paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9ab79aa4-944e-4a74-98d4-7983b5f89a1b | conditional-mutual-information-for | 2305.14133 | null | https://arxiv.org/abs/2305.14133v1 | https://arxiv.org/pdf/2305.14133v1.pdf | Conditional Mutual Information for Disentangled Representations in Reinforcement Learning | Reinforcement Learning (RL) environments can produce training data with spurious correlations between features due to the amount of training data or its limited feature coverage. This can lead to RL agents encoding these misleading correlations in their latent representation, preventing the agent from generalising if t... | ['Stefano V. Albrecht', 'Josiah P. Hanna', 'Kevin Sebastian Luck', 'Trevor McInroe', 'Mhairi Dunion'] | 2023-05-23 | null | null | null | null | ['disentanglement', 'continuous-control'] | ['methodology', 'playing-games'] | [ 3.64923686e-01 1.94859043e-01 9.71593056e-03 2.91031669e-03
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-6.46650791e-01 1.11372364e+00 2.65089154e-01 3.12769681e-01
-6.37101471e-01 -7.18081355e-01 -4.83974934e-01 -1.12874401e+00
-7.35852778e-01 5.20432055e-01 -2.73382932e-01 -2.04799145... | [4.142155170440674, 1.8440216779708862] |
f81300dd-3793-4ea6-8c18-403a741fa4d0 | towards-end-to-end-unsupervised-speech | 2204.02492 | null | https://arxiv.org/abs/2204.02492v2 | https://arxiv.org/pdf/2204.02492v2.pdf | Towards End-to-end Unsupervised Speech Recognition | Unsupervised speech recognition has shown great potential to make Automatic Speech Recognition (ASR) systems accessible to every language. However, existing methods still heavily rely on hand-crafted pre-processing. Similar to the trend of making supervised speech recognition end-to-end, we introduce wav2vec-U 2.0 whic... | ['Alexei Baevski', 'Michael Auli', 'Wei-Ning Hsu', 'Alexander H. Liu'] | 2022-04-05 | null | null | null | null | ['unsupervised-speech-recognition'] | ['speech'] | [ 2.63692081e-01 2.36644045e-01 -1.82990536e-01 -7.98929155e-01
-9.23271120e-01 -2.89941221e-01 7.76194155e-01 -2.02773675e-01
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6.25923723e-02 3.98233026e-01 -1.78798974e-01 -3.78697038... | [14.425724029541016, 6.66486120223999] |
d28a5ce9-39d9-476b-9885-2f97d21cb2bc | meshmvs-multi-view-stereo-guided-mesh-1 | 2010.08682 | null | https://arxiv.org/abs/2010.08682v3 | https://arxiv.org/pdf/2010.08682v3.pdf | MeshMVS: Multi-View Stereo Guided Mesh Reconstruction | Deep learning based 3D shape generation methods generally utilize latent features extracted from color images to encode the semantics of objects and guide the shape generation process. These color image semantics only implicitly encode 3D information, potentially limiting the accuracy of the generated shapes. In this p... | ['Zuozhuo Dai', 'Ping Tan', 'Qingkun Su', 'Siyu Zhu', 'Zhiwen Fan', 'Rakesh Shrestha'] | 2020-10-17 | meshmvs-multi-view-stereo-guided-mesh | https://openreview.net/forum?id=pULTvw9X313 | https://openreview.net/pdf?id=pULTvw9X313 | null | ['3d-shape-generation'] | ['computer-vision'] | [-8.61709341e-02 1.97460279e-01 3.55161637e-01 -5.19332469e-01
-7.56139815e-01 -6.60320163e-01 7.46874928e-01 4.00547124e-02
5.76567128e-02 3.61220956e-01 2.55491108e-01 1.96112007e-01
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3.30117792e-01 1.03934789e+00 2.85046577e-01 -1.64538115... | [8.811355590820312, -3.5410094261169434] |
6f691f2e-11ca-4dd9-9336-5088edfb5aca | a-strong-transfer-baseline-for-rgb-d-fusion | 2210.00843 | null | https://arxiv.org/abs/2210.00843v2 | https://arxiv.org/pdf/2210.00843v2.pdf | Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition | The Vision Transformer (ViT) architecture has established its place in computer vision literature, however, training ViTs for RGB-D object recognition remains an understudied topic, viewed in recent literature only through the lens of multi-task pretraining in multiple vision modalities. Such approaches are often compu... | ['Hamidreza Kasaei', 'Georgios Tziafas'] | 2022-10-03 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 3.54751885e-01 2.82906350e-02 1.85294390e-01 -2.37447307e-01
-1.21586430e+00 -6.61591351e-01 7.32995689e-01 -2.29747489e-01
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1.96517736e-01 5.92081130e-01 1.37234524e-01 -3.30071270... | [8.855507850646973, -1.883314847946167] |
313f4013-221e-426e-bd51-bb388fd25414 | benchmarking-automated-machine-learning | 2304.14735 | null | https://arxiv.org/abs/2304.14735v1 | https://arxiv.org/pdf/2304.14735v1.pdf | Benchmarking Automated Machine Learning Methods for Price Forecasting Applications | Price forecasting for used construction equipment is a challenging task due to spatial and temporal price fluctuations. It is thus of high interest to automate the forecasting process based on current market data. Even though applying machine learning (ML) to these data represents a promising approach to predict the re... | ['Christian Tutschku', 'Alexandre Beiderwellen-Bedrikow', 'Dennis Klau', 'Marc-André Zöller', 'Horst Stühler'] | 2023-04-28 | null | null | null | null | ['automl'] | ['methodology'] | [-3.83540392e-02 3.61325949e-01 8.71125534e-02 -2.53313929e-01
-3.05410951e-01 -6.25784338e-01 4.83141243e-01 4.56034660e-01
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6.08580075e-02 8.00627887e-01 8.22463706e-02 -2.73909450... | [9.053727149963379, 6.103189945220947] |
eb7eddec-de2f-4d2c-88e7-fa26979f8f2d | visual-and-semantic-knowledge-transfer-for | 1801.03145 | null | http://arxiv.org/abs/1801.03145v2 | http://arxiv.org/pdf/1801.03145v2.pdf | Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection | Deep CNN-based object detection systems have achieved remarkable success on
several large-scale object detection benchmarks. However, training such
detectors requires a large number of labeled bounding boxes, which are more
difficult to obtain than image-level annotations. Previous work addresses this
issue by transfor... | ['Yu-Xing Tang', 'Emmanuel Dellandrea', 'Boyang Gao', 'Xiaofang Wang', 'Robert Gaizauskas', 'Liming Chen', 'Josiah Wang'] | 2018-01-09 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.57281026e-01 5.44776693e-02 -1.64198324e-01 -5.81944168e-01
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1.95768341e-01 3.70523036e-01 8.37927103e-01 -8.58863965... | [9.511161804199219, 1.4875057935714722] |
bf73dcf2-3987-4904-a060-3da67afda7fc | text-categorization-for-conflict-event | null | null | https://aclanthology.org/2020.aespen-1.5 | https://aclanthology.org/2020.aespen-1.5.pdf | Text Categorization for Conflict Event Annotation | We cast the problem of event annotation as one of text categorization, and compare state of the art text categorization techniques on event data produced within the Uppsala Conflict Data Program (UCDP). Annotating a single text involves assigning the labels pertaining to at least 17 distinct categorization tasks, e.g.,... | ['Fehmi ben Abdesslem', 'Magnus Sahlgren', 'Ariel Ekgren', 'Kristine Eck', 'Fredrik Olsson'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['text-categorization'] | ['natural-language-processing'] | [-5.47663011e-02 -1.17557831e-02 -1.57727212e-01 -4.20838803e-01
-6.24234915e-01 -8.80444646e-01 1.24861443e+00 1.20053864e+00
-9.88332987e-01 4.99097824e-01 9.05667245e-01 -5.18351495e-01
-4.71135646e-01 -7.46113122e-01 -5.96357090e-03 -6.64348722e-01
-1.80805504e-01 9.27966952e-01 1.28006577e-01 -2.94064462... | [10.212337493896484, 8.850110054016113] |
fbcb6291-1cd4-4735-844e-513d1972bbad | a-convolutional-neural-network-for-language | 1904.00805 | null | http://arxiv.org/abs/1904.00805v1 | http://arxiv.org/pdf/1904.00805v1.pdf | A Convolutional Neural Network for Language-Agnostic Source Code Summarization | Descriptive comments play a crucial role in the software engineering process.
They decrease development time, enable better bug detection, and facilitate the
reuse of previously written code. However, comments are commonly the last of a
software developer's priorities and are thus either insufficient or missing
entirel... | ['David Slater', 'Jessica Moore', 'Ben Gelman'] | 2019-03-29 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 1.35941803e-01 2.00482458e-01 -5.54850221e-01 -2.49217555e-01
-9.32292998e-01 -5.87675273e-01 2.00063333e-01 8.55140209e-01
-1.41832635e-01 4.87917662e-01 5.56741655e-01 -5.25701046e-01
2.55882710e-01 -4.37742054e-01 -7.19950974e-01 1.83675557e-01
1.56863138e-01 -4.44617048e-02 2.50931889e-01 -2.88925976... | [7.630922317504883, 7.928878307342529] |
0203c7ba-961f-44c1-8aa0-9396d2875e5f | fuzzy-attention-neural-network-to-tackle | 2209.02048 | null | https://arxiv.org/abs/2209.02048v2 | https://arxiv.org/pdf/2209.02048v2.pdf | Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation | Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography ... | ['Guang Yang', 'Simon Walsh', 'Witold Pedrycz', 'Francisco Herrera', 'Yingying Fang', 'Xiaodan Xing', 'Peng Tang', 'Zeyu Tang', 'Javier Del Ser', 'Yang Nan'] | 2022-09-05 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 5.42085506e-02 -9.05105099e-02 -1.94197576e-02 -2.53381312e-01
-4.13021743e-01 -4.02800381e-01 -1.04280189e-01 -3.79656442e-02
-4.69267875e-01 5.73564172e-01 -1.35305926e-01 -1.96764573e-01
-4.92521942e-01 -8.15388262e-01 -3.27777982e-01 -8.68167937e-01
2.20859602e-01 6.63163960e-01 3.47485721e-01 7.27807656... | [15.072725296020508, -2.1668169498443604] |
590712e9-379f-473d-8908-9083c9b7ccd9 | a-meta-learning-based-generalizable-indoor | 2305.13453 | null | https://arxiv.org/abs/2305.13453v2 | https://arxiv.org/pdf/2305.13453v2.pdf | A Meta-learning based Generalizable Indoor Localization Model using Channel State Information | Indoor localization has gained significant attention in recent years due to its various applications in smart homes, industrial automation, and healthcare, especially since more people rely on their wireless devices for location-based services. Deep learning-based solutions have shown promising results in accurately es... | ['Kurt Turck', 'Jonathan Ashdown', 'Fatemeh Afghah', 'Linke Guo', 'ChunChih Lin', 'Ali Owfi'] | 2023-05-22 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-1.45303935e-01 -3.68571132e-01 -2.48650566e-01 -5.62020063e-01
-1.11064041e+00 -1.78741708e-01 3.20890397e-01 1.37061223e-01
-4.41410571e-01 9.78361785e-01 1.08053423e-01 -4.66406763e-01
-4.96716470e-01 -7.21480608e-01 -8.27057719e-01 -7.01503694e-01
-2.64767408e-01 2.84630954e-01 -1.86999410e-01 2.33840883... | [6.398256778717041, 0.9231898784637451] |
9914c214-6afb-4489-86dd-165d89d49588 | incremental-learning-for-heterogeneous | 2305.19404 | null | https://arxiv.org/abs/2305.19404v1 | https://arxiv.org/pdf/2305.19404v1.pdf | Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI | Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain. Yet, the static DL model is likely to perform poorly in a continually evolving environment, requiring appropriate model updates. In an incremental learning ... | ['Jonghye Woo', 'Georges El Fakhri', 'Emiliano Santarnecchi', 'Fangxu Xing', 'Helen A. Shih', 'Xiaofeng Liu'] | 2023-05-30 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation', 'incremental-learning', 'pseudo-label'] | ['computer-vision', 'medical', 'methodology', 'miscellaneous'] | [ 5.95809042e-01 4.83594894e-01 5.88568486e-03 -2.61260986e-01
-7.09388018e-01 -5.68725705e-01 4.33667481e-01 1.22585043e-01
-5.98677456e-01 9.05355513e-01 1.17985748e-01 -6.32266849e-02
-1.57215521e-01 -5.48883021e-01 -6.21242702e-01 -9.72754180e-01
-5.74726388e-02 7.91532278e-01 5.25747061e-01 -3.19570124... | [14.588056564331055, -2.1214759349823] |
9a23ad37-30a3-40a7-8008-0108ac23f09c | source-free-domain-adaptation-with-image | 2008.07514 | null | https://arxiv.org/abs/2008.07514v2 | https://arxiv.org/pdf/2008.07514v2.pdf | Source Free Domain Adaptation with Image Translation | Effort in releasing large-scale datasets may be compromised by privacy and intellectual property considerations. A feasible alternative is to release pre-trained models instead. While these models are strong on their original task (source domain), their performance might degrade significantly when deployed directly in ... | ['Yunzhong Hou', 'Liang Zheng'] | 2020-08-17 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 6.55046046e-01 9.58799571e-02 -2.45938078e-01 -5.85740566e-01
-8.36928129e-01 -9.26100433e-01 6.92371845e-01 -1.32496059e-01
-5.37403047e-01 9.60396588e-01 -1.28609493e-01 -3.92691651e-03
3.05845290e-01 -6.65402353e-01 -1.03593218e+00 -8.63577962e-01
5.46019495e-01 3.75515044e-01 -5.30699193e-02 -3.50750722... | [10.337627410888672, 3.1173887252807617] |
928e6a2b-5795-498d-adcd-e4929825a34a | affordance-transfer-learning-for-human-object | 2104.02867 | null | https://arxiv.org/abs/2104.02867v2 | https://arxiv.org/pdf/2104.02867v2.pdf | Affordance Transfer Learning for Human-Object Interaction Detection | Reasoning the human-object interactions (HOI) is essential for deeper scene understanding, while object affordances (or functionalities) are of great importance for human to discover unseen HOIs with novel objects. Inspired by this, we introduce an affordance transfer learning approach to jointly detect HOIs with novel... | ['DaCheng Tao', 'Xiaojiang Peng', 'Yu Qiao', 'Baosheng Yu', 'Zhi Hou'] | 2021-04-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hou_Affordance_Transfer_Learning_for_Human-Object_Interaction_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hou_Affordance_Transfer_Learning_for_Human-Object_Interaction_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['affordance-recognition', 'human-object-interaction-concept-discovery', 'affordance-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.77654088e-01 -1.70744415e-02 4.35477346e-02 -2.09537849e-01
