paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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91e1f26d-2bb7-476b-ae21-13a21c32e7e3 | rethinking-visual-geo-localization-for-large | 2204.02287 | null | https://arxiv.org/abs/2204.02287v2 | https://arxiv.org/pdf/2204.02287v2.pdf | Rethinking Visual Geo-localization for Large-Scale Applications | Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations. To investigate how existing techniques would perform on a real-world city-wide VG application, we build San Francisco eXtra Large, a new dataset covering ... | ['Barbara Caputo', 'Carlo Masone', 'Gabriele Berton'] | 2022-04-05 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Berton_Rethinking_Visual_Geo-Localization_for_Large-Scale_Applications_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Berton_Rethinking_Visual_Geo-Localization_for_Large-Scale_Applications_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-place-recognition'] | ['computer-vision'] | [-4.82587159e-01 -5.36176920e-01 -2.87510991e-01 -5.97382858e-02
-1.13873303e+00 -6.41645908e-01 7.26999819e-01 3.27197671e-01
-3.94417793e-01 5.87265074e-01 1.85012713e-01 -3.23567092e-01
1.77923188e-01 -9.29537416e-01 -8.21792006e-01 -4.46449786e-01
-2.20997944e-01 4.71700400e-01 5.69048703e-01 1.67955589... | [7.683736324310303, -1.8760429620742798] |
3583a8df-743a-441c-93de-dd752f1e8cc9 | gpu-accelerated-guided-source-separation-for | 2212.05271 | null | https://arxiv.org/abs/2212.05271v1 | https://arxiv.org/pdf/2212.05271v1.pdf | GPU-accelerated Guided Source Separation for Meeting Transcription | Guided source separation (GSS) is a type of target-speaker extraction method that relies on pre-computed speaker activities and blind source separation to perform front-end enhancement of overlapped speech signals. It was first proposed during the CHiME-5 challenge and provided significant improvements over the delay-a... | ['Sanjeev Khudanpur', 'Daniel Povey', 'Desh Raj'] | 2022-12-10 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 2.98685312e-01 -2.90046841e-01 2.17930764e-01 -6.09760106e-01
-1.93972492e+00 -8.43899727e-01 5.16573608e-01 5.21252528e-02
-8.13731626e-02 3.12445283e-01 8.48643124e-01 -3.57269347e-01
7.26719573e-02 1.61255404e-01 -3.72665197e-01 -6.52503014e-01
-3.23500097e-01 1.64062798e-01 2.56784596e-02 -1.09132752... | [14.804398536682129, 6.054569244384766] |
955763d2-7d1c-4fa3-89cf-88b7b26986d6 | multimodal-pathology-image-search-between-h-e | 2306.06780 | null | https://arxiv.org/abs/2306.06780v1 | https://arxiv.org/pdf/2306.06780v1.pdf | Multimodal Pathology Image Search Between H&E Slides and Multiplexed Immunofluorescent Images | We present an approach for multimodal pathology image search, using dynamic time warping (DTW) on Variational Autoencoder (VAE) latent space that is fed into a ranked choice voting scheme to retrieve multiplexed immunofluorescent imaging (mIF) that is most similar to a query H&E slide. Through training the VAE and appl... | ['Jacob M Luber', 'Helen H Shang', 'Michael Robben', 'Parisa Boodaghi Malidarreh', 'Aarti Darji', 'Jai Prakash Veerla', 'Mohammad S Nasr', 'MD Jillur Rahman Saurav', 'Amir Hajighasemi'] | 2023-06-11 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [ 3.04604590e-01 -4.37137961e-01 -3.94755036e-01 -1.06180541e-01
-1.10021079e+00 -9.24886107e-01 4.42112088e-01 3.67505074e-01
-7.14565873e-01 5.81886351e-01 2.79671758e-01 -4.21859324e-01
-4.50684607e-01 -6.45189404e-01 -5.86791709e-02 -1.44138348e+00
1.46613829e-02 6.64127827e-01 -3.25838745e-01 1.27938762... | [15.090066909790039, -2.9848921298980713] |
529f8820-75b9-4340-81a7-d59db2b7733d | scalable-lossless-coding-of-dynamic-medical | 2302.01589 | null | https://arxiv.org/abs/2302.01589v1 | https://arxiv.org/pdf/2302.01589v1.pdf | Scalable Lossless Coding of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Prediction and Update | Professional applications like telemedicine often require scalable lossless coding of sensitive data. 3-D subband coding has turned out to offer good compression results for dynamic CT data and additionally provides a scalable representation in terms of low- and highpass subbands. To improve the visual quality of the l... | ['André Kaup', 'Franz Schilling', 'Daniela Lanz'] | 2023-02-03 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.86275578e-01 1.55824333e-01 -1.81521982e-01 -1.61985293e-01
