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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
91274cc1-8935-4086-90eb-2d30897e5007 | what-s-this-learning-to-segment-unknown | 2011.03279 | null | https://arxiv.org/abs/2011.03279v2 | https://arxiv.org/pdf/2011.03279v2.pdf | "What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences | We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. Our method successively learns an agnostic foreground segmentation followed by a distinction between manipulator and object solely by observing the motion between consecutive RGB frames. In contrast to previous appr... | ['Rudolph Triebel', 'Maximilian Durner', 'Martin Sundermeyer', 'Wout Boerdijk'] | 2020-11-06 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 2.61481553e-01 2.64134794e-01 -6.81123808e-02 -3.84051621e-01
-6.41195416e-01 -9.73136127e-01 3.41845870e-01 -2.93659836e-01
-5.08157909e-01 2.75771976e-01 -4.02356088e-01 2.04383675e-02
-5.97762503e-02 -3.00781935e-01 -1.04981649e+00 -7.56189585e-01
1.41581995e-02 8.67229879e-01 4.12269533e-01 -6.98192120... | [6.153457164764404, -1.0260884761810303] |
b7357523-2c0d-4442-8457-22d618d42304 | noisy-self-knowledge-distillation-for-text | 2009.07032 | null | https://arxiv.org/abs/2009.07032v2 | https://arxiv.org/pdf/2009.07032v2.pdf | Noisy Self-Knowledge Distillation for Text Summarization | In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training on single reference and noisy datasets. Instead of relying on one-hot annotation labels, our student summarization model is trained with guidance from a teacher which generates... | ['Yang Liu', 'Mirella Lapata', 'Sheng Shen'] | 2020-09-15 | null | https://aclanthology.org/2021.naacl-main.56 | https://aclanthology.org/2021.naacl-main.56.pdf | naacl-2021-4 | ['self-knowledge-distillation'] | ['computer-vision'] | [ 4.28186327e-01 7.79981673e-01 -4.99618351e-01 -6.24154210e-01
-1.48872960e+00 -6.83642387e-01 7.41619229e-01 5.17493963e-01
-6.15991831e-01 1.01766479e+00 7.29577661e-01 -7.46422336e-02
2.10461706e-01 -3.99172336e-01 -8.88818324e-01 -2.74548411e-01
3.98426652e-01 6.75960064e-01 2.63023227e-01 -5.62551990... | [12.197084426879883, 9.183210372924805] |
162b93de-6bb8-4fa9-9e15-12e526b0d0cc | integrating-semantic-information-into-sketchy | 2301.00429 | null | https://arxiv.org/abs/2301.00429v1 | https://arxiv.org/pdf/2301.00429v1.pdf | Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension | Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro... | ['Ngan Luu-Thuy Nguyen', 'Duc-Vu Nguyen', 'Viet-Duc Ho', 'Hang Thi-Thu Le'] | 2023-01-01 | null | null | null | null | ['xlm-r', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.40313959e-01 6.12603188e-01 1.69526488e-01 -5.96285760e-01
-5.37589550e-01 -4.78561133e-01 6.00926042e-01 7.15504408e-01
-6.72478616e-01 5.20480156e-01 5.93682528e-01 -6.60027146e-01
-1.14233391e-02 -1.02172184e+00 -5.73574722e-01 2.51177624e-02
4.86058682e-01 2.15928376e-01 6.35533571e-01 -7.85584033... | [11.325849533081055, 8.21638298034668] |
bdd10a04-e00c-4150-9043-7c8395381b88 | conic-challenge-pushing-the-frontiers-of | 2303.06274 | null | https://arxiv.org/abs/2303.06274v2 | https://arxiv.org/pdf/2303.06274v2.pdf | CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting | Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellu... | ['Nasir M. Rajpoot', 'Fayyaz Minhas', 'Shan E Ahmed Raza', 'David Snead', 'Tianyi Miao', 'Hyun Jung', 'Dorsa Ziaei', 'Jin Tae Kwak', 'Trinh Thi Le Vuong', 'Ankush Jamthikar', 'Yash Dubey', 'Vedant Phuse', 'Pranay Dumbhare', 'Satoshi Kasai', 'Satoshi Kondo', 'Taebum Lee', 'Soo-Hyung Kim', 'Vi Thi-Tuong Vo', 'Min Wu', 'L... | 2023-03-11 | null | null | null | null | ['whole-slide-images', 'nuclear-segmentation', 'survival-analysis'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 1.25414789e-01 -1.69213817e-01 -1.70947507e-01 -1.69599161e-01
-1.39936519e+00 -8.29021335e-01 4.22983676e-01 1.19785976e+00
-6.70538843e-01 4.05739725e-01 4.32537377e-01 -3.50575805e-01
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-3.92713696e-01 5.91472447e-01 2.65929729e-01 2.37417556... | [15.088408470153809, -3.1437981128692627] |
72ea15e1-0cca-4a19-88ca-f4220df5a570 | exploring-the-potential-of-sar-data-for-cloud | 2206.02850 | null | https://arxiv.org/abs/2206.02850v3 | https://arxiv.org/pdf/2206.02850v3.pdf | GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion | The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However, the large domain gap between optical and SAR images as well as the severe speckle noise of SAR images may cause significant interference in SAR-based cloud removal, re... | ['Xiao Xiang Zhu', 'Wen Yang', 'Gui-Song Xia', 'Lei Yu', 'Patrick Ebel', 'Yilei Shi', 'Fang Xu'] | 2022-06-06 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 6.21046543e-01 -8.22700918e-01 5.58291256e-01 2.67094046e-01
-6.89638555e-01 -5.94350159e-01 1.11831278e-01 -3.92709285e-01
1.10562444e-02 6.79513037e-01 8.63981023e-02 1.81597397e-02
-1.91822648e-01 -7.27387249e-01 -1.72299668e-01 -1.32588100e+00
2.33843327e-01 -2.37475887e-01 3.73078555e-01 -3.56064618... | [10.244536399841309, -2.3502798080444336] |
8da80e16-001a-477c-ba0c-3a796437c5b7 | monocular-camera-localization-for-automated | 2109.06296 | null | https://arxiv.org/abs/2109.06296v1 | https://arxiv.org/pdf/2109.06296v1.pdf | Monocular Camera Localization for Automated Vehicles Using Image Retrieval | We address the problem of finding the current position and heading angle of an autonomous vehicle in real-time using a single camera. Compared to methods which require LiDARs and high definition (HD) 3D maps in real-time, the proposed approach is easily scalable and computationally efficient, at the price of lower prec... | ['Francesco Borrelli', 'Eunhyek Joa'] | 2021-09-13 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-1.43395662e-01 -3.96723330e-01 -4.67777811e-03 -3.90061080e-01
-3.72508973e-01 -4.88450199e-01 7.94021845e-01 5.84649518e-02
-8.09436381e-01 9.49731648e-01 -7.10305631e-01 -3.45937550e-01
-3.01371932e-01 -1.05066705e+00 -8.16948712e-01 -6.56968832e-01
-1.98143393e-01 7.91301608e-01 5.21013558e-01 -2.37183303... | [7.3230671882629395, -2.0689892768859863] |
bb848f0a-8186-40f4-a18a-4b096fd161fa | a-multi-stage-triple-path-method-for-speech | 2303.03732 | null | https://arxiv.org/abs/2303.03732v1 | https://arxiv.org/pdf/2303.03732v1.pdf | A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments | In noisy and reverberant environments, the performance of deep learning-based speech separation methods drops dramatically because previous methods are not designed and optimized for such situations. To address this issue, we propose a multi-stage end-to-end learning method that decouples the difficult speech separatio... | ['Wenjing Zhu', 'Xiangyuan Yang', 'Xinyu Yang', 'Zhaoxi Mu'] | 2023-03-07 | null | null | null | null | ['speech-separation', 'speech-denoising'] | ['speech', 'speech'] | [-5.88339195e-02 -3.87969196e-01 3.53386402e-01 -1.69190571e-01
-1.02183390e+00 -3.66734058e-01 7.35916123e-02 -8.17408487e-02
-4.15967762e-01 4.14180338e-01 2.89309025e-01 -5.72139084e-01
-2.09794119e-01 -1.29718080e-01 -2.36296728e-01 -9.43846107e-01
-2.10114615e-03 -4.47317250e-02 -1.56987354e-01 -2.74476837... | [14.986625671386719, 5.901123523712158] |
79bd6993-1c46-4d6d-a53f-9a4d97da5fe6 | exploiting-pairwise-mutual-information-for | null | null | https://ieeexplore.ieee.org/abstract/document/9739959 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9739959 | Exploiting Pairwise Mutual Information for Knowledge-Grounded Dialogue | External document knowledge is helpful for dialogue systems to generate high-quality responses. Although several knowledge-grounded dialogue models have been designed, external knowledge cannot be comprehensively exploited due to the complex relationships among dialogue context, knowledge, and responses. To this end, w... | ['Bo Xu', 'Hui Ma', 'Hongfei Lin', 'Jian Wang', 'Bo Zhang'] | 2022-03-22 | null | null | null | ieee-acm-transactions-on-audio-speech-and-8 | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 4.75312807e-02 1.33985594e-01 -2.35055774e-01 -4.49773759e-01
-7.21449673e-01 -6.31587029e-01 8.15072417e-01 -1.43602979e-03
-3.21630090e-01 9.99667704e-01 7.84573019e-01 -9.12011266e-02
-1.60594936e-02 -9.84188139e-01 -9.90069881e-02 -1.70047164e-01
5.61312020e-01 5.44687152e-01 4.78883862e-01 -1.02241397... | [12.442090034484863, 7.982668399810791] |
2c7348ec-19e1-4d80-9da4-f91ac15d73c8 | detailed-annotations-of-chest-x-rays-via-ct | 2210.03416 | null | https://arxiv.org/abs/2210.03416v1 | https://arxiv.org/pdf/2210.03416v1.pdf | Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding | In clinical radiology reports, doctors capture important information about the patient's health status. They convey their observations from raw medical imaging data about the inner structures of a patient. As such, formulating reports requires medical experts to possess wide-ranging knowledge about anatomical regions w... | ['Rainer Stiefelhagen', 'Jens Kleesiek', 'Klaus H. Maier-Hein', 'Moon Sung Kim', 'Jan Sellner', 'Victoria Mayer', 'Matthias A. Fink', 'Saquib Sarfraz', 'Simon Reiß', 'Constantin Seibold'] | 2022-10-07 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.69350475e-01 7.33954549e-01 -2.16972485e-01 -3.62394869e-01
-1.09220088e+00 -7.67279565e-01 5.10458291e-01 7.61100352e-01
-1.54330432e-01 4.12006736e-01 4.94334221e-01 -6.09162092e-01
-3.13843153e-02 -6.38792574e-01 -6.72015011e-01 -3.93642902e-01
-2.45161876e-01 7.49836862e-01 3.46714348e-01 -7.50050843... | [14.859095573425293, -2.16756272315979] |
03d55ce0-bbe9-485b-bc6b-122b7f36eceb | exploring-plausible-patches-using-source-code | 2103.16846 | null | https://arxiv.org/abs/2103.16846v1 | https://arxiv.org/pdf/2103.16846v1.pdf | Exploring Plausible Patches Using Source Code Embeddings in JavaScript | Despite the immense popularity of the Automated Program Repair (APR) field, the question of patch validation is still open. Most of the present-day approaches follow the so-called Generate-and-Validate approach, where first a candidate solution is being generated and after validated against an oracle. The latter, howev... | ['László Vidács', 'Márk Lajkó', 'Dániel Horváth', 'Viktor Csuvik'] | 2021-03-31 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 9.52354372e-02 -3.32275480e-02 3.10265124e-02 -2.69444376e-01
-6.97508097e-01 -8.18989277e-01 4.52241451e-01 6.04609072e-01
-8.26520845e-03 4.51151103e-01 6.11969084e-02 -7.20203578e-01
-2.23046526e-01 -7.93353379e-01 -7.30219483e-01 -4.41683143e-01
1.83067424e-03 2.10761577e-01 4.39004838e-01 -9.55712497... | [7.594992160797119, 7.714036464691162] |
bf470881-fd1c-4836-a966-c205069bd668 | cornet-a-neurosymbolic-approach-to-learning | 2208.06032 | null | https://arxiv.org/abs/2208.06032v4 | https://arxiv.org/pdf/2208.06032v4.pdf | CORNET: Learning Table Formatting Rules By Example | Spreadsheets are widely used for table manipulation and presentation. Stylistic formatting of these tables is an important property for both presentation and analysis. As a result, popular spreadsheet software, such as Excel, supports automatically formatting tables based on rules. Unfortunately, writing such formattin... | ['Gust Verbruggen', 'Mohammad Raza', 'Carina Negreanu', 'Vu Le', 'Sumit Gulwani', 'José Cambronero', 'Mukul Singh'] | 2022-08-11 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 2.65511632e-01 9.93156433e-02 -2.21405908e-01 -6.18920922e-01
-5.68148375e-01 -1.02011061e+00 2.78354943e-01 5.69174230e-01
-1.74105614e-01 8.00644994e-01 -2.33957525e-02 -7.64849305e-01
-3.66559893e-01 -1.06821144e+00 -1.17716110e+00 2.27562964e-01
-9.35624354e-03 7.78715193e-01 -3.50557454e-02 -3.17654938... | [9.656793594360352, 7.7375969886779785] |
6e267809-7207-4362-a812-a87aa4ba856e | a-large-scale-event-based-detection-dataset | 2001.08499 | null | https://arxiv.org/abs/2001.08499v3 | https://arxiv.org/pdf/2001.08499v3.pdf | A Large Scale Event-based Detection Dataset for Automotive | We introduce the first very large detection dataset for event cameras. The dataset is composed of more than 39 hours of automotive recordings acquired with a 304x240 ATIS sensor. It contains open roads and very diverse driving scenarios, ranging from urban, highway, suburbs and countryside scenes, as well as different ... | ['Pierre de Tournemire', 'Davide Nitti', 'Etienne Perot', 'Davide Migliore', 'Amos Sironi'] | 2020-01-23 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [-2.27694176e-02 -1.34569496e-01 -1.27341971e-01 -5.67265689e-01
-7.18177855e-01 -6.78153932e-01 6.87220633e-01 -7.44210929e-02
-4.21750665e-01 6.81353867e-01 1.04146205e-01 -1.92718968e-01
4.64833558e-01 -5.72323620e-01 -7.89017618e-01 -4.47423726e-01
-2.18266889e-01 6.25592619e-02 8.22832346e-01 -8.06963891... | [8.343466758728027, -1.0289843082427979] |