-1.62445262e-01 -4.94331717e-01 5.14413178e-01 2.25310400e-01
-9.08178017e-02 3.75940502e-01 1.17653228e-01 7.88914561e-02
-2.54806995e-01 -7.16082633e-01 -9.30424631e-01 -4.91919577e-01
-2.66257912e-01 4.28860396e-01 4.47148353e-01 -1.52863517... | [5.167304039001465, -0.11266295611858368] |
f7f8d9ac-4fd1-4e4f-8e4f-ebf0f3c82ca3 | stochastic-parallelizable-eigengap-dilation | 2207.14589 | null | https://arxiv.org/abs/2207.14589v1 | https://arxiv.org/pdf/2207.14589v1.pdf | Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering | Large graphs commonly appear in social networks, knowledge graphs, recommender systems, life sciences, and decision making problems. Summarizing large graphs by their high level properties is helpful in solving problems in these settings. In spectral clustering, we aim to identify clusters of nodes where most edges fal... | ['Richard Everett', 'Yoram Bachrach', 'Ian Gemp', 'Elise van der Pol'] | 2022-07-29 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-3.46047916e-02 1.45096213e-01 1.93594489e-02 2.27151021e-01
-1.63414583e-01 -9.32748854e-01 3.80028784e-02 3.89283776e-01
-6.13796599e-02 2.59725362e-01 1.30942971e-01 -5.89887738e-01
-4.31864887e-01 -7.61311650e-01 -3.72875184e-01 -7.24828720e-01
-5.45630991e-01 4.59321141e-01 7.80993775e-02 -1.62655145... | [7.031836986541748, 5.122690677642822] |
f9e6460a-47ad-437a-a768-32791afba232 | l-ac-learning-latent-decision-aware-models | 2306.17366 | null | https://arxiv.org/abs/2306.17366v1 | https://arxiv.org/pdf/2306.17366v1.pdf | $λ$-AC: Learning latent decision-aware models for reinforcement learning in continuous state-spaces | The idea of decision-aware model learning, that models should be accurate where it matters for decision-making, has gained prominence in model-based reinforcement learning. While promising theoretical results have been established, the empirical performance of algorithms leveraging a decision-aware loss has been lackin... | ['Amir-Massoud Farahmand', 'Igor Gilitschenski', 'Romina Abachi', 'Arash Ahmadian', 'Claas A Voelcker'] | 2023-06-30 | null | null | null | null | ['continuous-control', 'model-based-reinforcement-learning', 'decision-making'] | ['playing-games', 'reasoning', 'reasoning'] | [-9.64928884e-03 1.27538204e-01 -8.05197835e-01 -2.75259078e-01
-9.42583144e-01 -2.22847402e-01 3.94806355e-01 1.15557365e-01
-6.07725978e-01 1.08877051e+00 -6.66845590e-02 -4.99670506e-01
-7.05509186e-01 -5.62278509e-01 -5.11102438e-01 -8.19945991e-01
-4.89926964e-01 5.20853341e-01 -6.52860105e-02 -4.82749015... | [4.130195140838623, 2.191617965698242] |
85eba30c-8156-4467-8e0e-34a2fe82ed7a | es-un-platano-exploring-the-application-of-a | null | null | https://aclanthology.org/W19-1602 | https://aclanthology.org/W19-1602.pdf | ?`Es un pl\'atano? Exploring the Application of a Physically Grounded Language Acquisition System to Spanish | In this paper we describe a multilingual grounded language learning system adapted from an English-only system. This system learns the meaning of words used in crowd-sourced descriptions by grounding them in the physical representations of the objects they are describing. Our work presents a framework to compare the pe... | ['Francis Ferraro', 'Cynthia Matuszek', 'Caroline Kery'] | 2019-06-01 | null | null | null | ws-2019-6 | ['grounded-language-learning'] | ['natural-language-processing'] | [-3.08473766e-01 6.00456819e-02 2.59742647e-01 -3.03562492e-01
-8.04441452e-01 -7.20137715e-01 8.02553773e-01 3.39338541e-01
-8.07738662e-01 9.79754448e-01 2.65065283e-01 3.98024842e-02
3.68751466e-01 -8.67563069e-01 -7.34859109e-01 -4.31652635e-01
-3.74120958e-02 9.40999210e-01 4.08815056e-01 -5.92443645... | [4.357269287109375, 0.7647721767425537] |
2e875711-0ae7-4659-99e5-1e8f6d380a96 | research-on-smoking-behavior-detection-system | null | null | https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CMFD&dbname=CMFDTEMP&filename=1022545108.nh&uniplatform=NZKPT&v=EO9SeGGV79D5fqruvEuEUASMUu2YRCcwEnhyFqQY-l52O5A4MxkYJvMC3lR2N9KV | https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CMFD&dbname=CMFDTEMP&filename=1022545108.nh&uniplatform=NZKPT&v=EO9SeGGV79D5fqruvEuEUASMUu2YRCcwEnhyFqQY-l52O5A4MxkYJvMC3lR2N9KV | Research on Smoking Behavior Detection System Based on Deep Learning | As we all know,smoking endangers the health of smokers,and the harm of second-hand smoke to the health of people around us can not be ignored;in addition, improper smoking can sometimes cause many safety accidents,such as fire or explosion, and bring huge property losses to the society.Therefore,strengthening the super... | ['万里波'] | 2022-06-01 | null | null | null | 2022-2022-6 | ['face-detection'] | ['computer-vision'] | [-1.45978760e-02 -5.27638137e-01 -2.82146633e-01 -2.04383969e-01
2.93610126e-01 -1.48381546e-01 -5.91624975e-02 -8.15822124e-01
-4.04633939e-01 -2.54122801e-02 -2.40700334e-01 -4.83579457e-01
1.25094965e-01 -1.35652351e+00 1.62122503e-01 -9.73656356e-01
5.87700129e-01 -1.26797095e-01 4.61090505e-01 -1.58237889... | [13.26362419128418, 0.8344709873199463] |
7ce650b3-9cfe-4172-8551-d080417cef7b | semisupervised-regression-in-latent-structure | 2305.02473 | null | https://arxiv.org/abs/2305.02473v1 | https://arxiv.org/pdf/2305.02473v1.pdf | Semisupervised regression in latent structure networks on unknown manifolds | Random graphs are increasingly becoming objects of interest for modeling networks in a wide range of applications. Latent position random graph models posit that each node is associated with a latent position vector, and that these vectors follow some geometric structure in the latent space. In this paper, we consider ... | ['Carey E. Priebe', 'Youngser Park', 'Michael W. Trosset', 'Joshua Agterberg', 'Aranyak Acharyya'] | 2023-05-04 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [ 1.61753029e-01 5.72936356e-01 -3.52953583e-01 -2.44402602e-01
2.68828779e-01 -5.22162974e-01 6.78113997e-01 2.36478597e-01
-9.60218608e-02 2.56459624e-01 1.39771447e-01 -8.69830921e-02
-4.41657573e-01 -7.62670219e-01 -7.06524134e-01 -8.20286930e-01
-6.22130036e-01 7.12714076e-01 -1.42955974e-01 1.07217155... | [7.093810558319092, 5.223530292510986] |
9b40103d-4c5a-44b3-a1f6-892e20d9b2bc | applying-a-generic-sequence-to-sequence-model | 2201.05302 | null | https://arxiv.org/abs/2201.05302v1 | https://arxiv.org/pdf/2201.05302v1.pdf | Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation | In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies. In this work, we opt for simplicity and show how a commonly used seq2seq language model, BART, can be easily adapted to generate keyphrases fr... | ['Alfio Gliozzo', 'Nandana Mihindukulasooriya', 'Michael Glass', 'Gaetano Rossiello', 'Md Faisal Mahbub Chowdhury'] | 2022-01-14 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 8.30154568e-02 -3.21922712e-02 -6.56929836e-02 -3.75785120e-02
-9.33841407e-01 -6.45050168e-01 1.13370311e+00 1.36108130e-01
-7.12360084e-01 1.10699987e+00 3.35830390e-01 -6.50202215e-01
1.54366091e-01 -7.84527898e-01 -6.82155848e-01 -3.79745066e-01
9.99145284e-02 4.15880054e-01 2.84925878e-01 -8.47090006... | [12.263518333435059, 8.90402603149414] |
d852ece1-a362-415d-9629-27245b75a070 | generative-adversarial-network-applications | 2201.09152 | null | https://arxiv.org/abs/2201.09152v1 | https://arxiv.org/pdf/2201.09152v1.pdf | Generative Adversarial Network Applications in Creating a Meta-Universe | Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image trans... | ['Hamid R. Arabnia', 'Khaled Rasheed', 'Thiab R. Taha', 'Soheyla Amirian'] | 2022-01-23 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 1.01074851e+00 5.05593359e-01 1.05783194e-01 -2.82874823e-01
-6.10749960e-01 -7.09818482e-01 6.97562933e-01 -8.54750395e-01
1.55095577e-01 9.42191064e-01 2.23741561e-01 -1.78606972e-01
4.74024028e-01 -9.28216577e-01 -1.15940070e+00 -5.88640451e-01
6.50519967e-01 2.99008071e-01 -3.88560474e-01 -1.46733195... | [11.819926261901855, -0.38448426127433777] |
8a39ce85-7f88-44a6-8d01-29e593edc6b5 | ynu_dyx-at-semeval-2019-task-9-a-stacked | null | null | https://aclanthology.org/S19-2223 | https://aclanthology.org/S19-2223.pdf | YNU\_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification | In this paper we describe a deep-learning system that competed as SemEval 2019 Task 9-SubTask A: Suggestion Mining from Online Reviews and Forums. We use Word2Vec to learn the distributed representations from sentences. This system is composed of a Stacked Bidirectional Long-Short Memory Network (SBiLSTM) for enriching... | ['Xue-jie Zhang', 'Yunxia Ding', 'Xiaobing Zhou'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-2.60335475e-01 2.98643202e-01 -3.70497882e-01 -7.34390974e-01
-3.95911545e-01 -5.56935705e-02 7.97511339e-01 6.59794882e-02
-8.23561788e-01 8.76736581e-01 6.63035691e-01 -9.06116366e-01
2.38714561e-01 -4.94077027e-01 -6.23101950e-01 -2.92725652e-01
-5.12665398e-02 3.47146004e-01 1.36673272e-01 -6.52107716... | [10.917896270751953, 7.5656609535217285] |
48582cea-417e-4889-8a4e-75670826e3a3 | tedigan-text-guided-diverse-image-generation | 2012.03308 | null | https://arxiv.org/abs/2012.03308v3 | https://arxiv.org/pdf/2012.03308v3.pdf | TediGAN: Text-Guided Diverse Face Image Generation and Manipulation | In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and instance-level optimization. The inversion module maps real images to t... | ['Baoyuan Wu', 'Jing-Hao Xue', 'Yujiu Yang', 'Weihao Xia'] | 2020-12-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xia_TediGAN_Text-Guided_Diverse_Face_Image_Generation_and_Manipulation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xia_TediGAN_Text-Guided_Diverse_Face_Image_Generation_and_Manipulation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['face-sketch-synthesis'] | ['computer-vision'] | [ 4.78933901e-01 -2.43331753e-02 -1.43230423e-01 -3.89333308e-01
-8.51930678e-01 -7.79254198e-01 7.98294365e-01 -7.70079553e-01
8.88727978e-02 4.68684137e-01 -1.34498952e-02 2.05930248e-01
-5.54583361e-03 -8.74869287e-01 -9.16347802e-01 -6.94378614e-01
6.86469257e-01 5.66473901e-01 -2.52534002e-01 -1.08205482... | [12.281638145446777, -0.22180306911468506] |
78b92365-5bdc-4dfb-b459-80a14b6a1446 | multi-dataset-co-training-with-sharpness | 2305.19953 | null | https://arxiv.org/abs/2305.19953v2 | https://arxiv.org/pdf/2305.19953v2.pdf | Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing | Audio anti-spoofing for automatic speaker verification aims to safeguard users' identities from spoofing attacks. Although state-of-the-art spoofing countermeasure(CM) models perform well on specific datasets, they lack generalization when evaluated with different datasets. To address this limitation, previous studies ... | ['Tomi Kinnunen', 'Jee-weon Jung', 'Hye-jin Shim'] | 2023-05-31 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 5.23070693e-01 -2.91325569e-01 -1.82081386e-01 -2.99692333e-01
-1.15462601e+00 -5.89923680e-01 5.19360602e-01 -1.65717036e-01
-3.06590945e-01 4.15533632e-01 2.63781041e-01 -5.78544199e-01
1.73562676e-01 -1.73711017e-01 -5.93966126e-01 -5.22386014e-01
-2.41064727e-01 1.51245236e-01 1.58721015e-01 -2.80826956... | [14.061816215515137, 5.863542079925537] |
6103eda8-9ff0-43ed-aa26-258efea78c04 | opening-the-black-box-of-deep-neural-networks | 1703.00810 | null | http://arxiv.org/abs/1703.00810v3 | http://arxiv.org/pdf/1703.00810v3.pdf | Opening the Black Box of Deep Neural Networks via Information | Despite their great success, there is still no comprehensive theoretical
understanding of learning with Deep Neural Networks (DNNs) or their inner
organization. Previous work proposed to analyze DNNs in the \textit{Information
Plane}; i.e., the plane of the Mutual Information values that each layer
preserves on the inp... | ['Ravid Shwartz-Ziv', 'Naftali Tishby'] | 2017-03-02 | null | null | null | null | ['information-plane'] | ['methodology'] | [ 3.41940373e-01 5.69295943e-01 -2.92852782e-02 -2.46261179e-01
-1.14903590e-02 -3.25858563e-01 4.02284563e-01 1.01978600e-01
-6.61463499e-01 5.75053275e-01 1.11297496e-01 -3.30703795e-01
-5.22975445e-01 -6.18813217e-01 -7.42390215e-01 -9.55684781e-01
-1.74135119e-01 5.82789123e-01 2.70464510e-01 2.75902543... | [8.021390914916992, 3.538625717163086] |
c1ef34e9-ee6c-4ed2-a62f-cd3026d20391 | deep-adaptation-of-adult-child-facial | 2209.08614 | null | https://arxiv.org/abs/2209.08614v1 | https://arxiv.org/pdf/2209.08614v1.pdf | Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features | Imaging of facial affects may be used to measure psychophysiological attributes of children through their adulthood, especially for monitoring lifelong conditions like Autism Spectrum Disorder. Deep convolutional neural networks have shown promising results in classifying facial expressions of adults. However, classifi... | ['Khan M. Iftekharuddin', 'Haim Y. Bar', 'Norou Diawara', 'Manar D. Samad', 'Megan A. Witherow'] | 2022-09-18 | null | null | null | null | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.42362708e-01 8.57977271e-02 -3.51328403e-01 -1.12180221e+00
