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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ae2b5023-e014-4140-9dba-9f9dcb053400 | intent-discovery-with-or-without-labeled-data | null | null | https://openreview.net/forum?id=YZXFpEU8UHi | https://openreview.net/pdf?id=YZXFpEU8UHi | Intent Discovery With Or Without Labeled Data Using Dependency Parser | In dialogue applications, machine learning classification models are often used to classify user utterances into different intents that help to understand the users.
In real world scenarios, however, some utterances may not belong to any of the anticipated intent categories.
Furthermore, supervised classification mode... | ['Anonymous'] | 2020-10-15 | null | null | null | neurips-workshop-hamlets-2020-12 | ['intent-discovery'] | ['natural-language-processing'] | [ 1.07320383e-01 1.65054306e-01 -1.72472924e-01 -8.60876024e-01
-4.75899100e-01 -6.87754393e-01 6.27507985e-01 6.39004767e-01
-2.41400614e-01 6.26699090e-01 3.88312846e-01 -2.79227018e-01
-1.83803767e-01 -5.05203009e-01 2.00091526e-01 -5.08626282e-01
1.53714359e-01 1.09897983e+00 5.34433648e-02 -1.55103058... | [12.592101097106934, 7.635740280151367] |
1bee6427-3b75-4db7-8973-3dbcd33cc4f1 | meta-dm-applications-of-diffusion-models-on | 2305.08092 | null | https://arxiv.org/abs/2305.08092v1 | https://arxiv.org/pdf/2305.08092v1.pdf | Meta-DM: Applications of Diffusion Models on Few-Shot Learning | In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies. However, the role of data processing modules has not been fully explored. Therefore, in this paper, we propose Meta-DM, a generalized data processing module for FSL problems based on diffusio... | ['Hui Tian', 'YuQi Yang', 'Jiarun Liu', 'Xiurong Jiang', 'Wentao Hu'] | 2023-05-14 | null | null | null | null | ['unsupervised-few-shot-image-classification'] | ['computer-vision'] | [ 4.44224328e-02 -8.18921179e-02 -5.09895146e-01 -2.17391357e-01
-1.72447681e-01 -1.31494507e-01 8.20762813e-01 2.53943294e-01
-5.56269765e-01 4.58454281e-01 1.62607312e-01 -1.57408446e-01
-4.42782372e-01 -9.24444318e-01 -2.28748217e-01 -4.86613363e-01
-2.65973598e-01 4.24264610e-01 7.57795453e-01 -2.82993495... | [9.959403038024902, 3.160362482070923] |
f44d9ce2-9d96-4ca0-a2a8-26528bb3f897 | contextual-adversarial-attack-against-aerial | 2302.13487 | null | https://arxiv.org/abs/2302.13487v1 | https://arxiv.org/pdf/2302.13487v1.pdf | Contextual adversarial attack against aerial detection in the physical world | Deep Neural Networks (DNNs) have been extensively utilized in aerial detection. However, DNNs' sensitivity and vulnerability to maliciously elaborated adversarial examples have progressively garnered attention. Recently, physical attacks have gradually become a hot issue due to they are more practical in the real world... | ['Shaohui Mei', 'Mingyang Ma', 'Yuru Su', 'Xiaofei Wang', 'Jiawei Lian'] | 2023-02-27 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 4.50759113e-01 -1.61228389e-01 2.97224134e-01 2.65314519e-01
-1.44004807e-01 -8.30889404e-01 4.63101327e-01 -8.36345181e-02
-3.20976228e-01 6.36680126e-01 -1.21904992e-01 -2.57185400e-01
-3.59456807e-01 -8.78305376e-01 -4.45192337e-01 -1.16642809e+00
-4.12124246e-01 -7.41164923e-01 6.35255873e-01 -3.85264844... | [5.444695949554443, 7.913601398468018] |
bb8d19f8-80d4-4277-8dc1-ac24193dc1ac | task-oriented-dialog-systems-that-consider | 1911.10484 | null | https://arxiv.org/abs/1911.10484v2 | https://arxiv.org/pdf/1911.10484v2.pdf | Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context | Conversations have an intrinsic one-to-many property, which means that multiple responses can be appropriate for the same dialog context. In task-oriented dialogs, this property leads to different valid dialog policies towards task completion. However, none of the existing task-oriented dialog generation approaches tak... | ['Yichi Zhang', 'Zhou Yu', 'Zhijian Ou'] | 2019-11-24 | null | null | null | null | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [ 5.35697676e-02 1.82266235e-01 -4.12933975e-01 -7.62506723e-01
-7.36784995e-01 -7.44032085e-01 9.45348203e-01 -2.71835268e-01
-1.82716742e-01 1.14561129e+00 8.78678083e-01 -2.11292654e-01
1.57898188e-01 -5.83150923e-01 -3.49672511e-02 -5.24671435e-01
6.55402243e-01 9.32376266e-01 2.84115404e-01 -6.97437644... | [12.864645004272461, 8.1065092086792] |
42707463-ee57-4691-a95a-de511b9093f7 | rank-over-class-the-untapped-potential-of | 2009.05160 | null | https://arxiv.org/abs/2009.05160v4 | https://arxiv.org/pdf/2009.05160v4.pdf | Rank over Class: The Untapped Potential of Ranking in Natural Language Processing | Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection. With such a powerful solution, it is often tempting to use it as the go-to tool for all NLP problems since when you are ... | ['Amir Atapour-Abarghouei', 'Andrew Stephen McGough', 'Stephen Bonner'] | 2020-09-10 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 6.61395967e-01 -1.13377430e-01 -1.65915310e-01 -7.91191041e-01
-8.38271081e-01 -7.99855709e-01 9.11927223e-01 6.64893508e-01
-5.24368346e-01 5.18223643e-01 5.07402360e-01 -5.48433065e-01
-2.61052668e-01 -7.48499095e-01 -3.31939578e-01 -4.43565965e-01
2.36090168e-01 7.98738360e-01 2.08698466e-01 -7.51476824... | [10.461263656616211, 8.26561450958252] |
e28bfb2d-d140-4b77-8d46-34a55a2259d8 | representation-learning-by-rotating-your | 1705.11136 | null | http://arxiv.org/abs/1705.11136v2 | http://arxiv.org/pdf/1705.11136v2.pdf | Representation Learning by Rotating Your Faces | The large pose discrepancy between two face images is one of the fundamental
challenges in automatic face recognition. Conventional approaches to
pose-invariant face recognition either perform face frontalization on, or learn
a pose-invariant representation from, a non-frontal face image. We argue that
it is more desir... | ['Xiaoming Liu', 'Luan Tran', 'Xi Yin'] | 2017-05-31 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.58527058e-01 2.12342486e-01 -7.47273713e-02 -5.98179758e-01
-8.99750650e-01 -8.07100236e-01 6.40787125e-01 -1.19696414e+00
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1.43494442e-01 -5.86782277e-01 -8.26198936e-01 -1.00941503e+00
2.84770101e-01 6.30848289e-01 -7.16015518e-01 -1.57281280... | [12.91836166381836, 0.12970809638500214] |
41fc62c3-2c69-4f5f-bae2-7bebfc46696f | cross-lingual-contrastive-learning-for-fine | null | null | https://aclanthology.org/2022.acl-long.159 | https://aclanthology.org/2022.acl-long.159.pdf | Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages | Fine-grained entity typing (FGET) aims to classify named entity mentions into fine-grained entity types, which is meaningful for entity-related NLP tasks. For FGET, a key challenge is the low-resource problem — the complex entity type hierarchy makes it difficult to manually label data. Especially for those languages o... | ['Suncong Zheng', 'Hao Fei', 'Zhou Botong', 'Maosong Sun', 'Zhiyuan Liu', 'Weize Chen', 'Yuqi Luo', 'Xu Han'] | null | null | null | null | acl-2022-5 | ['entity-typing'] | ['natural-language-processing'] | [-5.24280667e-01 -4.63799722e-02 -6.32354498e-01 -5.63962519e-01
-9.28411663e-01 -7.58472562e-01 2.73448735e-01 1.67955570e-02
-9.13519263e-01 1.05344975e+00 1.83842778e-01 -4.17571753e-01
3.27979505e-01 -8.43307793e-01 -7.22329736e-01 -2.87837237e-01
2.11796820e-01 7.86940157e-01 -1.84348803e-02 -8.46907720... | [9.9757080078125, 9.697127342224121] |
50a39763-3dd4-4dbf-aeaf-cda8f6c836bc | effective-cascade-dual-decoder-model-for | 2106.14163 | null | https://arxiv.org/abs/2106.14163v1 | https://arxiv.org/pdf/2106.14163v1.pdf | Effective Cascade Dual-Decoder Model for Joint Entity and Relation Extraction | Extracting relational triples from texts is a fundamental task in knowledge graph construction. The popular way of existing methods is to jointly extract entities and relations using a single model, which often suffers from the overlapping triple problem. That is, there are multiple relational triples that share the sa... | ['Xiliang Zhang', 'Huimin Ren', 'Lianbo Ma'] | 2021-06-27 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-2.19603963e-02 5.25770247e-01 -4.49477822e-01 -2.46056661e-01
-9.21117544e-01 -4.79981631e-01 4.32499677e-01 6.22347653e-01
-4.21521187e-01 7.68726528e-01 2.82893330e-01 -1.88567370e-01
-5.91397509e-02 -1.10945153e+00 -8.78601551e-01 -3.71328890e-01
1.76344499e-01 7.26507664e-01 4.86061364e-01 -3.60124201... | [9.280936241149902, 8.643732070922852] |
17d473b6-f47f-4805-bec8-3f8f90700a40 | covidexpert-a-triplet-siamese-neural-network | 2302.09004 | null | https://arxiv.org/abs/2302.09004v1 | https://arxiv.org/pdf/2302.09004v1.pdf | CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19 | Patients with the COVID-19 infection may have pneumonia-like symptoms as well as respiratory problems which may harm the lungs. From medical images, coronavirus illness may be accurately identified and predicted using a variety of machine learning methods. Most of the published machine learning methods may need extensi... | ['Enamul Hassan', 'Gourab Roy', 'Tareque Rahman Ornob'] | 2023-02-17 | null | null | null | null | ['few-shot-image-classification', 'covid-19-detection'] | ['computer-vision', 'medical'] | [ 2.01848060e-01 -3.49148542e-01 7.16929138e-02 -2.78441459e-01
-6.78636849e-01 -9.20583382e-02 2.39873245e-01 2.04709202e-01
-5.15611768e-01 3.59466404e-01 -1.94938853e-02 -2.94858534e-02
-2.17351988e-01 -6.42020881e-01 -1.61550179e-01 -7.74345994e-01
-9.24640521e-02 7.35445023e-01 4.87308800e-01 2.15820178... | [15.585967063903809, -1.699184775352478] |
5bfef72f-db96-42dc-b21f-38010f63e2b5 | dark-beyond-deep-a-paradigm-shift-to | 2004.09044 | null | https://arxiv.org/abs/2004.09044v1 | https://arxiv.org/pdf/2004.09044v1.pdf | Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense | Recent progress in deep learning is essentially based on a "big data for small tasks" paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a "small data for big tasks" para... | ['Song-Chun Zhu', 'Feng Gao', 'Tao Gao', 'Yixin Zhu', 'Ying Nian Wu', 'Lifeng Fan', 'Siyuan Qi', 'Siyuan Huang', 'Hangxin Liu', 'Mark Edmonds', 'Joshua B. Tenenbaum', 'Chi Zhang'] | 2020-04-20 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 2.10674316e-01 2.55508512e-01 9.63623673e-02 -1.43545657e-01
2.58686900e-01 -7.51173317e-01 1.15060925e+00 -1.52977929e-01
-3.33314627e-01 5.35067141e-01 1.34046465e-01 -5.50756991e-01
-3.06878477e-01 -1.00816834e+00 -7.01518059e-01 -7.24518955e-01
5.65711558e-01 1.94614902e-01 1.53873712e-01 -4.99975711... | [9.159810066223145, 6.458734512329102] |
b20b2692-d8af-42b3-a4ff-3f999704b234 | augdiff-diffusion-based-feature-augmentation | 2303.06371 | null | https://arxiv.org/abs/2303.06371v1 | https://arxiv.org/pdf/2303.06371v1.pdf | AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image | Multiple Instance Learning (MIL), a powerful strategy for weakly supervised learning, is able to perform various prediction tasks on gigapixel Whole Slide Images (WSIs). However, the tens of thousands of patches in WSIs usually incur a vast computational burden for image augmentation, limiting the MIL model's improveme... | ['Yongbing Zhang', 'Haoqian Wang', 'Yifeng Wang', 'Liuxi Dai', 'Zhuchen Shao'] | 2023-03-11 | null | null | null | null | ['whole-slide-images', 'image-augmentation', 'multiple-instance-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.21680772e-01 2.61683874e-02 -5.42052925e-01 -2.43139938e-01
-1.04226303e+00 -1.15756266e-01 6.41846776e-01 1.86534226e-01
-3.09744954e-01 8.56158137e-01 2.46541008e-01 -1.54018670e-01
-1.43788725e-01 -7.93460488e-01 -6.71465397e-01 -1.10793519e+00
2.13694006e-01 4.09026593e-02 1.50455281e-01 -1.70928955... | [15.104113578796387, -2.6980693340301514] |
be5598d3-4b0a-425c-ba3a-1122a66e8085 | manipulated-object-proposal-a-discriminative | 1509.00651 | null | http://arxiv.org/abs/1509.00651v3 | http://arxiv.org/pdf/1509.00651v3.pdf | Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition | Detecting and recognizing objects interacting with humans lie in the center
of first-person (egocentric) daily activity recognition. However, due to noisy
camera motion and frequent changes in viewpoint and scale, most of the previous
egocentric action recognition methods fail to capture and model highly
discriminative... | ['Jian-Feng Wang', 'Bingbing Ni', 'Meng Wang', 'Jun Yuan', 'Changzhi Luo', 'Shuicheng Yan'] | 2015-09-02 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 2.04916552e-01 -5.59505761e-01 -2.95835435e-01 -2.60929167e-01
-5.36775410e-01 -3.45510334e-01 9.22479749e-01 -8.47266018e-02
-4.77561951e-01 3.22855294e-01 6.58554077e-01 6.84774518e-01
5.34769110e-02 -5.95705092e-01 -5.62714219e-01 -5.94409764e-01
-3.64594604e-03 9.87968445e-02 5.00954747e-01 3.53401899... | [8.109251976013184, 0.45140540599823] |
f923db3f-4681-4116-82b5-58c53d2e129b | unimib-at-trec-2021-clinical-trials-track | 2207.13514 | null | https://arxiv.org/abs/2207.13514v1 | https://arxiv.org/pdf/2207.13514v1.pdf | UNIMIB at TREC 2021 Clinical Trials Track | This contribution summarizes the participation of the UNIMIB team to the TREC 2021 Clinical Trials Track. We have investigated the effect of different query representations combined with several retrieval models on the retrieval performance. First, we have implemented a neural re-ranking approach to study the effective... | ['Gabriella Pasi', 'Oscar Espitia', 'Georgios Peikos'] | 2022-07-27 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 3.34823072e-01 1.68791324e-01 -2.92219400e-01 1.19381838e-01
-1.39409518e+00 -1.87139675e-01 1.03563678e+00 9.33230579e-01
-1.14775622e+00 7.93604612e-01 6.19868219e-01 -2.92471021e-01
-1.04042208e+00 -4.71629113e-01 -2.50964642e-01 -4.97972101e-01
-1.74831733e-01 6.91223502e-01 2.60698110e-01 -3.61964077... | [8.811883926391602, 8.575714111328125] |
0e4a7195-9e5a-4cd5-9877-6cc87902a7b8 | improving-machine-translation-of-rare-and | null | null | https://aclanthology.org/2021.wmt-1.66 | https://aclanthology.org/2021.wmt-1.66.pdf | Improving Machine Translation of Rare and Unseen Word Senses | The performance of NMT systems has improved drastically in the past few years but the translation of multi-sense words still poses a challenge. Since word senses are not represented uniformly in the parallel corpora used for training, there is an excessive use of the most frequent sense in MT output. In this work, we p... | ['Anna Korhonen', 'Alexander Fraser', 'Dario Stojanovski', 'Qianchu Liu', 'Viktor Hangya'] | null | null | null | null | wmt-emnlp-2021-11 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 6.94830298e-01 -2.73638725e-01 -2.87036419e-01 -4.09301102e-01
-1.28285980e+00 -1.11010826e+00 5.22332907e-01 1.00831270e-01
-6.56183183e-01 1.10910380e+00 3.72967720e-01 -7.73041308e-01
3.23919982e-01 -6.46795332e-01 -7.34445930e-01 -3.14627588e-01
5.31854093e-01 7.84076691e-01 -1.12228403e-02 -8.94829392... | [11.34272575378418, 10.226472854614258] |
3cd3dd7e-c183-46d9-9f62-308a010ae2c1 | a-unified-deep-learning-architecture-for | 1802.00385 | null | http://arxiv.org/abs/1802.00385v2 | http://arxiv.org/pdf/1802.00385v2.pdf | A Unified Deep Learning Architecture for Abuse Detection | Hate speech, offensive language, sexism, racism and other types of abusive
behavior have become a common phenomenon in many online social media platforms.