58d844b5-b7cd-4fd7-ac06-a53dcabcb8bf | multimodal-generalized-zero-shot-learning-for | 2111.07646 | null | https://arxiv.org/abs/2111.07646v1 | https://arxiv.org/pdf/2111.07646v1.pdf | Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning | Gleason grading from histopathology images is essential for accurate prostate cancer (PCa) diagnosis. Since such images are obtained after invasive tissue resection quick diagnosis is challenging under the existing paradigm. We propose a method to predict Gleason grades from magnetic resonance (MR) images which are non... | ['Dwarikanath Mahapatra'] | 2021-11-15 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 8.18433285e-01 7.08154678e-01 -3.88908803e-01 -7.15073287e-01
-1.37992322e+00 -4.23045516e-01 8.03211927e-01 -1.26968831e-01
-2.89918870e-01 9.00783002e-01 4.03644532e-01 8.83286353e-03
-3.94180298e-01 -1.04676962e+00 -4.45817202e-01 -1.15793407e+00
1.74386520e-02 1.05761003e+00 -2.47636348e-01 1.90738544... | [14.950797080993652, -2.711653470993042] |
68cf1f4d-6b16-4670-a2fa-5d54415b8a10 | hinerv-video-compression-with-hierarchical | 2306.09818 | null | https://arxiv.org/abs/2306.09818v1 | https://arxiv.org/pdf/2306.09818v1.pdf | HiNeRV: Video Compression with Hierarchical Encoding based Neural Representation | Learning-based video compression is currently one of the most popular research topics, offering the potential to compete with conventional standard video codecs. In this context, Implicit Neural Representations (INRs) have previously been used to represent and compress image and video content, demonstrating relatively ... | ['David Bull', 'Andrew Gower', 'Fan Zhang', 'Ge Gao', 'Ho Man Kwan'] | 2023-06-16 | null | null | null | null | ['video-compression', 'quantization', 'model-compression'] | ['computer-vision', 'methodology', 'methodology'] | [ 2.72082806e-01 -1.04976475e-01 -4.47684526e-01 -2.09350705e-01
-6.38335228e-01 2.14943603e-01 4.45554972e-01 2.37714067e-01
-5.12766123e-01 5.73960185e-01 4.68486577e-01 -3.24456573e-01
8.10501575e-02 -8.28615963e-01 -1.06846428e+00 -4.70341891e-01
-2.68938631e-01 1.67530179e-02 2.71115273e-01 -2.21922800... | [11.347722053527832, -1.6004142761230469] |
2c90863d-e2e9-4075-bc0a-c83f68eda845 | tridentadapt-learning-domain-invariance-via | 2111.15300 | null | https://arxiv.org/abs/2111.15300v2 | https://arxiv.org/pdf/2111.15300v2.pdf | TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation | Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation. From domain adaptation perspective, the key challenge is to learn domain-agnostic representation of the inputs in order to benefit from virtual data. ... | ['Alois Knoll', 'Onay Urfalioglu', 'Ahmet Faruk Tuna', 'Akhil Gurram', 'Fengyi Shen'] | 2021-11-30 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.77779400e-01 1.71310142e-01 -2.54072368e-01 -7.25143492e-01
-9.49941695e-01 -8.05697441e-01 6.64343536e-01 -4.61826175e-02
-4.60688174e-01 7.09332347e-01 -9.99263525e-02 -1.15461454e-01
9.74893272e-02 -6.78260326e-01 -8.65516841e-01 -4.03607160e-01
4.15429771e-01 8.71524274e-01 2.18606725e-01 -3.30498904... | [9.74100112915039, 1.4400057792663574] |
0c50e0e8-2c25-4180-a0b6-523b710933c8 | deep-residual-networks-for-automatic-sleep | 1810.03745 | null | http://arxiv.org/abs/1810.03745v1 | http://arxiv.org/pdf/1810.03745v1.pdf | Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms | We have developed an automatic sleep stage classification algorithm based on
deep residual neural networks and raw polysomnogram signals. Briefly, the raw
data is passed through 50 convolutional layers before subsequent classification
into one of five sleep stages. Three model configurations were trained on 1850
polyso... | ['Alexander Neergaard Olesen', 'Poul Jennum', 'Helge Bjarup Dissing Sorensen', 'Emmanuel Mignot', 'Paul Peppard'] | 2018-10-08 | null | null | null | null | ['automatic-sleep-stage-classification'] | ['medical'] | [ 1.04040027e-01 7.41962530e-03 4.79350891e-03 -6.93624496e-01
-4.54961240e-01 -3.43529671e-01 -1.20243303e-01 2.15688750e-01
-9.41008508e-01 1.01100874e+00 4.84591216e-01 -4.81590331e-01
3.52054723e-02 -3.70635450e-01 -9.17880237e-02 -2.63527602e-01
-4.84212130e-01 4.31566268e-01 1.71585325e-02 5.76629564... | [13.514674186706543, 3.5097362995147705] |
7ad671fa-49e2-42cb-8a15-9ba4d001a625 | context-aware-multi-task-learning-for-traffic | 2004.01351 | null | https://arxiv.org/abs/2004.01351v1 | https://arxiv.org/pdf/2004.01351v1.pdf | Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles | Traffic scene recognition, which requires various visual classification tasks, is a critical ingredient in autonomous vehicles. However, most existing approaches treat each relevant task independently from one another, never considering the entire system as a whole. Because of this, they are limited to utilizing a task... | ['Younkwan Lee', 'Moongu Jeon', 'Jihyo Jeon', 'Jongmin Yu'] | 2020-04-03 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.27009380e-01 -3.10368627e-01 -3.71276468e-01 -6.77065134e-01
-6.48365557e-01 -3.90602678e-01 7.37695217e-01 -4.59751427e-01
-3.97896081e-01 5.92163324e-01 -1.80014446e-02 -2.77927101e-01
-2.59095758e-01 -5.54544330e-01 -7.30144203e-01 -8.45254660e-01
3.35942447e-01 2.15148807e-01 3.72495234e-01 -3.19050513... | [9.79226016998291, 1.8250422477722168] |
d762c6b5-8959-44b0-a68a-61fb74702bff | unsupervised-pretraining-for-object-detection | 2103.04814 | null | https://arxiv.org/abs/2103.04814v2 | https://arxiv.org/pdf/2103.04814v2.pdf | Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors | Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks. However, most state-of-the-art unsupervised methods concentrate on learning global representations for image-level classification tasks instead of discriminative local region representations, which limits their tr... | ['Gui-Song Xia', 'Ping Luo', 'Zhenguo Li', 'Chenhan Jiang', 'Hang Xu', 'Enze Xie', 'Jian Ding'] | 2021-03-08 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.80595452e-01 7.74673298e-02 -5.51129937e-01 -2.81211674e-01
-7.67410278e-01 -3.82573962e-01 6.74545109e-01 2.83697248e-01
-3.11065823e-01 2.13856265e-01 -5.57936169e-02 -2.08923057e-01
2.00224221e-01 -9.32378650e-01 -8.10878217e-01 -7.52687514e-01
-5.99290840e-02 1.70424819e-01 6.27668440e-01 -5.00399014... | [9.651496887207031, 1.7572588920593262] |
1d8b31d7-0560-474e-819e-4d674d7ba162 | beyond-negativity-re-analysis-and-follow-up | 2306.01742 | null | https://arxiv.org/abs/2306.01742v1 | https://arxiv.org/pdf/2306.01742v1.pdf | Beyond Negativity: Re-Analysis and Follow-Up Experiments on Hope Speech Detection | Health experts assert that hope plays a crucial role in enhancing individuals' physical and mental well-being, facilitating their recovery, and promoting restoration. Hope speech refers to comments, posts and other social media messages that offer support, reassurance, suggestions, inspiration, and insight. The detecti... | ['Raghav Sahni', 'Diksha Sethi', 'Mohammad Aflah Khan', 'Neemesh Yadav'] | 2023-05-10 | null | null | null | null | ['hope-speech-detection'] | ['natural-language-processing'] | [-3.51683557e-01 3.24904203e-01 -7.89710701e-01 -1.52663857e-01
-7.99271822e-01 -6.94344938e-02 5.04480422e-01 1.12289762e+00
-6.09963275e-02 6.92109346e-01 1.46730101e+00 -5.82792163e-02
8.36854205e-02 -5.65903902e-01 3.70154560e-01 -2.83172399e-01
9.36888084e-02 -1.31742090e-01 -4.90586311e-01 -4.04549241... | [8.922789573669434, 10.681745529174805] |
86e586b8-d2ea-49ef-ac6d-383afd259587 | hierarchical-sparse-subspace-clustering-hessc | null | null | https://www.mdpi.com/2072-4292/12/15/2421 | https://www.mdpi.com/2072-4292/12/15/2421/htm | Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis | Hyperspectral imaging techniques are becoming one of the most important tools to remotely acquire fine spectral information on different objects. However, hyperspectral images (HSIs) require dedicated processing for most applications. Therefore, several machine learning techniques were proposed in the last decades. Amo... | ['and Richard Gloaguen', 'Raimon Tolosana-Delgado', 'Pedram Ghamisi', 'Laura Tusa', 'Mahdi Khodadadzadeh', 'Kasra Rafiezadeh Shahi'] | 2020-07-28 | null | null | null | null | ['sparse-subspace-based-clustering'] | ['computer-code'] | [ 2.30633542e-01 -6.69651210e-01 7.75788128e-02 -7.61028454e-02
-5.66738248e-01 -4.02270973e-01 3.97076130e-01 3.97131801e-01
-3.03265065e-01 5.28787315e-01 -1.42420650e-01 6.15424924e-02
-6.43643200e-01 -7.58580327e-01 -2.23697662e-01 -1.32291543e+00
-7.37842545e-02 3.54076743e-01 7.52918944e-02 8.64841267... | [9.990169525146484, -1.9727178812026978] |
864c83c1-c951-4d0a-bc81-840f8dea6f11 | self-annotated-training-for-controllable | 2110.08446 | null | https://arxiv.org/abs/2110.08446v2 | https://arxiv.org/pdf/2110.08446v2.pdf | Self-Annotated Training for Controllable Image Captioning | The Controllable Image Captioning (CIC) task aims to generate captions conditioned on designated control signals. Several structure-related control signals are proposed to control the semantic structure of sentences, such as sentence length and Part-of-Speech tag sequences. However, due to the fact that the accuracy-ba... | ['Hong Qu', 'Tianlei Wang', 'Zhangzi Zhu'] | 2021-10-16 | null | null | null | null | ['controllable-image-captioning'] | ['computer-vision'] | [ 6.15538239e-01 2.27298632e-01 -2.29514301e-01 -6.82261586e-01
-7.76584804e-01 -5.16165316e-01 5.29061675e-01 -3.09735388e-01
-3.86308908e-01 7.19958782e-01 3.90362829e-01 -1.65908873e-01
1.45582199e-01 -5.78512669e-01 -9.67907846e-01 -5.55829406e-01
3.96434009e-01 1.34758070e-01 7.55728409e-02 -2.15162218... | [10.989655494689941, 0.958912193775177] |
37851a24-d923-4dc9-9d7e-0b55b8bf3273 | latent-state-marginalization-as-a-low-cost | 2210.00999 | null | https://arxiv.org/abs/2210.00999v2 | https://arxiv.org/pdf/2210.00999v2.pdf | Latent State Marginalization as a Low-cost Approach for Improving Exploration | While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity. In this work,... | ['Ricky T. Q. Chen', 'Amy Zhang', 'Qinqing Zheng', 'Yoshua Bengio', 'Aaron Courville', 'Dinghuai Zhang'] | 2022-10-03 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-2.19800413e-01 3.70063633e-01 -5.30859292e-01 1.06143780e-01
-9.33737040e-01 -4.96604264e-01 9.72566128e-01 2.93530077e-02
-4.83991742e-01 1.21316040e+00 1.70070663e-01 -4.50613737e-01
-4.00456548e-01 -6.26548529e-01 -8.77489686e-01 -9.04776335e-01
-6.62876517e-02 4.64800179e-01 -7.34498426e-02 8.78590569... | [4.173191547393799, 2.2132465839385986] |
75a96551-e459-4aae-bb13-e23f4ff61944 | activemocap-optimized-drone-flight-for-active | 1912.08568 | null | https://arxiv.org/abs/1912.08568v2 | https://arxiv.org/pdf/1912.08568v2.pdf | ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture | The accuracy of monocular 3D human pose estimation depends on the viewpoint from which the image is captured. While freely moving cameras, such as on drones, provide control over this viewpoint, automatically positioning them at the location which will yield the highest accuracy remains an open problem. This is the pro... | ['Pascal Fua', 'Sena Kiciroglu', 'Mathieu Salzmann', 'Sudipta N. Sinha', 'Helge Rhodin'] | 2019-12-18 | activemocap-optimized-viewpoint-selection-for | http://openaccess.thecvf.com/content_CVPR_2020/html/Kiciroglu_ActiveMoCap_Optimized_Viewpoint_Selection_for_Active_Human_Motion_Capture_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Kiciroglu_ActiveMoCap_Optimized_Viewpoint_Selection_for_Active_Human_Motion_Capture_CVPR_2020_paper.pdf | cvpr-2020-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-8.93837214e-02 8.91349614e-02 -2.89066821e-01 -2.52069384e-01
-4.06174242e-01 -3.91686410e-01 4.19768274e-01 -2.50047058e-01
-7.15577126e-01 7.51529276e-01 3.69527668e-01 3.07649374e-01
1.29278511e-01 -4.05731201e-01 -7.44045079e-01 -4.42361981e-01
-9.63929892e-02 6.75114810e-01 4.30719368e-02 -1.96734309... | [7.060983180999756, -0.9913745522499084] |
5f0b2695-02ef-4314-96f2-de19418c84d1 | the-kitmus-test-evaluating-knowledge | 2212.08192 | null | https://arxiv.org/abs/2212.08192v2 | https://arxiv.org/pdf/2212.08192v2.pdf | The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources in Natural Language Understanding Systems | Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make inferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model's pretrain... | ['Jackie Chi Kit Cheung', 'Alexandra Olteanu', 'Adam Trischler', 'Kaheer Suleman', 'Martin Pömsl', 'Akshatha Arodi'] | 2022-12-15 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 2.42556155e-01 6.48585856e-01 -4.93807286e-01 -3.83267552e-01
-1.00661564e+00 -8.01111042e-01 9.85016406e-01 4.60693359e-01
-4.56715941e-01 1.25778496e+00 3.97068948e-01 -2.74370790e-01
-4.66770053e-01 -1.06735206e+00 -1.00324368e+00 2.23494079e-02
2.24401012e-01 1.21598911e+00 3.75308126e-01 -6.44578278... | [10.134949684143066, 8.186285972595215] |
eaa40d70-7405-46f0-b738-feb7536fcfe5 | cortical-microcircuits-as-gated-recurrent | 1711.02448 | null | http://arxiv.org/abs/1711.02448v2 | http://arxiv.org/pdf/1711.02448v2.pdf | Cortical microcircuits as gated-recurrent neural networks | Cortical circuits exhibit intricate recurrent architectures that are
remarkably similar across different brain areas. Such stereotyped structure
suggests the existence of common computational principles. However, such
principles have remained largely elusive. Inspired by gated-memory networks,
