paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f2c9d8b5-d814-47e5-aca9-39f739faf324 | generating-representative-samples-for-few | 2205.02918 | null | https://arxiv.org/abs/2205.02918v1 | https://arxiv.org/pdf/2205.02918v1.pdf | Generating Representative Samples for Few-Shot Classification | Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot class representations are often biased due to data scarcity. To mitigate this issue, we propose to generate visual samples based on semantic embeddings using a conditional variational autoencoder (CVAE) model. We train th... | ['Hieu Le', 'Jingyi Xu'] | 2022-05-05 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_Generating_Representative_Samples_for_Few-Shot_Classification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_Generating_Representative_Samples_for_Few-Shot_Classification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['classification'] | ['methodology'] | [ 4.92600687e-02 1.73734143e-01 -3.67077827e-01 -4.15332228e-01
-8.63498211e-01 -9.27938595e-02 7.99735665e-01 -2.02607527e-01
-2.97148913e-01 7.23960400e-01 3.40089172e-01 3.72038603e-01
2.43641078e-01 -9.70603883e-01 -8.69979441e-01 -6.34765387e-01
3.88176769e-01 3.18843395e-01 2.40840316e-01 -8.37986693... | [9.94788646697998, 2.8009345531463623] |
e369aebc-d442-48ef-8677-c5d398dbc9fb | kpt-keyword-guided-pre-training-for-grounded | 2212.01739 | null | https://arxiv.org/abs/2212.01739v1 | https://arxiv.org/pdf/2212.01739v1.pdf | KPT: Keyword-guided Pre-training for Grounded Dialog Generation | Incorporating external knowledge into the response generation process is essential to building more helpful and reliable dialog agents. However, collecting knowledge-grounded conversations is often costly, calling for a better pre-trained model for grounded dialog generation that generalizes well w.r.t. different types... | ['Minlie Huang', 'Xiaoyan Zhu', 'Qun Liu', 'Xin Jiang', 'Yitong Li', 'Yasheng Wang', 'Zheng Zhang', 'Fei Mi', 'Qi Zhu'] | 2022-12-04 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 8.12604502e-02 8.83760333e-01 -4.69888970e-02 -4.53843355e-01
-1.01448441e+00 -6.74304426e-01 8.04534733e-01 1.04549013e-01
-3.55460227e-01 1.20693982e+00 7.07813919e-01 -2.04617649e-01
1.59579381e-01 -9.36855257e-01 -3.58557820e-01 -3.78368914e-01
3.98514211e-01 1.16264760e+00 3.56745571e-01 -9.57468927... | [12.534274101257324, 8.103514671325684] |
31d82806-4c5f-44f1-ba7a-102826ec3197 | robust-human-detection-under-visual | 2307.03623 | null | https://arxiv.org/abs/2307.03623v1 | https://arxiv.org/pdf/2307.03623v1.pdf | Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion | The majority of human detection methods rely on the sensor using visible lights (e.g., RGB cameras) but such sensors are limited in scenarios with degraded vision conditions. In this paper, we present a multimodal human detection system that combines portable thermal cameras and single-chip mmWave radars. To mitigate t... | ['Chris Xiaoxuan Lu', 'John Stankovic', 'Peize Li', 'Qiyue Xia', 'Kaiwen Cai'] | 2023-07-07 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [ 3.21295291e-01 -5.05886257e-01 5.62975526e-01 -2.65747607e-01
-8.88787985e-01 -6.10321522e-01 5.95248640e-01 -6.24422610e-01
-6.45310163e-01 5.57536721e-01 -1.86088711e-01 1.66774929e-01
5.13676740e-02 -5.70631802e-01 -2.40420103e-01 -8.75983834e-01
6.55199885e-01 7.86549971e-02 5.64554155e-01 4.96348068... | [7.909421443939209, -1.4531307220458984] |
7983a384-1b92-46a7-bf55-4a3c31296f67 | learning-robust-visual-semantic-embeddings | 1703.05908 | null | http://arxiv.org/abs/1703.05908v2 | http://arxiv.org/pdf/1703.05908v2.pdf | Learning Robust Visual-Semantic Embeddings | Many of the existing methods for learning joint embedding of images and text
use only supervised information from paired images and its textual attributes.
Taking advantage of the recent success of unsupervised learning in deep neural
networks, we propose an end-to-end learning framework that is able to extract
more ro... | ['Liang-Kang Huang', 'Yao-Hung Hubert Tsai', 'Ruslan Salakhutdinov'] | 2017-03-17 | learning-robust-visual-semantic-embeddings-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Tsai_Learning_Robust_Visual-Semantic_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Tsai_Learning_Robust_Visual-Semantic_ICCV_2017_paper.pdf | iccv-2017-10 | ['generalized-few-shot-learning'] | ['methodology'] | [ 2.61257738e-01 -2.36235023e-01 -4.97439772e-01 -7.93733597e-01
-9.71672535e-01 -5.45484185e-01 1.01086879e+00 3.93397212e-01
-7.79831767e-01 6.13175511e-01 2.66916305e-01 1.89220041e-01
-2.76546568e-01 -6.43819511e-01 -7.63238847e-01 -5.52636206e-01
3.15111838e-02 6.82406485e-01 -1.09482259e-01 -1.08838202... | [10.049399375915527, 2.427971601486206] |
909793d0-dccd-444d-baef-6fc743b5687c | text-conditional-contextualized-avatars-for | 2304.07410 | null | https://arxiv.org/abs/2304.07410v1 | https://arxiv.org/pdf/2304.07410v1.pdf | Text-Conditional Contextualized Avatars For Zero-Shot Personalization | Recent large-scale text-to-image generation models have made significant improvements in the quality, realism, and diversity of the synthesized images and enable users to control the created content through language. However, the personalization aspect of these generative models is still challenging and under-explored.... | ['Sonal Gupta', 'Devi Parikh', 'Guan Pang', 'Akbar Shah', 'Thomas Hayes', 'Samaneh Azadi'] | 2023-04-14 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.49360299e-01 2.78281808e-01 3.37166071e-01 -3.22120100e-01
-7.06005871e-01 -8.91935110e-01 7.65902996e-01 -7.48117447e-01
-1.57142282e-01 4.11659271e-01 5.10697663e-01 3.88319314e-01
4.96386349e-01 -6.63486302e-01 -8.41928363e-01 -3.39110494e-01
4.76361662e-01 9.33620095e-01 8.60748067e-02 -4.45919752... | [12.01846981048584, -0.663482666015625] |
6277d512-00c7-4df3-8934-8c1d4e9248ca | estimating-and-detecting-random-processes-on | 2211.07884 | null | https://arxiv.org/abs/2211.07884v1 | https://arxiv.org/pdf/2211.07884v1.pdf | Estimating and detecting random processes on the unit circle | The problem of detecting a sinusoidal signal with randomly varying frequency has a long history. It is one of the core problems in signal processing, arising in many applications including, for example, underwater acoustic frequency line tracking, demodulation of FM radio communications, laser phase drift in optical co... | ['A. Melatos', 'B. Moran', 'R. J. Evans', 'S. Suvorova', 'Changrong Liu'] | 2022-11-15 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 2.88833857e-01 -2.72990048e-01 3.66012812e-01 -5.06738424e-02
-8.22813272e-01 -4.04750437e-01 4.64720935e-01 -4.85242270e-02
-7.87554622e-01 9.14992332e-01 -3.28529686e-01 -4.33492541e-01
-2.62297571e-01 -4.81671423e-01 -3.16139162e-01 -9.53022718e-01
-5.60392916e-01 4.74031061e-01 4.15128857e-01 1.30637527... | [6.756054401397705, 3.6839001178741455] |
0dd426ef-2ea9-4a20-af79-89e63590c8b2 | leveraging-information-bottleneck-for | 2110.01280 | null | https://arxiv.org/abs/2110.01280v1 | https://arxiv.org/pdf/2110.01280v1.pdf | Leveraging Information Bottleneck for Scientific Document Summarization | This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle. Inspired by previous work which uses the Information Bottleneck principle for sentence compression, we extend it to document level summarization with two separate steps. In the f... | ['Shirui Pan', 'Lan Du', 'Yuan Jin', 'Huan Yee Koh', 'Ming Liu', 'Jiaxin Ju'] | 2021-10-04 | null | https://aclanthology.org/2021.findings-emnlp.345 | https://aclanthology.org/2021.findings-emnlp.345.pdf | findings-emnlp-2021-11 | ['sentence-compression', 'scientific-article-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.50038779e-01 1.64658770e-01 -2.45399162e-01 -1.94072977e-01
-1.07500041e+00 -5.40440261e-01 5.49627364e-01 5.65124810e-01
-3.22075635e-01 8.76272142e-01 9.86099243e-01 -8.70553181e-02
-8.41663480e-02 -6.46292448e-01 -4.07962203e-01 -4.83521134e-01
1.41297251e-01 1.69254988e-01 2.88296580e-01 -1.64644718... | [12.535895347595215, 9.5143461227417] |
49e0c918-e52e-4904-b8b3-110259e243af | generating-code-with-the-help-of-retrieved | 2104.05310 | null | https://arxiv.org/abs/2104.05310v2 | https://arxiv.org/pdf/2104.05310v2.pdf | Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers | We approach the important challenge of code autocompletion as an open-domain task, in which a sequence-to-sequence code generator model is enhanced with the ability to attend to reference code snippets supplied by a semantic code search engine. In this work, we present a novel framework to precisely retrieve template f... | ['Changran Hu', 'Neel Sundaresan', 'Mikhail Breslav', 'Chen Wu', 'Dawn Drain'] | 2021-04-12 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 2.84606785e-01 -3.10855433e-02 -1.49611384e-01 -1.56472683e-01
-1.38925970e+00 -9.75181460e-01 3.03571850e-01 8.87821391e-02
-1.07975580e-01 4.87922788e-01 3.11952621e-01 -5.65537572e-01
-1.46931201e-01 -6.03710949e-01 -1.02604234e+00 -2.07967967e-01
-5.65494038e-02 3.40934932e-01 2.15318814e-01 -4.20932651... | [7.599555492401123, 8.021174430847168] |
d7747213-d629-44c9-86fe-de1e2af282a2 | zero-and-few-shot-semantic-parsing-with | 2306.00824 | null | https://arxiv.org/abs/2306.00824v1 | https://arxiv.org/pdf/2306.00824v1.pdf | Zero and Few-shot Semantic Parsing with Ambiguous Inputs | Despite the ubiquity of ambiguity in natural language, it is often ignored or deliberately removed in semantic parsing tasks, which generally assume that a given surface form has only one correct logical form. We attempt to address this shortcoming by introducing AmP, a framework, dataset, and challenge for parsing wit... | ['Benjamin Van Durme', 'Kyle Rawlins', 'Elias Stengel-Eskin'] | 2023-06-01 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.75578487e-01 5.29898345e-01 1.15167983e-01 -7.75696516e-01
-9.36200082e-01 -1.03747118e+00 6.80345416e-01 3.87864798e-01
-2.52100706e-01 6.25564814e-01 4.65756863e-01 -7.50783324e-01
-4.68197605e-03 -9.47966814e-01 -6.90408170e-01 5.06569333e-02
5.54385483e-01 7.44968355e-01 2.91472375e-01 -4.31063056... | [10.376376152038574, 8.9683198928833] |
65e08aa3-4dc2-44a0-9316-355fab785dff | playing-carcassonne-with-monte-carlo-tree | 2009.12974 | null | https://arxiv.org/abs/2009.12974v2 | https://arxiv.org/pdf/2009.12974v2.pdf | Playing Carcassonne with Monte Carlo Tree Search | Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with... | ['Anger Fernando Kuri Morales', 'Fred Valdez Ameneyro', 'Edgar Galvan'] | 2020-09-27 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.44391784e-02 -3.07638347e-01 -1.85461212e-02 3.20752710e-01
-7.75959313e-01 -6.43338263e-01 5.67063928e-01 -2.42036134e-01
-6.54898942e-01 1.12781036e+00 -1.54554725e-01 -4.24825341e-01
-5.64596713e-01 -7.19724774e-01 -2.18225464e-01 -8.44621480e-01
-2.15687931e-01 7.25349069e-01 8.75521004e-01 -6.80480480... | [3.523635149002075, 1.5250855684280396] |
28f13b68-c5c6-4f2d-97fc-d2d43f0b6143 | curaj-iiitdwd-lt-edi-acl-2022-hope-speech | null | null | https://aclanthology.org/2022.ltedi-1.25 | https://aclanthology.org/2022.ltedi-1.25.pdf | CURAJ_IIITDWD@LT-EDI-ACL 2022: Hope Speech Detection in English YouTube Comments using Deep Learning Techniques | Hope Speech are positive terms that help to promote or criticise a point of view without hurting the user’s or community’s feelings. Non-Hope Speech, on the other side, includes expressions that are harsh, ridiculing, or demotivating. The goal of this article is to find the hope speech comments in a YouTube dataset. Th... | ['Sunil Saumya', 'Ankit Mishra', 'Vanshita Jha'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-5.24592340e-01 3.11301589e-01 -2.07942843e-01 -2.04544038e-01
-8.84942412e-01 -1.14839636e-01 6.03984058e-01 7.17392936e-02
-1.37070313e-01 6.81034505e-01 1.18224823e+00 -3.46982270e-01
3.30373913e-01 -3.68693173e-01 -1.36189371e-01 -4.64151949e-01
2.08552465e-01 -1.86245933e-01 -5.06848633e-01 -6.27231598... | [9.009891510009766, 10.71493148803711] |
18e7a6ad-5b4a-4acf-8b0c-864d5ba51336 | mapformer-boosting-change-detection-by-using | 2303.17859 | null | https://arxiv.org/abs/2303.17859v1 | https://arxiv.org/pdf/2303.17859v1.pdf | MapFormer: Boosting Change Detection by Using Pre-change Information | Change detection in remote sensing imagery is essential for a variety of applications such as urban planning, disaster management, and climate research. However, existing methods for identifying semantically changed areas overlook the availability of semantic information in the form of existing maps describing features... | ['Matthias Schubert', 'Niklas Strauß', 'Maximilian Bernhard'] | 2023-03-31 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.59490919e-01 -3.96118969e-01 1.58070236e-01 -4.83678401e-01
-7.58487582e-01 -6.46782219e-01 1.03984952e+00 3.70047778e-01
-5.82713068e-01 6.27379477e-01 2.80946255e-01 -3.15674752e-01
-9.70192254e-02 -1.02539086e+00 -6.00022316e-01 -7.70503998e-01
-2.71434575e-01 -1.21434927e-01 3.51990938e-01 -4.95742500... | [9.662079811096191, -1.2805256843566895] |
0d32d23d-a2d2-439d-9869-67333c4b6eb1 | availability-adversarial-attack-and | 2301.01832 | null | https://arxiv.org/abs/2301.01832v1 | https://arxiv.org/pdf/2301.01832v1.pdf | Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting | The forecast of electrical loads is essential for the planning and operation of the power system. Recently, advances in deep learning have enabled more accurate forecasts. However, deep neural networks are prone to adversarial attacks. Although most of the literature focuses on integrity-based attacks, this paper propo... | ['Fei Teng', 'Wangkun Xu'] | 2023-01-04 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-2.49753147e-01 -5.88257983e-02 -1.29090667e-01 -1.49243131e-01
-3.90758842e-01 -8.22123230e-01 3.05618197e-01 6.28677979e-02
1.55688286e-01 8.33752275e-01 -2.95891076e-01 -8.30667436e-01
-1.79132774e-01 -1.15252411e+00 -6.80820227e-01 -9.12383497e-01
-4.41446513e-01 3.82777154e-01 -3.93199503e-01 -2.11689711... | [5.464780807495117, 7.405476093292236] |
