paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
60a909a7-5c4d-4d88-b253-4eefb8382566 | a-survey-on-stance-detection-for-mis-and | 2103.00242 | null | https://arxiv.org/abs/2103.00242v3 | https://arxiv.org/pdf/2103.00242v3.pdf | A Survey on Stance Detection for Mis- and Disinformation Identification | Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a ... | ['Isabelle Augenstein', 'Preslav Nakov', 'Arnav Arora', 'Momchil Hardalov'] | 2021-02-27 | null | https://aclanthology.org/2022.findings-naacl.94 | https://aclanthology.org/2022.findings-naacl.94.pdf | findings-naacl-2022-7 | ['rumour-detection'] | ['natural-language-processing'] | [ 4.52227026e-01 8.91923726e-01 -5.24585843e-01 -2.02974036e-01
-5.21614373e-01 -9.14627194e-01 1.07438219e+00 1.16040361e+00
-2.90329158e-01 9.01734471e-01 9.24760699e-01 -8.53741229e-01
2.18253225e-01 -8.86350930e-01 -4.35167611e-01 -2.68022060e-01
3.67755920e-01 2.38789842e-01 4.12082195e-01 -5.19687414... | [8.439858436584473, 10.042821884155273] |
b70a8e2b-fb0b-4360-b94e-693c65768ea6 | communication-efficient-edge-ai-inference | 2004.13351 | null | https://arxiv.org/abs/2004.13351v1 | https://arxiv.org/pdf/2004.13351v1.pdf | Communication-Efficient Edge AI Inference Over Wireless Networks | Given the fast growth of intelligent devices, it is expected that a large number of high-stake artificial intelligence (AI) applications, e.g., drones, autonomous cars, tactile robots, will be deployed at the edge of wireless networks in the near future. As such, the intelligent communication networks will be designed ... | ['Zhanpeng Yang', 'Yong Zhou', 'Kai Yang', 'Yuanming Shi'] | 2020-04-28 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 2.71480680e-01 4.11718309e-01 -3.46009552e-01 -8.00363347e-02
2.35404849e-01 -3.13172728e-01 3.41307342e-01 -2.34383136e-01
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-5.35427392e-01 6.24664068e-01 2.87049085e-01 -1.39947966... | [6.375155448913574, 1.871159553527832] |
44f3b227-4852-41e6-b5f9-018cff79f9bc | time-series-contrastive-learning-with | 2303.11911 | null | https://arxiv.org/abs/2303.11911v1 | https://arxiv.org/pdf/2303.11911v1.pdf | Time Series Contrastive Learning with Information-Aware Augmentations | Various contrastive learning approaches have been proposed in recent years and achieve significant empirical success. While effective and prevalent, contrastive learning has been less explored for time series data. A key component of contrastive learning is to select appropriate augmentations imposing some priors to co... | ['Xiang Zhang', 'Haifeng Chen', 'Yuncong Chen', 'Yanchi Liu', 'Xuchao Zhang', 'Wenchao Yu', 'Jingchao Ni', 'Dongkuan Xu', 'Yingheng Wang', 'Wei Cheng', 'Dongsheng Luo'] | 2023-03-21 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 6.38986766e-01 -1.61378190e-01 -4.75004554e-01 -4.24245119e-01
-9.65814114e-01 -4.73857611e-01 8.83397281e-01 1.81697518e-01
-5.58503389e-01 5.50050437e-01 2.53891293e-02 -2.80214787e-01
-3.33969712e-01 -4.50647205e-01 -6.42828703e-01 -8.29446316e-01
-4.63824242e-01 2.30482504e-01 -1.64501905e-01 -1.81270123... | [7.225512504577637, 2.930809497833252] |
cd0402c6-48d2-423b-bbb3-285274959935 | aspnet-action-segmentation-with-shared | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/van_Amsterdam_ASPnet_Action_Segmentation_With_Shared-Private_Representation_of_Multiple_Data_Sources_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/van_Amsterdam_ASPnet_Action_Segmentation_With_Shared-Private_Representation_of_Multiple_Data_Sources_CVPR_2023_paper.pdf | ASPnet: Action Segmentation With Shared-Private Representation of Multiple Data Sources | Most state-of-the-art methods for action segmentation are based on single input modalities or naive fusion of multiple data sources. However, effective fusion of complementary information can potentially strengthen segmentation models and make them more robust to sensor noise and more accurate with smaller training... | ['Danail Stoyanov', 'Imanol Luengo', 'Abdolrahim Kadkhodamohammadi', 'Beatrice van Amsterdam'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['action-segmentation', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 7.16807604e-01 4.22356138e-03 -6.85643435e-01 -4.69002634e-01
-1.43801761e+00 -7.25068569e-01 6.86892092e-01 1.76080331e-01
-4.38771278e-01 4.16540951e-01 9.28718328e-01 3.53867888e-01
-7.87233189e-02 -3.56873930e-01 -8.40599418e-01 -6.06469154e-01
2.56821781e-01 3.99598897e-01 4.51329887e-01 -7.42302686... | [8.69774055480957, 0.7908236980438232] |
a9a30f97-ccf6-42c1-a796-5d4468815c7c | coherent-false-seizure-prediction-in-epilepsy | 2110.13550 | null | https://arxiv.org/abs/2110.13550v1 | https://arxiv.org/pdf/2110.13550v1.pdf | Coherent False Seizure Prediction in Epilepsy, Coincidence or Providence? | Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rathe... | ['Ronald Tetzlaff', 'Levin Kuhlmann', 'Ortrud Uckermann', 'Georg Leonhardt', 'Matthias Eberlein', 'Hongliu Yang', 'Jens Müller'] | 2021-10-26 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.08698502e-01 3.80169675e-02 1.01283707e-01 -7.68757105e-01
-7.80675113e-01 -4.57238853e-01 8.40768099e-01 3.53201568e-01
-4.60444391e-01 1.09536517e+00 1.31694406e-01 -1.79275811e-01
-2.89365381e-01 -4.31159079e-01 -2.22816944e-01 -8.11375976e-01
-6.69509470e-01 5.46574712e-01 4.55829144e-01 -5.57584092... | [13.279234886169434, 3.517178535461426] |
ed105142-4cd5-4b2a-a68d-33e42a610cfa | on-the-integration-of-acoustics-and-lidar-a | 2206.03885 | null | https://arxiv.org/abs/2206.03885v1 | https://arxiv.org/pdf/2206.03885v1.pdf | On the Integration of Acoustics and LiDAR: a Multi-Modal Approach to Acoustic Reflector Estimation | Having knowledge on the room acoustic properties, e.g., the location of acoustic reflectors, allows to better reproduce the sound field as intended. Current state-of-the-art methods for room boundary detection using microphone measurements typically focus on a two-dimensional setting, causing a model mismatch when empl... | ['Richard C. Hendriks', 'Martin Møller', 'Jorge Martinez', 'Pablo Martínez-Nuevo', 'Ellen Riemens'] | 2022-06-08 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 3.52143526e-01 -2.26157904e-01 8.28411698e-01 7.51209818e-03
-6.01709187e-01 -4.64890152e-01 2.15001866e-01 1.49288714e-01
-2.86675245e-01 2.69600987e-01 3.29889417e-01 -2.82172978e-01
-1.59798220e-01 -9.36947703e-01 -3.39710265e-01 -1.05072403e+00
3.64876986e-01 -2.05122810e-02 3.76560271e-01 3.19755338... | [15.146363258361816, 5.7653069496154785] |
fe7a4894-61bc-4825-a416-22f51dc5d0ce | discodisco-at-the-disrpt2021-shared-task-a | 2109.09777 | null | https://arxiv.org/abs/2109.09777v1 | https://arxiv.org/pdf/2109.09777v1.pdf | DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection | This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwi... | ['Amir Zeldes', 'YIlun Zhu', 'Siyao Peng', 'Yang Janet Liu', 'Shabnam Behzad', 'Luke Gessler'] | 2021-09-20 | null | https://aclanthology.org/2021.disrpt-1.6 | https://aclanthology.org/2021.disrpt-1.6.pdf | emnlp-disrpt-2021-11 | ['discourse-segmentation', 'discourse-parsing', 'connective-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.65330088e-01 1.03045893e+00 -5.12940764e-01 -2.43944600e-01
-9.77648497e-01 -4.64838117e-01 1.00300717e+00 5.25298297e-01
-4.87483114e-01 7.97176063e-01 1.09003329e+00 -7.88158119e-01
3.44760157e-02 -6.99355006e-01 -3.10105979e-01 -1.77382052e-01
-2.30284452e-01 7.18707085e-01 4.05244678e-01 -6.41783774... | [10.774553298950195, 9.31557846069336] |
fda7099e-8b51-487b-8b47-2dd70f94f96e | multi-task-handwritten-document-layout | 1806.08852 | null | http://arxiv.org/abs/1806.08852v3 | http://arxiv.org/pdf/1806.08852v3.pdf | Multi-Task Handwritten Document Layout Analysis | Document Layout Analysis is a fundamental step in Handwritten Text Processing
systems, from the extraction of the text lines to the type of zone it belongs
to. We present a system based on artificial neural networks which is able to
determine not only the baselines of text lines present in the document, but
also perfor... | ['Lorenzo Quirós'] | 2018-06-22 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 3.49907845e-01 -4.32343572e-01 2.26468816e-02 -3.57304394e-01
-2.77654827e-02 -8.41941833e-01 7.85101414e-01 3.65522474e-01
-1.67380035e-01 3.94949079e-01 5.36622852e-02 -6.58804834e-01
-2.97958910e-01 -9.01648045e-01 -2.91354328e-01 -5.33419609e-01
1.19256914e-01 5.70179284e-01 4.38427210e-01 -2.82001466... | [11.801207542419434, 2.6385769844055176] |
376da50c-4e51-4d7d-a45b-d3180698b0bd | neural-basis-models-for-interpretability | 2205.14120 | null | https://arxiv.org/abs/2205.14120v4 | https://arxiv.org/pdf/2205.14120v4.pdf | Neural Basis Models for Interpretability | Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfulness limitations. Generalized Additive Models (GAMs) are an... | ['Dhruv Mahajan', 'Abhimanyu Dubey', 'Filip Radenovic'] | 2022-05-27 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 9.02302861e-02 2.52476990e-01 -3.46215427e-01 -5.66762805e-01
-5.43799579e-01 -4.56696689e-01 6.78639829e-01 -1.39610037e-01
2.60961741e-01 5.65529108e-01 1.07642591e-01 -5.64000845e-01
-3.24923724e-01 -6.16138995e-01 -1.02332890e+00 -4.56300646e-01
2.07581688e-02 7.04776287e-01 -2.81189997e-02 -3.24521631... | [8.829690933227539, 5.508686065673828] |
892ceaf0-1a9c-4034-9177-82c5a7894d57 | outlier-cluster-formation-in-spectral | 1703.01028 | null | http://arxiv.org/abs/1703.01028v1 | http://arxiv.org/pdf/1703.01028v1.pdf | Outlier Cluster Formation in Spectral Clustering | Outlier detection and cluster number estimation is an important issue for
clustering real data. This paper focuses on spectral clustering, a time-tested
clustering method, and reveals its important properties related to outliers.
