paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
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94be68c4-9e19-47b7-b119-1c9194d36728 | music-mood-detection-based-on-audio-and | 1809.07276 | null | http://arxiv.org/abs/1809.07276v1 | http://arxiv.org/pdf/1809.07276v1.pdf | Music Mood Detection Based On Audio And Lyrics With Deep Neural Net | We consider the task of multimodal music mood prediction based on the audio
signal and the lyrics of a track. We reproduce the implementation of
traditional feature engineering based approaches and propose a new model based
on deep learning. We compare the performance of both approaches on a database
containing 18,000 ... | ['Jimena Royo-Letelier', 'Francesco Piccoli', 'Rémi Delbouys', 'Romain Hennequin', 'Manuel Moussallam'] | 2018-09-19 | null | null | null | null | ['multimodal-emotion-recognition', 'music-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'music', 'speech'] | [ 2.26494744e-02 -2.31858239e-01 -8.71311650e-02 -5.09407341e-01
-1.12450707e+00 -7.74497867e-01 6.28004611e-01 3.25769395e-01
-1.73184380e-01 4.81110811e-01 5.77385306e-01 7.60949016e-01
-2.93807566e-01 -3.41153949e-01 -2.91647702e-01 -6.02616847e-01
-2.09621698e-01 3.70807946e-01 -4.38704908e-01 -3.93351734... | [15.84627628326416, 5.171200752258301] |
6218b855-7bd7-4be4-9efc-80746911b4ee | bop-challenge-2022-on-detection-segmentation | 2302.13075 | null | https://arxiv.org/abs/2302.13075v1 | https://arxiv.org/pdf/2302.13075v1.pdf | BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects | We present the evaluation methodology, datasets and results of the BOP Challenge 2022, the fourth in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB/RGB-D image. In 2022, we witnessed another significant improvement in the pose esti... | ['Jiri Matas', 'Carsten Rother', 'Bertram Drost', 'Eric Brachmann', 'Gu Wang', 'Yann Labbe', 'Tomas Hodan', 'Martin Sundermeyer'] | 2023-02-25 | null | null | null | null | ['2d-object-detection', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.75764501e-01 1.75957292e-01 2.63996631e-01 3.14940065e-02
-1.25278723e+00 -6.72930181e-01 4.73665029e-01 -9.51550603e-02
-6.66580200e-01 5.93318582e-01 -4.08367723e-01 2.08964609e-02
2.16880534e-02 -6.71965718e-01 -1.09711218e+00 -4.77695912e-01
-1.57806426e-01 9.11041558e-01 3.20098549e-01 -9.48601365... | [7.626652717590332, -2.666656255722046] |
a8394675-fec6-491d-9c8e-18de2e301267 | online-temporal-calibration-for-monocular | 1808.00692 | null | http://arxiv.org/abs/1808.00692v1 | http://arxiv.org/pdf/1808.00692v1.pdf | Online Temporal Calibration for Monocular Visual-Inertial Systems | Accurate state estimation is a fundamental module for various intelligent
applications, such as robot navigation, autonomous driving, virtual and
augmented reality. Visual and inertial fusion is a popular technology for 6-DOF
state estimation in recent years. Time instants at which different sensors'
measurements are r... | ['Tong Qin', 'Shaojie Shen'] | 2018-08-02 | null | null | null | null | ['time-offset-calibration'] | ['miscellaneous'] | [-1.03752151e-01 -5.33558190e-01 -1.94005996e-01 -3.15735966e-01
-4.32465345e-01 -6.12756371e-01 6.02430940e-01 2.65623271e-01
-5.78727961e-01 7.22991288e-01 -2.66375691e-01 -2.22925738e-01
-7.13626519e-02 -3.55540693e-01 -8.49564016e-01 -5.49485862e-01
-8.28940868e-02 2.89245754e-01 3.05514127e-01 -2.55393803... | [7.436136722564697, -1.9954513311386108] |
fdbb1de0-888f-4176-bc74-7f8c105dd433 | learning-semantic-role-labeling-from | 2305.14600 | null | https://arxiv.org/abs/2305.14600v1 | https://arxiv.org/pdf/2305.14600v1.pdf | Learning Semantic Role Labeling from Compatible Label Sequences | This paper addresses the question of how to efficiently learn from disjoint, compatible label sequences. We argue that the compatible structures between disjoint label sets help model learning and inference. We verify this hypothesis on the task of semantic role labeling (SRL), specifically, tagging a sentence with two... | ['Vivek Srikumar', 'Martha Palmer', 'Susan W. Brown', 'Ghazaleh Kazeminejad', 'Tao Li'] | 2023-05-24 | null | null | null | null | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 4.82264400e-01 7.45524704e-01 -6.30835593e-01 -7.21240222e-01
-1.21724808e+00 -1.06714761e+00 4.47230905e-01 2.65302122e-01
-6.88753188e-01 1.06787229e+00 4.19098914e-01 -4.94951427e-01
1.34479225e-01 -4.02239621e-01 -9.16436195e-01 -4.13536817e-01
1.36503130e-01 6.78710580e-01 4.45003688e-01 -2.81179965... | [10.382769584655762, 9.42599868774414] |
82d674d5-896c-4e29-a86e-64238bcb36fa | enhancing-retrieval-augmented-large-language | 2305.15294 | null | https://arxiv.org/abs/2305.15294v1 | https://arxiv.org/pdf/2305.15294v1.pdf | Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy | Large language models are powerful text processors and reasoners, but are still subject to limitations including outdated knowledge and hallucinations, which necessitates connecting them to the world. Retrieval-augmented large language models have raised extensive attention for grounding model generation on external kn... | ['Weizhu Chen', 'Nan Duan', 'Minlie Huang', 'Yelong Shen', 'Yeyun Gong', 'Zhihong Shao'] | 2023-05-24 | null | null | null | null | ['multi-hop-question-answering', 'fact-verification'] | ['knowledge-base', 'natural-language-processing'] | [ 1.64050609e-01 2.12868303e-01 -3.48362088e-01 6.44484758e-02
-1.36763728e+00 -6.78264558e-01 8.85665059e-01 4.10423130e-01
-4.63585436e-01 6.98299527e-01 7.89553940e-01 -3.85576874e-01
-2.38161922e-01 -1.07098329e+00 -7.06440151e-01 -6.54723793e-02
4.46097851e-01 9.70628440e-01 1.84086919e-01 -6.95128620... | [11.154084205627441, 7.929203033447266] |
d4612894-89cf-4810-983c-5e20bdafdba8 | halp-hallucinating-latent-positives-for | 2304.00387 | null | https://arxiv.org/abs/2304.00387v1 | https://arxiv.org/pdf/2304.00387v1.pdf | HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions | Supervised learning of skeleton sequence encoders for action recognition has received significant attention in recent times. However, learning such encoders without labels continues to be a challenging problem. While prior works have shown promising results by applying contrastive learning to pose sequences, the qualit... | ['Rama Chellappa', 'Anoop Cherian', 'David Jacobs', 'Shlok Kumar Mishra', 'Ketul Shah', 'Aniket Roy', 'Anshul Shah'] | 2023-04-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shah_HaLP_Hallucinating_Latent_Positives_for_Skeleton-Based_Self-Supervised_Learning_of_Actions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shah_HaLP_Hallucinating_Latent_Positives_for_Skeleton-Based_Self-Supervised_Learning_of_Actions_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-recognition-in-videos'] | ['computer-vision'] | [ 6.44514918e-01 1.95865706e-01 -3.87509972e-01 -3.85011792e-01
-1.19847906e+00 -4.81135070e-01 5.02326667e-01 -3.06436896e-01
-3.14357013e-01 6.92984700e-01 2.86159247e-01 3.84196360e-03
7.07066283e-02 -4.11865920e-01 -1.20641792e+00 -4.69223082e-01
-1.08734764e-01 5.74052215e-01 1.15593404e-01 -1.47413909... | [8.469904899597168, 0.6107240915298462] |
259c46f7-19da-4ec5-bfee-ad3db6f98ff4 | learning-visual-affordance-grounding-from | 2108.05675 | null | https://arxiv.org/abs/2108.05675v1 | https://arxiv.org/pdf/2108.05675v1.pdf | Learning Visual Affordance Grounding from Demonstration Videos | Visual affordance grounding aims to segment all possible interaction regions between people and objects from an image/video, which is beneficial for many applications, such as robot grasping and action recognition. However, existing methods mainly rely on the appearance feature of the objects to segment each region of ... | ['DaCheng Tao', 'Yang Cao', 'Jing Zhang', 'Wei Zhai', 'Hongchen Luo'] | 2021-08-12 | null | null | null | null | ['video-to-image-affordance-grounding'] | ['computer-vision'] | [ 1.26286075e-01 6.21209070e-02 -2.49867737e-01 -2.55975127e-01
-6.60990775e-02 -1.69261634e-01 2.67524570e-01 -3.61117452e-01
-3.35220337e-01 3.17588359e-01 3.37116510e-01 9.57521349e-02
-8.93975198e-02 -3.89311701e-01 -8.08721721e-01 -5.75519919e-01
1.30786806e-01 2.17137828e-01 5.32525957e-01 -1.84198543... | [5.17734956741333, -0.0705491453409195] |
59b92c2e-7b7e-4403-bc56-1c0d4e4d4531 | recognising-cardiac-abnormalities-in-wearable | 1807.04077 | null | http://arxiv.org/abs/1807.04077v1 | http://arxiv.org/pdf/1807.04077v1.pdf | Recognising Cardiac Abnormalities in Wearable Device Photoplethysmography (PPG) with Deep Learning | Cardiac abnormalities affecting heart rate and rhythm are commonly observed
in both healthy and acutely unwell people. Although many of these are benign,
they can sometimes indicate a serious health risk. ECG monitors are typically
used to detect these events in electrical heart activity, however they are
impractical f... | [] | 2018-07-11 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 5.24602652e-01 -2.73208432e-02 2.74869680e-01 -1.91267967e-01
-6.94699645e-01 -7.13755667e-01 -2.51803368e-01 2.28648245e-01
4.60983030e-02 7.84373283e-01 -2.67873015e-02 -6.47534192e-01
2.09553838e-02 -3.46494317e-01 -4.24178779e-01 -6.27586246e-01
-6.77899778e-01 1.54458597e-01 -4.00992453e-01 4.06156123... | [14.170363426208496, 3.1665008068084717] |
61fe9101-6857-48ef-8106-3b7e5fd99edd | modern-gpr-target-recognition-methods | 2211.01277 | null | https://arxiv.org/abs/2211.01277v1 | https://arxiv.org/pdf/2211.01277v1.pdf | Modern GPR Target Recognition Methods | Traditional GPR target recognition methods include pre-processing the data by removal of noisy signatures, dewowing (high-pass filtering to remove low-frequency noise), filtering, deconvolution, migration (correction of the effect of survey geometry), and can rely on the simulation of GPR responses. The techniques usua... | ['Maria Antonia Gonzalez-Huici', 'Kumar Vijay Mishra', 'Fabio Giovanneschi'] | 2022-11-02 | null | null | null | null | ['landmine', 'gpr', 'gpr'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 7.30862975e-01 -3.07908863e-01 5.79504907e-01 -6.31078899e-01
-8.04005206e-01 -8.76006708e-02 5.01767516e-01 2.69422472e-01
-4.23742175e-01 4.76846099e-01 4.30623978e-01 -1.09502554e-01
-4.35998112e-01 -9.96398211e-01 -2.75153488e-01 -7.15217054e-01
-4.02747631e-01 3.12009215e-01 3.12372029e-01 -5.43898284... | [6.877942085266113, 1.2386753559112549] |
43b2eefc-2509-41e1-b1f7-06dcaa228eff | static-fuzzy-bag-of-words-a-lightweight | 2304.03098 | null | https://arxiv.org/abs/2304.03098v1 | https://arxiv.org/pdf/2304.03098v1.pdf | Static Fuzzy Bag-of-Words: a lightweight sentence embedding algorithm | The introduction of embedding techniques has pushed forward significantly the Natural Language Processing field. Many of the proposed solutions have been presented for word-level encoding; anyhow, in the last years, new mechanism to treat information at an higher level of aggregation, like at sentence- and document-lev... | ['Vincenzo Scotti', 'Licia Sbattella', 'Roberto Tedesco', 'Matteo Muffo'] | 2023-04-06 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-textual-similarity'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 6.92126453e-02 6.93385974e-02 -7.95399621e-02 -4.65438545e-01
-4.47691530e-01 -3.57893765e-01 8.17966282e-01 1.41049170e+00
-8.04387927e-01 2.97283620e-01 5.65255523e-01 -1.59940332e-01
-4.66726393e-01 -1.12343812e+00 9.55663025e-02 -4.40032899e-01
-5.45481592e-02 3.30508798e-01 3.84051889e-01 -6.31904781... | [10.589359283447266, 8.663086891174316] |
0399ec85-0433-4ea8-8f96-8c05f72a7a61 | on-the-unlikelihood-of-d-separation | 2303.05628 | null | https://arxiv.org/abs/2303.05628v1 | https://arxiv.org/pdf/2303.05628v1.pdf | On the Unlikelihood of D-Separation | Causal discovery aims to recover a causal graph from data generated by it; constraint based methods do so by searching for a d-separating conditioning set of nodes in the graph via an oracle. In this paper, we provide analytic evidence that on large graphs, d-separation is a rare phenomenon, even when guaranteed to exi... | ['Devansh Arpit', 'Caiming Xiong', 'Weiran Yao', 'Juan Carlos Niebles', 'Shelby Heinecke', 'Huan Wang', 'Itai Feigenbaum'] | 2023-03-10 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.30432704e-01 7.72806644e-01 -4.33516234e-01 -1.57725215e-02
-7.74643898e-01 -8.52116525e-01 -1.67355016e-01 2.81268120e-01
-1.37296066e-01 8.38037014e-01 -2.46838465e-01 -6.87993348e-01
-7.40169942e-01 -1.40074790e+00 -9.78285074e-01 -9.21731591e-01
-1.08603156e+00 7.78949082e-01 1.21571928e-01 2.49721944... | [6.755875587463379, 4.993382453918457] |
f4f5baec-b7b9-4a95-b7f7-6ebe09facbad | exploring-high-order-structure-for-robust | 2203.11492 | null | https://arxiv.org/abs/2203.11492v1 | https://arxiv.org/pdf/2203.11492v1.pdf | Exploring High-Order Structure for Robust Graph Structure Learning | Recent studies show that Graph Neural Networks (GNNs) are vulnerable to adversarial attack, i.e., an imperceptible structure perturbation can fool GNNs to make wrong predictions. Some researches explore specific properties of clean graphs such as the feature smoothness to defense the attack, but the analysis of it has ... | ['Fengxiang He', 'Liu Liu', 'Baosheng Yu', 'Jinlong Li', 'Yibing Zhan', 'Guangqian Yang'] | 2022-03-22 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.86148994e-02 1.77965105e-01 -3.50633007e-03 -3.92483128e-03
