552873e-01 -7.80891180e-01 1.04527009e+00 2.99093425e-01
-7.76516616e-01 7.82154143e-01 4.34515566e-01 -3.77752364e-01
-1.24715343e-01 -2.87407368e-01 -3.89800631e-02 -5.63808799e-01
1.60471722e-01 7.37648606e-01 -5.56583293e-02 -6.55091345... | [8.800089836120605, 10.54399299621582] |
80a751aa-58a2-4c91-9b21-feb5e8dd824a | low-latency-time-domain-multichannel-speech | 2204.05609 | null | https://arxiv.org/abs/2204.05609v1 | https://arxiv.org/pdf/2204.05609v1.pdf | Low Latency Time Domain Multichannel Speech and Music Source Separation | The Goal is to obtain a simple multichannel source separation with very low latency. Applications can be teleconferencing, hearing aids, augmented reality, or selective active noise cancellation. These real time applications need a very low latency, usually less than about 6 ms, and low complexity, because they usually... | ['Gerald Schuller'] | 2022-04-12 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.08859020e-01 -3.91054749e-01 9.32835117e-02 9.96342674e-02
-1.24576461e+00 -6.67456448e-01 3.11945945e-01 -1.96478054e-01
-4.39393461e-01 6.93685710e-01 1.97681621e-01 -1.28482744e-01
-5.98382771e-01 -3.27585280e-01 -1.20785095e-01 -9.08775449e-01
-6.76386505e-02 3.12479615e-01 3.49890053e-01 -1.17436079... | [15.172436714172363, 5.653975486755371] |
3d9b335c-2c52-4627-b0e5-7910dad5a22d | from-hero-to-z-eroe-a-benchmark-of-low-level | null | null | https://aclanthology.org/2020.aacl-main.79 | https://aclanthology.org/2020.aacl-main.79.pdf | From Hero to Z\'eroe: A Benchmark of Low-Level Adversarial Attacks | Adversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. Natural Language Processing (NLP) has mostly focused on high-level attack scenarios such as paraphrasing input texts. We argue that these are less realistic in typical application s... | ['Yannik Benz', 'Steffen Eger'] | 2020-12-01 | null | null | null | asian-chapter-of-the-association-for | ['toxic-comment-classification'] | ['natural-language-processing'] | [ 5.80366910e-01 2.97756612e-01 1.58320606e-01 -4.51247357e-02
-8.83823752e-01 -1.41517174e+00 1.09631288e+00 8.85705575e-02
-4.05385524e-01 5.75428367e-01 -8.28087423e-03 -7.98637986e-01
1.67079210e-01 -6.86050951e-01 -8.77117038e-01 -3.35508466e-01
1.38828650e-01 4.20116931e-01 2.69198567e-01 -2.69545585... | [6.009475231170654, 8.071562767028809] |
3889cc04-8e03-40ff-936a-6a84c015f583 | hierarchical-neural-architecture-search-for-1 | 2010.13501 | null | https://arxiv.org/abs/2010.13501v1 | https://arxiv.org/pdf/2010.13501v1.pdf | Hierarchical Neural Architecture Search for Deep Stereo Matching | To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation. The underlying idea for the NAS algorithm is straightforward, namely, to enable the network the ability to ... | ['ZongYuan Ge', 'Hongdong Li', 'Tom Drummond', 'Xiaojun Chang', 'Yuchao Dai', 'Mehrtash Harandi', 'Yiran Zhong', 'Xuelian Cheng'] | 2020-10-26 | null | http://proceedings.neurips.cc/paper/2020/hash/fc146be0b230d7e0a92e66a6114b840d-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/fc146be0b230d7e0a92e66a6114b840d-Paper.pdf | neurips-2020-12 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 1.09145045e-01 -1.77668810e-01 1.30815744e-01 -4.03661340e-01
-4.96011138e-01 -4.34140563e-01 4.79388386e-01 -1.75537959e-01
-6.76483333e-01 1.50388315e-01 1.91887692e-02 -2.80679584e-01
2.96072569e-02 -7.91201174e-01 -8.38331878e-01 -4.10908073e-01
3.00436616e-01 5.85410774e-01 4.34579074e-01 -2.09154323... | [8.842385292053223, -2.246411085128784] |
0adea788-b850-4c52-8c8c-8318928e4dae | semantic-information-extraction-for-improved | null | null | https://aclanthology.org/W15-1523 | https://aclanthology.org/W15-1523.pdf | Semantic Information Extraction for Improved Word Embeddings | null | ['Gerard de Melo', 'Jiaqiang Chen'] | 2015-06-01 | null | null | null | ws-2015-6 | ['learning-word-embeddings'] | ['methodology'] | [-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... | [-7.293671607971191, 3.6729776859283447] |
a3d0a85b-7898-4fc1-9e0e-7206974312d8 | anatomy-guided-domain-adaptation-for-3d-in | 2211.12193 | null | https://arxiv.org/abs/2211.12193v2 | https://arxiv.org/pdf/2211.12193v2.pdf | Anatomy-guided domain adaptation for 3D in-bed human pose estimation | 3D human pose estimation is a key component of clinical monitoring systems. The clinical applicability of deep pose estimation models, however, is limited by their poor generalization under domain shifts along with their need for sufficient labeled training data. As a remedy, we present a novel domain adaptation method... | ['Mattias P. Heinrich', 'Philipp Rostalski', 'Carlotta Hennigs', 'Jasper Diesel', 'Lasse Hansen', 'Alexander Bigalke'] | 2022-11-22 | null | null | null | null | ['3d-human-pose-estimation', 'source-free-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 2.44782075e-01 6.08184695e-01 -3.64191681e-01 -3.92958999e-01
-1.21054792e+00 -6.60765350e-01 1.94626868e-01 2.37347841e-01
-4.16975558e-01 8.65357101e-01 3.96674037e-01 -1.61525384e-01
-2.51292378e-01 -4.32331204e-01 -7.59362996e-01 -6.65862978e-01
1.12839766e-01 9.55320895e-01 2.50979155e-01 -1.14327036... | [14.559030532836914, -2.0742273330688477] |
8e281cb5-800c-41fb-94e7-0e2a078db6f9 | symbolic-numeric-computation-of-integrals-in | 2303.10046 | null | https://arxiv.org/abs/2303.10046v1 | https://arxiv.org/pdf/2303.10046v1.pdf | Symbolic-Numeric Computation of Integrals in Successive Galerkin Approximation of Hamilton-Jacobi-Bellman Equation | This paper proposes an efficient symbolic-numeric method to compute the integrals in the successive Galerkin approximation (SGA) of the Hamilton-Jacobi-Bellman (HJB) equation. A solution of the HJB equation is first approximated with a linear combination of the Hermite polynomials. The coefficients of the combination a... | ['Tomoyuki Iori'] | 2023-03-17 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-4.11469907e-01 1.86131243e-02 3.26519579e-01 2.48064220e-01
-4.23024058e-01 -4.14175540e-01 1.87897265e-01 -1.09670654e-01
-2.29217529e-01 1.13971090e+00 -5.82306087e-01 -3.69801044e-01
-1.02589048e-01 -7.89720833e-01 -3.39578986e-01 -9.98986244e-01
-2.22280994e-01 4.18209553e-01 8.38274881e-02 -4.44412142... | [6.219139099121094, 3.495626449584961] |
cd3ee58b-de74-4821-b053-3be82e610f79 | a-multi-domain-virtual-network-embedding | 2202.01473 | null | https://arxiv.org/abs/2202.01473v1 | https://arxiv.org/pdf/2202.01473v1.pdf | A multi-domain virtual network embedding algorithm with delay prediction | Virtual network embedding (VNE) is an crucial part of network virtualization (NV), which aims to map the virtual networks (VNs) to a shared substrate network (SN). With the emergence of various delay-sensitive applications, how to improve the delay performance of the system has become a hot topic in academic circles. B... | ['Xin Li', 'Haipeng Yao', 'Yongjing Ni', 'Xue Pang', 'Peiying Zhang'] | 2022-02-03 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-3.12825561e-01 -3.42607409e-01 -2.51986474e-01 2.79853880e-01
7.21925437e-01 -5.09223878e-01 3.14035147e-01 -1.99952245e-01
-2.81860624e-02 8.95076871e-01 -2.10564688e-01 -4.88406032e-01
-4.40080315e-01 -9.56722736e-01 3.11419010e-01 -7.56528974e-01
-1.32890046e-01 4.52895850e-01 6.61598921e-01 -2.65370995... | [5.880164623260498, 1.7115333080291748] |
672e3ee8-c8f7-4e1c-a3a1-82cec2ef474a | action-recognition-by-hierarchical-mid-level | 1508.07654 | null | http://arxiv.org/abs/1508.07654v1 | http://arxiv.org/pdf/1508.07654v1.pdf | Action Recognition by Hierarchical Mid-level Action Elements | Realistic videos of human actions exhibit rich spatiotemporal structures at
multiple levels of granularity: an action can always be decomposed into
multiple finer-grained elements in both space and time. To capture this
intuition, we propose to represent videos by a hierarchy of mid-level action
elements (MAEs), where ... | ['Silvio Savarese', 'Yuke Zhu', 'Tian Lan', 'Amir Roshan Zamir'] | 2015-08-31 | action-recognition-by-hierarchical-mid-level-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Lan_Action_Recognition_by_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Lan_Action_Recognition_by_ICCV_2015_paper.pdf | iccv-2015-12 | ['action-parsing'] | ['natural-language-processing'] | [ 6.09785378e-01 -9.89751220e-02 -6.65273726e-01 -3.75249058e-01
-8.18984270e-01 -5.85014701e-01 6.31894112e-01 -6.77886531e-02
1.81375798e-02 3.54562372e-01 7.29817271e-01 1.21370725e-01
5.42581864e-02 -5.34905493e-01 -8.39162648e-01 -6.17950976e-01
-3.97472292e-01 8.77073184e-02 7.11409628e-01 3.29707950... | [8.379782676696777, 0.6105308532714844] |
37df107c-c6ff-40fd-9be0-532ce3680ff7 | orchard-a-benchmark-for-measuring-systematic | 2111.14034 | null | https://arxiv.org/abs/2111.14034v1 | https://arxiv.org/pdf/2111.14034v1.pdf | ORCHARD: A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical Reasoning | The ability to reason with multiple hierarchical structures is an attractive and desirable property of sequential inductive biases for natural language processing. Do the state-of-the-art Transformers and LSTM architectures implicitly encode for these biases? To answer this, we propose ORCHARD, a diagnostic dataset for... | ['Alvin Chan', 'Bill Tuck Weng Pung'] | 2021-11-28 | null | null | null | null | ['relational-reasoning', 'systematic-generalization'] | ['natural-language-processing', 'reasoning'] | [ 4.55828846e-01 4.73663360e-01 -1.59147874e-01 -3.19138229e-01
-3.49316120e-01 -6.90999269e-01 8.44002068e-01 4.34373975e-01
-3.08451682e-01 7.72429764e-01 5.93080521e-01 -8.69813263e-01
-2.64551908e-01 -1.20825529e+00 -8.74873459e-01 -5.34005225e-01
-2.55246639e-01 7.54691064e-01 3.87299448e-01 -5.00539839... | [9.584216117858887, 7.301270961761475] |
49350c13-5340-4970-aa92-7fe78cd7dcb3 | quantum-mechanics-and-machine-learning | 2103.14536 | null | https://arxiv.org/abs/2103.14536v1 | https://arxiv.org/pdf/2103.14536v1.pdf | Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity | There is a lack of scalable quantitative measures of reactivity for functional groups in organic chemistry. Measuring reactivity experimentally is costly and time-consuming and does not scale to the astronomical size of chemical space. In previous quantum chemistry studies, we have introduced Methyl Cation Affinities (... | ['Pierre Baldi', 'David Van Vranken', 'Aaron Mood', 'Mohammadamin Tavakoli'] | 2021-03-24 | null | null | null | null | ['chemical-reaction-prediction'] | ['medical'] | [ 3.49357754e-01 -8.72464776e-02 -2.03319073e-01 -2.67631054e-01
-9.66878295e-01 -9.27319407e-01 6.33924484e-01 7.65344083e-01
-4.00268286e-01 1.34564209e+00 1.79823115e-01 -7.83980072e-01
-3.49139236e-02 -1.00929260e+00 -8.37835968e-01 -8.58523548e-01
-2.00288042e-01 3.95016402e-01 2.08636560e-02 -1.80561468... | [5.0212225914001465, 5.62363862991333] |
df8ca513-1b49-4aa0-b9d6-226a884a3254 | learning-end-to-end-goal-oriented-dialog-with | 1808.09996 | null | http://arxiv.org/abs/1808.09996v1 | http://arxiv.org/pdf/1808.09996v1.pdf | Learning End-to-End Goal-Oriented Dialog with Multiple Answers | In a dialog, there can be multiple valid next utterances at any point. The
present end-to-end neural methods for dialog do not take this into account.