The highlights of this paper are the following two mathematical observations:
first, spect... | ['Michihiko Minoh', 'Masaaki Iiyama', 'Hidekazu Kasahara', 'Takuro Ina', 'Mikihiko Mori', 'Atsushi Hashimoto'] | 2017-03-03 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-3.54728609e-01 -5.78944802e-01 4.13962334e-01 -1.86836869e-01
-3.54271740e-01 -2.47913122e-01 4.69287395e-01 -1.78484991e-02
-8.14296678e-02 2.68731356e-01 2.98671663e-01 4.38005477e-01
-2.09240064e-01 -2.03755900e-01 -3.30899775e-01 -8.57919455e-01
-6.21660709e-01 4.90890920e-01 -1.74055621e-01 2.89259940... | [7.686348915100098, 4.434229850769043] |
4463c022-47d4-46e4-a767-41e611302713 | meta-sysid-a-meta-learning-approach-for | 2206.00694 | null | https://arxiv.org/abs/2206.00694v1 | https://arxiv.org/pdf/2206.00694v1.pdf | Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction | In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by their context. Inspired by classical modeling-and-identification approaches, Meta-SysId learns to represent the common law through shared pa... | ['Jinkyoo Park', 'Mykel J. Kochenderfer', 'Arec Jamgochian', 'Federico Berto', 'Junyoung Park'] | 2022-06-01 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-2.14935809e-01 -3.56127292e-01 -6.34482265e-01 -1.97197855e-01
-4.28334951e-01 -3.77419978e-01 6.82428956e-01 2.68438697e-01
1.65901259e-01 6.37066424e-01 -2.88613170e-01 -6.37819290e-01
-4.72304732e-01 -4.80794668e-01 -5.76167583e-01 -3.72819006e-01
-8.80596861e-02 7.39429653e-01 8.85996372e-02 -5.83290160... | [6.77682638168335, 3.189732789993286] |
96d936b9-ecaf-401d-871d-d0875a66e64b | safeaccess-towards-a-dialogue-enabled-access | 1904.01178 | null | http://arxiv.org/abs/1904.01178v2 | http://arxiv.org/pdf/1904.01178v2.pdf | Person Identification with Visual Summary for a Safe Access to a Smart Home | SafeAccess is an integrated system designed to provide easier and safer
access to a smart home for people with or without disabilities. The system is
designed to enhance safety and promote the independence of people with
disability (i.e., visually impaired). The key functionality of the system
includes the detection an... | ['Mohammed Yeasin', 'Shahinur Alam'] | 2019-04-02 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.13413773e-01 -2.04557896e-01 2.02894643e-01 -3.57192457e-01
-3.78787547e-01 -3.54885846e-01 1.08441412e-01 -2.48028219e-01
-4.10602897e-01 8.34335327e-01 3.48050326e-01 -9.13901776e-02
-1.05795540e-01 -8.23407590e-01 -1.58185869e-01 -6.41241729e-01
-1.71611663e-02 -1.06862158e-01 1.29006207e-01 -1.44536778... | [7.13539457321167, 0.3722545802593231] |
534c62a2-bd38-4fb6-96a1-ef503af6ce14 | towards-zero-shot-scale-aware-monocular-depth | 2306.17253 | null | https://arxiv.org/abs/2306.17253v1 | https://arxiv.org/pdf/2306.17253v1.pdf | Towards Zero-Shot Scale-Aware Monocular Depth Estimation | Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions. Even so, the resulting models will be geometry-specific, with learned scales that cannot be directly transferred across domains. Because of that, recent works focus instead on relative depth, eschewing scal... | ['Adrien Gaidon', 'Rares Ambrus', 'Dian Chen', 'Igor Vasiljevic', 'Vitor Guizilini'] | 2023-06-29 | null | null | null | null | ['depth-estimation', 'monocular-depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.52869248e-01 2.27169126e-01 -1.92048755e-02 -5.76096654e-01
-7.98646748e-01 -7.52149999e-01 7.11678684e-01 -2.87047267e-01
-4.66279417e-01 6.20337069e-01 2.80492723e-01 1.71809569e-01
2.91973710e-01 -8.76480758e-01 -9.11415279e-01 -4.95949954e-01
1.14400610e-01 5.74661374e-01 5.46415031e-01 6.00944720... | [8.619791984558105, -2.511573314666748] |
1d0ba38e-796c-4af5-a5f5-23c9f51a61f4 | surgical-phase-recognition-of-short-video | 1807.07853 | null | http://arxiv.org/abs/1807.07853v4 | http://arxiv.org/pdf/1807.07853v4.pdf | Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features | Recognizing the phases of a laparoscopic surgery (LS) operation form its
video constitutes a fundamental step for efficient content representation,
indexing and retrieval in surgical video databases. In the literature, most
techniques focus on phase segmentation of the entire LS video using
hand-crafted visual features... | ['Constantinos Loukas'] | 2018-07-20 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 4.84973133e-01 1.45261623e-02 -6.26640797e-01 1.09040655e-01
-6.03302419e-01 -3.36192578e-01 6.08518839e-01 5.73768497e-01
-8.87726367e-01 3.06729674e-01 3.42566997e-01 -2.41506547e-01
-3.36297542e-01 -4.49268669e-01 -8.02322626e-01 -7.66140819e-01
-3.41166764e-01 -2.79663980e-01 1.88656539e-01 -1.46254510... | [14.086641311645508, -3.3538718223571777] |
4fe9e96d-dbb1-45b0-9893-cb7a8d40fd87 | when-does-clip-generalize-better-than | null | null | https://aclanthology.org/2022.repl4nlp-1.4 | https://aclanthology.org/2022.repl4nlp-1.4.pdf | When does CLIP generalize better than unimodal models? When judging human-centric concepts | CLIP, a vision-language network trained with a multimodal contrastive learning objective on a large dataset of images and captions, has demonstrated impressive zero-shot ability in various tasks. However, recent work showed that in comparison to unimodal (visual) networks, CLIP’s multimodal training does not benefit ge... | ['Rufin VanRullen', 'Tim Van De Cruys', 'Benjamin Devillers', 'Romain Bielawski'] | null | null | null | null | repl4nlp-acl-2022-5 | ['genre-classification'] | ['computer-vision'] | [ 4.02921475e-02 -2.35979885e-01 -3.45411509e-01 -3.16053271e-01
-5.94109297e-01 -7.73201823e-01 1.00854635e+00 2.69810528e-01
-5.99678099e-01 5.66153586e-01 3.12874496e-01 -2.77376294e-01
3.47585231e-01 -3.99257660e-01 -8.21575522e-01 -4.85205114e-01
1.83183894e-01 2.69284666e-01 -1.45109981e-01 -6.06046557... | [11.002154350280762, 1.8450524806976318] |
97df8584-57dc-43b5-b16f-34e416e8399f | denoising-auto-encoder-with-recurrent-skip | 1807.01898 | null | http://arxiv.org/abs/1807.01898v1 | http://arxiv.org/pdf/1807.01898v1.pdf | Denoising Auto-encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation | Convolutional neural networks with skip connections have shown good
performance in music source separation. In this work, we propose a denoising
Auto-encoder with Recurrent skip Connections (ARC). We use 1D convolution along
the temporal axis of the time-frequency feature map in all layers of the
fully-convolutional ne... | ['Yi-Hsuan Yang', 'Jen-Yu Liu'] | 2018-07-05 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 2.77471840e-02 -2.81801254e-01 2.93797404e-01 -1.80714607e-01
-7.10935175e-01 -5.52695096e-01 3.31077367e-01 -5.20325422e-01
-4.75009143e-01 2.45191291e-01 4.94037330e-01 1.00535870e-01
-1.79687902e-01 -2.57206172e-01 -6.66762888e-01 -7.16886342e-01
-8.80009383e-02 -4.57453132e-01 -5.86930737e-02 -3.14198852... | [15.47588062286377, 5.519558906555176] |
3498bfbd-b18c-47b7-a727-e418058063b5 | recurrent-vision-transformers-for-object | 2212.05598 | null | https://arxiv.org/abs/2212.05598v3 | https://arxiv.org/pdf/2212.05598v3.pdf | Recurrent Vision Transformers for Object Detection with Event Cameras | We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object de... | ['Davide Scaramuzza', 'Mathias Gehrig'] | 2022-12-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gehrig_Recurrent_Vision_Transformers_for_Object_Detection_With_Event_Cameras_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gehrig_Recurrent_Vision_Transformers_for_Object_Detection_With_Event_Cameras_CVPR_2023_paper.pdf | cvpr-2023-1 | ['event-based-vision'] | ['computer-vision'] | [ 2.38550350e-01 -3.87234747e-01 -4.51443642e-02 -1.56004369e-01
-7.87709177e-01 -5.00604928e-01 7.06466496e-01 1.25579074e-01
-6.36064708e-01 2.17670072e-02 -7.36665949e-02 -4.46533918e-01
4.55470048e-02 -5.65953016e-01 -8.23967218e-01 -5.73632061e-01
7.08796754e-02 -4.33828356e-03 9.01576161e-01 2.60448873... | [8.45322036743164, -1.023681640625] |
5b1ade31-3940-48ac-9ec5-0c8b6f4de698 | learning-robust-hash-codes-for-multiple | 1703.05724 | null | http://arxiv.org/abs/1703.05724v1 | http://arxiv.org/pdf/1703.05724v1.pdf | Learning Robust Hash Codes for Multiple Instance Image Retrieval | In this paper, for the first time, we introduce a multiple instance (MI) deep
hashing technique for learning discriminative hash codes with weak bag-level
supervision suited for large-scale retrieval. We learn such hash codes by
aggregating deeply learnt hierarchical representations across bag members
through a dedicat... | ['Sailesh Conjeti', 'Amin Katouzian', 'Magdalini Paschali', 'Nassir Navab'] | 2017-03-16 | null | null | null | null | ['pose-retrieval'] | ['computer-vision'] | [ 2.70125717e-01 3.89463663e-01 -6.48078442e-01 -4.48317438e-01
-1.97823942e+00 -2.46316463e-01 4.18789715e-01 7.35810757e-01
-2.17425644e-01 5.72050214e-01 3.03243816e-01 2.30465215e-02
-3.76904398e-01 -7.26986647e-01 -6.79404557e-01 -1.12458396e+00
-5.00206470e-01 7.88347602e-01 2.26225078e-01 2.68429015... | [11.374991416931152, 0.9001979231834412] |
21831710-6dec-4a5f-8c7d-d982ce964aa7 | proper-scoring-rules-for-survival-analysis | 2305.00621 | null | https://arxiv.org/abs/2305.00621v3 | https://arxiv.org/pdf/2305.00621v3.pdf | Proper Scoring Rules for Survival Analysis | Survival analysis is the problem of estimating probability distributions for future event times, which can be seen as a problem in uncertainty quantification. Although there are fundamental theories on strictly proper scoring rules for uncertainty quantification, little is known about those for survival analysis. In th... | ['Hiroki Yanagisawa'] | 2023-05-01 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-5.23235612e-02 1.41510665e-01 -2.25499630e-01 -4.54963326e-01
-8.44868481e-01 -4.13629442e-01 3.68468910e-01 3.96401495e-01
-5.26835144e-01 1.48599911e+00 1.81576505e-01 -4.63681847e-01
-6.95615590e-01 -8.68129015e-01 -2.39431396e-01 -8.65431309e-01
-4.93235826e-01 6.21227086e-01 4.18223768e-01 1.80796196... | [7.608887672424316, 4.61508846282959] |
34e1cf91-826f-41d9-87b6-05769b21c7a3 | distilled-reverse-attention-network-for-open | 2303.00404 | null | https://arxiv.org/abs/2303.00404v1 | https://arxiv.org/pdf/2303.00404v1.pdf | Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning | Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects. In OW-CZSL, methods built on the conventional closed-world setting degrade severely due to the unconstrained OW test space. While previous works alleviate the issue by pruning compositions according ... | ['Lina Yao', 'Sally Cripps', 'Saurav Jha', 'Zhe Liu', 'Yun Li'] | 2023-03-01 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 8.61623958e-02 1.98641628e-01 -1.95957258e-01 -2.49823496e-01
-5.64385772e-01 -6.38255239e-01 8.77579510e-01 -1.41620815e-01
-2.03621522e-01 7.59600282e-01 4.60955620e-01 5.97487539e-02
2.18606591e-02 -1.05209196e+00 -9.81255054e-01 -7.79954910e-01
-8.79595354e-02 7.87308514e-01 2.28548437e-01 1.30157441... | [10.249664306640625, 2.2448463439941406] |
51e472c7-618b-4b11-b5f2-26ba6e5bd642 | ss-shapelets-semi-supervised-clustering-of | 2304.03292 | null | https://arxiv.org/abs/2304.03292v1 | https://arxiv.org/pdf/2304.03292v1.pdf | SS-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets | Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from a large pool of uninformative subsequences, and thus result in low clustering ac... | ['Chi-Hung Chi', 'Yong Xiang', 'Shuiqiao Yang', 'Guangyan Huang', 'Borui Cai'] | 2023-04-06 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-2.16909990e-01 -8.43180120e-01 -4.30572294e-02 -1.61011368e-01
-9.15916681e-01 -9.26849067e-01 2.36816645e-01 3.15583706e-01
-8.52402393e-03 1.91793337e-01 7.33079910e-02 -5.40179238e-02
-6.36609435e-01 -5.54854155e-01 -1.11625277e-01 -1.12743175e+00
-8.28775048e-01 4.74335492e-01 2.16140598e-01 -8.81601125... | [7.291308403015137, 3.3595988750457764] |
7fcaafdb-77ab-4cca-9d1c-600ae699add5 | a-pde-approach-to-the-prediction-of-a-binary | 2007.12732 | null | https://arxiv.org/abs/2007.12732v1 | https://arxiv.org/pdf/2007.12732v1.pdf | A PDE Approach to the Prediction of a Binary Sequence with Advice from Two History-Dependent Experts | The prediction of a binary sequence is a classic example of online machine learning. We like to call it the 'stock prediction problem,' viewing the sequence as the price history of a stock that goes up or down one unit at each time step. In this problem, an investor has access to the predictions of two or more 'experts... | ['Nadejda Drenska', 'Robert V. Kohn'] | 2020-07-24 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [ 1.76750913e-01 6.14959121e-01 -2.99851865e-01 2.95020118e-02
-5.20780742e-01 -8.15871119e-01 1.88903920e-02 2.69180804e-01
-6.27337813e-01 1.12074220e+00 -1.26805544e-01 -2.90898621e-01
-3.54246944e-01 -7.11198926e-01 -9.37297404e-01 -7.68899798e-01
-5.14433563e-01 6.37296319e-01 2.09039405e-01 -3.79245013... | [4.550292015075684, 3.2402713298797607] |
d6837479-5117-4034-8383-21f5590e3302 | hand-guided-high-resolution-feature | 2211.13694 | null | https://arxiv.org/abs/2211.13694v1 | https://arxiv.org/pdf/2211.13694v1.pdf | Hand Guided High Resolution Feature Enhancement for Fine-Grained Atomic Action Segmentation within Complex Human Assemblies | Due to the rapid temporal and fine-grained nature of complex human assembly atomic actions, traditional action segmentation approaches requiring the spatial (and often temporal) down sampling of video frames often loose vital fine-grained spatial and temporal information required for accurate classification within the ... | ['Nicholas Martin', 'Stephen McGough', 'Nick Wright', 'Matthew Kent Myers'] | 2022-11-24 | null | null | null | null | ['action-classification', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 8.87580335e-01 -1.58501074e-01 -1.21936940e-01 -2.75847048e-01
-8.14957738e-01 -5.14753878e-01 5.86312294e-01 -6.36545345e-02
-2.81525850e-01 6.27233565e-01 4.42314632e-02 2.08280504e-01
-4.12964493e-01 -4.84509051e-01 -6.95541024e-01 -5.53446233e-01
-2.90957063e-01 8.33397388e-01 7.32590914e-01 -1.58182949... | [7.968472957611084, 0.3052528202533722] |
d99d1e5d-46f6-4f4e-bde3-ff3038db908f | gpgait-generalized-pose-based-gait | 2303.05234 | null | https://arxiv.org/abs/2303.05234v1 | https://arxiv.org/pdf/2303.05234v1.pdf | GPGait: Generalized Pose-based Gait Recognition | Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has rece... | ['Yongzhen Huang', 'Xuecai Hu', 'Saihui Hou', 'Shibei Meng', 'Yang Fu'] | 2023-03-09 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-1.39225453e-01 -5.21581590e-01 -1.13692895e-01 -2.27809057e-01
-5.25193810e-01 -3.01651567e-01 4.29780483e-01 8.66165981e-02
-3.04398447e-01 6.31294131e-01 3.98140252e-02 2.69163609e-01
-3.14291328e-01 -8.78101587e-01 -2.97792673e-01 -6.75362587e-01
-5.12610793e-01 4.71192837e-01 5.68601489e-01 -4.54536885... | [14.284676551818848, 1.424831509590149] |