1.66943818e-01 -1.02053547e+00 5.01755953e-01 1.12494506e-01
1.65301621e-01 2.88097382e-01 2.78687656e-01 -7.23592341e-01
-2.01397941e-01 -1.11680150e+00 -1.00707018e+00 -8.68752182e-01
-3.03870231e-01 -1.53351784e-01 5.30070484e-01 -7.63279557... | [6.139091491699219, 7.311561107635498] |
17e40a04-0120-4854-9c12-1fc8e9d01a5a | hyperbolic-hierarchical-knowledge-graph | 2204.13704 | null | https://arxiv.org/abs/2204.13704v1 | https://arxiv.org/pdf/2204.13704v1.pdf | Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions | Knowledge graph embeddings (KGE) have been validated as powerful methods for inferring missing links in knowledge graphs (KGs) since they map entities into Euclidean space and treat relations as transformations of entities. Currently, some Euclidean KGE methods model semantic hierarchies prevalent in KGs and promote th... | ['Yanping Zhang', 'Shu Zhao', 'Fulan Qian', 'Wenxue Wang', 'Wenjie Zheng'] | 2022-04-28 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-7.86084116e-01 6.96148098e-01 -2.71995425e-01 -1.58382475e-01
-1.41095966e-01 -3.00194055e-01 2.31234014e-01 3.63997400e-01
7.96009973e-02 3.54482174e-01 4.30214107e-01 -3.39751035e-01
-6.35810494e-01 -1.38024032e+00 -7.29429543e-01 -4.19340372e-01
-2.16632038e-01 4.27000582e-01 6.81822121e-01 -4.61250514... | [8.647019386291504, 7.787686347961426] |
575f4b99-3674-4542-968c-d5bfc4f5fd74 | knowledge-distillation-for-action | 2004.07711 | null | https://arxiv.org/abs/2004.07711v2 | https://arxiv.org/pdf/2004.07711v2.pdf | Knowledge Distillation for Action Anticipation via Label Smoothing | Human capability to anticipate near future from visual observations and non-verbal cues is essential for developing intelligent systems that need to interact with people. Several research areas, such as human-robot interaction (HRI), assisted living or autonomous driving need to foresee future events to avoid crashes o... | ['Giovanni Maria Farinella', 'Pasquale Coscia', 'Guglielmo Camporese', 'Antonino Furnari', 'Lamberto Ballan'] | 2020-04-16 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 3.11509818e-01 5.19831300e-01 -3.18021178e-01 -9.56487179e-01
-3.19587708e-01 1.58155158e-01 7.97417223e-01 -2.46439740e-01
-6.45932257e-01 9.84553695e-01 6.44678295e-01 4.68379147e-02
7.40059987e-02 -3.45794350e-01 -4.57249612e-01 -3.35221976e-01
-3.35950494e-01 5.60901046e-01 7.06116855e-02 -8.62199664... | [7.921581268310547, 0.4516451060771942] |
a58d5a2e-12b4-4a26-af08-a8e1b984da42 | efficient-vertical-federated-learning-with | 2305.11236 | null | https://arxiv.org/abs/2305.11236v1 | https://arxiv.org/pdf/2305.11236v1.pdf | Efficient Vertical Federated Learning with Secure Aggregation | The majority of work in privacy-preserving federated learning (FL) has been focusing on horizontally partitioned datasets where clients share the same sets of features and can train complete models independently. However, in many interesting problems, such as financial fraud detection and disease detection, individual ... | ['Nicholas D. Lane', 'Pedro Porto Buarque de Gusmão', 'Chenyang Ma', 'Wanru Zhao', 'Heng Pan', 'Xinchi Qiu'] | 2023-05-18 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-8.64127055e-02 9.76992697e-02 -1.98509097e-01 -5.08339465e-01
-8.14731121e-01 -8.68148148e-01 2.47218147e-01 5.02981246e-01
-5.81251800e-01 9.12165999e-01 6.93842545e-02 -6.97465181e-01
-1.12360418e-01 -1.03655803e+00 -9.52377141e-01 -8.35528016e-01
-4.21502203e-01 8.61561149e-02 -1.73184380e-01 1.82125315... | [5.883149147033691, 6.6911845207214355] |
e655a5dc-b06d-4ca6-bf13-4f0a0e2a912b | curriculum-knowledge-switching-for-pancreas | 2306.12651 | null | https://arxiv.org/abs/2306.12651v1 | https://arxiv.org/pdf/2306.12651v1.pdf | Curriculum Knowledge Switching for Pancreas Segmentation | Pancreas segmentation is challenging due to the small proportion and highly changeable anatomical structure. It motivates us to propose a novel segmentation framework, namely Curriculum Knowledge Switching (CKS) framework, which decomposes detecting pancreas into three phases with different difficulty extent: straightf... | ['Xueming Wen', 'Saisai Wang', 'Mingxuan Zhang', 'Zhibo Tian', 'Kun Zhan', 'Yumou Tang'] | 2023-06-22 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-7.55643025e-02 1.02044448e-01 -3.93097818e-01 -2.94799775e-01
-4.43462938e-01 -6.14112794e-01 1.63771808e-01 1.38432370e-03
-5.21299243e-01 5.77570617e-01 9.19683650e-02 -2.40602463e-01
-2.05785334e-01 -5.09302974e-01 -7.21121848e-01 -7.92136669e-01
-9.21423435e-02 3.39935243e-01 5.47520995e-01 1.01674534... | [14.495694160461426, -2.6002581119537354] |
75faa026-df6a-4509-a614-98dde42811f1 | deep-hashing-learning-for-visual-and-semantic | 1909.04614 | null | https://arxiv.org/abs/1909.04614v1 | https://arxiv.org/pdf/1909.04614v1.pdf | Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images | Driven by the urgent demand for managing remote sensing big data, large-scale remote sensing image retrieval (RSIR) attracts increasing attention in the remote sensing field. In general, existing retrieval methods can be regarded as visual-based retrieval approaches which search and return a set of similar images from ... | ['Jon Atli Benediktsson', 'Shutao Li', 'Weiwei Song'] | 2019-09-10 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.14950538e-01 -5.58505774e-01 -1.61919191e-01 -6.39830112e-01
-1.09969878e+00 -2.72418380e-01 4.80847985e-01 1.85318723e-01
-4.65889841e-01 3.75723839e-01 -1.59166470e-01 -6.07986934e-02
-3.21812332e-01 -1.11202240e+00 -4.94545460e-01 -9.18688416e-01
-6.06291220e-02 8.99249241e-02 -4.45044748e-02 4.22207545... | [11.247323036193848, 0.8570356965065002] |
2a119fad-80cd-49f7-9ebc-95fe7c6ee85b | subtitles-to-segmentation-improving-low | null | null | https://aclanthology.org/2020.clssts-1.11 | https://aclanthology.org/2020.clssts-1.11.pdf | Subtitles to Segmentation: Improving Low-Resource Speech-to-TextTranslation Pipelines | In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation. ASR output segmentation is crucial, as ASR systems segment the input audio using purely acoustic information and are not guaranteed to output sentence-like segments. Since most MT systems expe... | ['Kathy Mckeown', 'Chris Kedzie', 'Zhengping Jiang', 'David Wan', 'Peter Bell', 'Elsbeth Turcan'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['speech-to-text-translation', 'cross-lingual-information-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.96717620e-01 4.32125717e-01 -1.92097351e-01 -4.27411407e-01
-1.90556800e+00 -9.12516057e-01 4.38255519e-01 -8.28534439e-02
-6.52651310e-01 4.74465668e-01 6.70323074e-01 -7.86303222e-01
5.34571230e-01 -2.42542356e-01 -7.57473707e-01 -2.22291365e-01
5.22252679e-01 8.57519150e-01 3.54946256e-01 -3.94570649... | [14.486135482788086, 7.135370254516602] |
27e6e1d1-85fb-40dd-99b0-ee3aadee1135 | optimal-target-assignment-and-path-finding | 1612.05693 | null | http://arxiv.org/abs/1612.05693v1 | http://arxiv.org/pdf/1612.05693v1.pdf | Optimal Target Assignment and Path Finding for Teams of Agents | We study the TAPF (combined target-assignment and path-finding) problem for
teams of agents in known terrain, which generalizes both the anonymous and
non-anonymous multi-agent path-finding problems. Each of the teams is given the
same number of targets as there are agents in the team. Each agent has to move
to exactly... | ['Sven Koenig', 'Hang Ma'] | 2016-12-17 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-7.00787902e-02 5.84753394e-01 3.00108418e-02 -7.78156146e-02
-3.52759510e-01 -1.13956261e+00 4.12522554e-02 7.67677724e-01
-4.49275851e-01 1.30741763e+00 -2.21386254e-01 -6.48938343e-02
-1.11789632e+00 -1.43836093e+00 -5.22879422e-01 -6.48727179e-01
-8.74028206e-01 1.81574368e+00 6.33715928e-01 -6.45276129... | [4.956528186798096, 1.7517321109771729] |
586eac6f-e2b5-42c7-a546-5d9a35467019 | a-new-manifold-distance-measure-for-visual | 1605.03865 | null | http://arxiv.org/abs/1605.03865v1 | http://arxiv.org/pdf/1605.03865v1.pdf | A New Manifold Distance Measure for Visual Object Categorization | Manifold distances are very effective tools for visual object recognition.
However, most of the traditional manifold distances between images are based on
the pixel-level comparison and thus easily affected by image rotations and
translations. In this paper, we propose a new manifold distance to model the
dissimilariti... | ['Hong Qiao', 'Fengfu Li', 'Xiayuan Huang', 'Bo Zhang'] | 2016-05-12 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [-2.67874271e-01 -6.07601702e-01 -7.44572580e-02 -4.99505550e-01
-1.94537923e-01 -3.99763286e-01 4.37487334e-01 2.68160045e-01
-3.96750748e-01 2.94800252e-02 -1.36361957e-01 -1.36298165e-01
-5.90559959e-01 -6.61621034e-01 -1.50540918e-02 -7.49213576e-01
-2.73553669e-01 9.27290842e-02 3.19912016e-01 1.32372146... | [7.863219261169434, 4.245777130126953] |
463cc164-fd9b-4b6a-83ef-84d9d23441f3 | semantic-binary-segmentation-using | 1805.00138 | null | http://arxiv.org/abs/1805.00138v2 | http://arxiv.org/pdf/1805.00138v2.pdf | Semantic Binary Segmentation using Convolutional Networks without Decoders | In this paper, we propose an efficient architecture for semantic image
segmentation using the depth-to-space (D2S) operation. Our D2S model is
comprised of a standard CNN encoder followed by a depth-to-space reordering of
the final convolutional feature maps. Our approach eliminates the decoder
portion of traditional e... | ['Ian Stavness', 'Shubhra Aich', 'William van der Kamp'] | 2018-05-01 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 5.35164893e-01 6.45960748e-01 -1.21570013e-01 -6.96099758e-01
-8.41914475e-01 -5.87957084e-01 5.27349949e-01 -3.35251577e-02
-5.78262687e-01 3.24618667e-01 1.07003562e-01 -6.22069716e-01
3.98020208e-01 -1.02849293e+00 -9.98500586e-01 5.68129569e-02
3.45199823e-01 2.69840389e-01 8.40845823e-01 -1.41006187... | [9.483966827392578, 0.10613437741994858] |
27b4e6f5-a92f-4247-b43a-bd344a2c8cb5 | disassemblable-fieldwork-ct-scanner-using-a | 2011.06671 | null | https://arxiv.org/abs/2011.06671v1 | https://arxiv.org/pdf/2011.06671v1.pdf | Disassemblable Fieldwork CT Scanner Using a 3D-printed Calibration Phantom | The use of computed tomography (CT) imaging has become of increasing interest to academic areas outside of the field of medical imaging and industrial inspection, e.g., to biology and cultural heritage research. The pecularities of these fields, however, sometimes require that objects need to be imaged on-site, e.g., i... | ['Oliver Cossairt', 'Michael Rubenstein', 'Tobias Würfl', 'Andre Aichert', 'Thomas Bochynek', 'Florian Schiffers'] | 2020-11-12 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 4.27058548e-01 -1.18260004e-01 5.34565330e-01 -2.56437868e-01
-5.70183814e-01 -3.05931926e-01 1.92989081e-01 2.67075747e-01
-5.76257825e-01 6.00514889e-01 -4.24615324e-01 -4.80155379e-01
-1.37167037e-01 -8.40932727e-01 -4.98515278e-01 -5.42099297e-01
1.13001075e-02 8.76810014e-01 5.93666553e-01 1.22902438... | [12.951370239257812, -2.762744665145874] |
bfcb0991-7ad4-46e9-8b90-826520d83f00 | mode-seeking-generative-adversarial-networks | 1903.05628 | null | https://arxiv.org/abs/1903.05628v6 | https://arxiv.org/pdf/1903.05628v6.pdf | Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis | Most conditional generation tasks expect diverse outputs given a single conditional context. However, conditional generative adversarial networks (cGANs) often focus on the prior conditional information and ignore the input noise vectors, which contribute to the output variations. Recent attempts to resolve the mode co... | ['Ming-Hsuan Yang', 'Hung-Yu Tseng', 'Hsin-Ying Lee', 'Qi Mao', 'Siwei Ma'] | 2019-03-13 | mode-seeking-generative-adversarial-networks-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Mao_Mode_Seeking_Generative_Adversarial_Networks_for_Diverse_Image_Synthesis_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Mao_Mode_Seeking_Generative_Adversarial_Networks_for_Diverse_Image_Synthesis_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multimodal-unsupervised-image-to-image'] | ['computer-vision'] | [ 7.15703130e-01 2.35359207e-01 -3.75297256e-02 -2.05786869e-01
-9.88543689e-01 -6.45022690e-01 8.69520366e-01 -2.80850798e-01
-6.63230196e-02 9.10332620e-01 2.95594305e-01 -1.76386476e-01
3.89263362e-01 -7.85311043e-01 -8.31305206e-01 -9.86127198e-01
4.53288794e-01 1.42622247e-01 -1.26306370e-01 -1.18071273... | [11.509248733520508, -0.2809615731239319] |
dc7c2399-9cd2-4380-ba50-587c7630216b | oss-net-memory-efficient-high-resolution | 2110.10640 | null | https://arxiv.org/abs/2110.10640v1 | https://arxiv.org/pdf/2110.10640v1.pdf | OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data | Convolutional neural networks (CNNs) are the current state-of-the-art meta-algorithm for volumetric segmentation of medical data, for example, to localize COVID-19 infected tissue on computer tomography scans or the detection of tumour volumes in magnetic resonance imaging. A key limitation of 3D CNNs on voxelised data... | ['Heinz Koeppl', 'Özdemir Cetin', 'Tim Prangemeier', 'Christoph Reich'] | 2021-10-20 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 3.48838776e-01 6.24375403e-01 -2.80914128e-01 -1.95718795e-01
-8.58997822e-01 -3.47676486e-01 4.39312667e-01 4.64488149e-01
-5.02570927e-01 4.87476975e-01 1.89086586e-01 -7.56238997e-01