52b7d307-c9d3-4d5a-935a-690bc1b2acb8 | neuroprim-an-attention-based-model-for | 2210.12453 | null | https://arxiv.org/abs/2210.12453v2 | https://arxiv.org/pdf/2210.12453v2.pdf | NeuroPrim: An Attention-based Model for Solving NP-hard Spanning Tree Problems | Spanning tree problems with specialized constraints can be difficult to solve in real-world scenarios, often requiring intricate algorithmic design and exponential time. Recently, there has been growing interest in end-to-end deep neural networks for solving routing problems. However, such methods typically produce seq... | ['Tiande Guo', 'Congying Han', 'Yuchen Shi'] | 2022-10-22 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 4.86361116e-01 2.46978059e-01 -3.13145548e-01 -1.68451160e-01
-4.87136722e-01 -8.38824868e-01 -9.73001644e-02 7.05433637e-02
-2.46828020e-01 7.31902719e-01 -5.57470977e-01 -7.83891559e-01
-7.75153100e-01 -9.82178450e-01 -8.58540535e-01 -6.87038362e-01
-5.52153826e-01 7.19385982e-01 2.33738750e-01 3.61015126... | [5.248636722564697, 2.9196431636810303] |
a5edf616-1bf9-49db-b859-22210c258294 | self-supervised-modality-invariant-and | null | null | https://openreview.net/forum?id=RunqFdkPuS | https://openreview.net/pdf?id=RunqFdkPuS | Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects | While most existing self-supervised 3D feature learning methods mainly focus on point cloud data, this paper explores the inherent multimodal attributes of 3D objects. We propose to jointly learn effective features from different modalities including image, point cloud, and mesh with heterogeneous networks from unlabel... | ['YingLi Tian', 'Bing Li', 'Zhimin Chen', 'Longlong Jing'] | 2021-09-29 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-2.29276419e-01 -1.81827024e-01 -5.88673353e-01 -6.05914056e-01
-1.23452830e+00 -6.81582689e-01 6.91948533e-01 4.43135649e-01
-1.29176062e-02 2.05026850e-01 2.60549843e-01 3.71805042e-01
-3.03221822e-01 -6.48729384e-01 -7.53192127e-01 -5.84589422e-01
-2.27068454e-01 5.66764653e-01 2.76524454e-01 5.10003306... | [8.126936912536621, -3.456651449203491] |
191dc3fa-46c1-425f-a23b-f2bbf94de764 | where-is-your-place-visual-place-recognition | 2103.06443 | null | https://arxiv.org/abs/2103.06443v2 | https://arxiv.org/pdf/2103.06443v2.pdf | Where is your place, Visual Place Recognition? | Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling robotic platforms and intelligent augmentation platforms such as augmented reality devices to perce... | ['Michael Milford', 'Tobias Fischer', 'Sourav Garg'] | 2021-03-11 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 6.10697307e-02 -1.74565226e-01 -1.73976392e-01 -4.64195348e-02
2.52192050e-01 -9.54275906e-01 7.91216075e-01 -2.06277687e-02
-5.29670775e-01 4.24130857e-01 4.19957265e-02 -9.95686203e-02
-1.21046335e-01 -5.43084502e-01 -5.07155061e-01 -6.21759355e-01
-1.20480500e-01 3.06465656e-01 3.09668392e-01 -4.92561847... | [7.47868537902832, -1.8831449747085571] |
59d626a5-c056-4542-8fb8-04868fc1f21d | predicting-skull-fractures-via-cnn-with | 2208.06756 | null | https://arxiv.org/abs/2208.06756v1 | https://arxiv.org/pdf/2208.06756v1.pdf | Predicting skull fractures via CNN with classification algorithms | Computer Tomography (CT) images have become quite important to diagnose diseases. CT scan slice contains a vast amount of data that may not be properly examined with the requisite precision and speed using normal visual inspection. A computer-assisted skull fracture classification expert system is needed to assist phys... | ['Moqsadur Rahman', 'Tareque Rahman Ornob', 'Md Moniruzzaman Emon'] | 2022-08-14 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [-1.04578994e-01 -5.64872734e-02 1.50747895e-01 -3.32108796e-01
-4.68957722e-01 9.84264305e-04 1.01899713e-01 6.23398542e-01
-6.78883195e-01 5.07890940e-01 5.87236807e-02 -4.97308224e-01
-3.47452700e-01 -8.61472726e-01 -1.05032437e-01 -5.54879010e-01
-6.03662968e-01 6.76704288e-01 4.74088669e-01 -2.38322854... | [14.946714401245117, -2.4079439640045166] |
530d3288-5a9c-4fa2-acba-31dc52a8faf7 | seeing-through-deception-a-computational | null | null | https://aclanthology.org/W12-0403 | https://aclanthology.org/W12-0403.pdf | Seeing through Deception: A Computational Approach to Deceit Detection in Written Communication | null | ["Rafael Valencia-Garc{\\'\\i}a", "{\\'A}ngela Almela", 'Pascual Cantos'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.181015491485596, 3.5639233589172363] |
03e9031a-6e4d-4cc2-a790-af9bb4c0f04f | reflection-invariant-and-symmetry-detection | 1705.10768 | null | http://arxiv.org/abs/1705.10768v2 | http://arxiv.org/pdf/1705.10768v2.pdf | Reflection Invariant and Symmetry Detection | Symmetry detection and discrimination are of fundamental meaning in science,
technology, and engineering. This paper introduces reflection invariants and
defines the directional moment to detect symmetry for shape analysis and object
recognition. And it demonstrates that detection of reflection symmetry can be
done in ... | ['Erbo Li', 'Hua Li'] | 2017-05-30 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 4.77854759e-01 -5.09078465e-02 -4.51236665e-02 -5.86202927e-02
1.00490727e-01 -4.73795384e-01 6.74938440e-01 -2.21103340e-01
-1.06121622e-01 5.05032659e-01 1.25928950e-02 -5.32099128e-01
-4.77831870e-01 -8.42166781e-01 -1.91633239e-01 -7.44712114e-01
-4.83219512e-03 7.07418561e-01 3.47666979e-01 -3.34701687... | [9.177483558654785, -1.7959234714508057] |
ebb9f790-df51-4d9e-9c42-7bd9b7a0c944 | evolutionary-reinforcement-learning-a-survey | 2303.04150 | null | https://arxiv.org/abs/2303.04150v3 | https://arxiv.org/pdf/2303.04150v3.pdf | Evolutionary Reinforcement Learning: A Survey | Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impressive achievements in a wide range of challenging tasks, including board games, arcade games, and ro... | ['Yaochu Jin', 'Ran Cheng', 'Hui Bai'] | 2023-03-07 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.51934499e-01 -3.10751021e-01 -3.93490762e-01 2.22146705e-01
-5.88508368e-01 -3.92440557e-01 3.43916982e-01 -1.51008070e-02
-8.10619831e-01 1.35112917e+00 -3.10918272e-01 -8.19650665e-03
-5.48608243e-01 -7.07652807e-01 -4.80862916e-01 -1.00214648e+00
-4.85315919e-01 6.23002708e-01 -1.94789786e-02 -6.04279339... | [3.9038450717926025, 1.9781428575515747] |
9649ef67-d3b4-4357-b618-df8b51e48718 | paste-inpaint-and-harmonize-via-denoising | 2306.07596 | null | https://arxiv.org/abs/2306.07596v1 | https://arxiv.org/pdf/2306.07596v1.pdf | Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model | Text-to-image generative models have attracted rising attention for flexible image editing via user-specified descriptions. However, text descriptions alone are not enough to elaborate the details of subjects, often compromising the subjects' identity or requiring additional per-subject fine-tuning. We introduce a new ... | ['Yusuke Iwasawa', 'Yutaka Matsuo', 'Paul Yoo', 'Jiaxian Guo', 'Xin Zhang'] | 2023-06-13 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 5.31934261e-01 1.00683630e-01 4.96687219e-02 -5.10574758e-01
-8.00071836e-01 -6.62108421e-01 8.19161057e-01 -1.82460546e-01
-1.96568415e-01 3.77156138e-01 2.88878530e-01 1.29871488e-01
2.32196063e-01 -5.11792660e-01 -7.27998912e-01 -5.67290664e-01
6.34873986e-01 3.52754921e-01 1.70282990e-01 -1.45106018... | [11.334070205688477, -0.6742768883705139] |
f7f2b791-69f9-4e5d-a199-70ceae19da72 | counting-guidance-for-high-fidelity-text-to | 2306.17567 | null | https://arxiv.org/abs/2306.17567v1 | https://arxiv.org/pdf/2306.17567v1.pdf | Counting Guidance for High Fidelity Text-to-Image Synthesis | Recently, the quality and performance of text-to-image generation significantly advanced due to the impressive results of diffusion models. However, text-to-image diffusion models still fail to generate high fidelity content with respect to the input prompt. One problem where text-to-diffusion models struggle is genera... | ['Hyung Il Koo', 'Kevin Galim', 'Wonjun Kang'] | 2023-06-30 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 4.73860234e-01 -4.05305684e-01 3.91260594e-01 -4.13500041e-01
-7.20995963e-01 -5.04816234e-01 6.36188328e-01 6.81821480e-02
-4.82431293e-01 3.67790639e-01 2.14225575e-01 -4.95336689e-02
1.09899811e-01 -9.75565791e-01 -6.90430760e-01 -6.74091578e-01
5.78469813e-01 4.45046067e-01 2.66588062e-01 5.03954068... | [11.428325653076172, -0.34015366435050964] |
4e2b1f9b-b35c-4c38-b8b3-f21e56a3b558 | fedhgn-a-federated-framework-for | 2305.09729 | null | https://arxiv.org/abs/2305.09729v1 | https://arxiv.org/pdf/2305.09729v1.pdf | FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks | Heterogeneous graph neural networks (HGNNs) can learn from typed and relational graph data more effectively than conventional GNNs. With larger parameter spaces, HGNNs may require more training data, which is often scarce in real-world applications due to privacy regulations (e.g., GDPR). Federated graph learning (FGL)... | ['Irwin King', 'Xinyu Fu'] | 2023-05-16 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-2.70814568e-01 5.18667698e-01 -5.84631085e-01 -4.45400864e-01
-3.53634000e-01 -8.86899054e-01 2.58785337e-01 5.53354919e-02
-1.58104241e-01 7.70487487e-01 5.03259227e-02 -4.94744182e-01
-2.26848915e-01 -1.29066885e+00 -7.29274035e-01 -4.85513240e-01
1.37795702e-01 5.06403804e-01 7.59312063e-02 -9.88974422... | [6.0009236335754395, 6.8851823806762695] |
0d28ed43-b34f-4bfc-a867-c8c01272e300 | inter-species-cell-detection-datasets-on | 2108.08529 | null | https://arxiv.org/abs/2108.08529v1 | https://arxiv.org/pdf/2108.08529v1.pdf | Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens | Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolarlavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-... | ['Christof A. Bertram', 'Katharina Breininger', 'Robert Klopfleisch', 'Andreas Maier', 'Marc Aubreville', 'Jörn Voigt', 'Frauke Wilm', 'Lutz Welker', 'Dorothee Bienzle', 'Jason Stayt', 'Jenny Hill', 'Christian Marzahl'] | 2021-08-19 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-4.18242849e-02 -5.37499450e-02 1.95535362e-01 8.16363543e-02
-6.25608742e-01 -6.89261615e-01 4.65969771e-01 5.93699753e-01
-8.46802175e-01 8.52688968e-01 -3.11224461e-01 -2.11118400e-01
3.07306275e-02 -6.34387553e-01 -4.30144787e-01 -7.60143936e-01
3.84539813e-02 1.06441116e+00 8.80020559e-01 3.52187485... | [15.044086456298828, -3.13150954246521] |
6c97b5aa-7a12-441f-9a6c-65e05759e7c4 | how-human-judgment-impairs-automated | 2003.13316 | null | https://arxiv.org/abs/2003.13316v1 | https://arxiv.org/pdf/2003.13316v1.pdf | How human judgment impairs automated deception detection performance | Background: Deception detection is a prevalent problem for security practitioners. With a need for more large-scale approaches, automated methods using machine learning have gained traction. However, detection performance still implies considerable error rates. Findings from other domains suggest that hybrid human-mach... | ['Bennett Kleinberg', 'Bruno Verschuere'] | 2020-03-30 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [ 2.13658303e-01 4.01975572e-01 -3.04728419e-01 -6.21891141e-01
-7.28505731e-01 -6.28331184e-01 7.71277905e-01 3.77958834e-01
-6.53683126e-01 6.99901879e-01 1.69435367e-01 -8.81039262e-01
1.78422153e-01 -3.12321603e-01 -1.82784393e-01 -4.73091990e-01
6.11613154e-01 2.31182456e-01 -1.71163857e-01 -1.68348223... | [8.1876802444458, 10.387429237365723] |
79d256d8-2133-48a6-984f-94b7bbcc2e24 | atypicality-for-heart-rate-variability-using | 1710.07319 | null | http://arxiv.org/abs/1710.07319v1 | http://arxiv.org/pdf/1710.07319v1.pdf | Atypicality for Heart Rate Variability Using a Pattern-Tree Weighting Method | Heart rate variability (HRV) is a vital measure of the autonomic nervous
system functionality and a key indicator of cardiovascular condition. This
paper proposes a novel method, called pattern tree which is an extension of
Willem's context tree to real-valued data, to investigate HRV via an
atypicality framework. In a... | ['Anders Høst-Madsen', 'Elyas Sabeti'] | 2017-10-12 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 6.39794394e-02 -4.18906137e-02 -3.10384817e-02 -3.32715571e-01
4.41482008e-01 -4.41798270e-01 -1.01533039e-02 2.64386475e-01
2.00994667e-02 1.03349757e+00 2.15834588e-01 -3.49285126e-01
-5.57739377e-01 -8.76142323e-01 1.07820936e-01 -2.79575288e-01
-4.59843010e-01 1.30581200e-01 -3.43581200e-01 -3.75663579... | [14.13066291809082, 3.147197961807251] |
a94e1d1b-aedf-48c1-8057-6c1adf376db3 | image-smoothing-via-unsupervised-learning | 1811.02804 | null | http://arxiv.org/abs/1811.02804v1 | http://arxiv.org/pdf/1811.02804v1.pdf | Image Smoothing via Unsupervised Learning | Image smoothing represents a fundamental component of many disparate computer
vision and graphics applications. In this paper, we present a unified
unsupervised (label-free) learning framework that facilitates generating
flexible and high-quality smoothing effects by directly learning from data
using deep convolutional... | ['Qingnan Fan', 'David Wipf', 'Jiaolong Yang', 'Xin Tong', 'Baoquan Chen'] | 2018-11-07 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 3.94597858e-01 -1.84511423e-01 -4.30577770e-02 -2.88221687e-01
-3.90183657e-01 -3.60262871e-01 4.37610120e-01 1.85817741e-02
-4.23591614e-01 4.81328428e-01 1.55883655e-01 -3.33944321e-01
2.72358477e-01 -7.79474914e-01 -7.53826678e-01 -6.93618298e-01
1.35790512e-01 -4.49591845e-01 4.30173844e-01 -1.48467675... | [10.952305793762207, -1.496046543121338] |
bee84a1b-2ea0-4811-8b24-db06ae1e5ca6 | few-shot-single-view-3-d-object | 2004.06302 | null | https://arxiv.org/abs/2004.06302v2 | https://arxiv.org/pdf/2004.06302v2.pdf | Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors | The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of the output space. However, recent work has challenged this belief, showing that complex encoder-decoder architectures perform similarly to ... | ['Stavros Tsogkas', 'Mateusz Michalkiewicz', 'Eugene Belilovsky', 'Anders Eriksson', 'Mahsa Baktashmotlagh', 'Sarah Parisot'] | 2020-04-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5140_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700613.pdf | eccv-2020-8 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 3.31414551e-01 4.88935471e-01 -5.12710959e-02 -9.04945731e-01