namely long short-term me... | ['Brendan Shillingford', 'Tim P. Vogels', 'Nando de Freitas', 'Yannis M. Assael', 'Rui Ponte Costa'] | 2017-11-07 | cortical-microcircuits-as-gated-recurrent-1 | http://papers.nips.cc/paper/6631-cortical-microcircuits-as-gated-recurrent-neural-networks | http://papers.nips.cc/paper/6631-cortical-microcircuits-as-gated-recurrent-neural-networks.pdf | neurips-2017-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 5.65724313e-01 2.56408215e-01 1.86140582e-01 -9.42121819e-02
-7.75840431e-02 -4.86407578e-01 1.09797382e+00 -2.47267663e-01
-3.51550460e-01 7.43138969e-01 4.45731819e-01 -4.59904194e-01
-4.77932282e-02 -8.43088210e-01 -6.83253586e-01 -9.81363535e-01
-2.75334001e-01 -1.03300601e-01 2.45153442e-01 -3.72876614... | [9.353259086608887, 2.7200427055358887] |
b0d882c8-787d-4db4-8e02-773160f281e8 | thuir2-at-ntcir-16-session-search-ss-task | 2307.00250 | null | https://arxiv.org/abs/2307.00250v1 | https://arxiv.org/pdf/2307.00250v1.pdf | THUIR2 at NTCIR-16 Session Search (SS) Task | Our team(THUIR2) participated in both FOSS and POSS subtasks of the NTCIR-161 Session Search (SS) Task. This paper describes our approaches and results. In the FOSS subtask, we submit five runs using learning-to-rank and fine-tuned pre-trained language models. We fine-tuned the pre-trained language model with ad-hoc da... | ['Shaoping Ma', 'Min Zhang', 'Yiqun Liu', 'Xiangsheng Li', 'Weihang Su'] | 2023-07-01 | null | null | null | null | ['learning-to-rank', 'learning-to-rank', 'session-search'] | ['graphs', 'miscellaneous', 'natural-language-processing'] | [-4.69602346e-01 -7.00741932e-02 -1.26972422e-01 -6.25519216e-01
-1.82534051e+00 -1.00482559e+00 8.76509905e-01 2.07269609e-01
-1.31604075e+00 9.33763444e-01 5.70809782e-01 -6.58396542e-01
1.34544209e-01 2.03762800e-01 -6.81178927e-01 1.99157238e-01
-2.92022228e-01 8.88095438e-01 5.23548186e-01 -7.28331804... | [11.12975025177002, 9.984793663024902] |
482a3fd5-31a7-45b3-8bc7-44aaf41316e3 | neural-morphological-tagging-from-characters | 1606.06640 | null | http://arxiv.org/abs/1606.06640v1 | http://arxiv.org/pdf/1606.06640v1.pdf | Neural Morphological Tagging from Characters for Morphologically Rich Languages | This paper investigates neural character-based morphological tagging for
languages with complex morphology and large tag sets. We systematically explore
a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain
character-based word vectors combined with bidirectional LSTMs to model
across-word con... | ['Guenter Neumann', 'Josef van Genabith', 'Georg Heigold'] | 2016-06-21 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [ 6.25220463e-02 -1.65431965e-02 -1.39094535e-02 -2.21884191e-01
-6.89150810e-01 -8.06056261e-01 4.11654919e-01 5.04343510e-01
-1.16043174e+00 5.39420187e-01 3.98446739e-01 -9.22818184e-01
2.91786820e-01 -5.88474810e-01 -3.12771291e-01 -6.65432811e-01
-6.17661811e-02 4.47230875e-01 3.54498625e-01 -1.23865098... | [10.352875709533691, 10.01153564453125] |
1b0647a2-6fb3-4bff-97d8-b1769406fac1 | asr-is-all-you-need-cross-modal-distillation | 1911.12747 | null | https://arxiv.org/abs/1911.12747v2 | https://arxiv.org/pdf/1911.12747v2.pdf | ASR is all you need: cross-modal distillation for lip reading | The goal of this work is to train strong models for visual speech recognition without requiring human annotated ground truth data. We achieve this by distilling from an Automatic Speech Recognition (ASR) model that has been trained on a large-scale audio-only corpus. We use a cross-modal distillation method that combin... | ['Triantafyllos Afouras', 'Joon Son Chung', 'Andrew Zisserman'] | 2019-11-28 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 5.49301088e-01 2.65322626e-01 -4.20187950e-01 -2.92486995e-01
-1.72659409e+00 -4.85526443e-01 8.78669143e-01 -3.72139841e-01
-4.65629995e-01 6.41752243e-01 4.08698082e-01 -6.49936557e-01
5.02748549e-01 1.70148909e-01 -8.82683814e-01 -4.15104598e-01
-4.60570194e-02 1.63518235e-01 1.13184392e-01 1.27147287... | [14.357876777648926, 5.063380718231201] |
29909c88-e20c-4014-91c7-8d1029cb6191 | framework-and-benchmarks-for-combinatorial | 2306.09803 | null | https://arxiv.org/abs/2306.09803v1 | https://arxiv.org/pdf/2306.09803v1.pdf | Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization | This paper introduces a modular framework for Mixed-variable and Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic benchmarking and standardized evaluation in the field. Current MCBO papers often introduce non-diverse or non-standard benchmarks to evaluate their methods, impeding the proper a... | ['Haitham Bou Ammar', 'Antoine Grosnit', 'Kamil Dreczkowski'] | 2023-06-16 | null | null | null | null | ['bayesian-optimization', 'benchmarking', 'benchmarking'] | ['methodology', 'miscellaneous', 'robots'] | [-3.89690958e-02 -2.79947549e-01 -3.77778947e-01 -3.58039439e-01
-1.18213117e+00 -3.81025523e-01 6.28582895e-01 -1.25402054e-02
-4.62493420e-01 1.04682398e+00 1.64797395e-01 -3.05753559e-01
-4.43552464e-01 -4.79567617e-01 -7.99063802e-01 -7.31582940e-01
3.11030913e-02 6.94310665e-01 1.35499701e-01 -4.13374528... | [6.4778151512146, 3.9147307872772217] |
476223cf-94f2-4dc3-be04-7d4def2515bc | traffic-sign-detection-and-recognition-for | 1911.05626 | null | https://arxiv.org/abs/1911.05626v1 | https://arxiv.org/pdf/1911.05626v1.pdf | Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment | This study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropping to address the issue of large image and small traffic signs; and using more anchors with various scales to detect traffic signs with dif... | ['Ziyuan Pu', 'Zhiyong Cui', 'Meixin Zhu', 'Yinhai Wang', 'Liangwu Yan', 'Jingyun Hu'] | 2019-10-27 | null | null | null | null | ['traffic-sign-detection', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [-1.82585754e-02 -4.50277001e-01 2.50509620e-01 -2.93297082e-01
2.97084153e-01 -2.67774701e-01 3.69675249e-01 -7.32256830e-01
-5.65652251e-01 7.74123788e-01 -2.88694203e-01 -6.91621304e-01
2.93917339e-02 -5.75180888e-01 -2.98310131e-01 -5.38916171e-01
1.54249460e-01 1.71458483e-01 1.06832755e+00 -3.82593900... | [7.959788799285889, -0.8422284722328186] |
45c756f8-2cc9-4e67-83aa-45d5f51ff404 | sampling-from-discrete-energy-based-models-1 | 2112.05702 | null | https://arxiv.org/abs/2112.05702v1 | https://arxiv.org/pdf/2112.05702v1.pdf | Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs | Energy-Based Models (EBMs) allow for extremely flexible specifications of probability distributions. However, they do not provide a mechanism for obtaining exact samples from these distributions. Monte Carlo techniques can aid us in obtaining samples if some proposal distribution that we can easily sample from is avail... | ['Marc Dymetman', 'Hady Elsahar', 'Germán Kruszewski', 'Bryan Eikema'] | 2021-12-10 | sampling-from-discrete-energy-based-models | https://openreview.net/forum?id=9zcjXdavnX | https://openreview.net/pdf?id=9zcjXdavnX | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [-1.18108010e-02 -2.79366851e-01 -3.46087873e-01 -2.76997626e-01
-1.22868240e+00 -5.53777039e-01 7.92686164e-01 3.34191889e-01
-3.90266538e-01 9.75498617e-01 -5.56887463e-02 -2.59758949e-01
9.93122812e-03 -1.08390546e+00 -9.39558864e-01 -6.25330031e-01
4.85709935e-01 9.28231418e-01 8.60356688e-02 2.26315513... | [6.986637115478516, 3.967517852783203] |
0afe2e7b-90d1-4c1d-8add-b05e184a49bb | learning-by-grouping-a-multilevel | 2304.00486 | null | https://arxiv.org/abs/2304.00486v1 | https://arxiv.org/pdf/2304.00486v1.pdf | Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy | The integration of machine learning models in various real-world applications is becoming more prevalent to assist humans in their daily decision-making tasks as a result of recent advancements in this field. However, it has been discovered that there is a tradeoff between the accuracy and fairness of these decision-ma... | ['Pengtao Xie', 'Bhanu Garg', 'Li Zhang', 'Ramtin Hosseini'] | 2023-04-02 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 3.80682588e-01 -6.17845468e-02 -4.39770550e-01 -7.07680225e-01
-4.14412051e-01 -1.69800654e-01 3.60506147e-01 1.88078612e-01
-6.90593004e-01 7.79603302e-01 -1.26086757e-01 -4.33769941e-01
-3.94417554e-01 -6.47520304e-01 -3.29962701e-01 -6.17700279e-01
2.67189145e-01 4.93035376e-01 -4.17333283e-02 5.85128441... | [9.257539749145508, 4.239013671875] |
a163b9a2-86c0-4158-87c3-8b4a2e68180e | foreground-segmentation-using-a-triplet | 1801.02225 | null | http://arxiv.org/abs/1801.02225v1 | http://arxiv.org/pdf/1801.02225v1.pdf | Foreground Segmentation Using a Triplet Convolutional Neural Network for Multiscale Feature Encoding | A common approach for moving objects segmentation in a scene is to perform a
background subtraction. Several methods have been proposed in this domain.
However, they lack the ability of handling various difficult scenarios such as
illumination changes, background or camera motion, camouflage effect, shadow
etc. To addr... | ['Long Ang Lim', 'Hacer Yalim Keles'] | 2018-01-07 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 6.21238768e-01 -4.08292592e-01 3.48357111e-01 -3.93873215e-01
-5.82218766e-01 -6.22722924e-01 3.96670073e-01 -4.14246172e-01
-5.97272336e-01 6.02868974e-01 -3.54920328e-01 -3.37475538e-01
4.52884912e-01 -8.10682952e-01 -1.08476377e+00 -6.68158114e-01
3.47436577e-01 -6.03228845e-02 9.70366895e-01 8.88933390... | [9.323158264160156, -0.5900633931159973] |
40b80587-b541-40a7-b475-8d8ee7298a20 | a-thousand-frames-in-just-a-few-words-lingual | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Das_A_Thousand_Frames_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Das_A_Thousand_Frames_2013_CVPR_paper.pdf | A Thousand Frames in Just a Few Words: Lingual Description of Videos through Latent Topics and Sparse Object Stitching | The problem of describing images through natural language has gained importance in the computer vision community. Solutions to image description have either focused on a top-down approach of generating language through combinations of object detections and language models or bottom-up propagation of keyword tags from t... | ['Jason J. Corso', 'Pradipto Das', 'Chenliang Xu', 'Richard F. Doell'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['video-description'] | ['computer-vision'] | [ 1.96927190e-01 1.70513481e-01 -2.14196146e-01 -7.31842518e-01
-1.07394993e+00 -7.45688260e-01 1.12997508e+00 4.97608751e-01
-3.79804641e-01 5.56879342e-01 4.45040107e-01 1.03021830e-01
1.20026790e-01 -5.45844793e-01 -5.34722209e-01 -3.65422964e-01
1.90866083e-01 7.57804275e-01 6.61457419e-01 -2.41310634... | [10.699257850646973, 0.8230736255645752] |
877a5139-dd23-4fcc-b36a-c9fde04dcf69 | feature-learning-with-gaussian-restricted | 1309.6176 | null | http://arxiv.org/abs/1309.6176v1 | http://arxiv.org/pdf/1309.6176v1.pdf | Feature Learning with Gaussian Restricted Boltzmann Machine for Robust Speech Recognition | In this paper, we first present a new variant of Gaussian restricted
Boltzmann machine (GRBM) called multivariate Gaussian restricted Boltzmann
machine (MGRBM), with its definition and learning algorithm. Then we propose
using a learned GRBM or MGRBM to extract better features for robust speech
recognition. Our experim... | ['Helen Meng', 'Xin Zheng', 'Zhiyong Wu', 'Weifeng Li', 'Lianhong Cai'] | 2013-09-23 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [-1.57181308e-01 -9.75401402e-02 -1.64063275e-01 -6.04516387e-01
-1.17297971e+00 -1.57039225e-01 9.10808265e-01 -6.26333475e-01
-8.89469385e-01 7.65704811e-01 2.85085618e-01 -5.64702630e-01
1.94949195e-01 -6.64838135e-01 -3.93366814e-01 -1.12731147e+00
-3.54538381e-01 2.76492417e-01 2.40066394e-01 1.61258236... | [14.54318618774414, 6.443105220794678] |
589773a7-2d55-4923-913e-03a4bee7d855 | semeval-2019-task-9-suggestion-mining-from | null | null | https://aclanthology.org/S19-2151 | https://aclanthology.org/S19-2151.pdf | SemEval-2019 Task 9: Suggestion Mining from Online Reviews and Forums | We present the pilot SemEval task on Suggestion Mining. The task consists of subtasks A and B, where we created labeled data from feedback forum and hotel reviews respectively. Subtask A provides training and test data from the same domain, while Subtask B evaluates the system on a test dataset from a different domain ... | ['Sapna Negi', 'Paul Buitelaar', 'Tobias Daudert'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 1.09045357e-01 3.04131031e-01 -2.94273257e-01 -6.91980898e-01
-8.40625823e-01 -6.74710810e-01 5.66494226e-01 3.68513793e-01
-5.71076095e-01 1.00198603e+00 4.55620021e-01 -5.43915272e-01
-1.29069552e-01 -3.27010781e-01 -3.46918583e-01 -3.36253047e-01
1.30172065e-02 6.34389877e-01 1.80753469e-01 -4.16880935... | [10.898763656616211, 7.487928867340088] |
258a1c83-96c7-48c9-9d68-85b39d283dde | a-review-of-audio-features-and-statistical | 1502.06811 | null | http://arxiv.org/abs/1502.06811v1 | http://arxiv.org/pdf/1502.06811v1.pdf | A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design | Audio fingerprinting, also named as audio hashing, has been well-known as a
powerful technique to perform audio identification and synchronization. It
basically involves two major steps: fingerprint (voice pattern) design and