d63e187d-fe8b-40bf-9c07-4e72d09df357 | fully-convolutional-network-with-multi-step | 1811.04323 | null | http://arxiv.org/abs/1811.04323v2 | http://arxiv.org/pdf/1811.04323v2.pdf | Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing | This paper tackles a new problem setting: reinforcement learning with
pixel-wise rewards (pixelRL) for image processing. After the introduction of
the deep Q-network, deep RL has been achieving great success. However, the
applications of deep RL for image processing are still limited. Therefore, we
extend deep RL to pi... | ['Naoto Inoue', 'Ryosuke Furuta', 'Toshihiko Yamasaki'] | 2018-11-10 | null | null | null | null | ['local-color-enhancement'] | ['computer-vision'] | [ 4.70021337e-01 -8.29039142e-02 -1.58877835e-01 -1.43504411e-01
-5.35898268e-01 4.21831161e-02 2.14121476e-01 -4.68777791e-02
-7.84538209e-01 8.98860633e-01 -3.01009536e-01 -2.10591868e-01
4.60200086e-02 -9.16384578e-01 -6.98103607e-01 -1.21412885e+00
8.64079630e-04 -2.78255254e-01 2.45913789e-01 -9.41752717... | [11.239496231079102, -1.4830424785614014] |
56cb7fd1-2c4c-488d-a648-b374da032e31 | convolutional-neural-networks-based-remote | null | null | https://ieeexplore.ieee.org/document/9607791 | https://openaccess.thecvf.com/content/ICCV2021W/LUAI/papers/Sun_Convolutional_Neural_Networks_Based_Remote_Sensing_Scene_Classification_Under_Clear_ICCVW_2021_paper.pdf | Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments | Remote sensing (RS) scene classification has wide ap- plications in the environmental monitoring and geological survey. In the real-world applications, the RS scene images taken by the satellite might have two scenarios: clear and cloudy environments. However, most of existing methods did not consider these two environ... | ['Hongkai Yu1∗', 'Jianwu Fang5', 'Shaoyue Song4', 'Qin Zou3', 'Yuewei Lin2', 'Huiming Sun1'] | 2021-12-30 | null | null | null | iccvw-2021-12 | ['scene-classification'] | ['computer-vision'] | [ 1.06168361e-02 -6.52810156e-01 2.12113634e-01 -8.74409676e-01
-2.73584872e-01 -1.59716979e-01 4.09078956e-01 -2.04549551e-01
-4.83249158e-01 6.94185793e-01 -1.46374464e-01 -4.04972643e-01
-2.66989052e-01 -1.31614220e+00 -3.31649691e-01 -1.03986645e+00
-2.57525682e-01 -8.58632941e-03 2.17537850e-01 -3.96805763... | [9.767596244812012, -1.5223957300186157] |
8abd159b-d1a2-4524-855e-e386f032ddbb | improved-target-specific-stance-detection-on | 2211.03061 | null | https://arxiv.org/abs/2211.03061v1 | https://arxiv.org/pdf/2211.03061v1.pdf | Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads | Target-specific stance detection on social media, which aims at classifying a textual data instance such as a post or a comment into a stance class of a target issue, has become an emerging opinion mining paradigm of importance. An example application would be to overcome vaccine hesitancy in combating the coronavirus ... | ['Yunya Song', 'Francis C. M. Lau', 'Shaonan Wang', 'Haorui He', 'Yupeng Li'] | 2022-11-06 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 4.81155992e-01 2.92068124e-01 -4.99359876e-01 -4.64744985e-01
-8.37467134e-01 -6.37202322e-01 1.07367373e+00 4.84914452e-01
-1.31499991e-01 6.13029003e-01 6.10616207e-01 -5.21550179e-01
4.19667691e-01 -8.96910131e-01 -4.05553758e-01 -7.45774150e-01
3.91771607e-02 7.14202225e-01 2.47719347e-01 -5.47263920... | [8.675176620483398, 9.696670532226562] |
d88f96a7-5ae1-41fc-b448-c1277d3e1f56 | user-level-membership-inference-attack | 2203.02077 | null | https://arxiv.org/abs/2203.02077v2 | https://arxiv.org/pdf/2203.02077v2.pdf | User-Level Membership Inference Attack against Metric Embedding Learning | Membership inference (MI) determines if a sample was part of a victim model training set. Recent development of MI attacks focus on record-level membership inference which limits their application in many real-world scenarios. For example, in the person re-identification task, the attacker (or investigator) is interest... | ['Xin Liu', 'Shahbaz Rezaei', 'Guoyao Li'] | 2022-03-04 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.47991496e-01 -4.81780082e-01 -2.29558647e-01 -4.31490868e-01
-4.68053937e-01 -6.84933603e-01 5.56428194e-01 3.02905202e-01
-8.36768329e-01 6.23984933e-01 -2.45584369e-01 -3.89556915e-01
-1.30163789e-01 -9.56250727e-01 -4.78010744e-01 -5.35291553e-01
-1.56257182e-01 5.91388047e-01 -1.31276269e-02 2.89029866... | [5.8733625411987305, 7.265180587768555] |
5af30ecc-5835-4d96-b634-f0bfe31e8328 | does-recommend-revise-produce-reliable | 2204.07980 | null | https://arxiv.org/abs/2204.07980v1 | https://arxiv.org/pdf/2204.07980v1.pdf | Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED | DocRED is a widely used dataset for document-level relation extraction. In the large-scale annotation, a \textit{recommend-revise} scheme is adopted to reduce the workload. Within this scheme, annotators are provided with candidate relation instances from distant supervision, and they then manually supplement and remov... | ['Dongyan Zhao', 'Yansong Feng', 'Shengqi Zhu', 'Yuan Ye', 'Shibo Hao', 'Quzhe Huang'] | 2022-04-17 | null | https://aclanthology.org/2022.acl-long.432 | https://aclanthology.org/2022.acl-long.432.pdf | acl-2022-5 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-9.30906609e-02 7.05859661e-01 -6.57282770e-01 -5.21607280e-01
-7.64106572e-01 -6.34658813e-01 5.71613908e-01 5.28323174e-01
-5.06481647e-01 9.78809178e-01 3.89272571e-01 -2.88756102e-01
-1.98949367e-01 -6.47338331e-01 -4.44037408e-01 -3.00235808e-01
3.71554285e-01 7.73207188e-01 3.78519505e-01 -3.14572424... | [9.437792778015137, 8.640647888183594] |
39f7cc92-184d-468f-a161-7fdab3ac5469 | enhancing-topic-modeling-for-short-texts-with | null | null | https://openreview.net/forum?id=SklnH9VoeV | https://openreview.net/pdf?id=SklnH9VoeV | Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings | Many applications require semantic understanding of short texts, and inferring discriminative and coherent latent topics is a critical and fundamental task in these applications. Conventional topic models largely rely on word co-occurrences to derive topics from a collection of documents. However, due to the length of ... | ['Zongyang Ma', 'Aixin Sun', 'Zhiqian Zhang', 'Haoran Wang', 'Yu Duan', 'Chenliang Li'] | 2018-12-22 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.01508059e-01 4.41720225e-02 -4.57700640e-01 -3.37906122e-01
-5.47253549e-01 -3.68619353e-01 8.67163301e-01 3.66466105e-01
-2.71413058e-01 4.50875610e-01 4.80226785e-01 -2.37342313e-01
2.31713623e-01 -1.31588709e+00 -5.56709588e-01 -6.89268887e-01
4.04014856e-01 8.48235428e-01 2.23142341e-01 -7.43278116... | [10.395795822143555, 6.94358491897583] |
4437c720-1f8e-4958-9d2c-529e3e63c898 | scalable-variational-bayes-methods-for-hawkes | 2212.00293 | null | https://arxiv.org/abs/2212.00293v1 | https://arxiv.org/pdf/2212.00293v1.pdf | Scalable Variational Bayes methods for Hawkes processes | Multivariate Hawkes processes are temporal point processes extensively applied to model event data with dependence on past occurrences and interaction phenomena. In the generalised nonlinear model, positive and negative interactions between the components of the process are allowed, therefore accounting for so-called e... | ['Judith Rousseau', 'Vincent Rivoirard', 'Deborah Sulem'] | 2022-12-01 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 4.71479446e-01 2.17015091e-02 5.22432886e-02 2.01211765e-01
-4.15363312e-01 -4.76002902e-01 7.70784378e-01 1.50295004e-01
-3.35700721e-01 7.27913201e-01 7.28226751e-02 -7.64249042e-02
-5.17030716e-01 -7.26958394e-01 -7.67534852e-01 -1.15521026e+00
-2.03925055e-02 7.49771059e-01 2.21062347e-01 -6.84213033... | [6.8583760261535645, 3.806253433227539] |
2b360654-e15d-4302-8dd8-59f8e5b1f617 | mapping-urban-population-growth-from-sentinel | 2303.08511 | null | https://arxiv.org/abs/2303.08511v1 | https://arxiv.org/pdf/2303.08511v1.pdf | Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda | To better understand current trends of urban population growth in Sub-Saharan Africa, high-quality spatiotemporal population estimates are necessary. While the joint use of remote sensing and deep learning has achieved promising results for population distribution estimation, most of the current work focuses on fine-sc... | ['Yifang Ban', 'Theodomir Mugiraneza', 'Stefanos Georganos', 'Sebastian Hafner'] | 2023-03-15 | null | null | null | null | ['change-detection', 'population-mapping'] | ['computer-vision', 'computer-vision'] | [-1.32444128e-01 -1.94447532e-01 -2.47161482e-02 -2.99785256e-01
-5.57973981e-01 2.56347116e-02 8.17920983e-01 3.62314492e-01
-8.62108529e-01 1.16929126e+00 7.22019255e-01 -5.78939736e-01
1.55451939e-01 -1.52450359e+00 -6.97574735e-01 -5.94591379e-01
-7.33264267e-01 5.86560726e-01 -2.81999826e-01 -4.82881814... | [9.351592063903809, -1.2285393476486206] |
221f869f-6fda-4e5d-a9e6-2aa9050ca9d2 | anatomy-x-net-a-semi-supervised-anatomy-aware | 2106.05915 | null | https://arxiv.org/abs/2106.05915v3 | https://arxiv.org/pdf/2106.05915v3.pdf | Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays | Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsible for significant contributions to the model's prediction. In contrast... | ['Taufiq Hasan', 'Nusrat Binta Nizam', 'Mohammad Zunaed', 'Uday Kamal'] | 2021-06-10 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 3.52557264e-02 3.18873614e-01 -3.59315425e-01 -2.58062840e-01
-1.09514141e+00 -4.53219861e-01 1.63285494e-01 2.97742367e-01
-3.16422999e-01 5.66897154e-01 4.16920304e-01 -5.88712633e-01
-4.21200424e-01 -6.36714518e-01 -5.60331881e-01 -8.33298087e-01
-8.90499353e-03 3.78633618e-01 4.35872495e-01 3.91096681... | [15.149526596069336, -2.1578755378723145] |
7d6820f0-e4e3-49dd-91d5-b83ce3f4c2a3 | transfuse-a-unified-transformer-based-image | 2201.07451 | null | https://arxiv.org/abs/2201.07451v1 | https://arxiv.org/pdf/2201.07451v1.pdf | TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning | Image fusion is a technique to integrate information from multiple source images with complementary information to improve the richness of a single image. Due to insufficient task-specific training data and corresponding ground truth, most existing end-to-end image fusion methods easily fall into overfitting or tedious... | ['Zhijian Song', 'Qin Qiao', 'Siqi Yin', 'Shiman Li', 'Manning Wang', 'Shaolei Liu', 'Linhao Qu'] | 2022-01-19 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 4.70706671e-01 -3.93150717e-01 -3.80738564e-02 -4.47609842e-01
-1.21962821e+00 -2.73032874e-01 3.92994434e-01 -2.97248363e-01
-4.92503315e-01 7.02534735e-01 3.16456020e-01 3.70213650e-02
1.45918950e-01 -6.51632428e-01 -7.65583158e-01 -9.28033412e-01
5.21765828e-01 -1.99134991e-01 9.29958150e-02 -3.38294655... | [10.572776794433594, -1.8323408365249634] |
eeef857c-23ba-4492-b44c-cdf8a4175c7a | causality-based-neural-network-repair | 2204.09274 | null | https://arxiv.org/abs/2204.09274v2 | https://arxiv.org/pdf/2204.09274v2.pdf | Causality-based Neural Network Repair | Neural networks have had discernible achievements in a wide range of applications. The wide-spread adoption also raises the concern of their dependability and reliability. Similar to traditional decision-making programs, neural networks can have defects that need to be repaired. The defects may cause unsafe behaviors, ... | ['Jie Shi', 'Hong Long Pham', 'Jun Sun', 'Bing Sun'] | 2022-04-20 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [ 2.22214699e-01 2.80119956e-01 -2.96819836e-01 -3.71642083e-01
-1.15698121e-01 -5.22633970e-01 -1.03374749e-01 6.72983006e-02
-3.76996160e-01 7.51561582e-01 -3.30960810e-01 -7.40103722e-01
-5.35554528e-01 -8.60089362e-01 -1.09400284e+00 -6.05862617e-01
-1.66486800e-01 -2.19299853e-01 1.24937542e-01 -5.12286648... | [6.28218936920166, 7.673478603363037] |
b538c5e4-8a8e-46bb-9ff4-3e89420a37ad | self-supervised-in-domain-representation | 2302.01793 | null | https://arxiv.org/abs/2302.01793v1 | https://arxiv.org/pdf/2302.01793v1.pdf | Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification | Transferring the ImageNet pre-trained weights to the various remote sensing tasks has produced acceptable results and reduced the need for labeled samples. However, the domain differences between ground imageries and remote sensing images cause the performance of such transfer learning to be limited. Recent research ha... | ['Hossein Soleimani', 'Ali Ghanbarzade'] | 2023-02-03 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 4.38448966e-01 -2.60013819e-01 -2.01414555e-01 -8.48605812e-01
-4.84216183e-01 -5.40464401e-01 6.46035075e-01 2.04024106e-01
-7.12608695e-01 8.09079707e-01 -4.95942608e-02 -3.27634603e-01
-3.65380824e-01 -1.23961401e+00 -6.93069160e-01 -6.51790380e-01
-5.12561798e-01 2.37417385e-01 1.02887653e-01 -2.86184192... | [9.638460159301758, -1.4101550579071045] |
9b808a1c-a081-473b-be65-a97912bab04b | divide-and-conquer-answering-questions-with | 2303.10482 | null | https://arxiv.org/abs/2303.10482v1 | https://arxiv.org/pdf/2303.10482v1.pdf | Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning | Humans have the innate capability to answer diverse questions, which is rooted in the natural ability to correlate different concepts based on their semantic relationships and decompose difficult problems into sub-tasks. On the contrary, existing visual reasoning methods assume training samples that capture every possi... | ['Qi Zhao', 'Shi Chen'] | 2023-03-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Divide_and_Conquer_Answering_Questions_With_Object_Factorization_and_Compositional_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Divide_and_Conquer_Answering_Questions_With_Object_Factorization_and_Compositional_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 8.93592462e-02 2.24499658e-01 -1.64989009e-01 -5.63259184e-01
-2.54110485e-01 -6.34073853e-01 7.39907742e-01 1.87945306e-01
-2.36622930e-01 3.29060793e-01 3.52926403e-01 -2.46143237e-01
-4.18098986e-01 -9.54589188e-01 -4.69220757e-01 -3.34521860e-01
4.87024486e-01 7.51671493e-01 2.18172684e-01 -3.03013146... | [10.568346977233887, 1.9807459115982056] |
b01e91a9-acc4-43f0-9a8c-54525e8f2c47 | metal-conscious-embedding-for-cbct-projection | 2211.16219 | null | https://arxiv.org/abs/2211.16219v1 | https://arxiv.org/pdf/2211.16219v1.pdf | Metal-conscious Embedding for CBCT Projection Inpainting | The existence of metallic implants in projection images for cone-beam computed tomography (CBCT) introduces undesired artifacts which degrade the quality of reconstructed images. In order to reduce metal artifacts, projection inpainting is an essential step in many metal artifact reduction algorithms. In this work, a h... | ['Andreas Maier', 'Steffen Kappler', 'Yixing Huang', 'Björn Kreher', 'Marcel Beister', 'Ramyar Biniazan', 'Ludwig Ritschl', 'Yangkong Wang', 'Fuxin Fan'] | 2022-11-29 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 1.9932906e-01 2.1428755e-01 2.6022565e-01 -1.0242654e-01
-9.3998021e-01 2.0998085e-01 1.3196726e-01 -2.6171008e-01