In recent years, such diverse abusive behaviors have been manifesting with
increased frequency and levels of intensity. This is due to the openness and
willingness o... | ['Antigoni-Maria Founta', 'Nicolas Kourtellis', 'Despoina Chatzakou', 'Athena Vakali', 'Jeremy Blackburn', 'Ilias Leontiadis'] | 2018-02-01 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-2.52619624e-01 -2.10994527e-01 -2.44909510e-01 -1.80296779e-01
-3.45768601e-01 -6.73020482e-01 9.15305972e-01 4.39964920e-01
-3.54447901e-01 6.19716108e-01 4.19959694e-01 -1.31922111e-01
9.63336304e-02 -5.31744003e-01 -1.82165533e-01 -4.79107171e-01
8.89281854e-02 7.59868324e-02 1.41420335e-01 -5.60392857... | [8.700592994689941, 10.516257286071777] |
bff75d7b-5220-4552-8386-3497aed534bc | track-to-detect-and-segment-an-online-multi | 2103.08808 | null | https://arxiv.org/abs/2103.08808v1 | https://arxiv.org/pdf/2103.08808v1.pdf | Track to Detect and Segment: An Online Multi-Object Tracker | Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking. In this paper, we present a new online joint detection and tracking model, TraDeS (TRAck to DEtect and Segment), exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking... | ['Junsong Yuan', 'Ming Yang', 'Yu Wang', 'Liangchen Song', 'Jiale Cao', 'Jialian Wu'] | 2021-03-16 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-instance-segmentation', 'online-multi-object-tracking', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.03505898e-01 -2.96148837e-01 -4.68899995e-01 -1.12850636e-01
-5.50046563e-01 -7.46206224e-01 2.86406636e-01 2.77677681e-02
-5.11850595e-01 6.02165580e-01 -2.48220459e-01 -1.02446340e-01
2.52405763e-01 -3.07296097e-01 -8.75050843e-01 -4.83042806e-01
-5.76889813e-02 3.18006575e-01 1.06394923e+00 3.32135856... | [6.318684101104736, -1.9976332187652588] |
d84d71f7-791d-454e-ae5f-9879518e099f | scaling-up-trustless-dnn-inference-with-zero | 2210.08674 | null | https://arxiv.org/abs/2210.08674v1 | https://arxiv.org/pdf/2210.08674v1.pdf | Scaling up Trustless DNN Inference with Zero-Knowledge Proofs | As ML models have increased in capabilities and accuracy, so has the complexity of their deployments. Increasingly, ML model consumers are turning to service providers to serve the ML models in the ML-as-a-service (MLaaS) paradigm. As MLaaS proliferates, a critical requirement emerges: how can model consumers verify th... | ['Yi Sun', 'Ion Stoica', 'Tatsunori Hashimoto', 'Daniel Kang'] | 2022-10-17 | null | null | null | null | ['snarks'] | ['natural-language-processing'] | [-2.01642543e-01 2.01792255e-01 -3.27976495e-01 -3.47068638e-01
-1.04731274e+00 -9.66217697e-01 1.87397882e-01 -4.84699979e-02
-1.64349213e-01 3.77221018e-01 -2.72090226e-01 -8.36486161e-01
-2.95133665e-02 -7.65341341e-01 -1.29045963e+00 -3.87120426e-01
-6.07770801e-01 6.42576396e-01 5.34245133e-01 -1.10606715... | [5.907912254333496, 7.022001266479492] |
434b4a01-3027-490d-bedc-b80802d877bc | medlens-improve-mortality-prediction-via | 2305.11742 | null | https://arxiv.org/abs/2305.11742v1 | https://arxiv.org/pdf/2305.11742v1.pdf | MedLens: Improve mortality prediction via medical signs selecting and regression interpolation | Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in electronic health records (EHR) are fitted into advanced machine learning models to make predictions. However, the data-quality problem of original clinic... | ['Weinan Dai', 'Chengjie Mou', 'Jun Wu', 'Xuesong Ye'] | 2023-05-19 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-1.38377234e-01 -1.66575417e-01 -2.92736411e-01 -5.45293570e-01
-8.85705352e-01 -1.33561820e-01 -7.22483099e-02 6.00383461e-01
-2.55851060e-01 7.80420899e-01 3.01333547e-01 -3.61434877e-01
-5.14211655e-01 -7.07710683e-01 -1.59140363e-01 -7.08587348e-01
-4.76121813e-01 4.89707202e-01 -3.75972018e-02 2.53014088... | [7.951623916625977, 6.204309940338135] |
7325b9d0-e66d-417b-a35c-4f008a1334e7 | a-novel-challenge-set-for-hebrew | 2010.02864 | null | https://arxiv.org/abs/2010.02864v1 | https://arxiv.org/pdf/2010.02864v1.pdf | A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration | One of the primary tasks of morphological parsers is the disambiguation of homographs. Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses is far more frequent than the others. In such cases, there may not exist sufficient examples of the minority analyses in order to properly e... | ['Reut Tsarfaty', 'Moshe Koppel', 'Shaltiel Shmidman', 'Joshua Guedalia', 'Avi Shmidman'] | 2020-10-06 | null | https://aclanthology.org/2020.findings-emnlp.297 | https://aclanthology.org/2020.findings-emnlp.297.pdf | findings-of-the-association-for-computational | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 2.00311884e-01 3.61941099e-01 -9.99554768e-02 -3.26853633e-01
-1.16443086e+00 -1.13846064e+00 4.13117528e-01 5.08939326e-01
-5.41257203e-01 9.06914651e-01 2.09503457e-01 -7.72291481e-01
-2.96183676e-01 -5.68113744e-01 -2.90911078e-01 -6.15261376e-01
1.60798505e-01 9.77580607e-01 4.54881519e-01 -5.90496838... | [10.410553932189941, 10.047950744628906] |
a6892ac2-2932-4752-837e-ad398decd3ca | prediction-of-repurposed-drugs-for-treating | 2003.14333 | null | https://arxiv.org/abs/2003.14333v2 | https://arxiv.org/pdf/2003.14333v2.pdf | Prediction of repurposed drugs for treating lung injury in COVID-19 | Coronavirus disease (COVID-19) is an infectious disease discovered in 2019 and currently in outbreak across the world. Lung injury with severe respiratory failure is the leading cause of death in COVID-19, brought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there still lacks efficient trea... | ['Lana Garmire', 'Bing He'] | 2020-03-30 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [ 3.77304181e-02 -8.10939372e-01 -1.80716753e-01 4.13083822e-01
-4.37752843e-01 -8.32357287e-01 1.34079486e-01 3.46473247e-01
-3.16123217e-01 7.42761016e-01 3.78886998e-01 -7.96816289e-01
-1.36914298e-01 -5.26970208e-01 -4.33020920e-01 -8.42624545e-01
9.24195256e-03 6.12934351e-01 5.76540157e-02 9.13240910... | [4.678311347961426, 5.087978839874268] |
569c2745-f506-4ed9-8580-0c7cbe10236d | learning-to-guide-multiple-heterogeneous | 2205.05784 | null | https://arxiv.org/abs/2205.05784v1 | https://arxiv.org/pdf/2205.05784v1.pdf | Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II | Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-quality data covering the most likely scenarios to be encountered when the agent is operating. However, in real-world scenarios, expert data is... | ['Derrik E. Asher', 'Anjon Basak', 'John Richardson', 'Mark Mittrick', 'Vinicius G. Goecks', 'James Hare', 'Nicholas Waytowich'] | 2022-05-11 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-4.15361635e-02 6.98293000e-02 -4.04352576e-01 -2.19786853e-01
-4.53086287e-01 -7.80256033e-01 7.13939309e-01 6.61707595e-02
-8.00219715e-01 9.73199546e-01 6.07440807e-02 -6.31940544e-01
4.92428914e-02 -6.15756214e-01 -8.16226542e-01 -6.23573244e-01
-3.81472379e-01 9.21399295e-01 2.10744157e-01 -6.62399173... | [4.168638706207275, 1.5916188955307007] |
d2149889-73d6-4737-aa4a-23a3ff17478d | lg4av-combining-language-models-and-graph | 2109.01479 | null | https://arxiv.org/abs/2109.01479v1 | https://arxiv.org/pdf/2109.01479v1.pdf | LG4AV: Combining Language Models and Graph Neural Networks for Author Verification | The automatic verification of document authorships is important in various settings. Researchers are for example judged and compared by the amount and impact of their publications and public figures are confronted by their posts on social media platforms. Therefore, it is important that authorship information in freque... | ['Gerd Stumme', 'Maximilian Stubbemann'] | 2021-09-03 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-2.01853618e-01 2.28207305e-01 -3.52139622e-01 -5.40138930e-02
-2.40401834e-01 -8.90232563e-01 8.98048937e-01 7.14279592e-01
-6.12114489e-01 6.75063372e-01 1.47460297e-01 -5.95436275e-01
-1.58892021e-01 -9.65124786e-01 -7.29904413e-01 -1.16609983e-01
4.53197032e-01 6.72122717e-01 -1.69980064e-01 -7.13620558... | [9.572616577148438, 10.59233570098877] |
66b3239d-593f-4a10-ab74-9111e410a54c | lasuie-unifying-information-extraction-with | 2304.06248 | null | https://arxiv.org/abs/2304.06248v1 | https://arxiv.org/pdf/2304.06248v1.pdf | LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model | Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM. Syntactic structure information, a type of effective feature whic... | ['Tat-Seng Chua', 'Min Zhang', 'Meishan Zhang', 'Libo Qin', 'Fei Li', 'Bobo Li', 'Jingye Li', 'Shengqiong Wu', 'Hao Fei'] | 2023-04-13 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 4.10187751e-01 5.46303213e-01 -4.66940403e-01 -6.16754830e-01
-1.12884557e+00 -4.70235407e-01 5.83014786e-01 -3.54219764e-01
2.76624616e-02 7.15550542e-01 9.09008801e-01 -4.06250715e-01
2.50357054e-02 -7.76104271e-01 -9.18670356e-01 -7.47947156e-01
1.82777569e-01 5.51613450e-01 5.62214218e-02 -2.60518521... | [10.827739715576172, 8.879724502563477] |
634f8784-dc0b-4e95-9069-6531ef4280f8 | trust-and-reliance-in-consensus-based | 2304.11279 | null | https://arxiv.org/abs/2304.11279v1 | https://arxiv.org/pdf/2304.11279v1.pdf | Trust and Reliance in Consensus-Based Explanations from an Anti-Misinformation Agent | The illusion of consensus occurs when people believe there is consensus across multiple sources, but the sources are the same and thus there is no "true" consensus. We explore this phenomenon in the context of an AI-based intelligent agent designed to augment metacognition on social media. Misinformation, especially on... | ['Katie Seaborn', 'Hiroki Oura', 'Yeongdae Kim', 'Takane Ueno'] | 2023-04-22 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-2.66313463e-01 7.25057006e-01 -3.14196080e-01 -3.70252192e-01
1.72395706e-01 -2.11958006e-01 1.03787196e+00 7.33439684e-01
-1.33580506e-01 4.23307031e-01 8.81485462e-01 -6.57583416e-01
1.74575429e-02 -6.06199682e-01 -4.40639287e-01 -1.11459404e-01
4.15378422e-01 2.75373697e-01 -2.48720318e-01 -6.19617224... | [9.095810890197754, 6.322657585144043] |
4023a0ac-081e-4579-8972-a7a80ec3a29a | record-deduplication-for-entity-distribution | 2306.06246 | null | https://arxiv.org/abs/2306.06246v1 | https://arxiv.org/pdf/2306.06246v1.pdf | Record Deduplication for Entity Distribution Modeling in ASR Transcripts | Voice digital assistants must keep up with trending search queries. We rely on a speech recognition model using contextual biasing with a rapidly updated set of entities, instead of frequent model retraining, to keep up with trends. There are several challenges with this approach: (1) the entity set must be frequently ... | ['Kanna Shimizu', 'Carl Wivagg', 'Chung Hoon Hong', 'Tianyu Huang'] | 2023-06-09 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [ 9.53238606e-02 1.18490115e-01 -4.31951702e-01 -1.71995804e-01
-1.32887769e+00 -8.02700341e-01 2.79070228e-01 2.10925534e-01
-5.58775187e-01 7.88959146e-01 5.58687925e-01 -3.48107576e-01
-2.49324709e-01 -3.08996409e-01 -6.52633727e-01 -2.44315621e-02
2.16400966e-01 9.66623306e-01 3.17975789e-01 -1.19213335... | [14.270198822021484, 6.644986629486084] |
9ca37bd1-f928-4f79-b6ed-4270766b3954 | adversarial-seeded-sequence-growing-for | 1908.02422 | null | https://arxiv.org/abs/1908.02422v1 | https://arxiv.org/pdf/1908.02422v1.pdf | Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization | Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos require amounts of labor expenditure, studies on the weakly-supervised action detection have been springing up. However, most of existing fr... | ['ShiLiang Pu', 'Zhanzhan Cheng', 'Yunlu Xu', 'Yi Niu', 'Fei Wu', 'Futai Zou', 'Chengwei Zhang'] | 2019-08-07 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 7.04067171e-01 3.20699215e-01 -4.27881092e-01 -1.91435609e-02
-6.49897993e-01 -4.89706159e-01 4.18596298e-01 -3.98550928e-01
-4.25999433e-01 6.77421570e-01 5.52178062e-02 1.18161663e-01
4.42225546e-01 -6.63991213e-01 -8.81055892e-01 -1.16671383e+00
-3.27436745e-01 -1.13957040e-01 8.32264662e-01 -5.66235632... | [8.426057815551758, 0.7312739491462708] |
55069bca-256f-4104-a2fa-ee5f1f59db3e | singing-voice-synthesis-using-deep | 1906.08977 | null | https://arxiv.org/abs/1906.08977v1 | https://arxiv.org/pdf/1906.08977v1.pdf | Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling | This paper presents a method of using autoregressive neural networks for the acoustic modeling of singing voice synthesis (SVS). Singing voice differs from speech and it contains more local dynamic movements of acoustic features, e.g., vibratos. Therefore, our method adopts deep autoregressive (DAR) models to predict t... | ['Li-Rong Dai', 'Zhen-Hua Ling', 'Yuan-Hao Yi', 'Yang Ai'] | 2019-06-21 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-2.12319493e-01 -4.15627062e-01 1.30434707e-01 3.32123749e-02
-5.22214532e-01 -3.81844252e-01 3.41726728e-02 -7.44836926e-01
-8.16167444e-02 1.82064086e-01 6.26875818e-01 -5.51443994e-02
3.04199904e-01 -4.74050611e-01 -2.87168562e-01 -8.60072672e-01