matching search. While the first step concerns the derivation of a robust and
compact audio si... | ['Hien-Thanh Duong', 'Ngoc Q. K. Duong'] | 2015-02-24 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 5.83884537e-01 -5.87100327e-01 -2.35517889e-01 -2.32623518e-01
-1.05386543e+00 -7.78250515e-01 1.42284453e-01 2.44988248e-01
-2.87213922e-01 4.07317191e-01 -3.06730531e-02 -1.04993634e-01
-3.34361404e-01 -3.85774821e-01 -2.41555139e-01 -5.18086195e-01
-4.83478397e-01 1.31475121e-01 3.99450123e-01 1.68666914... | [15.534284591674805, 5.436435222625732] |
9cafe45f-4b85-49f8-87a1-81b1f85c1798 | efficient-bilateral-cross-modality-cluster | 2305.12673 | null | https://arxiv.org/abs/2305.12673v2 | https://arxiv.org/pdf/2305.12673v2.pdf | Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID | Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match pedestrian images of the same identity from different modalities without annotations. Existing works mainly focus on alleviating the modality gap by aligning instance-level features of the unlabeled samples. However, the relationships be... | ['Xinbo Gao', 'Zhen Wang', 'Shizhou Zhang', 'Nannan Wang', 'Lingfeng He', 'De Cheng'] | 2023-05-22 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 1.79740742e-01 -2.50491679e-01 -4.20828938e-01 -6.26882434e-01
-1.07515681e+00 -4.95303571e-01 6.63894951e-01 -1.09960310e-01
-4.38725412e-01 4.43883210e-01 3.13051939e-01 1.20662354e-01
-1.38842851e-01 -4.15338099e-01 -8.67743850e-01 -7.72046506e-01
5.10540128e-01 2.53643721e-01 -1.01841472e-01 1.03321351... | [14.813061714172363, 1.0259755849838257] |
189e2191-5e24-4a57-bed1-de4aec87466a | emotion-recognition-in-conversations-with | 1910.04980 | null | https://arxiv.org/abs/1910.04980v3 | https://arxiv.org/pdf/1910.04980v3.pdf | Conversational Transfer Learning for Emotion Recognition | Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Recent approaches have focused on modeling these dependencies primarily via supervised learning. However, purely supervised strategies demand large amounts of annot... | ['Roger Zimmermann', 'Soujanya Poria', 'Devamanyu Hazarika', 'Rada Mihalcea'] | 2019-10-11 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.56852150e-01 4.00328726e-01 9.52560231e-02 -7.27971852e-01
-7.11043656e-01 -4.97137308e-01 9.14536774e-01 6.91130534e-02
-2.38793075e-01 9.13373888e-01 6.18390381e-01 -8.44664872e-02
4.51306075e-01 -6.13499939e-01 -4.60223049e-01 -4.25004184e-01
1.10287637e-01 3.52475643e-01 -1.14413217e-01 -5.33705354... | [12.98222827911377, 6.296786308288574] |
13be2c1f-fc7d-46ba-824f-3fc162256da4 | detection-and-attention-diagnosing-pulmonary | 1712.05114 | null | http://arxiv.org/abs/1712.05114v1 | http://arxiv.org/pdf/1712.05114v1.pdf | Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians | This paper proposes a novel and efficient method to build a Computer-Aided
Diagnoses (CAD) system for lung nodule detection based on Computed Tomography
(CT). This task was treated as an Object Detection on Video (VID) problem by
imitating how a radiologist reads CT scans. A lung nodule detector was trained
to automati... | ['Wei Guo', 'Ning Li', 'Kungang Li', 'Shijun Zhao', 'Jie He', 'Haopeng Liu', 'Bin Qiu'] | 2017-12-14 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.81490141e-01 5.47179654e-02 -4.48952258e-01 -1.86379790e-01
-1.18195009e+00 -5.01519799e-01 2.13677049e-01 -1.70059085e-01
-4.63968217e-01 2.92224258e-01 1.49503127e-01 -4.65358138e-01
-1.71102528e-02 -6.87669337e-01 -5.73441446e-01 -4.91282761e-01
-3.63372087e-01 8.73832405e-01 1.34923005e+00 4.30604100... | [15.373804092407227, -2.1518003940582275] |
03cccde2-1af0-4c6b-8caa-b1aab360b73d | from-representation-to-reasoning-towards-both | 2205.14895 | null | https://arxiv.org/abs/2205.14895v1 | https://arxiv.org/pdf/2205.14895v1.pdf | From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering | Video understanding has achieved great success in representation learning, such as video caption, video object grounding, and video descriptive question-answer. However, current methods still struggle on video reasoning, including evidence reasoning and commonsense reasoning. To facilitate deeper video understanding to... | ['Liqing Zhang', 'Li Niu', 'Jiangtong Li'] | 2022-05-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_From_Representation_to_Reasoning_Towards_Both_Evidence_and_Commonsense_Reasoning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_From_Representation_to_Reasoning_Towards_Both_Evidence_and_Commonsense_Reasoning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-question-answering'] | ['computer-vision'] | [ 2.10993662e-01 3.10710847e-01 -6.54743016e-01 -4.69720691e-01
-7.77218342e-01 -4.39058095e-01 6.81560636e-01 -1.44386113e-01
2.14014187e-01 9.53467250e-01 1.10739541e+00 -5.07241011e-01
-8.06133747e-02 -6.37032092e-01 -1.06871796e+00 1.63544007e-02
2.10592136e-01 7.22381324e-02 1.78628966e-01 -1.69240624... | [10.456403732299805, 1.0273951292037964] |
58dfaeca-e9bb-487d-aea8-382b0da0c7af | diameter-estimation-of-cylindrical-metal-bar | 2212.13021 | null | https://arxiv.org/abs/2212.13021v1 | https://arxiv.org/pdf/2212.13021v1.pdf | Diameter Estimation of Cylindrical Metal Bar Using Wideband Dual-Polarized Ground-Penetrating Radar | Ground-penetrating radar (GPR) has been an effective technology for locating metal bars in civil engineering structures. However, the accurate sizing of subsurface metal bars of small diameters remains a challenging problem for the existing reflection pattern-based method due to the limited resolution of GPR. To addres... | ['Zheng Fan', 'Weixia Cheng', 'Hai-Han Sun'] | 2022-12-26 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.34968528e-01 6.24447092e-02 8.13715756e-01 2.40647405e-01
-5.88726640e-01 -4.08800766e-02 2.56361160e-02 5.54548949e-02
1.05683304e-01 4.10663664e-01 1.80006947e-03 -3.82340997e-01
-7.17910469e-01 -1.09682679e+00 -6.93241581e-02 -1.04824901e+00
-5.32525079e-03 7.27763176e-02 2.57394791e-01 -4.07044023... | [6.7954487800598145, 1.4181901216506958] |
adef0ea9-655e-4c44-9ad0-6f680ce6f799 | hypertime-hyperparameter-optimization-for | 2305.18421 | null | https://arxiv.org/abs/2305.18421v1 | https://arxiv.org/pdf/2305.18421v1.pdf | HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts | In this work, we propose a hyperparameter optimization method named \emph{HyperTime} to find hyperparameters robust to potential temporal distribution shifts in the unseen test data. Our work is motivated by an important observation that it is, in many cases, possible to achieve temporally robust predictive performance... | ['Chi Wang', 'Qingyun Wu', 'Zhonghua Zheng', 'Yiran Wu', 'Shaokun Zhang'] | 2023-05-28 | null | null | null | null | ['hyperparameter-optimization', 'philosophy'] | ['methodology', 'miscellaneous'] | [ 8.26757103e-02 -1.56601816e-01 -2.69451052e-01 -2.36805633e-01
-1.16820025e+00 -8.30332875e-01 3.98881972e-01 3.54647823e-02
-5.28802454e-01 1.16563201e+00 -2.29845598e-01 -2.47492626e-01
-9.41433787e-01 -2.88961262e-01 -6.30185962e-01 -1.23024666e+00
-3.30898434e-01 5.01341403e-01 1.25140518e-01 5.81742711... | [6.666948318481445, 4.007574081420898] |
5e70f914-1b24-4c32-b25c-1be7c6451508 | semantic-neural-model-approach-for-face | 2305.01058 | null | https://arxiv.org/abs/2305.01058v1 | https://arxiv.org/pdf/2305.01058v1.pdf | semantic neural model approach for face recognition from sketch | Face sketch synthesis and reputation have wide range of packages in law enforcement. Despite the amazing progresses had been made in faces cartoon and reputation, maximum current researches regard them as separate responsibilities. On this paper, we propose a semantic neural version approach so that you can address fac... | ['Raghupathi Reddy Allapuram', 'Sandhya Jukanti', 'Chandana Navuluri'] | 2023-05-01 | null | null | null | null | ['face-recognition', 'face-sketch-synthesis', 'caricature'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.7370472e-03 1.9181396e-01 -9.9014409e-02 -5.7162058e-01
1.8380181e-01 -5.4505277e-01 5.3608948e-01 -1.0000752e+00
3.2787514e-01 6.5328455e-01 6.8203248e-02 1.9097541e-01
6.5732067e-03 -7.1745741e-01 -3.5767412e-01 -6.3159597e-01
1.6728324e-01 -2.5516763e-02 -1.1591351e-01 -2.9820040e-01
5.2863097e-01... | [13.100317001342773, 0.5369749665260315] |
1cd0f194-c3e2-4032-814f-2e8b2b9b35a5 | unsupervised-few-shot-learning-via-self | null | null | https://openreview.net/forum?id=Ske-ih4FPS | https://openreview.net/pdf?id=Ske-ih4FPS | Unsupervised Few Shot Learning via Self-supervised Training | Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a more natural procedure ... | ['Si Wu', 'Tiejun Huang', 'Xiaolong Zou', 'Zilong Ji'] | 2019-09-25 | null | null | null | null | ['unsupervised-few-shot-learning'] | ['computer-vision'] | [ 2.71736592e-01 -4.64437902e-02 -2.84428000e-01 -5.13996959e-01
-6.18110001e-01 -2.81780455e-02 7.43487477e-01 2.13334799e-01
-5.73670685e-01 9.12002981e-01 2.35204205e-01 4.46370631e-01
-3.59789968e-01 -1.01451612e+00 -5.09088159e-01 -6.42518878e-01
-4.40595672e-02 8.26461256e-01 4.76173878e-01 -1.49031475... | [10.021907806396484, 3.1553685665130615] |
202d3676-b15c-459b-aa31-f56ec3f48f55 | few-shot-named-entity-recognition-with | null | null | https://openreview.net/forum?id=5aeBigqvO-P | https://openreview.net/pdf?id=5aeBigqvO-P | Few-Shot Named Entity Recognition with Biaffine Span Representation | While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data (i.e., few-shot NER) has been challenging. Correspondingly, the N-way K-shot NER task is proposed to recognize entities in the given N categories with only K labeled samples for each category. Exist... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['few-shot-ner'] | ['natural-language-processing'] | [-2.39554700e-02 -1.25144482e-01 -1.05910301e-01 -6.37578785e-01
-7.62304902e-01 -4.81259048e-01 3.22137773e-01 3.22383821e-01
-1.06224132e+00 7.28827059e-01 4.32422876e-01 -4.70101833e-03
1.16798282e-01 -8.67330194e-01 -3.83371353e-01 -4.44252640e-01
1.57111913e-01 -6.09865412e-02 2.90148109e-01 -2.28821889... | [9.688023567199707, 9.402173042297363] |
9983c5aa-4d35-4203-ac77-30b6ceb40724 | using-fictitious-class-representations-to | 2111.13550 | null | https://arxiv.org/abs/2111.13550v1 | https://arxiv.org/pdf/2111.13550v1.pdf | Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners | Focusing on discriminative zero-shot learning, in this work we introduce a novel mechanism that dynamically augments during training the set of seen classes to produce additional fictitious classes. These fictitious classes diminish the model's tendency to fixate during training on attribute correlations that appear in... | ['Ran El-Yaniv', 'Mohammed Dabbah'] | 2021-11-26 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 1.51566625e-01 1.90646991e-01 -1.34928659e-01 -2.94524163e-01
-3.29459697e-01 -3.07828002e-02 7.58894563e-01 2.23216295e-01
-4.00547653e-01 7.73079634e-01 1.38158008e-01 1.21807918e-01
-2.46552736e-01 -1.10112190e+00 -5.98472238e-01 -6.85581803e-01
-1.43458042e-02 4.70520228e-01 9.10759330e-01 -2.63112664... | [9.909309387207031, 3.255009889602661] |
74f3bd8b-438c-47f5-8c1e-64b3a0b776db | timematch-unsupervised-cross-region | 2111.02682 | null | https://arxiv.org/abs/2111.02682v3 | https://arxiv.org/pdf/2111.02682v3.pdf | TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation | The recent developments of deep learning models that capture complex temporal patterns of crop phenology have greatly advanced crop classification from Satellite Image Time Series (SITS). However, when applied to target regions spatially different from the training region, these models perform poorly without any target... | ['Ira Assent', 'Sébastien Lefèvre', 'Charlotte Pelletier', 'Joachim Nyborg'] | 2021-11-04 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 3.32712978e-01 -5.63767254e-01 -5.54896295e-01 -4.83384728e-01
-4.62410867e-01 -1.15484977e+00 4.50430661e-01 4.92057681e-01
-1.93820745e-01 6.89096749e-01 -2.34098166e-01 -3.44250649e-01
1.15781993e-01 -1.00879145e+00 -1.03652060e+00 -7.58638680e-01
-2.77033776e-01 2.15353712e-01 1.68509364e-01 -4.19696182... | [9.400046348571777, -1.5908203125] |
ae9d108f-1ef1-43f5-a5c4-19154f620845 | planercnn-3d-plane-detection-and | 1812.04072 | null | http://arxiv.org/abs/1812.04072v2 | http://arxiv.org/pdf/1812.04072v2.pdf | PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image | This paper proposes a deep neural architecture, PlaneRCNN, that detects and
reconstructs piecewise planar surfaces from a single RGB image. PlaneRCNN
employs a variant of Mask R-CNN to detect planes with their plane parameters
and segmentation masks. PlaneRCNN then jointly refines all the segmentation
masks with a nove... | ['Yasutaka Furukawa', 'Jinwei Gu', 'Chen Liu', 'Jan Kautz', 'Kihwan Kim'] | 2018-12-10 | planercnn-3d-plane-detection-and-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_PlaneRCNN_3D_Plane_Detection_and_Reconstruction_From_a_Single_Image_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_PlaneRCNN_3D_Plane_Detection_and_Reconstruction_From_a_Single_Image_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-plane-detection'] | ['computer-vision'] | [ 4.17223155e-01 3.95821571e-01 7.95049295e-02 -4.51696038e-01