-3.11823517e-01 -4.62317020e-01 4.66244489e-01 9.68619362e-02
-4.22819704e-01 4.92434978e-01 1.21430710e-01 7.06020176e-01
2.48293709e-02 -4.67525035e-01 -2.88157731e-01 -8.72598827e-01
-1.35473996e-01 3.94784629e-01 -7.41954923e-01 7.79219344... | [13.54330062866211, 1.7002654075622559] |
7554ab7f-c20b-4265-927c-b6850a9d8324 | compressed-predictive-information-coding | 2203.02051 | null | https://arxiv.org/abs/2203.02051v1 | https://arxiv.org/pdf/2203.02051v1.pdf | Compressed Predictive Information Coding | Unsupervised learning plays an important role in many fields, such as artificial intelligence, machine learning, and neuroscience. Compared to static data, methods for extracting low-dimensional structure for dynamic data are lagging. We developed a novel information-theoretic framework, Compressed Predictive Informati... | ['Kristofer Bouchard', 'Tianyi Luo', 'Rui Meng'] | 2022-03-03 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 5.06651819e-01 3.87870461e-01 -4.00145531e-01 -1.75069854e-01
-7.52309144e-01 -5.33030391e-01 7.15400338e-01 -9.23231766e-02
-1.15065098e-01 6.10058844e-01 6.29159927e-01 1.14670090e-01
-5.88952243e-01 -3.49794120e-01 -7.81643748e-01 -9.14828598e-01
-4.94172692e-01 2.39727005e-01 -2.98410982e-01 4.19825077... | [7.971439838409424, 3.838175058364868] |
37193254-345a-4ce0-a7e5-646881968e2d | global-and-local-collaborative-learning-for | 2204.08917 | null | https://arxiv.org/abs/2204.08917v1 | https://arxiv.org/pdf/2204.08917v1.pdf | Global-and-Local Collaborative Learning for Co-Salient Object Detection | The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning... | ['Sam Kwong', 'Qingming Huang', 'Yao Zhao', 'Huazhu Fu', 'Chongyi Li', 'Ning Yang', 'Runmin Cong'] | 2022-04-19 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 2.38647208e-01 -4.22164977e-01 -2.01351851e-01 -3.77318531e-01
-6.43142462e-01 -2.79845685e-01 7.36013472e-01 3.04510385e-01
-3.68581206e-01 2.45847613e-01 4.38529849e-01 3.58596265e-01
-4.35645878e-01 -4.95667219e-01 -5.23659348e-01 -7.60212064e-01
5.86377122e-02 -2.14777932e-01 7.57888615e-01 -3.26817393... | [9.803727149963379, -0.31251269578933716] |
012887cd-80d9-4c7f-b8b6-96b4cb6480ef | ecnu-at-semeval-2017-task-1-leverage-kernel | null | null | https://aclanthology.org/S17-2028 | https://aclanthology.org/S17-2028.pdf | ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity | To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achi... | ['Zhiheng Zhou', 'Man Lan', 'Yuanbin Wu', 'Junfeng Tian'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['cross-lingual-semantic-textual-similarity'] | ['natural-language-processing'] | [-7.61632204e-01 -2.84935057e-01 -3.73350471e-01 -5.83076358e-01
-9.79849696e-01 -8.69880021e-01 6.97509527e-01 2.37020224e-01
-9.10062671e-01 1.07230616e+00 4.88079339e-01 -5.35425007e-01
-2.15268484e-03 -6.64889514e-01 -4.88520712e-01 1.09876044e-01
6.91861510e-02 6.32253289e-01 3.12056988e-02 -7.41129935... | [10.952589988708496, 9.779767036437988] |
c0eb036f-fd18-4c72-a4c4-70216669e654 | analysis-of-temporal-expressions-annotated-in | null | null | https://aclanthology.org/W15-0211 | https://aclanthology.org/W15-0211.pdf | Analysis of Temporal Expressions Annotated in Clinical Notes | null | ['Marcus Didonet Del Fabro', 'Genevieve Gorrell', 'Angus Roberts', 'Leon Derczynski', 'Hegler Tissot'] | 2015-04-01 | null | null | null | ws-2015-4 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.335432529449463, 3.614875078201294] |
d9715cb0-3440-40c1-9aa2-db2a04136527 | a-note-on-monotone-submodular-maximization | 2006.09327 | null | https://arxiv.org/abs/2006.09327v5 | https://arxiv.org/pdf/2006.09327v5.pdf | Submodular Maximization in Clean Linear Time | In this paper, we provide the first deterministic algorithm that achieves the tight $1-1/e$ approximation guarantee for submodular maximization under a cardinality (size) constraint while making a number of queries that scales only linearly with the size of the ground set $n$. To complement our result, we also show str... | ['Amin Karbasi', 'Ehsan Kazemi', 'Moran Feldman', 'Wenxin Li'] | 2020-06-16 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 2.17060894e-01 4.11047369e-01 -5.10716558e-01 -1.03336066e-01
-1.05915570e+00 -1.04842687e+00 -5.21794379e-01 6.50589466e-01
-6.08736217e-01 8.18287492e-01 -8.52477998e-02 -3.36617559e-01
-5.68451345e-01 -1.31370199e+00 -1.14172113e+00 -6.81602538e-01
-4.34080988e-01 9.49885249e-01 2.46045396e-01 -2.37757504... | [6.569189548492432, 4.8917083740234375] |
bb72cc20-b93f-4d4d-b458-18b55805c8ee | distinctive-image-features-from-scale | null | null | https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf | https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf | Distinctive Image Features from Scale-Invariant Keypoints | This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distor... | ['David Lowe'] | 2004-01-05 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 5.51107526e-01 -7.92337000e-01 5.93746789e-02 -6.48565471e-01
-1.12150609e+00 -7.28301048e-01 7.02639043e-01 -8.16918015e-02
-5.03092334e-02 1.70931518e-01 -1.02204971e-01 -8.71359557e-03
-2.81387091e-01 -3.54211271e-01 -4.06541824e-01 -6.58491015e-01
-7.58215338e-02 5.45969844e-01 5.09621978e-01 1.77174911... | [8.159666061401367, -2.286419630050659] |
3eaea165-1eeb-42a3-9688-1ada69a01eb8 | developing-an-icu-scoring-system-with | 1604.06730 | null | http://arxiv.org/abs/1604.06730v1 | http://arxiv.org/pdf/1604.06730v1.pdf | Developing an ICU scoring system with interaction terms using a genetic algorithm | ICU mortality scoring systems attempt to predict patient mortality using
predictive models with various clinical predictors. Examples of such systems
are APACHE, SAPS and MPM. However, most such scoring systems do not actively
look for and include interaction terms, despite physicians intuitively taking
such interactio... | ['Chee Chun Gan', 'Gerard Learmonth'] | 2016-04-22 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 3.84319127e-02 -2.12685481e-01 -3.12427044e-01 -4.84740108e-01
-4.50357556e-01 -2.69303083e-01 -3.11738923e-02 6.70347691e-01
-2.95058668e-01 9.57597852e-01 2.92847931e-01 -8.99627745e-01
-9.01382446e-01 -6.69624507e-01 2.76068658e-01 -6.09847188e-01
-5.25025189e-01 1.07472157e+00 -1.87726200e-01 -1.86123829... | [8.06434440612793, 6.001269340515137] |
d12f8fb3-d7d9-480f-a2f1-71cf0935c999 | simplebert-a-pre-trained-model-that-learns-to | 2204.07779 | null | https://arxiv.org/abs/2204.07779v1 | https://arxiv.org/pdf/2204.07779v1.pdf | SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words | Pre-trained models are widely used in the tasks of natural language processing nowadays. However, in the specific field of text simplification, the research on improving pre-trained models is still blank. In this work, we propose a continued pre-training method for text simplification. Specifically, we propose a new ma... | ['Xiaojun Wan', 'Renliang Sun'] | 2022-04-16 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 3.81777793e-01 1.49154738e-01 3.15700583e-02 -4.04277086e-01
-6.05245233e-01 -4.79700826e-02 5.11923850e-01 2.74722338e-01
-8.09844494e-01 7.73819566e-01 3.65801454e-01 -4.12968397e-01
3.03644776e-01 -6.67132795e-01 -4.85574484e-01 -3.00142467e-01
5.71256578e-01 4.28138435e-01 1.60108097e-02 -5.79267979... | [11.02657413482666, 10.311452865600586] |
5fe88cbf-ac0c-4967-bd21-676646882206 | missing-values-and-imputation-in-healthcare | 2304.11749 | null | https://arxiv.org/abs/2304.11749v1 | https://arxiv.org/pdf/2304.11749v1.pdf | Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? | Missing values are a fundamental problem in data science. Many datasets have missing values that must be properly handled because the way missing values are treated can have large impact on the resulting machine learning model. In medical applications, the consequences may affect healthcare decisions. There are many me... | ['Rich Caruana', 'Cynthia Rudin', 'Urszula Chajewska', 'Sarah Tan', 'Zhi Chen'] | 2023-04-23 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 5.38957119e-01 1.93995014e-01 -7.40795612e-01 -8.39988470e-01
-7.78887630e-01 3.93228084e-02 -1.68314055e-01 4.17062551e-01
1.18505679e-01 1.34975040e+00 5.01937211e-01 -4.56401080e-01
-3.65345150e-01 -8.26079488e-01 -8.81695032e-01 -8.41495275e-01
1.39824077e-01 4.49615598e-01 -5.65425158e-01 -7.43239969... | [7.726368427276611, 4.930809497833252] |
36509940-6547-420b-9826-11da1737f097 | adversarial-open-domain-adaption-for-sketch | 2104.05703 | null | https://arxiv.org/abs/2104.05703v2 | https://arxiv.org/pdf/2104.05703v2.pdf | Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis | In this paper, we explore open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of that class are missing in the training data. It is challenging due to the lack of training supervision and the large geometric distortion bet... | ['Jan P. Allebach', 'Xiaohui Shen', 'Yiheng Zhu', 'Xiao Yang', 'Ding Liu', 'Xiaoyu Xiang'] | 2021-04-12 | null | null | null | null | ['sketch-to-image-translation'] | ['computer-vision'] | [ 3.44330490e-01 3.33319634e-01 5.77485599e-02 -1.76826611e-01
-9.93128955e-01 -1.11892688e+00 7.37327874e-01 -9.01592374e-01
4.38121200e-01 8.45871985e-01 -7.77219236e-02 -2.24251323e-03
3.46603543e-01 -9.27859783e-01 -9.41583693e-01 -6.42156899e-01
6.21927202e-01 6.15002692e-01 -1.05188653e-01 -1.93773359... | [11.968707084655762, 0.007284647785127163] |
90b40aee-a8ba-45de-94bb-e92f26a61531 | shortest-path-networks-for-graph-property | 2206.01003 | null | https://arxiv.org/abs/2206.01003v4 | https://arxiv.org/pdf/2206.01003v4.pdf | Shortest Path Networks for Graph Property Prediction | Most graph neural network models rely on a particular message passing paradigm, where the idea is to iteratively propagate node representations of a graph to each node in the direct neighborhood. While very prominent, this paradigm leads to information propagation bottlenecks, as information is repeatedly compressed at... | ['İsmail İlkan Ceylan', 'Radoslav Dimitrov', 'Ralph Abboud'] | 2022-06-02 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [ 3.31778765e-01 6.69537187e-01 -4.39004064e-01 -1.78197652e-01
-3.48088816e-02 -3.52463067e-01 7.61926949e-01 1.00266933e+00
-3.29300702e-01 6.20993316e-01 -5.04730083e-02 -4.31254804e-01
-2.17356831e-01 -1.48111463e+00 -9.67520356e-01 -5.38795531e-01
-8.58781099e-01 4.23283190e-01 4.80040431e-01 -3.83797318... | [6.959756851196289, 6.200713634490967] |
8d347d91-4038-47ac-a705-636fa4e8f4b3 | udapter-typology-based-language-adapters-for | null | null | https://aclanthology.org/2022.cl-3.3 | https://aclanthology.org/2022.cl-3.3.pdf | UDapter: Typology-based Language Adapters for Multilingual Dependency Parsing and Sequence Labeling | Recent advances in multilingual language modeling have brought the idea of a truly universal parser closer to reality. However, such models are still not immune to the “curse of multilinguality”: Cross-language interference and restrained model capacity remain major obstacles. To address this, we propose a novel langua... | ['Gertjan van Noord', 'Gosse Bouma', 'Arianna Bisazza', 'Ahmet Üstün'] | null | null | null | null | cl-acl-2022-9 | ['morphological-tagging'] | ['natural-language-processing'] | [-2.23625153e-01 -2.85195392e-02 -4.54560041e-01 -3.27517331e-01
-1.23846245e+00 -9.76700425e-01 4.06227022e-01 2.05008626e-01
-8.73351872e-01 6.11081898e-01 4.68514889e-01 -7.03263104e-01
3.20578188e-01 -5.75550020e-01 -8.36335659e-01 -4.21695471e-01
2.69856155e-01 5.06370068e-01 2.03612298e-01 -3.33392084... | [10.549464225769043, 9.87064266204834] |
67f4323c-9ce2-45b5-9613-1b5a2bbc3e8b | clustering-human-mobility-with-multiple | 2301.08524 | null | https://arxiv.org/abs/2301.08524v1 | https://arxiv.org/pdf/2301.08524v1.pdf | Clustering Human Mobility with Multiple Spaces | Human mobility clustering is an important problem for understanding human mobility behaviors (e.g., work and school commutes). Existing methods typically contain two steps: choosing or learning a mobility representation and applying a clustering algorithm to the representation. However, these methods rely on strict vis... | ['Yao-Yi Chiang', 'Haowen Lin', 'Haoji Hu'] | 2023-01-20 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [-2.35654905e-01 -3.48700970e-01 -5.83412051e-01 -2.88910747e-01
-3.67143422e-01 -2.24323481e-01 4.90702868e-01 8.95276070e-02
-3.80605280e-01 5.72579801e-01 4.25404966e-01 -3.29661369e-01
-5.17015219e-01 -9.28790927e-01 -4.99802560e-01 -8.72327745e-01
2.34544277e-02 6.77501142e-01 4.15067554e-01 -7.10200220... | [6.517541885375977, 1.5581469535827637] |