-6.72309756e-01 -7.72960424e-01 1.00746202e+00 1.51095530e-02
-3.19395065e-01 2.99971640e-01 5.49085021e-01 -2.02668697e-01
1.81023657e-01 -8.57046247e-01 -1.23157763e+00 -3.63612145e-01
2.11748749e-01 8.17248940e-01 3.11302364e-01 -1.92744672... | [8.467633247375488, -3.1532816886901855] |
5ebbfda1-b4ed-4674-95d1-c763f7900353 | semi-supervised-deep-representation-learning | 1811.04480 | null | http://arxiv.org/abs/1811.04480v1 | http://arxiv.org/pdf/1811.04480v1.pdf | Semi-supervised Deep Representation Learning for Multi-View Problems | While neural networks for learning representation of multi-view data have
been previously proposed as one of the state-of-the-art multi-view dimension
reduction techniques, how to make the representation discriminative with only a
small amount of labeled data is not well-studied. We introduce a
semi-supervised neural n... | ['Lei Zheng', 'Philip S. Yu', 'Sihong Xie', 'Weixiang Shao', 'Vahid Noroozi', 'Sara Bahaadini'] | 2018-11-11 | null | null | null | null | ['learning-representation-of-multi-view-data'] | ['methodology'] | [-1.20076083e-01 -1.02502465e-01 -5.41624844e-01 -7.09979594e-01
-5.74156702e-01 -6.01059675e-01 4.66827631e-01 -5.07371187e-01
5.87004721e-02 3.72498870e-01 4.69712377e-01 3.62397343e-01
-1.89336464e-01 -5.14521182e-01 -3.66320044e-01 -7.83567667e-01
3.52505505e-01 7.50527859e-01 -3.87917995e-01 2.13064745... | [8.397510528564453, 4.581705093383789] |
cf784f9b-b57c-45e3-81f4-92ae0e2ada70 | decoupling-classifier-for-boosting-few-shot | null | null | https://openreview.net/pdf?id=dVXO3Orjmxk | https://openreview.net/pdf?id=dVXO3Orjmxk | Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation | This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances. The existing methods severely suffer from bias classification because of the missing label issue which naturally exists in a few-shot scenario and... | ['Chengjie Wang', 'Xi Wang', 'Guannan Jiang', 'Jinxiang Lai', 'Jun Liu', 'Congchong Nie', 'Zhongyi Huang', 'Xiaochen Chen', 'Bin-Bin Gao'] | 2022-09-05 | null | null | null | thirty-sixth-conference-on-neural-information | ['few-shot-object-detection'] | ['computer-vision'] | [ 3.78163069e-01 -1.11791983e-01 -3.72629076e-01 -5.78333080e-01
-9.85644221e-01 -5.42378068e-01 5.17881036e-01 -6.44994155e-02
-4.80034202e-01 7.60623217e-01 -5.14112771e-01 -3.18850614e-02
1.09922938e-01 -5.80869555e-01 -6.73269689e-01 -9.61531162e-01
3.65195394e-01 3.37968379e-01 7.59680569e-01 3.72632109... | [9.508142471313477, 1.7347148656845093] |
eb667cdf-4899-45b4-b94e-c80203bbdc1f | sangeet-a-xml-based-open-dataset-for-research | 2306.04148 | null | https://arxiv.org/abs/2306.04148v1 | https://arxiv.org/pdf/2306.04148v1.pdf | SANGEET: A XML based Open Dataset for Research in Hindustani Sangeet | It is very important to access a rich music dataset that is useful in a wide variety of applications. Currently, available datasets are mostly focused on storing vocal or instrumental recording data and ignoring the requirement of its visual representation and retrieval. This paper attempts to build an XML-based public... | ['Swarup Chattopadhyay', 'Chandan Misra'] | 2023-06-07 | null | null | null | null | ['music-information-retrieval', 'information-retrieval'] | ['music', 'natural-language-processing'] | [ 1.85924754e-01 -5.03934443e-01 -2.89116912e-02 6.08819984e-02
-9.67638612e-01 -1.04879415e+00 4.03277695e-01 4.07274336e-01
5.42874001e-02 4.17146981e-01 4.68783826e-01 3.87046598e-02
-7.03283012e-01 -7.66039848e-01 -1.41694814e-01 -6.74930215e-01
-9.53376666e-02 4.70204532e-01 4.30848263e-02 -4.35249120... | [15.98134708404541, 5.205749988555908] |
96af90a1-4d0e-4a29-8611-9de84b4c2f77 | cross-modal-contrastive-learning-for-speech-1 | 2205.02444 | null | https://arxiv.org/abs/2205.02444v1 | https://arxiv.org/pdf/2205.02444v1.pdf | Cross-modal Contrastive Learning for Speech Translation | How can we learn unified representations for spoken utterances and their written text? Learning similar representations for semantically similar speech and text is important for speech translation. To this end, we propose ConST, a cross-modal contrastive learning method for end-to-end speech-to-text translation. We eva... | ['Lei LI', 'Mingxuan Wang', 'Rong Ye'] | 2022-05-05 | null | https://aclanthology.org/2022.naacl-main.376 | https://aclanthology.org/2022.naacl-main.376.pdf | naacl-2022-7 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 8.12372193e-02 -1.27935885e-02 -4.44007069e-01 -5.72464287e-01
-1.94510043e+00 -7.66040683e-01 9.60004151e-01 -1.03829004e-01
-3.21749568e-01 6.08664811e-01 9.14380312e-01 -3.61276805e-01
4.82603431e-01 -9.11867917e-02 -5.78295708e-01 -3.93788844e-01
4.79900986e-01 7.78066933e-01 -1.01759024e-01 -3.98283780... | [14.494524002075195, 7.197785377502441] |
b89b0513-f785-4c05-b2e8-4416d4f74a86 | mem_ge-a-new-maximum-entropy-method-for-image | 2002.07921 | null | https://arxiv.org/abs/2002.07921v1 | https://arxiv.org/pdf/2002.07921v1.pdf | MEM_GE: a new maximum entropy method for image reconstruction from solar X-ray visibilities | Maximum Entropy is an image reconstruction method conceived to image a sparsely occupied field of view and therefore particularly appropriate to achieve super-resolution effects. Although widely used in image deconvolution, this method has been formulated in radio astronomy for the analysis of observations in the spati... | ['Federico Benvenuto', 'Michele Piana', 'Brian R Dennis', 'Richard Schwartz', 'Paolo Massa', 'Anna Maria Massone', 'A Kim Tolbert'] | 2020-02-18 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 3.57159525e-01 2.64270365e-01 3.09273839e-01 -3.22648972e-01
-4.03903753e-01 -2.96768427e-01 6.91935062e-01 -5.03485739e-01
-4.34190631e-01 8.56814981e-01 -8.51479098e-02 -2.44921908e-01
-4.07696009e-01 -6.47769272e-01 -3.62705261e-01 -9.58122611e-01
1.74567953e-01 5.47454357e-01 -9.01980028e-02 -2.03987807... | [11.526118278503418, -2.595055103302002] |
e17d3b7e-8e42-4cd4-8cf7-fe630c527105 | exploiting-timegraphs-in-temporal-relation | null | null | https://aclanthology.org/W14-3702 | https://aclanthology.org/W14-3702.pdf | Exploiting Timegraphs in Temporal Relation Classification | null | ['Natsuda Laokulrat', 'Makoto Miwa', 'Yoshimasa Tsuruoka'] | 2014-10-01 | null | null | null | ws-2014-10 | ['temporal-relation-classification'] | ['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... | [-7.481617450714111, 3.5874524116516113] |
e63baa1e-8446-429e-85f4-ff855c49f699 | fine-grained-causality-extraction-from | 2107.09980 | null | https://arxiv.org/abs/2107.09980v2 | https://arxiv.org/pdf/2107.09980v2.pdf | Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks | [Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to ext... | ['Daniel Mendez', 'Andreas Vogelsang', 'Henning Femmer', 'Julian Frattini', 'Tobias Springer', 'Jannik Fischbach'] | 2021-07-21 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 2.52947420e-01 3.46777171e-01 -4.34245169e-01 -5.43052435e-01
-2.31240064e-01 -6.76753461e-01 3.46517146e-01 2.64700413e-01
1.40493542e-01 8.34038675e-01 6.72007322e-01 -8.42813194e-01
-5.31470656e-01 -9.26804125e-01 -5.22508919e-01 -7.03083053e-02
-2.55799979e-01 3.91186744e-01 2.94113457e-01 -3.91664177... | [9.211508750915527, 9.006542205810547] |
dc323ced-5df7-465e-8c89-30c47ee3a1ea | neural-models-for-reasoning-over-multiple | 1804.05922 | null | http://arxiv.org/abs/1804.05922v1 | http://arxiv.org/pdf/1804.05922v1.pdf | Neural Models for Reasoning over Multiple Mentions using Coreference | Many problems in NLP require aggregating information from multiple mentions
of the same entity which may be far apart in the text. Existing Recurrent
Neural Network (RNN) layers are biased towards short-term dependencies and
hence not suited to such tasks. We present a recurrent layer which is instead
biased towards co... | ['Ruslan Salakhutdinov', 'Bhuwan Dhingra', 'Qiao Jin', 'Zhilin Yang', 'William W. Cohen'] | 2018-04-16 | neural-models-for-reasoning-over-multiple-1 | https://aclanthology.org/N18-2007 | https://aclanthology.org/N18-2007.pdf | naacl-2018-6 | ['lambada'] | ['natural-language-processing'] | [ 1.15367778e-01 9.66933608e-01 -1.65767297e-01 -5.79757154e-01
-1.00006247e+00 -6.37442946e-01 5.51519275e-01 3.87494773e-01
-7.25557268e-01 9.44583893e-01 8.07719052e-01 -4.47529227e-01
-2.49127612e-01 -5.69114149e-01 -8.88367593e-01 -3.81912380e-01
-1.43491970e-02 1.25789833e+00 1.84970289e-01 -5.79338074... | [9.49134349822998, 9.336053848266602] |
a701cd1c-cd52-4317-a2dc-3894da8f825b | gradient-hyperalignment-for-multi-subject | 1807.02612 | null | http://arxiv.org/abs/1807.02612v1 | http://arxiv.org/pdf/1807.02612v1.pdf | Gradient Hyperalignment for multi-subject fMRI data alignment | Multi-subject fMRI data analysis is an interesting and challenging problem in
human brain decoding studies. The inherent anatomical and functional
variability across subjects make it necessary to do both anatomical and
functional alignment before classification analysis. Besides, when it comes to
big data, time complex... | ['Daoqiang Zhang', 'Tonglin Xu', 'Muhammad Yousefnezhad'] | 2018-07-07 | null | null | null | null | ['brain-decoding', 'multi-subject-fmri-data-alignment', 'brain-decoding'] | ['medical', 'medical', 'miscellaneous'] | [ 9.88131203e-03 -6.32885754e-01 2.70837873e-01 -7.13747084e-01
-4.42760229e-01 -3.14855903e-01 2.96561599e-01 -1.44369408e-01
-7.98973203e-01 9.71545756e-01 2.00419456e-01 -9.94314104e-02
-4.13058460e-01 -2.61472017e-01 -5.03009319e-01 -7.59224474e-01
-4.51532602e-01 6.36054158e-01 6.32014573e-02 8.82703736... | [12.622687339782715, 3.378903388977051] |
b4462c19-58a3-40d0-834a-475835f2f5a9 | f2net-learning-to-focus-on-the-foreground-for | 2012.02534 | null | https://arxiv.org/abs/2012.02534v1 | https://arxiv.org/pdf/2012.02534v1.pdf | F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation | Although deep learning based methods have achieved great progress in unsupervised video object segmentation, difficult scenarios (e.g., visual similarity, occlusions, and appearance changing) are still not well-handled. To alleviate these issues, we propose a novel Focus on Foreground Network (F2Net), which delves into... | ['Pan Zhou', 'Changhu Wang', 'Dongdong Yu', 'Daizong Liu'] | 2020-12-04 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 7.51589388e-02 -3.84175509e-01 -3.00198913e-01 -3.79527718e-01
-4.20493215e-01 -2.34272093e-01 3.39019626e-01 -3.07803243e-01
-3.18895847e-01 4.11357582e-01 9.76408124e-02 2.95612782e-01
-1.76858194e-02 -6.44264877e-01 -6.08969569e-01 -8.50368559e-01
2.13192686e-01 5.13419770e-02 9.31133628e-01 9.16952044... | [9.276195526123047, -0.24351035058498383] |
b6c72516-38f9-45cc-8509-31211a502ea0 | unitopatho-a-labeled-histopathological | 2101.09991 | null | https://arxiv.org/abs/2101.09991v2 | https://arxiv.org/pdf/2101.09991v2.pdf | UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading | Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dyspl... | ['Marco Grangetto', 'Paola Cassoni', 'Luca Bertero', 'Attilio Fiandrotti', 'Enzo Tartaglione', 'Daniele Perlo', 'Carlo Alberto Barbano'] | 2021-01-25 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 3.54411185e-01 3.49813014e-01 -3.51891220e-01 -2.15717450e-01
-8.29604208e-01 -7.57027328e-01 9.08890292e-02 1.00151122e+00
-6.91348076e-01 3.23125809e-01 1.21728323e-01 -7.37855792e-01
6.03955947e-02 -1.13666224e+00 -4.39557910e-01 -8.93446386e-01
-4.95469570e-01 5.16184807e-01 2.69697666e-01 2.42953375... | [15.072669982910156, -2.9896798133850098] |
999027c3-95d2-4620-9552-a8878ace0627 | cris-clip-driven-referring-image-segmentation | 2111.15174 | null | https://arxiv.org/abs/2111.15174v2 | https://arxiv.org/pdf/2111.15174v2.pdf | CRIS: CLIP-Driven Referring Image Segmentation | Referring image segmentation aims to segment a referent via a natural linguistic expression.Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer t... | ['Tongliang Liu', 'Mingming Gong', 'Yandong Guo', 'Xunqiang Tao', 'Qiang Li', 'Yu Lu', 'Zhaoqing Wang'] | 2021-11-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_CRIS_CLIP-Driven_Referring_Image_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_CRIS_CLIP-Driven_Referring_Image_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['generalized-referring-expression-segmentation', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.03186572e-01 6.45191073e-02 -3.81705076e-01 -6.22380197e-01
-1.13566053e+00 -3.54178011e-01 5.59870899e-01 -1.18708834e-01
-4.70809817e-01 3.74568939e-01 1.65958479e-01 -1.42889842e-01
3.22406411e-01 -5.15906096e-01 -1.06579542e+00 -6.31713033e-01
7.28144646e-01 1.34155631e-01 3.18321407e-01 -3.88746299... | [10.274991989135742, 1.1622203588485718] |
e5232cde-ef16-42db-832d-65f9680e4ac0 | adaptive-and-dynamically-constrained-process | 1909.07921 | null | https://arxiv.org/abs/1909.07921v4 | https://arxiv.org/pdf/1909.07921v4.pdf | Adaptive and Dynamically Constrained Process Noise Estimation for Orbit Determination | This paper introduces two new algorithms to accurately estimate the process noise covariance of a discrete-time Kalman filter online for robust orbit determination in the presence of dynamics model uncertainties. Common orbit determination process noise techniques, such as state noise compensation and dynamic model com... | ["Simone D'Amico", 'Nathan Stacey'] | 2019-09-17 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 7.21248612e-02 -1.81597665e-01 1.90115109e-01 2.21683159e-01
-1.17086388e-01 -7.48633504e-01 5.84549904e-01 -2.24021330e-01
-3.02065074e-01 8.31352353e-01 -2.12844491e-01 -5.12944579e-01
-8.52189124e-01 -4.86341923e-01 -2.30271041e-01 -8.98309052e-01
-1.08937211e-01 4.43575382e-01 -1.15095926e-02 -1.28467754... | [5.541032314300537, 2.5704991817474365] |