-7.06785798e-01 -5.11478782e-01 3.10462505e-01 -2.92864859e-01
-2.33115330e-01 4.39807743e-01 -3.49603593e-01 -1.60037383e-01
-1.56385839e-01 -1.16497600e+00 -1.09113431e+00 -3.59502017e-01
5.77435680e-02 5.18478990e-01 5.83383739e-01 -3.09949845... | [8.53213882446289, -2.925299644470215] |
13d40601-2463-432f-b60e-17e55772f5d5 | construction-of-semantic-collocation-bank | null | null | https://aclanthology.org/Y15-2027 | https://aclanthology.org/Y15-2027.pdf | Construction of Semantic Collocation Bank Based on Semantic Dependency Parsing | null | ['Yu Ding', 'Yanqiu Shao', 'Shijun Liu', 'Lijuan Zheng'] | 2015-10-01 | construction-of-semantic-collocation-bank-1 | https://aclanthology.org/Y15-2027 | https://aclanthology.org/Y15-2027.pdf | paclic-2015-10 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3190412521362305, 3.683807611465454] |
7b9d5e5d-e3ff-448e-9bc8-a7a773bad01a | deep-attention-based-classification-network | 1807.03959 | null | http://arxiv.org/abs/1807.03959v1 | http://arxiv.org/pdf/1807.03959v1.pdf | Deep attention-based classification network for robust depth prediction | In this paper, we present our deep attention-based classification (DABC)
network for robust single image depth prediction, in the context of the Robust
Vision Challenge 2018 (ROB 2018). Unlike conventional depth prediction, our
goal is to design a model that can perform well in both indoor and outdoor
scenes with a sin... | ['Hao Lu', 'Chunhua Shen', 'Zhiguo Cao', 'Ke Xian', 'Ruibo Li', 'Lingxiao Hang'] | 2018-07-11 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 5.07198989e-01 -7.08331168e-02 -1.21627763e-01 -6.79228663e-01
-7.91799426e-01 -1.78245962e-01 4.17957962e-01 3.68409678e-02
-5.50862670e-01 4.51726615e-01 2.47684449e-01 -2.22570612e-03
-1.38233483e-01 -8.07834685e-01 -7.73464262e-01 -6.35572255e-01
-2.43989117e-02 -1.57492355e-01 4.10999298e-01 1.53395385... | [8.791135787963867, -2.2763757705688477] |
af53387a-6c4d-4679-bfc2-222365b53ab1 | multi-hop-reading-comprehension-via-deep | 1905.09438 | null | https://arxiv.org/abs/1905.09438v1 | https://arxiv.org/pdf/1905.09438v1.pdf | Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal | Reading Comprehension has received significant attention in recent years as high quality Question Answering (QA) datasets have become available. Despite state-of-the-art methods achieving strong overall accuracy, Multi-Hop (MH) reasoning remains particularly challenging. To address MH-QA specifically, we propose a Deep... | ['Alex Long', 'Alan Blair', 'Joel Mason', 'Wei Wang'] | 2019-05-23 | null | null | null | null | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 4.72746670e-01 3.52006823e-01 -1.89756319e-01 -4.35863376e-01
-1.65800476e+00 -8.43839288e-01 4.00015980e-01 8.37230265e-01
-4.35543954e-01 5.57477415e-01 6.84742630e-01 -7.30731189e-01
-3.01167607e-01 -1.05324447e+00 -9.14800763e-01 -3.30907516e-02
2.09235996e-01 7.22576439e-01 4.14490163e-01 -3.93278897... | [11.15841293334961, 7.962172985076904] |
67d63293-36c5-48e9-99a2-9a59e199e6b8 | clusterlog-clustering-logs-for-effective-log | 2301.07846 | null | https://arxiv.org/abs/2301.07846v1 | https://arxiv.org/pdf/2301.07846v1.pdf | ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection | With the increasing prevalence of scalable file systems in the context of High Performance Computing (HPC), the importance of accurate anomaly detection on runtime logs is increasing. But as it currently stands, many state-of-the-art methods for log-based anomaly detection, such as DeepLog, have encountered numerous ch... | ['Di Zhang', 'Dong Dai', 'Chris Egersdoerfer'] | 2023-01-19 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [-6.89142719e-02 -6.04036748e-01 3.32276195e-01 -3.30011159e-01
-1.70587376e-01 -4.35950309e-01 4.91552591e-01 1.05723512e+00
-3.52098703e-01 4.59737033e-01 1.39659643e-01 -4.62865144e-01
-4.71787423e-01 -7.55115867e-01 -2.56121486e-01 -5.14893293e-01
-8.09952378e-01 7.96947241e-01 4.32817787e-01 -2.15520725... | [7.483970642089844, 2.7107393741607666] |
74f37962-9c25-4488-ad8b-499f534c6ac0 | understanding-health-misinformation | 2101.01076 | null | https://arxiv.org/abs/2101.01076v7 | https://arxiv.org/pdf/2101.01076v7.pdf | Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning | Predicting video viewership is a top priority for content creators and video-sharing sites. Content creators live on such predictions to maximize influences and minimize budgets. Video-sharing sites rely on this prediction to promote credible videos and curb violative videos. Although deep learning champions viewership... | ['Xiao Liu', 'Jiaheng Xie'] | 2020-12-21 | null | null | null | null | ['video-description'] | ['computer-vision'] | [-5.37040271e-02 6.26193047e-01 -1.24957871e+00 -4.77750182e-01
-5.75963557e-01 -5.33188641e-01 2.29186714e-01 -7.14361072e-02
5.89114912e-02 1.56687483e-01 1.10941541e+00 -6.64124072e-01
-4.08354789e-01 -2.63871580e-01 -8.55939209e-01 8.63705873e-02
2.74616741e-02 -3.23669203e-02 -4.39353824e-01 1.07333526... | [9.808977127075195, 5.798613548278809] |
66d3299b-61a3-4197-9e24-91454c631854 | real-time-visual-tracking-promoting-the | 1608.08173 | null | http://arxiv.org/abs/1608.08173v2 | http://arxiv.org/pdf/1608.08173v2.pdf | Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning | Correlation filtering based tracking model has received lots of attention and
achieved great success in real-time tracking, however, the lost function in
current correlation filtering paradigm could not reliably response to the
appearance changes caused by occlusion and illumination variations. This study
intends to pr... | ['Ziming Zhang', 'Yao Sui', 'Li Zhang', 'Yafei Tang', 'Guanghui Wang'] | 2016-08-29 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [-2.52164006e-01 -6.44405901e-01 -2.01555729e-01 -9.69104543e-02
-2.58493185e-01 -4.12587881e-01 4.84313011e-01 -2.84999162e-01
-2.32318014e-01 8.62404168e-01 1.79440156e-02 2.17089683e-01
-2.79995978e-01 -2.44697452e-01 -4.39490914e-01 -9.50796008e-01
-9.19907913e-02 -3.63100827e-01 3.87937248e-01 3.94763760... | [6.405730724334717, -2.1254022121429443] |
44d6c1f8-f4ad-4b01-beed-1e7d6cb02d38 | neural-chinese-word-segmentation-as-sequence | 1911.12982 | null | https://arxiv.org/abs/1911.12982v1 | https://arxiv.org/pdf/1911.12982v1.pdf | Neural Chinese Word Segmentation as Sequence to Sequence Translation | Recently, Chinese word segmentation (CWS) methods using neural networks have made impressive progress. Most of them regard the CWS as a sequence labeling problem which construct models based on local features rather than considering global information of input sequence. In this paper, we cast the CWS as a sequence tran... | ['He-Yan Huang', 'Yi-Kun Tang', 'Yuhang Guo', 'Ping Jian', 'Xuewen Shi', 'Xiaochi Wei'] | 2019-11-29 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 8.45824718e-01 -3.17321092e-01 -2.89966136e-01 -5.43840408e-01
-1.27838624e+00 -5.09258449e-01 3.33426297e-01 -2.66771531e-03
-8.45701277e-01 6.58638179e-01 2.05464274e-01 -6.03746355e-01
6.69094920e-01 -3.97616178e-01 -7.84324229e-01 -6.14984632e-01
6.68665230e-01 4.46451426e-01 3.03249985e-01 -1.05608143... | [10.022212982177734, 10.066969871520996] |
6eb96052-e018-4c89-b59f-53b5f6b2010e | a-deep-learning-framework-for-traffic-data | 2304.09182 | null | https://arxiv.org/abs/2304.09182v1 | https://arxiv.org/pdf/2304.09182v1.pdf | A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies | Spatiotemporal (ST) data collected by sensors can be represented as multi-variate time series, which is a sequence of data points listed in an order of time. Despite the vast amount of useful information, the ST data usually suffer from the issue of missing or incomplete data, which also limits its applications. Imputa... | ['Chan', 'Wai Kin', 'George P. Chan', 'Chenyu Tian', 'Qiruyi Zuo', 'Ting Zhang', 'Li Jiang'] | 2023-04-18 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [ 4.47221771e-02 -8.45166683e-01 -4.59071428e-01 -2.78636664e-01
-3.84795040e-01 -5.41234791e-01 3.96358937e-01 1.69765279e-01
-1.28161356e-01 1.01095366e+00 3.34679604e-01 -3.75135332e-01
-6.47665024e-01 -8.60605657e-01 -7.56359339e-01 -6.90456629e-01
-1.44897372e-01 2.03621089e-02 3.02455544e-01 -2.53658235... | [6.7559356689453125, 2.514152765274048] |
e03c180c-1ec3-4bd1-af41-92a411b02356 | dual-distribution-discrepancy-for-anomaly | 2206.03935 | null | https://arxiv.org/abs/2206.03935v3 | https://arxiv.org/pdf/2206.03935v3.pdf | Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays | Chest X-ray (CXR) is the most typical radiological exam for diagnosis of various diseases. Due to the expensive and time-consuming annotations, detecting anomalies in CXRs in an unsupervised fashion is very promising. However, almost all of the existing methods consider anomaly detection as a one-class classification (... | ['Kwang-Ting Cheng', 'Yu Zhou', 'Xin Yang', 'Hao Chen', 'Yu Cai'] | 2022-06-08 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 1.09969482e-01 -2.53754139e-01 -1.81015551e-01 -3.70382965e-01
-8.81748974e-01 -3.80125165e-01 1.86069235e-01 4.30618137e-01
-3.73249173e-01 5.51969647e-01 -3.18500996e-01 -4.96704966e-01
1.41901046e-01 -6.78885221e-01 -3.65468800e-01 -9.37726557e-01
1.61099955e-01 6.44089699e-01 4.54537481e-01 3.60710829... | [7.635586261749268, 2.1961519718170166] |
21b58f15-71fc-4c95-9fd6-08452410739d | an-equivalent-graph-reconstruction-model-and | 2307.02183 | null | https://arxiv.org/abs/2307.02183v1 | https://arxiv.org/pdf/2307.02183v1.pdf | An Equivalent Graph Reconstruction Model and its Application in Recommendation Prediction | Recommendation algorithm plays an important role in recommendation system (RS), which predicts users' interests and preferences for some given items based on their known information. Recently, a recommendation algorithm based on the graph Laplacian regularization was proposed, which treats the prediction problem of the... | ['Zhihua Yang', 'Qing Zhang', 'Lihua Yang', 'Guangrui Yang'] | 2023-07-05 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-4.27372660e-03 -6.87198043e-02 -4.65857625e-01 -4.45277691e-01
-4.17531878e-01 -2.41889521e-01 9.29193646e-02 -8.52429494e-02
1.06653785e-02 4.23718899e-01 2.06804261e-01 -1.77739546e-01
-2.68517226e-01 -9.28239465e-01 -5.35816193e-01 -5.32203138e-01
1.23574063e-01 1.65865123e-01 8.14632848e-02 -1.65471658... | [10.104244232177734, 5.639612197875977] |
3d87c2dc-3d0d-403e-8ade-c91cc3b38990 | zenseact-open-dataset-a-large-scale-and | 2305.02008 | null | https://arxiv.org/abs/2305.02008v1 | https://arxiv.org/pdf/2305.02008v1.pdf | Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving | Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360{\deg} perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large-scale and diverse multimodal dataset collected over two years in various European countr... | ['Christoffer Petersson', 'Jenny Widahl', 'Junsheng Fu', 'Daria Motorniuk', 'Carl Lindstrom', 'Adam Lilja', 'Georg Hess', 'Adam Tonderski', 'William Ljungbergh', 'Mina Alibeigi'] | 2023-05-03 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.79511055e-01 -1.65051535e-01 -3.13801169e-01 -7.55658448e-01
-1.01658928e+00 -8.80098641e-01 8.35873902e-01 8.22903216e-03
-4.31204766e-01 4.03681993e-01 7.49653652e-02 -2.56884962e-01
-2.56694824e-01 -7.31438041e-01 -7.45580435e-01 -6.00222468e-01
-5.77110164e-02 4.25923944e-01 6.78592145e-01 -3.69383782... | [7.755643367767334, -1.9210164546966553] |
e99d8ece-68da-43cf-82af-d9955db46d2a | transformer-based-global-3d-hand-pose | 2210.11384 | null | https://arxiv.org/abs/2210.11384v1 | https://arxiv.org/pdf/2210.11384v1.pdf | Transformer-based Global 3D Hand Pose Estimation in Two Hands Manipulating Objects Scenarios | This report describes our 1st place solution to ECCV 2022 challenge on Human Body, Hands, and Activities (HBHA) from Egocentric and Multi-view Cameras (hand pose estimation). In this challenge, we aim to estimate global 3D hand poses from the input image where two hands and an object are interacting on the egocentric v... | ['Seungryul Baek', 'Seongyeong Lee', 'Chanwoo Kim', 'Donguk Kim', 'Hoseong Cho'] | 2022-10-20 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [-1.94189280e-01 -9.38980058e-02 1.96741849e-01 -2.59500220e-02
-5.65157831e-01 -7.28397548e-01 2.32831135e-01 -8.96799207e-01
-3.35147351e-01 5.39864063e-01 4.61900145e-01 4.63463902e-01
1.33870780e-01 -9.83444452e-02 -5.66977739e-01 -6.10853255e-01
3.10516685e-01 8.28878760e-01 1.17621832e-01 1.23736531... | [6.746607303619385, -0.8387802243232727] |
ebbf39d7-9d8b-4439-b7b7-3005647fccda | combining-scatter-transform-and-deep-neural | 2010.07639 | null | https://arxiv.org/abs/2010.07639v1 | https://arxiv.org/pdf/2010.07639v1.pdf | Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification | An essential part for the accurate classification of electrocardiogram (ECG) signals is the extraction of informative yet general features, which are able to discriminate diseases. Cardiovascular abnormalities manifest themselves in features on different time scales: small scale morphological features, such as missing ... | ['Jan Steffan', 'Felix P Kemeth', 'Maximilian Riehl', 'Maximilian P Oppelt'] | 2020-10-15 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.76566565e-01 -7.41873458e-02 2.23607793e-01 -5.09302974e-01