They learn with the assumption that at any time there is only one correct next
utterance. In this work, we focus on this problem in the goal-oriented dialog
setting wher... | ['Satinder Singh', 'Lazaros Polymenakos', 'Jatin Ganhotra', 'Janarthanan Rajendran'] | 2018-08-24 | learning-end-to-end-goal-oriented-dialog-with-1 | https://aclanthology.org/D18-1418 | https://aclanthology.org/D18-1418.pdf | emnlp-2018-10 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-2.24738732e-01 4.78985339e-01 3.08430761e-01 -9.92627323e-01
-1.04723454e+00 -8.71161878e-01 5.92231154e-01 -2.04348341e-01
-6.35966420e-01 1.12029433e+00 3.87831300e-01 -5.22897720e-01
1.56294972e-01 -4.87668157e-01 -4.01030958e-01 -3.70444834e-01
1.07795991e-01 1.17933321e+00 3.83286387e-01 -9.43492711... | [12.847248077392578, 8.012442588806152] |
9296f9fb-8064-4fbc-9352-ee29ce14955b | predicting-beauty-liking-and-aesthetic | 2307.00984 | null | https://arxiv.org/abs/2307.00984v1 | https://arxiv.org/pdf/2307.00984v1.pdf | Predicting beauty, liking, and aesthetic quality: A comparative analysis of image databases for visual aesthetics research | In the fields of Experimental and Computational Aesthetics, numerous image datasets have been created over the last two decades. In the present work, we provide a comparative overview of twelve image datasets that include aesthetic ratings (beauty, liking or aesthetic quality) and investigate the reproducibility of res... | ['Christoph Redies', 'Katja Thoemmes', 'Ralf Bartho'] | 2023-07-03 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 1.89683035e-01 -1.16198629e-01 1.67813063e-01 -5.50338209e-01
-2.81357437e-01 -5.93121648e-01 5.63237786e-01 3.41872305e-01
-3.77253652e-01 1.51585981e-01 3.67930114e-01 2.11445871e-03
-4.46291119e-01 -6.51592791e-01 -3.37149531e-01 -3.82084161e-01
1.25843957e-01 -1.28505245e-01 -3.57141137e-01 -1.82318479... | [11.540876388549805, -0.8914440870285034] |
5f7a5e8f-4535-4893-9ffe-9903a4ac350d | it-s-all-in-the-teacher-zero-shot | 2203.17008 | null | https://arxiv.org/abs/2203.17008v2 | https://arxiv.org/pdf/2203.17008v2.pdf | It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher | Model quantization is considered as a promising method to greatly reduce the resource requirements of deep neural networks. To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quantized networks. In real-world environments, however, such a method is fr... | ['Jinho Lee', 'Youngsok Kim', 'Noseong Park', 'Joonsang Yu', 'Deokki Hong', 'Hye Yoon Lee', 'Kanghyun Choi'] | 2022-03-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Choi_Its_All_in_the_Teacher_Zero-Shot_Quantization_Brought_Closer_to_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Choi_Its_All_in_the_Teacher_Zero-Shot_Quantization_Brought_Closer_to_CVPR_2022_paper.pdf | cvpr-2022-1 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 1.81123897e-01 -4.81030382e-02 -2.44762987e-01 -2.62921125e-01
-7.40380645e-01 -1.90866098e-01 3.38006496e-01 2.75334269e-01
-9.04340208e-01 1.01256216e+00 -2.97825187e-01 -1.98715404e-01
-1.83495179e-01 -8.72682393e-01 -8.13636363e-01 -9.21033919e-01
1.53862759e-01 1.71085432e-01 2.90805548e-01 -1.52670041... | [8.744338989257812, 3.031583309173584] |
891bc88f-a636-4cea-9734-1cce8eef092a | optimizing-fairness-tradeoffs-in-machine | 2304.12190 | null | https://arxiv.org/abs/2304.12190v1 | https://arxiv.org/pdf/2304.12190v1.pdf | Optimizing fairness tradeoffs in machine learning with multiobjective meta-models | Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learning methods transform the error-fairness trade-off into a single objective problem with a parameter controlling the relative importance of e... | ['William G. La Cava'] | 2023-04-21 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [ 2.91737735e-01 1.35706589e-01 -7.25355387e-01 -9.42060888e-01
-9.35801864e-01 -2.43670315e-01 4.04433221e-01 4.92414683e-01
-1.00405073e+00 1.03568721e+00 1.80822164e-01 -2.98911870e-01
-5.88161767e-01 -6.69726729e-01 -2.10014671e-01 -4.78277504e-01
3.61610919e-01 6.30787611e-01 -4.09248531e-01 2.23086867... | [8.899652481079102, 5.275594234466553] |
c87b11cd-f028-4b9c-a911-17e0483d2fca | extracting-complex-named-entities-in-legal | 2305.05836 | null | https://arxiv.org/abs/2305.05836v1 | https://arxiv.org/pdf/2305.05836v1.pdf | Extracting Complex Named Entities in Legal Documents via Weakly Supervised Object Detection | Accurate Named Entity Recognition (NER) is crucial for various information retrieval tasks in industry. However, despite significant progress in traditional NER methods, the extraction of Complex Named Entities remains a relatively unexplored area. In this paper, we propose a novel system that combines object detection... | ['Abhinav Agrawal', 'Hsiu-Wei Yang'] | 2023-05-10 | null | null | null | null | ['document-layout-analysis', 'weakly-supervised-object-detection', 'named-entity-recognition-ner'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 8.33696947e-02 2.69711494e-01 -2.80923933e-01 -3.33251208e-01
-1.17329037e+00 -9.05581832e-01 5.77612758e-01 2.63859659e-01
-6.71667337e-01 8.12443793e-01 1.45352513e-01 -6.59560978e-01
-6.46045282e-02 -4.89157289e-01 -6.60965741e-01 -1.56187654e-01
1.88781973e-02 1.49374485e-01 3.84842306e-01 7.66216069... | [9.637215614318848, 9.437087059020996] |
bb8a751b-5e9a-4f96-8ce2-53d9f4b21d17 | generalizing-gaze-estimation-with-rotation | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bao_Generalizing_Gaze_Estimation_With_Rotation_Consistency_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bao_Generalizing_Gaze_Estimation_With_Rotation_Consistency_CVPR_2022_paper.pdf | Generalizing Gaze Estimation With Rotation Consistency | Recent advances of deep learning-based approaches have achieved remarkable performance on appearance-based gaze estimation. However, due to the shortage of target domain data and absence of target labels, generalizing gaze estimation algorithm to unseen environments is still challenging. In this paper, we discover ... | ['Feng Lu', 'Haofei Wang', 'Yunfei Liu', 'Yiwei Bao'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['gaze-estimation'] | ['computer-vision'] | [ 2.88085341e-01 -9.74232629e-02 -3.55392247e-01 -6.10039473e-01
-3.99148107e-01 -4.46608126e-01 3.69560927e-01 -6.32516265e-01
-4.11919981e-01 7.01007783e-01 -9.56231728e-02 -4.74063568e-02
1.34395016e-02 1.82261601e-01 -6.49533331e-01 -9.46435332e-01
3.43918115e-01 5.41954860e-02 1.49033010e-01 -4.11020517... | [14.113128662109375, 0.025898484513163567] |
ac2f23e9-d8f9-4283-9a3e-b434544a853c | skip-connections-in-spiking-neural-networks | 2303.13563 | null | https://arxiv.org/abs/2303.13563v1 | https://arxiv.org/pdf/2303.13563v1.pdf | Skip Connections in Spiking Neural Networks: An Analysis of Their Effect on Network Training | Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and their ability to model spiking behavior in biological systems. However, the training of SNNs is still a challenging problem, and new technique... | ['Younes Bouhadjar', 'Imane Hamzaoui', 'Amine Ziad Ounnoughene', 'Hadjer Benmeziane'] | 2023-03-23 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 2.54979014e-01 -4.53353882e-01 4.37298603e-03 -1.57469139e-01
1.30142793e-01 -4.44941580e-01 3.87881130e-01 -9.73330587e-02
-9.93887663e-01 9.06988204e-01 -1.83209136e-01 -9.17776525e-02
1.49550125e-01 -5.77076614e-01 -8.32393110e-01 -1.00725496e+00
4.21893373e-02 1.95901424e-01 6.78883731e-01 -1.00232750... | [8.212352752685547, 2.5348784923553467] |
4145d16f-263f-48e2-8fed-0e7375c038f2 | planning-landmark-based-goal-recognition | 2306.15362 | null | https://arxiv.org/abs/2306.15362v1 | https://arxiv.org/pdf/2306.15362v1.pdf | Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense? | Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the... | ['Heiner Stuckenschmidt', 'Christian Bartelt', 'Lea Cohausz', 'Nils Wilken'] | 2023-06-27 | null | null | null | null | ['intrusion-detection'] | ['miscellaneous'] | [ 2.48308927e-01 2.36047998e-01 -5.48410267e-02 -1.68554246e-01
-4.25008118e-01 -4.41619068e-01 7.17711091e-01 5.16047299e-01
-4.02450114e-01 7.80716836e-01 1.09755574e-02 -2.95459330e-01
-3.29048306e-01 -1.11220884e+00 -2.08280504e-01 -3.88550311e-01
-3.22103888e-01 5.46162188e-01 5.50733626e-01 -4.09768045... | [3.7677464485168457, 1.316941261291504] |
e45635ce-56b5-4ef3-baa0-2b5f9d92182a | integrating-multiple-sources-knowledge-for | 2305.09893 | null | https://arxiv.org/abs/2305.09893v1 | https://arxiv.org/pdf/2305.09893v1.pdf | Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Images | In the existing unsupervised domain adaptation (UDA) methods for remote sensing images (RSIs) semantic segmentation, class symmetry is an widely followed ideal assumption, where the source and target RSIs have exactly the same class space. In practice, however, it is often very difficult to find a source RSI with exact... | ['Ningbo Huang', 'Ke Li', 'Wenyue Guo', 'Xiong You', 'Anzhu Yu', 'Kuiliang Gao'] | 2023-05-17 | null | null | null | null | ['unsupervised-domain-adaptation', 'pseudo-label'] | ['methodology', 'miscellaneous'] | [ 5.88640034e-01 2.41689733e-03 -3.56999785e-01 -4.53774333e-01
-6.52317762e-01 -4.34904367e-01 4.18254375e-01 1.43014103e-01
-8.20964202e-03 7.37417579e-01 -1.11001402e-01 2.85760243e-03