-3.5855642e-01 6.0690123e-01 4.9510679e-01 5.4743197e-02
3.4048578e-03 -7.0206922e-01 -6.9593096e-01 -8.2307994e-01
3.1712115e-01 -6.6748902e-02 4.3181244e-01 -1.2561165e-01
7.7864185e-02... | [13.50937271118164, -2.5305962562561035] |
6b551a2c-d195-462e-8d01-3600506ff716 | one-shot-federated-learning-for-leo | 2305.12316 | null | https://arxiv.org/abs/2305.12316v1 | https://arxiv.org/pdf/2305.12316v1.pdf | One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes | A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft trac... | ['Tie Luo', 'Mohamed Elmahallawy'] | 2023-05-21 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-3.61150235e-01 -7.13823512e-02 -2.76350468e-01 -5.47150262e-02
-5.95806539e-01 -7.72529423e-01 6.58765018e-01 -1.07068926e-01
-4.63155061e-01 1.29648149e+00 -4.37779456e-01 -5.89195192e-01
-4.81773674e-01 -7.97873974e-01 -8.11986685e-01 -1.01705933e+00
-8.37405264e-01 1.00115967e+00 3.79540235e-01 -2.27127120... | [5.954718112945557, 5.825336456298828] |
5d23600b-0682-4f7c-bc2d-ae13e9acea56 | r3sgm-real-time-raster-respecting-semi-global | 1810.12988 | null | http://arxiv.org/abs/1810.12988v1 | http://arxiv.org/pdf/1810.12988v1.pdf | R$^3$SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems | Stereo depth estimation is used for many computer vision applications. Though
many popular methods strive solely for depth quality, for real-time mobile
applications (e.g. prosthetic glasses or micro-UAVs), speed and power
efficiency are equally, if not more, important. Many real-world systems rely on
Semi-Global Match... | ['Simon Walker', 'Tommaso Cavallari', 'Philip H. S. Torr', 'Oscar Rahnama', 'Stuart Golodetz'] | 2018-10-30 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 4.31441456e-01 -1.56571835e-01 1.63797252e-02 -1.38769925e-01
-1.41045272e-01 -2.69517064e-01 3.76565814e-01 5.95485978e-02
-4.56986845e-01 4.64356214e-01 -3.03622723e-01 -4.36966509e-01
-4.18473594e-02 -9.97500956e-01 -4.51307565e-01 -5.12423933e-01
1.90311149e-01 9.53945071e-02 6.85320556e-01 -8.32856223... | [9.040251731872559, -2.241380453109741] |
e747bf9b-d3df-40d0-bed3-defff32d1215 | domain-adaptive-scene-text-detection-via | 2212.00377 | null | https://arxiv.org/abs/2212.00377v1 | https://arxiv.org/pdf/2212.00377v1.pdf | Domain Adaptive Scene Text Detection via Subcategorization | Most existing scene text detectors require large-scale training data which cannot scale well due to two major factors: 1) scene text images often have domain-specific distributions; 2) collecting large-scale annotated scene text images is laborious. We study domain adaptive scene text detection, a largely neglected yet... | ['Shijian Lu', 'Jingyi Zhang', 'Chuhui Xue', 'Zichen Tian'] | 2022-12-01 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 7.05188811e-01 -2.25719646e-01 -4.23583120e-01 -5.68156660e-01
-8.25830340e-01 -6.01282418e-01 7.27416158e-01 -3.91242355e-02
-5.28084815e-01 2.35483110e-01 3.23505737e-02 -2.88716942e-01
4.72379953e-01 -3.91729593e-01 -7.52893806e-01 -6.86630607e-01
6.61261678e-01 7.29232907e-01 1.09203732e+00 6.13276213... | [9.552106857299805, 1.5700204372406006] |
e61ae4d7-e96c-4be2-a8a9-c9c044ec0374 | qimera-data-free-quantization-with-synthetic | 2111.02625 | null | https://arxiv.org/abs/2111.02625v1 | https://arxiv.org/pdf/2111.02625v1.pdf | Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples | Model quantization is known as a promising method to compress deep neural networks, especially for inferences on lightweight mobile or edge devices. However, model quantization usually requires access to the original training data to maintain the accuracy of the full-precision models, which is often infeasible in real-... | ['Jinho Lee', 'Youngsok Kim', 'Noseong Park', 'Deokki Hong', 'Kanghyun Choi'] | 2021-11-04 | null | http://proceedings.neurips.cc/paper/2021/hash/7cc234202e98d2722580858573fd0817-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/7cc234202e98d2722580858573fd0817-Paper.pdf | neurips-2021-12 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 1.14255033e-01 -4.99394797e-02 -5.06791711e-01 -3.07182610e-01
-7.72510767e-01 -2.88252354e-01 4.51219112e-01 -8.12445208e-02
-5.21389365e-01 6.44326150e-01 1.68401554e-01 -2.36811906e-01
1.54218629e-01 -9.60549116e-01 -8.38696361e-01 -8.35067034e-01
2.32821092e-01 1.95641592e-01 -9.90848541e-02 9.21288598... | [8.739372253417969, 3.0051422119140625] |
65a20f02-a105-4951-b18e-9ab6925f2fdf | generating-coherent-drum-accompaniment-with | 2209.00291 | null | https://arxiv.org/abs/2209.00291v1 | https://arxiv.org/pdf/2209.00291v1.pdf | Generating Coherent Drum Accompaniment With Fills And Improvisations | Creating a complex work of art like music necessitates profound creativity. With recent advancements in deep learning and powerful models such as transformers, there has been huge progress in automatic music generation. In an accompaniment generation context, creating a coherent drum pattern with apposite fills and imp... | ['Prateek Verma', 'Preeti Rao', 'Vaibhav Talwadker', 'Rishabh Dahale'] | 2022-09-01 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.98568064e-01 -2.21466824e-01 2.81898111e-01 1.72176138e-01
-5.43989956e-01 -8.61226976e-01 4.76383448e-01 -4.34872031e-01
1.87858760e-01 6.60521030e-01 5.51690221e-01 7.21987709e-03
-1.88448355e-01 -6.08008742e-01 -7.64330506e-01 -6.73330188e-01
2.48534352e-01 5.50716519e-01 -5.23105673e-02 -7.11086392... | [16.032821655273438, 5.547758102416992] |
6049f7a1-06c6-4bed-8e8c-6b6203c57d81 | using-intermediate-representations-to-solve | null | null | https://aclanthology.org/P18-1039 | https://aclanthology.org/P18-1039.pdf | Using Intermediate Representations to Solve Math Word Problems | To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system. However, such mappings do not include the information of a few higher-order operations that cannot be explicitly represented in equations but are required to... | ['Chin-Yew Lin', 'Jin-Ge Yao', 'Jian Yin', 'Danqing Huang', 'Qingyu Zhou'] | 2018-07-01 | null | null | null | acl-2018-7 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 4.26826477e-01 1.16409771e-01 -3.23787093e-01 -7.61099935e-01
-5.48272252e-01 -7.64519751e-01 3.12566698e-01 2.30229691e-01
-1.72765523e-01 5.51764667e-01 1.12307481e-01 -5.82868993e-01
-4.27580774e-02 -8.56660366e-01 -8.35694313e-01 -1.85785949e-01
3.92197490e-01 3.48082870e-01 -1.28516838e-01 -1.33005276... | [9.666084289550781, 7.4859418869018555] |
248a018c-ca0f-4ca1-ba81-6a5f9b942b3d | dialoguecrn-contextual-reasoning-networks-for | 2106.01978 | null | https://arxiv.org/abs/2106.01978v2 | https://arxiv.org/pdf/2106.01978v2.pdf | DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations | Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines. Recently, many approaches have been devoted to perceiving conversational context by deep learning models. However, these approaches are insufficient in understanding the context due to lacking the ability to e... | ['Xiaoyong Huai', 'Lingwei Wei', 'Dou Hu'] | 2021-06-03 | null | https://aclanthology.org/2021.acl-long.547 | https://aclanthology.org/2021.acl-long.547.pdf | acl-2021-5 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-2.32003748e-01 1.28231898e-01 1.43334314e-01 -6.06670856e-01
-1.47797525e-01 -2.87336707e-01 6.32057011e-01 -1.31956171e-02
-7.80915767e-02 4.95610803e-01 5.96203804e-01 -1.62045240e-01
1.21091614e-02 -7.16161847e-01 1.77900180e-01 -3.66495103e-01
5.16325116e-01 7.77316168e-02 -4.31371152e-01 -6.17347240... | [12.99083137512207, 6.534669399261475] |
ba61c107-626a-450e-96e1-38205f27aec7 | potsdam-semantic-dependency-parsing-by | null | null | https://aclanthology.org/S14-2081 | https://aclanthology.org/S14-2081.pdf | Potsdam: Semantic Dependency Parsing by Bidirectional Graph-Tree Transformations and Syntactic Parsing | null | ['er', "{\\v{Z}}eljko Agi{\\'c}", 'Alex Koller'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.388085842132568, 3.915992498397827] |
69a1faba-bbcb-4510-a6de-62de27163623 | gldqn-explicitly-parameterized-quantile | 2205.15455 | null | https://arxiv.org/abs/2205.15455v2 | https://arxiv.org/pdf/2205.15455v2.pdf | A Simulation Environment and Reinforcement Learning Method for Waste Reduction | In retail (e.g., grocery stores, apparel shops, online retailers), inventory managers have to balance short-term risk (no items to sell) with long-term-risk (over ordering leading to product waste). This balancing task is made especially hard due to the lack of information about future customer purchases. In this paper... | ['Maarten de Rijke', 'Paul Groth', 'Mozhdeh Ariannezhad', 'Sami Jullien'] | 2022-05-30 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.84020305e-01 1.67987540e-01 -5.44961095e-01 -2.67687470e-01
-5.57610512e-01 -6.16584361e-01 9.45821851e-02 5.38862109e-01
-6.68882132e-01 9.29477751e-01 1.65855944e-01 -2.81561643e-01
-6.46428823e-01 -1.11092746e+00 -1.08767295e+00 -8.52386296e-01
-2.44243965e-01 1.09067500e+00 -1.87103167e-01 -3.57360005... | [4.2824883460998535, 2.565004825592041] |
23e845dd-d618-4441-a44c-a58bf487369f | novel-view-synthesis-from-single-images-via | 2009.08321 | null | https://arxiv.org/abs/2009.08321v2 | https://arxiv.org/pdf/2009.08321v2.pdf | Novel View Synthesis from Single Images via Point Cloud Transformation | In this paper the argument is made that for true novel view synthesis of objects, where the object can be synthesized from any viewpoint, an explicit 3D shape representation isdesired. Our method estimates point clouds to capture the geometry of the object, which can be freely rotated into the desired view and then pro... | ['Theo Gevers', 'Hoang-An Le', 'Thomas Mensink', 'Partha Das'] | 2020-09-17 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 2.52764255e-01 5.23882687e-01 2.17415795e-01 -3.39978486e-01
-4.20448691e-01 -8.53153288e-01 7.15878963e-01 -5.06711125e-01
5.63628711e-02 2.98557699e-01 7.45150670e-02 3.84268537e-02
4.17168468e-01 -7.44224906e-01 -1.03163111e+00 -6.89869821e-01
7.18208969e-01 8.71215105e-01 8.10317993e-02 -1.01871714... | [8.718608856201172, -3.0444841384887695] |
e7c1dcf6-c7f8-4419-9cb0-238ae78dae53 | data-centric-ai-approach-to-improve-optic | 2208.03868 | null | https://arxiv.org/abs/2208.03868v1 | https://arxiv.org/pdf/2208.03868v1.pdf | Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images | The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize classifier training for optic nerve head detection and localization in optical coherence tomography en face images of the retina. We examine ... | ['Tilman Schmoll', 'Rainer A. Leitgeb', 'Wolfgang Drexler', 'Ursula Schmidt-Erfurth', 'Andreas Pollreisz', 'Michael Niederleithner', 'Heiko Stino', 'Thomas Schlegl'] | 2022-08-08 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 2.71258920e-01 1.43269256e-01 -4.45149727e-02 -4.20170814e-01
-6.69523478e-01 -5.71366668e-01 2.26387352e-01 -3.74865681e-01
-8.95061314e-01 7.47594535e-01 1.78702116e-01 -4.84544903e-01
-4.23704147e-01 -1.75054967e-01 -3.17923486e-01 -6.35111392e-01
-2.10170131e-02 4.64181006e-01 2.97416925e-01 5.16568899... | [15.808899879455566, -3.9860026836395264] |
4e244506-1c6b-4062-8591-fd268cbca989 | reading-to-listen-at-the-cocktail-party-multi | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Rahimi_Reading_To_Listen_at_the_Cocktail_Party_Multi-Modal_Speech_Separation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Rahimi_Reading_To_Listen_at_the_Cocktail_Party_Multi-Modal_Speech_Separation_CVPR_2022_paper.pdf | Reading To Listen at the Cocktail Party: Multi-Modal Speech Separation | The goal of this paper is speech separation and enhancement in multi-speaker and noisy environments using a combination of different modalities. Previous works have shown good performance when conditioning on temporal or static visual evidence such as synchronised lip movements or face identity. In this paper we pr... | ['Andrew Zisserman', 'Triantafyllos Afouras', 'Akam Rahimi'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['speech-separation'] | ['speech'] | [ 5.19621670e-01 -2.09084779e-01 1.62250832e-01 -2.04677895e-01
-1.31514359e+00 -6.02547944e-01 9.82001960e-01 2.52209231e-02
-2.83279657e-01 2.85629660e-01 4.76341724e-01 -2.01652169e-01
-2.31628627e-01 4.56979126e-02 -5.01118600e-01 -7.54670858e-01
1.36059687e-01 -1.57110468e-01 2.88261741e-01 -3.31250131... | [14.41385555267334, 5.116034507751465] |
04a76f3e-151e-422b-910d-a51dc3aafdb0 | multi-sem-fusion-multimodal-semantic-fusion | 2212.05265 | null | https://arxiv.org/abs/2212.05265v2 | https://arxiv.org/pdf/2212.05265v2.pdf | Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection | LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2D feature maps imped... | ['Zhi-Xin Yang', 'Sifen Wang', 'Jin Fang', 'Ziying Song', 'Fang Li', 'Shaoqing Xu'] | 2022-12-10 | null | null | null | null | ['scene-parsing', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 8.33427459e-02 -1.93862960e-01 3.57790403e-02 -4.96509045e-01
-1.14634180e+00 -4.54721093e-01 6.40810490e-01 1.87907014e-02
-5.44863045e-01 -3.53730917e-02 -3.44554067e-01 -2.25011766e-01
1.29714280e-01 -8.73048007e-01 -9.46219027e-01 -5.52068949e-01
5.56107104e-01 5.53930700e-01 8.83887947e-01 -1.79262802... | [7.8376336097717285, -2.5247671604156494] |
85fa50e0-23db-4b7f-b32d-89313f40c76f | bidirectional-lstm-crf-for-clinical-concept-1 | 1610.05858 | null | http://arxiv.org/abs/1610.05858v1 | http://arxiv.org/pdf/1610.05858v1.pdf | Bidirectional LSTM-CRF for Clinical Concept Extraction | Extraction of concepts present in patient clinical records is an essential
step in clinical research. The 2010 i2b2/VA Workshop on Natural Language
Processing Challenges for clinical records presented concept extraction (CE)
task, with aim to identify concepts (such as treatments, tests, problems) and
classify them int... | ['Raghavendra Chalapathy', 'Massimo Piccardi', 'Ehsan Zare Borzeshi'] | 2016-10-19 | bidirectional-lstm-crf-for-clinical-concept-2 | https://aclanthology.org/W16-4202 | https://aclanthology.org/W16-4202.pdf | ws-2016-12 | ['clinical-concept-extraction'] | ['medical'] | [ 2.44157240e-01 1.10028662e-01 -5.70387483e-01 -4.78832006e-01
-1.14387691e+00 -2.51969099e-01 5.11596859e-01 1.13221300e+00
-1.02149975e+00 9.40261543e-01 6.05722427e-01 -5.73064148e-01
-1.51571780e-01 -5.74234426e-01 -1.64210171e-01 -6.29833877e-01
-2.12081984e-01 9.89526331e-01 -5.05466521e-01 -1.22232765... | [8.4722261428833, 8.705388069152832] |
abdcd5c1-c5bd-414f-b109-b5c068bfc76a | alternative-visual-units-for-an-optimized | 1909.07147 | null | http://arxiv.org/abs/1909.07147v1 | http://arxiv.org/pdf/1909.07147v1.pdf | Alternative Visual Units for an Optimized Phoneme-Based Lipreading System | Lipreading is understanding speech from observed lip movements. An observed
series of lip motions is an ordered sequence of visual lip gestures. These
gestures are commonly known, but as yet are not formally defined, as `visemes'.