-7.24406004e-01 -4.74271357e-01 2.16192424e-01 4.32187587e-01
-4.49684501e-01 6.55667961e-01 2.23519117e-01 -1.70587450e-01
-4.54230487e-01 -4.75938112e-01 -3.01841319e-01 -7.09285855e-01
-6.13918662e-01 2.38321409e-01 3.79542932e-02 -2.38425940... | [14.266572952270508, 3.30014967918396] |
d9d8a4a2-f4f4-41ef-b93f-7f20326839d5 | chain-of-thought-prompting-for-responding-to | 2305.11792 | null | https://arxiv.org/abs/2305.11792v1 | https://arxiv.org/pdf/2305.11792v1.pdf | Chain-of-thought prompting for responding to in-depth dialogue questions with LLM | The way and content in which users ask questions can provide insight into their current status, including their personality, emotions, and psychology. Instead of directly prompting the large language models (LLMs), we explore how chain-of-thought prompting helps in this scenario to perform reasoning and planning accord... | ['Kam-Fai Wong', 'Ruifeng Xu', 'Zezhong Wang', 'Fei Mi', 'Rui Wang', 'Hongru Wang'] | 2023-05-19 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-9.50364172e-02 3.98186892e-01 -5.52378334e-02 -6.60233676e-01
-6.65131211e-01 -6.95902228e-01 6.77011430e-01 2.71266907e-01
-2.39915565e-01 4.99203116e-01 4.76834059e-01 -2.63530105e-01
-2.49462038e-01 -5.88098705e-01 2.38496419e-02 -5.99224046e-02
5.57087839e-01 5.97417414e-01 1.88954309e-01 -5.57084024... | [12.375411033630371, 7.894015312194824] |
fb2cd3bb-aea3-437e-a604-01f23c86c2b7 | a-survey-on-deep-domain-adaptation-and-tiny | 2107.07927 | null | https://arxiv.org/abs/2107.07927v1 | https://arxiv.org/pdf/2107.07927v1.pdf | A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets | This survey paper specially analyzed computer vision-based object detection challenges and solutions by different techniques. We mainly highlighted object detection by three different trending strategies, i.e., 1) domain adaptive deep learning-based approaches (discrepancy-based, Adversarial-based, Reconstruction-based... | ['Xi Li', 'Muhammed Muzammul'] | 2021-07-16 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-4.71940562e-02 -2.26524055e-01 2.30727032e-01 1.01608291e-01
-2.97584802e-01 -4.97513294e-01 5.06645441e-01 -2.87613571e-01
-4.40703511e-01 5.28511763e-01 -2.62219220e-01 -2.46364877e-01
2.65670210e-01 -7.47099459e-01 -8.43901813e-01 -7.34078288e-01
-8.20478424e-02 7.21091256e-02 7.40697980e-01 -2.94271946... | [8.643514633178711, -0.5755419731140137] |
e8d4c912-ac2b-458f-9a29-87e300ac73c5 | characterizing-the-emotion-carriers-of-covid | 2306.13954 | null | https://arxiv.org/abs/2306.13954v1 | https://arxiv.org/pdf/2306.13954v1.pdf | Characterizing the Emotion Carriers of COVID-19 Misinformation and Their Impact on Vaccination Outcomes in India and the United States | The COVID-19 Infodemic had an unprecedented impact on health behaviors and outcomes at a global scale. While many studies have focused on a qualitative and quantitative understanding of misinformation, including sentiment analysis, there is a gap in understanding the emotion-carriers of misinformation and their differe... | ['Tavpritesh Sethi', 'Akshaya Devadiga', 'Sargun Nagpal', 'Gopal Mengi', 'Kriti Agrawal', 'Deepak Mahto', 'Sanjana S', 'Ridam Pal'] | 2023-06-24 | null | null | null | null | ['misinformation', 'sentiment-analysis', 'time-series'] | ['miscellaneous', 'natural-language-processing', 'time-series'] | [-3.40017974e-01 -2.34106898e-01 -4.96941417e-01 -8.60369578e-02
-3.57978404e-01 -5.05904973e-01 8.66146326e-01 9.91157472e-01
-4.13157254e-01 1.97705939e-01 9.71235454e-01 -5.78056157e-01
3.42383474e-01 -9.29637074e-01 -5.15838385e-01 -8.26152861e-01
-2.56747127e-01 -9.83104259e-02 -6.50248826e-01 -6.52878404... | [8.436784744262695, 9.766786575317383] |
80988545-1650-4ded-aa1f-0e53fca22ac1 | utilizing-every-image-object-for-semi | 2011.02655 | null | https://arxiv.org/abs/2011.02655v1 | https://arxiv.org/pdf/2011.02655v1.pdf | Utilizing Every Image Object for Semi-supervised Phrase Grounding | Phrase grounding models localize an object in the image given a referring expression. The annotated language queries available during training are limited, which also limits the variations of language combinations that a model can see during training. In this paper, we study the case applying objects without labeled qu... | ['Ram Nevatia', 'Zhaoheng Zheng', 'Arka Sadhu', 'Haidong Zhu'] | 2020-11-05 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 2.08289444e-01 5.82872748e-01 -3.90600085e-01 -3.81721169e-01
-1.11256611e+00 -6.12314522e-01 5.31174898e-01 1.14207655e-01
-6.20557010e-01 4.71275151e-01 -1.17238708e-01 -8.88865963e-02
4.18170333e-01 -7.77055681e-01 -1.18281555e+00 -3.83593827e-01
2.11516038e-01 5.71911454e-01 6.16101682e-01 1.38821065... | [10.178629875183105, 1.4875197410583496] |
62a24118-9f27-453d-bf23-d2bb8d3af675 | content-based-brain-tumor-retrieval-for-mr | null | null | https://ieeexplore.ieee.org/document/8611216 | https://doi.org/10.1109/ACCESS.2019.2892455 | Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning | This paper presents an automatic content-based image retrieval (CBIR) system for brain tumors
on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key challenge in CBIR
systems for MR images is the semantic gap between the low-level visual information captured by the MRI
machine and the high-leve... | ['Jianfeng Lu', 'Saeed Ahmad', 'Ali Zakir', 'Farman Ali', 'Muhammad Kabir', 'Qinghua Zhao3', 'Zar Nawab Khan Swati'] | 2019-01-04 | null | null | null | journal-2019-1 | ['content-based-image-retrieval', 'self-learning'] | ['computer-vision', 'natural-language-processing'] | [ 1.32755592e-01 -3.79116088e-01 -1.55671328e-01 -4.87848967e-01
-1.32677615e+00 -2.27187306e-01 5.66858649e-01 4.86731678e-01
-8.72946560e-01 3.11149210e-01 3.01813364e-01 -1.51994884e-01
-6.38246953e-01 -6.70297563e-01 -2.16014504e-01 -7.90703118e-01
-5.27039468e-01 3.43149155e-01 3.23442906e-01 -2.26036683... | [14.344049453735352, -1.6576117277145386] |
ea6bd3ce-f046-4349-8725-8a78070b5b06 | learning-transformation-aware-embeddings-for | 2001.04547 | null | https://arxiv.org/abs/2001.04547v1 | https://arxiv.org/pdf/2001.04547v1.pdf | Learning Transformation-Aware Embeddings for Image Forensics | A dramatic rise in the flow of manipulated image content on the Internet has led to an aggressive response from the media forensics research community. New efforts have incorporated increased usage of techniques from computer vision and machine learning to detect and profile the space of image manipulations. This paper... | ['Walter Scheirer', 'Aparna Bharati', 'Kevin Bowyer', 'Daniel Moreira', 'Anderson Rocha', 'Patrick Flynn'] | 2020-01-13 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.59942245e-01 -1.79203272e-01 -4.83432561e-02 -4.86302853e-01
-7.37549365e-01 -5.54113448e-01 9.02030468e-01 8.23763192e-01
-4.42506462e-01 3.34026814e-01 2.02005997e-01 1.76678598e-02
-2.89563060e-01 -6.02227807e-01 -1.05745614e+00 -5.33493519e-01
-1.89183831e-01 8.11354592e-02 2.12819114e-01 1.07262738... | [12.34280014038086, 1.0077283382415771] |
8889b213-dafd-4113-b94c-dcd3c31904c7 | news-driven-stock-prediction-using-noisy | null | null | https://openreview.net/forum?id=imnG4Ap9dAd | https://openreview.net/pdf?id=imnG4Ap9dAd | News-Driven Stock Prediction Using Noisy Equity State Representation | News-driven stock prediction investigates the correlation between news events and stock price movements.
Previous work has considered effective ways for representing news events and their sequences, but rarely exploited the representation of underlying equity states.
We address this issue by making use of a recurrent n... | ['Yue Zhang', 'Heyan Huang', 'Xiao Liu'] | 2021-01-01 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-5.21813214e-01 6.12828927e-03 -7.44172573e-01 -3.86223882e-01
-3.83557290e-01 -5.82932234e-01 1.19940197e+00 4.42112200e-02
-4.73237544e-01 6.68124497e-01 1.28136003e+00 -4.87973899e-01
4.73543286e-01 -1.14688230e+00 -7.49729633e-01 -3.33765559e-02
-4.60968129e-02 2.07277566e-01 3.16400886e-01 -4.71781403... | [4.418026447296143, 4.2824578285217285] |
1efa2041-6b82-49f0-bd77-c0085be5e76a | scene-labeling-with-lstm-recurrent-neural | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Byeon_Scene_Labeling_With_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Byeon_Scene_Labeling_With_2015_CVPR_paper.pdf | Scene Labeling With LSTM Recurrent Neural Networks | This paper addresses the problem of pixel-level segmentation and classification of scene images with an entirely learning-based approach using Long Short Term Memory (LSTM) recurrent neural networks, which are commonly used for sequence classification. We investigate two-dimensional (2D) LSTM networks for natural scene... | ['Marcus Liwicki', 'Wonmin Byeon', 'Thomas M. Breuel', 'Federico Raue'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['scene-labeling'] | ['computer-vision'] | [ 5.24789989e-01 -4.09597307e-01 3.43449228e-02 -3.85760903e-01
-3.60707790e-01 -3.03616703e-01 6.27468824e-01 5.35197817e-02
-9.48075831e-01 3.34915906e-01 -3.92729223e-01 -5.19266546e-01
2.20420226e-01 -9.30741012e-01 -6.33897841e-01 -8.09255660e-01
-2.04072893e-01 3.25443715e-01 5.87275028e-01 -8.57789889... | [9.411498069763184, -0.09903592616319656] |
87431f5f-63e7-470c-8a4a-e4e2b3d95a8c | semantic-adversarial-network-for-zero-shot | 1905.02327 | null | https://arxiv.org/abs/1905.02327v2 | https://arxiv.org/pdf/1905.02327v2.pdf | Semantic Adversarial Network for Zero-Shot Sketch-Based Image Retrieval | Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario. Previous works mostly focus on modeling the correspondence between images and sketches or synthesizing image features with sketch features. However, b... | ['Leida Li', 'Hao Wang', 'Xinxun Xu', 'Cheng Deng'] | 2019-05-07 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.20017046e-01 -4.28975075e-01 -1.68953180e-01 -2.45666683e-01
-1.22174799e+00 -5.11690915e-01 8.81209195e-01 -2.63363630e-01
-1.98357284e-01 5.05663753e-01 8.03598315e-02 3.39512318e-01
-4.17404622e-01 -7.96968400e-01 -6.08760774e-01 -5.91688573e-01
4.15424556e-01 3.85566086e-01 -8.18266906e-03 -2.82323718... | [11.62517261505127, 0.6753467917442322] |
e11cd528-8b24-4bb4-bd7b-007f8216c314 | potrika-raw-and-balanced-newspaper-datasets | 2210.09389 | null | https://arxiv.org/abs/2210.09389v1 | https://arxiv.org/pdf/2210.09389v1.pdf | Potrika: Raw and Balanced Newspaper Datasets in the Bangla Language with Eight Topics and Five Attributes | Knowledge is central to human and scientific developments. Natural Language Processing (NLP) allows automated analysis and creation of knowledge. Data is a crucial NLP and machine learning ingredient. The scarcity of open datasets is a well-known problem in machine and deep learning research. This is very much the case... | ['Rashid Mehmood', 'Fahad AlQurashi', 'Istiak Ahmad'] | 2022-10-17 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-5.67916155e-01 1.43894061e-01 -5.12676060e-01 -8.35831463e-02
-7.27604628e-01 -8.95574808e-01 8.71421039e-01 6.08116806e-01
-6.62158728e-01 1.17248428e+00 7.84131646e-01 -3.99037421e-01
-1.56951740e-01 -9.54236805e-01 -7.65343010e-01 -4.05156583e-01
3.15634608e-01 7.56786942e-01 -3.47576797e-01 -4.21246797... | [10.288538932800293, 9.709820747375488] |
37d6b966-79f4-442a-9c9e-c5e3da70ebc3 | deeploc-a-ubiquitous-accurate-and-low | 2106.13632 | null | https://arxiv.org/abs/2106.13632v1 | https://arxiv.org/pdf/2106.13632v1.pdf | DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization System | Recent years have witnessed fast growth in outdoor location-based services. While GPS is considered a ubiquitous localization system, it is not supported by low-end phones, requires direct line of sight to the satellites, and can drain the phone battery quickly. In this paper, we propose DeepLoc: a deep learning-based ... | ['Moustafa Youssef', 'Marwan Torki', 'Ahmed Shokry'] | 2021-06-25 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [-7.22621143e-01 -1.74129814e-01 -2.78115392e-01 -6.12981617e-02
-1.19834244e+00 -6.54657364e-01 -1.08910156e-02 6.14282154e-02
-1.43199384e-01 1.12327135e+00 1.25608578e-01 -6.41561270e-01
5.47719523e-02 -7.40430176e-01 -8.27608466e-01 -8.56563389e-01
-3.91921043e-01 2.45340496e-01 1.59477919e-01 9.89317968... | [6.411106586456299, 0.9182535409927368] |
733abe1f-8d75-44ce-9074-e67733a523af | single-model-deep-learning-on-imbalanced | 2102.01284 | null | https://arxiv.org/abs/2102.01284v2 | https://arxiv.org/pdf/2102.01284v2.pdf | Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification | Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance... | ['Ronald X. Xu', 'Benjamin Kaffenberger', 'Pengfei Shao', 'Jinyu Xing', 'Fan Zhang', 'Peng Liu', 'Mengjuan Xu', 'Shuwei Shen', 'Peng Yao'] | 2021-02-02 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.49348831e-01 -6.29170462e-02 -4.05145705e-01 -3.34169388e-01
-8.46893132e-01 -2.10167930e-01 1.61942676e-01 4.90522772e-01