c7b9fd4c-3197-40ff-a2b6-315ea1e0661d | improving-robustness-via-risk-averse | 2005.00585 | null | https://arxiv.org/abs/2005.00585v1 | https://arxiv.org/pdf/2005.00585v1.pdf | Improving Robustness via Risk Averse Distributional Reinforcement Learning | One major obstacle that precludes the success of reinforcement learning in real-world applications is the lack of robustness, either to model uncertainties or external disturbances, of the trained policies. Robustness is critical when the policies are trained in simulations instead of real world environment. In this wo... | ['Qinsheng Zhang', 'Rahul Singh', 'Yongxin Chen'] | 2020-05-01 | null | https://openreview.net/forum?id=6_48llFrKdm | https://openreview.net/pdf?id=6_48llFrKdm | l4dc-2020-6 | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.95242196e-01 3.13491523e-02 -6.15913868e-02 -9.22307447e-02
-7.36983359e-01 -6.43482268e-01 6.72466636e-01 -2.35818252e-02
-5.84990382e-01 1.27114010e+00 2.48286694e-01 -6.56556368e-01
-4.92186099e-01 -7.18303442e-01 -8.67466807e-01 -7.03095675e-01
-4.80504990e-01 6.21979907e-02 1.04378864e-01 -2.75357097... | [4.447775363922119, 2.3218441009521484] |
3d3659a8-d025-463f-b5d6-eb7f093fa1ca | mining-duplicate-questions-of-stack-overflow | 2210.01637 | null | https://arxiv.org/abs/2210.01637v1 | https://arxiv.org/pdf/2210.01637v1.pdf | Mining Duplicate Questions of Stack Overflow | There has a been a significant rise in the use of Community Question Answering sites (CQAs) over the last decade owing primarily to their ability to leverage the wisdom of the crowd. Duplicate questions have a crippling effect on the quality of these sites. Tackling duplicate questions is therefore an important step to... | ['Pranav Dheram', 'Radhika Parik', 'Anirudha Rayasam', 'Mihir Kale'] | 2022-10-04 | null | null | null | null | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [-4.85360026e-01 -2.14863308e-02 4.09224004e-01 -2.30004609e-01
-8.22459638e-01 -5.78150988e-01 5.23096025e-01 5.05162477e-01
-4.36776400e-01 3.81082982e-01 7.26694822e-01 -4.08177406e-01
-1.10044152e-01 -7.87252307e-01 -3.93705398e-01 5.32614142e-02
2.27776811e-01 1.08071484e-01 5.75323641e-01 -5.30243933... | [11.422355651855469, 8.141997337341309] |
45099a44-297e-4c6d-b8d4-9c4749e602c3 | a-method-for-detection-of-atrial-fibrillation | null | null | https://doi.org/10.1109/CIC.2000.898539 | http://www.medicine.mcgill.ca/physio/glasslab/pub_pdf/method_2001.pdf | A method for detection of atrial fibrillation using RR intervals | This work describes a method for automatic detection of atrial fibrillation (AF) based on RR intervals. We define /spl Delta/RR to be the difference between successive RR intervals. The standard density histograms of RR and /spl Delta/RR intervals are determined from data in the MIT-BIH atrial fibrillation/flutter data... | ['L. Glass', 'K. Tateno'] | 2000-09-24 | null | null | null | computers-in-cardiology-2000-9 | ['atrial-fibrillation-detection'] | ['medical'] | [-6.72235861e-02 -4.15991396e-01 -2.31445923e-01 -2.27230057e-01
-6.35981560e-01 -6.58938408e-01 -1.50714502e-01 4.55236256e-01
-2.77064770e-01 1.34508121e+00 -7.71309063e-02 -8.79632473e-01
-2.44942978e-01 -7.79775977e-01 -1.33695126e-01 -8.26647401e-01
-5.89124799e-01 5.59792280e-01 1.85023621e-01 3.34141493... | [14.1600923538208, 3.1771185398101807] |
9643b313-886b-44d2-a50f-3b114467f07d | semantic-line-detection-and-its-applications | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Lee_Semantic_Line_Detection_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Lee_Semantic_Line_Detection_ICCV_2017_paper.pdf | Semantic Line Detection and Its Applications | Semantic lines characterize the layout of an image. Despite their importance in image analysis and scene understanding, there is no reliable research for semantic line detection. In this paper, we propose a semantic line detector using a convolutional neural network with multi-task learning, by regarding the line detec... | ['Chang-Su Kim', 'Han-Ul Kim', 'Jun-Tae Lee', 'Chul Lee'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['line-detection'] | ['computer-vision'] | [ 3.91652972e-01 -3.95024538e-01 -3.35635208e-02 -4.33025420e-01
-3.49112481e-01 -2.24899366e-01 1.80154026e-01 4.60462064e-01
-5.05958080e-01 3.60910654e-01 -2.70615101e-01 -1.50392607e-01
-3.30808498e-02 -9.82500911e-01 -8.10691655e-01 -4.97111112e-01
3.46890211e-01 -9.57356021e-02 6.60144687e-01 -1.14762269... | [8.341418266296387, -1.5741368532180786] |
a2ab69a5-bd80-4dfb-9889-012e61c9ee55 | gaitmast-motion-aware-spatio-temporal-feature | 2210.11817 | null | https://arxiv.org/abs/2210.11817v2 | https://arxiv.org/pdf/2210.11817v2.pdf | Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning | As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security, and so on. Existing gait recognition methods suffer from changes in viewpoint and clothing and barely consider extracting diverse motion features, a fundamental characteristic in gaits, from... | ['Junping Zhang', 'Hongming Shan', 'Yuzhen Zhang', 'Jiaqi Gao', 'Jingqi Li'] | 2022-10-21 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-1.30604103e-01 -8.79126906e-01 -3.72994810e-01 -1.51198223e-01
-1.05050400e-01 -3.13917577e-01 2.03541398e-01 -5.15303791e-01
-3.56876224e-01 2.96647012e-01 3.72444272e-01 1.49150774e-01
2.45670930e-01 -6.61427557e-01 5.84210046e-02 -9.03148413e-01
-3.53788048e-01 -2.70820975e-01 2.16161564e-01 -2.32987136... | [14.270843505859375, 1.4307408332824707] |
a8449e73-6d90-4907-aa9f-dd09853c2532 | self-taught-convolutional-neural-networks-for | 1701.00185 | null | http://arxiv.org/abs/1701.00185v1 | http://arxiv.org/pdf/1701.00185v1.pdf | Self-Taught Convolutional Neural Networks for Short Text Clustering | Short text clustering is a challenging problem due to its sparseness of text
representation. Here we propose a flexible Self-Taught Convolutional neural
network framework for Short Text Clustering (dubbed STC^2), which can flexibly
and successfully incorporate more useful semantic features and learn non-biased
deep tex... | ['Guanhua Tian', 'Bo Xu', 'Suncong Zheng', 'Peng Wang', 'Jun Zhao', 'Jiaming Xu'] | 2017-01-01 | null | null | null | null | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-8.25753249e-03 -2.62086570e-01 -3.50478500e-01 -6.61831379e-01
-3.21216404e-01 -3.93559128e-01 5.96630573e-01 3.45446467e-02
-4.17846233e-01 1.16048656e-01 5.37745535e-01 -2.45499481e-02
-3.09728503e-01 -5.79038620e-01 -3.64765525e-01 -8.15984249e-01
3.03203940e-01 7.12714434e-01 -2.95404404e-01 5.84391020... | [10.403873443603516, 6.751841068267822] |
15ae1a6f-dc09-4a3e-ba3d-ed95fc991d1b | lambert-layout-aware-language-modeling-using | 2002.08087 | null | https://arxiv.org/abs/2002.08087v5 | https://arxiv.org/pdf/2002.08087v5.pdf | LAMBERT: Layout-Aware (Language) Modeling for information extraction | We introduce a simple new approach to the problem of understanding documents where non-trivial layout influences the local semantics. To this end, we modify the Transformer encoder architecture in a way that allows it to use layout features obtained from an OCR system, without the need to re-learn language semantics fr... | ['Michał Turski', 'Filip Graliński', 'Tomasz Stanisławek', 'Rafał Powalski', 'Łukasz Garncarek', 'Piotr Halama', 'Bartosz Topolski'] | 2020-02-19 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 1.06722862e-01 1.56286910e-01 3.39849368e-02 -4.51136023e-01
-1.01301408e+00 -1.11548245e+00 8.17565918e-01 2.67669767e-01
-5.49634576e-01 4.56006289e-01 4.51903552e-01 -5.01157880e-01
-1.54662490e-01 -8.91874254e-01 -9.59362209e-01 -4.97247785e-01
-2.10319068e-02 4.98506457e-01 1.36294305e-01 -2.66047984... | [11.623963356018066, 2.597076177597046] |
53e8c822-764e-48cc-b88d-68bcd53bd0f4 | learning-rich-features-for-gait-recognition | 2110.13408 | null | https://arxiv.org/abs/2110.13408v2 | https://arxiv.org/pdf/2110.13408v2.pdf | Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes | Gait recognition captures gait patterns from the walking sequence of an individual for identification. Most existing gait recognition methods learn features from silhouettes or skeletons for the robustness to clothing, carrying, and other exterior factors. The combination of the two data modalities, however, is not ful... | ['Zhiqiang He', 'Yang Zhang', 'Kang Ma', 'Yunjie Peng'] | 2021-10-26 | null | null | null | null | ['gait-identification'] | ['computer-vision'] | [ 0.01880165 -0.45592475 -0.38836527 -0.26863524 -0.39564875 -0.03645656
0.18193781 -0.2381882 -0.14876387 0.51234525 0.35918054 0.5657741
-0.01443955 -0.7811022 -0.19182831 -0.9448516 -0.21427526 0.19661665
0.27175543 -0.3000722 -0.12223334 0.3175098 -1.5962294 -0.05692229
0.6216201 1.0823066 -0.1... | [14.291923522949219, 1.4104026556015015] |
2e4fd196-8b8f-4b54-a227-4ac743e8ebc7 | functional-intrusive-load-monitor-film-a | 1809.08910 | null | http://arxiv.org/abs/1809.08910v1 | http://arxiv.org/pdf/1809.08910v1.pdf | Functional Intrusive Load Monitor (FILM): A Model-based Platform for Non-Intrusive Load Monitoring System Development | Non-Intrusive Load Monitoring (NILM) is an important application to monitor
household appliance activities and provide related information to house owner
or/and utility company via a single sensor installed at the electrical entry of
the house. It can be used for different purposes in residential and industrial
sectors... | [] | 2018-09-19 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.70421064e-01 -2.56543458e-01 -2.74102122e-01 -3.42042893e-01
-1.96678832e-01 -5.26811242e-01 2.94741690e-01 -7.19037503e-02
4.67138171e-01 7.34130621e-01 -1.82472333e-01 4.39105034e-02
-2.53866225e-01 -9.99711692e-01 1.33774251e-01 -9.50620592e-01
1.61066815e-01 4.80056435e-01 1.13790870e-01 -5.58518060... | [5.984971523284912, 2.565417766571045] |
10f0a349-9087-4718-82fa-42be19915b3b | non-autoregressive-conditional-diffusion | 2306.05043 | null | https://arxiv.org/abs/2306.05043v1 | https://arxiv.org/pdf/2306.05043v1.pdf | Non-autoregressive Conditional Diffusion Models for Time Series Prediction | Recently, denoising diffusion models have led to significant breakthroughs in the generation of images, audio and text. However, it is still an open question on how to adapt their strong modeling ability to model time series. In this paper, we propose TimeDiff, a non-autoregressive diffusion model that achieves high-qu... | ['James Kwok', 'Lifeng Shen'] | 2023-06-08 | null | null | null | null | ['open-question', 'time-series-prediction'] | ['natural-language-processing', 'time-series'] | [-1.11888401e-01 -3.28700870e-01 -1.48398429e-01 -5.02790749e-01
-9.50729132e-01 -6.92590952e-01 1.13491261e+00 -1.02731757e-01
-2.17485696e-01 2.46360555e-01 7.70104706e-01 -3.92184317e-01
-1.09692395e-01 -7.34396398e-01 -6.49293303e-01 -7.07925797e-01
-3.40103269e-01 3.82611573e-01 3.06635410e-01 -3.50658894... | [7.1767578125, 3.2450811862945557] |
1bb198e1-111a-42dc-a5f4-2694a6ec5cd8 | simple-and-deep-graph-convolutional-networks-1 | 2007.02133 | null | https://arxiv.org/abs/2007.02133v1 | https://arxiv.org/pdf/2007.02133v1.pdf | Simple and Deep Graph Convolutional Networks | Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to the {\em over-smoothi... | ['Yaliang Li', 'Ming Chen', 'Zhewei Wei', 'Zengfeng Huang', 'Bolin Ding'] | 2020-07-04 | simple-and-deep-graph-convolutional-networks | https://proceedings.icml.cc/static/paper_files/icml/2020/2172-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2172-Paper.pdf | icml-2020-1 | ['node-classification-on-non-homophilic'] | ['graphs'] | [ 9.04712901e-02 4.58349675e-01 -2.77143091e-01 -4.06458080e-01
-3.15012991e-01 -3.02064955e-01 7.03639686e-01 -1.87636372e-02
-3.44372302e-01 5.10318935e-01 -4.51778388e-03 -6.77219033e-01
-8.58160183e-02 -9.03826535e-01 -8.34557474e-01 -4.47562456e-01
-3.53631049e-01 5.72504818e-01 1.14585049e-01 -8.13977420... | [7.002048969268799, 6.219116687774658] |
fec4f6b8-eb1a-4a7b-a2c7-a11fbd0aaf39 | differentiable-programming-for-hyperspectral | 2007.05996 | null | https://arxiv.org/abs/2007.05996v1 | https://arxiv.org/pdf/2007.05996v1.pdf | Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model | Hyperspectral unmixing is an important remote sensing task with applications including material identification and analysis. Characteristic spectral features make many pure materials identifiable from their visible-to-infrared spectra, but quantifying their presence within a mixture is a challenging task due to nonline... | ['Suren Jayasuriya', 'Philip Christensen', 'John Janiczek', 'Gautam Dasarathy', 'Christopher S. Edwards', 'Parth Thaker'] | 2020-07-12 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5893_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720647.pdf | eccv-2020-8 | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.35784292e-01 -7.45226085e-01 7.62519985e-02 -1.34713128e-01
-9.30036426e-01 -6.63688600e-01 5.88255465e-01 -3.25505793e-01
1.17265701e-01 4.81563330e-01 -1.06523614e-02 -2.51566768e-01
-3.78941029e-01 -9.02393639e-01 -5.31014264e-01 -1.14375889e+00
1.36885911e-01 3.74013633e-01 -5.75493693e-01 -2.94342816... | [10.083846092224121, -2.0253851413726807] |