-5.44383347e-01 -1.13715208e+00 -3.04956347e-01 -8.24154437e-01
3.46690655e-01 6.50021374e-01 4.21256453e-01 -1.17735416... | [9.800823211669922, 1.5834035873413086] |
2a2e016c-d48d-4d0c-89c9-5c73721f9bb1 | reducing-the-label-bias-for-timestamp | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Reducing_the_Label_Bias_for_Timestamp_Supervised_Temporal_Action_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Reducing_the_Label_Bias_for_Timestamp_Supervised_Temporal_Action_Segmentation_CVPR_2023_paper.pdf | Reducing the Label Bias for Timestamp Supervised Temporal Action Segmentation | Timestamp supervised temporal action segmentation (TSTAS) is more cost-effective than fully supervised counterparts. However, previous approaches suffer from severe label bias due to over-reliance on sparse timestamp annotations, resulting in unsatisfactory performance. In this paper, we propose the Debiasing-TSTAS... | ['Zihang Shao', 'Chenwei Tan', 'Shenglan Liu', 'Yunheng Li', 'Kaiyuan Liu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['action-segmentation'] | ['computer-vision'] | [ 6.15774691e-01 1.57584354e-01 -7.08890259e-01 -5.55006027e-01
-1.08904791e+00 -2.49208376e-01 4.76170301e-01 5.92122190e-02
-5.22807062e-01 6.85395718e-01 3.33434016e-01 7.30093867e-02
1.80316418e-01 -3.38405281e-01 -5.35632253e-01 -6.02112949e-01
-2.89862286e-02 3.51298839e-01 8.20065975e-01 3.91150922... | [8.477771759033203, 0.5956428647041321] |
a52d0eae-c31f-472b-8496-dedf0e765ca6 | an-open-world-lottery-ticket-for-out-of | 2210.07071 | null | https://arxiv.org/abs/2210.07071v1 | https://arxiv.org/pdf/2210.07071v1.pdf | An Open-World Lottery Ticket for Out-of-Domain Intent Classification | Most existing methods of Out-of-Domain (OOD) intent classification, which rely on extensive auxiliary OOD corpora or specific training paradigms, are underdeveloped in the underlying principle that the models should have differentiated confidence in In- and Out-of-domain intent. In this work, we demonstrate that calibr... | ['Xipeng Qiu', 'Yuxin Wang', 'Peiju Liu', 'Yunhua Zhou'] | 2022-10-13 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 6.57855161e-03 3.85112077e-01 -1.00609159e+00 -6.01597428e-01
-8.08110535e-01 -6.22214496e-01 7.59221494e-01 -1.25157788e-01
-1.68805987e-01 7.14779973e-01 4.50611621e-01 -5.04856169e-01
-8.93073231e-02 -4.80185568e-01 -4.73338097e-01 -1.43677086e-01
-1.44051969e-01 7.65278399e-01 1.87376663e-01 -2.71791160... | [11.584813117980957, 7.651508331298828] |
b5d6dda2-c122-483b-aed2-9328301a1621 | synthetic-data-for-english-lexical | null | null | https://aclanthology.org/2020.lrec-1.773 | https://aclanthology.org/2020.lrec-1.773.pdf | Synthetic Data for English Lexical Normalization: How Close Can We Get to Manually Annotated Data? | Social media is a valuable data resource for various natural language processing (NLP) tasks. However, standard NLP tools were often designed with standard texts in mind, and their performance decreases heavily when applied to social media data. One solution to this problem is to adapt the input text to a more standard... | ['Rob van der Goot', 'Kelly Dekker'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['lexical-normalization'] | ['natural-language-processing'] | [ 2.61836082e-01 1.63512096e-01 1.24910660e-01 -4.08090353e-01
-5.33506632e-01 -4.85287786e-01 7.27324903e-01 6.74906015e-01
-1.06336534e+00 8.26705337e-01 1.66383535e-01 -2.42151663e-01
3.18227321e-01 -1.12823856e+00 -3.67049485e-01 -3.41477871e-01
4.25645411e-01 5.56875527e-01 4.00255054e-01 -5.57150543... | [10.153371810913086, 9.77626895904541] |
d0b74230-920d-47f0-9ba1-80138d88f9c9 | sheffield-multimt-using-object-posterior | null | null | https://aclanthology.org/W17-4752 | https://aclanthology.org/W17-4752.pdf | Sheffield MultiMT: Using Object Posterior Predictions for Multimodal Machine Translation | null | ['Pranava Swaroop Madhyastha', 'Lucia Specia', 'Josiah Wang'] | 2017-09-01 | null | null | null | ws-2017-9 | ['multimodal-machine-translation'] | ['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... | [-7.346563816070557, 3.7064201831817627] |
e42645c8-b241-4c71-a8c3-c487283a25b7 | multi-task-multi-channel-multi-input-learning | null | null | https://aclanthology.org/D19-6208 | https://aclanthology.org/D19-6208.pdf | Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text | We investigate the impact of using emotional patterns identified by the clinical practitioners and computational linguists to enhance the prediction capabilities of a mental illness detection (in our case depression and post-traumatic stress disorder) model built using a deep neural network architecture. Over the years... | ['Prasadith Kirinde Gamaarachchige', 'Diana Inkpen'] | 2019-11-01 | null | null | null | ws-2019-11 | ['3d-object-classification'] | ['computer-vision'] | [ 2.52292007e-01 3.44890594e-01 5.42752966e-02 -6.57136261e-01
-6.38407648e-01 -6.65888339e-02 2.84307182e-01 6.55582070e-01
-6.82492614e-01 5.72809219e-01 3.46531600e-01 -3.18493992e-02
-3.10661167e-01 -6.37614429e-01 -5.13989059e-03 -2.92576700e-01
-3.67670864e-01 5.59656799e-01 -5.47724545e-01 -3.00374061... | [12.885971069335938, 5.978109359741211] |
c5818d4b-8509-4ea2-9bef-3b85722ad70c | estimation-with-low-rank-time-frequency | 1804.09497 | null | http://arxiv.org/abs/1804.09497v2 | http://arxiv.org/pdf/1804.09497v2.pdf | Estimation with Low-Rank Time-Frequency Synthesis Models | Many state-of-the-art signal decomposition techniques rely on a low-rank
factorization of a time-frequency (t-f) transform. In particular, nonnegative
matrix factorization (NMF) of the spectrogram has been considered in many audio
applications. This is an analysis approach in the sense that the factorization
is applied... | ['Matthieu Kowalski', 'Cédric Févotte'] | 2018-04-25 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 6.08533084e-01 -1.50038168e-01 5.97100612e-03 9.42604095e-02
-6.40772104e-01 -5.60260117e-01 3.89037937e-01 -1.90849021e-01
-1.07738376e-01 5.03025413e-01 6.11396313e-01 -7.28910742e-03
-5.13684273e-01 -3.74482423e-01 -4.99827743e-01 -9.17442083e-01
-9.67026129e-02 -2.63034344e-01 -3.73606265e-01 -2.61136025... | [15.40606689453125, 5.6601243019104] |
1447b16d-a8ff-4694-aefd-37fabd3edf54 | self-attention-in-vision-transformers | 2303.01542 | null | https://arxiv.org/abs/2303.01542v1 | https://arxiv.org/pdf/2303.01542v1.pdf | Self-attention in Vision Transformers Performs Perceptual Grouping, Not Attention | Recently, a considerable number of studies in computer vision involves deep neural architectures called vision transformers. Visual processing in these models incorporates computational models that are claimed to implement attention mechanisms. Despite an increasing body of work that attempts to understand the role of ... | ['John K. Tsotsos', 'Paria Mehrani'] | 2023-03-02 | null | null | null | null | ['saliency-detection'] | ['computer-vision'] | [ 9.61671695e-02 1.11480184e-01 2.15065747e-01 1.15755284e-02
4.90410149e-01 -3.33703369e-01 8.50212395e-01 2.15491518e-01
-5.10453641e-01 7.81495497e-02 3.56897593e-01 -3.11894864e-01
-2.09275693e-01 -8.06317866e-01 -7.11149216e-01 -5.99540174e-01
2.24814475e-01 -7.47937635e-02 6.45686865e-01 -4.69400793... | [10.022332191467285, 1.9219814538955688] |
775cbe2f-4505-4484-ad22-fbd3c6c16401 | gridformer-residual-dense-transformer-with | 2305.17863 | null | https://arxiv.org/abs/2305.17863v1 | https://arxiv.org/pdf/2305.17863v1.pdf | GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions | Image restoration in adverse weather conditions is a difficult task in computer vision. In this paper, we propose a novel transformer-based framework called GridFormer which serves as a backbone for image restoration under adverse weather conditions. GridFormer is designed in a grid structure using a residual dense tra... | ['Hongdong Li', 'Wei Liu', 'Tae-Kyun Kim', 'Tong Lu', 'Bjorn Stenger', 'Wenhan Luo', 'Ziqian Shao', 'Kaihao Zhang', 'Tao Wang'] | 2023-05-29 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 3.09416771e-01 -3.29158843e-01 2.27339044e-01 -3.01975101e-01
-6.63600624e-01 -1.17863365e-01 5.82263291e-01 -4.30754244e-01
-2.14282766e-01 5.69715261e-01 8.11738849e-01 -2.67060965e-01
1.99396715e-01 -6.38098776e-01 -6.67962790e-01 -1.30501294e+00
5.53012639e-03 -5.89266181e-01 1.88190684e-01 -3.13918918... | [11.091840744018555, -2.803161144256592] |
c45c4424-fb5a-41e2-9ac6-116c3948987d | amdet-a-tool-for-mitotic-cell-detection-in | 2108.03676 | null | https://arxiv.org/abs/2108.03676v1 | https://arxiv.org/pdf/2108.03676v1.pdf | AMDet: A Tool for Mitotic Cell Detection in Histopathology Slides | Breast Cancer is the most prevalent cancer in the world. The World Health Organization reports that the disease still affects a significant portion of the developing world citing increased mortality rates in the majority of low to middle income countries. The most popular protocol pathologists use for diagnosing breast... | ['Jimmy Hall', 'Walt Williams'] | 2021-08-08 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-1.39285982e-01 4.14757580e-02 -4.55744922e-01 6.68864921e-02
-6.36157334e-01 -5.90544343e-01 2.40740418e-01 9.47067857e-01
-5.08256316e-01 7.00962842e-01 -8.77753645e-02 -6.19191587e-01
3.05814356e-01 -7.67494321e-01 8.78594145e-02 -9.29698169e-01