-1.59454474e-03 -9.78370547e-01 -7.48007655e-01 -4.26359504e-01
-3.82754207e-01 8.83133471e-01 5.17527580e-01 3.10074270... | [14.51259994506836, -2.523165225982666] |
b5e50014-3b1c-4611-97a5-0660389cbd69 | styleipsb-identity-preserving-semantic-basis | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_StyleIPSB_Identity-Preserving_Semantic_Basis_of_StyleGAN_for_High_Fidelity_Face_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_StyleIPSB_Identity-Preserving_Semantic_Basis_of_StyleGAN_for_High_Fidelity_Face_CVPR_2023_paper.pdf | StyleIPSB: Identity-Preserving Semantic Basis of StyleGAN for High Fidelity Face Swapping | Recent researches reveal that StyleGAN can generate highly realistic images, inspiring researchers to use pretrained StyleGAN to generate high-fidelity swapped faces. However, existing methods fail to meet the expectations in two essential aspects of high-fidelity face swapping. Their results are blurry without por... | ['Min Tang', 'Ruofeng Tong', 'Dan Song', 'Diqiong Jiang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['face-swapping'] | ['computer-vision'] | [ 2.78640538e-01 8.07506368e-02 4.29723002e-02 -4.07705277e-01
-3.67369682e-01 -5.95506907e-01 4.69198942e-01 -1.12252033e+00
2.46444479e-01 6.32907450e-01 3.66420358e-01 2.26517007e-01
1.19798817e-01 -8.40751648e-01 -6.81417644e-01 -9.43030536e-01
3.96186918e-01 1.11958027e-01 -5.14183402e-01 -3.60080421... | [12.663434982299805, -0.09527446329593658] |
6c1c6e13-a511-4239-bfd9-e9c8c379d666 | contraction-mapping-of-feature-norms-for | 2007.13406 | null | https://arxiv.org/abs/2007.13406v2 | https://arxiv.org/pdf/2007.13406v2.pdf | Contraction Mapping of Feature Norms for Classifier Learning on the Data with Different Quality | The popular softmax loss and its recent extensions have achieved great success in the deep learning-based image classification. However, the data for training image classifiers usually has different quality. Ignoring such problem, the correct classification of low quality data is hard to be solved. In this paper, we di... | ['Xiabi Liu', 'Weihua Liu', 'Ling Ma', 'Murong Wang'] | 2020-07-27 | null | null | null | null | ['handwritten-digit-recognition', 'lung-nodule-classification'] | ['computer-vision', 'medical'] | [ 2.95640051e-01 -1.75810620e-01 -3.77664953e-01 -7.49055445e-01
-4.67000008e-01 1.50079459e-01 1.57007828e-01 -1.51407257e-01
-5.63855052e-01 7.42415905e-01 -2.69098848e-01 -5.83679788e-02
-5.06142676e-01 -6.94897950e-01 -4.44569796e-01 -1.07516646e+00
-2.40886956e-02 2.81365141e-02 -1.18830793e-01 2.29674384... | [9.431173324584961, 2.3407845497131348] |
59cba718-a0fd-4c65-82f8-f441eb067a06 | mwp-bert-numeracy-augmented-pre-training-for | null | null | https://openreview.net/forum?id=1c5HGUkc1SL | https://openreview.net/pdf?id=1c5HGUkc1SL | MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving | Math word problem (MWP) solving faces a dilemma in number representation learning. In order to avoid the number representation issue and reduce the search space of feasible solutions, existing works striving for MWP solving usually replace real numbers with symbolic placeholders to focus on logic reasoning. However, di... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 1.06010400e-01 3.03437978e-01 -5.13071954e-01 -1.96091518e-01
-4.20575917e-01 -6.53697670e-01 2.47127891e-01 3.84026140e-01
-3.28238726e-01 7.23655224e-01 2.25597173e-01 -9.45433557e-01
-1.74469918e-01 -1.58177495e+00 -9.00254250e-01 -1.01307549e-01
1.53146505e-01 3.92102122e-01 9.71403942e-02 -5.06534696... | [9.503569602966309, 7.4110107421875] |
5e0e1452-2682-4752-8fdf-f4d016641ed8 | context-aware-selective-label-smoothing-for | 2303.06946 | null | https://arxiv.org/abs/2303.06946v1 | https://arxiv.org/pdf/2303.06946v1.pdf | Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model | Despite the success of deep neural network (DNN) on sequential data (i.e., scene text and speech) recognition, it suffers from the over-confidence problem mainly due to overfitting in training with the cross-entropy loss, which may make the decision-making less reliable. Confidence calibration has been recently propose... | ['Yongpan Wang', 'Mengchao He', 'Jin-Gang Yu', 'Zhenzhou Zhuang', 'Yu Luo', 'Shuangping Huang'] | 2023-03-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.27855837e-01 -4.78015661e-01 -1.95311487e-01 -9.27717865e-01
-1.00839150e+00 -2.80248135e-01 6.82380557e-01 1.37461051e-01
-5.80458760e-01 5.73953152e-01 9.61281583e-02 -4.41206902e-01
7.70088881e-02 -3.05318713e-01 -6.04308188e-01 -9.30777371e-01
4.69895601e-01 1.88585173e-03 3.18686068e-01 3.11302632... | [11.949773788452148, 2.275439977645874] |
96f4bce6-6c79-4f04-97a2-a1388d4beec7 | pranet-parallel-reverse-attention-network-for | 2006.11392 | null | https://arxiv.org/abs/2006.11392v4 | https://arxiv.org/pdf/2006.11392v4.pdf | PraNet: Parallel Reverse Attention Network for Polyp Segmentation | Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy images is of great importance since it provides valuable information for diagnosis and surgery. However, accurate polyp segmentation is a challen... | ['Geng Chen', 'Ge-Peng Ji', 'Deng-Ping Fan', 'Huazhu Fu', 'Jianbing Shen', 'Tao Zhou', 'Ling Shao'] | 2020-06-13 | null | null | null | null | ['camouflage-segmentation', 'camouflaged-object-segmentation', 'video-polyp-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.84116021e-01 6.19887859e-02 -1.43008843e-01 -1.65494978e-01
-5.78406274e-01 -5.17070293e-01 1.89769566e-01 6.13087416e-01
-3.26286554e-01 1.34314477e-01 3.06578487e-01 -3.27681154e-01
4.38484326e-02 -9.31211770e-01 -6.80976689e-01 -7.08911777e-01
-1.04565278e-01 1.37508199e-01 5.73909521e-01 -1.23521544... | [14.588854789733887, -2.707505941390991] |
59d3bcc3-0cff-4926-aa04-eafe5b1024c3 | enhanced-detection-of-the-presence-and | 2303.09440 | null | https://arxiv.org/abs/2303.09440v2 | https://arxiv.org/pdf/2303.09440v2.pdf | Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentation | Improving automated analysis of medical imaging will provide clinicians more options in providing care for patients. The 2023 AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D) provides an opportunity to test and refine machine learning methods for detecting the presence and s... | ['Robert Turnbull'] | 2023-03-16 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 8.55922699e-02 3.02116513e-01 -4.05284995e-03 -2.01197088e-01
-1.11522663e+00 -2.98480064e-01 2.75189310e-01 2.45288134e-01
-4.66906756e-01 3.70309979e-01 2.69734859e-01 -4.75968748e-01
-1.76230714e-01 -2.30851740e-01 -3.50591928e-01 -5.89373827e-01
-3.74002367e-01 9.91503775e-01 3.50532800e-01 4.49427336... | [15.340465545654297, -1.9230289459228516] |
8e6d1f1c-fa7b-4271-b7cd-e10c82e6f726 | cellvit-vision-transformers-for-precise-cell | 2306.15350 | null | https://arxiv.org/abs/2306.15350v1 | https://arxiv.org/pdf/2306.15350v1.pdf | CellViT: Vision Transformers for Precise Cell Segmentation and Classification | Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural netw... | ['Jens Kleesiek', 'Jan Egger', 'Barbara Grünwald', 'Jens Siveke', 'Selma Ugurel', 'Giulia Baldini', 'Julius Keyl', 'Constantin Seibold', 'Lukas Heine', 'Moritz Rempe', 'Fabian Hörst'] | 2023-06-27 | null | null | null | null | ['panoptic-segmentation', 'cell-detection', 'instance-segmentation', 'cell-segmentation', 'classification-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'medical', 'methodology'] | [ 2.28726104e-01 2.41618484e-01 -4.31769667e-03 -1.01180032e-01
-1.29657137e+00 -6.72687590e-01 2.98842400e-01 2.71088630e-01
-8.39536965e-01 5.98566234e-01 -1.69678748e-01 -2.05343708e-01
3.01821202e-01 -6.33732736e-01 -5.33170044e-01 -1.09387481e+00
1.45736173e-01 7.81464875e-01 4.69535977e-01 2.18136813... | [15.01533317565918, -3.1032397747039795] |
b5697012-d4f1-4143-9488-af9348367da8 | a-learning-exploring-method-to-generate | null | null | https://aclanthology.org/2020.coling-main.209 | https://aclanthology.org/2020.coling-main.209.pdf | A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning | Paraphrase generation (PG) is of great importance to many downstream tasks in natural language processing. Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream applications. Recently, neural sequence-to-sequence (Seq2Seq) models have shown promising results in PG. ... | ['Yufeng Chen', 'Jinan Xu', 'Changjian Hu', 'Yao Meng', 'Yujie Zhang', 'Deyi Xiong', 'Erguang Yang', 'Mingtong Liu'] | 2020-12-01 | null | null | null | coling-2020-8 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.58608842e-01 -1.28028274e-01 -3.00014228e-01 -4.96035486e-01
-1.16398752e+00 -3.71875316e-01 6.55006707e-01 -8.25890824e-02
-2.99161971e-01 1.10835123e+00 5.23527801e-01 5.47338836e-03
-2.04605777e-02 -9.01371181e-01 -7.58050740e-01 -5.98643959e-01
2.74863750e-01 3.67350787e-01 -1.18159287e-01 -5.16206503... | [11.866119384765625, 9.228275299072266] |
daa99916-6562-4784-a737-927e5250fdd4 | binary-coding-for-partial-action-analysis | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Qin_Binary_Coding_for_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Qin_Binary_Coding_for_CVPR_2017_paper.pdf | Binary Coding for Partial Action Analysis With Limited Observation Ratios | Traditional action recognition methods aim to recognize actions with complete observations/executions. However, it is often difficult to capture fully executed actions due to occlusions, interruptions, etc. Meanwhile, action prediction/recognition in advance based on partial observations is essential for preventing the... | ['Bingbing Ni', 'Jie Qin', 'Chen Chen', 'Yunhong Wang', 'Li Liu', 'Fumin Shen', 'Ling Shao'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['action-analysis'] | ['computer-vision'] | [ 7.25139081e-01 -5.60464799e-01 -5.01057744e-01 -1.44495666e-01
-8.77985060e-01 -1.05996870e-01 5.09441972e-01 3.31762768e-02
-2.97061265e-01 6.07449651e-01 2.19341710e-01 8.05227309e-02
-3.97196412e-01 -3.46108735e-01 -4.71609622e-01 -8.57212961e-01
6.10647351e-02 3.00788283e-01 1.45704076e-01 2.90032983... | [8.446305274963379, 0.614672839641571] |
b6c16312-b351-45db-b7d2-bd9efc35cd40 | chemical-protein-interaction-extraction-via | 1911.09487 | null | https://arxiv.org/abs/1911.09487v2 | https://arxiv.org/pdf/1911.09487v2.pdf | Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge | Motivation: The biomedical literature contains a wealth of chemical-protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for drug discovery, precision medicine, as well as basic biomedical research. Most existing methods focus only on the sentence sequence to identi... | ['Jian Wang', 'Cong Sun', 'Yin Zhang', 'Leilei Su', 'Zhihao Yang', 'Lei Wang', 'Hongfei Lin'] | 2019-11-21 | null | null | null | null | ['chemical-protein-interaction-extraction'] | ['medical'] | [ 4.13733423e-01 -1.02527410e-01 -4.94416058e-01 -3.45480651e-01
-8.09466839e-01 -2.76895463e-01 1.66526571e-01 5.92614591e-01
-4.57121849e-01 1.16601801e+00 2.50922769e-01 -3.36905807e-01
-1.26968384e-01 -6.83448970e-01 -7.09310293e-01 -9.29466486e-01
3.03586066e-01 2.82317489e-01 5.99712878e-02 1.59737781... | [8.417847633361816, 8.838783264160156] |
1557dde8-9eb7-4817-aa72-1a802aecbae7 | policy-learning-and-evaluation-with | 2202.07808 | null | https://arxiv.org/abs/2202.07808v2 | https://arxiv.org/pdf/2202.07808v2.pdf | Policy Learning and Evaluation with Randomized Quasi-Monte Carlo | Reinforcement learning constantly deals with hard integrals, for example when computing expectations in policy evaluation and policy iteration. These integrals are rarely analytically solvable and typically estimated with the Monte Carlo method, which induces high variance in policy values and gradients. In this work, ... | ['Fei Sha', 'Yi-fan Chen', 'Liyu Chen', "Pierre L'Ecuyer", 'Sebastien M. R. Arnold'] | 2022-02-16 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-4.23349231e-01 -4.46031317e-02 -6.35395229e-01 4.87035140e-02
-1.06594241e+00 -5.30106843e-01 6.07834101e-01 1.78025626e-02
-8.59791636e-01 1.76258326e+00 1.02317184e-01 -5.90429068e-01
-1.02016322e-01 -5.89466453e-01 -7.96519339e-01 -4.50595587e-01
-2.79951066e-01 6.06841683e-01 -1.71866622e-02 -1.26312464... | [4.106679439544678, 2.371483325958252] |
4f2efe4c-627a-41ec-8117-f881c4bb33fa | a-least-square-approach-to-semi-supervised | 2202.02904 | null | https://arxiv.org/abs/2202.02904v2 | https://arxiv.org/pdf/2202.02904v2.pdf | A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction | A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix. The algorithm is based on a two-stage approach similar to the one in \cite{LaiMckenzie2020}. However, under a weaker assumption and with less compu... | ['Zhaiming Shen', 'Ming-Jun Lai'] | 2022-02-07 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.93301171e-01 3.04397732e-01 -7.63925165e-02 -2.16947809e-01
-7.35541821e-01 -3.70440334e-01 2.90818065e-01 1.27406686e-03
-1.45587221e-01 7.06946850e-01 -1.05549723e-01 -1.68657273e-01
-6.37878776e-01 -7.04421401e-01 -6.42221272e-01 -1.08459604e+00
-3.83898795e-01 4.99058187e-01 6.69083968e-02 4.14886773... | [7.155086994171143, 5.018365383148193] |