7f9d0a63-1e23-4210-90c1-1475b2df6ac6 | efficient-plane-based-optimization-of | 1905.08853 | null | https://arxiv.org/abs/1905.08853v1 | https://arxiv.org/pdf/1905.08853v1.pdf | Efficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction | We propose a novel approach to reconstruct RGB-D indoor scene based on plane primitives. Our approach takes as input a RGB-D sequence and a dense coarse mesh reconstructed from it, and generates a lightweight, low-polygonal mesh with clear face textures and sharp features without losing geometry details from the origin... | ['Chao Wang', 'Xiaohu Guo'] | 2019-05-21 | null | null | null | null | ['rgb-d-reconstruction'] | ['computer-vision'] | [ 4.62387204e-01 3.55388552e-01 6.12309396e-01 -2.82359391e-01
-5.59129298e-01 -5.00543118e-01 2.74915248e-01 -1.03075795e-01
2.22132921e-01 6.62501991e-01 -1.08988456e-01 1.05462754e-02
3.07838209e-02 -1.48840380e+00 -9.12789524e-01 -3.43931109e-01
3.48014802e-01 6.74930930e-01 5.86308002e-01 -2.11976692... | [9.053975105285645, -3.106006145477295] |
1c73a750-69d5-4eb5-8275-5e5cba59bb2d | cdjur-br-a-golden-collection-of-legal | 2305.18315 | null | https://arxiv.org/abs/2305.18315v1 | https://arxiv.org/pdf/2305.18315v1.pdf | CDJUR-BR -- A Golden Collection of Legal Document from Brazilian Justice with Fine-Grained Named Entities | A basic task for most Legal Artificial Intelligence (Legal AI) applications is Named Entity Recognition (NER). However, texts produced in the context of legal practice make references to entities that are not trivially recognized by the currently available NERs. There is a lack of categorization of legislation, jurispr... | ['Nilsiton Aragão', 'Raquel Silveira', 'André Câmara Ferreira da Costa', 'Francisco das Chagas Jucá Bomfim', 'João Araújo Monteiro Neto', 'Vasco Furtado', 'Vladia Pinheiro', 'Antonio Mauricio'] | 2023-05-20 | null | null | null | null | ['jurisprudence', 'named-entity-recognition-ner'] | ['miscellaneous', 'natural-language-processing'] | [-3.05202454e-01 2.17454702e-01 -1.20315351e-01 -2.98146993e-01
-7.55374074e-01 -7.94394791e-01 6.60768926e-01 7.64142811e-01
-8.84615541e-01 1.02470720e+00 5.32556474e-01 -8.06295455e-01
-6.21751130e-01 -8.98789048e-01 -3.16547990e-01 -2.25025713e-01
4.81250137e-01 5.81167758e-01 2.19436228e-01 -4.71155018... | [9.704315185546875, 9.243600845336914] |
b76f733b-5d3d-48fa-81cf-0282e924d65a | pro-uigan-progressive-face-hallucination-from | 2108.00602 | null | https://arxiv.org/abs/2108.00602v6 | https://arxiv.org/pdf/2108.00602v6.pdf | Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails | In this paper, we study the task of hallucinating an authentic high-resolution (HR) face from an occluded thumbnail. We propose a multi-stage Progressive Upsampling and Inpainting Generative Adversarial Network, dubbed Pro-UIGAN, which exploits facial geometry priors to replenish and upsample (8*) the occluded and tiny... | ['Ping Liu', 'Xiaobo Lu', 'Xin Yu', 'Yang Zhang'] | 2021-08-02 | null | null | null | null | ['face-alignment', 'face-parsing', 'face-hallucination'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.03445750e-01 3.90941232e-01 1.05285816e-01 -5.32063007e-01
-1.04412782e+00 -4.44008708e-01 2.45822594e-01 -1.06195199e+00
1.62819341e-01 7.10577369e-01 2.81741977e-01 3.15656960e-01
3.16687614e-01 -7.59636581e-01 -1.03895152e+00 -6.83423698e-01
3.47276151e-01 2.70774066e-01 -5.88089347e-01 2.69036852... | [12.783344268798828, -0.11673842370510101] |
dd3e431c-e623-492e-a364-42785ec53b6a | guided-adaptive-credit-assignment-for-sample | null | null | https://openreview.net/forum?id=SyxBgkBFPS | https://openreview.net/pdf?id=SyxBgkBFPS | Guided Adaptive Credit Assignment for Sample Efficient Policy Optimization | Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from sparse reward tasks, which leads to poor sample efficiency during training. In this work, we propose a guided adaptive credit assignment method to do effectively credit... | ['Caiming Xiong', 'Richard Socher', 'Hao liu'] | 2019-09-25 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 3.13653558e-01 -1.24498367e-01 -9.42198575e-01 -3.41300040e-01
-7.46219456e-01 -3.22508961e-01 5.09310186e-01 1.81243196e-03
-7.74742365e-01 1.35885191e+00 1.41878769e-01 -7.84591913e-01
-1.06616460e-01 -5.00658989e-01 -7.34745681e-01 -5.50908029e-01
1.75476387e-01 3.58623177e-01 1.13291137e-01 -1.78508043... | [4.12155294418335, 2.220228433609009] |
9d546522-9f3e-4782-a73b-3390a703d788 | joint-lemmatization-and-morphological-tagging | null | null | https://aclanthology.org/D15-1272 | https://aclanthology.org/D15-1272.pdf | Joint Lemmatization and Morphological Tagging with Lemming | null | ['Hinrich Sch{\\"u}tze', 'er', 'Thomas M{\\"u}ller', 'Alex Fraser', 'Ryan Cotterell'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['morphological-tagging'] | ['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... | [-7.376143932342529, 3.7354094982147217] |
ae86d3c2-db0d-4887-8e1f-74cff0140b63 | deep-curvilinear-editing-commutative-and | 2211.14573 | null | https://arxiv.org/abs/2211.14573v2 | https://arxiv.org/pdf/2211.14573v2.pdf | Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model | Semantic editing of images is the fundamental goal of computer vision. Although deep learning methods, such as generative adversarial networks (GANs), are capable of producing high-quality images, they often do not have an inherent way of editing generated images semantically. Recent studies have investigated a way of ... | ['Takashi Matsubara', 'Takehiro Aoshima'] | 2022-11-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Aoshima_Deep_Curvilinear_Editing_Commutative_and_Nonlinear_Image_Manipulation_for_Pretrained_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Aoshima_Deep_Curvilinear_Editing_Commutative_and_Nonlinear_Image_Manipulation_for_Pretrained_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-manipulation'] | ['computer-vision'] | [ 4.76682842e-01 6.68016747e-02 1.07695013e-01 -2.52066970e-01
-1.74409613e-01 -8.57948422e-01 9.99462545e-01 -4.20554787e-01
-2.62860388e-01 7.89187849e-01 1.28707783e-02 2.35602446e-02
-2.34158933e-01 -1.05639124e+00 -1.00680506e+00 -8.72133434e-01
3.80421638e-01 2.24763423e-01 -2.83999443e-01 -2.30498746... | [11.708251953125, -0.3684540390968323] |
cf43e438-1a61-4db6-a488-4068bc301495 | eliminating-gradient-conflict-in-reference | 2207.06095 | null | https://arxiv.org/abs/2207.06095v3 | https://arxiv.org/pdf/2207.06095v3.pdf | Eliminating Gradient Conflict in Reference-based Line-Art Colorization | Reference-based line-art colorization is a challenging task in computer vision. The color, texture, and shading are rendered based on an abstract sketch, which heavily relies on the precise long-range dependency modeling between the sketch and reference. Popular techniques to bridge the cross-modal information and mode... | ['Yibo Yang', 'Wenyu Chen', 'Zhao Kang', 'Zhengyang Geng', 'Zekun Li'] | 2022-07-13 | null | null | null | null | ['colorization', 'line-art-colorization'] | ['computer-vision', 'computer-vision'] | [ 8.95819515e-02 -3.16824406e-01 1.42682577e-02 -2.44490594e-01
-6.26243472e-01 -5.23222506e-01 5.11167705e-01 -2.64467865e-01
-2.07634449e-01 6.03370428e-01 -5.32061458e-02 -2.28679687e-01
6.99722255e-03 -6.98663116e-01 -8.50282669e-01 -7.73108125e-01
3.51946026e-01 -6.08610623e-02 2.26492584e-01 -3.98763001... | [11.422110557556152, -0.8411617279052734] |
a70e7b74-b7e6-4c01-bd44-255bbaf97f0e | attention-based-aspect-reasoning-for | 2108.00513 | null | https://arxiv.org/abs/2108.00513v2 | https://arxiv.org/pdf/2108.00513v2.pdf | Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes | Question Answering (QA) in clinical notes has gained a lot of attention in the past few years. Existing machine reading comprehension approaches in clinical domain can only handle questions about a single block of clinical texts and fail to retrieve information about multiple patients and their clinical notes. To handl... | ['Chandan K. Reddy', 'Sutanay Choudhury', 'Khushbu Agarwal', 'Tian Shi', 'Ping Wang'] | 2021-08-01 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 5.92540298e-03 6.50967240e-01 -1.68452218e-01 -3.31751466e-01
-1.04332423e+00 -3.30131024e-01 1.93071187e-01 7.82320917e-01
-2.14399397e-01 7.14348674e-01 7.97001660e-01 -5.75106382e-01
-4.95916486e-01 -8.37705731e-01 -3.27893496e-01 -1.53201684e-01
2.29341209e-01 8.09846282e-01 5.91991961e-01 -3.35075766... | [8.769164085388184, 8.5711030960083] |
13853259-bed4-4dd8-92c9-bfb922466ab9 | driveability-constrained-models-for-optimal | 2303.12603 | null | https://arxiv.org/abs/2303.12603v1 | https://arxiv.org/pdf/2303.12603v1.pdf | Driveability Constrained Models for Optimal Control of Hybrid Electric Vehicles | This work investigates the effect of three different driveability constraints on the optimal energy management strategy for a p2 parallel hybrid. Two of these constraints are used to prevent frequent gear shifting and engine start/stops, while the third is used to increase the sportiness of the vehicle by maximizing th... | ['Daniela Misul', 'Federico Miretti'] | 2023-03-22 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 1.19739033e-01 2.38269180e-01 -5.50987959e-01 1.60985425e-01
3.90681744e-01 -4.67047006e-01 4.89475727e-01 2.33503833e-01
-2.86092758e-01 5.32316387e-01 -3.43757212e-01 -5.46666980e-01
-1.00777352e+00 -8.10130835e-01 -2.11644545e-01 -8.05079043e-01
1.93019062e-01 2.09877059e-01 3.07827204e-01 -3.03601354... | [5.497677803039551, 2.0799129009246826] |
50e0cd8e-0c02-47b7-8652-f46b722a23fa | diffdock-diffusion-steps-twists-and-turns-for | 2210.01776 | null | https://arxiv.org/abs/2210.01776v2 | https://arxiv.org/pdf/2210.01776v2.pdf | DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking | Predicting the binding structure of a small molecule ligand to a protein -- a task known as molecular docking -- is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial impro... | ['Tommi Jaakkola', 'Regina Barzilay', 'Bowen Jing', 'Hannes Stärk', 'Gabriele Corso'] | 2022-10-04 | null | null | null | null | ['blind-docking', 'molecular-docking'] | ['medical', 'medical'] | [-1.90679342e-01 -7.35774636e-02 -1.11263938e-01 -2.03972921e-01
-1.15829957e+00 -8.15872967e-01 3.88097882e-01 7.30019882e-02
-4.91477787e-01 1.35255623e+00 -1.13483509e-02 -6.97274983e-01
5.13524823e-02 -6.26006246e-01 -1.13909805e+00 -1.15709662e+00
-3.61101925e-01 9.83642876e-01 -7.74660707e-02 -9.59611759... | [4.87790584564209, 5.597963333129883] |
9e806fcf-47e1-48fa-846b-ca5745d0643c | part-of-speech-tagging-of-swedish-texts-in | null | null | https://aclanthology.org/2021.nodalida-main.20 | https://aclanthology.org/2021.nodalida-main.20.pdf | Part-of-speech tagging of Swedish texts in the neural era | We train and test five open-source taggers, which use different methods, on three Swedish corpora, which are of comparable size but use different tagsets. The KB-Bert tagger achieves the highest accuracy for part-of-speech and morphological tagging, while being fast enough for practical use. We also compare the perform... | ['Aleksandrs Berdicevskis', 'Yvonne Adesam'] | null | null | null | null | nodalida-2021-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-2.30211057e-02 7.37726912e-02 1.80411572e-03 -2.87002325e-01
-1.49110985e+00 -1.05925810e+00 5.45464158e-01 3.37288171e-01
-8.72045815e-01 8.45225751e-01 4.89555866e-01 -4.43979710e-01
-9.72399712e-02 -3.66305977e-01 -3.02347809e-01 -6.41866446e-01
-1.75490618e-01 8.98700893e-01 7.49804795e-01 -1.09036170... | [10.329988479614258, 10.046083450317383] |
922e494f-b92c-4db5-979a-57d39c3e0af1 | deeplung-deep-3d-dual-path-nets-for-automated | 1801.09555 | null | http://arxiv.org/abs/1801.09555v1 | http://arxiv.org/pdf/1801.09555v1.pdf | DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification | In this work, we present a fully automated lung computed tomography (CT)
cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule
detection (identifying the locations of candidate nodules) and classification
(classifying candidate nodules into benign or malignant). Considering the 3D
nature of lun... | ['Wei Fan', 'Wentao Zhu', 'Chaochun Liu', 'Xiaohui Xie'] | 2018-01-25 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [-1.16232544e-01 4.84853864e-01 -5.97579539e-01 -4.34349589e-02
-9.77109969e-01 -2.15558961e-01 3.12444299e-01 -2.87832081e-01
-1.28645167e-01 2.26500019e-01 2.38173395e-01 -6.96495831e-01
-3.44805866e-02 -8.66668284e-01 -4.96875644e-01 -6.54143989e-01
-5.92396781e-02 8.38226736e-01 7.66554236e-01 3.18837315... | [15.414968490600586, -2.116024971008301] |
224e8b45-f4c0-4444-bc96-59cacd30cebe | videoglue-video-general-understanding | 2307.03166 | null | https://arxiv.org/abs/2307.03166v1 | https://arxiv.org/pdf/2307.03166v1.pdf | VideoGLUE: Video General Understanding Evaluation of Foundation Models | We evaluate existing foundation models video understanding capabilities using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring a f... | ['Boqing Gong', 'Ting Liu', 'Ming-Hsuan Yang', 'Hartwig Adam', 'Florian Schroff', 'Huisheng Wang', 'Mikhail Sirotenko', 'Luke Friedman', 'Tobias Weyand', 'Menglin Jia', 'Xuan Yang', 'Lu Jiang', 'Yin Cui', 'Hao Zhou', 'Long Zhao', 'Nitesh Bharadwaj Gundavarapu', 'Liangzhe Yuan'] | 2023-07-06 | null | null | null | null | ['temporal-localization', 'action-recognition-in-videos', 'video-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.03845966e-01 -3.12382430e-01 -5.42609274e-01 -3.34826440e-01
-6.80129945e-01 -5.70611298e-01 4.41002876e-01 -6.22916341e-01