-2.12365035e-02 -4.03870165e-01 2.64114469e-01 -4.00704354... | [15.519309997558594, 6.19665002822876] |
a313acce-647a-4b25-a113-4ac8e0da7547 | power-up-what-can-generative-models-do-for | 2307.02243 | null | https://arxiv.org/abs/2307.02243v1 | https://arxiv.org/pdf/2307.02243v1.pdf | Power-up! What Can Generative Models Do for Human Computation Workflows? | We are amidst an explosion of artificial intelligence research, particularly around large language models (LLMs). These models have a range of applications across domains like medicine, finance, commonsense knowledge graphs, and crowdsourcing. Investigation into LLMs as part of crowdsourcing workflows remains an under-... | ['Ujwal Gadiraju', 'Gaole He', 'Garrett Allen'] | 2023-07-05 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-4.10616472e-02 4.27278847e-01 1.78627521e-01 -1.92999169e-01
-3.98966998e-01 -9.88536596e-01 8.69612515e-01 6.28413916e-01
-5.42411268e-01 3.53910565e-01 6.98020756e-01 -4.34849054e-01
3.16937491e-02 -3.94853652e-01 -3.74718934e-01 -2.42823027e-02
3.44827831e-01 6.41933680e-01 4.59493667e-01 -6.78697407... | [9.375472068786621, 6.464956760406494] |
61100f8d-f69d-4388-861e-fc1035aa0366 | sparse-to-dense-motion-transfer-for-face | 2109.00471 | null | https://arxiv.org/abs/2109.00471v2 | https://arxiv.org/pdf/2109.00471v2.pdf | Sparse to Dense Motion Transfer for Face Image Animation | Face image animation from a single image has achieved remarkable progress. However, it remains challenging when only sparse landmarks are available as the driving signal. Given a source face image and a sequence of sparse face landmarks, our goal is to generate a video of the face imitating the motion of landmarks. We ... | ['Guodong Guo', 'Tianyi Wu', 'Ruiqi Zhao'] | 2021-09-01 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 2.43059963e-01 -2.14216903e-01 -1.24173760e-01 -3.78595769e-01
-9.57235873e-01 -6.03472829e-01 6.98326826e-01 -9.94813383e-01
-9.19267982e-02 6.82194531e-01 1.09393328e-01 3.82554412e-01
5.10313272e-01 -2.51473337e-01 -1.05342960e+00 -8.37925613e-01
-8.49421322e-02 4.14939851e-01 1.42354295e-01 1.70728266... | [11.034517288208008, -0.761620819568634] |
66dc9a20-7e24-4ac6-8663-fe40cd139911 | atm-r-an-adaptive-tradeoff-model-with | 2301.03317 | null | https://arxiv.org/abs/2301.03317v1 | https://arxiv.org/pdf/2301.03317v1.pdf | ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization | The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and welldistributed feasible solutions. To complete this goal, there should be a tradeoff among feasibility, diversity, and convergence. However, it is nontrivial to balance these three elements simultaneously by using... | ['Zhi-Zhong Liu', 'Xian-Bing Meng', 'Yunchuan Qin', 'Bing-Chuan Wang'] | 2023-01-09 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [-1.36571556e-01 -6.93854451e-01 -2.17544377e-01 -1.97102979e-01
-3.55052352e-01 -3.93461525e-01 -6.09420203e-02 3.61019224e-02
-1.17120266e-01 9.00178790e-01 -4.21047285e-02 -3.34738530e-02
-7.29537606e-01 -9.98713672e-01 -2.00772986e-01 -1.01621521e+00
-3.56232785e-02 3.08490217e-01 -9.46622118e-02 -4.30422902... | [5.7114152908325195, 3.5604076385498047] |
80278995-d96e-483e-b3ae-114c6c4d9cc2 | the-pcg-aiid-system-for-l3das22-challenge | 2202.10017 | null | https://arxiv.org/abs/2202.10017v1 | https://arxiv.org/pdf/2202.10017v1.pdf | The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional recurrent Network for Multi Channel Speech Enhancement and Speech Recognition | This paper described the PCG-AIID system for L3DAS22 challenge in Task 1: 3D speech enhancement in office reverberant environment. We proposed a two-stage framework to address multi-channel speech denoising and dereverberation. In the first stage, a multiple input and multiple output (MIMO) network is applied to remove... | ['Zhaoxia Li', 'Guohui Cui', 'Yun Liu', 'Dawei Luo', 'Yuanyuan Zhu', 'Jingdong Li'] | 2022-02-21 | null | null | null | null | ['speech-denoising'] | ['speech'] | [-1.12613097e-01 -1.35450944e-01 6.35507584e-01 -1.46187410e-01
-1.33138680e+00 -5.03073692e-01 3.25873047e-01 -3.21702272e-01
-4.61361766e-01 3.84231776e-01 6.23013735e-01 -5.36974669e-01
1.34023324e-01 1.26330793e-01 -5.37688076e-01 -9.57230926e-01
1.61341697e-01 -4.34405029e-01 9.33072865e-02 -2.07696378... | [14.924630165100098, 5.8965044021606445] |
af8468c9-bbbc-4676-beb8-a984a9fb4f43 | adaptive-feature-processing-for-robust-human | 1901.02858 | null | http://arxiv.org/abs/1901.02858v1 | http://arxiv.org/pdf/1901.02858v1.pdf | Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset | Human Activity Recognition (HAR) is a key building block of many emerging
applications such as intelligent mobility, sports analytics, ambient-assisted
living and human-robot interaction. With robust HAR, systems will become more
human-aware, leading towards much safer and empathetic autonomous systems.
While human pos... | ['Varuna De Silva', 'Ahmet Kondoz', 'Mirco Moencks', 'Jamie Roche'] | 2019-01-09 | null | null | null | null | ['sports-analytics', 'multimodal-activity-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.82789847e-01 1.48194600e-02 -2.50138253e-01 -2.55705714e-01
-5.36594868e-01 -1.92691833e-01 1.55074656e-01 4.15442027e-02
-7.77265191e-01 7.56230056e-01 3.46739441e-01 8.35305974e-02
-1.81279957e-01 -6.71287000e-01 -6.02655172e-01 -6.97077692e-01
-2.66920447e-01 2.63398856e-01 2.07890779e-01 -4.37144190... | [7.602644443511963, 0.472729355096817] |
15cb399b-0314-4afe-b818-5f1f6ac8f2d2 | rayleigh-gauss-newton-optimization-with | 2106.10558 | null | https://arxiv.org/abs/2106.10558v4 | https://arxiv.org/pdf/2106.10558v4.pdf | Rayleigh-Gauss-Newton optimization with enhanced sampling for variational Monte Carlo | Variational Monte Carlo (VMC) is an approach for computing ground-state wavefunctions that has recently become more powerful due to the introduction of neural network-based wavefunction parametrizations. However, efficiently training neural wavefunctions to converge to an energy minimum remains a difficult problem. In ... | ['Michael Lindsey', 'Robert J. Webber'] | 2021-06-19 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 2.47557670e-01 -2.83092201e-01 1.31835207e-01 -1.61770016e-01
-1.09637892e+00 -3.03680927e-01 6.32930338e-01 1.94724500e-01
-7.78928697e-01 1.06312299e+00 -4.27929945e-02 -2.34573185e-01
-3.87756042e-02 -7.92202413e-01 -8.57153773e-01 -1.19243038e+00
-1.21997699e-01 5.66817641e-01 2.78547760e-02 -3.36807311... | [5.679833889007568, 4.839364528656006] |
77f72c1f-a40e-4408-b7b9-bf2d8d6bf8f6 | dial2desc-end-to-end-dialogue-description | 1811.00185 | null | http://arxiv.org/abs/1811.00185v1 | http://arxiv.org/pdf/1811.00185v1.pdf | Dial2Desc: End-to-end Dialogue Description Generation | We first propose a new task named Dialogue Description (Dial2Desc). Unlike
other existing dialogue summarization tasks such as meeting summarization, we
do not maintain the natural flow of a conversation but describe an object or an
action of what people are talking about. The Dial2Desc system takes a dialogue
text as ... | ['Zhou Zhao', 'Junpei Zhou', 'Haojie Pan', 'Min Yang', 'Deng Cai', 'Yan Liu'] | 2018-11-01 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 2.78836936e-01 7.24964797e-01 5.18207774e-02 -6.53286040e-01
-9.72828150e-01 -6.42504215e-01 1.24269891e+00 4.37649071e-01
-2.30619878e-01 9.89283800e-01 1.27117229e+00 4.70788665e-02
4.41725016e-01 -5.15488088e-01 -1.14286609e-01 -3.27561766e-01
1.62123501e-01 7.44089246e-01 3.07048354e-02 -7.15795517... | [12.598427772521973, 8.798038482666016] |
ab57a255-df4e-40c3-a142-a5da8a435e17 | action-conditioned-on-demand-motion | 2207.08164 | null | https://arxiv.org/abs/2207.08164v1 | https://arxiv.org/pdf/2207.08164v1.pdf | Action-conditioned On-demand Motion Generation | We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization. ODMO shows improvements over SOTA approaches on all traditional motion evaluation metrics when evalu... | ['Vwani Roychowdhury', 'Mingjian Lu', 'YiPeng Zhang', 'QIUJING LU'] | 2022-07-17 | null | null | null | null | ['human-action-generation'] | ['computer-vision'] | [-1.38876885e-01 -1.54957563e-01 -3.58750403e-01 2.84129716e-02
-6.62382782e-01 -7.30450153e-01 9.05844629e-01 -4.90242064e-01
-2.40374237e-01 5.75559318e-01 1.01352882e+00 -9.57036689e-02
1.64885327e-01 -6.20306432e-01 -7.84826934e-01 -7.74823248e-01
-3.13926607e-01 2.33698159e-01 1.80658951e-01 -1.68763608... | [7.2991461753845215, -0.14826346933841705] |
af290059-d916-46f8-a53f-a87d92fde8f3 | proposal-distribution-calibration-for-few | 2212.07618 | null | https://arxiv.org/abs/2212.07618v1 | https://arxiv.org/pdf/2212.07618v1.pdf | Proposal Distribution Calibration for Few-Shot Object Detection | Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step training paradigm is widely adopted to mitigate the severe sample imbalance, i.e., holistic pre-training on base classes, then partial fine... | ['Qixiang Ye', 'Xiangyang Ji', 'Xiaozhong Chen', 'Mengnan Shi', 'Chang Liu', 'Bohao Li'] | 2022-12-15 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 3.56753945e-01 1.08022586e-01 -3.61680299e-01 -3.45473349e-01
-7.99597502e-01 -3.38670552e-01 4.32675898e-01 1.34738833e-01
-7.29156494e-01 7.21351564e-01 -2.35499173e-01 1.96057796e-01
-1.45996688e-02 -7.48420477e-01 -7.64556229e-01 -9.85633314e-01
4.23618615e-01 5.01071811e-01 7.40578234e-01 -1.22375488... | [9.39404582977295, 1.5412683486938477] |
b928f95c-5191-44e8-a9cc-34f2de0a87fc | exploring-transformers-for-on-line | 2307.01663 | null | https://arxiv.org/abs/2307.01663v2 | https://arxiv.org/pdf/2307.01663v2.pdf | Exploring Transformers for On-Line Handwritten Signature Verification | The application of mobile biometrics as a user-friendly authentication method has increased in the last years. Recent studies have proposed novel behavioral biometric recognition systems based on Transformers, which currently outperform the state of the art in several application scenarios. On-line handwritten signatur... | ['Javier Ortega-Garcia', 'Julian Fierrez', 'Giuseppe Stragapede', 'Paula Delgado-Santos', 'Ruben Vera-Rodriguez', 'Ruben Tolosana', 'Pietro Melzi'] | 2023-07-04 | null | null | null | null | ['activity-recognition'] | ['computer-vision'] | [ 4.71218020e-01 -3.47454756e-01 -1.92537084e-01 -2.26025090e-01
-3.81654620e-01 -4.36508685e-01 6.19921148e-01 -1.83823593e-02
-6.18814230e-01 4.22666460e-01 -1.10312276e-01 -2.38616526e-01
-3.55063885e-01 -4.05277133e-01 -7.02499375e-02 -7.36676991e-01
-4.03071083e-02 3.27880591e-01 2.55743057e-01 -1.89188331... | [13.960722923278809, 1.5168546438217163] |
9a0ffbf5-e518-4693-8d6e-bc5d3c54ab82 | inducing-distant-supervision-in-suggestion | 1709.07403 | null | http://arxiv.org/abs/1709.07403v2 | http://arxiv.org/pdf/1709.07403v2.pdf | Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings | Mining suggestion expressing sentences from a given text is a less
investigated sentence classification task, and therefore lacks hand labeled
benchmark datasets. In this work, we propose and evaluate two approaches for
distant supervision in suggestion mining. The distant supervision is obtained
through a large silver... | ['Sapna Negi', 'Paul Buitelaar'] | 2017-09-21 | null | null | null | null | ['suggestion-mining'] | ['natural-language-processing'] | [ 3.41330767e-01 5.69494784e-01 -4.87840056e-01 -5.94463170e-01
-4.42528695e-01 -1.31221432e-02 8.66120577e-01 5.02288938e-01
-7.99322426e-01 8.29379737e-01 4.94462579e-01 -3.34975064e-01
-2.45629176e-01 -8.65514278e-01 -5.84158897e-01 -5.75535595e-01
7.44191231e-03 3.15866083e-01 2.63873577e-01 -3.92798573... | [10.850863456726074, 7.643332481384277] |
337289ef-872e-466a-a166-1f0c2fb4bf96 | place-recognition-in-forests-with-urquhart | 2010.03026 | null | https://arxiv.org/abs/2010.03026v2 | https://arxiv.org/pdf/2010.03026v2.pdf | Place Recognition in Forests with Urquhart Tessellations | In this letter, we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest. We propose a framework that uses these descriptors to detect previously seen observations and landmark correspondences, even with partial overlap and noise. We run loop closure detection experim... | ['Vijay Kumar', 'Roseli A. F. Romero', 'Vaibhav Arcot', 'Xu Liu', 'Steven W. Chen', 'Avraham Cohen', 'Guilherme V. Nardari'] | 2020-09-23 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 3.26806784e-01 -2.98315883e-01 8.49214792e-02 -2.80981392e-01
-3.32203150e-01 -9.58368182e-01 7.44060397e-01 3.16970468e-01
-4.55006242e-01 7.28762746e-01 -5.38008511e-01 -3.20350438e-01
-4.96179879e-01 -8.48773122e-01 -5.41099787e-01 -2.87924469e-01
-8.32898557e-01 4.93419111e-01 7.57263064e-01 -1.45317867... | [7.310038089752197, -1.9454975128173828] |