In this article, we describe a structured approach which allows us to create
speaker-depe... | [] | 2019-09-16 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 3.28697622e-01 6.86361641e-02 -2.86408246e-01 -1.71177447e-01
-8.90664279e-01 -5.66851020e-01 7.40323782e-01 -3.51658493e-01
-3.02450687e-01 4.44990337e-01 6.34287715e-01 -4.65727091e-01
2.51716733e-01 -1.01387948e-01 -6.95783198e-01 -7.13271022e-01
1.15436718e-01 3.80961299e-01 4.38306332e-01 -6.49052933... | [14.307446479797363, 4.992704391479492] |
c555b5ae-59ae-423f-956f-1ed51768d8be | learning-to-reduce-information-bottleneck-for | 2204.02033 | null | https://arxiv.org/abs/2204.02033v4 | https://arxiv.org/pdf/2204.02033v4.pdf | Learning to Reduce Information Bottleneck for Object Detection in Aerial Images | Object detection in aerial images is a fundamental research topic in the geoscience and remote sensing domain. However, the advanced approaches on this topic mainly focus on designing the elaborate backbones or head networks but ignore neck networks. In this letter, we first underline the importance of the neck network... | ['Zhihao Song', 'Dong Zhang', 'Qiaolin Ye', 'Xuesong Jiang', 'Yuchen Shen'] | 2022-04-05 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 3.25728118e-01 -2.90088534e-01 1.06847115e-01 -2.04477355e-01
5.27765900e-02 -2.87641108e-01 2.64680237e-01 -5.14533743e-02
-2.07323775e-01 4.95761245e-01 -1.53566688e-01 -1.53051466e-01
-6.35688126e-01 -1.33597279e+00 -2.51925260e-01 -8.05248976e-01
-5.89288771e-02 -4.11206990e-01 6.65889800e-01 -3.37173343... | [9.169838905334473, -0.9224028587341309] |
ce15592d-bb0b-4cc1-83e7-240849835aa2 | hit-scir-at-mrp-2019-a-unified-pipeline-for | null | null | https://aclanthology.org/K19-2007 | https://aclanthology.org/K19-2007.pdf | HIT-SCIR at MRP 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding | This paper describes our system (HIT-SCIR) for CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing. We extended the basic transition-based parser with two improvements: a) Efficient Training by realizing Stack LSTM parallel training; b) Effective Encoding via adopting deep contextualized word embeddi... | ['Longxu Dou', 'Wanxiang Che', 'Yuxuan Wang', 'Yang Xu', 'Ting Liu', 'Yijia Liu'] | 2019-11-01 | null | null | null | conll-2019-11 | ['ucca-parsing'] | ['natural-language-processing'] | [ 5.78817606e-01 3.98014694e-01 -1.69037893e-01 -6.72503293e-01
-1.49484062e+00 -5.35144508e-01 1.40831649e-01 3.08822423e-01
-6.99024618e-01 6.50081217e-01 5.28805912e-01 -7.38879621e-01
3.76138464e-02 -8.36435318e-01 -6.93451703e-01 -2.95120239e-01
3.02350875e-02 2.65111297e-01 8.00467879e-02 -1.35817289... | [10.422658920288086, 9.54794979095459] |
ece315e3-9f80-4238-b611-d8ece48fc704 | frame-flexible-network | 2303.14817 | null | https://arxiv.org/abs/2303.14817v1 | https://arxiv.org/pdf/2303.14817v1.pdf | Frame Flexible Network | Existing video recognition algorithms always conduct different training pipelines for inputs with different frame numbers, which requires repetitive training operations and multiplying storage costs. If we evaluate the model using other frames which are not used in training, we observe the performance will drop signifi... | ['Yun Fu', 'Sheng Li', 'Huan Wang', 'Chang Liu', 'Yue Bai', 'Yitian Zhang'] | 2023-03-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Frame_Flexible_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Frame_Flexible_Network_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-recognition'] | ['computer-vision'] | [ 1.48348674e-01 -6.40930951e-01 -4.57344741e-01 -3.84069443e-01
-3.91599268e-01 -4.61161822e-01 3.73278230e-01 -4.72299308e-01
-4.08476412e-01 4.34818327e-01 1.35155886e-01 -1.40878588e-01
9.91224125e-02 -6.45278633e-01 -8.73640120e-01 -7.62415707e-01
-2.72548229e-01 -2.68540502e-01 3.39166731e-01 -2.41286486... | [9.012784004211426, 0.41481122374534607] |
d4dcfb81-bb38-46fb-aff2-3d350e199c42 | multilingual-ontology-matching-based-on | 1109.0732 | null | http://arxiv.org/abs/1109.0732v2 | http://arxiv.org/pdf/1109.0732v2.pdf | Multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint | Interoperability is a feature required by the Semantic Web. It is provided by
the ontology matching methods and algorithms. But now ontologies are presented
not only in English, but in other languages as well. It is important to use an
automatic translation for obtaining correct matching pairs in multilingual
ontology ... | ['Andrew Krizhanovsky', 'Feiyu Lin'] | 2011-09-04 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-5.07516682e-01 1.59773812e-01 -1.76292717e-01 -1.48056984e-01
-3.03942353e-01 -5.33616841e-01 6.59727812e-01 7.05856264e-01
-8.30088496e-01 7.02332437e-01 1.45644248e-01 -1.48161471e-01
-5.63667119e-01 -1.31036997e+00 -5.11738420e-01 -6.36890233e-02
5.52804589e-01 1.24385762e+00 5.31447113e-01 -9.17741954... | [9.23299789428711, 8.110440254211426] |
24d2acd9-99e6-43ba-a3c2-f0510083891f | real-time-mortality-prediction-using-mimic-iv | 2110.08949 | null | https://arxiv.org/abs/2110.08949v3 | https://arxiv.org/pdf/2110.08949v3.pdf | Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards | Electronic Health Record (EHR) systems provide critical, rich and valuable information at high frequency. One of the most exciting applications of EHR data is in developing a real-time mortality warning system with tools from survival analysis. However, most of the survival analysis methods used recently are based on (... | ['Bobak J. Mortazavi', 'Donald K. K. Lee', 'James Royalty', 'Arash Pakbin', 'Zhale Nowroozilarki'] | 2021-10-17 | null | null | null | null | ['icu-mortality'] | ['medical'] | [-3.39070320e-01 -2.75315821e-01 -1.24599315e-01 -4.40192759e-01
-7.85794914e-01 -1.87564492e-01 -1.02949783e-01 7.11051464e-01
-2.00462937e-01 7.44619489e-01 4.24105018e-01 -7.20026433e-01
-3.29647124e-01 -6.45622313e-01 -2.95679718e-02 -3.24998051e-01
-6.71762407e-01 3.57543737e-01 -1.74776390e-01 1.08831868... | [7.939025402069092, 6.144029140472412] |
147ab1a2-81e6-4d97-8be4-853bff32182b | ochadai-kyodai-at-semeval-2021-task-1 | 2105.05535 | null | https://arxiv.org/abs/2105.05535v3 | https://arxiv.org/pdf/2105.05535v3.pdf | OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction | We propose an ensemble model for predicting the lexical complexity of words and multiword expressions (MWEs). The model receives as input a sentence with a target word or MWEand outputs its complexity score. Given that a key challenge with this task is the limited size of annotated data, our model relies on pretrained ... | ['Ichiro Kobayashi', 'Fei Cheng', 'Lis Kanashiro Pereira', 'Yuki Taya'] | 2021-05-12 | null | https://aclanthology.org/2021.semeval-1.2 | https://aclanthology.org/2021.semeval-1.2.pdf | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [ 3.40318412e-01 -1.54673547e-01 -7.86132663e-02 -4.33972269e-01
-1.14183497e+00 -7.08987296e-01 6.00097358e-01 4.26230282e-02
-7.26240993e-01 4.71150398e-01 2.94055790e-01 -5.90891004e-01
3.56213570e-01 -7.85937190e-01 -6.91722512e-01 -1.36249125e-01
3.45221572e-02 4.50914353e-01 1.72117464e-02 -6.93490505... | [10.733131408691406, 8.45449447631836] |
a24c16d0-49e1-459f-b91b-9d8f53c284b7 | covid-19-named-entity-recognition-for | 2104.03879 | null | https://arxiv.org/abs/2104.03879v1 | https://arxiv.org/pdf/2104.03879v1.pdf | COVID-19 Named Entity Recognition for Vietnamese | The current COVID-19 pandemic has lead to the creation of many corpora that facilitate NLP research and downstream applications to help fight the pandemic. However, most of these corpora are exclusively for English. As the pandemic is a global problem, it is worth creating COVID-19 related datasets for languages other ... | ['Dat Quoc Nguyen', 'Mai Hoang Dao', 'Thinh Hung Truong'] | 2021-04-08 | null | https://aclanthology.org/2021.naacl-main.173 | https://aclanthology.org/2021.naacl-main.173.pdf | naacl-2021-4 | ['vietnamese-word-segmentation', 'named-entity-recognition-in-vietnamese'] | ['natural-language-processing', 'natural-language-processing'] | [-5.25173008e-01 -2.39687830e-01 -4.32645887e-01 -1.09845683e-01
-8.04933131e-01 -8.56430888e-01 7.20541775e-01 4.08475608e-01
-1.05984628e+00 1.10134804e+00 5.53029895e-01 -5.75862467e-01
4.15991783e-01 -8.15225065e-01 -2.33913511e-01 -2.72932947e-01
-4.71312031e-02 1.01803160e+00 8.05155560e-02 -5.34573317... | [9.750560760498047, 9.63266372680664] |
28baedb1-fcb4-45ca-a7e1-2432e6847a88 | domain-adaptive-robotic-gesture-recognition | 2103.04075 | null | https://arxiv.org/abs/2103.04075v2 | https://arxiv.org/pdf/2103.04075v2.pdf | Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment | Automated surgical gesture recognition is of great importance in robot-assisted minimally invasive surgery. However, existing methods assume that training and testing data are from the same domain, which suffers from severe performance degradation when a domain gap exists, such as the simulator and real robot. In this ... | ['Pheng-Ann Heng', 'Jing Qin', 'Qi Dou', 'Yueming Jin', 'Xueying Shi'] | 2021-03-06 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 0.20661813 0.05886614 -0.34007278 -0.3006639 -0.7829329 -0.56954765
0.41546565 -0.2168316 -0.7471768 0.60961455 0.3722859 0.16437699
-0.46811864 -0.3065478 -0.66167015 -0.96025956 0.01876353 0.16196783
0.2897071 -0.38371375 0.06904449 0.37788296 -1.234062 0.28433126
0.83286816 0.792537 0.... | [14.07978343963623, -3.2625317573547363] |
db1963e4-a7ff-4d4f-ae5f-bd097c7aea30 | neuragen-a-low-resource-neural-network-based | 2203.15253 | null | https://arxiv.org/abs/2203.15253v1 | https://arxiv.org/pdf/2203.15253v1.pdf | NeuraGen-A Low-Resource Neural Network based approach for Gender Classification | Human voice is the source of several important information. This is in the form of features. These Features help in interpreting various features associated with the speaker and speech. The speaker dependent work researchersare targeted towards speaker identification, Speaker verification, speaker biometric, forensics ... | ['Naagamani Molakathaala', 'Chhanda Saha', 'Shankhanil Ghosh'] | 2022-03-29 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-2.10904386e-02 -4.28405777e-02 3.04560483e-01 -8.09273839e-01
-7.12710679e-01 -5.63476980e-01 5.91680348e-01 -3.52201015e-02
-4.02400702e-01 6.54021621e-01 2.60165066e-01 -1.83179900e-01
-1.77137479e-01 -3.67565632e-01 -1.26399785e-01 -8.07282925e-01
2.55045682e-01 5.43860734e-01 -3.42684388e-01 -9.00815129... | [14.327861785888672, 5.982324600219727] |
e3a92ad5-1c26-4cc4-870b-b84eba2d1d21 | patching-as-translation-the-data-and-the | 2008.10707 | null | https://arxiv.org/abs/2008.10707v2 | https://arxiv.org/pdf/2008.10707v2.pdf | Patching as Translation: the Data and the Metaphor | Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model's initial success at a given task does not necessarily mean it is well-suited for the task. In this work, we examine a common example of thi... | ['Vincent J. Hellendoorn', 'Premkumar Devanbu', 'Yangruibo Ding', 'Baishakhi Ray'] | 2020-08-24 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 1.08028658e-01 3.07094723e-01 -2.72071868e-01 -2.79660702e-01
-6.76393330e-01 -6.04158938e-01 4.34937179e-01 1.84450656e-01
1.01158991e-01 3.39638829e-01 3.04625183e-01 -1.07728827e+00
-1.63917542e-02 -5.96678197e-01 -9.72715199e-01 -1.60461172e-01
1.91884309e-01 6.08773790e-02 7.14380518e-02 -5.29675663... | [7.731932640075684, 7.7627692222595215] |
4a1dd9d8-da26-4669-8cac-b934d9c92b48 | a-survey-of-point-of-interest-recommendation | 1607.00647 | null | https://arxiv.org/abs/1607.00647v1 | https://arxiv.org/pdf/1607.00647v1.pdf | A Survey of Point-of-interest Recommendation in Location-based Social Networks | Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field... | ['Michael R. Lyu', 'Irwin King', 'Shenglin Zhao'] | 2016-07-03 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.46752343e-01 -1.30618557e-01 -8.81615162e-01 -1.24658339e-01
-8.73627514e-02 -4.11576688e-01 8.23441684e-01 1.17021963e-01
-1.86951756e-01 8.86022806e-01 6.34095967e-01 -4.09481704e-01
-1.11229873e+00 -1.10096169e+00 -2.46805936e-01 -6.79207742e-01
-6.27361953e-01 4.20121968e-01 5.60262382e-01 -4.10758317... | [9.986424446105957, 5.727001667022705] |
249e8d4f-377c-44e3-a1a5-bba4be148dbd | p-fp-extraction-classification-and-prediction | 1711.03656 | null | http://arxiv.org/abs/1711.03656v2 | http://arxiv.org/pdf/1711.03656v2.pdf | p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning | Recent advances in learning Deep Neural Network (DNN) architectures have
received a great deal of attention due to their ability to outperform
state-of-the-art classifiers across a wide range of applications, with little