dd6a470f-50a4-4f1c-bf76-cb248d35b943 | human-centered-prior-guided-and-task | 2204.12729 | null | https://arxiv.org/abs/2204.12729v1 | https://arxiv.org/pdf/2204.12729v1.pdf | Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training | Recently, much progress has been made for self-supervised action recognition. Most existing approaches emphasize the contrastive relations among videos, including appearance and motion consistency. However, two main issues remain for existing pre-training methods: 1) the learned representation is neutral and not inform... | ['Gaoang Wang', 'Zhanhao He', 'Yang Zhou', 'Keyu Lu', 'Guanhong Wang'] | 2022-04-27 | null | null | null | null | ['human-parsing', 'self-supervised-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.45657670e-01 -2.24799514e-01 -5.91007948e-01 -3.65629673e-01
-8.09924781e-01 -2.04458311e-01 6.03180528e-01 -3.18272829e-01
-3.89642000e-01 7.85906434e-01 5.28925478e-01 3.13918531e-01
1.30275292e-02 -3.12389106e-01 -5.76043189e-01 -7.93663085e-01
3.33456546e-01 2.23591685e-01 5.53667486e-01 -3.31558324... | [8.517196655273438, 0.779137909412384] |
b55ce6d8-9cda-4054-a96f-dde1ca3c1c9c | 3d-mesh-processing-using-gamer-2-to-enable | 1901.11008 | null | http://arxiv.org/abs/1901.11008v3 | http://arxiv.org/pdf/1901.11008v3.pdf | 3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries | Recent advances in electron microscopy have enabled the imaging of single
cells in 3D at nanometer length scale resolutions. An uncharted frontier for in
silico biology is the ability to simulate cellular processes using these
observed geometries. Enabling such simulations requires watertight meshing of
electron microg... | [] | 2019-12-17 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 3.62362266e-01 -1.87324271e-01 1.00254178e+00 2.94267964e-02
-3.61080825e-01 -4.37410772e-01 4.58925605e-01 5.25030673e-01
-7.65599966e-01 9.55035329e-01 -4.26730663e-01 -5.41747868e-01
1.24452271e-01 -9.57165480e-01 -5.04908264e-01 -7.08998859e-01
-1.28021166e-01 1.22337902e+00 5.13908207e-01 -1.37118787... | [13.635560035705566, -3.0808465480804443] |
34c4479f-cb67-4674-8b31-6ecba00c620e | a-critical-re-evaluation-of-benchmark | 2307.01231 | null | https://arxiv.org/abs/2307.01231v1 | https://arxiv.org/pdf/2307.01231v1.pdf | A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms | Entity resolution (ER) is the process of identifying records that refer to the same entities within one or across multiple databases. Numerous techniques have been developed to tackle ER challenges over the years, with recent emphasis placed on machine and deep learning methods for the matching phase. However, the qual... | ['Themis Palpanas', 'Peter Christen', 'Nishadi Kirielle', 'George Papadakis'] | 2023-07-03 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-1.00681223e-01 -1.33239534e-02 -3.19740504e-01 -4.29966211e-01
-1.10301316e+00 -5.50580919e-01 6.65846467e-01 6.85876131e-01
-5.81049562e-01 7.69164264e-01 -6.48382157e-02 -1.65779218e-01
-6.14126801e-01 -9.51808691e-01 -8.95633101e-01 -3.57402295e-01
-2.40723789e-01 7.54995823e-01 1.23027675e-01 -1.42269984... | [9.456721305847168, 8.457087516784668] |
c8340226-8335-4304-b38a-f191d4e20ee3 | symmetry-breaking-for-k-robust-multi-agent | 2102.08689 | null | https://arxiv.org/abs/2102.08689v2 | https://arxiv.org/pdf/2102.08689v2.pdf | Symmetry Breaking for k-Robust Multi-Agent Path Finding | During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by unexpected events. To address such situations recent work describes k-Robust Conflict-BasedSearch (k-CBS): an algorithm that produces coordinated and collision-free plan that is robust for up to k delays. In this work we introducing a variety of ... | ['Peter J. Stuckey', 'Jiaoyang Li', 'Daniel Harabor', 'Zhe Chen'] | 2021-02-17 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.00372770e-01 4.66760635e-01 -1.38724580e-01 -2.94818014e-01
-9.59786355e-01 -1.12291276e+00 5.27226210e-01 4.97793347e-01
-3.53672415e-01 1.37771380e+00 1.92485064e-01 -2.88551331e-01
-1.24594462e+00 -8.34594250e-01 -5.73727787e-01 -4.51995075e-01
-1.04511011e+00 1.52346766e+00 9.52997267e-01 -8.98924589... | [4.9651994705200195, 1.7822085618972778] |
76c71bd6-b015-4861-a5a1-f1d4678f5882 | dependable-intrusion-detection-system-for-iot | 2204.04837 | null | https://arxiv.org/abs/2204.04837v1 | https://arxiv.org/pdf/2204.04837v1.pdf | Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach | Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid de... | ['Rafiqul Islam', 'Kawsar Ahmed', 'Ziaur Rahman', 'Adnan Anwar', 'Sk. Tanzir Mehedi'] | 2022-04-11 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-2.48997256e-01 -7.43616998e-01 1.21555757e-02 -3.54428768e-01
-4.05005328e-02 -4.69082534e-01 3.86489630e-01 1.19297951e-01
-4.94945019e-01 6.23874128e-01 -3.53924960e-01 -4.14750785e-01
-6.53339565e-01 -1.09160686e+00 -7.86458403e-02 -7.23172247e-01
-3.47039074e-01 6.64823949e-01 3.27355832e-01 -3.08539242... | [5.277160167694092, 7.201507568359375] |
cc4836ee-e201-4b8f-89e8-dba34f83bff3 | how-does-information-bottleneck-help-deep | 2305.18887 | null | https://arxiv.org/abs/2305.18887v1 | https://arxiv.org/pdf/2305.18887v1.pdf | How Does Information Bottleneck Help Deep Learning? | Numerous deep learning algorithms have been inspired by and understood via the notion of information bottleneck, where unnecessary information is (often implicitly) minimized while task-relevant information is maximized. However, a rigorous argument for justifying why it is desirable to control information bottlenecks ... | ['Jiaoyang Huang', 'Xu Ji', 'Zhun Deng', 'Kenji Kawaguchi'] | 2023-05-30 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [-2.28703991e-02 1.43318713e-01 -2.65989244e-01 -1.05847314e-01
-3.21737081e-01 -6.24851942e-01 3.85939568e-01 3.88988614e-01
-6.88303530e-01 6.69328034e-01 1.21999212e-01 -4.44866717e-01
-5.37661731e-01 -5.72370589e-01 -7.90410936e-01 -7.07236290e-01
-9.66767743e-02 6.76226392e-02 2.27202382e-02 -1.23280458... | [8.05884838104248, 3.585968255996704] |
d17ec16b-68c0-4cc4-9958-3c15ce3b0dbf | noisy-multi-label-semi-supervised | 1902.07517 | null | http://arxiv.org/abs/1902.07517v1 | http://arxiv.org/pdf/1902.07517v1.pdf | Noisy multi-label semi-supervised dimensionality reduction | Noisy labeled data represent a rich source of information that often are
easily accessible and cheap to obtain, but label noise might also have many
negative consequences if not accounted for. How to fully utilize noisy labels
has been studied extensively within the framework of standard supervised
machine learning ove... | ['Cristina Soguero-Ruiz', 'Karl Øyvind Mikalsen', 'Filippo Maria Bianchi', 'Robert Jenssen'] | 2019-02-20 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 5.71271718e-01 -1.38800576e-01 -1.04483396e-01 -6.58194780e-01
-1.14914370e+00 -5.14419019e-01 5.34380734e-01 9.38718840e-02
-3.72393340e-01 6.98303103e-01 1.04994759e-01 3.32655013e-01
-2.77449548e-01 -4.93838340e-01 -3.36490929e-01 -1.20097744e+00
5.62653720e-01 5.43892801e-01 -4.36742097e-01 2.74115384... | [9.453397750854492, 3.948620319366455] |
a4a54ee8-1731-498f-9ff0-95a2f68d1d23 | surge-routing-event-informed-multiagent | 2307.02637 | null | https://arxiv.org/abs/2307.02637v1 | https://arxiv.org/pdf/2307.02637v1.pdf | Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare | Large events such as conferences, concerts and sports games, often cause surges in demand for ride services that are not captured in average demand patterns, posing unique challenges for routing algorithms. We propose a learning framework for an autonomous fleet of taxis that scrapes event data from the internet to pre... | ['Stephanie Gil', 'Daniel Garces'] | 2023-07-05 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [-5.13909876e-01 2.37141788e-01 -5.08949876e-01 -5.57906568e-01
-1.00247943e+00 -4.25572634e-01 5.38591683e-01 3.29767853e-01
-4.01446015e-01 1.24620509e+00 6.20613813e-01 -3.73346120e-01
-5.90373695e-01 -1.53968191e+00 -8.27779412e-01 -3.40626329e-01
-7.68127978e-01 1.66263509e+00 2.38856182e-01 -6.45628393... | [5.443620681762695, 1.5459234714508057] |
5670c767-5864-4a0a-b98f-0838cf21b2d5 | unsupervised-heterophilous-network-embedding | 2203.10866 | null | https://arxiv.org/abs/2203.10866v3 | https://arxiv.org/pdf/2203.10866v3.pdf | Unsupervised Network Embedding Beyond Homophily | Network embedding (NE) approaches have emerged as a predominant technique to represent complex networks and have benefited numerous tasks. However, most NE approaches rely on a homophily assumption to learn embeddings with the guidance of supervisory signals, leaving the unsupervised heterophilous scenario relatively u... | ['Jun Pang', 'Daniele Grattarola', 'Guadalupe Gonzalez', 'Zhiqiang Zhong'] | 2022-03-21 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 1.17774121e-01 3.47318411e-01 -2.69063920e-01 -2.98746616e-01
7.00606853e-02 -6.55234218e-01 8.82787824e-01 2.92589039e-01
-2.18434617e-01 3.88777196e-01 1.44779682e-01 -1.58624604e-01
-6.12014890e-01 -1.09612823e+00 -3.66066962e-01 -7.60691941e-01
-3.99900168e-01 5.94890118e-01 -4.69489880e-02 -1.17647417... | [7.02653169631958, 6.085538387298584] |
4f750c52-7d20-4f47-af00-c8f7f6a2d265 | improving-ecg-classification-interpretability | 2201.04070 | null | https://arxiv.org/abs/2201.04070v1 | https://arxiv.org/pdf/2201.04070v1.pdf | Improving ECG Classification Interpretability using Saliency Maps | Cardiovascular disease is a large worldwide healthcare issue; symptoms often present suddenly with minimal warning. The electrocardiogram (ECG) is a fast, simple and reliable method of evaluating the health of the heart, by measuring electrical activity recorded through electrodes placed on the skin. ECGs often need to... | ['Dr Jeff Dalton', 'Dr Fani Deligianni', 'Ms Yola Jones'] | 2022-01-10 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 5.20596743e-01 8.02751109e-02 2.66967565e-01 -4.52108920e-01
-4.76552755e-01 -5.91158330e-01 4.58530225e-02 7.41572559e-01
-2.77852476e-01 7.51541793e-01 5.78967715e-03 -3.34747106e-01
-2.46158555e-01 -4.48566049e-01 -3.72712880e-01 -6.69351995e-01
-4.11189020e-01 3.63803446e-01 2.26794511e-01 -1.45083433... | [14.295592308044434, 3.2970781326293945] |
3befba5a-0058-442e-8ff6-8894f835f970 | towards-personalized-and-semantic-retrieval | 2006.02282 | null | https://arxiv.org/abs/2006.02282v3 | https://arxiv.org/pdf/2006.02282v3.pdf | Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning | Nowadays e-commerce search has become an integral part of many people's shopping routines. Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query terms, and how to retrieve items that are more personalized to different users for th... | ['Wen-Yun Yang', 'Songlin Wang', 'Kang Zhang', 'Han Zhang', 'Yunjiang Jiang', 'Zhiling Tang', 'Yun Xiao', 'Weipeng Yan'] | 2020-06-03 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [-3.40156376e-01 -8.18729520e-01 -4.65145856e-01 -4.30675417e-01
-1.01699221e+00 -6.97797596e-01 1.53072163e-01 9.53569189e-02
-4.11437273e-01 2.40850255e-01 7.40582496e-02 -1.47997096e-01
-4.75140005e-01 -1.00451076e+00 -4.47059780e-01 -8.53131637e-02
9.37135965e-02 1.09534872e+00 6.61706328e-01 -7.74081528... | [11.281376838684082, 7.3926100730896] |
033b5869-6929-4ab4-8360-a616951b3ca1 | crop-classification-under-varying-cloud-cover | 2012.02542 | null | https://arxiv.org/abs/2012.02542v2 | https://arxiv.org/pdf/2012.02542v2.pdf | Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations | Optical satellite sensors cannot see the Earth's surface through clouds. Despite the periodic revisit cycle, image sequences acquired by Earth observation satellites are therefore irregularly sampled in time. State-of-the-art methods for crop classification (and other time series analysis tasks) rely on techniques that... | ['Konrad Schindler', 'Jan Dirk Wegner', "Stefano D'Aronco", 'Mehmet Ozgur Turkoglu', 'Nando Metzger'] | 2020-12-04 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 2.08016858e-01 -5.47796965e-01 -2.96621919e-01 2.19825599e-02
1.08750202e-01 -7.09296823e-01 6.27287388e-01 2.12486580e-01
-2.30463967e-01 6.66592538e-01 -2.24928617e-01 -6.36047959e-01
8.99402276e-02 -1.01722252e+00 -7.25755930e-01 -1.09092927e+00
-3.12853038e-01 2.16995925e-02 -1.01954348e-01 -3.28795224... | [9.52322006225586, -1.5708585977554321] |
74896b9f-bc20-4b83-b799-9500a472c5e7 | conditional-gans-for-multi-illuminant-color | 1811.06604 | null | http://arxiv.org/abs/1811.06604v2 | http://arxiv.org/pdf/1811.06604v2.pdf | Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach? | Non-uniform and multi-illuminant color constancy are important tasks, the
solution of which will allow to discard information about lighting conditions
in the image. Non-uniform illumination and shadows distort colors of real-world
objects and mostly do not contain valuable information. Thus, many computer
vision and i... | ['Oleksii Sidorov'] | 2018-11-15 | null | null | null | null | ['shadow-removal', 'color-constancy', 'shadow-detection-and-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 7.13655114e-01 -1.52021751e-01 7.01533020e-01 -2.56497830e-01
-1.68887094e-01 -8.08635533e-01 5.53993940e-01 -6.47507250e-01
-3.13291252e-01 1.18940532e+00 -1.98416933e-01 -3.65206301e-01
3.60645115e-01 -6.49374485e-01 -7.45138645e-01 -1.00755477e+00
3.54029417e-01 2.09878325e-01 4.37488019e-01 -1.10492907... | [10.797304153442383, -4.039897918701172] |