d11c8ef1-5cb5-4aa4-bda4-025ee59a04bd | refinevis-video-instance-segmentation-with | 2306.04774 | null | https://arxiv.org/abs/2306.04774v1 | https://arxiv.org/pdf/2306.04774v1.pdf | RefineVIS: Video Instance Segmentation with Temporal Attention Refinement | We introduce a novel framework called RefineVIS for Video Instance Segmentation (VIS) that achieves good object association between frames and accurate segmentation masks by iteratively refining the representations using sequence context. RefineVIS learns two separate representations on top of an off-the-shelf frame-le... | ['Zicheng Liu', 'Quanzeng You', 'Peng Chu', 'Jiang Wang', 'Andre Abrantes'] | 2023-06-07 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.19182804e-01 2.34277407e-03 -3.29559624e-01 -3.81484181e-01
-1.01867175e+00 -5.53803980e-01 4.83412862e-01 -1.25851691e-01
-4.85769063e-01 5.87671876e-01 -2.16214154e-02 4.39488217e-02
1.55418903e-01 -6.17246747e-01 -1.10079026e+00 -4.90522146e-01
-2.87235916e-01 3.04371119e-01 6.66169107e-01 7.85293877... | [9.130172729492188, -0.078250452876091] |
19f83960-972a-4c1b-bb2f-6bc2b959aeff | development-of-the-multilingual-semantic | null | null | https://aclanthology.info/papers/N15-1137/n15-1137 | https://www.aclweb.org/anthology/N15-1137 | Development of the Multilingual Semantic Annotation System | null | ["Angela D'Egidio", 'Scott Piao', 'Carmen Dayrell', 'Paul Rayson', 'Francesca Bianchi'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391634702682495, 15.869173049926758] |
62abb48e-e2b8-419a-9eac-5acaab839207 | single-image-calibration-using-knowledge | 2212.02379 | null | https://arxiv.org/abs/2212.02379v1 | https://arxiv.org/pdf/2212.02379v1.pdf | Single image calibration using knowledge distillation approaches | Although recent deep learning-based calibration methods can predict extrinsic and intrinsic camera parameters from a single image, their generalization remains limited by the number and distribution of training data samples. The huge computational and space requirement prevents convolutional neural networks (CNNs) from... | ['Antoine Letienne', 'Mohamed Abbas Hedjazi', 'Oussama Hadjerci', 'Khadidja Ould Amer'] | 2022-12-05 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [-3.10627408e-02 -2.48163179e-01 -2.89664090e-01 -6.87582672e-01
-5.25785863e-01 -7.94360518e-01 2.75063246e-01 -3.77046108e-01
-6.66286111e-01 8.12697351e-01 -2.93787301e-01 -2.30420515e-01
-2.52128951e-02 -4.24102366e-01 -9.55934823e-01 -7.21814036e-01
3.43489408e-01 1.05688624e-01 1.60035521e-01 6.50350526... | [8.248605728149414, -2.2430670261383057] |
c0d1ab66-6f1a-4c23-bfab-50d059d4800d | idll-inverse-depth-line-based-visual | 2304.11748 | null | https://arxiv.org/abs/2304.11748v1 | https://arxiv.org/pdf/2304.11748v1.pdf | IDLL: Inverse Depth Line based Visual Localization in Challenging Environments | Precise and real-time localization of unmanned aerial vehicles (UAVs) or robots in GNSS denied indoor environments are critically important for various logistics and surveillance applications. Vision-based simultaneously locating and mapping (VSLAM) are key solutions but suffer location drifts in texture-less, man-made... | ['Deying Li', 'Xuewei Bai', 'Shuo Wang', 'Yongcai Wang', 'Yu Shao', 'Wanting Li'] | 2023-04-23 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-1.51258126e-01 -7.19287574e-01 2.23357260e-01 -3.03892165e-01
-1.47123575e-01 -8.67435277e-01 5.46766818e-01 -4.96128649e-02
-6.24353647e-01 7.05084562e-01 -5.73912919e-01 -2.87185222e-01
-2.75557041e-01 -7.80222893e-01 -5.49257278e-01 -5.48483312e-01
7.54662380e-02 2.75871664e-01 3.96797001e-01 -3.57858241... | [7.4730939865112305, -2.0389554500579834] |
05b4d60b-fff9-400c-b012-f23aa55a1a31 | relational-graph-learning-for-grounded-video | 2112.00967 | null | https://arxiv.org/abs/2112.00967v1 | https://arxiv.org/pdf/2112.00967v1.pdf | Relational Graph Learning for Grounded Video Description Generation | Grounded video description (GVD) encourages captioning models to attend to appropriate video regions (e.g., objects) dynamically and generate a description. Such a setting can help explain the decisions of captioning models and prevents the model from hallucinating object words in its description. However, such design ... | ['William Yang Wang', 'Yueting Zhuang', 'Jun Xiao', 'Haocheng Shi', 'Haizhou Shi', 'Siliang Tang', 'Xin Eric Wang', 'Wenqiao Zhang'] | 2021-12-02 | null | null | null | null | ['video-description'] | ['computer-vision'] | [-6.09920584e-02 3.13976198e-01 -4.72689718e-01 -3.26881677e-01
-4.38916683e-01 -3.73220205e-01 6.56022429e-01 1.40052542e-01
9.97443572e-02 5.89124382e-01 8.40520442e-01 -1.06882557e-01
4.31236327e-02 -9.90871727e-01 -8.35207224e-01 -4.66386944e-01
2.30369523e-01 3.97582442e-01 2.51521051e-01 -2.98687667... | [10.636953353881836, 1.1099368333816528] |
0e90c9a1-79a6-48fb-ba9a-b3bad837c749 | temporal-relational-ranking-for-stock | 1809.09441 | null | http://arxiv.org/abs/1809.09441v1 | http://arxiv.org/pdf/1809.09441v1.pdf | Temporal Relational Ranking for Stock Prediction | Stock prediction aims to predict the future trends of a stock in order to
help investors to make good investment decisions. Traditional solutions for
stock prediction are based on time-series models. With the recent success of
deep neural networks in modeling sequential data, deep learning has become a
promising choice... | ['Tat-Seng Chua', 'Yiqun Liu', 'Cheng Luo', 'Xiang Wang', 'Fuli Feng', 'Xiangnan He'] | 2018-09-25 | null | null | null | null | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-7.96606600e-01 -4.65519547e-01 -5.80464840e-01 -2.10277393e-01
1.96502674e-02 -4.32705730e-01 5.73773026e-01 -6.61398098e-03
-1.66203193e-02 5.58300555e-01 1.70062542e-01 -5.19666255e-01
-4.05407250e-01 -1.30935347e+00 -6.18979275e-01 -5.30528247e-01
-4.54285741e-01 4.16476637e-01 2.96537668e-01 -6.73437536... | [4.352682113647461, 4.315592288970947] |
538f223b-32f4-4f42-917b-7926fe1e0f0d | a-vector-quantized-approach-for-text-to | 2302.04215 | null | https://arxiv.org/abs/2302.04215v1 | https://arxiv.org/pdf/2302.04215v1.pdf | A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech | Recent Text-to-Speech (TTS) systems trained on reading or acted corpora have achieved near human-level naturalness. The diversity of human speech, however, often goes beyond the coverage of these corpora. We believe the ability to handle such diversity is crucial for AI systems to achieve human-level communication. Our... | ['Alexander Rudnicky', 'Shinji Watanabe', 'Li-Wei Chen'] | 2023-02-08 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 3.28623772e-01 2.81226963e-01 1.56149240e-02 -5.48866808e-01
-1.38359058e+00 -7.11082220e-01 6.95278823e-01 -3.21771652e-01
-2.72079688e-02 6.55769944e-01 6.36712551e-01 -7.61567116e-01
3.66416305e-01 -7.51031190e-02 -5.25859833e-01 -3.37494493e-01
1.69340502e-02 3.32700104e-01 2.65300721e-01 -5.44677615... | [14.764078140258789, 6.759161949157715] |
05af2f13-f22d-438c-9924-522ac42603fd | cross-lingual-question-answering-using-common | null | null | https://aclanthology.org/W16-1403 | https://aclanthology.org/W16-1403.pdf | Cross-Lingual Question Answering Using Common Semantic Space | null | ['Amir Pouran Ben Veyseh'] | 2016-06-01 | null | null | null | ws-2016-6 | ['cross-lingual-question-answering'] | ['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... | [-7.496878147125244, 3.5826504230499268] |
9bd7ef10-e2a6-4fbb-a671-98268b1e70c4 | learning-stylometric-representations-for | 1606.01219 | null | http://arxiv.org/abs/1606.01219v1 | http://arxiv.org/pdf/1606.01219v1.pdf | Learning Stylometric Representations for Authorship Analysis | Authorship analysis (AA) is the study of unveiling the hidden properties of
authors from a body of exponentially exploding textual data. It extracts an
author's identity and sociolinguistic characteristics based on the reflected
writing styles in the text. It is an essential process for various areas, such
as cybercrim... | ['Steven H. H. Ding', 'William K. Cheung', 'Benjamin C. M. Fung', 'Farkhund Iqbal'] | 2016-06-03 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-6.40272424e-02 -2.71399617e-01 -1.50531560e-01 -2.74626911e-01
-2.39721742e-02 -7.21946359e-01 9.87969637e-01 4.61552531e-01
-4.43421245e-01 3.39622557e-01 6.27271891e-01 -2.31113821e-01
-9.97601748e-02 -4.94315743e-01 1.01562604e-01 -5.18701732e-01
4.51136708e-01 4.14576799e-01 -3.44166547e-01 -1.17408335... | [9.598326683044434, 10.553476333618164] |
68797e1b-4d9d-4d78-b639-9118de5c5490 | the-value-improvement-path-towards-better | 2006.02243 | null | https://arxiv.org/abs/2006.02243v2 | https://arxiv.org/pdf/2006.02243v2.pdf | The Value-Improvement Path: Towards Better Representations for Reinforcement Learning | In value-based reinforcement learning (RL), unlike in supervised learning, the agent faces not a single, stationary, approximation problem, but a sequence of value prediction problems. Each time the policy improves, the nature of the problem changes, shifting both the distribution of states and their values. In this pa... | ['Robert Dadashi', 'André Barreto', 'Mark Rowland', 'David Silver', 'Will Dabney', 'Marc G. Bellemare', 'John Quan'] | 2020-06-03 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 1.77259538e-02 3.67586792e-01 -7.11417854e-01 -7.76197091e-02
-8.90429378e-01 -8.16945314e-01 7.01270938e-01 2.17370927e-01
-7.32510090e-01 1.33419454e+00 6.01076186e-01 -4.28524941e-01
-3.24697524e-01 -6.07261598e-01 -8.09074759e-01 -8.54298532e-01
-1.87408194e-01 7.00248182e-01 -1.21960267e-01 -5.08621573... | [4.0744781494140625, 1.947529673576355] |
90aca550-d590-433b-bcc3-0a1d5d5008af | shadow-detection-with-conditional-generative | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Nguyen_Shadow_Detection_With_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Nguyen_Shadow_Detection_With_ICCV_2017_paper.pdf | Shadow Detection With Conditional Generative Adversarial Networks | We introduce scGAN, a novel extension of conditional Generative Adversarial Networks (GAN) tailored for the challenging problem of shadow detection in images. Previous methods for shadow detection focus on learning the local appearance of shadow regions, while using limited local context reasoning in the form of pairwi... | ['Minh Hoai', 'Tomas F. Yago Vicente', 'Vu Nguyen', 'Maozheng Zhao', 'Dimitris Samaras'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['shadow-detection'] | ['computer-vision'] | [ 7.30658591e-01 5.73062062e-01 4.08549458e-01 -2.05928177e-01
-9.41030204e-01 -5.39179265e-01 7.74191558e-01 -2.37194344e-01
-3.03742170e-01 8.17987502e-01 -1.63827971e-01 -2.97797740e-01
5.02000034e-01 -1.03582299e+00 -9.49350536e-01 -1.12927878e+00
2.10501149e-01 6.03074014e-01 4.43936557e-01 -3.95601243... | [10.846348762512207, -4.102905750274658] |
3f446d2b-95df-4101-8bb0-3826538e8459 | omnixai-a-library-for-explainable-ai | 2206.01612 | null | https://arxiv.org/abs/2206.01612v8 | https://arxiv.org/pdf/2206.01612v8.pdf | OmniXAI: A Library for Explainable AI | We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of understanding and interpreting the decisions made by machine learning (ML) in... | ['Tanmay Laud', 'Steven C. H. Hoi', 'Silvio Savarese', 'Hung Le', 'Wenzhuo Yang'] | 2022-06-01 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [-3.34820479e-01 5.54780543e-01 -3.20772231e-01 -4.83132511e-01
9.94871035e-02 -4.72616225e-01 6.20091319e-01 -8.07462856e-02
4.14571494e-01 7.15742767e-01 3.84016365e-01 -8.68302941e-01
-5.10619283e-01 -3.92118126e-01 -5.34788609e-01 -3.90094191e-01
-1.28366753e-01 6.45015419e-01 -6.16325021e-01 -8.67215097... | [8.8135986328125, 5.878915309906006] |
f414c712-384f-4937-9d10-74ce9755d663 | real-time-tone-mapping-a-state-of-the-art | 2003.03074 | null | https://arxiv.org/abs/2003.03074v1 | https://arxiv.org/pdf/2003.03074v1.pdf | Real-time Tone Mapping: A State of the Art Report | The rising demand for high quality display has ensued active research in high dynamic range (HDR) imaging, which has the potential to replace the standard dynamic range imaging. This is due to HDR's features like accurate reproducibility of a scene with its entire spectrum of visible lighting and color depth. But this ... | ['Tetsuya Asai', 'Masato Motomura', 'Shinya Takamaeda', 'Masayuki Ikebe', 'Prasoon Ambalathankandy', 'Yafei Ou'] | 2020-03-06 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 6.66175306e-01 -6.56121790e-01 2.62341917e-01 -3.93306851e-01
-2.83391684e-01 -4.42949265e-01 1.18279710e-01 -4.51198429e-01
-2.45647073e-01 4.87629265e-01 -9.88572985e-02 -2.29745567e-01
-5.22896126e-02 -7.69270599e-01 -3.93033892e-01 -5.63966334e-01
-1.46442726e-01 -1.25973761e-01 5.12636721e-01 -4.62555826... | [10.818747520446777, -2.401249647140503] |
6cc81608-2c70-4a2a-8a28-e52960210758 | kurdish-handwritten-character-recognition | 2210.13734 | null | https://arxiv.org/abs/2210.13734v1 | https://arxiv.org/pdf/2210.13734v1.pdf | Kurdish Handwritten Character Recognition using Deep Learning Techniques | Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and convertin... | ['Amit Chhabra', 'S. Vimal', 'Seyedali Mirjalili', 'Nebojsa Bacanin', 'Abeer Alsadoon', 'Polla Fattah', 'Tarik A. Rashid', 'Rebin M. Ahmed'] | 2022-10-18 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [-2.94485148e-02 -4.31782931e-01 -5.25887311e-03 -2.91950613e-01
8.16374049e-02 -4.29702044e-01 5.83446026e-01 -2.63993919e-01
-4.86267388e-01 6.78823471e-01 -6.15232438e-02 -5.63096702e-01
-1.20249219e-01 -8.68285120e-01 -2.00948775e-01 -7.58462191e-01
3.91938120e-01 5.71487606e-01 2.40328442e-02 -2.66113192... | [11.843999862670898, 2.64717173576355] |
190c8dea-fc86-4f67-9e4a-8df816b1e321 | fast-shadow-detection-from-a-single-image | 1709.09283 | null | http://arxiv.org/abs/1709.09283v2 | http://arxiv.org/pdf/1709.09283v2.pdf | Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network | In recent years, various shadow detection methods from a single image have
been proposed and used in vision systems; however, most of them are not
appropriate for the robotic applications due to the expensive time complexity.