2.90307850e-01 7.07796991e-01 3.73471946e-01 1.17140366... | [15.071793556213379, -3.117854118347168] |
81ad0731-4cef-4905-954a-d3eb6d006766 | high-resolution-image-editing-via-multi-stage | 2210.12965 | null | https://arxiv.org/abs/2210.12965v1 | https://arxiv.org/pdf/2210.12965v1.pdf | High-Resolution Image Editing via Multi-Stage Blended Diffusion | Diffusion models have shown great results in image generation and in image editing. However, current approaches are limited to low resolutions due to the computational cost of training diffusion models for high-resolution generation. We propose an approach that uses a pre-trained low-resolution diffusion model to edit ... | ['Minjun Li', 'Johannes Ackermann'] | 2022-10-24 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 4.01998699e-01 7.78530538e-02 2.87820667e-01 -5.26816174e-02
-8.19715917e-01 -3.34531575e-01 7.64285564e-01 -3.31954956e-01
-2.64306664e-01 9.75252509e-01 1.37365073e-01 1.69161439e-01
1.39255688e-01 -1.14303732e+00 -5.21643400e-01 -3.95096600e-01
3.42185557e-01 2.54654825e-01 7.61714041e-01 -2.64045596... | [11.20821762084961, -1.4833232164382935] |
f7e6f8d3-01a8-4999-9b8d-945f4ed6e6f6 | graph-convolution-based-efficient-re-ranking | 2306.08792 | null | https://arxiv.org/abs/2306.08792v1 | https://arxiv.org/pdf/2306.08792v1.pdf | Graph Convolution Based Efficient Re-Ranking for Visual Retrieval | Visual retrieval tasks such as image retrieval and person re-identification (Re-ID) aim at effectively and thoroughly searching images with similar content or the same identity. After obtaining retrieved examples, re-ranking is a widely adopted post-processing step to reorder and improve the initial retrieval results b... | ['Fan Wang', 'Weihua Chen', 'Chong Liu', 'Hongsong Wang', 'Qi Qian', 'Yuqi Zhang'] | 2023-06-15 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-5.29736653e-02 -9.35457647e-01 -1.26297027e-01 -3.95089626e-01
-8.15480471e-01 -6.44959450e-01 7.75227785e-01 4.78263110e-01
-6.55303180e-01 3.44486594e-01 1.75657406e-01 1.98654577e-01
-6.78329945e-01 -6.99282110e-01 -3.52682859e-01 -5.49356222e-01
-6.20519370e-02 3.23172748e-01 6.03157766e-02 -1.38695374... | [14.708818435668945, 0.9314708709716797] |
3a449a0c-8ef8-4c0e-873e-8b7c89ad0b38 | learning-from-good-trajectories-in-offline | 2211.15612 | null | https://arxiv.org/abs/2211.15612v2 | https://arxiv.org/pdf/2211.15612v2.pdf | Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning | Offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets, which is an important step toward the deployment of multi-agent systems in real-world applications. However, in practice, each individual behavior policy that generates multi-agent joint trajector... | ['Baoxiang Wang', 'Furui Liu', 'Kun Kuang', 'Qi Tian'] | 2022-11-28 | null | null | null | null | ['starcraft-ii', 'continuous-control', 'starcraft'] | ['playing-games', 'playing-games', 'playing-games'] | [-5.52854896e-01 -7.02109635e-02 -1.76747024e-01 4.35902268e-01
-6.68813586e-01 -5.49787700e-01 5.95179021e-01 2.71598101e-01
-5.51206529e-01 1.13458896e+00 -1.73158050e-02 -2.11184751e-02
-3.67714792e-01 -7.65303135e-01 -8.26932967e-01 -1.05023515e+00
-3.68762463e-01 9.69785810e-01 1.22833669e-01 -4.30982560... | [3.75110125541687, 2.028992176055908] |
c53db476-f3e2-4719-a0de-8e1a590e5ac0 | analyzing-codebert-s-performance-on-natural | null | null | https://openreview.net/forum?id=canvJzQNs09 | https://openreview.net/pdf?id=canvJzQNs09 | Analyzing CodeBERT's Performance on Natural Language Code Search | Large language models such as CodeBERT perform very well on tasks such as natural language code search. We show that this is most likely due to the high token overlap and similarity between the queries and the code in datasets obtained from large codebases, rather than any deeper understanding of the syntax or semantic... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-5.84860623e-01 6.63633496e-02 -7.72700250e-01 -3.54858845e-01
-6.64317012e-01 -6.93155587e-01 5.51451445e-01 7.78999329e-01
-3.22088331e-01 1.60247520e-01 5.60444772e-01 -6.66981816e-01
1.18064567e-01 -7.22396672e-01 -7.31261075e-01 2.28473186e-01
-3.85427624e-01 1.88580707e-01 5.33463955e-01 -2.70819634... | [7.545039176940918, 8.066950798034668] |
6022525e-15ce-4e15-ad69-bfcb28de325c | deepsleep-2-0-automated-sleep-arousal | null | null | https://www.mdpi.com/2673-2688/3/1/10 | https://www.mdpi.com/2673-2688/3/1/10/pdf | DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning | DeepSleep 2.0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully convolutional deep neural network, which achieved the highest unofficial score in the 2018 PhysioNet Computing Challenge. The proposed network architecture has a compact encoder/decoder structure containing only 740,551 trainable... | ['Robert Fonod'] | 2022-03-01 | null | null | null | ai-2022-3 | ['sleep-quality-prediction', 'sleep-micro-event-detection', 'sleep-arousal-detection'] | ['medical', 'medical', 'medical'] | [-2.11590491e-02 1.51389912e-01 -2.74890624e-02 -5.06634414e-01
-5.73738277e-01 -6.88731000e-02 -2.56157704e-02 4.22918856e-01
-7.85276651e-01 1.00491893e+00 2.17720658e-01 -8.27913359e-02
-1.91943765e-01 -3.12927634e-01 -6.25769317e-01 -4.21637505e-01
-4.70292002e-01 -4.74795327e-02 -3.16892527e-02 2.88324207... | [13.484424591064453, 3.5222930908203125] |
966c03d8-7795-4109-8e8b-13e5dc8727ba | the-task-2-of-cips-sighan-2012-named-entity | null | null | https://aclanthology.org/W12-6321 | https://aclanthology.org/W12-6321.pdf | The Task 2 of CIPS-SIGHAN 2012 Named Entity Recognition and Disambiguation in Chinese Bakeoff | null | ['Houfeng Wang', 'Zhengyan He', 'Sujian Li'] | 2012-12-01 | the-task-2-of-cips-sighan-2012-named-entity-1 | https://aclanthology.org/W12-6321 | https://aclanthology.org/W12-6321.pdf | ws-2012-12 | ['chinese-named-entity-recognition'] | ['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... | [-7.406741619110107, 3.6685519218444824] |
609bf0b8-4eb0-41aa-bc0a-b3b2f897ccb4 | multistage-pruning-of-cnn-based-ecg | 2109.00516 | null | https://arxiv.org/abs/2109.00516v1 | https://arxiv.org/pdf/2109.00516v1.pdf | Multistage Pruning of CNN Based ECG Classifiers for Edge Devices | Using smart wearable devices to monitor patients electrocardiogram (ECG) for real-time detection of arrhythmias can significantly improve healthcare outcomes. Convolutional neural network (CNN) based deep learning has been used successfully to detect anomalous beats in ECG. However, the computational complexity of exis... | ['Deepu John', 'Barry Cardiff', 'Rajesh Panicker', 'Xiaolin Li'] | 2021-08-31 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.26035655e-01 5.77856004e-02 -2.53731050e-02 -3.70235652e-01
-3.60662669e-01 -6.79437891e-02 -5.35126567e-01 6.51024938e-01
-4.64920640e-01 6.47462189e-01 -5.22450097e-02 -5.30433238e-01
-1.44775495e-01 -7.80197144e-01 -2.35840529e-01 -3.39560747e-01
-8.56993198e-02 -8.36241469e-02 1.69084035e-02 2.88454980... | [14.068614959716797, 3.275242328643799] |
ccba85b4-1caf-43cf-b59b-957746c14da5 | reknow-enhanced-knowledge-for-joint-entity | 2206.05123 | null | https://arxiv.org/abs/2206.05123v3 | https://arxiv.org/pdf/2206.05123v3.pdf | REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction | Relation extraction is an important but challenging task that aims to extract all hidden relational facts from the text. With the development of deep language models, relation extraction methods have achieved good performance on various benchmarks. However, we observe two shortcomings of previous methods: first, there ... | ['Bing Xiang', 'Zhiguo Wang', 'Patrick Ng', 'Sheng Zhang'] | 2022-06-10 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.02407724e-01 5.88088870e-01 -6.80648863e-01 -1.91953331e-01
-8.77485335e-01 -3.56582314e-01 6.16691649e-01 1.00454144e-01
6.89531043e-02 1.11794293e+00 2.44646460e-01 -4.27179903e-01
-4.30455267e-01 -1.20518804e+00 -5.09157479e-01 -1.09688580e-01
1.76933452e-01 8.89576972e-01 3.34497362e-01 -4.70867723... | [9.205423355102539, 8.502617835998535] |
e1875e98-dcaf-428a-97cc-0c07c6c7413c | object-discovery-via-contrastive-learning-for | 2208.07576 | null | https://arxiv.org/abs/2208.07576v2 | https://arxiv.org/pdf/2208.07576v2.pdf | Object Discovery via Contrastive Learning for Weakly Supervised Object Detection | Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations. Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common ... | ['Daijin Kim', 'Junhyug Noh', 'Danica J. Sutherland', 'Wonho Bae', 'Jinhwan Seo'] | 2022-08-16 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 2.67206550e-01 -1.25207767e-01 -5.47558963e-01 -6.29478693e-01
-1.00246656e+00 -3.17743748e-01 6.83703005e-01 2.39416078e-01
-7.12981105e-01 6.97844386e-01 -2.55356371e-01 3.64882320e-01
2.05333814e-01 -5.44642270e-01 -1.19175279e+00 -6.90127194e-01
-9.03422311e-02 3.62032950e-01 5.74099541e-01 3.24335605... | [9.295049667358398, 1.273382544517517] |