or no feature engineering. In this paper, we broadly study the applicability of
deep learning to we... | ['Saikrishna Sunkam', 'Nicholas Hopper', 'Se Eun Oh'] | 2017-11-10 | null | null | null | null | ['website-fingerprinting-attacks'] | ['adversarial'] | [-9.32549965e-03 -4.85237449e-01 -7.50740707e-01 -5.03987849e-01
-7.53674805e-01 -1.18916106e+00 5.40545702e-01 -9.72289219e-02
-2.34944627e-01 2.15847299e-01 -5.25992326e-02 -7.90264428e-01
-1.18261166e-01 -9.72172916e-01 -1.01612890e+00 -2.85483122e-01
-1.66955188e-01 3.65397006e-01 3.35038543e-01 7.60609880... | [5.3038458824157715, 7.28789758682251] |
8826e706-4b28-4a26-af21-792bba90d31a | times-series-averaging-and-denoising-from-a | 1611.09194 | null | http://arxiv.org/abs/1611.09194v4 | http://arxiv.org/pdf/1611.09194v4.pdf | Times series averaging and denoising from a probabilistic perspective on time-elastic kernels | In the light of regularized dynamic time warping kernels, this paper
re-considers the concept of time elastic centroid for a setof time series. We
derive a new algorithm based on a probabilistic interpretation of kernel
alignment matrices. This algorithm expressesthe averaging process in terms of a
stochastic alignment... | ['Pierre-François Marteau'] | 2016-11-28 | null | null | null | null | ['time-series-denoising'] | ['time-series'] | [ 3.42776090e-01 -3.97755086e-01 1.71152011e-01 2.14112084e-02
-1.04084957e+00 -6.51119471e-01 9.49050426e-01 1.25815287e-01
-8.33645821e-01 5.39080083e-01 1.74266756e-01 1.79886132e-01
-8.81869614e-01 -4.74664211e-01 -4.08499479e-01 -1.25636673e+00
-3.96818250e-01 4.79544371e-01 2.38730192e-01 -1.38840035... | [7.3468403816223145, 3.3694612979888916] |
1edb171b-1a6c-4116-8f47-14dc5676beff | on-the-strength-of-character-language-models | 1809.05157 | null | http://arxiv.org/abs/1809.05157v2 | http://arxiv.org/pdf/1809.05157v2.pdf | On the Strength of Character Language Models for Multilingual Named Entity Recognition | Character-level patterns have been widely used as features in English Named
Entity Recognition (NER) systems. However, to date there has been no direct
investigation of the inherent differences between name and non-name tokens in
text, nor whether this property holds across multiple languages. This paper
analyzes the c... | ['Xiaodong Yu', 'Mark Sammons', 'Dan Roth', 'Stephen Mayhew'] | 2018-09-13 | on-the-strength-of-character-language-models-1 | https://aclanthology.org/D18-1345 | https://aclanthology.org/D18-1345.pdf | emnlp-2018-10 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-3.40566069e-01 -4.53792512e-01 -2.41060525e-01 -3.92645031e-01
-8.93411338e-01 -9.38236594e-01 8.96041274e-01 5.86359859e-01
-1.04823089e+00 7.94206202e-01 2.10680112e-01 -4.43228692e-01
1.17513081e-02 -7.36990690e-01 -7.80751109e-02 -1.59084931e-01
-2.55482532e-02 4.54547942e-01 3.00359488e-01 -2.54679233... | [9.786344528198242, 9.666894912719727] |
48e9f393-6b04-466f-beba-20e8b82b8f59 | high-probability-bounds-for-stochastic-1 | 2302.00999 | null | https://arxiv.org/abs/2302.00999v1 | https://arxiv.org/pdf/2302.00999v1.pdf | High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance | During recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization methods has been growing. One of the main reasons for this is that high-probability complexity bounds are more accurate and less studied than in-expectation ones. However, SOTA hi... | ['Peter Richtárik', 'Alexander Gasnikov', 'Pavel Dvurechensky', 'Gauthier Gidel', 'Samuel Horváth', 'Eduard Gorbunov', 'Marina Danilova', 'Abdurakhmon Sadiev'] | 2023-02-02 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.62857508e-02 6.72459006e-02 -2.36410392e-03 -1.26464441e-01
-8.63876700e-01 -2.59039342e-01 -1.97730497e-01 2.95879930e-01
-7.51418054e-01 1.16458130e+00 -1.63628533e-01 -7.94962272e-02
-5.63112438e-01 -4.71938342e-01 -8.39102805e-01 -1.16268063e+00
-8.67809355e-02 4.54965591e-01 -2.57473320e-01 -6.93923980... | [6.649425029754639, 4.409657001495361] |
e1c927bb-d094-41d5-bcc5-4ae891a6c5a7 | xtrimoabfold-improving-antibody-structure | 2212.00735 | null | https://arxiv.org/abs/2212.00735v3 | https://arxiv.org/pdf/2212.00735v3.pdf | xTrimoABFold: De novo Antibody Structure Prediction without MSA | In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence... | ['Le Song', 'Cheng Yang', 'Yangang Wang', 'Hui Li', 'Chuan Shi', 'YiWu Sun', 'Bing Yang', 'Shaochuan Li', 'Xumeng Gong', 'Yining Wang'] | 2022-11-30 | null | null | null | null | ['protein-language-model'] | ['medical'] | [ 2.07505658e-01 -2.29989290e-01 -9.49307457e-02 -3.89855206e-01
-4.36128855e-01 -6.54759407e-01 1.80708143e-04 3.88316542e-01
-3.46174538e-01 1.27018416e+00 -3.20587903e-01 -7.12327361e-01
1.82533875e-01 -5.37016869e-01 -1.19518399e+00 -1.00533211e+00
5.62230833e-02 8.77873838e-01 8.23799819e-02 -4.83121336... | [4.7497782707214355, 5.646662712097168] |
ad6796b1-3652-4dc8-bb50-b855950a14a5 | deep-hough-transform-for-semantic-line | 2003.04676 | null | https://arxiv.org/abs/2003.04676v4 | https://arxiv.org/pdf/2003.04676v4.pdf | Deep Hough Transform for Semantic Line Detection | We focus on a fundamental task of detecting meaningful line structures, a.k.a. semantic line, in natural scenes. Many previous methods regard this problem as a special case of object detection and adjust existing object detectors for semantic line detection. However, these methods neglect the inherent characteristics o... | ['Chang-Bin Zhang', 'Ming-Ming Cheng', 'Qi Han', 'Jun Xu', 'Kai Zhao'] | 2020-03-10 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/779_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540239.pdf | eccv-2020-8 | ['line-detection'] | ['computer-vision'] | [ 7.70619214e-02 -2.18314171e-01 -8.40276480e-02 -3.19449902e-01
-6.09603703e-01 -6.49389744e-01 4.99134004e-01 4.66899067e-01
-5.23873150e-01 3.26737165e-01 -1.14118405e-01 -5.28359264e-02
-1.01328507e-01 -1.04974461e+00 -8.64580810e-01 -3.95036578e-01
-4.15850468e-02 1.05895087e-01 6.37386620e-01 -8.53717476... | [8.245926856994629, -1.6615735292434692] |
2fa442f2-7957-4135-85ff-fe1cd615413f | video-compressive-sensing-for-spatial | 1503.02727 | null | http://arxiv.org/abs/1503.02727v2 | http://arxiv.org/pdf/1503.02727v2.pdf | Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models | Spatial multiplexing cameras (SMCs) acquire a (typically static) scene
through a series of coded projections using a spatial light modulator (e.g., a
digital micro-mirror device) and a few optical sensors. This approach finds use
in imaging applications where full-frame sensors are either too expensive
(e.g., for short... | ['Aswin C. Sankaranarayanan', 'Yun Li', 'Kevin Kelly', 'Richard G. Baraniuk', 'Lina Xu', 'Christoph Studer'] | 2015-03-09 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 9.80557561e-01 -4.36177939e-01 -5.37290871e-02 6.14557900e-02
-6.39823794e-01 -5.79296291e-01 2.46091664e-01 -8.06960762e-01
-3.12670350e-01 6.25978887e-01 2.44456381e-01 -1.49099365e-01
-9.92038846e-02 -4.67227101e-01 -7.93280661e-01 -7.07399845e-01
2.96831019e-02 -3.51523101e-01 2.37663582e-01 5.97946420... | [10.96054744720459, -2.236140012741089] |
5922c692-fab6-42e7-baf6-c1ab020a7461 | a-data-driven-methodology-for-considering | 2204.08094 | null | https://arxiv.org/abs/2204.08094v1 | https://arxiv.org/pdf/2204.08094v1.pdf | A Data-Driven Methodology for Considering Feasibility and Pairwise Likelihood in Deep Learning Based Guitar Tablature Transcription Systems | Guitar tablature transcription is an important but understudied problem within the field of music information retrieval. Traditional signal processing approaches offer only limited performance on the task, and there is little acoustic data with transcription labels for training machine learning models. However, guitar ... | ['Zhiyao Duan', 'Jonathan Driedger', 'Frank Cwitkowitz'] | 2022-04-17 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 4.74896252e-01 -8.25461447e-02 -1.55747116e-01 -3.94075811e-01
-1.28734720e+00 -1.15185332e+00 3.26457709e-01 1.60045356e-01
-1.64293841e-01 3.69850278e-01 3.87795329e-01 -1.99146077e-01
-3.32352489e-01 -2.16551974e-01 -7.01541781e-01 -6.05812788e-01
9.32623148e-02 4.16437536e-01 -2.23561853e-01 -6.50656745... | [15.832707405090332, 5.403995990753174] |
d53d0456-26ad-45ca-a0c6-fbfb3f3fc810 | a-unifying-bayesian-approach-for-preterm | 1809.07102 | null | http://arxiv.org/abs/1809.07102v1 | http://arxiv.org/pdf/1809.07102v1.pdf | A unifying Bayesian approach for preterm brain-age prediction that models EEG sleep transitions over age | Preterm newborns undergo various stresses that may materialize as learning
problems at school-age. Sleep staging of the Electroencephalogram (EEG),
followed by prediction of their brain-age from these sleep states can quantify
deviations from normal brain development early (when compared to the known
age). Current auto... | ['Kirubin Pillay', 'Maarten De Vos'] | 2018-09-19 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 1.45018533e-01 -3.10810027e-03 4.85840067e-03 -4.78072643e-01
-3.92763138e-01 -4.06279206e-01 3.04094046e-01 4.17457342e-01
-5.56285977e-01 5.65434396e-01 -6.15864210e-02 -2.56484091e-01
-4.06487733e-01 -5.11443377e-01 -3.27199996e-01 -7.11464584e-01
8.17178339e-02 7.11380005e-01 4.25324917e-01 6.94474220... | [13.494174003601074, 3.5598597526550293] |
64b439cd-3ad0-41db-8424-6074cb85ba54 | hdformer-a-higher-dimensional-transformer-for | 2303.11340 | null | https://arxiv.org/abs/2303.11340v1 | https://arxiv.org/pdf/2303.11340v1.pdf | HDformer: A Higher Dimensional Transformer for Diabetes Detection Utilizing Long Range Vascular Signals | Diabetes mellitus is a worldwide concern, and early detection can help to prevent serious complications. Low-cost, non-invasive detection methods, which take cardiovascular signals into deep learning models, have emerged. However, limited accuracy constrains their clinical usage. In this paper, we present a new Transfo... | ['Ella Lan'] | 2023-03-17 | null | null | null | null | ['photoplethysmography-ppg', 'specificity'] | ['medical', 'natural-language-processing'] | [ 1.99023753e-01 -8.82742107e-02 -1.14213891e-01 -4.93583202e-01
-8.91890824e-01 -1.86075270e-01 -1.26118064e-01 -2.18091279e-01
-1.48343638e-01 6.22399628e-01 1.85946330e-01 -1.82443157e-01
-1.21186741e-01 -5.68807364e-01 -5.07617235e-01 -8.98480058e-01
-4.46525991e-01 5.67088742e-03 -2.60473013e-01 2.07256317... | [14.17728042602539, 3.182568311691284] |
31b7d243-b879-47eb-8f12-dc7f48583594 | two-sides-of-the-same-coin-heterophily-and | 2102.06462 | null | https://arxiv.org/abs/2102.06462v8 | https://arxiv.org/pdf/2102.06462v8.pdf | Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks | In node classification tasks, graph convolutional neural networks (GCNs) have demonstrated competitive performance over traditional methods on diverse graph data. However, it is known that the performance of GCNs degrades with increasing number of layers (oversmoothing problem) and recent studies have also shown that G... | ['Danai Koutra', 'Yaoqing Yang', 'Kevin Swersky', 'Milad Hashemi', 'Yujun Yan'] | 2021-02-12 | two-sides-of-the-same-coin-heterophily-and-1 | https://openreview.net/forum?id=R2aCiGQ9Qc | https://openreview.net/pdf?id=R2aCiGQ9Qc | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-5.16286939e-02 3.67263705e-01 -2.11209163e-01 -1.13737278e-01
1.00667745e-01 -5.03579974e-01 4.97112632e-01 5.33663094e-01
-1.27363550e-02 4.22903746e-01 -5.23715392e-02 -2.66851932e-01
-2.89869398e-01 -1.15091908e+00 -6.23184323e-01 -7.76904404e-01
-6.47558093e-01 4.18108821e-01 2.88116395e-01 -2.72591710... | [6.9413909912109375, 6.086587905883789] |
13799acf-987a-4313-adaa-9402c5f4526c | scientific-computing-algorithms-to-learn | 2304.00338 | null | https://arxiv.org/abs/2304.00338v1 | https://arxiv.org/pdf/2304.00338v1.pdf | Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics | Data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. Among them, graph neural networks (GNNs) that operate on mesh-based data are desirable because they possess inductive biases that promote physical faithfulness, but hardware limitations have precluded their application to... | ['Phan Nguyen', 'Brenda Ng', 'Zhijie Xu', 'Jie Bao', 'Yucheng Fu', 'Jose Cadena', 'Amar Saini', 'Yeping Hu', 'Brian R. Bartoldson'] | 2023-04-01 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-9.45566967e-03 2.93730140e-01 1.80286214e-01 -7.48427510e-02
-3.89514089e-01 -3.03547144e-01 3.97291690e-01 2.34131709e-01
-2.09036097e-01 1.12548292e+00 -2.45432839e-01 -7.07634151e-01
-3.96039665e-01 -1.49459898e+00 -1.17288196e+00 -4.95836586e-01
-6.29051626e-01 8.25787604e-01 1.41566873e-01 -3.64109218... | [6.400871753692627, 3.41469669342041] |