This paper introduces a fast shadow detection method using a deep learning
framework, with a t... | ['Sepideh Hosseinzadeh', 'Moein Shakeri', 'Hong Zhang'] | 2017-09-26 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 5.49564898e-01 -3.95303816e-02 3.47525150e-01 -4.30093825e-01
-2.78052628e-01 -1.01071015e-01 4.54664528e-01 -1.36589691e-01
-5.25196970e-01 7.60876834e-01 -4.30923402e-01 -3.96251440e-01
3.55329573e-01 -6.20224774e-01 -8.03393066e-01 -8.97663653e-01
4.08131719e-01 2.87788987e-01 1.16446984e+00 8.49583596... | [10.850624084472656, -4.114041805267334] |
1cdf0d26-636e-4db4-8ecc-f6298ca7542b | ascertaining-price-formation-in | 2003.00803 | null | https://arxiv.org/abs/2003.00803v1 | https://arxiv.org/pdf/2003.00803v1.pdf | Ascertaining price formation in cryptocurrency markets with DeepLearning | The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep le... | ['Fan Wu', 'Michail Basios', 'Waichung Chung', 'Leslie Kanthan', 'Carmine Ventre', 'Lingbo Li', 'Fan Fang'] | 2020-02-09 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-1.07155716e+00 -2.52355903e-01 2.81060878e-02 -2.10658014e-01
-6.36281908e-01 -7.38689780e-01 8.94933403e-01 1.97336450e-02
-4.70577955e-01 8.36808920e-01 1.48902640e-01 -6.89960420e-01
-1.08960345e-01 -1.07349861e+00 -5.44441938e-01 -6.35741234e-01
-6.36489689e-01 6.29221618e-01 -2.07815275e-01 -4.84080523... | [4.542211055755615, 4.1563286781311035] |
89ea73d7-4c26-45c1-8ca7-6567e989a17e | designing-a-prospective-covid-19-therapeutic | 2012.01736 | null | https://arxiv.org/abs/2012.01736v1 | https://arxiv.org/pdf/2012.01736v1.pdf | Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning | The SARS-CoV-2 pandemic has created a global race for a cure. One approach focuses on designing a novel variant of the human angiotensin-converting enzyme 2 (ACE2) that binds more tightly to the SARS-CoV-2 spike protein and diverts it from human cells. Here we formulate a novel protein design framework as a reinforceme... | ['Karim Beguir', 'Uğur Şahin', 'Amine Kerkeni', 'Alexandre Laterre', 'Slim Said', 'Joe Phillips', 'Thomas Pierrot', 'Nicolás López Carranza', 'Marcin J. Skwark'] | 2020-12-03 | null | null | null | null | ['protein-design'] | ['medical'] | [ 2.72505552e-01 -4.87990491e-02 -3.66048492e-03 -1.17540091e-01
-7.73028255e-01 -8.09289038e-01 2.21042216e-01 4.85132754e-01
-5.07360160e-01 1.39693105e+00 1.10374823e-01 -6.45744026e-01
2.02678949e-01 -4.59384739e-01 -9.65031087e-01 -6.77515209e-01
-3.95176321e-01 7.91534781e-01 -3.22776616e-01 -4.56824899... | [4.794200897216797, 5.592466354370117] |
287e0482-e948-4f25-a713-083ca17ffc34 | confidence-regularized-self-training | 1908.09822 | null | https://arxiv.org/abs/1908.09822v3 | https://arxiv.org/pdf/1908.09822v3.pdf | Confidence Regularized Self-Training | Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation. These methods often involve an iterative process of predicting on target domain and then taking the confident predictions as pseudo-labels for retraining. However, since pseudo-labels can be n... | ['B. V. K. Vijaya Kumar', 'Yang Zou', 'Zhiding Yu', 'Xiaofeng Liu', 'Jinsong Wang'] | 2019-08-26 | confidence-regularized-self-training-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zou_Confidence_Regularized_Self-Training_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zou_Confidence_Regularized_Self-Training_ICCV_2019_paper.pdf | iccv-2019-10 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.87176901e-01 4.81411844e-01 -5.86271822e-01 -8.63546133e-01
-9.42779481e-01 -4.81000423e-01 4.36073750e-01 -1.96689352e-01
-4.09818858e-01 9.94370818e-01 -1.04242027e-01 -1.67034239e-01
1.28119931e-01 -5.08261740e-01 -9.15832818e-01 -7.05623388e-01
4.81618196e-01 6.04852378e-01 2.19922766e-01 3.27454329... | [9.611597061157227, 1.413488745689392] |
1fecd337-31e2-49a1-bc1a-9a4f9a30e56b | sequential-3d-human-pose-and-shape-estimation | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Sequential_3D_Human_Pose_and_Shape_Estimation_From_Point_Clouds_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Sequential_3D_Human_Pose_and_Shape_Estimation_From_Point_Clouds_CVPR_2020_paper.pdf | Sequential 3D Human Pose and Shape Estimation From Point Clouds | This work addresses the problem of 3D human pose and shape estimation from a sequence of point clouds. Existing sequential 3D human shape estimation methods mainly focus on the template model fitting from a sequence of depth images or the parametric model regression from a sequence of RGB images. In this paper, we prop... | [' Jian Yang', ' Lei Liu', ' Guofeng Zhang', ' Jin Xie', 'Kangkan Wang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-5.45784123e-02 -3.11718255e-01 3.00438583e-01 -4.16320115e-01
-5.33700466e-01 -1.00596100e-01 2.77827412e-01 -2.16076940e-01
-5.66909432e-01 2.39932001e-01 -1.17733501e-01 5.22546768e-01
-6.98706717e-05 -6.61300957e-01 -1.00864303e+00 -3.61694515e-01
-1.41668320e-01 1.11202312e+00 3.48356158e-01 -9.55112576... | [7.019306182861328, -1.094046950340271] |
38761ff7-16de-44ba-87a2-52bc22af9882 | unified-feature-and-instance-based-domain | null | null | https://aclanthology.org/2020.emnlp-main.572 | https://aclanthology.org/2020.emnlp-main.572.pdf | Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis | The supervised models for aspect-based sentiment analysis (ABSA) rely heavily on labeled data. However, fine-grained labeled data are scarce for the ABSA task. To alleviate the dependence on labeled data, prior works mainly focused on feature-based adaptation, which used the domain-shared knowledge to construct auxilia... | ['Rui Xia', 'Jianfei Yu', 'Chenggong Gong'] | null | null | null | null | emnlp-2020-11 | ['aspect-extraction'] | ['natural-language-processing'] | [ 3.73273462e-01 -2.24612392e-02 -1.99579760e-01 -8.58300090e-01
-1.10983014e+00 -6.12580955e-01 6.23213530e-01 -4.48728167e-02
-4.16665524e-01 6.16179109e-01 2.85311460e-01 -9.29332674e-02
1.97786793e-01 -7.36420214e-01 -5.81445515e-01 -6.25695825e-01
4.94032115e-01 4.27343935e-01 3.26625668e-02 -4.13559109... | [11.428532600402832, 6.660813808441162] |
5b6a9969-fda9-44b7-890a-95359cb19945 | decomposed-soft-prompt-guided-fusion | 2211.10681 | null | https://arxiv.org/abs/2211.10681v1 | https://arxiv.org/pdf/2211.10681v1.pdf | Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning | Compositional Zero-Shot Learning (CZSL) aims to recognize novel concepts formed by known states and objects during training. Existing methods either learn the combined state-object representation, challenging the generalization of unseen compositions, or design two classifiers to identify state and object separately fr... | ['Jingcai Guo', 'Song Guo', 'Ziming Liu', 'Xiaocheng Lu'] | 2022-11-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lu_Decomposed_Soft_Prompt_Guided_Fusion_Enhancing_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lu_Decomposed_Soft_Prompt_Guided_Fusion_Enhancing_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['compositional-zero-shot-learning', 'novel-concepts'] | ['computer-vision', 'reasoning'] | [ 0.39340416 -0.2747917 -0.30217898 -0.21143156 -0.72625786 -0.42651564
0.8585403 -0.09914853 -0.18289852 0.38978693 0.11810937 0.27652922
0.14710878 -0.5101245 -0.8160197 -1.1496173 0.38622752 0.22756033
0.39261192 -0.06402393 -0.11870778 -0.03272313 -1.8868563 0.5420902
0.6560556 1.4747643 0.3... | [10.164388656616211, 2.2918319702148438] |
3c5f3fd3-50cd-4352-b2ab-03345f604b6a | pointnorm-normalization-is-all-you-need-for | 2207.06324 | null | https://arxiv.org/abs/2207.06324v4 | https://arxiv.org/pdf/2207.06324v4.pdf | PointNorm: Dual Normalization is All You Need for Point Cloud Analysis | Point cloud analysis is challenging due to the irregularity of the point cloud data structure. Existing works typically employ the ad-hoc sampling-grouping operation of PointNet++, followed by sophisticated local and/or global feature extractors for leveraging the 3D geometry of the point cloud. Unfortunately, the samp... | ['Gaurav Gupta', 'Changjie Lu', 'Jinqian Pan', 'Shen Zheng'] | 2022-07-13 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.35745877e-01 -4.13285404e-01 -9.47017514e-04 -5.00287414e-01
-6.20935977e-01 -4.80618358e-01 4.57978278e-01 3.77661824e-01
-2.35266626e-01 1.70272931e-01 -4.45218891e-01 -2.57221073e-01
-2.68582493e-01 -1.09915650e+00 -7.87980199e-01 -6.45041704e-01
6.52732179e-02 6.20223999e-01 5.15385449e-01 -2.09320728... | [7.9549970626831055, -3.230024576187134] |
3f0b6103-0c83-4148-b7d2-d9f419e42ab5 | towards-robust-video-instance-segmentation | 2301.09416 | null | https://arxiv.org/abs/2301.09416v1 | https://arxiv.org/pdf/2301.09416v1.pdf | Towards Robust Video Instance Segmentation with Temporal-Aware Transformer | Most existing transformer based video instance segmentation methods extract per frame features independently, hence it is challenging to solve the appearance deformation problem. In this paper, we observe the temporal information is important as well and we propose TAFormer to aggregate spatio-temporal features both in... | ['Siyu Zhu', 'Zuozhuo Dai', 'Fangtao Shao', 'Zhenghao Zhang'] | 2023-01-20 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.17260031e-01 -2.79445201e-01 -2.28359312e-01 -3.76134127e-01
-7.89882362e-01 -5.32155335e-01 3.56987864e-01 -4.36243936e-02
-4.75131482e-01 4.14437294e-01 2.02269971e-01 2.37299904e-01
-2.04999402e-01 -6.78948581e-01 -8.37102592e-01 -6.29806519e-01
2.09727615e-01 -7.21173286e-02 7.70436108e-01 1.13094591... | [9.187912940979004, -0.02568202279508114] |
95c49548-eaba-4d4d-bd30-7d13b5191e84 | bert-based-simplification-of-japanese | null | null | https://aclanthology.org/2020.inlg-1.31 | https://aclanthology.org/2020.inlg-1.31.pdf | BERT-Based Simplification of Japanese Sentence-Ending Predicates in Descriptive Text | Japanese sentence-ending predicates intricately combine content words and functional elements, such as aspect, modality, and honorifics; this can often hinder the understanding of language learners and children. Conventional lexical simplification methods, which replace difficult target words with simpler synonyms acqu... | ['Satoshi Sato', 'Rei Miyata', 'Taichi Kato'] | null | null | null | null | inlg-acl-2020-12 | ['lexical-simplification'] | ['natural-language-processing'] | [ 4.38642055e-02 5.41040152e-02 5.34598269e-02 -4.24394697e-01
-5.36799371e-01 -5.33085108e-01 1.64004102e-01 2.79883713e-01
-8.98676932e-01 1.04015160e+00 2.12328956e-01 -6.69391155e-02
7.07197487e-02 -8.17299068e-01 -6.56849921e-01 -7.18729258e-01
4.82456297e-01 2.38935485e-01 4.44511920e-01 -5.93446195... | [10.801602363586426, 10.212812423706055] |
c4241d1c-b62d-45eb-aed1-faad2cb1d502 | learning-physical-intuition-of-block-towers | 1603.01312 | null | http://arxiv.org/abs/1603.01312v1 | http://arxiv.org/pdf/1603.01312v1.pdf | Learning Physical Intuition of Block Towers by Example | Wooden blocks are a common toy for infants, allowing them to develop motor
skills and gain intuition about the physical behavior of the world. In this
paper, we explore the ability of deep feed-forward models to learn such
intuitive physics. Using a 3D game engine, we create small towers of wooden
blocks whose stabilit... | ['Sam Gross', 'Adam Lerer', 'Rob Fergus'] | 2016-03-03 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [-4.07453150e-01 1.11033797e-01 1.12506092e-01 6.09760219e-03
3.98978412e-01 -6.49962902e-01 5.03191888e-01 -1.84482674e-03
-1.41606694e-02 4.88285929e-01 1.09584227e-01 -4.27879125e-01
-1.25673831e-01 -1.05558932e+00 -1.17947888e+00 -4.56014603e-01
-5.35853863e-01 4.08299923e-01 6.72907829e-01 -3.35635155... | [8.404227256774902, 0.9464970827102661] |
b67b6560-46ac-4213-b76c-fc2d8579a28e | benchmark-of-deep-learning-models-on-large | 1710.08531 | null | http://arxiv.org/abs/1710.08531v1 | http://arxiv.org/pdf/1710.08531v1.pdf | Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets | Deep learning models (aka Deep Neural Networks) have revolutionized many
fields including computer vision, natural language processing, speech
recognition, and is being increasingly used in clinical healthcare