bc7b89ae-8ccd-4a9d-b6ad-3cb8fff09c06 | harnessing-the-power-of-multi-task | 2212.04231 | null | https://arxiv.org/abs/2212.04231v2 | https://arxiv.org/pdf/2212.04231v2.pdf | Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations | Natural language explanations promise to offer intuitively understandable explanations of a neural network's decision process in complex vision-language tasks, as pursued in recent VL-NLE models. While current models offer impressive performance on task accuracy and explanation plausibility, they suffer from a range of... | ['Stefan Wermter', 'Jae Hee Lee', 'Lukas Braach', 'Jakob Ambsdorf', 'Björn Plüster'] | 2022-12-08 | null | null | null | null | ['explanation-generation', 'visual-entailment'] | ['natural-language-processing', 'reasoning'] | [ 5.46044350e-01 1.09216774e+00 -1.16656363e-01 -7.18260527e-01
-9.83208299e-01 -3.03296804e-01 1.01311052e+00 -3.43729645e-01
-1.40631735e-01 9.49468732e-01 5.71388841e-01 -6.49858415e-01
-1.62209570e-01 -4.01329957e-02 -8.77453983e-01 -1.91577449e-01
4.46771502e-01 8.80392432e-01 3.66971008e-02 1.47721311... | [10.836188316345215, 1.9552754163742065] |
8badde80-1f8f-4a21-bcaf-fc72fce2f7a3 | learning-from-partially-labeled-data-for | 2211.06894 | null | https://arxiv.org/abs/2211.06894v1 | https://arxiv.org/pdf/2211.06894v1.pdf | Learning from partially labeled data for multi-organ and tumor segmentation | Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream approaches follow the practice of one network solving one task. With this pipeline, not only the performance is limited by the typically small ... | ['Chunhua Shen', 'Yong Xia', 'Jianpeng Zhang', 'Yutong Xie'] | 2022-11-13 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [-2.85816994e-02 3.71532679e-01 -3.01269889e-01 -3.47734541e-01
-8.22663963e-01 -4.86062676e-01 3.47858548e-01 1.45907653e-02
-4.90010947e-01 3.28557760e-01 7.99723789e-02 -4.21998650e-01
2.54243404e-01 -5.30499160e-01 -6.29436791e-01 -8.93408597e-01
2.29026690e-01 5.91861427e-01 4.75687623e-01 7.63736013... | [14.675503730773926, -2.5243349075317383] |
7bdf0502-8a8f-49e7-bd40-32ad415558d7 | towards-high-fidelity-monocular-face | 2103.15432 | null | https://arxiv.org/abs/2103.15432v3 | https://arxiv.org/pdf/2103.15432v3.pdf | Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing | Robust face reconstruction from monocular image in general lighting conditions is challenging. Methods combining deep neural network encoders with differentiable rendering have opened up the path for very fast monocular reconstruction of geometry, lighting and reflectance. They can also be trained in self-supervised ma... | ['Louis Chevallier', 'Christian Theobalt', 'Philippe-Henri Gosselin', 'Junghyun Ahn', 'Cedric Thebault', 'Abdallah Dib'] | 2021-03-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Dib_Towards_High_Fidelity_Monocular_Face_Reconstruction_With_Rich_Reflectance_Using_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Dib_Towards_High_Fidelity_Monocular_Face_Reconstruction_With_Rich_Reflectance_Using_ICCV_2021_paper.pdf | iccv-2021-1 | ['face-reconstruction'] | ['computer-vision'] | [ 1.80049777e-01 -1.40121967e-01 1.44743070e-01 -4.76059556e-01
-3.10839802e-01 -2.79735327e-01 5.96483588e-01 -5.86416841e-01
4.04103436e-02 7.17242718e-01 -3.35238711e-03 -3.03540509e-02
2.43084803e-01 -9.97634709e-01 -8.24224412e-01 -7.88623154e-01
4.01102662e-01 1.79917082e-01 -3.03382240e-02 -3.75485539... | [9.907610893249512, -2.9204137325286865] |
e49d4253-fb0c-4f48-b125-93c45d8c03a4 | chinese-zero-pronoun-resolution-an | null | null | https://aclanthology.org/D14-1084 | https://aclanthology.org/D14-1084.pdf | Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers | null | ['Vincent Ng', 'Chen Chen'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['chinese-zero-pronoun-resolution'] | ['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... | [-7.1648030281066895, 3.7107908725738525] |
72af4293-48e6-43fd-8dc0-168bf0b34bd0 | enhancing-opinion-role-labeling-with-semantic | null | null | https://aclanthology.org/N19-1066 | https://aclanthology.org/N19-1066.pdf | Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling | Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger. The task is highly correlative with semantic role labeling (SRL), which identifies important semantic arguments such as agent and patient ... | ['Peili Liang', 'Meishan Zhang', 'Guohong Fu'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [ 5.01052439e-01 4.57227468e-01 -4.80815619e-01 -6.51476562e-01
-8.19360018e-01 -7.34582365e-01 6.78755760e-01 6.59915626e-01
-4.65926915e-01 1.06217456e+00 7.16416419e-01 -2.28038624e-01
-1.05028667e-01 -7.90669262e-01 -5.57768583e-01 -6.54019356e-01
4.78666395e-01 6.06454432e-01 3.49820822e-01 -5.90700150... | [11.445561408996582, 6.709410190582275] |
6fc5452d-b15c-4fca-bb62-279073face32 | unsupervised-recurrent-neural-network | 1904.03746 | null | https://arxiv.org/abs/1904.03746v6 | https://arxiv.org/pdf/1904.03746v6.pdf | Unsupervised Recurrent Neural Network Grammars | Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. Supervised RNNGs achieve strong language modeling and parsing performance, but require an annotated corpu... | ['Gábor Melis', 'Alexander M. Rush', 'Lei Yu', 'Chris Dyer', 'Yoon Kim', 'Adhiguna Kuncoro'] | 2019-04-07 | unsupervised-recurrent-neural-network-1 | https://aclanthology.org/N19-1114 | https://aclanthology.org/N19-1114.pdf | naacl-2019-6 | ['constituency-grammar-induction'] | ['natural-language-processing'] | [ 3.19380283e-01 8.53572249e-01 -1.84931502e-01 -7.77550876e-01
-1.07627141e+00 -6.46363556e-01 5.74572444e-01 -1.91992074e-01
-3.77716988e-01 6.77613854e-01 4.98537958e-01 -9.63416576e-01
5.97141623e-01 -1.10801566e+00 -9.80140984e-01 -3.59240174e-01
-1.64117347e-02 8.13521683e-01 -3.69576365e-01 4.38596262... | [10.394906044006348, 9.586811065673828] |
8ba58179-04c4-44e2-8958-b12d7eeb8c54 | improved-reinforcement-learning-with | 1903.12328 | null | https://arxiv.org/abs/1903.12328v2 | https://arxiv.org/pdf/1903.12328v2.pdf | Improved Reinforcement Learning with Curriculum | Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner. For instance, when learning how to play a board game, usually one of the first concepts learned is how the game ends, i.e. the actions that lead to a terminal state (win, lose or draw). The advantage of learning end-... | ['Cameron Browne', 'Simon Denman', 'Frederic Maire', 'Joseph West'] | 2019-03-29 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 3.36093247e-01 4.44680452e-01 1.62082747e-01 -1.29121348e-01
-1.92412272e-01 -6.27124548e-01 5.17293692e-01 3.54810297e-01
-6.53026462e-01 7.75750577e-01 -2.10538521e-01 -4.77891356e-01
-8.98406282e-02 -1.19027376e+00 -9.27011132e-01 -4.97096330e-01
-1.79836705e-01 7.21282899e-01 5.88644028e-01 -5.16495168... | [3.896543025970459, 1.4744749069213867] |
86280fa8-483b-4b28-8a5c-d1bd26557962 | leveraging-user-triggered-supervision-in | 2302.03784 | null | https://arxiv.org/abs/2302.03784v1 | https://arxiv.org/pdf/2302.03784v1.pdf | Leveraging User-Triggered Supervision in Contextual Bandits | We study contextual bandit (CB) problems, where the user can sometimes respond with the best action in a given context. Such an interaction arises, for example, in text prediction or autocompletion settings, where a poor suggestion is simply ignored and the user enters the desired text instead. Crucially, this extra fe... | ['Teodor V. Marinov', 'Claudio Gentile', 'Alekh Agarwal'] | 2023-02-07 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 4.11371768e-01 2.92425952e-03 -8.52829337e-01 -3.45946580e-01
-1.04692149e+00 -9.71959651e-01 4.82935846e-01 2.89168090e-01
-5.04375398e-01 9.64760900e-01 5.49790680e-01 -6.33885562e-01
-4.48064357e-01 -3.64130706e-01 -8.16047728e-01 -6.82136714e-01
2.58498579e-01 4.84937727e-01 -1.65841803e-01 -2.00133935... | [4.505217552185059, 3.2296671867370605] |
465821f8-cd15-49d1-827d-b56826d53ba2 | integrating-uncertainty-into-neural-network | 2305.08744 | null | https://arxiv.org/abs/2305.08744v1 | https://arxiv.org/pdf/2305.08744v1.pdf | Integrating Uncertainty into Neural Network-based Speech Enhancement | Supervised masking approaches in the time-frequency domain aim to employ deep neural networks to estimate a multiplicative mask to extract clean speech. This leads to a single estimate for each input without any guarantees or measures of reliability. In this paper, we study the benefits of modeling uncertainty in clean... | ['Timo Gerkmann', 'Stefan Wermter', 'Dennis Becker', 'Huajian Fang'] | 2023-05-15 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-1.29975811e-01 3.76535542e-02 1.93569168e-01 -3.98108542e-01
-1.53782094e+00 -3.10344458e-01 6.98955059e-01 -2.38963068e-02
-1.99006990e-01 9.47248697e-01 5.36159217e-01 -2.99322367e-01
-4.50937152e-01 -6.51321948e-01 -7.80078650e-01 -9.23078239e-01
1.25029340e-01 3.61887589e-02 -2.54713982e-01 1.96713701... | [7.2916412353515625, 3.6626293659210205] |
a75c6f22-2e4c-4840-9839-d991f3778669 | machine-learning-algorithms-for-depression | 2301.03222 | null | https://arxiv.org/abs/2301.03222v1 | https://arxiv.org/pdf/2301.03222v1.pdf | Machine Learning Algorithms for Depression Detection and Their Comparison | Textual emotional intelligence is playing a ubiquitously important role in leveraging human emotions on social media platforms. Social media platforms are privileged with emotional content and are leveraged for various purposes like opinion mining, emotion mining, and sentiment analysis. This data analysis is also leve... | ['Mubashir Qayoom', 'Furqan Yaqub Khan', 'Danish Muzafar'] | 2023-01-09 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-2.40879074e-01 2.51497298e-01 -4.02327567e-01 -3.70641142e-01
-1.19451247e-01 6.03223394e-04 1.03778340e-01 5.96224844e-01
-5.24361670e-01 6.81208551e-01 1.87334672e-01 -2.45569423e-01
1.87617421e-01 -7.79794157e-01 1.41046226e-01 -4.19338167e-01
-2.38095626e-01 -2.02335730e-01 -3.92186642e-01 -4.33616400... | [12.157289505004883, 6.397804260253906] |
46d194bf-3f7a-4471-8a07-da9368ca600f | refinement-of-direction-of-arrival-estimators | 2106.01011 | null | https://arxiv.org/abs/2106.01011v1 | https://arxiv.org/pdf/2106.01011v1.pdf | Refinement of Direction of Arrival Estimators by Majorization-Minimization Optimization on the Array Manifold | We propose a generalized formulation of direction of arrival estimation that includes many existing methods such as steered response power, subspace, coherent and incoherent, as well as speech sparsity-based methods. Unlike most conventional methods that rely exclusively on grid search, we introduce a continuous optimi... | ['Masahito Togami', 'Robin Scheibler'] | 2021-06-02 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-7.38015026e-02 -1.15433209e-01 2.04544857e-01 -3.21085118e-02
-1.00709319e+00 -6.14640951e-01 3.94437939e-01 -1.44596219e-01
-2.99514383e-01 8.68930757e-01 6.44012332e-01 -2.82621831e-01
-4.05271918e-01 -4.30135220e-01 -4.29487526e-01 -1.00771487e+00
-1.26300797e-01 2.21650880e-02 1.07984081e-01 -6.92190528... | [6.5186920166015625, 1.401112675666809] |
da9f28e7-906b-489f-b38b-4bed82a5c0c5 | levenshtein-ocr | 2209.03594 | null | https://arxiv.org/abs/2209.03594v2 | https://arxiv.org/pdf/2209.03594v2.pdf | Levenshtein OCR | A novel scene text recognizer based on Vision-Language Transformer (VLT) is presented. Inspired by Levenshtein Transformer in the area of NLP, the proposed method (named Levenshtein OCR, and LevOCR for short) explores an alternative way for automatically transcribing textual content from cropped natural images. Specifi... | ['Cong Yao', 'Peng Wang', 'Cheng Da'] | 2022-09-08 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.20866716e-01 -2.25623056e-01 7.78733864e-02 -1.20605603e-01
-6.96466863e-01 -8.14862192e-01 8.87698233e-01 -1.57055393e-01
-3.11744481e-01 4.33426559e-01 2.70201147e-01 -3.27202320e-01
3.73363912e-01 -4.19130266e-01 -8.25424314e-01 -6.34091794e-01
7.28048682e-01 5.80113471e-01 1.17355518e-01 -9.28142763... | [11.835088729858398, 2.147157669067383] |
045d5686-a8d0-4671-b2fd-568adb5f76c4 | dynamic-weights-in-multi-objective-deep | 1809.07803 | null | https://arxiv.org/abs/1809.07803v2 | https://arxiv.org/pdf/1809.07803v2.pdf | Dynamic Weights in Multi-Objective Deep Reinforcement Learning | Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) ... | ['Ann Nowé', 'Tom Lenaerts', 'Denis Steckelmacher', 'Axel Abels', 'Diederik M. Roijers'] | 2018-09-20 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 2.05097154e-01 -2.18905255e-01 -2.76607871e-01 -1.43775508e-01
-7.42158055e-01 -5.63689053e-01 2.99108088e-01 3.04094970e-01
-9.18218732e-01 1.28545141e+00 3.91097404e-02 -1.13809161e-01
-7.85055935e-01 -5.82203090e-01 -6.03595853e-01 -7.96921253e-01
-1.00138754e-01 8.26807439e-01 1.70009196e-01 -5.05636930... | [4.172831058502197, 2.2688469886779785] |
b0604cda-a7bd-4212-ba7f-a039e5c0c1e6 | on-the-opportunities-and-challenges-of | 2304.06798 | null | https://arxiv.org/abs/2304.06798v1 | https://arxiv.org/pdf/2304.06798v1.pdf | On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence | Large pre-trained models, also known as foundation models (FMs), are trained in a task-agnostic manner on large-scale data and can be adapted to a wide range of downstream tasks by fine-tuning, few-shot, or even zero-shot learning. Despite their successes in language and vision tasks, we have yet seen an attempt to dev... | ['Ni Lao', 'Rui Zhu', 'Ziyuan Li', 'Chris Cundy', 'Yingjie Hu', 'Gao Cong', 'Tianming Liu', 'Song Gao', 'Ninghao Liu', 'Deepak Mishra', 'Suhang Song', 'Jin Sun', 'Weiming Huang', 'Gengchen Mai'] | 2023-04-13 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 3.19004357e-01 -2.80015200e-01 -3.32631350e-01 -2.70197868e-01