5edac015-012b-4ba9-a3ee-47fe38236f77 | binary-classification-of-proteins-by-a | 2111.01975 | null | https://arxiv.org/abs/2111.01975v1 | https://arxiv.org/pdf/2111.01975v1.pdf | Binary classification of proteins by a Machine Learning approach | In this work we present a system based on a Deep Learning approach, by using a Convolutional Neural Network, capable of classifying protein chains of amino acids based on the protein description contained in the Protein Data Bank. Each protein is fully described in its chemical-physical-geometric properties in a file i... | ['Osvaldo Gervasi', 'Noelia Faginas-Lago', 'Andrea Lombardi', 'Marco Simonetti', 'Damiano Perri'] | 2021-11-03 | null | null | null | null | ['classification'] | ['methodology'] | [ 3.37357596e-02 -2.93125473e-02 2.74599083e-02 -6.67525828e-01
-8.40182304e-02 -4.14923638e-01 4.63019222e-01 7.96197474e-01
-6.09624565e-01 1.04771245e+00 -4.02848274e-02 -6.26610816e-01
-3.19614649e-01 -8.30480039e-01 -9.12395179e-01 -7.75166690e-01
-3.17720175e-01 8.08651268e-01 1.70593970e-02 -3.22696209... | [4.72590446472168, 5.595959663391113] |
91cd8822-4ce9-4b8e-bb69-9ae7afc76a80 | give-me-more-feedback-ii-annotating-thesis | null | null | https://aclanthology.org/P19-1390 | https://aclanthology.org/P19-1390.pdf | Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays | While the vast majority of existing work on automated essay scoring has focused on holistic scoring, researchers have recently begun work on scoring specific dimensions of essay quality. Nevertheless, progress on dimension-specific essay scoring is limited in part by the lack of annotated corpora. To facilitate advance... | ['Vincent Ng', 'Zixuan Ke', 'Hui Lin', 'Hrishikesh Inamdar'] | 2019-07-01 | null | null | null | acl-2019-7 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-6.77982420e-02 4.06699777e-01 -6.41349137e-01 -3.99687052e-01
-9.89233017e-01 -7.08985865e-01 2.74119735e-01 5.93113005e-01
-1.89379379e-01 9.69294667e-01 8.30344617e-01 -7.23045051e-01
-5.58562040e-01 -7.20521152e-01 1.73596352e-01 -2.91177407e-02
7.27886438e-01 4.10974622e-01 -2.61103719e-01 -2.03903198... | [11.308167457580566, 9.26874828338623] |
45f943f7-62e0-4162-a81c-65cc96695252 | globally-injective-and-bijective-neural | 2306.03982 | null | https://arxiv.org/abs/2306.03982v1 | https://arxiv.org/pdf/2306.03982v1.pdf | Globally injective and bijective neural operators | Recently there has been great interest in operator learning, where networks learn operators between function spaces from an essentially infinite-dimensional perspective. In this work we present results for when the operators learned by these networks are injective and surjective. As a warmup, we combine prior work in b... | ['Maarten V. de Hoop', 'Matti Lassas', 'Michael Puthawala', 'Takashi Furuya'] | 2023-06-06 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [ 2.42492333e-01 5.97271979e-01 -1.26565546e-01 -6.76871464e-02
-3.73155683e-01 -6.24901950e-01 1.89256772e-01 -4.49647248e-01
-2.37300843e-01 8.55617642e-01 2.68038481e-01 -2.97743142e-01
-7.09485829e-01 -7.91097164e-01 -1.20403278e+00 -7.42607892e-01
-5.85850358e-01 3.29653561e-01 -3.70890237e-02 -4.89609033... | [7.5599446296691895, 3.739657163619995] |
1e7085ca-fc19-4ed7-8097-c0785cacc09b | skin-lesion-classification-with-ensemble-of | 1809.02568 | null | http://arxiv.org/abs/1809.02568v1 | http://arxiv.org/pdf/1809.02568v1.pdf | Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning | In this report, we introduce the outline of our system in Task 3: Disease
Classification of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection.
We fine-tuned multiple pre-trained neural network models based on
Squeeze-and-Excitation Networks (SENet) which achieved state-of-the-art results
in the field of image ... | ['Shunsuke Kitada', 'Hitoshi Iyatomi'] | 2018-09-07 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 1.03804874e+00 1.24702565e-01 -5.01323998e-01 -1.79619223e-01
-1.13525259e+00 -2.09753603e-01 7.36847281e-01 -6.56732768e-02
-7.60058165e-01 6.20432854e-01 -3.54445539e-02 -5.42931139e-01
-1.57678705e-02 -4.10251975e-01 -4.49042469e-01 -1.07260597e+00
-7.63378292e-02 -1.07201889e-01 -1.42311845e-02 -3.01783048... | [15.710053443908691, -2.98388671875] |
f2dc8496-2fec-4ca5-a597-1cb80ea88e01 | protein-folding-a-meeting-point-for-leibniz | 2208.03150 | null | https://arxiv.org/abs/2208.03150v2 | https://arxiv.org/pdf/2208.03150v2.pdf | Protein Folding: From Classical Issues to a New Perspective | The Levinthal paradox exposes many critical questions on the protein folding problem, among which we could point out why proteins can reach their native state in a biologically reasonable time. A proper answer to this question is of foremost importance for evolutive biology since it enables us to understand life as we ... | ['Jorge A. Vila'] | 2022-08-05 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 3.49687457e-01 3.22051316e-01 -6.96034878e-02 -3.24950546e-01
-8.73457119e-02 -6.53620124e-01 1.53597906e-01 3.64488155e-01
-5.07556617e-01 1.15690994e+00 -1.12888247e-01 -9.75978315e-01
-1.88962705e-02 -5.22167683e-01 -8.15964818e-01 -1.26373053e+00
-2.56570965e-01 3.28474492e-01 9.10898969e-02 -7.38139212... | [4.72942590713501, 5.26641845703125] |
89ec0cfe-71cd-47c1-9b43-e1dc71611c52 | positive-unlabeled-learning-for-cell | 2106.15918 | null | https://arxiv.org/abs/2106.15918v1 | https://arxiv.org/pdf/2106.15918v1.pdf | Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations | Cell detection in histopathology images is of great value in clinical practice. \textit{Convolutional neural networks} (CNNs) have been applied to cell detection to improve the detection accuracy, where cell annotations are required for network training. However, due to the variety and large number of cells, complete a... | ['Chuyang Ye', 'Zhiwen Liu', 'Fengqian Pang', 'Zipei Zhao'] | 2021-06-30 | null | null | null | null | ['cell-detection', 'mitosis-detection'] | ['computer-vision', 'medical'] | [ 3.41892064e-01 1.23811640e-01 -3.25924933e-01 -2.85284221e-01
-6.23158872e-01 -3.72326076e-01 4.59495708e-02 5.78128994e-01
-6.57517850e-01 1.17476249e+00 -3.79893392e-01 -2.66333461e-01
3.25273454e-01 -8.06828678e-01 -5.32930076e-01 -1.26682150e+00
2.19077930e-01 3.73555928e-01 3.72361422e-01 1.81653753... | [14.928933143615723, -3.0348830223083496] |
36d05ef6-ef8b-476f-8546-be54faf6b4fe | cocatt-a-cognitive-conditioned-driver-1 | 2207.04028 | null | https://arxiv.org/abs/2207.04028v1 | https://arxiv.org/pdf/2207.04028v1.pdf | CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material) | The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preventing high-risk events, like collisions and casualties. However, existing driver attention predictio... | ['Katherine Driggs-Campbell', 'Peter Du', 'Aamir Hasan', 'Pranav Sriram', 'Niviru Wijayaratne', 'Yuan Shen'] | 2022-07-08 | null | null | null | null | ['driver-attention-monitoring'] | ['computer-vision'] | [-3.87821704e-01 -2.12100577e-02 -4.75760520e-01 -1.68175951e-01
-1.92140520e-01 -1.43632740e-01 4.97116536e-01 1.88608263e-02
-4.33413088e-01 2.40506381e-01 2.14433491e-01 -5.60784042e-01
-7.65756294e-02 -2.42625311e-01 -3.59207660e-01 -3.54937822e-01
6.43454731e-01 -1.43027892e-02 4.53511268e-01 -4.55822438... | [7.59960412979126, -0.05926382541656494] |
455133e7-835a-4aa2-8ff0-fa2f37f6e7ab | lite-pose-efficient-architecture-design-for | 2205.01271 | null | https://arxiv.org/abs/2205.01271v4 | https://arxiv.org/pdf/2205.01271v4.pdf | Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation | Pose estimation plays a critical role in human-centered vision applications. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-constrained edge devices due to the high computational cost (more than 150 GMACs per frame). In this paper, we study efficient architecture desi... | ['Song Han', 'Wei-Ming Chen', 'Han Cai', 'Muyang Li', 'Yihan Wang'] | 2022-05-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Lite_Pose_Efficient_Architecture_Design_for_2D_Human_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Lite_Pose_Efficient_Architecture_Design_for_2D_Human_Pose_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['2d-human-pose-estimation', 'multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-5.62517941e-01 -1.62295520e-01 1.25884578e-01 -2.87202418e-01
-6.78580344e-01 -2.30806187e-01 6.98525161e-02 -3.14450651e-01
-7.38154709e-01 4.29154515e-01 3.36914808e-01 -1.14972740e-02
2.90747911e-01 -6.10504627e-01 -6.67071462e-01 -2.54700154e-01
-3.08245923e-02 2.44576797e-01 5.25605857e-01 -1.65287524... | [7.214210033416748, -0.7235434055328369] |
5a98875f-8cf7-4c4c-a7fe-b6d0276274e1 | toward-fair-facial-expression-recognition | 2306.06696 | null | https://arxiv.org/abs/2306.06696v1 | https://arxiv.org/pdf/2306.06696v1.pdf | Toward Fair Facial Expression Recognition with Improved Distribution Alignment | We present a novel approach to mitigate bias in facial expression recognition (FER) models. Our method aims to reduce sensitive attribute information such as gender, age, or race, in the embeddings produced by FER models. We employ a kernel mean shrinkage estimator to estimate the kernel mean of the distributions of th... | ['Ali Etemad', 'Mojtaba Kolahdouzi'] | 2023-06-11 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 8.05656835e-02 2.57983208e-01 -7.20316321e-02 -7.28111804e-01
-3.23826730e-01 -1.93540588e-01 3.83503407e-01 2.48025015e-01
-6.70827329e-01 6.60497725e-01 2.00569391e-01 2.90844262e-01
-1.11887693e-01 -7.38027334e-01 -5.73052227e-01 -8.87424707e-01
-8.86291713e-02 6.80955127e-02 -4.23613608e-01 -2.68510669... | [13.111485481262207, 1.343023419380188] |