applications. However, few works exist which have benchmarked the performance
of the deep learning models with... | ['Sanjay Purushotham', 'Chuizheng Meng', 'Zhengping Che', 'Yan Liu'] | 2017-10-23 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [-2.74918437e-01 -3.17823648e-01 -1.08363688e-01 -4.50782418e-01
-7.89955616e-01 8.27959105e-02 1.23602934e-01 6.98159575e-01
-5.72427809e-01 7.61809766e-01 5.20227373e-01 -6.85868442e-01
-6.05388105e-01 -6.24788046e-01 4.05568928e-02 -8.18103731e-01
-6.57060385e-01 1.01443315e+00 -2.78686106e-01 3.82203492... | [8.020005226135254, 6.237532615661621] |
d2d75bbf-0d10-49ef-b888-3e8b84267b21 | semantic-aware-generation-of-multi-view | 2305.02618 | null | https://arxiv.org/abs/2305.02618v1 | https://arxiv.org/pdf/2305.02618v1.pdf | Semantic-aware Generation of Multi-view Portrait Drawings | Neural radiance fields (NeRF) based methods have shown amazing performance in synthesizing 3D-consistent photographic images, but fail to generate multi-view portrait drawings. The key is that the basic assumption of these methods -- a surface point is consistent when rendered from different views -- doesn't hold for d... | ['Gang Xu', 'Nannan Wang', 'Chang Jiang', 'Fei Gao', 'Biao Ma'] | 2023-05-04 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 2.98923850e-01 -2.05132559e-01 4.79635084e-03 -5.25647044e-01
-4.23488498e-01 -7.98888326e-01 5.10512233e-01 -8.73550296e-01
5.72861731e-01 3.93323988e-01 1.21462591e-01 1.28681839e-01
1.98562890e-01 -1.01201916e+00 -6.78419352e-01 -2.59759009e-01
7.03875065e-01 1.65662453e-01 -3.18013936e-01 -5.21661997... | [12.013776779174805, -0.46215716004371643] |
a07b4687-d7d3-42a7-8f9c-04a4a82dc968 | ccml-a-novel-collaborative-learning-model-for | 2012.10715 | null | https://arxiv.org/abs/2012.10715v6 | https://arxiv.org/pdf/2012.10715v6.pdf | Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification | The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on convolutional neural networks (CNNs) have shown strong performance gains in RS. However, they usually require a high number of reliable trai... | ['Begüm Demir', 'Mahdyar Ravanbakhsh', 'Ahmet Kerem Aksoy'] | 2020-12-19 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 2.45713368e-01 -1.99187934e-01 2.07734033e-02 -5.77879310e-01
-9.56927121e-01 -4.41646963e-01 2.21496031e-01 4.52832319e-02
-3.94535154e-01 5.71466446e-01 -1.61122978e-01 -1.70038059e-01
-2.46802062e-01 -9.63143826e-01 -6.14846647e-01 -1.02868509e+00
1.95934922e-01 1.37119204e-01 -4.44604084e-02 2.70996373... | [9.54385757446289, 3.8437845706939697] |
c80b65dd-e0c8-4e1f-80e4-69e701f508c6 | network-compression-for-machine-learnt-fluid-1 | null | null | https://openreview.net/forum?id=6Qy9aoCms0C | https://openreview.net/pdf?id=6Qy9aoCms0C | NETWORK COMPRESSION FOR MACHINE-LEARNT FLUID SIMULATIONS | Multi-scale, multi-fidelity numerical simulations form the pillar of scientific applications
related to numerically modeling fluids. However, simulating the fluid
behavior characterized by the non-linear Navier Stokes equations are often times
computational expensive. Physics informed machine learning methods is a viab... | ['Anonymous'] | 2021-03-04 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.98082581e-01 -1.06002413e-01 6.69397935e-02 3.30918789e-01
-1.41957954e-01 -4.66570765e-01 7.44600594e-01 5.44657111e-01
-3.77716780e-01 1.09070551e+00 -2.29664087e-01 -6.15988851e-01
-7.60644376e-01 -9.32738304e-01 -5.72479486e-01 -6.91485405e-01
-4.46929067e-01 7.91202247e-01 2.66278684e-02 -1.71118289... | [6.414342403411865, 3.444453477859497] |
9a1cc607-0c4b-41ca-8de4-6431dc59826b | comprehensive-and-delicate-an-efficient | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Comprehensive_and_Delicate_An_Efficient_Transformer_for_Image_Restoration_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Comprehensive_and_Delicate_An_Efficient_Transformer_for_Image_Restoration_CVPR_2023_paper.pdf | Comprehensive and Delicate: An Efficient Transformer for Image Restoration | Vision Transformers have shown promising performance in image restoration, which usually conduct window- or channel-based attention to avoid intensive computations. Although the promising performance has been achieved, they go against the biggest success factor of Transformers to a certain extent by capturing the l... | ['Xi Peng', 'Jiancheng Lv', 'Dezhong Peng', 'Boyun Li', 'Yuanbiao Gou', 'Haiyu Zhao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['superpixels'] | ['computer-vision'] | [ 2.79759526e-01 -3.16635430e-01 -9.06026810e-02 -2.69321710e-01
-7.85076678e-01 1.53035680e-02 4.49187070e-01 6.14077412e-02
-2.21410275e-01 4.81386542e-01 4.81339842e-01 -1.99017450e-01
4.38857339e-02 -9.20625210e-01 -7.55165458e-01 -9.49199319e-01
2.99870253e-01 -2.72522271e-01 6.22453451e-01 -1.26635239... | [10.973854064941406, -1.8547015190124512] |
78548b15-cb09-4f60-af83-dd21a15a32b9 | learning-logic-programs-from-noisy-failures | 2201.03702 | null | https://arxiv.org/abs/2201.03702v2 | https://arxiv.org/pdf/2201.03702v2.pdf | Learning Logic Programs From Noisy Failures | Inductive Logic Programming (ILP) is a form of machine learning (ML) which in contrast to many other state of the art ML methods typically produces highly interpretable and reusable models. However, many ILP systems lack the ability to naturally learn from any noisy or partially misclassified training data. We introduc... | ['John Wahlig'] | 2021-12-28 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 4.32751924e-01 5.88957489e-01 -3.76542151e-01 -3.75851274e-01
-9.22304332e-01 -7.19683826e-01 4.54314739e-01 4.97223496e-01
-2.53287017e-01 1.02144408e+00 -3.37268621e-01 -5.90667963e-01
-4.53792959e-01 -1.03840685e+00 -1.06429291e+00 -4.52849537e-01
-2.01838329e-01 8.20580959e-01 3.91353726e-01 1.55234069... | [8.725825309753418, 6.6622138023376465] |
fd4bcf17-9ebe-4ab3-ab62-09440e18763e | ntire-2023-challenge-on-light-field-image | 2304.10415 | null | https://arxiv.org/abs/2304.10415v1 | https://arxiv.org/pdf/2304.10415v1.pdf | NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results | In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a tool... | ['Yulan Guo', 'Radu Timofte', 'Jungang Yang', 'Zhengyu Liang', 'Longguang Wang', 'Yingqian Wang'] | 2023-04-20 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 7.00669289e-01 -2.74626583e-01 -1.42169592e-03 -2.68341243e-01
-1.08408093e+00 -3.48412067e-01 2.04267800e-01 -9.38806474e-01
-7.53303692e-02 1.07241535e+00 5.65428078e-01 1.38279393e-01
4.62731067e-03 -2.78929055e-01 -7.49812782e-01 -6.35596395e-01
8.77191722e-02 -2.02965364e-01 4.08017069e-01 -3.06256592... | [10.919062614440918, -2.130563735961914] |
ba7bdbaf-9fa6-499f-b23d-9378a15f887f | calibration-of-p-values-for-calibration-and | 2202.00100 | null | https://arxiv.org/abs/2202.00100v7 | https://arxiv.org/pdf/2202.00100v7.pdf | Calibration of P-values for calibration and for deviation of a subpopulation from the full population | The author's recent research papers, "Cumulative deviation of a subpopulation from the full population" and "A graphical method of cumulative differences between two subpopulations" (both published in volume 8 of Springer's open-access "Journal of Big Data" during 2021), propose graphical methods and summary statistics... | ['Mark Tygert'] | 2022-01-31 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 4.23831819e-03 7.87880719e-02 -3.94313961e-01 -4.66963947e-01
-1.06357419e+00 -5.11610210e-01 5.35810411e-01 5.23267210e-01
-2.02878684e-01 1.58228195e+00 2.05962569e-01 -4.36937809e-01
-6.94330037e-01 -9.22826648e-01 -6.77789092e-01 -9.83829916e-01
-3.63735855e-01 4.67748612e-01 -1.01749949e-01 3.06214988... | [7.580937385559082, 4.595036029815674] |
35ce3820-8fb3-4f60-a106-98342234a547 | directed-acyclic-graph-neural-networks-1 | 2101.07965 | null | https://arxiv.org/abs/2101.07965v3 | https://arxiv.org/pdf/2101.07965v3.pdf | Directed Acyclic Graph Neural Networks | Graph-structured data ubiquitously appears in science and engineering. Graph neural networks (GNNs) are designed to exploit the relational inductive bias exhibited in graphs; they have been shown to outperform other forms of neural networks in scenarios where structure information supplements node features. The most co... | ['Jie Chen', 'Veronika Thost'] | 2021-01-20 | directed-acyclic-graph-neural-networks | https://openreview.net/forum?id=JbuYF437WB6 | https://openreview.net/pdf?id=JbuYF437WB6 | iclr-2021-1 | ['graph-property-prediction'] | ['graphs'] | [ 1.18436471e-01 6.49251938e-01 -2.89945722e-01 -4.46270376e-01
2.84331024e-01 -4.39224124e-01 7.15345204e-01 3.37808430e-01
-6.41667917e-02 4.68809426e-01 3.68499398e-01 -6.87250555e-01
-5.47011614e-01 -1.25772381e+00 -8.22790563e-01 -5.12705803e-01
-6.35344684e-01 4.99331862e-01 1.67256340e-01 -2.43592367... | [6.932179927825928, 6.308597564697266] |
f6fccff5-40fa-4b41-bbfa-5960f5d7901f | depression-recognition-using-remote | 2206.04399 | null | https://arxiv.org/abs/2206.04399v1 | https://arxiv.org/pdf/2206.04399v1.pdf | Depression Recognition using Remote Photoplethysmography from Facial Videos | Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive state... | ['Miguel Bordallo López', 'Manuel Lage Cañellas', 'Constantino Álvarez Casado'] | 2022-06-09 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 2.8453562e-01 7.2450370e-02 1.1478646e-01 -6.0562348e-01
-2.8306645e-01 -1.5316191e-01 3.2570732e-01 2.8766650e-01
-3.8849583e-01 6.4935911e-01 1.7956330e-01 1.2335186e-01
3.1624809e-02 -7.1428645e-01 -8.3891407e-02 -8.3296508e-01
-1.7679307e-01 -1.2724850e-01 -3.5252061e-01 -2.7005452e-01
8.0575839e-02... | [13.77697467803955, 2.9236643314361572] |
d84cb436-8760-4c5d-808f-628bb2084a40 | a-high-resolution-chest-ct-scan-image-dataset | 2205.03408 | null | https://arxiv.org/abs/2205.03408v1 | https://arxiv.org/pdf/2205.03408v1.pdf | A High-Resolution Chest CT-Scan Image Dataset for COVID-19 Diagnosis and Differentiation | During the COVID-19 pandemic, computed tomography (CT) is a good way to diagnose COVID-19 patients. HRCT (High-Resolution Computed Tomography) is a form of computed tomography that uses advanced methods to improve image resolution. Publicly accessible COVID-19 CT image datasets are very difficult to come by due to priv... | ['Hamidreza Bolhasani', 'Bentolhoda Otroshi Shahreza', 'Mahsa Vali', 'Iraj Abedi'] | 2022-05-06 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-3.25164534e-02 -4.47252005e-01 -2.61826694e-01 1.29325673e-01
-6.75571620e-01 -5.19856453e-01 1.39431849e-01 4.05620635e-01
-4.05845970e-01 7.20744014e-01 2.37473488e-01 -8.98119092e-01
-3.71281862e-01 -8.77869070e-01 -3.28018308e-01 -7.10795462e-01
-1.25596300e-01 1.49829257e+00 6.84666112e-02 3.47759813... | [15.442106246948242, -1.8317177295684814] |
d71e7f91-149a-4d54-b54e-1af25fdb92cb | weakly-supervised-body-part-parsing-with-pose | 1907.13051 | null | https://arxiv.org/abs/1907.13051v2 | https://arxiv.org/pdf/1907.13051v2.pdf | Weakly Supervised Body Part Segmentation with Pose based Part Priors | Human body part segmentation refers to the task of predicting the semantic segmentation mask for each body part. Fully supervised body part segmentation methods achieve good performances but require an enormous amount of effort to annotate part masks for training. In contrast to high annotation costs needed for a limit... | ['Yuncheng Li', 'Jiebo Luo', 'Linjie Yang', 'Zhengyuan Yang', 'Ning Zhang'] | 2019-07-30 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 5.14643967e-01 9.09928977e-01 -4.79779065e-01 -6.50419295e-01
-9.12968040e-01 -3.96786034e-01 3.63037527e-01 -2.25969195e-01
-2.71556735e-01 5.40709674e-01 1.78988233e-01 3.55822265e-01
3.05666715e-01 -4.72143918e-01 -9.52314615e-01 -4.34218705e-01
2.30160162e-01 8.18995357e-01 5.17982841e-01 -9.30017829... | [8.22274398803711, -0.2438848614692688] |