9281343a-947c-4873-865f-cd16d99dda50 | a-low-rank-weighted-graph-convolutional | null | null | https://ieeexplore.ieee.org/document/8594887 | https://ieeexplore.ieee.org/document/8594887 | A Low Rank Weighted Graph Convolutional Approach to Weather Prediction | Weather forecasting is an important but challenging problem as one must contend with the inherent non-linearities and spatiotemporal autocorrelation present in the data. This paper presents a novel deep learning approach based on a coupled weighted graph convolutional LSTM (WGC-LSTM) to address these challenges. Specif... | ['Lifeng Luo', 'Pang-Ning Tan', 'Tyler Wilson'] | 2018-11-17 | null | null | null | ieee-2018-11 | ['weather-forecasting'] | ['miscellaneous'] | [-1.69363007e-01 -2.33773679e-01 2.70871460e-01 -3.28685969e-01
-3.25823799e-02 -6.86320126e-01 5.59327722e-01 3.63169044e-01
-4.52473164e-01 5.48588991e-01 3.01775753e-01 -4.90194410e-01
-3.99915755e-01 -9.68156874e-01 -8.29251468e-01 -5.93396187e-01
-6.48080587e-01 1.16800234e-01 3.36515278e-01 -2.78076172... | [6.6783270835876465, 2.7402191162109375] |
3ba88b0e-5717-4a27-b570-1e8b2648f246 | weakly-supervised-graph-clustering | null | null | https://openreview.net/forum?id=gaYko_Y2_l | https://openreview.net/pdf?id=gaYko_Y2_l | Weakly Supervised Graph Clustering | Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications. While this task is common, more complex cases arise in practice—can we cluster nodes better wit... | ['Hong Cheng', 'Junzhou Huang', 'Peilin Zhao', 'Xi Xiao', 'Wenbing Huang', 'Yu Rong', 'Tingyang Xu', 'Tian Bian'] | 2021-09-29 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-6.43410371e-04 5.52468598e-01 -2.69176692e-01 -2.40946665e-01
-1.55267552e-01 -3.46826196e-01 7.35040784e-01 3.48432660e-01
3.47947702e-02 3.24558377e-01 -1.19799905e-01 -3.23283225e-01
-3.24411809e-01 -9.31263328e-01 -5.28262556e-01 -9.57605779e-01
-4.05600011e-01 4.84624982e-01 -3.20669040e-02 1.36611924... | [7.277731418609619, 5.904614448547363] |
579afcf6-72f2-4dc5-85e4-a4c353f25dec | cs-af-a-cost-sensitive-multi-classifier | 2004.12064 | null | https://arxiv.org/abs/2004.12064v2 | https://arxiv.org/pdf/2004.12064v2.pdf | CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification | Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in skin lesion analysis. Compared with single CNN classifier, combining the results of multiple classifiers via fusion approaches shows to be more effective and robust. Since the skin lesion datasets are usually limited and statisticall... | ['J. Morris Chang', 'Di Zhuang', 'Keyu Chen'] | 2020-04-25 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.02556300e-01 -1.17587252e-02 -4.27952796e-01 -2.05135211e-01
-8.13746333e-01 -2.44983301e-01 3.66609454e-01 6.49154246e-01
-4.05964941e-01 8.28043342e-01 -9.66208056e-02 -1.16706625e-01
-1.79053366e-01 -1.02328885e+00 -4.25817698e-01 -1.37808657e+00
4.83280271e-01 2.18175724e-01 2.24541321e-01 4.11026515... | [15.535296440124512, -2.847846031188965] |
78950cc9-78ad-4314-b59d-1d2c1d63480a | dice-loss-for-data-imbalanced-nlp-tasks | 1911.02855 | null | https://arxiv.org/abs/1911.02855v3 | https://arxiv.org/pdf/1911.02855v3.pdf | Dice Loss for Data-imbalanced NLP Tasks | Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training. The most commonly used cross entropy (CE) criteria... | ['Fei Wu', 'Xiaofei Sun', 'Junjun Liang', 'Yuxian Meng', 'Jiwei Li', 'Xiaoya Li'] | 2019-11-07 | dice-loss-for-data-imbalanced-nlp-tasks-1 | https://aclanthology.org/2020.acl-main.45 | https://aclanthology.org/2020.acl-main.45.pdf | acl-2020-6 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 1.81984529e-01 3.36781800e-01 -3.61773789e-01 -6.11222148e-01
-9.33320284e-01 -6.10349000e-01 2.77663410e-01 6.60454571e-01
-8.60815763e-01 9.79723632e-01 5.65537028e-02 -3.57569188e-01
-9.79820937e-02 -5.88954866e-01 -4.41337824e-01 -5.25152147e-01
3.03482324e-01 7.16006637e-01 -3.03415637e-02 -1.50738254... | [9.176472663879395, 4.414519309997559] |
dd0d11d6-7054-4753-a161-df80dbfc955d | mitigating-negative-transfer-with-task | 2307.03377 | null | https://arxiv.org/abs/2307.03377v1 | https://arxiv.org/pdf/2307.03377v1.pdf | Mitigating Negative Transfer with Task Awareness for Sexism, Hate Speech, and Toxic Language Detection | This paper proposes a novelty approach to mitigate the negative transfer problem. In the field of machine learning, the common strategy is to apply the Single-Task Learning approach in order to train a supervised model to solve a specific task. Training a robust model requires a lot of data and a significant amount of ... | ['Damiano Spina', 'Paolo Rosso', 'Angel Felipe Magnossão de Paula'] | 2023-07-07 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 3.46736610e-01 1.25442639e-01 2.25512281e-01 -2.12403312e-01
-7.69217312e-01 -1.84329733e-01 8.69261444e-01 4.66935575e-01
-6.89351678e-01 8.31281304e-01 -9.69356522e-02 9.30218920e-02
1.36029888e-02 -4.91632193e-01 -4.97777194e-01 -7.74897575e-01
2.32708529e-01 3.89834255e-01 3.06188315e-01 -4.27177250... | [10.548968315124512, 9.718487739562988] |
eb1ed678-597f-4913-890a-f1754296c99e | impact-of-dataset-on-acoustic-models-for | 2203.13590 | null | https://arxiv.org/abs/2203.13590v1 | https://arxiv.org/pdf/2203.13590v1.pdf | Impact of Dataset on Acoustic Models for Automatic Speech Recognition | In Automatic Speech Recognition, GMM-HMM had been widely used for acoustic modelling. With the current advancement of deep learning, the Gaussian Mixture Model (GMM) from acoustic models has been replaced with Deep Neural Network, namely DNN-HMM Acoustic Models. The GMM models are widely used to create the alignments o... | ['Siddhesh Singh'] | 2022-03-25 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [-1.43073753e-01 -1.74231678e-01 1.71745986e-01 -5.21153033e-01
-7.30055928e-01 -1.40355796e-01 2.50065118e-01 3.08890082e-02
-5.21405816e-01 4.20814186e-01 4.28754538e-02 -4.99003738e-01
2.06934988e-01 -5.28705299e-01 -6.22425616e-01 -8.02678466e-01
3.32097113e-01 6.31399989e-01 -2.07483722e-03 -1.05040772... | [14.45707893371582, 6.647143840789795] |
7af0dea5-5d6a-4586-ad71-13268e90da18 | an-efficient-method-for-face-quality | 2207.09505 | null | https://arxiv.org/abs/2207.09505v1 | https://arxiv.org/pdf/2207.09505v1.pdf | An Efficient Method for Face Quality Assessment on the Edge | Face recognition applications in practice are composed of two main steps: face detection and feature extraction. In a sole vision-based solution, the first step generates multiple detection for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detection of iden... | ['Cevahir Çığla', 'Burak Oğuz Özkalaycı', 'Sefa Burak Okcu'] | 2022-07-19 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 4.96928722e-01 -1.83063596e-01 2.23739415e-01 -5.79284489e-01
-4.28412884e-01 -3.27234864e-01 6.17716610e-01 -1.07156694e-01
-5.54958642e-01 2.70082116e-01 -2.27639630e-01 -7.76188225e-02
3.62505466e-01 -7.31417656e-01 -5.86758137e-01 -5.04263759e-01
2.77811617e-01 4.11942780e-01 1.18930757e-01 2.11976796... | [13.327000617980957, 0.7187061905860901] |
288e3e44-cffb-4989-b8cb-cb284be0e805 | improving-continuous-time-conflict-based | 2101.09723 | null | https://arxiv.org/abs/2101.09723v2 | https://arxiv.org/pdf/2101.09723v2.pdf | Improving Continuous-time Conflict Based Search | Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time.... | ['Roni Stern', 'Eli Boyarski', 'Konstantin Yakovlev', 'Anton Andreychuk'] | 2021-01-24 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 6.69639036e-02 2.31593698e-01 -2.88184762e-01 1.17475748e-01
-5.11061847e-01 -8.39690506e-01 4.53027517e-01 4.43859011e-01
-4.88489628e-01 1.56085026e+00 -3.69417191e-01 -6.61527932e-01
-1.04866135e+00 -1.10644388e+00 -3.94522756e-01 -6.45733595e-01
-9.30817008e-01 9.74207163e-01 9.20667470e-01 -7.05148101... | [4.982669830322266, 1.8291999101638794] |
716bcd3a-608b-441d-8b4b-133877714eec | quantum-pufferfish-privacy-a-flexible-privacy | 2306.13054 | null | https://arxiv.org/abs/2306.13054v1 | https://arxiv.org/pdf/2306.13054v1.pdf | Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems | We propose a versatile privacy framework for quantum systems, termed quantum pufferfish privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and addresses limitations of quantum differential privacy by offering flexibility in specifying private information, feasible measurements, and dom... | ['Mark M. Wilde', 'Ziv Goldfeld', 'Theshani Nuradha'] | 2023-06-22 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 4.78905618e-01 3.29400480e-01 1.50353787e-03 -4.40266281e-01
-9.59578991e-01 -1.26263344e+00 4.03828979e-01 1.91342190e-01
-5.01858830e-01 6.05249822e-01 -7.39614107e-03 -6.96486950e-01
-4.72843826e-01 -8.06756556e-01 -7.04258204e-01 -1.07412374e+00
-1.50582522e-01 1.40467957e-01 -2.00967833e-01 -2.56592333... | [5.899650573730469, 6.650055408477783] |
5fa89616-9dc2-470d-8bc4-e8a8de7dfd06 | perception-de-la-langue-francaise-parlee | null | null | https://aclanthology.org/F12-1103 | https://aclanthology.org/F12-1103.pdf | Perception de la Langue fran\ccaise Parl\'ee Compl\'et\'ee (LPC) et effet d'expertise chez les normo-entendants (French Cued Speech perception and expertise effect in hearing people) [in French] | null | ["C{\\'e}cile Colin", 'Anne-Sophie Tilmant', "Cl{\\'e}mence Bayard", 'Jacqueline Leybaert'] | 2012-06-01 | perception-de-la-langue-franccaise-parlee | https://aclanthology.org/F12-1103 | https://aclanthology.org/F12-1103.pdf | jeptalnrecital-2012-6 | ['lipreading'] | ['computer-vision'] | [-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... | [-7.396218299865723, 3.706688642501831] |
3b17f0e7-ea74-4b76-a1d2-2f6eec755b91 | wikir-a-python-toolkit-for-building-a-large | 1912.01901 | null | https://arxiv.org/abs/1912.01901v4 | https://arxiv.org/pdf/1912.01901v4.pdf | WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset | Over the past years, deep learning methods allowed for new state-of-the-art results in ad-hoc information retrieval. However such methods usually require large amounts of annotated data to be effective. Since most standard ad-hoc information retrieval datasets publicly available for academic research (e.g. Robust04, Cl... | ['Jean-Pierre Chevallet', 'Jibril Frej', 'Didier Schwab'] | 2019-12-04 | wikir-a-python-toolkit-for-building-a-large-1 | https://aclanthology.org/2020.lrec-1.237 | https://aclanthology.org/2020.lrec-1.237.pdf | lrec-2020-5 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-6.81521535e-01 -4.40119475e-01 -3.69623363e-01 -1.46903053e-01
-1.49106884e+00 -8.17607582e-01 8.25533748e-01 4.77892697e-01
-9.80275750e-01 6.71312213e-01 2.58424848e-01 -1.57320406e-02
-6.79152369e-01 -7.07331836e-01 -6.09104395e-01 -1.83581620e-01
6.51673153e-02 9.59330738e-01 1.93307459e-01 -5.16177356... | [11.50192642211914, 7.700483798980713] |
ee0a6df8-609a-426d-b288-0424142588ce | a-new-benchmark-for-evaluation-of-cross | 1912.07200 | null | https://arxiv.org/abs/1912.07200v2 | https://arxiv.org/pdf/1912.07200v2.pdf | A Broader Study of Cross-Domain Few-Shot Learning | Recent progress on few-shot learning largely relies on annotated data for meta-learning: base classes sampled from the same domain as the novel classes. However, in many applications, collecting data for meta-learning is infeasible or impossible. This leads to the cross-domain few-shot learning problem, where there is ... | ['Tajana Rosing', 'Leonid Karlinsky', 'Yunhui Guo', 'Kate Saenko', 'James V. Codella', 'John R. Smith', 'Rogerio Feris', 'Noel C. Codella'] | 2019-12-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5643_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720120.pdf | eccv-2020-8 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 6.03573084e-01 -1.23624772e-01 -2.90900737e-01 -1.55905992e-01
-1.10987151e+00 -1.42082244e-01 8.53978157e-01 2.76006281e-01
-4.84460890e-01 7.45077312e-01 1.60424579e-02 2.73029417e-01
-4.06776428e-01 -7.73723841e-01 -5.71136713e-01 -7.21606791e-01
-2.30788678e-01 4.58041161e-01 5.02122939e-01 -4.62478071... | [9.954070091247559, 2.8805201053619385] |
8a1c638f-8030-461a-996f-1ff35b5f14b7 | empirical-exploration-of-zone-by-zone-energy | 2304.13175 | null | https://arxiv.org/abs/2304.13175v1 | https://arxiv.org/pdf/2304.13175v1.pdf | Empirical Exploration of Zone-by-zone Energy Flexibility: a Non-intrusive Load Disaggregation Approach for Commercial Buildings | Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of intermittent renewable energy sources. Adjusting zonal temperature set-points is one of ... | ['Jacques A. de Chalendar', 'Ram Rajagopal', 'Maomao Hu'] | 2023-04-25 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-4.16987032e-01 -3.34373564e-02 -1.59793451e-01 2.05318034e-02
-2.82415181e-01 -9.49127555e-01 3.35795850e-01 -2.11722348e-02
3.13379198e-01 8.77984941e-01 8.29347894e-02 -5.15224397e-01
-5.79710960e-01 -1.43045747e+00 -1.85556278e-01 -1.10081577e+00
9.24186781e-02 3.34549308e-01 -2.62371480e-01 -2.79996455... | [5.740937232971191, 2.461754083633423] |
7f1faf35-0a62-40f0-ba0c-5fc2d353974e | data-summarization-at-scale-a-two-stage | 1806.02815 | null | http://arxiv.org/abs/1806.02815v1 | http://arxiv.org/pdf/1806.02815v1.pdf | Data Summarization at Scale: A Two-Stage Submodular Approach | The sheer scale of modern datasets has resulted in a dire need for
summarization techniques that identify representative elements in a dataset.