c37af03f-1950-483e-9a51-24bbfa362f46 | upsampling-artifacts-in-neural-audio | 2010.14356 | null | https://arxiv.org/abs/2010.14356v2 | https://arxiv.org/pdf/2010.14356v2.pdf | Upsampling artifacts in neural audio synthesis | A number of recent advances in neural audio synthesis rely on upsampling layers, which can introduce undesired artifacts. In computer vision, upsampling artifacts have been studied and are known as checkerboard artifacts (due to their characteristic visual pattern). However, their effect has been overlooked so far in a... | ['Joan Serrà', 'Giulio Cengarle', 'Santiago Pascual', 'Jordi Pons'] | 2020-10-27 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 6.27933204e-01 -2.35923052e-01 4.45171356e-01 2.37478137e-01
-5.86433411e-01 -3.54504049e-01 5.91849267e-01 5.90241933e-03
-3.29971403e-01 8.03748906e-01 2.74317384e-01 5.08299842e-02
-6.60216948e-03 -6.58663869e-01 -8.03485453e-01 -6.55489743e-01
-1.69040024e-01 -3.95985156e-01 5.58957458e-01 -6.87032118... | [15.435489654541016, 5.7347917556762695] |
aa168ee9-4d55-4d84-a7fc-a19711f775a9 | developing-a-curated-topic-model-for-covid-19 | null | null | https://aclanthology.org/2020.nlpcovid19-2.30 | https://aclanthology.org/2020.nlpcovid19-2.30.pdf | Developing a Curated Topic Model for COVID-19 Medical Research Literature | Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an e... | ['Mike Moran', 'Katherine E. Goodman', 'Philip Resnik'] | null | null | null | null | emnlp-nlp-covid19-2020-12 | ['topic-models'] | ['natural-language-processing'] | [-1.49618149e-01 3.82336915e-01 -7.16487288e-01 -2.31935740e-01
-9.33999658e-01 -5.74411690e-01 6.35378182e-01 8.02414596e-01
-4.79561836e-01 5.19627333e-01 6.58433199e-01 -9.00971770e-01
-6.34976566e-01 -5.58712304e-01 8.50178301e-03 6.41804561e-03
1.50295764e-01 9.64243889e-01 1.73385099e-01 8.42429101... | [9.000285148620605, 8.34588623046875] |
967c1261-b859-414a-bb7c-3f30dbfba200 | interactive-learning-from-activity | 2102.07024 | null | https://arxiv.org/abs/2102.07024v2 | https://arxiv.org/pdf/2102.07024v2.pdf | Interactive Learning from Activity Description | We present a novel interactive learning protocol that enables training request-fulfilling agents by verbally describing their activities. Unlike imitation learning (IL), our protocol allows the teaching agent to provide feedback in a language that is most appropriate for them. Compared with reward in reinforcement lear... | ['Patrick Shafto', 'Miro Dudík', 'Robert Schapire', 'Dipendra Misra', 'Khanh Nguyen'] | 2021-02-13 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.08709084e-02 5.73869824e-01 -4.68085498e-01 -1.85714379e-01
-1.14507329e+00 -7.55315065e-01 8.93571734e-01 8.90569761e-02
-7.70721436e-01 1.12934148e+00 1.77687518e-02 -3.96278918e-01
-1.78505138e-01 -5.66366911e-01 -9.01529610e-01 -8.03102195e-01
-5.23730934e-01 8.47539902e-01 3.39875907e-01 5.49405208... | [4.070488452911377, 1.677091121673584] |
1a29e24a-6317-4a5e-badc-ef5bb6bd1d4f | stabiliser-states-are-efficiently-pac | 1705.00345 | null | http://arxiv.org/abs/1705.00345v2 | http://arxiv.org/pdf/1705.00345v2.pdf | Stabiliser states are efficiently PAC-learnable | The exponential scaling of the wave function is a fundamental property of
quantum systems with far reaching implications in our ability to process
quantum information. A problem where these are particularly relevant is quantum
state tomography. State tomography, whose objective is to obtain a full
description of a quan... | ['Andrea Rocchetto'] | 2017-04-30 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 5.02725601e-01 2.29447961e-01 8.01529214e-02 -1.37043938e-01
-9.91632700e-01 -7.34699309e-01 5.54163456e-01 8.96615684e-02
-6.45218194e-01 9.19780433e-01 -1.85763270e-01 -6.77479804e-01
-2.62328565e-01 -1.14530933e+00 -7.46482313e-01 -1.15287960e+00
-3.76843750e-01 8.16443741e-01 -2.79469267e-02 -1.99351177... | [5.589860916137695, 4.912106513977051] |
f4420124-9235-4402-b533-7ba96c890e62 | 3d-human-pose-regression-using-graph | 2105.10379 | null | https://arxiv.org/abs/2105.10379v2 | https://arxiv.org/pdf/2105.10379v2.pdf | 3D Human Pose Regression using Graph Convolutional Network | 3D human pose estimation is a difficult task, due to challenges such as occluded body parts and ambiguous poses. Graph convolutional networks encode the structural information of the human skeleton in the form of an adjacency matrix, which is beneficial for better pose prediction. We propose one such graph convolutiona... | ['Alois Knoll', 'Alejandro Mendoza Gracia', 'Soubarna Banik'] | 2021-05-21 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-2.82690465e-01 3.72123480e-01 -3.80414844e-01 -2.68223077e-01
7.05067068e-02 -2.25658149e-01 1.26594320e-01 -2.20235690e-01
-3.57536942e-01 5.01470149e-01 4.59359556e-01 1.71199515e-01
-3.37602906e-02 -5.91173291e-01 -7.47232258e-01 -1.13617152e-01
-6.48334801e-01 8.83273244e-01 4.08316731e-01 -4.34772760... | [7.017577648162842, -0.7214995622634888] |
c0158f9d-47cc-4d77-90a9-ac68c8531674 | masked-and-adaptive-transformer-for-exemplar | 2303.17123 | null | https://arxiv.org/abs/2303.17123v1 | https://arxiv.org/pdf/2303.17123v1.pdf | Masked and Adaptive Transformer for Exemplar Based Image Translation | We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; an... | ['Gang Xu', 'Nannan Wang', 'YuHao Lin', 'Biao Ma', 'Fei Gao', 'Chang Jiang'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Masked_and_Adaptive_Transformer_for_Exemplar_Based_Image_Translation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Masked_and_Adaptive_Transformer_for_Exemplar_Based_Image_Translation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semantic-correspondence'] | ['computer-vision'] | [ 5.80573082e-01 -3.24803621e-01 -1.21060364e-01 -2.42450118e-01
-1.05372536e+00 -5.45675218e-01 5.72032511e-01 -1.87636495e-01
-2.28554830e-01 6.65826023e-01 1.08910218e-01 -4.28278036e-02
8.34574923e-02 -8.58633518e-01 -9.76352036e-01 -6.78489208e-01
7.10171461e-01 3.12824428e-01 7.71288425e-02 -3.47216338... | [11.621200561523438, -0.5068408846855164] |
56f5afc1-8d06-4bb8-b6f8-6ec44a1cee9c | multiframe-based-adaptive-despeckling | 1912.00815 | null | https://arxiv.org/abs/1912.00815v4 | https://arxiv.org/pdf/1912.00815v4.pdf | Multiframe-based Adaptive Despeckling Algorithm for Ultrasound B-mode Imaging with Superior Edge and Texture | Removing speckle noise from medical ultrasound images while preserving image features without introducing artifact and distortion is a major challenge in ultrasound image restoration. In this paper, we propose a multiframe-based adaptive despeckling (MADS) algorithm to reconstruct a high-resolution B-mode image from ra... | ['Md. Kamrul Hasan', 'Jayanta Dey'] | 2019-12-02 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 7.12670743e-01 -5.27346544e-02 8.48710120e-01 -2.34925061e-01
-8.04751515e-01 -3.01937640e-01 1.36436924e-01 -2.45150030e-01
-4.17949200e-01 5.81038058e-01 3.81822854e-01 -1.45816863e-01
-6.84340715e-01 -3.57227057e-01 -5.54710507e-01 -1.26976836e+00
-1.94312319e-01 -3.78944248e-01 4.01064664e-01 -1.79880753... | [12.268121719360352, -2.572789192199707] |
c0dd862d-a012-41c3-9c4e-eb3f86fc0256 | synbody-synthetic-dataset-with-layered-human | 2303.17368 | null | https://arxiv.org/abs/2303.17368v1 | https://arxiv.org/pdf/2303.17368v1.pdf | SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling | Synthetic data has emerged as a promising source for 3D human research as it offers low-cost access to large-scale human datasets. To advance the diversity and annotation quality of human models, we introduce a new synthetic dataset, Synbody, with three appealing features: 1) a clothed parametric human model that can g... | ['Lei Yang', 'Ziwei Liu', 'Dahua Lin', 'Chen Qian', 'Wayne Wu', 'Bo Dai', 'Chen Wei', 'Zhongfei Qing', 'Yukun Wei', 'Weiye Xiao', 'Zhaoxi Chen', 'Shuai Liu', 'Haiyi Mei', 'Zhongang Cai', 'Zhitao Yang'] | 2023-03-30 | null | null | null | null | ['neural-rendering', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [ 1.92009434e-01 1.19325504e-01 -6.00060299e-02 -4.61647034e-01
-8.29757333e-01 -7.24917352e-02 5.11950612e-01 -3.67239267e-01
2.48870756e-02 6.20204687e-01 3.68983090e-01 4.36653256e-01
3.68177563e-01 -6.92240655e-01 -7.80729890e-01 -5.16570210e-01
-6.74149767e-02 7.79574156e-01 2.19393075e-01 -5.83854258... | [7.211093902587891, -1.1807082891464233] |
d0936875-5eb3-4376-939f-1480ea4d1131 | spectral-clustering-under-the-degree | 2105.00987 | null | https://arxiv.org/abs/2105.00987v2 | https://arxiv.org/pdf/2105.00987v2.pdf | Spectral clustering under degree heterogeneity: a case for the random walk Laplacian | This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree. Under a generalised random dot product graph, the embedding provides uniformly consistent estimates of degree-corrected latent positions, with asymptotically Gau... | ['Patrick Rubin-Delanchy', 'Alexander Modell'] | 2021-05-03 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.67458773e-01 6.85687840e-01 -1.32384583e-01 8.43568146e-02
-5.21811843e-01 -7.76901126e-01 7.19765842e-01 6.99597970e-02
-2.05554128e-01 3.96608979e-01 1.60444647e-01 -4.02155668e-01
-3.48372728e-01 -7.46252120e-01 -2.89103478e-01 -1.03629422e+00
-3.82877052e-01 7.91084051e-01 2.18624860e-01 3.00535977... | [7.047217845916748, 5.274139881134033] |
d6ff63c3-cdae-42b3-912f-fc84ad52f2de | model-based-offline-meta-reinforcement-1 | 2202.02929 | null | https://arxiv.org/abs/2202.02929v2 | https://arxiv.org/pdf/2202.02929v2.pdf | Model-Based Offline Meta-Reinforcement Learning with Regularization | Existing offline reinforcement learning (RL) methods face a few major challenges, particularly the distributional shift between the learned policy and the behavior policy. Offline Meta-RL is emerging as a promising approach to address these challenges, aiming to learn an informative meta-policy from a collection of tas... | ['Junshan Zhang', 'Yingbin Liang', 'Tengyu Xu', 'Jialin Wan', 'Sen Lin'] | 2022-02-07 | model-based-offline-meta-reinforcement | https://openreview.net/forum?id=EBn0uInJZWh | https://openreview.net/pdf?id=EBn0uInJZWh | iclr-2022-4 | ['safe-exploration'] | ['robots'] | [-1.40735731e-01 2.29691252e-01 -6.30041659e-01 9.34170838e-03
-1.06609440e+00 -4.10786122e-01 6.37117743e-01 2.13782992e-02
-7.19104826e-01 9.68381882e-01 2.74829566e-01 -3.43149930e-01
-3.43892813e-01 -1.67187095e-01 -1.00950396e+00 -1.01790655e+00
-2.45595634e-01 5.00304163e-01 -1.21236548e-01 -3.96850020... | [4.069243431091309, 2.152120590209961] |
21b37e78-fe7a-4a5b-ae2a-6ebb57067686 | interpretable-scientific-discovery-with | 2211.10873 | null | https://arxiv.org/abs/2211.10873v2 | https://arxiv.org/pdf/2211.10873v2.pdf | Interpretable Scientific Discovery with Symbolic Regression: A Review | Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discove... | ['Sanjay Chawla', 'Nour Makke'] | 2022-11-20 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 3.73732775e-01 1.77360132e-01 -8.80170822e-01 -6.34990275e-01
-4.31880563e-01 -2.46634439e-01 6.54921412e-01 2.46982515e-01
-1.21178895e-01 9.01356339e-01 -4.71132934e-01 -6.57043874e-01
-4.47467238e-01 -7.16084063e-01 -5.83711982e-01 -7.19310999e-01
-3.85724515e-01 6.20889246e-01 -4.06235874e-01 -4.11527634... | [8.662342071533203, 6.78120756149292] |
ab6318ea-1f45-4466-93c1-f0a309a5b2de | vocabulary-informed-zero-shot-and-open-set | 2301.00998 | null | https://arxiv.org/abs/2301.00998v2 | https://arxiv.org/pdf/2301.00998v2.pdf | Vocabulary-informed Zero-shot and Open-set Learning | Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has ... | ['Leonid Sigal', 'xiangyang xue', 'Meng Wang', 'Yu-Gang Jiang', 'Hanze Dong', 'Xiaomei Wang', 'Yanwei Fu'] | 2023-01-03 | null | null | null | null | ['object-categorization', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 3.92330378e-01 2.53427297e-01 -5.62625110e-01 -7.65906751e-01
-5.97396970e-01 -4.90213871e-01 5.46702445e-01 2.79928535e-01
-3.22233409e-01 5.29480517e-01 1.12229325e-01 1.79739609e-01
-4.51630414e-01 -6.31242573e-01 -4.63310957e-01 -6.64774060e-01
-7.14812800e-02 8.37615013e-01 6.38621971e-02 -1.52081838... | [9.977252006530762, 2.5108683109283447] |
c234bb80-83d3-4106-8bd6-20a3eaa0f76e | supervised-deep-learning-for-content-aware | 2306.07383 | null | https://arxiv.org/abs/2306.07383v1 | https://arxiv.org/pdf/2306.07383v1.pdf | Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions | Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, w... | ['Shadrokh Samavi', 'Shahram Shirani', 'Nader Karimi', 'Mohammadreza Naderi', 'MohammadHossein Givkashi'] | 2023-06-12 | null | null | null | null | ['image-quality-assessment', 'image-retargeting'] | ['computer-vision', 'computer-vision'] | [ 6.45331681e-01 4.66300160e-01 7.29415864e-02 -2.71475077e-01