6fc1a918-a212-4469-96d8-89ffa2df9f5f | machine-learning-for-advancing-low | 2307.00131 | null | https://arxiv.org/abs/2307.00131v1 | https://arxiv.org/pdf/2307.00131v1.pdf | Machine learning for advancing low-temperature plasma modeling and simulation | Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be carefully assessed in general, many aspects of plasma modeling and simulation hav... | ['Tobias Gergs', 'Luca Vialetto', 'Jan Trieschmann'] | 2023-06-30 | null | null | null | null | ['known-unknowns'] | ['miscellaneous'] | [ 3.47338915e-01 8.02266747e-02 -4.03727069e-02 -3.54454339e-01
-1.80687845e-01 -6.06821813e-02 1.03566074e+00 1.14203833e-01
2.93156393e-02 7.64994740e-01 -2.32927158e-01 -5.36011219e-01
-2.92763501e-01 -6.67142153e-01 -1.97041810e-01 -1.18835032e+00
-3.82048696e-01 1.05465508e+00 -2.35907927e-01 -4.66038674... | [6.37091588973999, 3.5888671875] |
23ccb018-7522-4b98-a86e-e7d1562064be | low-rank-random-tensor-for-bilinear-pooling | 1906.01004 | null | https://arxiv.org/abs/1906.01004v2 | https://arxiv.org/pdf/1906.01004v2.pdf | Frontal Low-rank Random Tensors for Fine-grained Action Segmentation | Fine-grained action segmentation in long untrimmed videos is an important task for many applications such as surveillance, robotics, and human-computer interaction. To understand subtle and precise actions within a long time period, second-order information (e.g. feature covariance) or higher is reported to be effectiv... | ['Yan Zhang', 'Qianli Ma', 'Heiko Neumann', 'Siyu Tang', 'Krikamol Muandet'] | 2019-06-03 | null | null | null | null | ['action-parsing'] | ['natural-language-processing'] | [ 1.53049484e-01 -1.74112022e-01 -2.19714075e-01 -1.29365772e-01
-5.46656251e-01 -4.75554824e-01 3.31570894e-01 -2.40357950e-01
-3.78658831e-01 5.58553040e-01 2.57131517e-01 -2.58402713e-02
-4.23384100e-01 -4.37335759e-01 -9.15608644e-01 -7.69892693e-01
-3.65396768e-01 3.24023068e-01 2.59837389e-01 -6.55154660... | [7.952866077423096, 0.2758459746837616] |
edf8a00d-5ed4-4114-85db-f376f4f43c9a | attention-based-point-cloud-edge-sampling | 2302.14673 | null | https://arxiv.org/abs/2302.14673v2 | https://arxiv.org/pdf/2302.14673v2.pdf | Attention-based Point Cloud Edge Sampling | Point cloud sampling is a less explored research topic for this data representation. The most commonly used sampling methods are still classical random sampling and farthest point sampling. With the development of neural networks, various methods have been proposed to sample point clouds in a task-based learning manner... | ['Jürgen Beyerer', 'Julius Pfrommer', 'Junwei Zheng', 'Chengzhi Wu'] | 2023-02-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Attention-Based_Point_Cloud_Edge_Sampling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Attention-Based_Point_Cloud_Edge_Sampling_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-point-cloud-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-8.45066980e-02 -1.98545069e-01 -9.70005020e-02 -1.13578811e-01
-7.03250051e-01 3.92881781e-02 7.68860936e-01 3.48228551e-02
-1.79322973e-01 4.71242130e-01 -9.21825692e-02 8.56919363e-02
-7.05028847e-02 -1.04697776e+00 -7.68203139e-01 -6.41011119e-01
2.72585690e-01 6.54984176e-01 2.82054722e-01 5.77623025... | [8.248215675354004, -3.5071539878845215] |
d700fef9-e661-48ef-8aa4-6cb0e45d38b9 | deep-image-prior-inpainting-of-ancient | 2306.14209 | null | https://arxiv.org/abs/2306.14209v1 | https://arxiv.org/pdf/2306.14209v1.pdf | Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc | The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the s... | ['Rosa Maria Dessì', 'Luca Calatroni', 'Elena Loli Piccolomini', 'Elena Morotti', 'Oceane Acquier', 'Perrine Saillard', 'Fabio Merizzi'] | 2023-06-25 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.47519469e-01 -1.01839140e-01 4.26516891e-01 1.12635046e-01
-5.57037234e-01 -2.86206663e-01 6.95774376e-01 2.47885644e-01
-5.10960996e-01 8.76171470e-01 1.65577978e-01 1.43430784e-01
-3.30031544e-01 -1.06234384e+00 -6.99308872e-01 -8.12010884e-01
3.04242790e-01 3.57654691e-01 8.22942108e-02 -4.30547982... | [11.369420051574707, -2.116116762161255] |
92fdffd6-1d14-4f51-bbf8-2b93e9dca507 | deep-learning-models-for-multilingual-hate | 2004.06465 | null | https://arxiv.org/abs/2004.06465v3 | https://arxiv.org/pdf/2004.06465v3.pdf | Deep Learning Models for Multilingual Hate Speech Detection | Hate speech detection is a challenging problem with most of the datasets available in only one language: English. In this paper, we conduct a large scale analysis of multilingual hate speech in 9 languages from 16 different sources. We observe that in low resource setting, simple models such as LASER embedding with log... | ['Sai Saketh Aluru', 'Punyajoy Saha', 'Animesh Mukherjee', 'Binny Mathew'] | 2020-04-14 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [-6.28050327e-01 -4.77483928e-01 -3.17243546e-01 1.37668356e-01
-8.66050005e-01 -7.34526157e-01 6.21304631e-01 1.31383557e-02
-6.56718433e-01 7.42336631e-01 3.86865437e-01 -2.77611375e-01
6.00579083e-01 -4.63814110e-01 -3.68961990e-01 -7.43728757e-01
8.35806355e-02 9.05312821e-02 4.00326401e-01 -1.37149841... | [8.763197898864746, 10.53953742980957] |
50ab7288-9f54-4f58-89df-dbd40c92fe58 | memory-efficient-network-for-large-scale | 2103.03089 | null | https://arxiv.org/abs/2103.03089v2 | https://arxiv.org/pdf/2103.03089v2.pdf | Memory-Efficient Network for Large-scale Video Compressive Sensing | Video snapshot compressive imaging (SCI) captures a sequence of video frames in a single shot using a 2D detector. The underlying principle is that during one exposure time, different masks are imposed on the high-speed scene to form a compressed measurement. With the knowledge of masks, optimization algorithms or deep... | ['Xin Yuan', 'Zhengjue Wang', 'Ruiying Lu', 'Hao Zhang', 'Guanliang Liu', 'Bo Chen', 'Ziheng Cheng'] | 2021-03-04 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Cheng_Memory-Efficient_Network_for_Large-Scale_Video_Compressive_Sensing_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Cheng_Memory-Efficient_Network_for_Large-Scale_Video_Compressive_Sensing_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-compressive-sensing'] | ['computer-vision'] | [ 4.50404286e-01 -7.31882453e-01 1.30428806e-01 -7.40466192e-02
-6.79139197e-01 -4.00706351e-01 5.22843711e-02 -7.85611570e-01
-4.26764816e-01 4.57361847e-01 -1.53696258e-02 -3.23194623e-01
7.24351108e-02 -5.51767647e-01 -9.93217468e-01 -7.70616949e-01
-1.37542889e-01 -1.43388808e-01 8.94863307e-02 2.65700549... | [11.031530380249023, -2.127836227416992] |
904dfa2f-c080-4438-b97b-03fff410a91b | improving-weakly-supervised-visual-grounding | 2007.01951 | null | https://arxiv.org/abs/2007.01951v2 | https://arxiv.org/pdf/2007.01951v2.pdf | Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation | Weakly supervised phrase grounding aims at learning region-phrase correspondences using only image-sentence pairs. A major challenge thus lies in the missing links between image regions and sentence phrases during training. To address this challenge, we leverage a generic object detector at training time, and propose a... | ['Liwei Wang', 'Kun Xu', 'Yin Li', 'Jing Huang', 'Zhengyuan Yang', 'Dong Yu'] | 2020-07-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Improving_Weakly_Supervised_Visual_Grounding_by_Contrastive_Knowledge_Distillation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Improving_Weakly_Supervised_Visual_Grounding_by_Contrastive_Knowledge_Distillation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['phrase-grounding'] | ['natural-language-processing'] | [ 3.85293961e-01 3.04095060e-01 -1.95817545e-01 -4.54299569e-01
-1.35432637e+00 -6.42709672e-01 6.19181514e-01 4.89962131e-01
-4.54091817e-01 3.81887794e-01 -2.06439227e-01 -9.21583474e-02
3.54828745e-01 -6.44093275e-01 -1.09356630e+00 -5.24836659e-01
1.81082889e-01 3.82831544e-01 7.76315033e-01 -3.26470099... | [10.328133583068848, 1.439555048942566] |
ee2d56e4-6e72-4fa8-aa7f-9333f92942f0 | action-understanding-with-multiple-classes-of | 1704.08723 | null | http://arxiv.org/abs/1704.08723v1 | http://arxiv.org/pdf/1704.08723v1.pdf | Action Understanding with Multiple Classes of Actors | Despite the rapid progress, existing works on action understanding focus
strictly on one type of action agent, which we call actor---a human adult,
ignoring the diversity of actions performed by other actors. To overcome this
narrow viewpoint, our paper marks the first effort in the computer vision
community to jointly... | ['Chenliang Xu', 'Caiming Xiong', 'Jason J. Corso'] | 2017-04-27 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 7.33215690e-01 2.60585636e-01 -5.34872055e-01 -4.33092177e-01
-4.10519779e-01 -6.21900797e-01 9.19875681e-01 -2.74117708e-01
-2.44196191e-01 3.99310559e-01 5.76293230e-01 -4.99412715e-02
-1.78098232e-01 -1.49196431e-01 -7.38993645e-01 -7.08204031e-01
-1.23992674e-01 4.02357638e-01 2.94946015e-01 2.17410251... | [8.38583755493164, 0.5241627097129822] |
b3b2ccab-d400-4442-aa2c-ac271b94114c | geofault-a-well-founded-fault-ontology-for | 2302.07059 | null | https://arxiv.org/abs/2302.07059v1 | https://arxiv.org/pdf/2302.07059v1.pdf | GeoFault: A well-founded fault ontology for interoperability in geological modeling | Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level by means of ontologies is essential for making these applications interoperable. Since geo-modeling is currently part of multidisciplinary projects, semantic harmonization is required to model not only geolo... | ['Martin Giese', 'Mara Abel', 'Anita Torabi', 'Michel Perrin', 'Yuanwei Qu'] | 2023-02-14 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-4.21461821e-01 6.49820745e-01 1.76821932e-01 -3.01125795e-01
-9.01390761e-02 -5.17421424e-01 6.86162055e-01 3.35568100e-01
-1.18719697e-01 7.90049434e-01 3.49205732e-01 -3.68617028e-01
-7.33742118e-01 -1.58553219e+00 -5.84400415e-01 -3.82908702e-01
-3.97491813e-01 8.21654737e-01 9.15233791e-01 -1.01501167... | [9.164803504943848, 7.988325595855713] |
be60cdbf-b5be-486d-a79c-8cd47488c9df | virapart-a-text-refinement-framework-for-asr | 2110.09086 | null | https://arxiv.org/abs/2110.09086v3 | https://arxiv.org/pdf/2110.09086v3.pdf | ViraPart: A Text Refinement Framework for Automatic Speech Recognition and Natural Language Processing Tasks in Persian | The Persian language is an inflectional subject-object-verb language. This fact makes Persian a more uncertain language. However, using techniques such as Zero-Width Non-Joiner (ZWNJ) recognition, punctuation restoration, and Persian Ezafe construction will lead us to a more understandable and precise language. In most... | ['Saeed Bibak', 'Hamed Babaei Giglou', 'Saman Jamalabbasi', 'Milad Molazadeh', 'Narges Farokhshad'] | 2021-10-18 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 2.44307548e-01 2.06976622e-01 2.24507555e-01 -1.96018592e-01
-5.43604136e-01 -6.75299525e-01 6.40049160e-01 3.43101472e-01
-4.70151842e-01 1.05078697e+00 1.71151876e-01 -4.14047927e-01
-2.19316974e-01 -8.12679112e-01 -1.95908979e-01 -4.69124913e-01
3.68185729e-01 7.15523124e-01 3.93464625e-01 -4.08470362... | [10.539507865905762, 10.21861457824707] |
ce1a946b-fdb6-4467-8c44-9e61907ebdfb | onepose-one-shot-object-pose-estimation | 2205.12257 | null | https://arxiv.org/abs/2205.12257v1 | https://arxiv.org/pdf/2205.12257v1.pdf | OnePose: One-Shot Object Pose Estimation without CAD Models | We propose a new method named OnePose for object pose estimation. Unlike existing instance-level or category-level methods, OnePose does not rely on CAD models and can handle objects in arbitrary categories without instance- or category-specific network training. OnePose draws the idea from visual localization and only... | ['Xiaowei Zhou', 'Guofeng Zhang', 'Hongcheng Zhao', 'Xingyi He', 'Siyu Zhang', 'ZiHao Wang', 'Jiaming Sun'] | 2022-05-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Sun_OnePose_One-Shot_Object_Pose_Estimation_Without_CAD_Models_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sun_OnePose_One-Shot_Object_Pose_Estimation_Without_CAD_Models_CVPR_2022_paper.pdf | cvpr-2022-1 | ['6d-pose-estimation-1'] | ['computer-vision'] | [-8.65854919e-02 5.45052905e-03 -1.90141827e-01 -3.92671138e-01
-5.55766046e-01 -5.57044923e-01 2.79448241e-01 3.73092256e-02
-1.95306256e-01 5.30357398e-02 -2.37920254e-01 1.79760441e-01