-5.22935748e-01 4.72689897e-01 5.20006239e-01 -1.62893966e-01
2.52671149e-02 -1.36514768e-01 -1.14117408e+00 -4.81396407e-01
-2.70809621e-01 -1.06994053e-02 3.32320869e-01 -8.11024383... | [9.276544570922852, 0.7750512361526489] |
6ce8a106-a9e5-4b61-8550-ea2938b43ea1 | rpg-learning-recursive-point-cloud-generation | 2105.14322 | null | https://arxiv.org/abs/2105.14322v1 | https://arxiv.org/pdf/2105.14322v1.pdf | RPG: Learning Recursive Point Cloud Generation | In this paper we propose a novel point cloud generator that is able to reconstruct and generate 3D point clouds composed of semantic parts. Given a latent representation of the target 3D model, the generation starts from a single point and gets expanded recursively to produce the high-resolution point cloud via a seque... | ['Wei-Chen Chiu', 'Li-Heng Wang', 'Chen-Yi Chiu', 'Yu-Liang Kuo', 'Hui-Yu Huang', 'Wei-Jan Ko'] | 2021-05-29 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 1.71520725e-01 2.97309577e-01 2.41752028e-01 -2.31824443e-01
-8.67148876e-01 -6.02438509e-01 7.70712793e-01 1.38956144e-01
3.25239569e-01 1.59443870e-01 -2.76955426e-01 4.64907512e-02
8.29988569e-02 -1.22109544e+00 -7.81375945e-01 -4.76845145e-01
1.75152019e-01 1.30595660e+00 5.48586130e-01 -1.60044245... | [8.402247428894043, -3.508168935775757] |
6e1d7183-c0d8-4da4-887f-5767c71304aa | automatic-sleep-staging-recent-development | 2111.08446 | null | https://arxiv.org/abs/2111.08446v3 | https://arxiv.org/pdf/2111.08446v3.pdf | Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions | Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Re... | ['Kaare Mikkelsen', 'Huy Phan'] | 2021-11-03 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 5.72495237e-02 9.93633270e-02 -2.50962138e-01 -6.88616037e-01
-5.58482230e-01 -1.58066183e-01 -2.16927320e-01 1.93899766e-01
-6.97656572e-01 6.05494797e-01 1.27845287e-01 -5.34870446e-01
1.07604675e-01 -9.54818912e-03 4.68018264e-01 -7.88740814e-01
-7.76055874e-03 6.08062267e-01 1.21188276e-01 5.26535176... | [13.523439407348633, 3.503833055496216] |
c365cff9-9f64-4dce-8520-2187d2604792 | pneumococcus-and-the-stress-gradient | 2205.12629 | null | https://arxiv.org/abs/2205.12629v1 | https://arxiv.org/pdf/2205.12629v1.pdf | Pneumococcus and the stress-gradient hypothesis: a trade-off links $R_0$ and susceptibility to co-colonization across countries | Modern molecular technologies have revolutionized our understanding of bacterial epidemiology, but reported data across different settings remain under-integrated in common theoretical frameworks. Pneumococcus serotype co-colonization, caused by the polymorphic bacteria Streptococcus pneumoniae, has been increasingly i... | ['Erida Gjini', 'Ermanda Dekaj'] | 2022-05-25 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 3.99096906e-01 -4.77828145e-01 1.23452730e-01 1.62350565e-01
2.25296065e-01 -7.24822521e-01 2.23339856e-01 5.70339441e-01
-4.55464900e-01 8.07612121e-01 2.56952971e-01 -6.29497588e-01
-7.68962204e-01 -6.65877223e-01 -9.56512809e-01 -1.16861284e+00
-6.76519573e-01 3.42467934e-01 2.04783961e-01 -9.72075760... | [5.663492679595947, 4.534782409667969] |
fdfce4e5-d75c-42ac-9188-7c4ff0ea6bab | reliability-hierarchical-memory-network-for | 2303.14384 | null | https://arxiv.org/abs/2303.14384v1 | https://arxiv.org/pdf/2303.14384v1.pdf | Reliability-Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation | This paper aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only trained by the sparse scribble annotations but also initialized with the sparse target scribbles for inference. Thus, the annotation burdens for both training and initialization can be sub... | ['Zhenyu He', 'YaoWei Wang', 'Hongpeng Wang', 'Wenjie Pei', 'Kaige Mao', 'Zikun Zhou'] | 2023-03-25 | null | null | null | null | ['video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.28411973e-02 2.92556822e-01 -3.13619912e-01 -4.63764250e-01
-6.95537388e-01 -1.26950666e-01 -2.24701618e-03 -1.96981892e-01
-1.42440706e-01 5.70285380e-01 -8.41376558e-02 1.79388866e-01
6.50161207e-02 -8.15493643e-01 -9.48573351e-01 -8.33199382e-01
2.95827925e-01 6.97920799e-01 7.58515358e-01 1.87154729... | [9.567122459411621, 0.003120080102235079] |
8aa839f2-736f-4d38-8dbe-42582245e6a5 | entity-extraction-from-wikipedia-list-pages | 2003.05146 | null | https://arxiv.org/abs/2003.05146v1 | https://arxiv.org/pdf/2003.05146v1.pdf | Entity Extraction from Wikipedia List Pages | When it comes to factual knowledge about a wide range of domains, Wikipedia is often the prime source of information on the web. DBpedia and YAGO, as large cross-domain knowledge graphs, encode a subset of that knowledge by creating an entity for each page in Wikipedia, and connecting them through edges. It is well kno... | ['Heiko Paulheim', 'Nicolas Heist'] | 2020-03-11 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [-6.59639955e-01 6.41202450e-01 -6.99398398e-01 -4.95081842e-02
-3.15592676e-01 -9.68795419e-01 7.53063083e-01 9.01767015e-01
-5.12769818e-01 1.51193762e+00 2.27174118e-01 -2.02455148e-01
-3.81309241e-01 -1.45931256e+00 -9.93390441e-01 3.65633070e-02
-3.60435963e-01 8.97964180e-01 8.09040248e-01 -5.15897632... | [9.313729286193848, 8.354809761047363] |
95b4e294-1923-42f2-bef3-7ba7136e135c | wide-baseline-stereo-matching-with-convex-1 | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Galun_Wide_Baseline_Stereo_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Galun_Wide_Baseline_Stereo_ICCV_2015_paper.pdf | Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints | Finding correspondences in wide baseline setups is a challenging problem. Existing approaches have focused largely on developing better feature descriptors for correspondence and on accurate recovery of epipolar line constraints. This paper focuses on the challenging problem of finding correspondences once approximate ... | ['Tal Amir', 'Yaron Lipman', 'Meirav Galun', 'Tal Hassner', 'Ronen Basri'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [-2.28893962e-02 -9.96737257e-02 -1.67305365e-01 -3.40827495e-01
-9.33618963e-01 -8.08804989e-01 5.43555915e-01 1.62212938e-01
-3.31149817e-01 4.46518272e-01 2.12797523e-01 8.07916820e-02
-2.65008330e-01 -6.10847950e-01 -9.55955446e-01 -3.77559513e-01
8.04189816e-02 6.07775688e-01 2.04738110e-01 -2.84072697... | [7.913965225219727, -2.335557222366333] |
e490c3de-f2f0-497b-98ce-d360e50cfd9b | neural-network-hardware-co-design-for | 1811.02187 | null | http://arxiv.org/abs/1811.02187v2 | http://arxiv.org/pdf/1811.02187v2.pdf | Neural Network-Hardware Co-design for Scalable RRAM-based BNN Accelerators | Recently, RRAM-based Binary Neural Network (BNN) hardware has been gaining
interests as it requires 1-bit sense-amp only and eliminates the need for
high-resolution ADC and DAC. However, RRAM-based BNN hardware still requires
high-resolution ADC for partial sum calculation to implement large-scale neural
network using ... | ['Jae-Joon Kim', 'HyungJun Kim', 'Yulhwa Kim'] | 2018-11-06 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [ 3.52846861e-01 -8.33637044e-02 -1.99423432e-01 -5.16283214e-01
-2.35115349e-01 -3.46546441e-01 -1.50831556e-02 -2.25802332e-01
-6.50026798e-01 8.76309752e-01 -4.79069024e-01 -4.44610864e-01
-8.72316211e-03 -6.67284548e-01 -7.63654113e-01 -6.77687645e-01
3.38881403e-01 1.49140032e-02 2.95264363e-01 2.09314361... | [8.37532901763916, 2.743797540664673] |
fc5d801b-7128-4cb8-90cc-9b852ebdffdd | spder-semiperiodic-damping-enabled-object | 2306.15242 | null | https://arxiv.org/abs/2306.15242v1 | https://arxiv.org/pdf/2306.15242v1.pdf | SPDER: Semiperiodic Damping-Enabled Object Representation | We present a neural network architecture designed to naturally learn a positional embedding and overcome the spectral bias towards lower frequencies faced by conventional implicit neural representation networks. Our proposed architecture, SPDER, is a simple MLP that uses an activation function composed of a sinusoidal ... | ['Chawin Sitawarin', 'Kathan Shah'] | 2023-06-27 | null | null | null | null | ['image-super-resolution', 'super-resolution', 'video-frame-interpolation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.42033505e-01 6.20256782e-01 -3.22650671e-01 -2.13356853e-01
-6.84747517e-01 -4.76049520e-02 4.25419360e-01 -2.81321198e-01
-3.16613257e-01 7.17179894e-01 1.49626762e-01 -8.83532986e-02
1.20904483e-01 -7.18869388e-01 -9.10118580e-01 -8.27391982e-01
-1.19554311e-01 8.07286277e-02 1.09363627e-03 -1.87742025... | [11.208733558654785, -1.7655855417251587] |
a1359bf4-f3fe-4546-8b48-4db036079f19 | comparing-unsupervised-word-translation | null | null | http://papers.nips.cc/paper/8836-comparing-unsupervised-word-translation-methods-step-by-step | http://papers.nips.cc/paper/8836-comparing-unsupervised-word-translation-methods-step-by-step.pdf | Comparing Unsupervised Word Translation Methods Step by Step | Cross-lingual word vector space alignment is the task of mapping the vocabularies of two languages into a shared semantic space, which can be used for dictionary induction, unsupervised machine translation, and transfer learning. In the unsupervised regime, an initial seed dictionary is learned in the absence of any kn... | ['Anders Søgaard', 'Yova Kementchedjhieva', 'Mareike Hartmann'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.40648264e-01 -3.39605473e-02 -3.72864544e-01 -1.85890377e-01
-1.02539408e+00 -9.26208615e-01 1.14850652e+00 3.96566540e-01
-7.24987864e-01 8.37790668e-01 3.48731697e-01 -4.37560529e-01
2.14783907e-01 -6.23590529e-01 -6.02352977e-01 -9.45806980e-01
2.53450096e-01 1.09306192e+00 -1.16372578e-01 -4.24206465... | [11.110671997070312, 10.100927352905273] |
054fd934-ca74-4b2f-a655-99032d868fe5 | unsupervised-source-separation-by-steering | 2110.13071 | null | https://arxiv.org/abs/2110.13071v1 | https://arxiv.org/pdf/2110.13071v1.pdf | Unsupervised Source Separation By Steering Pretrained Music Models | We showcase an unsupervised method that repurposes deep models trained for music generation and music tagging for audio source separation, without any retraining. An audio generation model is conditioned on an input mixture, producing a latent encoding of the audio used to generate audio. This generated audio is fed to... | ['Bryan Pardo', 'Prem Seetharaman', "Patrick O'Reilly", 'Ethan Manilow'] | 2021-10-25 | null | null | null | null | ['audio-generation', 'audio-source-separation', 'music-generation', 'music-generation'] | ['audio', 'audio', 'audio', 'music'] | [ 5.01743495e-01 3.11670870e-01 9.82731804e-02 -5.80147952e-02
-1.27943003e+00 -8.58208358e-01 4.44386631e-01 -3.85273427e-01
-2.12341100e-02 4.54545587e-01 6.62924290e-01 1.48380861e-01
-2.26443917e-01 -4.56695914e-01 -4.84308869e-01 -1.01240909e+00
-2.84486175e-01 8.22789073e-01 1.41176239e-01 6.82867924... | [15.526710510253906, 5.605590343475342] |
938df489-b15b-4ebd-98de-415b114983c3 | towards-early-prediction-of-human-ipsc | 2305.14575 | null | https://arxiv.org/abs/2305.14575v1 | https://arxiv.org/pdf/2305.14575v1.pdf | Towards Early Prediction of Human iPSC Reprogramming Success | This paper presents advancements in automated early-stage prediction of the success of reprogramming human induced pluripotent stem cells (iPSCs) as a potential source for regenerative cell therapies.The minuscule success rate of iPSC-reprogramming of around $ 0.01% $ to $ 0.1% $ makes it labor-intensive, time-consumin... | ['James Shapiro', 'Nilanjan Ray', 'Nidheesh Dadheech', 'Omar Mouhammed', 'Ila Jasra', 'Abhineet Singh'] | 2023-05-23 | null | null | null | null | ['cell-segmentation'] | ['medical'] | [ 2.38144949e-01 -3.13892603e-01 3.78809571e-02 1.70959443e-01
-8.71611714e-01 -9.54958737e-01 2.86666393e-01 6.38874948e-01
-4.22462434e-01 1.04157460e+00 -3.05995554e-01 -3.21247488e-01
3.92467737e-01 -8.13712716e-01 -5.74258089e-01 -9.43022728e-01
1.18913449e-01 8.76249671e-01 1.31550720e-02 2.04038039... | [14.561763763427734, -3.1678571701049805] |
ad6f2ce2-09c3-4b85-a7ec-6073eabd89d9 | mplug-2-a-modularized-multi-modal-foundation | 2302.00402 | null | https://arxiv.org/abs/2302.00402v1 | https://arxiv.org/pdf/2302.00402v1.pdf | mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video | Recent years have witnessed a big convergence of language, vision, and multi-modal pretraining. In this work, we present mPLUG-2, a new unified paradigm with modularized design for multi-modal pretraining, which can benefit from modality collaboration while addressing the problem of modality entanglement. In contrast t... | ['Jingren Zhou', 'Fei Huang', 'Songfang Huang', 'Ji Zhang', 'Guohai Xu', 'Wei Wang', 'Qi Qian', 'Bin Bi', 'Chenliang Li', 'Yuanhong Xu', 'Jiabo Ye', 'Yaya Shi', 'Ming Yan', 'Qinghao Ye', 'Haiyang Xu'] | 2023-02-01 | null | null | null | null | ['visual-grounding', 'action-classification', 'video-question-answering', 'video-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.60315502e-01 -2.46549193e-02 -5.65288901e-01 -2.06850126e-01
-1.34972179e+00 -8.07984173e-01 9.41555738e-01 -5.18225789e-01
-4.46046114e-01 5.02318859e-01 4.54068273e-01 -4.08932745e-01
2.92804271e-01 -3.07303369e-01 -1.03303504e+00 -6.24304771e-01
2.64341831e-01 3.73865724e-01 -1.12068936e-01 -3.15136969... | [10.865400314331055, 1.358234167098999] |