-6.10065937e-01 8.72214437e-01 -3.17317873e-01 -2.49929428e-01
-2.70473599e-01 -8.73922586e-01 -4.61410582e-01 -8.20851505e-01
2.53260314e-01 1.78695530e-01 2.81339735e-01 2.35812947... | [15.623140335083008, -2.9128313064575195] |
bbe266c0-225c-4630-8318-50000b3dffc7 | joint-super-resolution-and-rectification-for | 2011.05003 | null | https://arxiv.org/abs/2011.05003v2 | https://arxiv.org/pdf/2011.05003v2.pdf | Joint Super-Resolution and Rectification for Solar Cell Inspection | Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multi-frame super-resolution (MFSR) to a sequence of low resolution me... | ['Vincent Christlein', 'Andreas Maier', 'Christoph J. Brabec', 'Ian Marius Peters', 'Florian Talkenberg', 'Frank Schebesch', 'Bernd Doll', 'Thomas Köhler', 'Mathis Hoffmann'] | 2020-11-10 | null | null | null | null | ['crack-segmentation', 'multi-frame-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 9.05497134e-01 -2.34747276e-01 3.65590572e-01 7.18922094e-02
-7.37310708e-01 -3.07686865e-01 -4.24503163e-02 2.11583227e-02
-2.06549808e-01 9.04778361e-01 -3.52493942e-01 -9.45744440e-02
-1.57190293e-01 -8.13805342e-01 -5.42343259e-01 -9.29406226e-01
5.65322459e-01 -3.98543067e-02 7.27723122e-01 2.58118026... | [11.006957054138184, -2.296703338623047] |
df9c76d4-a9e2-4628-b50c-ec4d72d3b88b | endonet-a-deep-architecture-for-recognition | 1602.03012 | null | http://arxiv.org/abs/1602.03012v2 | http://arxiv.org/pdf/1602.03012v2.pdf | EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos | Surgical workflow recognition has numerous potential medical applications,
such as the automatic indexing of surgical video databases and the optimization
of real-time operating room scheduling, among others. As a result, phase
recognition has been studied in the context of several kinds of surgeries, such
as cataract,... | ['Michel de Mathelin', 'Didier Mutter', 'Andru P. Twinanda', 'Sherif Shehata', 'Nicolas Padoy', 'Jacques Marescaux'] | 2016-02-09 | null | null | null | null | ['offline-surgical-phase-recognition', 'online-surgical-phase-recognition', 'surgical-tool-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.85848880e-01 -2.30879068e-01 -3.84081334e-01 -1.72665358e-01
-2.27312982e-01 -4.51644838e-01 5.85801184e-01 3.96004170e-01
-5.99697232e-01 7.35136494e-02 -1.25895187e-01 -2.42538154e-01
-3.63004953e-01 -5.39022028e-01 -4.89293873e-01 -6.75701380e-01
3.50766852e-02 8.97579119e-02 2.24577382e-01 -8.49439427... | [14.103002548217773, -3.326690196990967] |
087f7565-f38d-43c7-94e3-8b9a207a4217 | txallo-dynamic-transaction-allocation-in | 2212.11584 | null | https://arxiv.org/abs/2212.11584v1 | https://arxiv.org/pdf/2212.11584v1.pdf | TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems | The scalability problem has been one of the most significant barriers limiting the adoption of blockchains. Blockchain sharding is a promising approach to this problem. However, the sharding mechanism introduces a significant number of cross-shard transactions, which are expensive to process. This paper focuses on the ... | ['Jiangshan Yu', 'Shirui Pan', 'Yuanzhe Zhang'] | 2022-12-22 | null | null | null | null | ['community-detection'] | ['graphs'] | [-3.99814963e-01 7.63842538e-02 -6.02476478e-01 3.90760526e-02
-5.43388188e-01 -5.15732944e-01 6.73826694e-01 4.72190440e-01
-3.17042410e-01 8.13972592e-01 -2.74358899e-03 -7.24756837e-01
3.97092581e-01 -1.04914629e+00 -4.60160553e-01 -7.32774556e-01
-3.00268501e-01 1.04808652e+00 5.78812897e-01 -1.10450365... | [4.800865650177002, 4.155876636505127] |
c836f9e5-7fb5-4565-bbc5-5a80b28d418a | decision-attentive-regularization-to-improve | 2110.15729 | null | https://arxiv.org/abs/2110.15729v2 | https://arxiv.org/pdf/2110.15729v2.pdf | Decision Attentive Regularization to Improve Simultaneous Speech Translation Systems | Simultaneous translation systems start producing the output while processing the partial source sentence in the incoming input stream. These systems need to decide when to read more input and when to write the output. These decisions depend on the structure of source/target language and the information contained in the... | ['Sangha Kim', 'Chanwoo Kim', 'Beomseok Lee', 'Mohd Abbas Zaidi'] | 2021-10-13 | null | null | null | null | ['speech-to-text-translation', 'simultaneous-speech-to-text-translation'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.08166957e-01 -1.41698569e-01 -3.33135307e-01 -3.53325307e-01
-1.29718959e+00 -9.35514152e-01 7.25070417e-01 -1.26626894e-01
-6.03115916e-01 5.25036097e-01 4.15965676e-01 -9.60436165e-01
6.37302339e-01 -2.21583709e-01 -7.40996420e-01 -5.37621677e-01
6.31549478e-01 6.47554636e-01 7.56165534e-02 -2.98041224... | [14.504080772399902, 7.171857833862305] |
bd044dd9-92a8-4779-80b4-f9afbe37fa77 | scene-graph-generation-from-hierarchical | 2303.06842 | null | https://arxiv.org/abs/2303.06842v1 | https://arxiv.org/pdf/2303.06842v1.pdf | Scene Graph Generation from Hierarchical Relationship Reasoning | This paper describes a novel approach to deducing relationships between objects in a visual scene. It explicitly exploits an informative hierarchical structure that can be imposed to divide the object and relationship categories into disjoint super-categories. Specifically, our proposed scheme implements a Bayes predic... | ['Camillo J. Taylor', 'Bowen Jiang'] | 2023-03-13 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.88171798e-01 3.40811670e-01 -4.07562435e-01 -7.74019778e-01
-3.08513343e-01 -4.07075554e-01 5.10585189e-01 5.35954177e-01
-6.96407035e-02 5.19680321e-01 1.15402713e-01 -2.77693152e-01
-3.73242170e-01 -7.82930434e-01 -6.86410427e-01 -5.49596071e-01
-1.81954101e-01 4.46790248e-01 8.15284967e-01 2.07560211... | [10.259928703308105, 1.6617276668548584] |
10a161a8-0a37-4fb7-94e5-1b6550735188 | learning-driven-exploration-for-reinforcement | 1906.06890 | null | https://arxiv.org/abs/1906.06890v2 | https://arxiv.org/pdf/1906.06890v2.pdf | Learning-Driven Exploration for Reinforcement Learning | Effective and intelligent exploration has been an unresolved problem for reinforcement learning. Most contemporary reinforcement learning relies on simple heuristic strategies such as $\epsilon$-greedy exploration or adding Gaussian noise to actions. These heuristics, however, are unable to intelligently distinguish th... | ['Muhammad Usama', 'Dong Eui Chang'] | 2019-06-17 | null | null | null | null | ['fps-games'] | ['playing-games'] | [-1.67718396e-01 -6.38004020e-02 -5.03684878e-01 1.36680543e-01
-4.62268144e-01 -6.03839338e-01 3.21824282e-01 4.56703380e-02
-6.90184474e-01 1.34581637e+00 -2.00349048e-01 -6.98945642e-01
-4.02203381e-01 -8.66686463e-01 -4.54137981e-01 -8.63148272e-01
-3.87262374e-01 3.37040931e-01 2.90604562e-01 -2.71480948... | [3.9383299350738525, 1.8371281623840332] |
e093b4d1-d7f2-443e-8c82-b1a43d786dd9 | robust-m-estimation-based-bayesian-cluster | 2005.01404 | null | https://arxiv.org/abs/2005.01404v3 | https://arxiv.org/pdf/2005.01404v3.pdf | Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions | Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications. Cluster enumeration becomes challenging when the true underlying structure in the observed data is corrupted by heavy-tailed noise and outliers. Recently, Bayesian cluster enumeration criteria have ... | ['Christian A. Schroth', 'Michael Muma'] | 2020-05-04 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-1.79435648e-02 -3.28509480e-01 1.32158011e-01 -5.94347060e-01
-8.53685677e-01 -4.05906409e-01 4.39263493e-01 6.89263269e-02
-5.52644610e-01 5.26849389e-01 -1.30409241e-01 -1.81294426e-01
-5.37369311e-01 -5.80076218e-01 -1.91407099e-01 -1.04293418e+00
-4.33559150e-01 8.86552930e-01 -8.61587822e-02 3.04556936... | [7.327394962310791, 4.358190059661865] |
47873548-9c2e-440c-8b97-6fa9233b3d0b | multi-label-detection-and-classification-of | 1910.02672 | null | https://arxiv.org/abs/1910.02672v2 | https://arxiv.org/pdf/1910.02672v2.pdf | Multi-label Detection and Classification of Red Blood Cells in Microscopic Images | Cell detection and cell type classification from biomedical images play an important role for high-throughput imaging and various clinical application. While classification of single cell sample can be performed with standard computer vision and machine learning methods, analysis of multi-label samples (region containi... | ['Quanzheng Li', 'Wei Qiu', 'Mo Zhang', 'Mengjia Xu', 'Jiaming Guo', 'Xiang Li', 'Ning Guo'] | 2019-10-07 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 4.74938661e-01 -2.20407575e-01 1.38144732e-01 -1.39003426e-01
-5.28204918e-01 -3.15471917e-01 2.07228377e-01 7.51665652e-01
-5.00031233e-01 9.05361652e-01 -3.53508919e-01 -4.37594391e-02
3.94744277e-01 -1.08698797e+00 -5.13068616e-01 -1.12745488e+00
2.52503529e-02 9.01463687e-01 3.62389266e-01 2.52280325... | [14.884809494018555, -3.1271800994873047] |
91919cdb-9bb8-4fb7-81ab-454cfa831c82 | challenges-in-building-intelligent-open | 1905.05709 | null | https://arxiv.org/abs/1905.05709v3 | https://arxiv.org/pdf/1905.05709v3.pdf | Challenges in Building Intelligent Open-domain Dialog Systems | There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connection... | ['Minlie Huang', 'Xiaoyan Zhu', 'Jianfeng Gao'] | 2019-05-13 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-3.09602767e-01 6.61552608e-01 6.75693303e-02 -7.95947313e-01
-3.89475450e-02 -5.04727483e-01 7.30276763e-01 -5.27678570e-03
-1.53842449e-01 9.79803383e-01 4.15436685e-01 2.34476626e-01
-5.72428368e-02 -4.39443201e-01 2.85055399e-01 -1.38186291e-01
3.03628724e-02 8.20621908e-01 1.08897544e-01 -8.40362191... | [12.888398170471191, 7.883219242095947] |
0b9c4973-25e8-4e76-aa83-2d686b73e42f | a-supervised-geometry-aware-mapping-approach | 1807.02682 | null | http://arxiv.org/abs/1807.02682v1 | http://arxiv.org/pdf/1807.02682v1.pdf | A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images | The lack of proper class discrimination among the Hyperspectral (HS) data
points poses a potential challenge in HS classification. To address this issue,
this paper proposes an optimal geometry-aware transformation for enhancing the
classification accuracy. The underlying idea of this method is to obtain a
linear proje... | ['S. L. Happy', 'Ramanarayan Mohanty', 'Aurobinda Routray'] | 2018-07-07 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 5.42233646e-01 -3.29128802e-01 2.53798533e-02 -3.64423811e-01
-5.39246619e-01 -5.40456533e-01 3.03972781e-01 -4.25339878e-01
-1.21744454e-01 4.61846650e-01 1.13553636e-01 2.11019665e-02
-7.33340025e-01 -8.29602301e-01 -7.02152774e-02 -1.25463629e+00
2.63005227e-01 -6.83026686e-02 -2.89790273e-01 -7.89286196... | [10.03786563873291, -1.799551248550415] |
0593fec2-252f-419d-826a-4ac0523aac5c | privacy-preserving-by-design-indoor | 2306.02211 | null | https://arxiv.org/abs/2306.02211v1 | https://arxiv.org/pdf/2306.02211v1.pdf | Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA | Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services. However, the growing demand for accurate localization has heightened concerns over privacy, as many localization systems rely on active signals that can be mis... | ['Moustafa Youssef', 'Hamada Rizk', 'Mohamed Mohsen'] | 2023-06-03 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-7.83093721e-02 -3.41747552e-01 1.60377882e-02 -5.05440056e-01
-9.75950658e-01 -9.79486823e-01 1.50871575e-01 1.18198961e-01
-6.50182962e-01 1.14045238e+00 -1.08669184e-01 -8.86812449e-01
-4.80557531e-01 -8.03510308e-01 -4.90954757e-01 -7.17435539e-01
-5.30153871e-01 -1.37065247e-01 -1.30914167e-01 3.01020712... | [6.396030902862549, 0.9026083946228027] |
ff1e06e0-c102-4350-9293-5edf7a3a81f1 | sparse-recovery-beyond-compressed-sensing | 1905.04627 | null | https://arxiv.org/abs/1905.04627v2 | https://arxiv.org/pdf/1905.04627v2.pdf | Sparse Recovery Beyond Compressed Sensing: Separable Nonlinear Inverse Problems | Extracting information from nonlinear measurements is a fundamental challenge in data analysis. In this work, we consider separable inverse problems, where the data are modeled as a linear combination of functions that depend nonlinearly on certain parameters of interest. These parameters may represent neuronal activit... | ['Carlos Fernandez-Granda', 'Chrysa Papadaniil', 'Sheng Liu', 'Brett Bernstein'] | 2019-05-12 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 6.16999328e-01 -2.11783692e-01 2.99055099e-01 -2.52151281e-01
-5.31909883e-01 -6.50402486e-01 2.46846154e-01 -3.15337270e-01
-5.12552142e-01 9.75275278e-01 2.45462075e-01 6.10847808e-02
-6.02167606e-01 -1.67077363e-01 -6.63652956e-01 -1.42039764e+00
-2.91974634e-01 4.22939837e-01 -4.92110133e-01 -1.23896897... | [7.025950908660889, 4.306422710418701] |
da38ca50-1506-4a28-bfc2-d2d0d8dab2ab | nonmyopic-view-planning-for-active-object | 1309.5401 | null | http://arxiv.org/abs/1309.5401v1 | http://arxiv.org/pdf/1309.5401v1.pdf | Nonmyopic View Planning for Active Object Detection | One of the central problems in computer vision is the detection of
semantically important objects and the estimation of their pose. Most of the
work in object detection has been based on single image processing and its
performance is limited by occlusions and ambiguity in appearance and geometry.