2.12828238e-02 -5.94205022e-01 -1.16216648e+00 -3.18179876e-01
6.26984285e-03 9.00675893e-01 5.21475136e-01 3.31032813... | [7.48704719543457, -2.586268901824951] |
72d850a9-a19b-4aae-94e1-0a64f9f31dd4 | domain-knowledge-informed-self-supervised | 2202.14019 | null | https://arxiv.org/abs/2202.14019v2 | https://arxiv.org/pdf/2202.14019v2.pdf | Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment | Maintaining proper form while exercising is important for preventing injuries and maximizing muscle mass gains. Detecting errors in workout form naturally requires estimating human's body pose. However, off-the-shelf pose estimators struggle to perform well on the videos recorded in gym scenarios due to factors such as... | ['Helge Rhodin', 'Amol Gharat', 'Paritosh Parmar'] | 2022-02-28 | null | null | null | null | ['action-quality-assessment', 'action-understanding', 'action-analysis', 'action-assessment', 'pose-contrastive-learning', 'motion-disentanglement', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.10481042e-01 8.33425373e-02 -5.05947113e-01 -2.56975353e-01
-7.83528507e-01 -5.28429091e-01 -9.37671587e-02 -2.60570198e-01
-3.24853659e-01 5.58798909e-01 3.61811250e-01 3.07839632e-01
-3.12114656e-01 -3.34599435e-01 -8.52670014e-01 -3.66201550e-01
-2.06585571e-01 3.22175980e-01 8.90686214e-02 -2.78466940... | [7.216451168060303, -0.6972991228103638] |
b2a4e037-ab0b-4e2f-a7ad-5f6e3d17e44b | attacking-graph-classification-via-bayesian | null | null | https://openreview.net/forum?id=7oziDfK4Fs | https://openreview.net/pdf?id=7oziDfK4Fs | Attacking Graph Classification via Bayesian Optimisation | Graph neural networks have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to attacks on graph-level classification, an important problem with numerous real-life applications such ... | ['Xiaowen Dong', 'Michael Osborne', 'Arno Blaas', 'Binxin Ru', 'Henry Kenlay', 'Xingchen Wan'] | 2021-06-18 | null | null | null | icml-workshop-aml-2021-7 | ['bayesian-optimisation'] | ['methodology'] | [ 5.89627445e-01 1.96393073e-01 -8.84942617e-03 1.30639508e-01
-3.40363353e-01 -9.34464395e-01 5.57118952e-01 5.35470366e-01
-2.13363424e-01 7.33845592e-01 -3.61335188e-01 -7.70997226e-01
-4.14976001e-01 -1.05259883e+00 -7.03366876e-01 -8.41498315e-01
-5.58526874e-01 4.81592476e-01 4.20940071e-01 -2.23425850... | [5.961103916168213, 7.415004253387451] |
7c2da72b-9747-4b46-9222-e7f99814c734 | using-generic-summarization-to-improve-music | 1503.06666 | null | http://arxiv.org/abs/1503.06666v3 | http://arxiv.org/pdf/1503.06666v3.pdf | Using Generic Summarization to Improve Music Information Retrieval Tasks | In order to satisfy processing time constraints, many MIR tasks process only
a segment of the whole music signal. This practice may lead to decreasing
performance, since the most important information for the tasks may not be in
those processed segments. In this paper, we leverage generic summarization
algorithms, prev... | ['Ricardo Ribeiro', 'David Martins de Matos', 'Francisco Raposo'] | 2015-03-23 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 4.22899306e-01 -2.57160906e-02 -3.53784740e-01 -6.72515035e-02
-1.04752171e+00 -7.99944937e-01 6.45620763e-01 6.84343636e-01
-5.23593903e-01 7.71177888e-01 9.44924235e-01 9.85310748e-02
-6.06313288e-01 -3.72184724e-01 -2.20341325e-01 -5.29916286e-01
-1.79687887e-01 3.22471559e-01 6.97163045e-02 -1.05251074... | [12.530291557312012, 9.533792495727539] |
19b06dd2-9a55-4efb-b6ff-35f34a210a5d | noisy-tensor-ring-approximation-for-computing | 2307.03884 | null | https://arxiv.org/abs/2307.03884v1 | https://arxiv.org/pdf/2307.03884v1.pdf | Noisy Tensor Ring approximation for computing gradients of Variational Quantum Eigensolver for Combinatorial Optimization | Variational Quantum algorithms, especially Quantum Approximate Optimization and Variational Quantum Eigensolver (VQE) have established their potential to provide computational advantage in the realm of combinatorial optimization. However, these algorithms suffer from classically intractable gradients limiting the scala... | ['Vaneet Aggarwal', 'Utkarsh Priyam', 'Dheeraj Peddireddy'] | 2023-07-08 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [-1.31488489e-02 7.99212530e-02 3.14948052e-01 -2.79046060e-03
-4.95625883e-01 -9.63756979e-01 5.11284232e-01 4.90546338e-02
-7.44467318e-01 8.08121204e-01 -1.29481986e-01 -5.61808825e-01
-2.05935836e-01 -1.05251634e+00 -6.50176406e-01 -1.16890097e+00
-3.33484888e-01 3.56001347e-01 -4.20630872e-02 -7.23861158... | [5.643873691558838, 4.874456405639648] |
f48be3cf-6718-4992-9b50-9c6b2236f451 | improving-neural-morphological-tagging-using | null | null | https://www.researchgate.net/publication/330224906_Improving_neural_morphological_Tagging_using_Language_Models | http://www.dialog-21.ru/media/4530/sorokinaa.pdf | Improving neural morphological Tagging using Language Models | This paper addresses the task of morphological tagging and demonstrates how neural network architectures bene t from using language models for morphological tags. We show that incorporating the probabilities from language model on morphological tags improves the quality of character-based morphological tagging, reducin... | ['Alexey Sorokin'] | 2018-06-02 | null | null | null | dialogue-international-conference-on | ['morphological-tagging'] | ['natural-language-processing'] | [-4.00035363e-03 3.17296147e-01 -2.31868416e-01 -5.41160762e-01
-8.73675108e-01 -8.79909694e-01 4.25844789e-02 5.84014356e-01
-1.05183434e+00 5.39419293e-01 4.08330202e-01 -8.50661278e-01
3.32506120e-01 -7.28454471e-01 -4.23885196e-01 -4.24953699e-01
-2.22927332e-01 2.17981964e-01 3.32767129e-01 1.81391940... | [10.352559089660645, 10.027551651000977] |
bea78c5b-1d71-4c5c-b4d1-9b825bce032a | watt-effnet-a-lightweight-and-accurate-model | 2304.10811 | null | https://arxiv.org/abs/2304.10811v2 | https://arxiv.org/pdf/2304.10811v2.pdf | WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images | Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's ability to promptly comprehend the crisis and optimally utilize its limited power and processing reso... | ['Vu N. Duong', 'Daniel Puiu Poenar', 'Md Meftahul Ferdaus', 'Tanmoy Dam', 'Gao Yu Lee'] | 2023-04-21 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [-5.70469163e-02 -3.57463419e-01 -9.77729186e-02 -2.03257576e-01
-3.63138616e-01 -5.57291031e-01 2.89843023e-01 2.09936723e-01
-7.51739323e-01 3.91506523e-01 8.07426423e-02 -6.78483665e-01
-3.77292514e-01 -8.81385088e-01 -3.79639506e-01 -3.58065993e-01
-3.68172109e-01 -1.75662972e-02 5.35195060e-02 -3.57190788... | [8.88074016571045, -0.3328262269496918] |
b7dff2ec-18ef-4834-9455-58ce1e3e6f60 | application-of-adversarial-examples-to | 2108.08972 | null | https://arxiv.org/abs/2108.08972v1 | https://arxiv.org/pdf/2108.08972v1.pdf | Application of Adversarial Examples to Physical ECG Signals | This work aims to assess the reality and feasibility of the adversarial attack against cardiac diagnosis system powered by machine learning algorithms. To this end, we introduce adversarial beats, which are adversarial perturbations tailored specifically against electrocardiograms (ECGs) beat-by-beat classification sys... | ['Tatsuya Mori', 'Jun Sakuma', 'Takeshi Sugawara', 'Taiga Ono'] | 2021-08-20 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 6.46190166e-01 5.77925622e-01 5.17026544e-01 -9.25010536e-03
-8.07994246e-01 -1.07393265e+00 1.47990122e-01 -9.85089168e-02
-1.48658708e-01 5.96387804e-01 -3.00181985e-01 -7.02956915e-01
-3.13547738e-02 -4.83635366e-01 -6.04203403e-01 -6.01134777e-01
-9.05664921e-01 1.25794873e-01 -2.10089147e-01 -1.11115657... | [14.353813171386719, 3.113445997238159] |
5c2a5c4c-eafd-43d5-98aa-2060238e4c6c | incorporating-subjectivity-into-gendered | null | null | https://aclanthology.org/2022.gebnlp-1.28 | https://aclanthology.org/2022.gebnlp-1.28.pdf | Incorporating Subjectivity into Gendered Ambiguous Pronoun (GAP) Resolution using Style Transfer | The GAP dataset is a Wikipedia-based evaluation dataset for gender bias detection in coreference resolution, containing mostly objective sentences. Since subjectivity is ubiquitous in our daily texts, it becomes necessary to evaluate models for both subjective and objective instances. In this work, we present a new eva... | ['Tanvi Dadu', 'Kartikey Pant'] | null | null | null | null | naacl-gebnlp-2022-7 | ['gender-bias-detection', 'gender-bias-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 2.37613946e-01 4.65414733e-01 -2.90114641e-01 -7.31133342e-01
-9.37162042e-01 -7.90328681e-01 6.55608177e-01 2.48235270e-01
-5.44039488e-01 1.07386136e+00 7.08374679e-01 3.03559434e-02
-2.37293139e-01 -5.45846581e-01 -2.59498745e-01 -4.42490608e-01
4.18027520e-01 9.10294116e-01 1.07903577e-01 -6.44294798... | [11.068286895751953, 9.8075590133667] |
05de5389-f5d6-4bba-9d92-01597a40ff18 | katildakat-at-semeval-2021-task-1-lexical | null | null | https://aclanthology.org/2021.semeval-1.91 | https://aclanthology.org/2021.semeval-1.91.pdf | katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English | This paper describes systems submitted to Se- mEval 2021 Task 1: Lexical Complexity Prediction (LCP). We compare a linear and a non-linear regression models trained to work for both tracks of the task. We show that both systems are able to generalize better when supplied with information about complexities of single wo... | ['Katja Voskoboinik'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [ 8.28428864e-02 -2.88871288e-01 -3.43022108e-01 -5.77293277e-01
-1.03450453e+00 -6.54681206e-01 6.52376294e-01 2.72765338e-01
-8.33389640e-01 9.71805394e-01 2.08774731e-01 -5.21884501e-01
4.76409234e-02 -4.68772501e-01 -3.35152477e-01 -3.80335391e-01
-1.49012014e-01 4.76917773e-01 8.76672715e-02 -6.23504519... | [10.631973266601562, 10.440585136413574] |
42bcd3ea-0394-4b66-9176-e2f2fa1494f1 | linear-mode-connectivity-in-multitask-and-1 | 2010.04495 | null | https://arxiv.org/abs/2010.04495v1 | https://arxiv.org/pdf/2010.04495v1.pdf | Linear Mode Connectivity in Multitask and Continual Learning | Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all considered tasks. The main difference is in the training regimes, where continual learning can only have access to one task at a time, which for ne... | ['Hassan Ghasemzadeh', 'Razvan Pascanu', 'Dilan Gorur', 'Mehrdad Farajtabar', 'Seyed Iman Mirzadeh'] | 2020-10-09 | linear-mode-connectivity-in-multitask-and | https://openreview.net/forum?id=Fmg_fQYUejf | https://openreview.net/pdf?id=Fmg_fQYUejf | iclr-2021-1 | ['linear-mode-connectivity'] | ['knowledge-base'] | [ 9.70349684e-02 1.25045404e-01 1.36008129e-01 -5.56326285e-02
-3.24155509e-01 -4.24959809e-01 6.55676246e-01 4.95640874e-01
-6.93056941e-01 1.02640009e+00 -4.23400730e-01 -3.06374848e-01
-6.60786510e-01 -4.59817708e-01 -9.60469425e-01 -1.19922936e+00
-5.34612015e-02 6.40562296e-01 5.34926832e-01 -3.31910610... | [9.399831771850586, 2.005295991897583] |
e300e9e3-ba1f-4980-898d-c7c133bbf5fc | batch-normalized-joint-training-for-dnn-based | 1703.08471 | null | http://arxiv.org/abs/1703.08471v1 | http://arxiv.org/pdf/1703.08471v1.pdf | Batch-normalized joint training for DNN-based distant speech recognition | Improving distant speech recognition is a crucial step towards flexible
human-machine interfaces. Current technology, however, still exhibits a lack of
robustness, especially when adverse acoustic conditions are met. Despite the
significant progress made in the last years on both speech enhancement and
speech recogniti... | ['Yoshua Bengio', 'Maurizio Omologo', 'Philemon Brakel', 'Mirco Ravanelli'] | 2017-03-24 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 6.25437737e-01 5.03046960e-02 3.34904313e-01 -4.29115504e-01
-5.63020945e-01 -3.00906241e-01 4.86025870e-01 -1.66192815e-01
-6.92666352e-01 6.19545579e-01 2.78086156e-01 -2.42538422e-01
-2.40892284e-02 -2.59789079e-01 -5.85077643e-01 -8.44055235e-01
4.79143888e-01 5.77966645e-02 9.76286754e-02 -2.84396797... | [14.902549743652344, 5.901392936706543] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.