a09d138f-8db3-4f38-a329-981024aee5d2 | an-order-complexity-model-for-aesthetic | 2301.05908 | null | https://arxiv.org/abs/2301.05908v1 | https://arxiv.org/pdf/2301.05908v1.pdf | An Order-Complexity Model for Aesthetic Quality Assessment of Symbolic Homophony Music Scores | Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored. Although the existing work of music generation is very substantial, the quality of music score generated by AI is relatively poor compared with that created by human co... | ['Shuai Cui', 'Yiqing Rong', 'Duo Xu', 'Jinyu Wang', 'Wu Zhou', 'Xin Jin'] | 2023-01-14 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [-3.36036161e-02 -2.51223207e-01 2.75865704e-01 9.56598744e-02
-5.05763113e-01 -5.13440549e-01 6.81643486e-02 -1.16791993e-01
1.30426377e-01 2.78154939e-01 3.40010613e-01 4.72532421e-01
-3.60021204e-01 -7.12686300e-01 1.33609250e-01 -4.96412009e-01
1.72795817e-01 3.11363310e-01 -1.79354951e-01 -4.43172187... | [16.023849487304688, 5.467034816741943] |
d1a7b52c-a9f5-4763-9b3a-fbbf7c709c00 | temporal-phenotyping-using-deep-predictive | 2006.08600 | null | https://arxiv.org/abs/2006.08600v1 | https://arxiv.org/pdf/2006.08600v1.pdf | Temporal Phenotyping using Deep Predictive Clustering of Disease Progression | Due to the wider availability of modern electronic health records, patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial for patient phenotyping, anticipating patients' prognoses by identifying "similar" patients, and designing treatment guidelines that are tail... | ['Mihaela van der Schaar', 'Changhee Lee'] | 2020-06-15 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1742-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1742-Paper.pdf | icml-2020-1 | ['patient-phenotyping', 'time-series-clustering'] | ['medical', 'time-series'] | [-1.15981981e-01 2.53849570e-03 -4.85126406e-01 -6.29939020e-01
-9.43843603e-01 -1.83142290e-01 1.35368139e-01 1.13750887e+00
-3.33434343e-02 5.43861747e-01 7.80442595e-01 -3.50291908e-01
-6.85720384e-01 -6.77227795e-01 -2.16372818e-01 -8.08971167e-01
-6.86604917e-01 9.78569388e-01 -5.23883343e-01 4.37618554... | [7.835720062255859, 6.15600061416626] |
6065b42b-2ce9-4da3-86c5-f4395d8d5775 | launching-a-robust-backdoor-attack-under | 2304.10985 | null | https://arxiv.org/abs/2304.10985v1 | https://arxiv.org/pdf/2304.10985v1.pdf | Launching a Robust Backdoor Attack under Capability Constrained Scenarios | As deep neural networks continue to be used in critical domains, concerns over their security have emerged. Deep learning models are vulnerable to backdoor attacks due to the lack of transparency. A poisoned backdoor model may perform normally in routine environments, but exhibit malicious behavior when the input conta... | ['Xiaolei Liu', 'Mingyong Yin', 'Kangyi Ding', 'Yixiao Xu', 'Ming Yi'] | 2023-04-21 | null | null | null | null | ['backdoor-attack', 'image-augmentation'] | ['adversarial', 'computer-vision'] | [ 1.81430459e-01 1.70370981e-01 -2.46117264e-01 -1.49081443e-02
-3.60329330e-01 -1.47144437e+00 7.21130371e-01 -7.64216781e-02
-6.03421509e-01 7.81372413e-02 -5.61118543e-01 -9.80882764e-01
5.62394917e-01 -6.99479878e-01 -1.06383145e+00 -5.39396226e-01
-1.65298879e-01 1.02736214e-02 3.67656201e-01 -2.20865086... | [5.749709129333496, 7.639607906341553] |
65b14ab6-f5fd-45bc-ab08-e25a85d295c1 | unsupervised-person-re-identification-via | 2004.03547 | null | https://arxiv.org/abs/2004.03547v1 | https://arxiv.org/pdf/2004.03547v1.pdf | Unsupervised Person Re-identification via Softened Similarity Learning | Person re-identification (re-ID) is an important topic in computer vision. This paper studies the unsupervised setting of re-ID, which does not require any labeled information and thus is freely deployed to new scenarios. There are very few studies under this setting, and one of the best approach till now used iterativ... | ['Lingxi Xie', 'Yu Wu', 'Qi Tian', 'Yutian Lin', 'Chenggang Yan'] | 2020-04-07 | unsupervised-person-re-identification-via-2 | http://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Unsupervised_Person_Re-Identification_via_Softened_Similarity_Learning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.42707795e-01 -3.16245317e-01 -2.69791912e-02 -4.58216906e-01
-5.02703905e-01 -4.64808285e-01 6.07179165e-01 2.74049729e-01
-8.89812469e-01 6.11252964e-01 -1.20126814e-01 2.27019817e-01
-8.32324848e-02 -4.96584326e-01 -3.97463351e-01 -1.04189038e+00
-8.02052580e-03 7.68851459e-01 2.98582435e-01 1.91446617... | [14.7271728515625, 1.0541502237319946] |
f3570c63-3d1f-47e5-a314-d815fdeac7e3 | sequential-attention-based-network-for-noetic | 1901.02609 | null | https://arxiv.org/abs/1901.02609v3 | https://arxiv.org/pdf/1901.02609v3.pdf | Sequential Attention-based Network for Noetic End-to-End Response Selection | The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the m... | ['Qian Chen', 'Wen Wang'] | 2019-01-09 | null | null | null | null | ['goal-oriented-dialog', 'conversational-response-selection'] | ['natural-language-processing', 'natural-language-processing'] | [-2.30821818e-02 1.36453927e-01 -1.39338508e-01 -9.50130045e-01
-1.06467414e+00 -5.23971677e-01 7.61572838e-01 1.37314409e-01
-4.95478421e-01 7.44529605e-01 6.39318407e-01 -3.81358385e-01
6.00833148e-02 -2.29888678e-01 2.97147512e-01 -1.54409572e-01
2.42098376e-01 1.18609202e+00 5.36173642e-01 -1.32999504... | [12.73274040222168, 7.878838539123535] |
828eb89c-7534-44ac-a1ad-e9192c10d4c8 | prompt-based-metric-learning-for-few-shot-ner | 2211.04337 | null | https://arxiv.org/abs/2211.04337v1 | https://arxiv.org/pdf/2211.04337v1.pdf | Prompt-Based Metric Learning for Few-Shot NER | Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples. Existing metric learning methods compute token-level similarities between query and support sets, but are not able to fully incorporate label semantics into modeling. To address this issue, we propose... | ['Zhilin Yang', 'Yanan Zheng', 'Yanru Chen'] | 2022-11-08 | null | null | null | null | ['few-shot-ner'] | ['natural-language-processing'] | [-5.76757342e-02 1.80967874e-03 -4.26849037e-01 -7.18440890e-01
-1.28344560e+00 -7.78088927e-01 7.11555004e-01 4.16423231e-01
-9.01458085e-01 7.71285176e-01 2.03860939e-01 -1.87538993e-02
-1.01455897e-01 -7.74847031e-01 -3.77636522e-01 -2.66323209e-01
1.74589634e-01 5.75796664e-01 3.14375192e-01 -2.00592816... | [9.676888465881348, 9.337662696838379] |
7db2f3a0-1179-4a28-8786-e5f164d290fd | synthcity-facilitating-innovative-use-cases | 2301.07573 | null | https://arxiv.org/abs/2301.07573v1 | https://arxiv.org/pdf/2301.07573v1.pdf | Synthcity: facilitating innovative use cases of synthetic data in different data modalities | Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time series, data with censoring, multi-source data, composite data, and more. Synthcity provides the prac... | ['Mihaela van der Schaar', 'Bogdan-Constantin Cebere', 'Zhaozhi Qian'] | 2023-01-18 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [-5.91613233e-01 -1.38720855e-01 -4.59259540e-01 -4.98637706e-01
-1.16904724e+00 -8.01379204e-01 4.64801580e-01 2.87378460e-01
1.81559417e-02 7.78997481e-01 3.91730666e-01 -4.54115659e-01
4.54929955e-02 -6.32448196e-01 -4.36315298e-01 -3.53828818e-01
-3.88089031e-01 4.76651698e-01 -2.12431848e-01 -2.20084473... | [7.2228617668151855, 3.7023627758026123] |
0df1e9d0-40f0-4ea5-83f0-da60a5ad62b3 | lidarmultinet-towards-a-unified-multi-task | 2209.09385 | null | https://arxiv.org/abs/2209.09385v2 | https://arxiv.org/pdf/2209.09385v2.pdf | LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception | LiDAR-based 3D object detection, semantic segmentation, and panoptic segmentation are usually implemented in specialized networks with distinctive architectures that are difficult to adapt to each other. This paper presents LidarMultiNet, a LiDAR-based multi-task network that unifies these three major LiDAR perception ... | ['Hassan Foroosh', 'Panqu Wang', 'Yu Wang', 'Yufei Xie', 'Weijia Chen', 'Zixiang Zhou', 'Dongqiangzi Ye'] | 2022-09-19 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.44954759e-01 -1.09247841e-01 -4.63499390e-02 -6.34066939e-01
-1.15287971e+00 -4.09732521e-01 5.61866343e-01 -4.33287658e-02
-6.63773835e-01 2.83062607e-01 -4.47021216e-01 -3.26070279e-01
1.58724234e-01 -7.96923339e-01 -8.73805940e-01 -4.81124490e-01
1.37978584e-01 8.26292336e-01 8.61908615e-01 -5.20945229... | [8.125592231750488, -2.7149081230163574] |
e8ac1f98-ccf5-4f42-8e06-cf17350ef299 | multi-modal-dense-video-captioning | 2003.07758 | null | https://arxiv.org/abs/2003.07758v2 | https://arxiv.org/pdf/2003.07758v2.pdf | Multi-modal Dense Video Captioning | Dense video captioning is a task of localizing interesting events from an untrimmed video and producing textual description (captions) for each localized event. Most of the previous works in dense video captioning are solely based on visual information and completely ignore the audio track. However, audio, and speech, ... | ['Esa Rahtu', 'Vladimir Iashin'] | 2020-03-17 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 5.11268020e-01 1.17102526e-01 -2.46706441e-01 -2.48835415e-01
-1.29717267e+00 -7.35728383e-01 7.56809115e-01 -1.90351292e-01
-1.10221155e-01 7.47084737e-01 8.91413569e-01 6.88814521e-02
4.45907921e-01 -8.88906270e-02 -1.12349427e+00 -5.25566280e-01
6.54485375e-02 2.00977504e-01 1.39088526e-01 6.97932616... | [10.518889427185059, 0.8202311396598816] |
d35832bf-49e9-4401-99cd-454e9558af8e | hierarchical-bayesian-inference-for-community | 2301.07386 | null | https://arxiv.org/abs/2301.07386v1 | https://arxiv.org/pdf/2301.07386v1.pdf | Hierarchical Bayesian inference for community detection and connectivity of functional brain networks | Many functional magnetic resonance imaging (fMRI) studies rely on estimates of hierarchically organised brain networks whose segregation and integration reflect the dynamic transitions of latent cognitive states. However, most existing methods for estimating the community structure of networks from both individual and ... | ['Adeel Razi', 'Jonathan Keith', 'Leonardo Novelli', 'Nizhuan Wang', 'Lingbin Bian'] | 2023-01-18 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 1.73179463e-01 4.08099405e-02 3.43094736e-01 -3.76811206e-01
4.79676604e-01 -2.77950615e-01 5.60026944e-01 -1.74892634e-01
-2.32972309e-01 6.31228864e-01 3.69196385e-01 -1.50177136e-01
-5.18839598e-01 -7.20397949e-01 -8.51342976e-02 -8.39759469e-01
-7.78318048e-01 6.02942526e-01 4.44357336e-01 2.54125834... | [12.399659156799316, 3.4270384311676025] |
30b3dd28-bc9b-4c17-9a1c-1d8b01ce8142 | keyphrase-generation-with-fine-grained | 2104.08799 | null | https://arxiv.org/abs/2104.08799v2 | https://arxiv.org/pdf/2104.08799v2.pdf | Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning | Aiming to generate a set of keyphrases, Keyphrase Generation (KG) is a classical task for capturing the central idea from a given document. Based on Seq2Seq models, the previous reinforcement learning framework on KG tasks utilizes the evaluation metrics to further improve the well-trained neural models. However, these... | ['Qi Zhang', 'Xipeng Qiu', 'Jiacheng Ye', 'Yige Xu', 'Yichao Luo'] | 2021-04-18 | null | https://aclanthology.org/2021.findings-emnlp.45 | https://aclanthology.org/2021.findings-emnlp.45.pdf | findings-emnlp-2021-11 | ['keyphrase-generation'] | ['natural-language-processing'] | [-1.87896147e-01 2.16761194e-02 -3.60142827e-01 -2.57496119e-01
-9.72224951e-01 -4.72038597e-01 4.75272387e-01 2.29175732e-01
-5.55854917e-01 1.01942301e+00 4.27947074e-01 -5.29293828e-02
-8.31000730e-02 -1.12826431e+00 -8.92116547e-01 -5.68989992e-01
2.18488753e-01 1.48597494e-01 2.59546731e-02 -4.77349490... | [11.9387845993042, 8.81673526763916] |
9890009b-e8fe-4572-beb8-1aa1bcfa75ab | revisiting-image-deblurring-with-an-efficient | 2302.02234 | null | https://arxiv.org/abs/2302.02234v1 | https://arxiv.org/pdf/2302.02234v1.pdf | Revisiting Image Deblurring with an Efficient ConvNet | Image deblurring aims to recover the latent sharp image from its blurry counterpart and has a wide range of applications in computer vision. The Convolution Neural Networks (CNNs) have performed well in this domain for many years, and until recently an alternative network architecture, namely Transformer, has demonstra... | ['Bin Chen', 'Karol Myszkowski', 'Hans-Peter Seidel', 'Mojtaba Bemana', 'Lingyan Ruan'] | 2023-02-04 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 7.50834420e-02 -3.53111506e-01 -3.53071131e-02 -1.68826386e-01
-4.57084447e-01 -2.48795509e-01 3.82614553e-01 -6.12168849e-01
-4.72837090e-01 5.75779498e-01 4.42152441e-01 -3.16028744e-01
-1.16904236e-01 -4.39502507e-01 -6.78073823e-01 -1.07424366e+00
1.16898768e-01 -4.18350458e-01 2.60867357e-01 -5.65789752... | [11.391231536865234, -2.46476674079895] |
bbcb7d77-5f76-45d1-b7b5-5e0a33874b34 | semi-supervised-vision-transformers-at-scale | 2208.05688 | null | https://arxiv.org/abs/2208.05688v1 | https://arxiv.org/pdf/2208.05688v1.pdf | Semi-supervised Vision Transformers at Scale | We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this problem, we propose a new SSL pipeline, consisting of first un/self-supervised pre-training, followed by supervised fine-tuning, and finall... | ['Stefano Soatto', 'Zhuowen Tu', 'Rahul Bhotika', 'Davide Modolo', 'Manchen Wang', 'Paolo Favaro', 'Avinash Ravichandran', 'Zhaowei Cai'] | 2022-08-11 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 2.05606312e-01 2.44109154e-01 -2.92350024e-01 -6.54673934e-01