Fortunately, the vast majority of data summarization tasks satisfy an intuitive
diminishing returns condition known as submodularity, which allows us to find
nearly-optimal sol... | ['Amin Karbasi', 'Morteza Zadimoghaddam', 'Marko Mitrovic', 'Ehsan Kazemi'] | 2018-06-07 | data-summarization-at-scale-a-two-stage-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2144 | http://proceedings.mlr.press/v80/mitrovic18a/mitrovic18a.pdf | icml-2018-7 | ['data-summarization'] | ['miscellaneous'] | [ 2.40720034e-01 3.19333732e-01 -5.72990119e-01 -2.36960277e-01
-1.02253425e+00 -7.12595105e-01 -3.41952890e-02 4.81903434e-01
-2.63757914e-01 9.68268514e-01 5.55092156e-01 1.73029676e-01
-3.92858416e-01 -4.55389708e-01 -8.91429842e-01 -7.25332499e-01
-9.53121409e-02 5.97220957e-01 -1.33624762e-01 -1.86278284... | [6.568754196166992, 4.934501647949219] |
cd32a5de-d2cf-47cd-b187-6afcbf48a976 | parallel-coordinates-for-discovery-of | 2305.18434 | null | https://arxiv.org/abs/2305.18434v1 | https://arxiv.org/pdf/2305.18434v1.pdf | Parallel Coordinates for Discovery of Interpretable Machine Learning Models | This work uses visual knowledge discovery in parallel coordinates to advance methods of interpretable machine learning. The graphic data representation in parallel coordinates made the concepts of hypercubes and hyperblocks (HBs) simple to understand for end users. It is suggested to use mixed and pure hyperblocks in t... | ['Boris Kovalerchuk', 'Dustin Hayes'] | 2023-05-28 | null | null | null | null | ['dimensionality-reduction', 'interpretable-machine-learning'] | ['methodology', 'methodology'] | [-3.38372886e-01 3.24947029e-01 -1.56496137e-01 -2.91749984e-01
1.32225960e-01 -5.52166879e-01 5.13626099e-01 3.15733075e-01
6.61627129e-02 8.68229985e-01 -3.61295938e-02 -7.82714307e-01
-7.58047581e-01 -5.27732491e-01 -2.78944939e-01 -9.36333120e-01
-6.70542777e-01 4.76598710e-01 -2.05329552e-01 -5.09575866... | [8.02209186553955, 4.671634674072266] |
040b5217-cba9-45fe-a0d6-552f21923467 | common-conversational-community-prototype | 2001.06910 | null | https://arxiv.org/abs/2001.06910v1 | https://arxiv.org/pdf/2001.06910v1.pdf | Common Conversational Community Prototype: Scholarly Conversational Assistant | This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions. Specifically, we propose to develop and operate a prototype conversational search system f... | ['Svitlana Vakulenko', 'Martin Potthast', 'Lucie Flekova', 'Matthias Hagen', 'Hamed Zamani', 'Mark Sanderson', 'Rosie Jones', 'Krisztian Balog', 'Filip Radlinski'] | 2020-01-19 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-5.24794817e-01 1.84660301e-01 -5.20024598e-01 -2.78725713e-01
-7.58103549e-01 -6.68594241e-01 1.39854598e+00 3.93150538e-01
-2.06200480e-01 6.60207987e-01 6.88062608e-01 -8.21564555e-01
-4.20446843e-01 -4.36047256e-01 2.67865568e-01 -5.72482124e-02
1.24807999e-01 9.49367464e-01 6.76527470e-02 -6.62198365... | [12.288835525512695, 7.807748794555664] |
8e8dc237-29c3-41d7-b18f-fb01ee261376 | improved-drug-target-interaction-prediction | 2110.07347 | null | https://arxiv.org/abs/2110.07347v2 | https://arxiv.org/pdf/2110.07347v2.pdf | Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer | The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieved better performance than molecular docking, existing models oft... | ['Tie-Yan Liu', 'Nanning Zheng', 'Jian Yin', 'Bin Shao', 'Liang He', 'Yifan Deng', 'Tong Wang', 'Yusong Wang', 'Siyuan Liu'] | 2021-10-14 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.69280827e-01 -2.21096814e-01 -3.45304281e-01 -1.44330636e-01
-6.54393137e-01 -5.84538877e-01 1.89822972e-01 3.83765608e-01
-1.85763970e-01 1.38583720e+00 -5.37416749e-02 -6.83708966e-01
-2.96222031e-01 -6.63645327e-01 -8.92925739e-01 -8.95311058e-01
-1.52471602e-01 7.53214359e-01 1.07158422e-01 -3.54234427... | [4.998838424682617, 5.699443340301514] |
803aed5a-0cde-4f3a-9d2f-2e245b564c51 | fpga-based-acceleration-system-for-visual | 1810.05367 | null | http://arxiv.org/abs/1810.05367v2 | http://arxiv.org/pdf/1810.05367v2.pdf | FPGA-based Acceleration System for Visual Tracking | Visual tracking is one of the most important application areas of computer
vision. At present, most algorithms are mainly implemented on PCs, and it is
difficult to ensure real-time performance when applied in the real scenario. In
order to improve the tracking speed and reduce the overall power consumption of
visual t... | ['Yunxu Sun', 'Peng Gao', 'Ke Song', 'Chun Yuan'] | 2018-10-12 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [ 8.33218545e-02 -6.83827162e-01 3.98793742e-02 9.12395567e-02
1.97441354e-01 -5.31841516e-01 1.29685074e-01 -1.77547720e-03
-7.18304098e-01 1.46960527e-01 -4.12927419e-01 -6.97586596e-01
2.65877217e-01 -6.90581858e-01 -1.43594205e-01 -6.17706239e-01
3.87014151e-01 -3.03281546e-01 9.10287440e-01 1.72298685... | [9.014934539794922, -2.0710182189941406] |
7ed14f12-20bb-4df7-8d32-1f0fe4eecf3c | transferd2-automated-defect-detection | 2302.13317 | null | https://arxiv.org/abs/2302.13317v1 | https://arxiv.org/pdf/2302.13317v1.pdf | TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques | Quality assurance is crucial in the smart manufacturing industry as it identifies the presence of defects in finished products before they are shipped out. Modern machine learning techniques can be leveraged to provide rapid and accurate detection of these imperfections. We, therefore, propose a transfer learning appro... | ['Rickey Dubay', 'Monica Wachowicz', 'Joshua Pickard', 'Hung Cao', 'Atah Nuh Mih'] | 2023-02-26 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.69661462e-01 1.21451430e-02 2.00695410e-01 -3.78775954e-01
-5.61788619e-01 -2.93258011e-01 1.68289185e-01 3.04175258e-01
1.48749799e-01 3.17015976e-01 -4.28801596e-01 -1.32128134e-01
-2.01505885e-01 -1.07987475e+00 -8.43720436e-01 -4.71238405e-01
4.99868505e-02 4.92823571e-01 5.83451271e-01 -2.88525641... | [7.395875453948975, 1.924378514289856] |
8119b98f-ac91-49f4-ac28-957c25870ca3 | the-agent-based-modelling-for-human-behaviour | 2302.01789 | null | https://arxiv.org/abs/2302.01789v2 | https://arxiv.org/pdf/2302.01789v2.pdf | The Agent-based Modelling for Human Behaviour Special Issue | If human societies are so complex, then how can we hope to understand them? Artificial Life gives us one answer. The field of Artificial Life comprises a diverse set of introspective studies that largely ask the same questions, albeit from many different perspectives: Why are we here? Who are we? Why do we behave as we... | ['Peter J. Bentley', 'Soo Ling Lim'] | 2023-02-03 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.27638608e-01 2.38080874e-01 1.25449114e-02 -2.33243704e-02
4.95685130e-01 -8.17435086e-01 8.00085008e-01 2.85106655e-02
-3.56720716e-01 1.12397897e+00 1.33836403e-01 -5.97664356e-01
1.58364788e-01 -8.38242114e-01 -4.41106915e-01 -8.57354462e-01
2.80688275e-02 3.91642600e-01 2.68056095e-01 -9.91828322... | [5.635099411010742, 4.234460830688477] |
359dbade-7521-450a-8e3e-54a4c9f79525 | migs-meta-image-generation-from-scene-graphs | 2110.11918 | null | https://arxiv.org/abs/2110.11918v1 | https://arxiv.org/pdf/2110.11918v1.pdf | MIGS: Meta Image Generation from Scene Graphs | Generation of images from scene graphs is a promising direction towards explicit scene generation and manipulation. However, the images generated from the scene graphs lack quality, which in part comes due to high difficulty and diversity in the data. We propose MIGS (Meta Image Generation from Scene Graphs), a meta-le... | ['Nassir Navab', 'Helisa Dhamo', 'Sabrina Musatian', 'Azade Farshad'] | 2021-10-22 | null | null | null | null | ['scene-generation', 'image-generation-from-scene-graphs'] | ['computer-vision', 'computer-vision'] | [ 3.69197130e-01 1.30665645e-01 1.09476000e-01 -3.44495893e-01
-7.38897979e-01 -2.16551483e-01 8.11007440e-01 -7.46983737e-02
9.08823088e-02 4.05375302e-01 4.39231753e-01 2.27101341e-01
5.15619479e-02 -1.02668321e+00 -9.51478541e-01 -4.26217020e-01
2.71688133e-01 4.01165485e-01 1.89626142e-01 -3.21376085... | [11.168612480163574, -0.129219651222229] |
bccfd920-de23-474d-8972-8012b69d65ce | une-version-polyatomique-de-l-algorithme | 2204.13557 | null | https://arxiv.org/abs/2204.13557v1 | https://arxiv.org/pdf/2204.13557v1.pdf | Une version polyatomique de l'algorithme Frank-Wolfe pour résoudre le problème LASSO en grandes dimensions | Nous nous int\'eressons \`a la reconstruction parcimonieuse d'images \`a l'aide du probl\`eme d'optimisation r\'egularis\'e LASSO. Dans de nombreuses applications pratiques, les grandes dimensions des objets \`a reconstruire limitent, voire emp\^echent, l'utilisation des m\'ethodes de r\'esolution proximales classiques... | ['Julien Fageot', 'Matthieu Simeoni', 'Adrian Jarret'] | 2022-04-28 | null | null | null | null | ['radio-interferometry'] | ['miscellaneous'] | [ 2.54656732e-01 2.54763931e-01 5.44420540e-01 -1.38921849e-02
-6.52894437e-01 -3.77271444e-01 4.15110379e-01 -6.94821626e-02
-7.77081609e-01 1.07409286e+00 -1.29571453e-01 -8.41610879e-02
-2.57319748e-01 -8.29924524e-01 -7.34059155e-01 -8.10654581e-01
-2.10364044e-01 5.82026720e-01 -2.99002588e-01 -2.39594162... | [11.829873085021973, -2.4702413082122803] |
9374bb0e-cd2f-467f-8873-0cd2ecd4a478 | revisiting-the-uniform-information-density | 2109.11635 | null | https://arxiv.org/abs/2109.11635v1 | https://arxiv.org/pdf/2109.11635v1.pdf | Revisiting the Uniform Information Density Hypothesis | The uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. While its implications on language production have been well explored, the hypothesis potentially makes predictions about language comprehen... | ['Roger Levy', 'Ryan Cotterell', 'Lena Jäger', 'Patrick Haller', 'Tiago Pimentel', 'Clara Meister'] | 2021-09-23 | null | https://aclanthology.org/2021.emnlp-main.74 | https://aclanthology.org/2021.emnlp-main.74.pdf | emnlp-2021-11 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 2.29015455e-01 5.25190175e-01 -4.44621295e-01 -6.69793963e-01
-6.07620478e-01 -7.69926429e-01 7.68676460e-01 6.61509693e-01
-5.48647285e-01 3.76010507e-01 9.13193643e-01 -1.10856390e+00
-2.64231235e-01 -6.67756259e-01 -4.55880761e-01 -2.21328467e-01
2.73665071e-01 7.10129961e-02 1.70871913e-02 -2.34181806... | [10.320063591003418, 8.70556640625] |
07bef5b5-2fd1-41c3-a1b4-195405c220c2 | isolated-sign-recognition-from-rgb-video | null | null | https://openaccess.thecvf.com/content/CVPR2021W/ChaLearn/html/De_Coster_Isolated_Sign_Recognition_From_RGB_Video_Using_Pose_Flow_and_CVPRW_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021W/ChaLearn/papers/De_Coster_Isolated_Sign_Recognition_From_RGB_Video_Using_Pose_Flow_and_CVPRW_2021_paper.pdf | Isolated Sign Recognition from RGB Video using Pose Flow and Self-Attention | Automatic sign language recognition lies at the intersection of natural language processing (NLP) and computer vision. The highly successful transformer architectures, based on multi-head attention, originate from the field of NLP. The Video Transformer Network (VTN) is an adaptation of this concept for tasks that requ... | ['Joni Dambre', 'Mieke Van Herreweghe', 'Mathieu De Coster'] | 2021-06-11 | null | null | null | computer-vision-and-pattern-recognition | ['sign-language-recognition'] | ['computer-vision'] | [ 2.04868257e-01 -6.85952976e-02 5.21433763e-02 -2.14126170e-01
-7.41581738e-01 -5.23851454e-01 6.92641735e-01 -6.55008018e-01
-8.54554951e-01 4.61264044e-01 4.99911219e-01 1.91425934e-01
-9.84547734e-02 -2.88714141e-01 -6.89685106e-01 -8.29818547e-01
2.41079256e-01 6.41188145e-01 4.92095888e-01 -2.36827672... | [9.154718399047852, -6.4647088050842285] |
a1cafe2c-8b39-4d61-b17c-99bb5e830db8 | privacy-preserving-visual-feature-descriptors | 2006.06634 | null | https://arxiv.org/abs/2006.06634v3 | https://arxiv.org/pdf/2006.06634v3.pdf | Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings | Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new priva... | ['Johannes L. Schönberger', 'Mihai Dusmanu', 'Marc Pollefeys', 'Sudipta N. Sinha'] | 2020-06-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 3.70411366e-01 -1.71869159e-01 5.70285320e-03 -3.62916589e-01
-6.03242576e-01 -1.17355180e+00 4.71058846e-01 9.85686779e-02
-4.51788455e-01 3.57898653e-01 -8.55627656e-02 -1.04319043e-01
8.46710056e-03 -7.43401051e-01 -7.28228688e-01 -1.04431450e+00
8.55802447e-02 -2.70805985e-01 -3.32629420e-02 1.83835730... | [12.638975143432617, 0.7564692497253418] |
9098b8b6-08ce-41e2-9b4b-8ee6c5e0c0b2 | cross-class-feature-augmentation-for-class | 2304.01899 | null | https://arxiv.org/abs/2304.01899v1 | https://arxiv.org/pdf/2304.01899v1.pdf | Cross-Class Feature Augmentation for Class Incremental Learning | We propose a novel class incremental learning approach by incorporating a feature augmentation technique motivated by adversarial attacks. We employ a classifier learned in the past to complement training examples rather than simply play a role as a teacher for knowledge distillation towards subsequent models. The prop... | ['Bohyung Han', 'Jaeyoo Park', 'TaeHoon Kim'] | 2023-04-04 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 4.57731396e-01 1.61228091e-01 -4.91999477e-01 -1.75572276e-01
-5.82103670e-01 -7.77076006e-01 7.62099743e-01 1.90846100e-01
-6.38457060e-01 1.24165332e+00 -3.16436827e-01 -2.54064709e-01
2.45189164e-02 -9.74644065e-01 -9.32590544e-01 -9.45419073e-01