e0a3d2c9-f21a-4c5b-82d9-da3d94755d43 | stardata-a-starcraft-ai-research-dataset | 1708.02139 | null | http://arxiv.org/abs/1708.02139v1 | http://arxiv.org/pdf/1708.02139v1.pdf | STARDATA: A StarCraft AI Research Dataset | We release a dataset of 65646 StarCraft replays that contains 1535 million
frames and 496 million player actions. We provide full game state data along
with the original replays that can be viewed in StarCraft. The game state data
was recorded every 3 frames which ensures suitability for a wide variety of
machine learn... | ['Jonas Gehring', 'Gabriel Synnaeve', 'Zeming Lin', 'Vasil Khalidov'] | 2017-08-07 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-1.90630168e-01 -1.71192423e-01 -4.65891093e-01 -2.60684118e-02
-6.80059552e-01 -8.82510900e-01 7.12062180e-01 -1.79277167e-01
-7.39756286e-01 8.01159501e-01 4.93493110e-01 -1.28670067e-01
-6.35392293e-02 -5.63135624e-01 -5.84538400e-01 -5.64054430e-01
-2.95077175e-01 3.30187321e-01 6.02209032e-01 -5.12523055... | [3.8884732723236084, 1.5185869932174683] |
438c63a7-11e7-4734-8fcf-29d9f3fe3d0d | real-time-and-robust-3d-object-detection-with | 2207.05200 | null | https://arxiv.org/abs/2207.05200v1 | https://arxiv.org/pdf/2207.05200v1.pdf | Real-Time And Robust 3D Object Detection with Roadside LiDARs | This work aims to address the challenges in autonomous driving by focusing on the 3D perception of the environment using roadside LiDARs. We design a 3D object detection model that can detect traffic participants in roadside LiDARs in real-time. Our model uses an existing 3D detector as a baseline and improves its accu... | ['Alois C. Knoll', 'Xingcheng Zhou', 'Jialong Wu', 'Walter Zimmer'] | 2022-07-11 | null | null | null | null | ['robust-3d-object-detection'] | ['computer-vision'] | [-1.80526838e-01 8.92572328e-02 -3.07737857e-01 -5.79186559e-01
-7.72466540e-01 -5.04315972e-01 5.88596940e-01 -1.96022719e-01
-6.86733186e-01 2.53517658e-01 -3.23911935e-01 -9.69928682e-01
2.14963511e-01 -1.16388750e+00 -9.05563951e-01 -3.34964097e-01
-8.54691863e-02 6.09038413e-01 8.20251346e-01 -2.72559136... | [7.8606462478637695, -1.7277733087539673] |
c4e5e2b5-cd5b-4814-9387-ac40315e6306 | 3d-context-enhanced-region-based | 1806.09648 | null | http://arxiv.org/abs/1806.09648v2 | http://arxiv.org/pdf/1806.09648v2.pdf | 3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection | Detecting lesions from computed tomography (CT) scans is an important but
difficult problem because non-lesions and true lesions can appear similar. 3D
context is known to be helpful in this differentiation task. However, existing
end-to-end detection frameworks of convolutional neural networks (CNNs) are
mostly design... | ['Mohammadhadi Bagheri', 'Ke Yan', 'Ronald M. Summers'] | 2018-06-25 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [-5.53677976e-02 -1.69937566e-01 -2.57574648e-01 -4.43048090e-01
-1.01732278e+00 -3.79139245e-01 3.86097699e-01 6.80407584e-02
-4.24969047e-01 2.59681582e-01 1.83680817e-01 -4.43313748e-01
1.30988017e-01 -7.62767017e-01 -4.79761422e-01 -6.41688287e-01
-2.07516611e-01 3.95996183e-01 7.36845136e-01 1.87986091... | [15.058865547180176, -2.350785732269287] |
7513ebde-b465-4f70-bdc4-b75b8b4612f0 | gennet-framework-interpretable-deep-learning | null | null | https://www.nature.com/articles/s42003-021-02622-z | https://www.nature.com/articles/s42003-021-02622-z.pdf | GenNet framework: interpretable deep learning for predicting phenotypes from genetic data | Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network archit... | ['Gennady V. Roshchupkin', 'Wiro J. Niessen', 'Caroline C. W. Klaver', 'Hieab H. H. Adams', 'M. Arfan Ikram', 'Manfred Kayser', 'Seven A. Kushner', 'Arno van Hilten'] | 2021-09-17 | null | null | null | nature-communications-biology-2021-9 | ['genetic-risk-prediction', 'medical-genetics'] | ['medical', 'miscellaneous'] | [-3.82686891e-02 4.00548428e-01 2.42590513e-02 -6.01760805e-01
-3.18765819e-01 -2.92149127e-01 -2.38623307e-03 4.20735657e-01
3.43454666e-02 1.03775132e+00 3.33976567e-01 -2.79910862e-01
-4.83124703e-01 -6.26738429e-01 -8.18109095e-01 -4.67610598e-01
-4.40577269e-01 6.01810277e-01 -4.80844826e-01 1.38860270... | [6.123726844787598, 5.678106307983398] |
567f67ae-db80-44c5-8c5d-1c64d426ae16 | optimal-low-rank-matrix-completion | 2305.12292 | null | https://arxiv.org/abs/2305.12292v1 | https://arxiv.org/pdf/2305.12292v1.pdf | Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions | Low-rank matrix completion consists of computing a matrix of minimal complexity that recovers a given set of observations as accurately as possible, and has numerous applications such as product recommendation. Unfortunately, existing methods for solving low-rank matrix completion are heuristics that, while highly scal... | ['Jean Pauphilet', 'Sean Lo', 'Ryan Cory-Wright', 'Dimitris Bertsimas'] | 2023-05-20 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion', 'product-recommendation'] | ['methodology', 'methodology', 'miscellaneous'] | [ 4.06324208e-01 3.35217237e-01 -2.19800949e-01 -3.55976894e-02
-1.08480310e+00 -1.00573087e+00 2.59244628e-03 2.75358707e-02
-1.68316402e-02 6.50335610e-01 2.38318831e-01 -7.28286505e-01
-7.59670258e-01 -5.10999143e-01 -1.00937819e+00 -7.12414980e-01
-5.01926780e-01 9.14710522e-01 -5.73323011e-01 -2.66526699... | [6.85284423828125, 4.810906410217285] |
69e81f72-6132-4d4a-a77b-3fe2aa44a9e7 | learning-semantic-relatedness-in-community | null | null | https://aclanthology.org/W16-1616 | https://aclanthology.org/W16-1616.pdf | Learning Semantic Relatedness in Community Question Answering Using Neural Models | null | ['Mitra Mohtarami', 'James Glass', 'Henry Nassif'] | 2016-08-01 | null | null | null | ws-2016-8 | ['question-similarity'] | ['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... | [-7.355537414550781, 3.8220536708831787] |
e46222c1-85ad-4598-a369-33a914523bc7 | modeling-generalized-rate-distortion | 1906.05178 | null | http://arxiv.org/abs/1906.05178v1 | http://arxiv.org/pdf/1906.05178v1.pdf | Modeling Generalized Rate-Distortion Functions | Many multimedia applications require precise understanding of the
rate-distortion characteristics measured by the function relating visual
quality to media attributes, for which we term it the generalized
rate-distortion (GRD) function. In this study, we explore the GRD behavior of
compressed digital videos in a three-... | [] | 2019-06-12 | null | null | null | null | ['pgtask'] | ['natural-language-processing'] | [ 4.09169197e-01 -5.61531603e-01 -3.39160234e-01 -4.33932513e-01
-1.03619230e+00 -4.21254724e-01 2.90779561e-01 -6.44285530e-02
-3.51166390e-02 5.16382515e-01 2.49684095e-01 -2.23780423e-01
-5.21633148e-01 -5.67312241e-01 -7.13526785e-01 -6.42306030e-01
-2.36431196e-01 -1.01104915e-01 4.12867397e-01 2.41369918... | [11.469207763671875, -1.839739441871643] |
0fdd11bf-b765-42c2-8bdc-fd36d630ab06 | rich-feature-distillation-with-feature | 2207.11250 | null | https://arxiv.org/abs/2207.11250v1 | https://arxiv.org/pdf/2207.11250v1.pdf | Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing | Single-image haze removal is a long-standing hurdle for computer vision applications. Several works have been focused on transferring advances from image classification, detection, and segmentation to the niche of image dehazing, primarily focusing on contrastive learning and knowledge distillation. However, these appr... | ['Varun P. Gopi', 'Nisha J. S.', 'Anushri Suresh', 'Sai Mitheran'] | 2022-07-13 | null | null | null | null | ['image-dehazing', 'single-image-haze-removal'] | ['computer-vision', 'computer-vision'] | [ 4.20375824e-01 1.09652117e-01 1.55403381e-02 -1.56827956e-01
-7.55908847e-01 -1.20310307e-01 4.77214456e-01 -1.76612601e-01
-4.93682265e-01 5.02988398e-01 -9.11303684e-02 -1.52367100e-01
-4.93052155e-02 -7.32966900e-01 -8.18767846e-01 -9.64613438e-01
1.44090533e-01 -1.12167656e-01 6.59345865e-01 -2.96097040... | [10.901259422302246, -2.9497783184051514] |
1b5f18f2-068f-49d6-932b-26c8760947ad | structural-models-for-policy-making-coping | 2103.01115 | null | https://arxiv.org/abs/2103.01115v4 | https://arxiv.org/pdf/2103.01115v4.pdf | Structural models for policy-making: Coping with parametric uncertainty | The ex-ante evaluation of policies using structural econometric models is based on estimated parameters as a stand-in for the true parameters. This practice ignores uncertainty in the counterfactual policy predictions of the model. We develop a generic approach that deals with parametric uncertainty using uncertainty s... | ['Christopher Walsh', 'Lena Janys', 'Janoś Gabler', 'Philipp Eisenhauer'] | 2021-03-01 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-3.27566504e-01 8.25747490e-01 -9.47013140e-01 -2.74155617e-01
-3.34862143e-01 -6.73433363e-01 7.40774214e-01 -9.91702676e-02
-5.81026912e-01 1.22910309e+00 7.56354749e-01 -1.42852747e+00
-5.88384569e-01 -6.59625769e-01 -4.99989033e-01 -2.94430465e-01
2.92236209e-01 6.62832558e-01 -3.37219924e-01 2.84540713... | [7.906651973724365, 5.1545939445495605] |