-1.09155416e+00 -3.00791711e-01 9.61635828e-01 2.22363636e-01
-4.91367877e-01 5.97502351e-01 7.41088390e-01 -6.59997225e-01
-4.31902677e-01 -1.06294906e+00 -5.21811724e-01 -3.25986266e-01
-8.54204297e-02 2.50695556e-01 -7.23795267e-03 -4.09474134... | [9.387527465820312, -1.1977037191390991] |
f6ad6e95-8191-462b-bad8-856c18635a84 | focus-on-details-online-multi-object-tracking | 2302.14589 | null | https://arxiv.org/abs/2302.14589v3 | https://arxiv.org/pdf/2302.14589v3.pdf | Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation | Discriminative representation is essential to keep a unique identifier for each target in Multiple object tracking (MOT). Some recent MOT methods extract features of the bounding box region or the center point as identity embeddings. However, when targets are occluded, these coarse-grained global representations become... | ['Faquan Wang', 'Hongwei Wang', 'Ziwen Zhang', 'Huilin Ding', 'Shoudong Han', 'Hao Ren'] | 2023-02-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ren_Focus_on_Details_Online_Multi-Object_Tracking_With_Diverse_Fine-Grained_Representation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ren_Focus_on_Details_Online_Multi-Object_Tracking_With_Diverse_Fine-Grained_Representation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multiple-object-tracking', 'online-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 4.23904508e-01 -3.52520019e-01 -3.68630320e-01 -3.39958489e-01
-8.77027690e-01 -6.30206704e-01 6.21116757e-01 -4.39981818e-01
-1.56293914e-01 8.70778143e-01 1.76462844e-01 4.32156086e-01
7.77069386e-03 -8.13976526e-01 -8.24364185e-01 -9.59135056e-01
2.22670153e-01 5.46787560e-01 5.64075589e-01 -1.62931308... | [6.479081630706787, -2.1026859283447266] |
9a229652-9c84-4b9f-83fd-993eb31097bf | lbdmids-lstm-based-deep-learning-model-for | 2207.00424 | null | https://arxiv.org/abs/2207.00424v1 | https://arxiv.org/pdf/2207.00424v1.pdf | LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks | In the recent years, we have witnessed a huge growth in the number of Internet of Things (IoT) and edge devices being used in our everyday activities. This demands the security of these devices from cyber attacks to be improved to protect its users. For years, Machine Learning (ML) techniques have been used to develop ... | ['Rahamatullah Khondoker', 'O. P. Vyas', 'Ranjana Vyas', 'Uphar Singh', 'P. Aditya Kumar', 'Saksham Sood', 'Kumar Saurabh'] | 2022-06-23 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-2.79544383e-01 -2.31867164e-01 -4.04059924e-02 -3.07156593e-01
2.34437972e-01 -1.72191918e-01 8.63058150e-01 -6.83017001e-02
-5.28073013e-01 8.97432804e-01 -1.19414970e-01 -7.04646349e-01
-2.97620535e-01 -1.02923930e+00 -3.32902521e-01 -5.55336118e-01
-1.67582512e-01 5.41625500e-01 4.29962635e-01 -1.92652017... | [5.206057548522949, 7.178478717803955] |
42610569-6482-44f6-acfa-6b52e646072a | anode-unconditionally-accurate-memory | 1902.10298 | null | https://arxiv.org/abs/1902.10298v3 | https://arxiv.org/pdf/1902.10298v3.pdf | ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs | Residual neural networks can be viewed as the forward Euler discretization of an Ordinary Differential Equation (ODE) with a unit time step. This has recently motivated researchers to explore other discretization approaches and train ODE based networks. However, an important challenge of neural ODEs is their prohibitiv... | ['George Biros', 'Kurt Keutzer', 'Amir Gholami'] | 2019-02-27 | null | null | null | null | ['multivariate-time-series-imputation', 'clustering-multivariate-time-series'] | ['time-series', 'time-series'] | [-3.08352083e-01 -8.49739835e-03 4.37149465e-01 -9.00514424e-02
-3.20704579e-01 -3.91880959e-01 2.42512703e-01 -2.48232663e-01
-8.50667357e-01 1.11200237e+00 -3.11811239e-01 -4.96195644e-01
-1.38172694e-02 -6.56345785e-01 -7.29809225e-01 -7.82098532e-01
-3.46674144e-01 1.44483492e-01 1.53996482e-01 -2.15700492... | [6.865442276000977, 3.5000619888305664] |
b11a9732-96d3-4295-a72b-51cb609a6e4d | deep-deformable-3d-caricatures-with-learned | 2207.14593 | null | https://arxiv.org/abs/2207.14593v1 | https://arxiv.org/pdf/2207.14593v1.pdf | Deep Deformable 3D Caricatures with Learned Shape Control | A 3D caricature is an exaggerated 3D depiction of a human face. The goal of this paper is to model the variations of 3D caricatures in a compact parameter space so that we can provide a useful data-driven toolkit for handling 3D caricature deformations. To achieve the goal, we propose an MLP-based framework for buildin... | ['Seungyong Lee', 'Xin Tong', 'Jiaolong Yang', 'Soongjin Kim', 'Wonjong Jang', 'Yucheol Jung'] | 2022-07-29 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 2.43580133e-01 8.22051108e-01 1.97175965e-01 -6.83364689e-01
-3.91986638e-01 -5.45102596e-01 6.32581830e-01 -5.27111709e-01
2.41307765e-01 1.49704084e-01 7.21221194e-02 -6.90598711e-02
9.62510183e-02 -9.83541369e-01 -1.19728780e+00 -5.05210459e-01
3.98754254e-02 9.73372400e-01 -1.46405604e-02 -1.46999344... | [8.909466743469238, -3.629671573638916] |
aab7f104-9f2b-49ae-a06c-a161034e83a3 | srl4e-semantic-role-labeling-for-emotions-a | null | null | https://aclanthology.org/2022.acl-long.314 | https://aclanthology.org/2022.acl-long.314.pdf | SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework | In the field of sentiment analysis, several studies have highlighted that a single sentence may express multiple, sometimes contrasting, sentiments and emotions, each with its own experiencer, target and/or cause. To this end, over the past few years researchers have started to collect and annotate data manually, in or... | ['Roberto Navigli', 'Simone Conia', 'Cesare Campagnano'] | null | null | null | null | acl-2022-5 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 2.30887130e-01 -1.78533167e-01 -1.03134654e-01 -7.25957096e-01
-2.45661303e-01 -1.02595317e+00 6.86872482e-01 7.69650042e-01
-5.25996864e-01 8.50521624e-01 4.71369863e-01 3.42315435e-01
1.26216441e-01 -4.43246245e-01 -7.15003386e-02 -6.79193735e-01
2.52449244e-01 3.69089454e-01 -1.06119625e-01 -4.76780862... | [12.603468894958496, 6.326849937438965] |
65039fce-7ea5-488b-9697-ed89e7dff9e5 | obstacle-tower-a-generalization-challenge-in | 1902.01378 | null | https://arxiv.org/abs/1902.01378v2 | https://arxiv.org/pdf/1902.01378v2.pdf | Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning | The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to competitive video games. We propose a new benchmark - Obstacle Tower: a high fidelity, 3D, 3rd ... | ['Jonathan Harper', 'Vincent-Pierre Berges', 'Ahmed Khalifa', 'Danny Lange', 'Adam Crespi', 'Julian Togelius', 'Ervin Teng', 'Arthur Juliani', 'Hunter Henry'] | 2019-02-04 | null | null | null | null | ['board-games'] | ['playing-games'] | [-9.95843261e-02 1.36984080e-01 2.44713947e-01 8.47600251e-02
-8.17573607e-01 -7.05152392e-01 8.24278712e-01 -1.21371247e-01
-7.58391380e-01 1.05134046e+00 1.02592163e-01 -1.26060173e-01
4.26386669e-02 -8.37841749e-01 -5.54232001e-01 -4.46160555e-01
-4.68310148e-01 1.03695476e+00 4.73505020e-01 -5.74517071... | [3.9435184001922607, 1.3190914392471313] |
b517b3ae-36d2-4a6c-9535-8f441b1b349a | automatic-extraction-of-time-expressions | null | null | https://aclanthology.org/D15-1055 | https://aclanthology.org/D15-1055.pdf | Automatic Extraction of Time Expressions Accross Domains in French Narratives | null | ["Aur{\\'e}lie N{\\'e}v{\\'e}ol", 'Mike Donald Tapi Nzali', 'Xavier Tannier'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['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... | [-7.248049736022949, 3.7637124061584473] |
a93b4ac0-b972-4d7f-9462-7da7814b7688 | fine-tuning-siamese-networks-to-assess-sport | null | null | https://www.scitepress.org/PublicationsDetail.aspx?ID=qYIEOJLsu40=&t=1 | https://www.scitepress.org/PublicationsDetail.aspx?ID=qYIEOJLsu40=&t=1 | Fine-tuning Siamese Networks to Assess Sport Gestures Quality | This paper presents an Action Quality Assessment (AQA) approach that learns to automatically score action
realization from temporal sequences like videos. To manage the small size of most of databases capturing
actions or gestures, we propose to use Siamese Networks. In the literature, Siamese Networks are widely use... | ['Mégane Millan', 'Catherine Achard'] | 2020-02-27 | null | null | null | international-joint-conference-on-computer | ['action-quality-assessment'] | ['computer-vision'] | [ 3.45527261e-01 -2.07097337e-01 -2.98559576e-01 -4.77124661e-01
-1.08959448e+00 -4.05910790e-01 4.07216102e-01 -1.65244704e-03
-7.28402913e-01 6.45660102e-01 4.34423268e-01 4.10688370e-01
-2.59490788e-01 -7.82755911e-01 -7.29176521e-01 -4.97745812e-01
-3.59328151e-01 4.92973089e-01 6.76481307e-01 -2.50812173... | [8.052640914916992, 0.6198624968528748] |
6a70853c-5658-443c-832f-57a9a558a057 | fast-interactive-video-object-segmentation | 2103.03821 | null | https://arxiv.org/abs/2103.03821v2 | https://arxiv.org/pdf/2103.03821v2.pdf | Fast Interactive Video Object Segmentation with Graph Neural Networks | Pixelwise annotation of image sequences can be very tedious for humans. Interactive video object segmentation aims to utilize automatic methods to speed up the process and reduce the workload of the annotators. Most contemporary approaches rely on deep convolutional networks to collect and process information from huma... | ['András Lőrincz', 'Viktor Varga'] | 2021-03-05 | null | null | null | null | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 3.48798990e-01 1.95193693e-01 -4.77845930e-02 -4.41507310e-01
-6.43390417e-01 -6.72224104e-01 2.49788657e-01 2.71781236e-01
-8.20870996e-01 5.68889141e-01 -5.97541988e-01 -3.71148348e-01
2.94459045e-01 -6.59617901e-01 -8.99026513e-01 -5.45783937e-01
5.39946370e-02 8.99079621e-01 6.62701666e-01 2.05410138... | [9.31657886505127, 0.04311717674136162] |
1ccdcaa0-955e-43ab-888a-52b047bf14f3 | egohumans-an-egocentric-3d-multi-human | 2305.16487 | null | https://arxiv.org/abs/2305.16487v1 | https://arxiv.org/pdf/2305.16487v1.pdf | EgoHumans: An Egocentric 3D Multi-Human Benchmark | We present EgoHumans, a new multi-view multi-human video benchmark to advance the state-of-the-art of egocentric human 3D pose estimation and tracking. Existing egocentric benchmarks either capture single subject or indoor-only scenarios, which limit the generalization of computer vision algorithms for real-world appli... | ['Kris Kitani', 'Minh Vo', 'Richard Newcombe', 'Lingni Ma', 'Aayush Bansal', 'Rawal Khirodkar'] | 2023-05-25 | null | null | null | null | ['pose-estimation', '3d-pose-estimation', 'human-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.55750525e-01 -4.34825540e-01 7.75966123e-02 1.06430076e-01
-4.76347029e-01 -5.57630360e-01 4.07624871e-01 -4.95521218e-01
-3.01370740e-01 3.41270566e-01 5.48522413e-01 6.28908575e-01
2.10203543e-01 -4.67146963e-01 -8.25597823e-01 -4.35859710e-01
1.09888576e-02 8.40197325e-01 3.81823599e-01 -4.33295041... | [7.03298807144165, -0.8572015762329102] |
8a93cfde-560b-46e9-8e84-4fde90353f2e | click-carving-segmenting-objects-in-video | 1607.01115 | null | http://arxiv.org/abs/1607.01115v1 | http://arxiv.org/pdf/1607.01115v1.pdf | Click Carving: Segmenting Objects in Video with Point Clicks | We present a novel form of interactive video object segmentation where a few
clicks by the user helps the system produce a full spatio-temporal segmentation
of the object of interest. Whereas conventional interactive pipelines take the
user's initialization as a starting point, we show the value in the system
taking th... | ['Kristen Grauman', 'Suyog Dutt Jain'] | 2016-07-05 | null | null | null | null | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 3.52737576e-01 -1.28252655e-01 -4.02648784e-02 -3.36760074e-01
-9.28156137e-01 -8.63578141e-01 2.73971617e-01 2.84446299e-01
-6.73455060e-01 4.13178116e-01 -2.99197465e-01 -1.90482900e-01
2.47036591e-01 -3.66687000e-01 -5.46397924e-01 -5.53423643e-01
-1.01827726e-01 7.48994231e-01 1.23021448e+00 1.27754271... | [9.244595527648926, -0.3002196252346039] |
46abb2ea-0692-4cb3-895f-4aad8ad7ff88 | mv-mr-multi-views-and-multi-representations | 2303.12130 | null | https://arxiv.org/abs/2303.12130v1 | https://arxiv.org/pdf/2303.12130v1.pdf | MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation | We present a new method of self-supervised learning and knowledge distillation based on the multi-views and multi-representations (MV-MR). The MV-MR is based on the maximization of dependence between learnable embeddings from augmented and non-augmented views, jointly with the maximization of dependence between learnab... | ['Slava Voloshynovskiy', 'Mariia Drozdova', 'Vitaliy Kinakh'] | 2023-03-21 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [-1.83619127e-01 1.50864556e-01 -3.25191945e-01 -3.43067616e-01
-7.98395991e-01 -5.00773430e-01 5.85876048e-01 -1.06380023e-01
-5.64190745e-01 6.26849651e-01 1.71833426e-01 1.05890013e-01
-3.18811834e-01 -6.63809597e-01 -9.28544402e-01 -8.46824110e-01
6.26237988e-02 4.68607575e-01 1.02426611e-01 -1.27357587... | [9.423757553100586, 2.6362264156341553] |