-2.21466422e-02 3.16643506e-01 5.24734437e-01 -7.54396766... | [9.82524299621582, 3.3780882358551025] |
d9ef1e06-fbca-40eb-a67a-29e7795db565 | ded-diagnostic-evidence-distillation-for-acne | null | null | http://dx.doi.org/10.1016/j.eswa.2023.120312 | http://dx.doi.org/10.1016/j.eswa.2023.120312 | DED: Diagnostic Evidence Distillation for acne severity grading on face images | Acne seriously affects people’s daily life. Acne severity level grading plays a decisive role in the cure. However, the acne criterion is not unified in the medical field. Most of the current studies explore the application of advanced visual models on acne severity grading but lack the adaptation to the characteristic... | ['Jing Yang', 'Haiyan You', 'Xiguang Liu', 'Yi Guan', 'Zhaoyang Ma', 'Dongxin Chen', 'Jingchi Jiang', 'Yi Lin'] | 2023-10-05 | null | null | null | expert-systems-with-applications-2023-10 | ['acne-severity-grading', 'specificity'] | ['medical', 'natural-language-processing'] | [-1.68486103e-01 -1.58649713e-01 -2.33773589e-01 -8.79181102e-02
-3.20881397e-01 -5.31877220e-01 4.94394630e-01 2.83873715e-02
-1.85171053e-01 6.95636392e-01 -1.78456798e-01 -1.90783143e-01
-6.08141780e-01 -6.80721462e-01 5.20664081e-03 -8.79579306e-01
2.59448975e-01 6.38812125e-01 6.86901733e-02 -5.84718548... | [15.742077827453613, -3.0563175678253174] |
acc882be-f04c-4226-b5f5-fef176e13164 | accurate-ground-truth-depth-image-generation | 2207.07016 | null | https://arxiv.org/abs/2207.07016v2 | https://arxiv.org/pdf/2207.07016v2.pdf | Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets | Accurate three-dimensional perception is a fundamental task in several computer vision applications. Recently, commercial RGB-depth (RGB-D) cameras have been widely adopted as single-view depth-sensing devices owing to their efficient depth-sensing abilities. However, the depth quality of most RGB-D sensors remains ins... | ['Minyoung Chung', 'Yeong-Gil Shin', 'Minchang Kim', 'Jiwan Kim'] | 2022-07-14 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 5.00784874e-01 -2.80198246e-01 9.12751555e-02 -5.27314425e-01
-7.45439053e-01 -2.43527181e-02 3.22356075e-01 -2.96799868e-01
-6.17795467e-01 4.62722093e-01 -6.47694096e-02 1.42036706e-01
-1.02181062e-02 -1.21307576e+00 -5.26792228e-01 -1.00295460e+00
5.18151402e-01 8.73266757e-02 5.29856145e-01 -1.95364550... | [9.05777645111084, -2.508068799972534] |
19c71260-9d68-4687-94ae-ab7b4b930122 | spatial-and-semantic-consistency | 2109.05686 | null | https://arxiv.org/abs/2109.05686v1 | https://arxiv.org/pdf/2109.05686v1.pdf | Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition | While recent studies on pedestrian attribute recognition have shown remarkable progress in leveraging complicated networks and attention mechanisms, most of them neglect the inter-image relations and an important prior: spatial consistency and semantic consistency of attributes under surveillance scenarios. The spatial... | ['Kaiqi Huang', 'Xiaotang Chen', 'Jian Jia'] | 2021-09-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jia_Spatial_and_Semantic_Consistency_Regularizations_for_Pedestrian_Attribute_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jia_Spatial_and_Semantic_Consistency_Regularizations_for_Pedestrian_Attribute_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-2.94600129e-01 -3.80042762e-01 -1.48611655e-02 -8.51562262e-01
-3.09243947e-01 -3.38753074e-01 4.18116152e-01 2.61781573e-01
-3.64710659e-01 5.85821390e-01 1.88475594e-01 1.95912391e-01
-9.75871310e-02 -6.65435910e-01 -8.83000314e-01 -8.51651251e-01
2.16418445e-01 1.96465001e-01 5.55533528e-01 -6.50073886... | [14.452401161193848, 0.9398728609085083] |
5e819187-7583-4cbf-a0cd-9795446b813e | that-is-a-suspicious-reaction-interpreting | null | null | https://openreview.net/forum?id=UrtZRnO6VB | https://openreview.net/pdf?id=UrtZRnO6VB | "That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks | Adversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications. Extensive research in computer vision has been carried to develop reliable defense strategies. However, th... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['adversarial-text'] | ['adversarial'] | [ 4.51701254e-01 -1.45176351e-01 -2.14413881e-01 -1.61840826e-01
-7.56469429e-01 -1.26829183e+00 1.16420555e+00 2.19772682e-01
-5.51563919e-01 4.26780492e-01 -9.60960388e-02 -7.87640989e-01
1.32569507e-01 -6.67083442e-01 -6.46464288e-01 -7.71011472e-01
-4.05264795e-02 2.45096922e-01 4.14388537e-01 -4.35976416... | [5.861874580383301, 7.9522705078125] |
594317e9-e1ad-41d3-bf69-63648cb5f924 | diversity-based-generalization-for-neural | 2002.10937 | null | https://arxiv.org/abs/2002.10937v2 | https://arxiv.org/pdf/2002.10937v2.pdf | Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift | Domain adaptation approaches seek to learn from a source domain and generalize it to an unseen target domain. At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data. In this paper, we propose a ... | ['Huzefa Rangwala', 'Jitin Krishnan', 'Hemant Purohit'] | 2020-02-25 | null | null | null | null | ['unsupervised-text-classification'] | ['natural-language-processing'] | [ 2.69634277e-01 3.23725700e-01 -4.42483276e-01 -7.99292386e-01
-5.53354800e-01 -6.40053928e-01 6.54999077e-01 6.89306259e-02
-4.68884945e-01 9.32681561e-01 2.97108293e-01 -2.73024440e-01
9.35336798e-02 -5.45794427e-01 -4.97966975e-01 -5.28704882e-01
4.04581815e-01 7.84959972e-01 2.99439341e-01 -4.97811317... | [10.787056922912598, 7.921026229858398] |
447b9fbf-b198-4615-86cf-6be5021f362d | incorporating-l2-phonemes-using-articulatory | 2306.02534 | null | https://arxiv.org/abs/2306.02534v1 | https://arxiv.org/pdf/2306.02534v1.pdf | Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognition | The limited availability of non-native speech datasets presents a major challenge in automatic speech recognition (ASR) to narrow the performance gap between native and non-native speakers. To address this, the focus of this study is on the efficient incorporation of the L2 phonemes, which in this work refer to Korean ... | ['Myungwoo Oh', 'Haram Lee', 'Jisung Wang'] | 2023-06-05 | null | null | null | null | ['robust-speech-recognition', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 2.17684895e-01 -9.28954408e-02 -2.16195226e-01 -3.10645431e-01
-1.38713109e+00 -5.92496157e-01 2.24212334e-01 -3.36352177e-02
-4.94117945e-01 4.23735857e-01 4.45028901e-01 -5.76365829e-01
1.32938847e-01 -1.12439863e-01 -2.44045079e-01 -4.33604389e-01
3.23309988e-01 7.68977329e-02 -2.09773093e-01 4.58789654... | [14.554245948791504, 6.770931243896484] |
7703f605-5314-491e-a53b-f1359159014c | low-complexity-cnns-for-acoustic-scene-1 | 2208.01555 | null | https://arxiv.org/abs/2208.01555v1 | https://arxiv.org/pdf/2208.01555v1.pdf | Low-complexity CNNs for Acoustic Scene Classification | This technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC framework should have maximum 128K parameters, and (b) there should be a maximum of 30 millions multiply-accumulate operations (MACs) per i... | ['Mark D. Plumbley', 'Wenwu Wang', 'Xubo Liu', 'James A King', 'Arshdeep Singh'] | 2022-08-02 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 1.73553228e-01 -2.40704283e-01 3.98835927e-01 -7.36970305e-01
-1.19882953e+00 -3.79890442e-01 5.26581883e-01 -1.75398424e-01
-7.52176583e-01 4.37738776e-01 -1.87083043e-03 -6.39935732e-01
1.38209671e-01 1.41547918e-02 -7.37859130e-01 -5.87576210e-01
-3.79690200e-01 1.12487070e-01 7.37940013e-01 -6.32966608... | [14.549206733703613, 5.9712724685668945] |
7dd85aca-949d-49c2-901d-cde17838d1d8 | explaining-transition-systems-through-program | 1705.08320 | null | http://arxiv.org/abs/1705.08320v1 | http://arxiv.org/pdf/1705.08320v1.pdf | Explaining Transition Systems through Program Induction | Explaining and reasoning about processes which underlie observed black-box
phenomena enables the discovery of causal mechanisms, derivation of suitable
abstract representations and the formulation of more robust predictions. We
propose to learn high level functional programs in order to represent abstract
models which ... | ['Subramanian Ramamoorthy', 'Svetlin Penkov'] | 2017-05-23 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 4.36676443e-01 6.00251436e-01 -2.53386289e-01 -5.58077276e-01
-3.26050483e-02 1.30275413e-02 8.58029366e-01 4.45227087e-01
7.88879246e-02 6.45569682e-01 -1.39914081e-01 -9.68575358e-01
-4.35661137e-01 -7.81793773e-01 -1.18934762e+00 -2.68527746e-01
-7.47610271e-01 6.42826855e-01 4.94300053e-02 -1.19308650... | [8.407050132751465, 7.257181167602539] |
506b09f2-528b-4ef6-9392-d2bb4f43278d | structure-representation-network-and | 2210.03061 | null | https://arxiv.org/abs/2210.03061v1 | https://arxiv.org/pdf/2210.03061v1.pdf | Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal | Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog. Dealing with these dense and/or non-uniform distributions can be intractable, since fog's attenuation and airlight (or veiling effect) significantly weaken the background ... | ['Robby T. Tan', 'Wenhan Yang', 'Wending Yan', 'Yeying Jin'] | 2022-10-06 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.28846258e-01 -2.67801136e-01 5.27168274e-01 -2.58161068e-01
7.73704275e-02 -4.42631602e-01 1.92784250e-01 -4.20376599e-01
-9.00011361e-02 9.20129478e-01 -1.93684106e-03 5.47781412e-04
2.92830970e-02 -1.23957384e+00 -7.34254241e-01 -1.15226519e+00
4.45996046e-01 3.53303224e-01 5.29158413e-01 -1.13535360... | [10.915739059448242, -3.2214765548706055] |
5d4951e8-2d2c-401d-bf09-8ef14848d13e | an-inductive-bias-for-distances-neural-nets-1 | 2002.05825 | null | https://arxiv.org/abs/2002.05825v3 | https://arxiv.org/pdf/2002.05825v3.pdf | An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality | Distances are pervasive in machine learning. They serve as similarity measures, loss functions, and learning targets; it is said that a good distance measure solves a task. When defining distances, the triangle inequality has proven to be a useful constraint, both theoretically--to prove convergence and optimality guar... | ['Silviu Pitis', 'Jimmy Ba', 'Harris Chan', 'Kiarash Jamali'] | 2020-02-14 | null | https://openreview.net/forum?id=HJeiDpVFPr | https://openreview.net/pdf?id=HJeiDpVFPr | iclr-2020-1 | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-5.87873533e-02 2.39848599e-01 -1.64885804e-01 -6.56735599e-01
-6.40788674e-01 -6.51010573e-01 1.93652198e-01 2.58082241e-01
-6.76420748e-01 6.22506082e-01 1.28447220e-01 -4.03641611e-01
-7.03116119e-01 -9.09274042e-01 -6.72573388e-01 -6.96841717e-01
-4.00197148e-01 6.93113983e-01 -2.27134079e-01 -3.61361980... | [9.288412094116211, 3.1429898738861084] |
35276aff-8215-41eb-8957-68160136948c | counterfactual-explanations-for-survival | null | null | https://www.researchgate.net/publication/352203927_Counterfactual_Explanations_for_Survival_Prediction_of_Cardiovascular_ICU_Patients | https://www.researchgate.net/publication/352203927_Counterfactual_Explanations_for_Survival_Prediction_of_Cardiovascular_ICU_Patients | Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients | In recent years, machine learning methods have been rapidly implemented in the medical domain. However, current state-of-the-art methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to develop interpretable machine learning methods. In the med... | ['Panagiotis Papapetrou', 'Isak Samsten', 'Zhendong Wang'] | 2021-06-15 | null | null | null | international-conference-on-artificial-6 | ['style-transfer', 'interpretable-machine-learning', 'counterfactual-explanation', 'text-style-transfoer'] | ['computer-vision', 'methodology', 'miscellaneous', 'natural-language-processing'] | [ 6.90648556e-01 1.06835794e+00 -3.00796121e-01 -5.41094005e-01
-4.76875871e-01 -1.92276224e-01 6.79912627e-01 7.81367570e-02
5.95789962e-02 1.35373056e+00 5.05090892e-01 -1.00561810e+00
9.63875800e-02 -6.67403400e-01 -6.55590594e-01 -2.12240294e-01
-1.12841882e-01 4.39736545e-01 -4.97977793e-01 2.47570768... | [8.581344604492188, 5.688521385192871] |
13241be9-fcc2-414a-b62c-5b877b8434db | automated-translation-of-rebar-information | 2110.15448 | null | https://arxiv.org/abs/2110.15448v1 | https://arxiv.org/pdf/2110.15448v1.pdf | Automated Translation of Rebar Information from GPR Data into As-Built BIM: A Deep Learning-based Approach | Building Information Modeling (BIM) is increasingly used in the construction industry, but existing studies often ignore embedded rebars. Ground Penetrating Radar (GPR) provides a potential solution to develop as-built BIM with surface elements and rebars. However, automatically translating rebars from GPR into BIM is ... | ['Abbas Rashidi', 'Ge Ou', 'Zhongming Xiang'] | 2021-10-28 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.94613799e-01 -2.96046063e-02 3.70067745e-01 -4.16671902e-01
-9.79029834e-01 7.72998407e-02 -9.51378942e-02 8.46729055e-02
6.43360466e-02 2.57633835e-01 -1.64541637e-03 -1.07145652e-01
-1.86264232e-01 -1.74876356e+00 -8.45679998e-01 -5.65911353e-01
2.13596970e-01 4.66780841e-01 3.46959859e-01 -3.54764462... | [8.32464599609375, -2.580246925354004] |
05d8fdf7-be72-4dc3-8233-58d73b3a45b2 | learning-to-control-highly-accelerated | 1904.03665 | null | http://arxiv.org/abs/1904.03665v1 | http://arxiv.org/pdf/1904.03665v1.pdf | Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots | High-speed and high-acceleration movements are inherently hard to control.