f53ab91a-7f55-41de-992f-45391d757b9f | pups-point-cloud-unified-panoptic | 2302.06185 | null | https://arxiv.org/abs/2302.06185v2 | https://arxiv.org/pdf/2302.06185v2.pdf | PUPS: Point Cloud Unified Panoptic Segmentation | Point cloud panoptic segmentation is a challenging task that seeks a holistic solution for both semantic and instance segmentation to predict groupings of coherent points. Previous approaches treat semantic and instance segmentation as surrogate tasks, and they either use clustering methods or bounding boxes to gather ... | ['Xi Li', 'Dayang Hao', 'Xin Zhan', 'Zhenwei Miao', 'Huanyu Wang', 'Jianyun Xu', 'Shihao Su'] | 2023-02-13 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.05278122e-01 -3.75372507e-02 -4.73022491e-01 -5.29234231e-01
-9.65085626e-01 -6.83926880e-01 3.74768078e-01 3.27997245e-02
1.30409464e-01 2.79117227e-02 -2.28181675e-01 -2.79129475e-01
-2.20468324e-02 -1.00209641e+00 -8.31221759e-01 -6.18206024e-01
1.62990421e-01 8.25743675e-01 3.39616209e-01 -1.96330383... | [7.937295913696289, -3.1003193855285645] |
5f5e19a8-45b5-418e-a1a1-c21c7af185a7 | the-impact-of-spelling-correction-and-task | null | null | https://aclanthology.org/W19-6310 | https://aclanthology.org/W19-6310.pdf | The Impact of Spelling Correction and Task Context on Short Answer Assessment for Intelligent Tutoring Systems | null | ['Bj{\\"o}rn Rudzewitz', 'Florian Nuxoll', 'Detmar Meurers', 'Ramon Ziai', 'Kordula De Kuthy'] | 2019-09-01 | null | null | null | ws-2019-9 | ['spelling-correction'] | ['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... | [-7.441454887390137, 3.667285442352295] |
b72477d7-25e6-47b5-8b09-34910df6818d | multi-frame-joint-enhancement-for-early | 2109.14151 | null | https://arxiv.org/abs/2109.14151v1 | https://arxiv.org/pdf/2109.14151v1.pdf | Multi-frame Joint Enhancement for Early Interlaced Videos | Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality. Although the high-definition reconstruction technology for early videos has made great progress in recent years, related research on deinterlacing is still lacking. Traditio... | ['Xiaoping Liu', 'Ronggang Wang', 'Wei Jia', 'Yuan Chen', 'Yanbo Ma', 'Yang Zhao'] | 2021-09-29 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 9.56175253e-02 -7.53113449e-01 -7.85788987e-03 9.68539622e-03
-1.12409338e-01 -1.44805387e-02 3.18209976e-01 -2.40645871e-01
-5.12135029e-01 5.74384332e-01 3.00906926e-01 1.08913206e-01
-1.51912078e-01 -4.09287333e-01 -6.67395294e-01 -7.22809136e-01
-3.37994881e-02 -5.69524109e-01 6.71207786e-01 -8.48911852... | [11.040820121765137, -1.8521862030029297] |
9f4b3a35-3ccb-4ee2-bb65-b4d91592bac1 | learning-versatile-3d-shape-generation-with | 2303.14700 | null | https://arxiv.org/abs/2303.14700v1 | https://arxiv.org/pdf/2303.14700v1.pdf | Learning Versatile 3D Shape Generation with Improved AR Models | Auto-Regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space. While this approach has been extended to the 3D domain for powerful shape generation, it still has two limitations: expensive computations on volumetric grids and ambiguous auto-regress... | ['xiangyang xue', 'Chengjie Wang', 'Zhenyu Zhang', 'Ying Tai', 'yinda zhang', 'Yanwei Fu', 'Xuelin Qian', 'Simian Luo'] | 2023-03-26 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 8.98507386e-02 5.46373501e-02 1.73347294e-01 -4.72899228e-02
-1.02932644e+00 -5.70888638e-01 9.89252806e-01 -3.33574682e-01
2.25524515e-01 7.09997535e-01 2.28389546e-01 -2.82632619e-01
8.27442482e-02 -1.23632646e+00 -8.22345018e-01 -7.23365963e-01
2.97998816e-01 8.15784395e-01 -1.52885541e-01 -1.36664957... | [8.916244506835938, -3.6184093952178955] |
a0c7efa9-0c41-4374-8e67-a79344ac7abf | recurrent-video-restoration-transformer-with | 2206.02146 | null | https://arxiv.org/abs/2206.02146v3 | https://arxiv.org/pdf/2206.02146v3.pdf | Recurrent Video Restoration Transformer with Guided Deformable Attention | Video restoration aims at restoring multiple high-quality frames from multiple low-quality frames. Existing video restoration methods generally fall into two extreme cases, i.e., they either restore all frames in parallel or restore the video frame by frame in a recurrent way, which would result in different merits and... | ['Luc van Gool', 'Radu Timofte', 'Kai Zhang', 'JieZhang Cao', 'Simon Green', 'Eddy Ilg', 'Rakesh Ranjan', 'Xiaoyu Xiang', 'Yuchen Fan', 'Jingyun Liang'] | 2022-06-05 | null | null | null | null | ['video-super-resolution', 'video-denoising', 'video-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.78596318e-01 -5.73949635e-01 -1.23648778e-01 -1.46364212e-01
-9.56010818e-01 -7.60659501e-02 2.44426370e-01 -2.94909954e-01
-1.16758063e-01 4.89783287e-01 6.92437351e-01 2.72296846e-01
-1.48768499e-01 -5.94919026e-01 -6.90183282e-01 -9.95781779e-01
2.87903905e-01 -2.05194116e-01 5.84420562e-01 -8.08444172... | [11.108933448791504, -1.9059652090072632] |
ca68d985-cc1c-4c18-bf8e-62f36029ec1e | we-learn-better-road-pothole-detection-from | 2008.06840 | null | https://arxiv.org/abs/2008.06840v2 | https://arxiv.org/pdf/2008.06840v2.pdf | We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation | Manual visual inspection performed by certified inspectors is still the main form of road pothole detection. This process is, however, not only tedious, time-consuming and costly, but also dangerous for the inspectors. Furthermore, the road pothole detection results are always subjective, because they depend entirely o... | ['Hengli Wang', 'Rui Fan', 'Mohammud J. Bocus', 'Ming Liu'] | 2020-08-16 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 3.61093283e-01 1.54172080e-02 2.78778553e-01 -1.54689461e-01
-6.79842353e-01 -2.56794423e-01 1.67652428e-01 -2.28678748e-01
-5.00723183e-01 4.52032983e-01 -1.72931746e-01 -4.76373166e-01
6.65946901e-02 -1.33342409e+00 -6.89176083e-01 -8.13382387e-01
4.63806897e-01 2.20004156e-01 6.69577658e-01 -3.98712397... | [9.571032524108887, 0.06574205309152603] |
70b140e0-40e5-4ce3-b5c3-930f3d520c42 | improving-continual-relation-extraction | 2210.04513 | null | https://arxiv.org/abs/2210.04513v1 | https://arxiv.org/pdf/2210.04513v1.pdf | Improving Continual Relation Extraction through Prototypical Contrastive Learning | Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this critical problem for enhanced CRE performance, we propose a novel Continual Relation Extraction fra... | ['Yanghua Xiao', 'Zhen Chen', 'Haoliang Jin', 'Deqing Yang', 'Chengwei Hu'] | 2022-10-10 | null | https://aclanthology.org/2022.coling-1.163 | https://aclanthology.org/2022.coling-1.163.pdf | coling-2022-10 | ['continual-relation-extraction'] | ['natural-language-processing'] | [ 1.97128087e-01 3.55680555e-01 -2.54883409e-01 -1.86148092e-01
-3.81326973e-01 -2.13174313e-01 7.23963141e-01 3.42599899e-01
-4.87249076e-01 8.74542058e-01 -3.14283781e-02 -4.07003105e-01
-3.89027089e-01 -8.32105279e-01 -5.88212132e-01 -4.35419232e-01
-9.31137651e-02 7.78399765e-01 5.65873682e-01 -3.59674156... | [9.19079303741455, 8.536351203918457] |
9b2f6c9d-88ee-4458-8b82-534ba1e82b5a | audio-guided-attention-network-for-weakly | null | null | https://ieeexplore.ieee.org/document/9712793 | https://ieeexplore.ieee.org/document/9712793 | Audio-Guided Attention Network for Weakly Supervised Violence Detection | Detecting violence in video is a challenging task due to its complex scenarios and great intra-class variability. Most previous works specialize in the analysis of appearance or motion information, ignoring the co-occurrence of some audio and visual events. Physical conflicts such as abuse and fighting are usually acco... | ['Xiaoyu Wu', 'Yujiang Pu'] | 2022-02-21 | null | null | null | conference-2022-2 | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 1.94117710e-01 -6.19155705e-01 2.02973172e-01 -2.55827785e-01
-8.20164979e-01 -1.97291076e-01 6.04959428e-01 2.71364927e-01
-5.72774589e-01 4.41182405e-01 4.62068945e-01 2.26175934e-01
-1.00466385e-01 -6.71161473e-01 -4.95168507e-01 -7.54609466e-01
-3.36463094e-01 -3.65651131e-01 2.10109726e-01 -1.72381744... | [13.439847946166992, 4.842841148376465] |
894c19b0-8009-4ff5-a8ca-ebe3eb7e9cd5 | deception-detection-in-russian-texts | null | null | https://aclanthology.org/E17-4005 | https://aclanthology.org/E17-4005.pdf | Deception detection in Russian texts | Humans are known to detect deception in speech randomly and it is therefore important to develop tools to enable them to detect deception. The problem of deception detection has been studied for a significant amount of time, however the last 10-15 years have seen methods of computational linguistics being employed. Tex... | ['Olga Litvinova', 'Pavel Seredin', 'John Lyell', 'Tatiana Litvinova'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['deception-detection'] | ['miscellaneous'] | [-7.99008533e-02 -1.76388189e-01 -5.52392080e-02 -6.27188861e-01
-6.97917104e-01 -7.20015466e-01 1.00664639e+00 2.63124734e-01
-5.95203876e-01 8.09066892e-01 3.30821365e-01 -6.89328134e-01
1.48180509e-02 -1.90734103e-01 1.93424135e-01 -4.43754494e-01
4.19784784e-01 2.46753827e-01 -6.27112612e-02 -4.05035585... | [8.244256973266602, 10.443354606628418] |
67e5c6a6-8633-4d41-9f37-ce4558234e44 | a-multi-task-framework-for-skin-lesion | 1808.01676 | null | http://arxiv.org/abs/1808.01676v1 | http://arxiv.org/pdf/1808.01676v1.pdf | A Multi-task Framework for Skin Lesion Detection and Segmentation | Early detection and segmentation of skin lesions is crucial for timely
diagnosis and treatment, necessary to improve the survival rate of patients.