-9.62707281e-01 -4.88077372e-01 7.89900839e-01 -1.97858706e-01
-4.37339813e-01 4.77836400e-01 3.68439080e-03 -3.55474353e-01
1.41885191e-01 -5.87362766e-01 -9.81421888e-01 -5.57510197e-01
6.17397785e-01 6.45067871e-01 5.26435137e-01 2.01259270... | [9.627009391784668, 1.6054438352584839] |
62a9e89c-c106-42e0-94db-087713dfa294 | question-answering-over-biological-knowledge | 2210.06040 | null | https://arxiv.org/abs/2210.06040v1 | https://arxiv.org/pdf/2210.06040v1.pdf | Question Answering Over Biological Knowledge Graph via Amazon Alexa | Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A kno... | ['Stefan Decker', 'Mohamed Abdelwaheb', 'Prinon Das', 'Hussain Ali', 'Md. Rezaul Karim'] | 2022-10-12 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-5.83345830e-01 3.59693050e-01 -3.98261219e-01 -1.64505050e-01
-4.04834300e-01 -7.01862156e-01 3.16004395e-01 8.28845322e-01
-1.64273113e-01 1.19759142e+00 4.64470536e-01 -4.96495157e-01
-4.14662302e-01 -1.15428424e+00 -7.19439209e-01 -2.66744524e-01
-8.04342702e-02 7.33136654e-01 4.36255842e-01 -4.01788056... | [8.732985496520996, 8.410316467285156] |
bea9ddd2-fbcb-4365-933d-a0c0f7ba647b | applying-second-order-quantifier-elimination | 2110.11108 | null | https://arxiv.org/abs/2110.11108v1 | https://arxiv.org/pdf/2110.11108v1.pdf | Applying Second-Order Quantifier Elimination in Inspecting Gödel's Ontological Proof | In recent years, G\"odel's ontological proof and variations of it were formalized and analyzed with automated tools in various ways. We supplement these analyses with a modeling in an automated environment based on first-order logic extended by predicate quantification. Formula macros are used to structure complex form... | ['Christoph Wernhard'] | 2021-10-21 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 3.30640048e-01 1.18597639e+00 1.16830021e-02 -2.71474153e-01
-1.59430593e-01 -8.37817788e-01 8.45068455e-01 3.36696297e-01
1.50433391e-01 1.00564718e+00 -1.26437038e-01 -1.19388831e+00
-7.36589968e-01 -7.93877602e-01 -5.83261073e-01 1.92628801e-01
-3.15443993e-01 6.24986768e-01 4.78451520e-01 -3.53943676... | [8.779589653015137, 6.82900333404541] |
be1d8ad2-cda0-4cc4-88a4-7ecd1bf69a99 | test-time-adaptation-vs-training-time | 2212.06242 | null | https://arxiv.org/abs/2212.06242v1 | https://arxiv.org/pdf/2212.06242v1.pdf | Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation | We consider the problem of improving the human instance segmentation mask quality for a given test image using keypoints estimation. We compare two alternative approaches. The first approach is a test-time adaptation (TTA) method, where we allow test-time modification of the segmentation network's weights using a singl... | ['Fatih Porikli', 'Hyojin Park', 'Debasmit Das', 'Kambiz Azarian'] | 2022-12-12 | null | null | null | null | ['human-instance-segmentation'] | ['computer-vision'] | [ 4.43982750e-01 2.60654151e-01 -7.64196739e-02 -3.41989756e-01
-8.93229425e-01 -8.21813881e-01 5.18468797e-01 1.00524954e-01
-7.42455184e-01 6.67349756e-01 -3.25412095e-01 -4.21293914e-01
-1.73450679e-01 -5.03082097e-01 -8.63339424e-01 -7.23747909e-01
1.34786278e-01 5.81698060e-01 7.50561237e-01 2.17877571... | [9.44581127166748, 1.238126277923584] |
febffb69-bb6a-4c0f-969b-6a798dd3607e | homeomorphic-image-registration-via-conformal | 2303.08113 | null | https://arxiv.org/abs/2303.08113v2 | https://arxiv.org/pdf/2303.08113v2.pdf | Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation | Deformable image registration is a fundamental task in medical image analysis and plays a crucial role in a wide range of clinical applications. Recently, deep learning-based approaches have been widely studied for deformable medical image registration and achieved promising results. However, existing deep learning ima... | ['Angelica I Aviles-Rivero', 'Carola-Bibiane Schönlieb', 'Jing Qin', 'Lihao Liu', 'Noémie Debroux', 'Jing Zou'] | 2023-03-14 | null | null | null | null | ['deformable-medical-image-registration', 'medical-image-registration'] | ['medical', 'medical'] | [ 3.68465006e-01 2.37103313e-01 -8.83484632e-02 -4.48478341e-01
-7.36404061e-01 -6.37828648e-01 6.21715546e-01 4.42503273e-01
-4.94653255e-01 5.15647829e-01 1.62824765e-01 -9.80852172e-02
-6.26133442e-01 -7.51036584e-01 -7.41385937e-01 -8.85558367e-01
-2.10030973e-01 5.71338058e-01 2.55289137e-01 -4.01053876... | [14.038735389709473, -2.5914697647094727] |
da1cc2d4-ef27-41f4-b9f4-3e4969fad55b | are-all-point-clouds-suitable-for-completion | 2303.01804 | null | https://arxiv.org/abs/2303.01804v1 | https://arxiv.org/pdf/2303.01804v1.pdf | Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion | In the practical application of point cloud completion tasks, real data quality is usually much worse than the CAD datasets used for training. A small amount of noisy data will usually significantly impact the overall system's accuracy. In this paper, we propose a quality evaluation network to score the point clouds an... | ['Shaojie Shen', 'Xiaozhi Chen', 'Peiliang Li', 'Jieqi Shi'] | 2023-03-03 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-3.72506887e-01 -4.52006847e-01 -7.43470788e-02 -7.26184726e-01
-5.42256236e-01 -5.32125831e-01 2.18494490e-01 1.00475304e-01
-4.09747422e-01 2.39262313e-01 -4.98480618e-01 -3.99520725e-01
-1.13791965e-01 -1.10396409e+00 -6.55261397e-01 -2.98232198e-01
6.27629012e-02 8.26409936e-01 5.90576410e-01 -2.49250039... | [7.912431716918945, -2.944645643234253] |
e0da45ec-d582-4a3d-84e3-011de35954bd | neural-block-slot-representations | 2211.01177 | null | https://arxiv.org/abs/2211.01177v3 | https://arxiv.org/pdf/2211.01177v3.pdf | Neural Systematic Binder | The key to high-level cognition is believed to be the ability to systematically manipulate and compose knowledge pieces. While token-like structured knowledge representations are naturally provided in text, it is elusive how to obtain them for unstructured modalities such as scene images. In this paper, we propose a ne... | ['Sungjin Ahn', 'Yeongbin Kim', 'Gautam Singh'] | 2022-11-02 | null | null | null | null | ['scene-generation', 'systematic-generalization'] | ['computer-vision', 'reasoning'] | [ 2.86947012e-01 1.11060023e-01 -7.85715878e-02 -2.71592766e-01
-3.71372551e-01 -7.37574220e-01 7.35789537e-01 -8.57209191e-02
-1.89829126e-01 4.62481111e-01 4.14214015e-01 -3.07208449e-01
-4.30369318e-01 -7.55313277e-01 -9.83244479e-01 -7.06044316e-01
2.50964314e-01 3.24465185e-01 2.93226331e-01 -2.44823053... | [10.023052215576172, 1.2325763702392578] |
c3bbfac3-11d5-4623-8e22-85c4bcf85d1b | salient-sign-detection-in-safe-autonomous | 2301.05804 | null | https://arxiv.org/abs/2301.05804v2 | https://arxiv.org/pdf/2301.05804v2.pdf | Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context | Detecting road traffic signs and accurately determining how they can affect the driver's future actions is a critical task for safe autonomous driving systems. However, various traffic signs in a driving scene have an unequal impact on the driver's decisions, making detecting the salient traffic signs a more important ... | ['Mohan Trivedi', 'Akshay Rangesh', 'Nachiket Deo', 'Akshay Gopalkrishnan', 'Ross Greer'] | 2023-01-14 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 3.01219106e-01 -1.87096391e-02 -4.44087178e-01 -6.54904246e-01
-6.17222190e-01 -2.20242050e-02 8.53217244e-01 -3.04687694e-02
-6.64977968e-01 3.59107435e-01 4.75597590e-01 -4.09483641e-01
-3.46655816e-01 -3.79625231e-01 -5.71163833e-01 -5.56584060e-01
4.64576595e-02 -1.62985161e-01 9.00762916e-01 -2.85467327... | [7.950728893280029, -0.7848660349845886] |
16eec3ef-6aa9-402d-b58e-01a02da31399 | making-video-quality-assessment-models-robust | 2304.13092 | null | https://arxiv.org/abs/2304.13092v1 | https://arxiv.org/pdf/2304.13092v1.pdf | Making Video Quality Assessment Models Robust to Bit Depth | We introduce a novel feature set, which we call HDRMAX features, that when included into Video Quality Assessment (VQA) algorithms designed for Standard Dynamic Range (SDR) videos, sensitizes them to distortions of High Dynamic Range (HDR) videos that are inadequately accounted for by these algorithms. While these feat... | ['Alan C. Bovik', 'Sriram Sethuraman', 'Hai Wei', 'Yongjun Wu', 'Zaixi Shang', 'Joshua P. Ebenezer'] | 2023-04-25 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [ 1.06635347e-01 -4.77324188e-01 -3.30894962e-02 -2.88062572e-01
-5.95949173e-01 -5.36620498e-01 6.17194533e-01 -1.98588073e-01
-1.39788285e-01 5.24015605e-01 6.41279399e-01 -3.76211922e-03
-2.21321642e-01 -5.89371800e-01 -6.85146332e-01 -5.34318805e-01
-4.67447519e-01 -7.36238156e-03 5.23462772e-01 -4.39544261... | [11.59286880493164, -1.8814352750778198] |
6174ba20-1658-49a7-bd10-eefcfd13d778 | contextual-modeling-for-3d-dense-captioning | 2210.03925 | null | https://arxiv.org/abs/2210.03925v1 | https://arxiv.org/pdf/2210.03925v1.pdf | Contextual Modeling for 3D Dense Captioning on Point Clouds | 3D dense captioning, as an emerging vision-language task, aims to identify and locate each object from a set of point clouds and generate a distinctive natural language sentence for describing each located object. However, the existing methods mainly focus on mining inter-object relationship, while ignoring contextual ... | ['Lin Ma', 'Jiebo Luo', 'Long Xu', 'Yufeng Zhong'] | 2022-10-08 | null | null | null | null | ['dense-captioning', '3d-dense-captioning'] | ['computer-vision', 'computer-vision'] | [ 5.74608939e-03 -1.79455251e-01 -2.55853925e-02 -6.22145534e-01
-8.08947027e-01 -5.34423709e-01 6.24912679e-01 2.98479855e-01
1.35363936e-01 2.99562603e-01 3.57159734e-01 1.69259444e-01
-1.30541295e-01 -7.15467334e-01 -9.28593755e-01 -8.47885549e-01
3.20210040e-01 6.42085075e-01 3.59387904e-01 6.48424495... | [8.213172912597656, -3.1914725303649902] |
c64d9fbf-e8a4-40d4-9ac8-d0c93b7be3c3 | unsupervised-segmentation-via-semantic | 2005.10513 | null | https://arxiv.org/abs/2005.10513v1 | https://arxiv.org/pdf/2005.10513v1.pdf | Unsupervised segmentation via semantic-apparent feature fusion | Foreground segmentation is an essential task in the field of image understanding. Under unsupervised conditions, different images and instances always have variable expressions, which make it difficult to achieve stable segmentation performance based on fixed rules or single type of feature. In order to solve this prob... | ['Xi Li', 'Huimin Ma', 'Yidong Wang', 'Hongbing Ma'] | 2020-05-21 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 6.79550827e-01 -2.83419043e-01 -3.12414318e-01 -7.86401212e-01
-3.13252568e-01 -1.61645472e-01 3.68795007e-01 -2.03515887e-01
-3.49983662e-01 3.93663675e-01 1.41840741e-01 2.40881130e-01
-1.30831748e-01 -7.88720846e-01 -4.78096575e-01 -8.98137629e-01
4.88748103e-01 2.90740002e-02 8.29046905e-01 5.84038794... | [9.575494766235352, -0.3182496726512909] |
43e273de-498d-4b47-b47f-2f881f16eb1a | mathematics-assisted-directed-evolution-and | 2306.04658 | null | https://arxiv.org/abs/2306.04658v1 | https://arxiv.org/pdf/2306.04658v1.pdf | Mathematics-assisted directed evolution and protein engineering | Directed evolution is a molecular biology technique that is transforming protein engineering by creating proteins with desirable properties and functions. However, it is experimentally impossible to perform the deep mutational scanning of the entire protein library due to the enormous mutational space, which scales as ... | ['Guo-Wei Wei', 'Yuchi Qiu'] | 2023-06-06 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 1.42883971e-01 -7.04807788e-02 3.05915982e-01 1.83948845e-01
-1.19414367e-01 -4.49682802e-01 3.97722244e-01 2.35166788e-01
-2.40335956e-01 9.84984696e-01 -1.06491774e-01 -5.97619176e-01
-3.68881166e-01 -9.21184659e-01 -9.20767665e-01 -1.13905537e+00
-5.24035156e-01 5.49528658e-01 1.56093076e-01 -7.44781613... | [4.892721652984619, 5.639756679534912] |
0299bd90-f2e3-48db-b955-830e0fd38c71 | multi-source-pointer-network-for-product | 1808.06885 | null | http://arxiv.org/abs/1808.06885v3 | http://arxiv.org/pdf/1808.06885v3.pdf | Multi-Source Pointer Network for Product Title Summarization | In this paper, we study the product title summarization problem in E-commerce
applications for display on mobile devices. Comparing with conventional
sentence summarization, product title summarization has some extra and
essential constraints. For example, factual errors or loss of the key
information are intolerable f... | ['Peng Jiang', 'Hanxiao Sun', 'Xiaobo Wang', 'Fei Sun', 'Wenwu Ou', 'Changhua Pei'] | 2018-08-21 | null | null | null | null | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 5.06245673e-01 3.11688632e-01 -5.83316147e-01 -2.41762877e-01
-7.27843761e-01 -4.42485809e-01 1.45367026e-01 4.75860864e-01
-4.46287185e-01 8.17811847e-01 7.17945576e-01 -2.06894130e-01
5.26777431e-02 -5.90602279e-01 -9.89422381e-01 -2.28588849e-01
2.32005939e-01 6.26979992e-02 2.71763533e-01 -3.60951036... | [12.393524169921875, 9.353321075439453] |
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