Applying learning to the control of such motions on anthropomorphic robot arms
can improve the accuracy of the control but might damage the system. The
inherent exploration of learning approaches can lead to instabilities and the
robot reaching ... | ['Dieter Büchler', 'Jan Peters', 'Roberto Calandra'] | 2019-04-07 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.92641303e-01 8.32225859e-01 -3.66499960e-01 3.18421990e-01
1.05201602e-02 -3.27914387e-01 4.13160384e-01 -5.02708495e-01
-7.21997440e-01 9.22265172e-01 -3.07272494e-01 -1.85994938e-01
-8.19857240e-01 -3.32947731e-01 -7.44609952e-01 -9.81907606e-01
-4.78016049e-01 7.17470646e-01 4.99202281e-01 -3.70632559... | [4.821750640869141, 1.4803392887115479] |
76f6e84c-f8a2-49dd-bcfe-14fb02a977cc | redet-a-rotation-equivariant-detector-for | 2103.07733 | null | https://arxiv.org/abs/2103.07733v1 | https://arxiv.org/pdf/2103.07733v1.pdf | ReDet: A Rotation-equivariant Detector for Aerial Object Detection | Recently, object detection in aerial images has gained much attention in computer vision. Different from objects in natural images, aerial objects are often distributed with arbitrary orientation. Therefore, the detector requires more parameters to encode the orientation information, which are often highly redundant an... | ['Gui-Song Xia', 'Nan Xue', 'Jian Ding', 'Jiaming Han'] | 2021-03-13 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-4.79878820e-02 -5.10742307e-01 1.84415445e-01 -2.78212219e-01
-2.20035195e-01 -7.99367607e-01 3.20941389e-01 -3.08713317e-01
-4.91160274e-01 1.37533963e-01 -8.84035304e-02 -2.00421274e-01
-6.13783021e-03 -7.56759942e-01 -8.14372241e-01 -6.94845200e-01
1.18880123e-01 -1.13556251e-01 5.68744123e-01 -2.84834683... | [8.755918502807617, -0.7625021934509277] |
5913d714-56d8-4815-938a-359fb535935b | visual7w-grounded-question-answering-in | 1511.03416 | null | http://arxiv.org/abs/1511.03416v4 | http://arxiv.org/pdf/1511.03416v4.pdf | Visual7W: Grounded Question Answering in Images | We have seen great progress in basic perceptual tasks such as object
recognition and detection. However, AI models still fail to match humans in
high-level vision tasks due to the lack of capacities for deeper reasoning.
Recently the new task of visual question answering (QA) has been proposed to
evaluate a model's cap... | ['Li Fei-Fei', 'Michael Bernstein', 'Yuke Zhu', 'Oliver Groth'] | 2015-11-11 | visual7w-grounded-question-answering-in-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhu_Visual7W_Grounded_Question_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhu_Visual7W_Grounded_Question_CVPR_2016_paper.pdf | cvpr-2016-6 | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 1.66144475e-01 1.97594434e-01 2.11056605e-01 -5.23412168e-01
-9.46803927e-01 -4.84844714e-01 7.28005946e-01 1.64219230e-01
-4.63030756e-01 4.07112956e-01 4.17379558e-01 -5.46327353e-01
6.00376353e-02 -7.47798800e-01 -8.79488170e-01 -3.09185922e-01
2.72192001e-01 6.40606821e-01 7.85436630e-01 -5.05222797... | [10.854866981506348, 1.8092767000198364] |
28d86d1f-07f5-49ac-b8d3-bd017685e160 | scene-restoration-from-scaffold-occlusion | 2305.18810 | null | https://arxiv.org/abs/2305.18810v1 | https://arxiv.org/pdf/2305.18810v1.pdf | Scene restoration from scaffold occlusion using deep learning-based methods | The occlusion issues of computer vision (CV) applications in construction have attracted significant attention, especially those caused by the wide-coverage, crisscrossed, and immovable scaffold. Intuitively, removing the scaffold and restoring the occluded visual information can provide CV agents with clearer site vie... | ['Xiaowei Luo', 'Muyang Liu', 'Yuexiong Ding'] | 2023-05-30 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.63063383e-01 1.24659866e-01 -3.11031342e-02 4.70839366e-02
-3.74018788e-01 -2.95866877e-01 9.14409831e-02 5.62575050e-02
2.91761130e-01 7.70975351e-01 1.25361765e-02 -2.01937348e-01
-7.35294148e-02 -7.92274833e-01 -6.01139247e-01 -7.00198650e-01
6.01755738e-01 6.13597296e-02 4.41880584e-01 -6.65114522... | [10.87879753112793, -1.34256911277771] |
289450a7-0e7d-4b27-988f-acb781faaae2 | molecular-orbital-based-machine-learning-for | 2207.08317 | null | https://arxiv.org/abs/2207.08317v1 | https://arxiv.org/pdf/2207.08317v1.pdf | Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression | We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The l... | ['Thomas F. Miller III', 'Vignesh C. Bhethanabotla', 'J. Emiliano Deustua', 'Jiace Sun', 'Lixue Cheng'] | 2022-07-17 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-6.39278516e-02 4.51529771e-03 -2.28929788e-01 1.50866687e-01
-9.94784355e-01 -2.04576954e-01 3.43078911e-01 6.21599972e-01
-2.96491057e-01 1.22687781e+00 -2.64215320e-01 -6.96321070e-01
-1.27245896e-02 -7.78262258e-01 -4.88168091e-01 -1.21971416e+00
-5.25291860e-01 3.79864931e-01 2.31130421e-01 -2.67815113... | [5.141482830047607, 5.3916916847229] |
a7797099-1daa-41ca-a7a8-d1e184e507bb | aunet-learning-relations-between-action-units | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.pdf | AUNet: Learning Relations Between Action Units for Face Forgery Detection | Face forgery detection becomes increasingly crucial due to the serious security issues caused by face manipulation techniques. Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same domain. However, the problem remains challenging when one ... | ['Weiming Hu', 'Bing Li', 'Zhipeng Zhang', 'Yufan Liu', 'Weiming Bai'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 4.38785672e-01 1.64558813e-02 1.49774864e-01 -3.19027960e-01
-6.15506351e-01 -3.74351948e-01 4.88972187e-01 -8.36092710e-01
3.03038448e-01 3.39781880e-01 2.65960768e-02 -2.87022870e-02
4.48558539e-01 -7.87829816e-01 -9.28970516e-01 -1.01830578e+00
-3.36343981e-02 -3.32816005e-01 2.67808028e-02 -3.26083630... | [12.734223365783691, 1.0361828804016113] |
6aea2f8d-b91d-43fe-afd7-ad71482d6b8d | semantic-term-blurring-and-stochastic | 1811.02456 | null | http://arxiv.org/abs/1811.02456v1 | http://arxiv.org/pdf/1811.02456v1.pdf | Semantic Term "Blurring" and Stochastic "Barcoding" for Improved Unsupervised Text Classification | The abundance of text data being produced in the modern age makes it
increasingly important to intuitively group, categorize, or classify text data
by theme for efficient retrieval and search. Yet, the high dimensionality and
imprecision of text data, or more generally language as a whole, prove to be
challenging when ... | ['Robert Frank Martorano III'] | 2018-11-06 | null | null | null | null | ['unsupervised-text-classification'] | ['natural-language-processing'] | [ 6.26963750e-03 -3.06060255e-01 -4.49603908e-02 -4.95031178e-01
-5.71615696e-01 -8.28814089e-01 7.70413637e-01 7.15304434e-01
-4.49867070e-01 9.64084938e-02 5.08525431e-01 -3.42460185e-01
-6.33032858e-01 -5.59964955e-01 1.93053469e-01 -8.26084256e-01
2.15175766e-02 6.65127873e-01 -1.68043762e-01 -1.88909546... | [10.303627967834473, 7.392019271850586] |
ef194b6c-2467-4ba0-b21a-a651d90152a8 | 2d-motion-detection-using-snns-with-graphene | 2111.15250 | null | https://arxiv.org/abs/2111.15250v1 | https://arxiv.org/pdf/2111.15250v1.pdf | 2D-Motion Detection using SNNs with Graphene-Insulator-Graphene Memristive Synapses | The event-driven nature of spiking neural networks makes them biologically plausible and more energy-efficient than artificial neural networks. In this work, we demonstrate motion detection of an object in a two-dimensional visual field. The network architecture presented here is biologically plausible and uses CMOS an... | ['Anjan Chakravorty', 'Bhaswar Chakrabarti', 'Suresh Balanethiram', 'Karthi Srinivasan', 'Shubham Pande'] | 2021-11-30 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 5.44728577e-01 -5.26844203e-01 3.85158360e-01 1.42734975e-01
6.73257589e-01 -4.55560744e-01 4.09171462e-01 -6.36237934e-02
-9.44290936e-01 8.14712942e-01 -5.72225809e-01 -7.49988481e-02
3.08628738e-01 -7.24105418e-01 -6.81118906e-01 -7.50846088e-01
-4.47791405e-02 -3.89961243e-01 1.09990859e+00 -1.17270604... | [8.2121000289917, 2.4233710765838623] |
aebcfb5a-c2ec-4b66-80d3-cba79a4c1fde | learning-6d-pose-estimation-from-synthetic | 2208.14288 | null | https://arxiv.org/abs/2208.14288v2 | https://arxiv.org/pdf/2208.14288v2.pdf | 6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping | 6D pose recognition has been a crucial factor in the success of robotic grasping, and recent deep learning based approaches have achieved remarkable results on benchmarks. However, their generalization capabilities in real-world applications remain unclear. To overcome this gap, we introduce 6IMPOSE, a novel framework ... | ['Marco Caccamo', 'Cristina Piazza', 'Daniele Bernardini', 'Lukas Dirnberger', 'Hongpeng Cao'] | 2022-08-30 | null | null | null | null | ['6d-pose-estimation-1', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 8.75941068e-02 -1.31741658e-01 3.58591825e-01 -3.93451571e-01
-6.47873402e-01 -7.38971233e-01 3.03112149e-01 -5.88296168e-02
-3.63828152e-01 1.44079149e-01 -4.09660816e-01 -7.65752718e-02
3.42232361e-02 -7.28876889e-01 -1.14325631e+00 -7.54491806e-01
-2.20803991e-01 8.55933428e-01 3.27539712e-01 -1.19146734... | [5.861690521240234, -0.9204932451248169] |
5f48aae3-745b-45eb-bab1-d8a5bc7238d4 | pedestrain-detection-for-low-light-vision | 2303.12725 | null | https://arxiv.org/abs/2303.12725v1 | https://arxiv.org/pdf/2303.12725v1.pdf | Pedestrain detection for low-light vision proposal | The demand for pedestrian detection has created a challenging problem for various visual tasks such as image fusion. As infrared images can capture thermal radiation information, image fusion between infrared and visible images could significantly improve target detection under environmental limitations. In our project... | ['Wenliang Jia', 'Ruiling Ma', 'Zhipeng Chang'] | 2023-03-17 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 3.96299094e-01 -5.38710117e-01 1.89091474e-01 -3.33389282e-01
-3.39695364e-01 -5.87205052e-01 7.90052950e-01 -2.63576776e-01
-5.29365778e-01 7.19949365e-01 -1.83104388e-02 -4.69395816e-01
5.25405407e-01 -9.98869479e-01 -4.57955331e-01 -7.85189033e-01
5.28625667e-01 -2.17996195e-01 4.54633594e-01 -3.74653995... | [9.701127052307129, -1.432047724723816] |
fcfeb2a4-5243-46b8-a1a2-c50541085066 | simple-and-effective-multi-paragraph-reading | 1710.10723 | null | http://arxiv.org/abs/1710.10723v2 | http://arxiv.org/pdf/1710.10723v2.pdf | Simple and Effective Multi-Paragraph Reading Comprehension | We consider the problem of adapting neural paragraph-level question answering
models to the case where entire documents are given as input. Our proposed
solution trains models to produce well calibrated confidence scores for their
results on individual paragraphs. We sample multiple paragraphs from the
documents during... | ['Christopher Clark', 'Matt Gardner'] | 2017-10-29 | simple-and-effective-multi-paragraph-reading-1 | https://aclanthology.org/P18-1078 | https://aclanthology.org/P18-1078.pdf | acl-2018-7 | ['triviaqa'] | ['miscellaneous'] | [ 2.43978739e-01 4.32140291e-01 -4.40670699e-02 -7.89994121e-01
-1.87470734e+00 -9.29556549e-01 7.09687114e-01 1.76791847e-01
-5.09578049e-01 7.65616715e-01 3.05496842e-01 -6.22275472e-01
1.44612387e-01 -7.26433873e-01 -1.05748630e+00 -2.44453743e-01
5.67513466e-01 1.05186772e+00 4.10002619e-01 -5.87821722... | [11.306944847106934, 8.106430053710938] |
05788dd0-bffc-4c21-85a2-bb06456703b6 | forcing-the-whole-video-as-background-an | 2207.06659 | null | https://arxiv.org/abs/2207.06659v1 | https://arxiv.org/pdf/2207.06659v1.pdf | Forcing the Whole Video as Background: An Adversarial Learning Strategy for Weakly Temporal Action Localization | With video-level labels, weakly supervised temporal action localization (WTAL) applies a localization-by-classification paradigm to detect and classify the action in untrimmed videos. Due to the characteristic of classification, class-specific background snippets are inevitably mis-activated to improve the discriminabi... | ['Zhongming Chen', 'Jiaruo Yu', 'Yongxin Ge', 'Ziqiang Li'] | 2022-07-14 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 5.71049690e-01 -1.99223962e-03 -5.45180559e-01 2.85908151e-02