However, manual delineation is time consuming and subject to intra- and
inter-observer variations among dermatologists. This underlines the need for an
accurate and automat... | ['Shreyas Malakarjun Patil', 'Sulaiman Vesal', 'Nishant Ravikumar', 'Andreas Maier'] | 2018-08-05 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 4.74367887e-01 4.72948840e-03 -1.24890395e-01 -2.52933085e-01
-9.99816716e-01 -6.32764578e-01 3.80445123e-01 5.04666507e-01
-7.75723457e-01 6.81147158e-01 -2.48866007e-01 -1.05161637e-01
-9.67085660e-02 -6.73850477e-01 -4.31292236e-01 -9.42045808e-01
-1.90492541e-01 2.64077157e-01 5.71713030e-01 1.95937514... | [15.625797271728516, -2.9382357597351074] |
cf650c69-a916-4059-ba4c-1983b2d0bd9a | continual-learning-for-class-and-domain | 2209.08023 | null | https://arxiv.org/abs/2209.08023v1 | https://arxiv.org/pdf/2209.08023v1.pdf | Continual Learning for Class- and Domain-Incremental Semantic Segmentation | The field of continual deep learning is an emerging field and a lot of progress has been made. However, concurrently most of the approaches are only tested on the task of image classification, which is not relevant in the field of intelligent vehicles. Only recently approaches for class-incremental semantic segmentatio... | ['Jürgen Beyerer', 'Miriam Ruf', 'Masoud Roschani', 'Tobias Kalb'] | 2022-09-16 | null | null | null | null | ['class-incremental-semantic-segmentation', 'continual-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.88273358e-01 -3.34964134e-02 -3.76388878e-01 -5.15649974e-01
-2.62486964e-01 -4.18218374e-01 7.68059850e-01 4.57325578e-01
-7.97005117e-01 6.80551410e-01 -6.36514187e-01 -2.70389438e-01
-3.33862245e-01 -7.91867554e-01 -8.66711617e-01 -6.35089993e-01
2.29066104e-01 8.24628472e-01 9.01045680e-01 -3.60882282... | [9.507844924926758, 1.9306806325912476] |
9b7a89b9-4417-4b50-b2ba-04a9c9b143ae | hvtsurv-hierarchical-vision-transformer-for | 2306.17373 | null | https://arxiv.org/abs/2306.17373v1 | https://arxiv.org/pdf/2306.17373v1.pdf | HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image | Survival prediction based on whole slide images (WSIs) is a challenging task for patient-level multiple instance learning (MIL). Due to the vast amount of data for a patient (one or multiple gigapixels WSIs) and the irregularly shaped property of WSI, it is difficult to fully explore spatial, contextual, and hierarchic... | ['Yongbing Zhang', 'Guojun Liu', 'Jian Zhang', 'Hao Bian', 'Yang Chen', 'Zhuchen Shao'] | 2023-06-30 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 1.55804381e-01 -4.78746220e-02 -3.22294563e-01 -2.20899194e-01
-1.28462660e+00 -1.59352779e-01 3.05921435e-01 5.36896229e-01
-3.49915594e-01 8.58759522e-01 7.43545771e-01 -3.99245590e-01
-5.71108937e-01 -7.36502647e-01 -4.84826207e-01 -1.23521376e+00
-1.85142577e-01 2.89913654e-01 1.57889366e-01 -5.43129221... | [15.106377601623535, -2.8883988857269287] |
1332919f-714e-43e3-ac25-ce77db2cc4c4 | mbptrack-improving-3d-point-cloud-tracking | 2303.05071 | null | https://arxiv.org/abs/2303.05071v1 | https://arxiv.org/pdf/2303.05071v1.pdf | MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors | 3D single object tracking has been a crucial problem for decades with numerous applications such as autonomous driving. Despite its wide-ranging use, this task remains challenging due to the significant appearance variation caused by occlusion and size differences among tracked targets. To address these issues, we pres... | ['Song-Hai Zhang', 'Yu-Kun Lai', 'Yuan-Chen Guo', 'Tian-Xing Xu'] | 2023-03-09 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-1.32969141e-01 -5.52403510e-01 -3.16920221e-01 -2.34137222e-01
-5.74730277e-01 -4.98149127e-01 4.94808793e-01 3.90120894e-02
-4.59343463e-01 4.58803058e-01 -1.53771713e-01 1.08321644e-01
2.34054521e-01 -7.64717460e-01 -9.46656108e-01 -7.93886185e-01
-2.66534574e-02 3.07977855e-01 1.11181462e+00 1.11831121... | [6.585860252380371, -2.254053831100464] |
4f1fc457-4f2a-4362-aee6-daa1e919834d | comparing-statistical-and-neural-models-for | null | null | https://aclanthology.org/2020.lt4hala-1.12 | https://aclanthology.org/2020.lt4hala-1.12.pdf | Comparing Statistical and Neural Models for Learning Sound Correspondences | Cognate prediction and proto-form reconstruction are key tasks in computational historical linguistics that rely on the study of sound change regularity. Solving these tasks appears to be very similar to machine translation, though methods from that field have barely been applied to historical linguistics. Therefore, i... | ['Beno{\\^\\i}t Sagot', "Cl{\\'e}mentine Fourrier"] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cognate-prediction'] | ['natural-language-processing'] | [ 2.50829816e-01 1.80260316e-01 -6.86792657e-02 -9.75061581e-02
-3.53091478e-01 -6.23453200e-01 8.27994466e-01 2.52368093e-01
-5.70125639e-01 6.37437284e-01 2.79727101e-01 -6.56881392e-01
-1.40317231e-01 -5.60524940e-01 -7.61645734e-01 -5.60795426e-01
1.83663592e-01 6.26303792e-01 4.94981110e-01 -3.20050031... | [10.740528106689453, 9.767122268676758] |
d7ac5b50-afd1-4cd4-ac13-810c1c7691bf | fmfcc-a-a-challenging-mandarin-dataset-for | 2110.09441 | null | https://arxiv.org/abs/2110.09441v1 | https://arxiv.org/pdf/2110.09441v1.pdf | FMFCC-A: A Challenging Mandarin Dataset for Synthetic Speech Detection | As increasing development of text-to-speech (TTS) and voice conversion (VC) technologies, the detection of synthetic speech has been suffered dramatically. In order to promote the development of synthetic speech detection model against Mandarin TTS and VC technologies, we have constructed a challenging Mandarin dataset... | ['Xianfeng Zhao', 'Xiaowei Yi', 'Yewei Gu', 'Zhenyu Zhang'] | 2021-10-18 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 3.32499027e-01 -2.78143555e-01 3.89569163e-01 -2.11751796e-02
-1.52785420e+00 -5.79474211e-01 8.07194650e-01 -4.60490435e-01
-1.44490927e-01 2.58066118e-01 2.24700600e-01 -5.88014305e-01
5.97188115e-01 -5.27888089e-02 -8.76663208e-01 -5.80951214e-01
1.30260155e-01 2.00946704e-01 4.58397746e-01 2.07084939... | [14.170060157775879, 5.814658164978027] |
6c7041a6-d0bb-4caa-84e7-79857d45c30d | opinion-mining-with-deep-recurrent-neural | null | null | https://aclanthology.org/D14-1080 | https://aclanthology.org/D14-1080.pdf | Opinion Mining with Deep Recurrent Neural Networks | null | ['Ozan {\\.I}rsoy', 'Claire Cardie'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['fine-grained-opinion-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... | [-7.253962516784668, 3.5042145252227783] |
bdbb7163-46bf-4243-a008-a497eff45363 | end-to-end-offline-goal-oriented-dialog | 1712.02838 | null | http://arxiv.org/abs/1712.02838v1 | http://arxiv.org/pdf/1712.02838v1.pdf | End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient | Learning a goal-oriented dialog policy is generally performed offline with
supervised learning algorithms or online with reinforcement learning (RL).
Additionally, as companies accumulate massive quantities of dialog transcripts
between customers and trained human agents, encoder-decoder methods have gained
popularity ... | ['Li Zhou', 'Kevin Small', 'Charles Elkan', 'Oleg Rokhlenko'] | 2017-12-07 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 9.12942514e-02 8.12600553e-01 -2.64221698e-01 -7.83902287e-01
-1.02721786e+00 -1.00906467e+00 8.81986678e-01 3.26617628e-01
-6.27372265e-01 1.15001464e+00 2.25848421e-01 -5.50621927e-01
4.03128654e-01 -4.84262228e-01 -3.99186462e-01 -3.55165273e-01
1.81477681e-01 9.62507486e-01 -7.21701980e-02 -4.12304074... | [13.016447067260742, 8.068645477294922] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.