9cd5a928-51c5-476a-96f6-0d3c2834f214 | facenet-a-unified-embedding-for-face | 1503.03832 | null | http://arxiv.org/abs/1503.03832v3 | http://arxiv.org/pdf/1503.03832v3.pdf | FaceNet: A Unified Embedding for Face Recognition and Clustering | Despite significant recent advances in the field of face recognition,
implementing face verification and recognition efficiently at scale presents
serious challenges to current approaches. In this paper we present a system,
called FaceNet, that directly learns a mapping from face images to a compact
Euclidean space whe... | ['Florian Schroff', 'Dmitry Kalenichenko', 'James Philbin'] | 2015-03-12 | facenet-a-unified-embedding-for-face-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Schroff_FaceNet_A_Unified_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf | cvpr-2015-6 | ['disguised-face-verification'] | ['computer-vision'] | [ 8.59126542e-03 -2.10821643e-01 -1.31170571e-01 -8.95463228e-01
-6.75659597e-01 -4.43570793e-01 5.89034736e-01 -2.45140269e-01
-3.92405868e-01 2.69964188e-01 -9.85842198e-03 -2.21639901e-01
-1.17354795e-01 -7.96206057e-01 -7.89959788e-01 -4.85669792e-01
-4.63395029e-01 5.44867277e-01 -3.08578819e-01 -4.44513783... | [13.287086486816406, 0.7962698340415955] |
6cb35d66-2ecd-4b3b-a472-6de7864475b3 | sgd-qa-fast-schema-guided-dialogue-state | 2105.08049 | null | https://arxiv.org/abs/2105.08049v1 | https://arxiv.org/pdf/2105.08049v1.pdf | SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services | Dialogue state tracking is an essential part of goal-oriented dialogue systems, while most of these state tracking models often fail to handle unseen services. In this paper, we propose SGD-QA, a simple and extensible model for schema-guided dialogue state tracking based on a question answering approach. The proposed m... | ['Boris Ginsburg', 'Evelina Bakhturina', 'Vahid Noroozi', 'Yang Zhang'] | 2021-05-17 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 5.10560162e-02 6.41137898e-01 -1.15850613e-01 -5.22902966e-01
-1.04364014e+00 -8.36393356e-01 1.13321006e+00 2.33443573e-01
-4.35704291e-01 6.53288841e-01 6.60604835e-01 -4.51014072e-01
3.32992673e-01 -3.09884429e-01 -1.01269372e-02 -1.85945079e-01
1.19833477e-01 1.15831971e+00 8.08085144e-01 -1.13722539... | [12.836320877075195, 7.927868843078613] |
dc2d5bcb-2a72-43b9-a914-a42db936834a | facial-feature-point-detection-a | 1410.1037 | null | http://arxiv.org/abs/1410.1037v1 | http://arxiv.org/pdf/1410.1037v1.pdf | Facial Feature Point Detection: A Comprehensive Survey | This paper presents a comprehensive survey of facial feature point detection
with the assistance of abundant manually labeled images. Facial feature point
detection favors many applications such as face recognition, animation,
tracking, hallucination, expression analysis and 3D face modeling. Existing
methods can be ca... | ['Xuelong. Li', 'DaCheng Tao', 'Nannan Wang', 'Xinbo Gao'] | 2014-10-04 | null | null | null | null | ['3d-face-modeling'] | ['computer-vision'] | [-1.40992165e-01 -2.06022248e-01 -3.56162637e-01 -6.21707499e-01
-4.76997107e-01 -2.14038983e-01 5.67315221e-01 -4.85633463e-01
-9.31941047e-02 1.61256149e-01 -3.51628102e-02 3.42803091e-01
1.18267328e-01 -2.47516260e-01 -2.90131629e-01 -9.16607320e-01
-1.30527288e-01 2.80110776e-01 -1.18275613e-01 -2.36817718... | [13.29920768737793, 0.5050673484802246] |
3b3c922c-af8f-4648-852a-ced7176f84e3 | zero-shot-cross-lingual-opinion-target | 1904.09122 | null | http://arxiv.org/abs/1904.09122v1 | http://arxiv.org/pdf/1904.09122v1.pdf | Zero-Shot Cross-Lingual Opinion Target Extraction | Aspect-based sentiment analysis involves the recognition of so called opinion
target expressions (OTEs). To automatically extract OTEs, supervised learning
algorithms are usually employed which are trained on manually annotated
corpora. The creation of these corpora is labor-intensive and sufficiently
large datasets ar... | ['Soufian Jebbara', 'Philipp Cimiano'] | 2019-04-19 | zero-shot-cross-lingual-opinion-target-1 | https://aclanthology.org/N19-1257 | https://aclanthology.org/N19-1257.pdf | naacl-2019-6 | ['multilingual-word-embeddings'] | ['methodology'] | [-2.44418606e-02 -4.33622524e-02 -3.51541907e-01 -4.78110850e-01
-1.18952847e+00 -7.09857464e-01 6.34891033e-01 4.57878590e-01
-8.02472353e-01 5.93428850e-01 8.01046863e-02 -2.91129291e-01
6.22309685e-01 -7.26976812e-01 -5.59257805e-01 -3.84449363e-01
3.86273772e-01 5.95650196e-01 -1.85058080e-02 -3.78577024... | [11.33719539642334, 7.0341477394104] |
fc7dcd8f-2d42-42b1-81ef-e9c82ec00cb9 | semantically-self-aligned-network-for-text-to | 2107.12666 | null | https://arxiv.org/abs/2107.12666v2 | https://arxiv.org/pdf/2107.12666v2.pdf | Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification | Text-to-image person re-identification (ReID) aims to search for images containing a person of interest using textual descriptions. However, due to the significant modality gap and the large intra-class variance in textual descriptions, text-to-image ReID remains a challenging problem. Accordingly, in this paper, we pr... | ['DaCheng Tao', 'Zhiyin Shao', 'Changxing Ding', 'Zefeng Ding'] | 2021-07-27 | null | null | null | null | ['nlp-based-person-retrival'] | ['computer-vision'] | [ 1.32360533e-01 -3.18475366e-01 -3.27645093e-01 -5.93689680e-01
-7.49473214e-01 -4.57395971e-01 6.43995941e-01 -6.60242885e-02
-5.28909147e-01 4.15228248e-01 5.24598598e-01 5.23903430e-01
-1.45865366e-01 -4.25161004e-01 -5.15721440e-01 -5.52592337e-01
3.87041509e-01 3.87793958e-01 -4.70042862e-02 7.99103230... | [14.643595695495605, 0.8452453017234802] |
fde697cd-9bbe-4c6c-9bc4-8a9a15ac3ad8 | investigating-glyph-phonetic-information-for | 2212.04068 | null | https://arxiv.org/abs/2212.04068v3 | https://arxiv.org/pdf/2212.04068v3.pdf | Investigating Glyph Phonetic Information for Chinese Spell Checking: What Works and What's Next | While pre-trained Chinese language models have demonstrated impressive performance on a wide range of NLP tasks, the Chinese Spell Checking (CSC) task remains a challenge. Previous research has explored using information such as glyphs and phonetics to improve the ability to distinguish misspelled characters, with good... | ['Xipeng Qiu', 'Hang Yan', 'Yanjun Zheng', 'Xiaotian Zhang'] | 2022-12-08 | null | null | null | null | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 2.36837089e-01 -4.11032468e-01 -3.31918329e-01 -3.54276866e-01
-1.02452409e+00 -8.26591611e-01 5.23798704e-01 2.22456425e-01
-5.37376046e-01 4.62578207e-01 5.07977903e-01 -7.68480241e-01
3.04452360e-01 -3.91391546e-01 -5.74015498e-01 -4.01912391e-01
3.24274510e-01 1.04320906e-01 1.21993661e-01 5.50450794... | [10.923233032226562, 10.61236572265625] |
f79c4a28-8467-49f1-b1c7-637abaefd115 | deploying-offline-reinforcement-learning-with | 2303.07046 | null | https://arxiv.org/abs/2303.07046v1 | https://arxiv.org/pdf/2303.07046v1.pdf | Deploying Offline Reinforcement Learning with Human Feedback | Reinforcement learning (RL) has shown promise for decision-making tasks in real-world applications. One practical framework involves training parameterized policy models from an offline dataset and subsequently deploying them in an online environment. However, this approach can be risky since the offline training may n... | ['Peilin Zhao', 'Deheng Ye', 'Lanqing Li', 'Liu Liu', 'Ke Xu', 'Ziniu Li'] | 2023-03-13 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 2.75343925e-01 2.43627146e-01 -3.96849781e-01 -2.77899563e-01
-7.08466470e-01 -3.97177428e-01 3.36076289e-01 9.57580805e-02
-7.22865224e-01 9.80630577e-01 -5.75562775e-01 -5.21705091e-01
-2.12782577e-01 -7.22477376e-01 -6.86699092e-01 -9.53935206e-01
-2.75859773e-01 8.23010564e-01 4.48263109e-01 -2.11289406... | [4.203892707824707, 2.2060322761535645] |
977c0a67-b1da-48ac-b13e-48bc158e0cde | abusive-language-detection-using-syntactic | null | null | https://aclanthology.org/2020.alw-1.6 | https://aclanthology.org/2020.alw-1.6.pdf | Abusive Language Detection using Syntactic Dependency Graphs | Automated detection of abusive language online has become imperative. Current sequential models (LSTM) do not work well for long and complex sentences while bi-transformer models (BERT) are not computationally efficient for the task. We show that classifiers based on syntactic structure of the text, dependency graphica... | ['Chris Brew', 'Kanika Narang'] | null | null | null | null | emnlp-alw-2020-11 | ['abusive-language'] | ['natural-language-processing'] | [-3.38876247e-01 -9.39441249e-02 -3.87016118e-01 -5.01782298e-01
-7.82799244e-01 -5.99079311e-01 7.25596189e-01 3.51900250e-01
-7.01713622e-01 6.63640916e-01 2.93333918e-01 -7.58824646e-01
3.54105532e-01 -5.13783276e-01 -3.69063854e-01 -1.24880753e-01
-1.74232349e-01 6.37567163e-01 4.01366740e-01 -6.08255744... | [8.800048828125, 10.530421257019043] |
040b109b-8977-4840-8d10-ffbe1a5f8d2d | learn-to-adapt-for-generalized-zero-shot-text | null | null | https://openreview.net/forum?id=2NOSFKxicWQ | https://openreview.net/pdf?id=2NOSFKxicWQ | Learn to Adapt for Generalized Zero-Shot Text Classification | Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the parameters keep stationar... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 3.66693348e-01 2.27307510e-02 -4.67790902e-01 -6.03292763e-01
-8.04645240e-01 -3.35136771e-01 7.22571552e-01 1.94993436e-01
-3.25434774e-01 8.37849081e-01 -3.95188108e-03 1.19797103e-01
-1.32723719e-01 -1.01518536e+00 -4.51050550e-01 -7.61063278e-01
3.04510564e-01 1.05604994e+00 4.72143710e-01 -2.62724608... | [10.126744270324707, 3.462315559387207] |
c4145b0e-5aca-4265-86e1-6606e63629bd | graph-level-anomaly-detection-via | 2307.00755 | null | https://arxiv.org/abs/2307.00755v1 | https://arxiv.org/pdf/2307.00755v1.pdf | Graph-level Anomaly Detection via Hierarchical Memory Networks | Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or i... | ['Ling Chen', 'Guansong Pang', 'Chaoxi Niu'] | 2023-07-03 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [-1.25992224e-02 4.09104526e-01 4.51480411e-02 -9.95191932e-02
2.25139745e-02 -2.23179564e-01 4.48964179e-01 7.13734806e-01
5.02104223e-01 2.62052156e-02 -1.97842736e-02 -2.00465485e-01
-1.33467570e-01 -1.30581367e+00 -6.33647442e-01 -5.35495937e-01
-9.32323098e-01 4.73385185e-01 2.90892482e-01 -1.02057472... | [6.654242515563965, 5.835501670837402] |
f4ce8ab6-efa7-484d-8319-cee9b39696a1 | modeling-sequential-sentence-relation-to | 2302.01626 | null | https://arxiv.org/abs/2302.01626v1 | https://arxiv.org/pdf/2302.01626v1.pdf | Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval | Recently multi-lingual pre-trained language models (PLM) such as mBERT and XLM-R have achieved impressive strides in cross-lingual dense retrieval. Despite its successes, they are general-purpose PLM while the multilingual PLM tailored for cross-lingual retrieval is still unexplored. Motivated by an observation that th... | ['Nan Duan', 'Daxin Jiang', 'Ming Gong', 'Yaobo Liang', 'Shunyu Zhang'] | 2023-02-03 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-4.47831824e-02 -3.64272386e-01 -6.15518808e-01 -5.63466907e-01
-1.56464148e+00 -4.89776075e-01 8.81711006e-01 1.09672405e-01
-5.11586487e-01 5.73048651e-01 4.70620692e-01 -3.31365943e-01
2.07528472e-01 -4.49960947e-01 -8.62335086e-01 -3.17061365e-01
2.66633332e-02 5.01374543e-01 -8.66677612e-02 -5.32738984... | [11.300262451171875, 9.80214786529541] |
70e6bbd3-c6d1-45da-ace4-c2bc9cf27672 | constraints-on-parameter-choices-for | 2206.02575 | null | https://arxiv.org/abs/2206.02575v1 | https://arxiv.org/pdf/2206.02575v1.pdf | Constraints on parameter choices for successful reservoir computing | Echo-state networks are simple models of discrete dynamical systems driven by a time series. By selecting network parameters such that the dynamics of the network is contractive, characterized by a negative maximal Lyapunov exponent, the network may synchronize with the driving signal. Exploiting this synchronization, ... | ['B. Mehlig', 'K. Gustavsson', 'L. Storm'] | 2022-06-03 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.33502775e-01 1.67553514e-01 -2.25675359e-01 1.21591790e-02
2.59461194e-01 -8.09085608e-01 6.82656825e-01 -1.72875628e-01
-2.49552444e-01 7.67349958e-01 -2.61082381e-01 -3.13315362e-01
-2.04528674e-01 -5.45069754e-01 -5.18361211e-01 -1.05919099e+00
-6.50535166e-01 2.83842146e-01 3.25507700e-01 -4.77222085... | [6.61785364151001, 3.5565025806427] |
376c9528-5fd1-4f82-8440-3d16af4244d8 | a-maintenance-planning-framework-using-online | 2208.00808 | null | https://arxiv.org/abs/2208.00808v2 | https://arxiv.org/pdf/2208.00808v2.pdf | A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning | Cost-effective asset management is an area of interest across several industries. Specifically, this paper develops a deep reinforcement learning (DRL) solution to automatically determine an optimal rehabilitation policy for continuously deteriorating water pipes. We approach the problem of rehabilitation planning in a... | ['Hajo Molegraaf', 'Nils Jansen', 'Zaharah A. Bukhsh'] | 2022-08-01 | null | null | null | null | ['dqn-replay-dataset', 'dqn-replay-dataset'] | ['miscellaneous', 'playing-games'] | [-1.35761276e-01 1.63660347e-01 -2.69350465e-02 1.07023440e-01
-7.11981177e-01 -5.69330513e-01 -1.12567656e-01 3.79030049e-01
-2.99275577e-01 9.43945885e-01 -3.35281454e-02 -5.20355821e-01
-7.91438878e-01 -1.04309881e+00 -8.29976916e-01 -1.11028051e+00