-2.20351338e-01 -2.36934200e-01 4.10853773e-01 -4.46924835e-01
-2.33018950e-01 7.01181948e-01 -5.16412482e-02 -3.67398486e-02
2.74823517e-01 -6.44820929e-01 -7.37309694e-01 -1.25756526e+00
9.58797932e-02 -2.51537144e-01 7.36889601e-01 1.19793572... | [8.47288990020752, 0.7012536525726318] |
85399f21-4a47-4f74-ab4a-5386deab3bef | non-local-graph-neural-networks | 2005.14612 | null | https://arxiv.org/abs/2005.14612v2 | https://arxiv.org/pdf/2005.14612v2.pdf | Non-Local Graph Neural Networks | Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on disassortative graphs usually require non-local aggregation. In addition, we find that local aggregation is even harmful for some disassortati... | ['Shuiwang Ji', 'Meng Liu', 'Zhengyang Wang'] | 2020-05-29 | null | https://openreview.net/forum?id=heqv8eIweMY | https://openreview.net/pdf?id=heqv8eIweMY | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-2.71147579e-01 -8.78395885e-02 -3.38461310e-01 -3.32288206e-01
1.02747763e-02 -4.96437222e-01 4.32819158e-01 5.40991127e-01
-4.33232427e-01 5.52914143e-01 -2.75620054e-02 -6.29390955e-01
-4.00289178e-01 -1.42502403e+00 -8.58983278e-01 -5.85862458e-01
-7.10767925e-01 6.47398770e-01 1.24318764e-01 -2.37069055... | [7.013609886169434, 6.239759922027588] |
4e7b38d3-8ae6-4761-9ae8-1858e2b0ca35 | filler-word-detection-and-classification-a | 2203.15135 | null | https://arxiv.org/abs/2203.15135v2 | https://arxiv.org/pdf/2203.15135v2.pdf | Filler Word Detection and Classification: A Dataset and Benchmark | Filler words such as `uh' or `um' are sounds or words people use to signal they are pausing to think. Finding and removing filler words from recordings is a common and tedious task in media editing. Automatically detecting and classifying filler words could greatly aid in this task, but few studies have been published ... | ['Justin Salamon', 'Juan-Pablo Caceres', 'Ge Zhu'] | 2022-03-28 | null | null | null | null | ['sound-event-localization-and-detection', 'keyword-spotting'] | ['audio', 'speech'] | [ 3.00734192e-01 -6.14731014e-02 6.16477728e-02 -2.04084948e-01
-1.41499984e+00 -8.40463042e-01 4.68220174e-01 3.20148200e-01
-4.61038619e-01 2.63847858e-01 8.54450703e-01 -1.81170851e-01
2.38444299e-01 -3.48424643e-01 -6.50001287e-01 -2.10083440e-01
1.18359558e-01 2.99258947e-01 1.68158367e-01 -1.52464360... | [15.08265209197998, 5.341324806213379] |
0ae5738b-0cfa-4b1a-b8c5-1bb5cd055764 | comparing-span-extraction-methods-for | null | null | https://aclanthology.org/2021.spnlp-1.8 | https://aclanthology.org/2021.spnlp-1.8.pdf | Comparing Span Extraction Methods for Semantic Role Labeling | In this work, we empirically compare span extraction methods for the task of semantic role labeling (SRL). While recent progress incorporating pre-trained contextualized representations into neural encoders has greatly improved SRL F1 performance on popular benchmarks, the potential costs and benefits of structured dec... | ['Eduard Hovy', 'Emma Strubell', 'Zhisong Zhang'] | null | null | null | null | acl-spnlp-2021-8 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 6.49373412e-01 3.06894004e-01 -7.07817197e-01 -5.99284947e-01
-1.14188766e+00 -7.40081251e-01 6.16666675e-01 4.25826132e-01
-9.26202655e-01 9.64256287e-01 1.00285947e+00 -2.92828351e-01
1.01853631e-01 -2.60619491e-01 -4.73817497e-01 -3.46657574e-01
-1.63303465e-01 1.61110103e-01 1.74979344e-01 -1.68496326... | [10.362037658691406, 9.42813491821289] |
3e5b2625-19ba-401a-9bd2-8bfa02ed4c5b | divide-and-conquer-from-complexity-to | null | null | https://aclanthology.org/2020.sdp-1.40 | https://aclanthology.org/2020.sdp-1.40.pdf | Divide and Conquer: From Complexity to Simplicity for Lay Summarization | We describe our approach for the 1st Computational Linguistics Lay Summary Shared Task CL-LaySumm20. The task is to produce non-technical summaries of scholarly documents. The summary should be within easy grasp of a layman who may not be well versed with the domain of the research article. We propose a two step divide... | ['Vasudha Bhatnagar', 'Alka Khurana', 'Swagata Duari', 'Neha Tomar', 'Ankush Khanna', 'Anurag Joshi', 'Jaspreet Singh Dhani', 'Saachi .', 'Rochana Chaturvedi'] | null | null | null | null | emnlp-sdp-2020-11 | ['lay-summarization'] | ['natural-language-processing'] | [ 3.41763347e-01 6.36783242e-01 -4.04745311e-01 -1.64410979e-01
-1.57158160e+00 -5.73270977e-01 6.60025179e-01 6.40535295e-01
-5.37172079e-01 8.85432899e-01 8.66707504e-01 -5.75061321e-01
-1.60001308e-01 -4.93075252e-01 -9.22293067e-01 -1.00217633e-01
2.39792824e-01 4.92158920e-01 -7.33393654e-02 -1.40567780... | [12.479584693908691, 9.520506858825684] |
6be1524b-dd48-43d1-b74f-9ffb5792ab73 | weakly-supervised-learning-significantly | 2211.15924 | null | https://arxiv.org/abs/2211.15924v1 | https://arxiv.org/pdf/2211.15924v1.pdf | Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT | Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing strong supervision on what constitutes positive and neg... | ['Jeremias Sulam', 'Paul H. Yi', 'Jacopo Teneggi'] | 2022-11-29 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 2.45276198e-01 4.01261717e-01 -1.36911109e-01 -5.54481208e-01
-1.10797048e+00 -3.12943339e-01 3.11031461e-01 8.76875877e-01
-9.20481563e-01 6.16565347e-01 -3.35476920e-02 -6.51288331e-01
-5.73493727e-02 -7.53992558e-01 -7.08271921e-01 -8.52404594e-01
-3.64451438e-01 6.04044616e-01 3.23573321e-01 1.18858941... | [14.746174812316895, -2.2565815448760986] |
4d15d3f2-1c0f-427f-a1b9-2aeeec3e175f | first-order-transition-in-trigonal-structure | 2012.01863 | null | https://arxiv.org/abs/2012.01863v1 | https://arxiv.org/pdf/2012.01863v1.pdf | First order transition in trigonal structure ${\textbf{Ca}}{\textbf{Mn}}_{2}{\textbf{P}}_{2}$ | We report structural and physical properties of the single crystalline ${\mathrm{Ca}}{\mathrm{Mn}}_{2}{\mathrm{P}}_{2}$. The X-ray diffraction(XRD) results show that ${\mathrm{Ca}}{\mathrm{Mn}}_{2}{\mathrm{P}}_{2}$ adopts the trigonal ${\mathrm{Ca}}{\mathrm{Al}}_{2}{\mathrm{Si}}_{2}$-type structure. Temperature depende... | ['J. L. Luo', 'G. Li', 'T. Xiang', 'Q. M. Zhang', 'L. L. Sun', 'K. Liu', 'Z. Li', 'X. B. Zhou', 'C. Mu', 'S. H. Na', 'D. S. Wu', 'W. Wu', 'J. Guo', 'Z. Y. Mi', 'F. Jin', 'Y. J. Li'] | 2020-12-03 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [ 4.40951854e-01 3.49625707e-01 5.49579673e-02 3.34088430e-02
-6.58983111e-01 -1.56056061e-01 3.38797122e-01 1.95110012e-02
-3.81028473e-01 9.83521700e-01 -3.90246063e-01 -8.37963402e-01
-6.57687128e-01 -1.03844452e+00 -6.03330493e-01 -1.36180604e+00
-2.04811543e-01 5.01036644e-01 5.50065935e-01 -2.96821862... | [5.839770317077637, 4.754453182220459] |
44d69134-c13b-471a-87f2-04558418781d | multi-dimensional-refinement-graph | 2306.15321 | null | https://arxiv.org/abs/2306.15321v1 | https://arxiv.org/pdf/2306.15321v1.pdf | Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition | Graph convolutional networks have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of inter-class data. Moreover, the noisy data from pose extraction increases the challenge of fine-grained recognition. In this work,... | ['Gao Huang', 'Fei-Long Wang', 'Si-Fan Zhang', 'Kai-Yuan Liu', 'Jin-Rong Zhang', 'Yu-Ning Ding', 'Sheng-Lan Liu'] | 2023-06-27 | null | null | null | null | ['skeleton-based-action-recognition', 'action-recognition-in-videos', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.73969084e-01 -3.33203375e-01 -2.75527716e-01 -3.02430779e-01
-7.19759405e-01 -7.95150623e-02 5.70670068e-01 -2.10149109e-01
-3.77044111e-01 5.94243884e-01 6.83439255e-01 3.52225840e-01
-3.67650300e-01 -7.33195603e-01 -6.63889110e-01 -7.08553612e-01
2.86732405e-01 9.64327976e-02 5.62325895e-01 -1.37283698... | [7.897792816162109, 0.3829604983329773] |
94cbbd99-73ad-427c-9b7a-d16313a72940 | contextual-information-and-commonsense-based | 2207.13254 | null | https://arxiv.org/abs/2207.13254v1 | https://arxiv.org/pdf/2207.13254v1.pdf | Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation | Emotion recognition in conversation (ERC) aims to detect the emotion for each utterance in a given conversation. The newly proposed ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance. However, these models seldom exploit PLMs' advant... | ['Yanghua Xiao', 'Zhiyao Zhang', 'Caiyan Cao', 'Siyu Yuan', 'Deqing Yang', 'Jingjie Yi'] | 2022-07-27 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.99992269e-01 -1.31462160e-02 -9.83696058e-02 -6.61754310e-01
-6.83574200e-01 -2.46775717e-01 4.85126853e-01 -1.80181846e-01
-2.28130639e-01 3.03923190e-01 5.72901189e-01 1.42477676e-02
3.37463617e-01 -1.92458943e-01 -7.40361735e-02 -7.60852337e-01
6.19502105e-02 -1.06622934e-01 -3.65659773e-01 -4.65304106... | [13.032731056213379, 6.094903469085693] |
57886316-24f1-4e91-a600-5fa170206780 | ukp-square-an-interactive-tool-for-teaching | 2305.19748 | null | https://arxiv.org/abs/2305.19748v2 | https://arxiv.org/pdf/2305.19748v2.pdf | UKP-SQuARE: An Interactive Tool for Teaching Question Answering | The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course. Additionally, the breadth of QA derived from this exponential growth makes it an ideal scenario for teaching related NLP topics such as information retrieval, explainability, and adversa... | ['Iryna Gurevych', 'Haritz Puerto', 'Haishuo Fang'] | 2023-05-31 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-2.38131523e-01 2.15987965e-01 2.20657662e-01 -2.70980746e-01
-7.44830728e-01 -1.02468145e+00 2.48937726e-01 8.47980440e-01
-2.13109940e-01 3.44343483e-01 -2.67822772e-01 -8.66092861e-01
-3.96533728e-01 -9.76925611e-01 -7.03134358e-01 -4.17070031e-01
1.82960451e-01 2.99757093e-01 5.47345698e-01 -7.52395868... | [10.583152770996094, 7.540399074554443] |
3f96ce72-a196-4ab3-a43c-e062fd272e12 | hyperparameter-optimization-quantum-assisted | 2303.15053 | null | https://arxiv.org/abs/2303.15053v1 | https://arxiv.org/pdf/2303.15053v1.pdf | Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC | Training and Hyperparameter Optimization (HPO) of deep learning-based AI models are often compute resource intensive and calls for the use of large-scale distributed resources as well as scalable and resource efficient hyperparameter search algorithms. This work studies the potential of using model performance predicti... | ['Eduard Cuba', 'Juan Pablo García Amboage', 'David Southwick', 'Maria Girone', 'Eric Wulff'] | 2023-03-27 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-8.84764194e-02 -1.66768894e-01 1.66897088e-01 -3.02590489e-01
-5.99419773e-01 -2.17589647e-01 7.17666447e-01 6.24217331e-01
-1.02485013e+00 6.99328482e-01 -1.60957307e-01 -4.89942372e-01
-3.05125803e-01 -1.13422108e+00 -7.14466155e-01 -1.04747796e+00
3.98433395e-02 1.33608341e+00 2.31809199e-01 -4.43096519... | [5.566711902618408, 4.942492961883545] |
1fbec278-cb39-497d-a46f-072bcfc39454 | mdetr-modulated-detection-for-end-to-end | 2104.12763 | null | https://arxiv.org/abs/2104.12763v2 | https://arxiv.org/pdf/2104.12763v2.pdf | MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding | Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challenging for such systems t... | ['Gabriel Synnaeve', 'Nicolas Carion', 'Ishan Misra', 'Yann Lecun', 'Mannat Singh', 'Aishwarya Kamath'] | 2021-04-26 | null | null | null | null | ['referring-image-matting-refmatte-rw100', 'referring-image-matting-keyword-based', 'referring-expression-segmentation', 'referring-image-matting-expression-based', 'phrase-grounding'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.25477332e-01 2.99417466e-01 -3.34030725e-02 -6.14177525e-01
-1.22451293e+00 -8.63859296e-01 7.66166389e-01 2.06988588e-01
-7.25356221e-01 1.90398708e-01 -9.63633657e-02 -2.25379422e-01
2.32005179e-01 -7.10365891e-01 -1.17049849e+00 -5.41753173e-01
4.08893079e-01 9.78086233e-01 5.33616364e-01 -1.78819373... | [10.47502613067627, 1.4396226406097412] |
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