This paper proposes an... | ['Jerome Le Ny', 'Bharath Sankaran', 'Nikolay Atanasov', 'Kostas Daniilidis', 'George J. Pappas'] | 2013-09-20 | null | null | null | null | ['active-object-detection'] | ['computer-vision'] | [ 4.67182308e-01 4.09133255e-01 -2.84179062e-01 -2.24632367e-01
-5.31897306e-01 -6.15954816e-01 5.08020163e-01 5.61443508e-01
-7.56068528e-01 5.92466056e-01 -5.32974184e-01 -3.14927064e-02
-1.25500515e-01 -7.02470124e-01 -6.88336551e-01 -8.38944733e-01
-7.58433118e-02 1.14342713e+00 9.20580387e-01 7.38341138... | [7.189414978027344, -2.11311411857605] |
05c32a35-06ea-4bad-b0b8-5113302ad051 | from-renyi-entropy-power-to-information-scan | 2102.09415 | null | https://arxiv.org/abs/2102.09415v1 | https://arxiv.org/pdf/2102.09415v1.pdf | From Rényi Entropy Power to Information Scan of Quantum States | In the estimation theory context, we generalize the notion of Shannon's entropy power to the R\'{e}nyi-entropy setting. This not only allows to find new estimation inequalities, such as the R\'{e}nyi-entropy based De Bruijn identity, isoperimetric inequality or Stam inequality, but it also provides a convenient technic... | ['Martin Prokš', 'Jacob Dunningham', 'Petr Jizba'] | 2021-02-18 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.35399264e-01 4.41922277e-01 1.94331333e-02 -3.17324817e-01
-5.23400307e-01 -7.26414859e-01 3.46364498e-01 -1.57563195e-01
-7.13984966e-01 1.10849786e+00 -1.72869548e-01 -6.21907353e-01
-6.62889600e-01 -8.39087844e-01 -1.39788687e-01 -9.03945565e-01
-4.21324104e-01 3.20788294e-01 -3.39199245e-01 -3.02523583... | [5.6406683921813965, 4.889890670776367] |
08b1583d-9e0c-433f-a142-5858bf94f066 | towards-neural-variational-monte-carlo-that | 2212.11296 | null | https://arxiv.org/abs/2212.11296v1 | https://arxiv.org/pdf/2212.11296v1.pdf | Towards Neural Variational Monte Carlo That Scales Linearly with System Size | Quantum many-body problems are some of the most challenging problems in science and are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, coupled with the Variational Monte Carlo (VMC) algorithm, has be... | ['Anima Anandkumar', 'Garnet Kin-Lic Chan', 'Or Sharir'] | 2022-12-21 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 2.08236873e-01 -1.22214623e-01 -3.93125042e-02 -8.53305832e-02
-8.19470346e-01 -3.21124673e-01 6.84236765e-01 7.22457934e-03
-5.62471211e-01 9.85160828e-01 -1.87698126e-01 -6.54423118e-01
1.37414664e-01 -1.05965269e+00 -7.61052191e-01 -1.05700839e+00
-1.69859573e-01 5.35156906e-01 1.71508729e-01 -8.54347527... | [5.566542148590088, 4.964407444000244] |
ae730eeb-0f73-46dc-b536-8ace8a63b16d | towards-meta-learning-for-multi-target | 1907.11277 | null | https://arxiv.org/abs/1907.11277v1 | https://arxiv.org/pdf/1907.11277v1.pdf | Towards meta-learning for multi-target regression problems | Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. However, none of these methods outperforms the others for all problems. This motivates the development of automatic approachesto recommend the mos... | ['Saulo Martiello Mastelini', 'Everton José Santana', 'Gabriel Jonas Aguiar', 'Sylvio Barbon Jr', 'Rafael Gomes Mantovani'] | 2019-07-25 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 1.62237003e-01 -1.76848099e-01 -8.28322649e-01 -6.06320322e-01
-1.40706110e+00 -3.34184527e-01 1.13377404e+00 2.83812821e-01
-2.93972522e-01 9.02590811e-01 4.79699410e-02 8.79584774e-02
-6.02309644e-01 -6.41225338e-01 -5.15108824e-01 -8.41893137e-01
-9.49804783e-02 8.12846601e-01 1.93898827e-01 -2.95606613... | [9.012736320495605, 4.31868314743042] |
53356746-c2c1-46b7-b880-c2e43f8c2c65 | attention-to-fires-multi-channel-deep | null | null | https://www.mdpi.com/2076-3417/11/22/11060 | https://www.mdpi.com/2076-3417/11/22/11060/pdf?version=1637581659 | Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction | Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact... | ['Elena Baralis', 'Tania Cerquitelli', 'Paolo Garza', 'Daniele Apiletti', 'Luca Colomba', 'Alessandro Farasin', 'Salvatore Greco', 'Simone Monaco'] | 2021-11-22 | null | null | null | applied-sciences-2021-11 | ['severity-prediction'] | ['computer-vision'] | [ 4.07722920e-01 -3.90084349e-02 1.45444959e-01 -1.20610937e-01
-8.65960181e-01 -4.15071607e-01 6.86444044e-01 8.23428154e-01
-7.59070873e-01 8.63215268e-01 4.93097365e-01 -2.93646365e-01
-6.23117089e-01 -1.18170118e+00 -6.07552528e-01 -9.97531593e-01
-5.66426754e-01 6.19506598e-01 -1.39918655e-01 -7.14946568... | [9.584096908569336, -1.5273113250732422] |
08077a3f-ad42-40b6-9ca5-1f68a85ed042 | router-for-wireless-power-packet-transmission | 2301.05368 | null | https://arxiv.org/abs/2301.05368v1 | https://arxiv.org/pdf/2301.05368v1.pdf | Router for wireless power packet transmission: Design and application to intersystem power management | Power supply for small-scale battery-powered systems such as electric vehicles (EVs and mobile robots) is being actively researched. We are particularly interested in energy management, which considers the interconnection of such systems close to each other. This allows for overall redundancy to be maintained without a... | ['Takashi Hikihara', 'Shiu Mochiyama', 'Takahiro Mamiya'] | 2023-01-13 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 1.62848651e-01 2.37074286e-01 -8.55590165e-01 7.93493316e-02
1.90777957e-01 -7.11589515e-01 4.30991769e-01 5.56357279e-02
-3.51149559e-01 1.14774525e+00 -6.87201142e-01 -2.62402236e-01
-3.69309813e-01 -9.95706975e-01 -5.91947377e-01 -1.01305819e+00
-1.07225351e-01 8.26995671e-02 3.35051447e-01 -1.79585159... | [5.854426383972168, 1.8613777160644531] |
1026272b-3bfd-4b4a-9f0b-8f5a3144c1c3 | three-dimensional-ultrasound-matrix-imaging | 2303.07483 | null | https://arxiv.org/abs/2303.07483v1 | https://arxiv.org/pdf/2303.07483v1.pdf | Three-Dimensional Ultrasound Matrix Imaging | Matrix imaging paves the way towards a next revolution in wave physics. Based on the response matrix recorded between a set of sensors, it enables an optimized compensation of aberration phenomena and multiple scattering events that usually drastically hinder the focusing process in heterogeneous media. Although it gav... | ['Alexandre Aubry', 'Mathias Fink', 'William Lambert', 'Arthur Le Ber', 'Justine Robin', 'Flavien Bureau'] | 2023-03-13 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 6.03657424e-01 4.02293429e-02 7.58631825e-01 1.63430974e-01
-5.18421888e-01 -4.98628497e-01 2.73254365e-01 5.17322831e-02
-5.27368724e-01 3.87788922e-01 1.90570503e-01 -3.35622936e-01
-3.07000399e-01 -5.40508628e-01 -4.52576965e-01 -1.28425181e+00
-5.69178104e-01 4.59551603e-01 5.68673670e-01 2.86783092... | [12.669169425964355, -2.6779487133026123] |
30b37b2a-9b54-495d-98ef-ec3c05266c03 | ernie-sparse-learning-hierarchical-efficient | null | null | https://openreview.net/forum?id=Nctx6hVAQYf | https://openreview.net/pdf?id=Nctx6hVAQYf | ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention | Sparse Transformer has recently attracted a lot of attention since the ability for reducing the quadratic dependency on the sequence length. We argue that two factors, information bottleneck sensitivity and inconsistency between different attention topologies, could affect the performance of the Sparse Transformer. Thi... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['sparse-learning'] | ['methodology'] | [ 2.18072027e-01 -6.93119392e-02 -1.99109316e-02 -1.91416696e-01
-1.39469278e+00 -2.90639967e-01 4.27917689e-01 -2.50880271e-01
-2.69575864e-01 5.84148824e-01 6.44369781e-01 -2.59813219e-01
-6.63671494e-02 -3.38000506e-01 -7.24956572e-01 -7.58085907e-01
6.16806000e-03 5.04531741e-01 2.55098313e-01 -4.58010912... | [10.902341842651367, 6.638364791870117] |
8b54b9fc-d7f3-458f-9dcc-168ed4db172b | distributed-graph-embedding-with-information | 2303.15702 | null | https://arxiv.org/abs/2303.15702v1 | https://arxiv.org/pdf/2303.15702v1.pdf | Distributed Graph Embedding with Information-Oriented Random Walks | Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most... | ['Yuchao Cao', 'Wei Yin', 'Zhenli Li', 'Dan Feng', 'Fang Wang', 'Siqiang Luo', 'Arijit Khan', 'Peng Fang'] | 2023-03-28 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-7.73740709e-01 6.38886467e-02 -4.91275549e-01 6.20160513e-02
-4.53119874e-01 -5.92466831e-01 4.39823061e-01 8.91359389e-01
-3.73279750e-01 2.34066024e-01 3.78195316e-01 -7.85644293e-01
-1.56095773e-01 -1.26660013e+00 -2.72824258e-01 -3.57090861e-01
-6.84019387e-01 8.45724940e-01 3.53560627e-01 -9.98546630... | [7.1189446449279785, 6.148777961730957] |
8fba5534-f661-4272-9ea3-7727ce2a5610 | iit-gandhinagar-at-semeval-2020-task-9-code | 2006.14465 | null | https://arxiv.org/abs/2006.14465v3 | https://arxiv.org/pdf/2006.14465v3.pdf | IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection | Code-mixing is the phenomenon of using multiple languages in the same utterance of a text or speech. It is a frequently used pattern of communication on various platforms such as social media sites, online gaming, product reviews, etc. Sentiment analysis of the monolingual text is a well-studied task. Code-mixing adds ... | ['Vivek Srivastava', 'Mayank Singh'] | 2020-06-25 | null | https://aclanthology.org/2020.semeval-1.168 | https://aclanthology.org/2020.semeval-1.168.pdf | semeval-2020 | ['humor-detection'] | ['natural-language-processing'] | [ 6.50665686e-02 -1.37630418e-01 -6.69819713e-02 -3.95488322e-01
-5.08668423e-01 -5.02367735e-01 7.10022151e-01 4.34742004e-01
-2.80568078e-02 4.80474800e-01 4.60861117e-01 -4.96396065e-01
3.93074036e-01 -4.21535283e-01 -3.07913274e-01 -6.06739223e-01
3.14488143e-01 1.36852739e-02 -1.14665709e-01 -5.62320769... | [9.184361457824707, 10.47307014465332] |
458034e5-cd14-421e-91fa-c6467244294b | forensic-discrimination-between-traditional | 1811.03157 | null | http://arxiv.org/abs/1811.03157v1 | http://arxiv.org/pdf/1811.03157v1.pdf | Forensic Discrimination between Traditional and Compressive Imaging Systems | Compressive sensing is a new technology for modern computational imaging
systems. In comparison to widespread conventional image sensing, the
compressive imaging paradigm requires specific forensic analysis techniques and
tools. In this regards, one of basic scenarios in image forensics is to
distinguish traditionally ... | ['Farokh Marvasti', 'Ali Taimori'] | 2018-11-07 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 7.81473279e-01 -5.80552340e-01 1.27538502e-01 2.99806852e-04
-5.03613591e-01 -6.17586792e-01 5.22101879e-01 -4.92514670e-01
-3.63943577e-02 5.71168840e-01 -1.53297931e-01 -3.97521675e-01
-3.69407713e-01 -1.47723360e-02 -3.85264784e-01 -8.03162694e-01
-7.61176571e-02 -1.53130135e-02 -2.21925765e-01 7.41583332... | [12.358181953430176, 0.9307592511177063] |
63da9045-83a8-42b4-af68-f76c30ad2709 | properties-on-n-dimensional-convolution-for | 1711.11224 | null | http://arxiv.org/abs/1711.11224v1 | http://arxiv.org/pdf/1711.11224v1.pdf | Properties on n-dimensional convolution for image deconvolution | Convolution system is linear and time invariant, and can describe the optical
imaging process. Based on convolution system, many deconvolution techniques
have been developed for optical image analysis, such as boosting the space
resolution of optical images, image denoising, image enhancement and so on.
Here, we gave p... | ['Zhou Hang', 'Song Yizhi', 'Ding Daoxin', 'Xu Cheng', 'Li Shiwei', 'Quan Tingwei'] | 2017-11-30 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 8.57826173e-02 -7.83512831e-01 7.92651713e-01 -1.35940447e-01
3.65690321e-01 -4.15029138e-01 3.02474380e-01 -8.26203406e-01
-6.37523293e-01 6.08552158e-01 2.62550145e-01 -3.10663581e-01
4.44074534e-02 -5.10714233e-01 -1.71005070e-01 -9.45757568e-01
2.07380876e-01 -2.19083250e-01 6.20913386e-01 -1.74190879... | [11.658316612243652, -2.6506357192993164] |
5c813664-327e-4357-8657-d60e4ea7527d | image-animation-with-refined-masking | null | null | https://openreview.net/forum?id=VKoY98_IN3- | https://openreview.net/pdf?id=VKoY98_IN3- | Image Animation with Refined Masking | We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object. We do not assume the existence of pose models and our method is able to animate arbitrary objects without knowledge of the object's structure. Furthermore, both the driving video and the source ... | ['Lior Wolf', 'Yoav Shalev'] | 2021-01-01 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 6.41924918e-01 3.33254397e-01 5.09875119e-01 5.05563319e-02
-4.33056325e-01 -6.30200267e-01 5.36945045e-01 -2.64389813e-01
-1.46859482e-01 3.40433657e-01 -3.25464666e-01 2.41341591e-02
5.54339707e-01 -7.38997757e-01 -1.11283290e+00 -7.75955260e-01
3.15402746e-01 6.88599765e-01 8.92705142e-01 -2.14587972... | [11.085343360900879, -0.809800386428833] |
0923de4d-3e47-4610-92ef-f3267e2226c2 | towards-data-driven-sign-language | null | null | https://openreview.net/forum?id=HpQA6JhTL7x | https://openreview.net/pdf?id=HpQA6JhTL7x | Towards data-driven sign language interpreting virtual assistant | Sign Languages (SL) are a form of communication in the visual-gestural modality, and are full-fledged natural languages. Recent years have seen the increase in the use of virtual agents as assistants for the sign language users. Research into sign language recognition has demonstrated promising potential for the improv... | ['Anonymous'] | 2021-06-23 | null | null | null | acm-icmi-workshop-genea-2021-10 | ['sign-language-recognition'] | ['computer-vision'] | [-2.09153920e-01 2.79762864e-01 -7.85148963e-02 -3.89285654e-01
-5.01439124e-02 -4.63274300e-01 8.20734024e-01 -6.52969241e-01
-6.45864367e-01 5.74752331e-01 5.13434112e-01 -4.69730198e-01
-2.14425430e-01 -2.15571508e-01 -5.94626218e-02 -7.04113483e-01
1.52199373e-01 6.06501937e-01 2.20712081e-01 -6.60830796... | [9.078335762023926, -6.375583648681641] |
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