-3.82149816e-01 -5.97928166e-01 3.69668871e-01 -5.02161160e-02
-5.86961269e-01 7.04353094e-01 3.25858593e-02 -1.59462482e-01
3.82234544e-01 -1.02574849e+00 -1.07487249e+00 -6.75863743e-01
5.77250957e-01 7.86264837e-02 3.56500030e-01 -1.87970251... | [11.218738555908203, -0.9651939272880554] |
b36a69f4-59f2-4315-85d6-9c8d4c2eba1f | generalised-gillespie-algorithms-for | 2210.09511 | null | https://arxiv.org/abs/2210.09511v2 | https://arxiv.org/pdf/2210.09511v2.pdf | Generalised Gillespie Algorithms for Simulations in a Rule-Based Epidemiological Model Framework | Rule-based models have been successfully used to represent different aspects of the COVID-19 pandemic, including age, testing, hospitalisation, lockdowns, immunity, infectivity, behaviour, mobility and vaccination of individuals. These rule-based approaches are motivated by chemical reaction rules which are traditional... | ['Lisa Maria Kreusser', 'Luca Sbano', 'Markus Kirkilionis', 'Steffen Bauer', 'David Alonso'] | 2022-10-17 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 2.37128779e-01 -2.31935069e-01 3.26815575e-01 1.10331729e-01
3.30680788e-01 -5.65226257e-01 7.49666333e-01 8.41974914e-01
-7.44279146e-01 1.16894674e+00 -1.77513510e-01 -4.67349112e-01
-7.44418204e-01 -9.37227368e-01 -5.18970370e-01 -8.88256252e-01
-6.17260158e-01 9.58959520e-01 2.99903363e-01 -7.24060655... | [5.926288604736328, 4.390689373016357] |
a2565197-7935-42a7-947f-49d0e74ced29 | multilingual-protest-news-detection-shared | null | null | https://aclanthology.org/2021.case-1.11 | https://aclanthology.org/2021.case-1.11.pdf | Multilingual Protest News Detection - Shared Task 1, CASE 2021 | Benchmarking state-of-the-art text classification and information extraction systems in multilingual, cross-lingual, few-shot, and zero-shot settings for socio-political event information collection is achieved in the scope of the shared task Socio-political and Crisis Events Detection at the workshop CASE @ ACL-IJCNLP... | ['Shyam Ratan', 'Ritesh Kumar', 'Farhana Ferdousi Liza', 'Erdem Yörük', 'Osman Mutlu', 'Ali Hürriyetoğlu'] | null | null | null | null | acl-case-2021-8 | ['sentence-classification'] | ['natural-language-processing'] | [-1.18035838e-01 -8.18330571e-02 -1.81725904e-01 -3.54180872e-01
-1.75090122e+00 -9.42241311e-01 1.11547315e+00 6.48572803e-01
-8.05373251e-01 9.95526016e-01 7.21934080e-01 -2.05549553e-01
1.01358697e-01 -3.31791103e-01 -5.20278633e-01 -5.13591647e-01
4.13840301e-02 5.94785511e-01 2.25530222e-01 -5.48862636... | [9.081841468811035, 9.69400691986084] |
e4625baa-3e18-4861-a20d-835f4b60d1f6 | scotch-and-soda-a-transformer-video-shadow | 2211.06885 | null | https://arxiv.org/abs/2211.06885v2 | https://arxiv.org/pdf/2211.06885v2.pdf | SCOTCH and SODA: A Transformer Video Shadow Detection Framework | Shadows in videos are difficult to detect because of the large shadow deformation between frames. In this work, we argue that accounting for shadow deformation is essential when designing a video shadow detection method. To this end, we introduce the shadow deformation attention trajectory (SODA), a new type of video s... | ['Angelica I Aviles-Rivero', 'Carola-Bibiane Schönlieb', 'Pietro Liò', 'Nicolas Papadakis', 'Lei Zhu', 'Jean Prost', 'Lihao Liu'] | 2022-11-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_SCOTCH_and_SODA_A_Transformer_Video_Shadow_Detection_Framework_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_SCOTCH_and_SODA_A_Transformer_Video_Shadow_Detection_Framework_CVPR_2023_paper.pdf | cvpr-2023-1 | ['shadow-detection'] | ['computer-vision'] | [ 1.35648787e-01 8.15558508e-02 6.10495694e-02 -3.61833163e-02
-3.33768904e-01 -4.77665752e-01 4.54810113e-01 -6.05455041e-01
2.22700741e-02 5.98905325e-01 3.93664151e-01 -4.72496033e-01
3.61844093e-01 -4.39636141e-01 -1.03031719e+00 -7.97175884e-01
9.17006051e-04 -1.35947645e-01 7.71327317e-01 -1.54906716... | [10.841449737548828, -4.104833602905273] |
23ef5a8e-54ea-4965-bd49-a57c79cb20c6 | fast-optimization-of-wildfire-suppression | 1703.09391 | null | http://arxiv.org/abs/1703.09391v1 | http://arxiv.org/pdf/1703.09391v1.pdf | Fast Optimization of Wildfire Suppression Policies with SMAC | Managers of US National Forests must decide what policy to apply for dealing
with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber
companies, home owners, and wildlife biologists) have often led to spirited
political debates and even violent eco-terrorism. One way to transform these
conflicts into... | ['Claire Montgomery', 'Rachel Houtman', 'Sean McGregor', 'Thomas G. Dietterich', 'Ronald Metoyer'] | 2017-03-28 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 1.08220771e-01 -1.70338079e-01 -1.63752630e-01 -1.54021472e-01
-3.56486231e-01 -7.14377224e-01 3.99659276e-01 2.66609281e-01
-7.92632878e-01 1.09215736e+00 1.66110277e-01 -8.81560743e-01
-5.91716528e-01 -1.02925277e+00 -3.67527783e-01 -5.05773723e-01
-3.80544245e-01 6.16158009e-01 2.37156898e-01 -5.25697052... | [5.28387975692749, 2.390507936477661] |
01c8a61b-4487-4534-a86c-c9670014b0ca | agnostic-multi-group-active-learning | 2306.01922 | null | https://arxiv.org/abs/2306.01922v1 | https://arxiv.org/pdf/2306.01922v1.pdf | Agnostic Multi-Group Active Learning | Inspired by the problem of improving classification accuracy on rare or hard subsets of a population, there has been recent interest in models of learning where the goal is to generalize to a collection of distributions, each representing a ``group''. We consider a variant of this problem from the perspective of active... | ['Kamalika Chaudhuri', 'Nick Rittler'] | 2023-06-02 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 2.32524604e-01 7.07524478e-01 -3.96271944e-01 -3.04219246e-01
-1.40004778e+00 -7.03427851e-01 -8.54263976e-02 3.91524911e-01
-8.64877999e-01 1.13323736e+00 -6.61694229e-01 -3.25611532e-01
-7.99761236e-01 -1.11810529e+00 -8.06997538e-01 -1.21841061e+00
-5.81090569e-01 9.02571619e-01 9.50033367e-02 2.27195937... | [6.308594226837158, 4.480869770050049] |
5c9e2d01-47be-41ab-9fc8-734955333d71 | adaptive-channel-estimation-based-on-deep | null | null | https://ieeexplore.ieee.org/document/9348501 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9348501 | Adaptive Channel Estimation based on Deep Learning | Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show tha... | ['Gerhard Fettweis', 'Ahmad Nimr', 'Marwa Chafii', 'Abdul Karim Gizzini'] | 2020-11-18 | null | null | null | ieee-92nd-vehicular-technology-conference | ['indoor-localization'] | ['computer-vision'] | [ 1.51444137e-01 -4.74884780e-03 1.68654453e-02 -7.15957731e-02
-8.39850664e-01 8.08107257e-02 1.33990854e-01 3.27730209e-01
-8.30910504e-01 1.19244277e+00 -2.20362812e-01 -9.26787436e-01
-1.45587742e-01 -8.66960704e-01 -7.41256297e-01 -1.04275608e+00
-9.35560465e-01 -5.17811418e-01 4.14138734e-02 -6.28081784... | [6.352067947387695, 1.4502772092819214] |
764c8351-679c-4586-ade9-fbe2cd8cca75 | the-replica-dataset-a-digital-replica-of | 1906.05797 | null | https://arxiv.org/abs/1906.05797v1 | https://arxiv.org/pdf/1906.05797v1.pdf | The Replica Dataset: A Digital Replica of Indoor Spaces | We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-dynamic-range (HDR) textures, per-primitive semantic class and instance information, and planar mirror and glass reflectors. The goal of Repl... | ['Steven Lovegrove', 'Michael Goesele', 'Renzo De Nardi', 'Luis Pesqueira', 'Kimberly Leon', 'Shobhit Verma', 'Raul Mur-Artal', 'Lingni Ma', 'June Yon', 'Hauke M. Strasdat', 'Erik Wijmans', 'Carl Ren', 'Richard Newcombe', 'Nigel Carter', 'Manolis Savva', 'Jesus Briales', 'Brian Budge', 'Yufan Chen', 'Simon Green', 'Jak... | 2019-06-13 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 1.00093037e-01 2.06905901e-01 5.33752978e-01 -3.20476711e-01
-3.28475654e-01 -5.65458238e-01 7.50585735e-01 -1.59091860e-01
-2.14199334e-01 3.64465624e-01 2.09162354e-01 -3.03948849e-01
6.70574233e-02 -1.11903930e+00 -1.16364563e+00 -3.83691281e-01
-2.40472496e-01 8.70191157e-01 2.11372361e-01 -3.68870139... | [4.608093738555908, 0.5842369198799133] |
c55a3d6a-4b0f-4549-8d1f-1f9cb82e6b12 | marine-iot-systems-with-space-air-sea | 2301.03815 | null | https://arxiv.org/abs/2301.03815v1 | https://arxiv.org/pdf/2301.03815v1.pdf | Marine IoT Systems with Space-Air-Sea Integrated Networks: Hybrid LEO and UAV Edge Computing | Marine Internet of Things (IoT) systems have grown substantially with the development of non-terrestrial networks (NTN) via aerial and space vehicles in the upcoming sixth-generation (6G), thereby assisting environment protection, military reconnaissance, and sea transportation. Due to unpredictable climate changes and... | ['Joonhyuk Kang', 'Jinkyu Kang', 'Seongah Jeong', 'Sooyeob Jung'] | 2023-01-10 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-1.57931000e-01 -5.20282723e-02 6.42139539e-02 2.61398643e-01
2.88474679e-01 -1.05085289e+00 2.16389939e-01 -2.40859315e-01
-4.45609003e-01 8.99624884e-01 -5.34388542e-01 -6.15941584e-01
-7.51818180e-01 -1.07792640e+00 -4.69890594e-01 -9.10034359e-01
-7.49602139e-01 1.43884346e-01 -1.34047300e-01 -2.66849369... | [5.9394636154174805, 1.493317723274231] |
66a63003-fdf7-4066-a289-3ba9081e6121 | pointconv-deep-convolutional-networks-on-3d | 1811.07246 | null | https://arxiv.org/abs/1811.07246v3 | https://arxiv.org/pdf/1811.07246v3.pdf | PointConv: Deep Convolutional Networks on 3D Point Clouds | Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. In this paper, we extend the dynamic filter to a new convolution operation, named PointConv. PointConv can be applied on point clouds to build deep convolutional ... | ['Li Fuxin', 'Zhongang Qi', 'Wenxuan Wu'] | 2018-11-17 | pointconv-deep-convolutional-networks-on-3d-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wu_PointConv_Deep_Convolutional_Networks_on_3D_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_PointConv_Deep_Convolutional_Networks_on_3D_Point_Clouds_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-part-segmentation'] | ['computer-vision'] | [-4.05495971e-01 -3.69784772e-01 2.10481763e-01 -4.60699201e-01
-2.34986737e-01 -4.68740225e-01 5.89409173e-01 -2.13489030e-02
-6.29535556e-01 3.33732247e-01 -4.23349947e-01 -2.28608876e-01
-1.89023465e-02 -1.39703619e+00 -1.33067131e+00 -5.83214521e-01
-1.80340514e-01 8.30537617e-01 5.44291019e-01 8.36015344... | [7.927619457244873, -3.6274032592773438] |
b0ed060e-1f99-44cf-9a8a-716f20744706 | nlfiit-at-semeval-2020-task-11-neural-network | null | null | https://aclanthology.org/2020.semeval-1.232 | https://aclanthology.org/2020.semeval-1.232.pdf | NLFIIT at SemEval-2020 Task 11: Neural Network Architectures for Detection of Propaganda Techniques in News Articles | Since propaganda became more common technique in news, it is very important to look for possibilities of its automatic detection. In this paper, we present neural model architecture submitted to the SemEval-2020 Task 11 competition: {``}Detection of Propaganda Techniques in News Articles{''}. We participated in both su... | ['Marian Simko', 'Samuel Pecar', 'Matej Martinkovic'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-span-identification'] | ['natural-language-processing'] | [ 1.29352123e-01 2.58483082e-01 -2.70310313e-01 -6.02778532e-02
-7.36644506e-01 -5.40066481e-01 1.32760119e+00 3.46602350e-01
-6.98332131e-01 7.13245690e-01 8.37260187e-01 -6.00426137e-01
1.02724373e-01 -6.92471683e-01 -7.59633660e-01 -4.52558547e-01
3.31396982e-02 5.26704714e-02 -5.87713458e-02 -4.62982327... | [8.49721908569336, 10.683889389038086] |
26ad996d-4f2d-4b23-846b-9d6cb841b745 | pretraining-de-biased-language-model-with | 2302.13498 | null | https://arxiv.org/abs/2302.13498v1 | https://arxiv.org/pdf/2302.13498v1.pdf | Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking | Pre-trained language models have achieved great success in various large-scale information retrieval tasks. However, most of pretraining tasks are based on counterfeit retrieval data where the query produced by the tailored rule is assumed as the user's issued query on the given document or passage. Therefore, we explo... | ['Zhanhui Kang', 'Zeqian Huang', 'Lei Jiang', 'Bin Hu', 'Kunliang Wei', 'Xiaoshu Chen', 'Xiangsheng Li'] | 2023-02-27 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-2.41658807e-01 -3.49822164e-01 -5.09729147e-01 -3.57981831e-01
-1.24016380e+00 -5.30071855e-01 8.20798039e-01 1.02780528e-01
-1.00759506e+00 5.42258739e-01 1.35474935e-01 -7.33569682e-01
-2.91789800e-01 -3.35261762e-01 -7.37152755e-01 -1.19536854e-01
1.81111030e-03 7.67775655e-01 5.65680921e-01 -4.45702106... | [11.558622360229492, 7.66481876373291] |
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