-5.59474289e-01 7.56172121e-01 1.27476171e-01 -5.10820448... | [4.438408851623535, 2.251450300216675] |
d349acfe-96af-49d8-9555-c1dcf59a8333 | d3vo-deep-depth-deep-pose-and-deep | 2003.01060 | null | https://arxiv.org/abs/2003.01060v2 | https://arxiv.org/pdf/2003.01060v2.pdf | D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry | We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. In particular, it aligns t... | ['Rui Wang', 'Nan Yang', 'Lukas von Stumberg', 'Daniel Cremers'] | 2020-03-02 | d3vo-deep-depth-deep-pose-and-deep-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.pdf | cvpr-2020-6 | ['monocular-visual-odometry'] | ['robots'] | [-5.35462499e-01 2.65934691e-02 -4.13113624e-01 -5.00565171e-01
-5.57921410e-01 -2.73999125e-01 5.93530536e-01 -4.98251498e-01
-6.10428572e-01 6.34375095e-01 2.10805312e-01 1.65277630e-01
4.22746807e-01 -5.78993261e-01 -1.20921075e+00 -5.21926880e-01
4.28861141e-01 8.92591238e-01 3.12066734e-01 5.27529269... | [8.142760276794434, -2.2437946796417236] |
5499acf3-5dc1-4165-a9b6-101453404d73 | adequacy-of-the-gradient-descent-method-for | 1704.01704 | null | http://arxiv.org/abs/1704.01704v2 | http://arxiv.org/pdf/1704.01704v2.pdf | Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks | Despite the wide use of machine learning in adversarial settings including
computer security, recent studies have demonstrated vulnerabilities to evasion
attacks---carefully crafted adversarial samples that closely resemble
legitimate instances, but cause misclassification. In this paper, we examine
the adequacy of the... | ['Benjamin I. P. Rubinstein', 'Yi Han'] | 2017-04-06 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 3.72911394e-01 2.31287464e-01 -1.71963692e-01 -3.74892712e-01
-6.62680447e-01 -1.08806503e+00 9.60438371e-01 1.19824652e-02
-3.83706331e-01 7.60569334e-01 -1.54075503e-01 -1.01007211e+00
-2.29330540e-01 -9.90508735e-01 -6.40626132e-01 -4.64424938e-01
-3.66247684e-01 3.31239194e-01 3.24889988e-01 -5.37843704... | [5.7113823890686035, 7.682979106903076] |
ac32c6dc-60b0-4b84-8d1f-ed7338bb0ba7 | few-shots-is-all-you-need-a-progressive-few | 2107.10064 | null | https://arxiv.org/abs/2107.10064v3 | https://arxiv.org/pdf/2107.10064v3.pdf | Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition | Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. The main difficulty comes from the very few annotated data and the limited linguistic information (e.g. dictionaries and language models). Thus, we propose a few-shot learning-based handwriting rec... | ['Beáta Megyesi', 'Yousri Kessentini', 'Alicia Fornés', 'Mohamed Ali Souibgui'] | 2021-07-21 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 4.56664383e-01 -1.66715249e-01 -5.30316867e-02 -3.00763160e-01
-9.16630447e-01 -6.81852341e-01 4.45346504e-01 7.03309402e-02
-4.27029431e-01 8.94331098e-01 -1.10050708e-01 -9.80190337e-02
1.81565389e-01 -5.43742120e-01 -6.77588761e-01 -6.33811891e-01
6.17283285e-01 7.79906988e-01 4.10044461e-01 -1.05640925... | [11.816390991210938, 2.4538464546203613] |
9756e96f-6b7a-4917-a099-6a9628593544 | drugehrqa-a-question-answering-dataset-on-1 | 2205.01290 | null | https://arxiv.org/abs/2205.01290v1 | https://arxiv.org/pdf/2205.01290v1.pdf | DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries | This paper develops the first question answering dataset (DrugEHRQA) containing question-answer pairs from both structured tables and unstructured notes from a publicly available Electronic Health Record (EHR). EHRs contain patient records, stored in structured tables and unstructured clinical notes. The information in... | ['Daisy Zhe Wang', 'Kirk Roberts', 'Anthony Colas', 'Jayetri Bardhan'] | 2022-05-03 | null | https://aclanthology.org/2022.lrec-1.117 | https://aclanthology.org/2022.lrec-1.117.pdf | lrec-2022-6 | ['text-to-sql'] | ['computer-code'] | [ 0.05530621 0.58343136 -0.11705893 -0.6199553 -1.4335594 -0.82259655
0.06517588 1.0712956 -0.07276095 0.7982391 0.70154124 -0.7803476
-0.6316555 -1.1245301 -0.5284573 0.18857652 0.09967741 1.1590321
0.39734632 -0.5691644 -0.24624126 0.07260022 -1.0751652 1.1471002
1.053648 0.9909971 -0.251... | [8.776321411132812, 8.52126693725586] |
39efe4a9-f9ae-4286-a255-9ca85c16ac21 | unsupervised-continuous-object-representation | 2007.15627 | null | https://arxiv.org/abs/2007.15627v2 | https://arxiv.org/pdf/2007.15627v2.pdf | Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision | Novel View Synthesis (NVS) is concerned with synthesizing views under camera viewpoint transformations from one or multiple input images. NVS requires explicit reasoning about 3D object structure and unseen parts of the scene to synthesize convincing results. As a result, current approaches typically rely on supervised... | ['Jun-Jee Chao', 'Nicolai Häni', 'Volkan Isler', 'Selim Engin'] | 2020-07-30 | null | http://proceedings.neurips.cc/paper/2020/hash/43a7c24e2d1fe375ce60d84ac901819f-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/43a7c24e2d1fe375ce60d84ac901819f-Paper.pdf | neurips-2020-12 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 3.38852793e-01 2.19200507e-01 -1.24751702e-01 -5.10670662e-01
-7.85919070e-01 -6.87484145e-01 8.15684438e-01 -5.22286117e-01
2.97424316e-01 3.50311279e-01 1.56442001e-01 -2.58747160e-01
4.97998774e-01 -7.37153530e-01 -1.29729152e+00 -2.09787875e-01
4.88671690e-01 5.99320471e-01 2.45556518e-01 -6.67884126... | [9.058724403381348, -3.060664653778076] |
9ffa7a8a-1bb7-4628-9497-94c94d5348d7 | distributional-reinforcement-learning-for-5 | 2203.00636 | null | https://arxiv.org/abs/2203.00636v2 | https://arxiv.org/pdf/2203.00636v2.pdf | Distributional Reinforcement Learning for Scheduling of Chemical Production Processes | Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application of RL to identify optimal scheduling decision in the presence of uncertainty. In this work, we present a RL methodology tailored to efficie... | ['Ehecatl Antonio del Rio Chanona', 'Dongda Zhang', 'Max Mowbray'] | 2022-03-01 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 3.26210856e-01 3.75580102e-01 -1.40090257e-01 -4.65808213e-02
-5.80635369e-01 -4.77113754e-01 4.36090976e-01 7.96828032e-01
-2.18047515e-01 1.08839226e+00 -3.31033170e-01 -3.28790128e-01
-9.88823593e-01 -9.95183051e-01 -6.85130596e-01 -7.69490123e-01
-4.58963186e-01 7.76682198e-01 -3.25581402e-01 -1.22561380... | [4.704955101013184, 2.4353713989257812] |
0d511a01-194d-4b3d-8dc2-896adc5af925 | autonomous-asteroid-characterization-through | 2210.05518 | null | https://arxiv.org/abs/2210.05518v1 | https://arxiv.org/pdf/2210.05518v1.pdf | Autonomous Asteroid Characterization Through Nanosatellite Swarming | This paper first defines a class of estimation problem called simultaneous navigation and characterization (SNAC), which is a superset of simultaneous localization and mapping (SLAM). A SNAC framework is then developed for the Autonomous Nanosatellite Swarming (ANS) mission concept to autonomously navigate about and ch... | ["Simone D'Amico", 'Nathan Stacey', 'Kaitlin Dennison'] | 2022-10-11 | null | null | null | null | ['simultaneous-localization-and-mapping', 'landmark-tracking'] | ['computer-vision', 'computer-vision'] | [-4.08565909e-01 -2.30033040e-01 3.04794729e-01 7.88599849e-02
-8.12040269e-02 -1.05746865e+00 8.87154162e-01 -2.91612476e-01
-6.40087247e-01 9.61129904e-01 -5.26571989e-01 -2.47291997e-01
-5.73321581e-01 -5.72441399e-01 -5.47721267e-01 -6.43444121e-01
-4.76668447e-01 9.50544238e-01 2.05574736e-01 -6.35308266... | [7.307408332824707, -1.8262383937835693] |
333eb8a1-f866-4815-b693-0811e4e6bad6 | a-boundary-aware-neural-model-for-nested | null | null | https://aclanthology.org/D19-1034 | https://aclanthology.org/D19-1034.pdf | A Boundary-aware Neural Model for Nested Named Entity Recognition | In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones. We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categ... | ['ong', 'Ho-fung Leung', 'Jingyun Xu', 'Yi Cai', 'Gu Xu', 'Changmeng Zheng'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-3.90005052e-01 1.23253353e-01 -4.07184750e-01 -5.78094542e-01
-5.55375040e-01 -9.05865610e-01 9.45481360e-02 4.34287369e-01
-6.34235084e-01 7.24599361e-01 3.59399676e-01 -2.29444936e-01
2.44014740e-01 -9.69548583e-01 -6.71928048e-01 -1.56309813e-01
-3.24866056e-01 3.35929930e-01 4.02392298e-01 1.76200226... | [9.534306526184082, 9.4483060836792] |
0996155a-ff03-4edc-8ab9-0a355feb24f5 | simulating-user-satisfaction-for-the | 2105.03748 | null | https://arxiv.org/abs/2105.03748v1 | https://arxiv.org/pdf/2105.03748v1.pdf | Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems | Evaluation is crucial in the development process of task-oriented dialogue systems. As an evaluation method, user simulation allows us to tackle issues such as scalability and cost-efficiency, making it a viable choice for large-scale automatic evaluation. To help build a human-like user simulator that can measure the ... | ['Maarten de Rijke', 'Zhumin Chen', 'Pengjie Ren', 'Zhaochun Ren', 'Krisztian Balog', 'Shuo Zhang', 'Weiwei Sun'] | 2021-05-08 | null | null | null | null | ['movie-recommendation', 'user-simulation'] | ['miscellaneous', 'natural-language-processing'] | [-1.9235784e-01 3.4448105e-01 1.1937593e-01 -8.5154754e-01
-7.2868431e-01 -5.0301355e-01 5.3111231e-01 7.4643729e-04
-4.3380353e-01 6.2935805e-01 5.7515764e-01 -2.2486727e-01
9.7677276e-02 -5.6633246e-01 1.3431673e-01 3.6640245e-02
2.0842475e-01 1.0091921e+00 1.8090160e-01 -1.1531436e+00
2.6652503e-01... | [12.87171459197998, 8.05517864227295] |
b6fd360d-f762-40d1-8595-10b2f75e91f1 | deep-sky-modeling-for-single-image-outdoor | 1905.03897 | null | https://arxiv.org/abs/1905.03897v1 | https://arxiv.org/pdf/1905.03897v1.pdf | Deep Sky Modeling for Single Image Outdoor Lighting Estimation | We propose a data-driven learned sky model, which we use for outdoor lighting estimation from a single image. As no large-scale dataset of images and their corresponding ground truth illumination is readily available, we use complementary datasets to train our approach, combining the vast diversity of illumination cond... | ['Jean-François Lalonde', 'Yannick Hold-Geoffroy', 'Akshaya Athawale'] | 2019-05-10 | deep-sky-modeling-for-single-image-outdoor-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Hold-Geoffroy_Deep_Sky_Modeling_for_Single_Image_Outdoor_Lighting_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Hold-Geoffroy_Deep_Sky_Modeling_for_Single_Image_Outdoor_Lighting_Estimation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['lighting-estimation'] | ['computer-vision'] | [ 2.43298888e-01 -2.98052192e-01 3.25801879e-01 -5.31280816e-01
-1.01334310e+00 -1.05480146e+00 4.77887064e-01 -5.45985758e-01
8.84236544e-02 6.50460124e-01 1.66353181e-01 -2.24114463e-01
2.55368471e-01 -7.83072531e-01 -9.75324392e-01 -5.92225492e-01
2.14598089e-01 2.89293945e-01 9.21634883e-02 -2.51908869... | [9.772184371948242, -2.9588284492492676] |
1a1f79da-0ca1-4571-a48b-d7aab58a5fcc | rethinking-backdoor-data-poisoning-attacks-in | 2212.02582 | null | https://arxiv.org/abs/2212.02582v1 | https://arxiv.org/pdf/2212.02582v1.pdf | Rethinking Backdoor Data Poisoning Attacks in the Context of Semi-Supervised Learning | Semi-supervised learning methods can train high-accuracy machine learning models with a fraction of the labeled training samples required for traditional supervised learning. Such methods do not typically involve close review of the unlabeled training samples, making them tempting targets for data poisoning attacks. In... | ['Vincent Emanuele', 'Marissa Connor'] | 2022-12-05 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 8.06545168e-02 1.09310828e-01 -7.37322271e-01 -2.90289789e-01
-8.51047099e-01 -1.19717538e+00 5.65813005e-01 4.78514373e-01
-5.67461193e-01 9.04066384e-01 -2.87574381e-01 -5.81974030e-01
3.20961505e-01 -8.90332162e-01 -7.24319160e-01 -8.48919749e-01
-1.92790464e-01 5.98415017e-01 1.57918811e-01 1.07294559... | [5.831053733825684, 7.503674030303955] |
c229ad15-5abb-40fd-9635-0d205bbb01cd | 3d-face-reconstruction-from-single-image-with | null | null | https://doi.org/10.1016/j.jksuci.2022.11.014 | https://reader.elsevier.com/reader/sd/pii/S131915782200413X?token=585B515A57883E1FC38B2C59EA999ADE93A1CD1FA4472234FB6507500E717B7477F6BE947D46B1573BF0B3D67BD6EAAD&originRegion=eu-west-1&originCreation=20221224162725 | 3D face reconstruction from single image with generative adversarial networks | Traditional reconstruction techniques extract information from the object’s geometry or one or more 2D images. On the other hand, the limit of the existing methods is that they generate less precise objects. Thus the lack of robustness towards several face reconstruction problems, such as the position of the head, occl... | ['Mehdi Malah;Mounir Hemam;Fayçal Abbas'] | 2022-12-01 | null | null | null | journal-of-king-saud-university-computer-and | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.03235794e-02 3.27767998e-01 3.48806947e-01 -4.34049636e-01
-2.99235702e-01 -3.06863248e-01 6.58795059e-01 -3.17023814e-01
3.14894388e-03 5.25356650e-01 -9.92709398e-02 1.04932211e-01
2.33786687e-01 -1.27116799e+00 -6.56350911e-01 -5.53382158e-01
3.55335027e-01 6.01047456e-01 -2.01895609e-01 -3.25934440... | [12.733809471130371, -0.2692641615867615] |
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