-8.47008705e-01 -9.76481810e-02 8.55832621e-02 5.81001401e-01
-4.57294345e-01 5.16644955e-01 6.84251904e-01 -2.08537936e-01
-7.60331899e-02 -7.89893866e-01 -3.70278925e-01 -7.75496483e-01
-1.82204664e-01 -2.56713182e-01 7.53140211e-01 2.22057793... | [11.498762130737305, -2.30005145072937] |
fcfa82f8-c0d3-43bd-8834-a75118498063 | building-hvac-scheduling-using-reinforcement | 1910.05313 | null | https://arxiv.org/abs/1910.05313v2 | https://arxiv.org/pdf/1910.05313v2.pdf | Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation | Buildings sector is one of the major consumers of energy in the United States. The buildings HVAC (Heating, Ventilation, and Air Conditioning) systems, whose functionality is to maintain thermal comfort and indoor air quality (IAQ), account for almost half of the energy consumed by the buildings. Thus, intelligent sche... | ['Viktor K. Prasanna', 'Rajgopal Kannan', 'Sanmukh R. Kuppannagari', 'Chi Zhang'] | 2019-10-11 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.11372687e-01 2.94317663e-01 1.78745482e-02 1.61047876e-01
-7.53218055e-01 -6.24879360e-01 3.51732224e-01 2.35604122e-01
-7.13606104e-02 1.20401406e+00 -2.12657899e-01 -4.71101433e-01
-4.58544642e-01 -1.08628607e+00 -7.76851058e-01 -9.76402223e-01
-1.46838501e-01 9.25861150e-02 -1.88562393e-01 -1.83076903... | [5.531165599822998, 2.4461286067962646] |
a10ca705-f6c4-4445-8f88-eab0a6583e16 | convformer-parameter-reduction-in-transformer | 2304.02147 | null | https://arxiv.org/abs/2304.02147v1 | https://arxiv.org/pdf/2304.02147v1.pdf | ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention | Recently, fully-transformer architectures have replaced the defacto convolutional architecture for the 3D human pose estimation task. In this paper we propose \textbf{\textit{ConvFormer}}, a novel convolutional transformer that leverages a new \textbf{\textit{dynamic multi-headed convolutional self-attention}} mechanis... | ['Dmitriy Shin', 'Alec Diaz-Arias'] | 2023-04-04 | null | null | null | null | ['3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.15032691e-01 6.53847307e-02 2.29861531e-02 -2.50250667e-01
-9.12861049e-01 -3.79025936e-01 3.22321028e-01 -6.45794868e-01
-6.17508709e-01 4.19295698e-01 4.59143966e-01 1.69956863e-01
-7.59135485e-02 -1.96760982e-01 -8.30181360e-01 -4.45472300e-01
-4.12573576e-01 5.63922107e-01 2.85092980e-01 -2.27517635... | [7.110671043395996, -0.6745200157165527] |
b73b5d75-8592-4b51-8153-ee579a93126b | spectral-response-function-guided-deep | 2011.09701 | null | https://arxiv.org/abs/2011.09701v2 | https://arxiv.org/pdf/2011.09701v2.pdf | Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution | Hyperspectral images are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high spatial resolution (HR) hyperspectral images from HR multispectral images. Traditional SSR methods include model-driven algorithms and deep learning. By unfolding a variational method, this paper pr... | ['Liangpei Zhang', 'Huanfeng Shen', 'Qiangqiang Yuan', 'Jie Li', 'Jiang He'] | 2020-11-19 | null | null | null | null | ['spectral-super-resolution'] | ['computer-vision'] | [ 8.58675182e-01 -3.06147784e-01 1.88303308e-03 -4.16028887e-01
-8.32641065e-01 -4.54697907e-02 3.54543358e-01 -6.34990394e-01
-1.16199113e-01 9.64931011e-01 1.61391303e-01 -2.62980819e-01
-4.64199513e-01 -1.00338089e+00 -6.08071268e-01 -1.10198307e+00
1.92947581e-01 -3.47768784e-01 -4.38778877e-01 -4.46670145... | [10.137588500976562, -1.9806994199752808] |
373cad38-02e7-4e10-af24-c202c02b7e1e | gibbonr-an-r-package-for-the-detection-and | 1906.02572 | null | https://arxiv.org/abs/1906.02572v2 | https://arxiv.org/pdf/1906.02572v2.pdf | GIBBONFINDR: An R package for the detection and classification of acoustic signals | The recent improvements in recording technology, data storage and battery life have led to an increased interest in the use of passive acoustic monitoring for a variety of research questions. One of the main obstacles in implementing wide scale acoustic monitoring programs in terrestrial environments is the lack of use... | ['Dena J. Clink', 'Holger Klinck'] | 2019-06-06 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 1.17398977e-01 -6.65214896e-01 9.46489573e-01 -4.47321504e-01
-8.52043986e-01 -6.58111989e-01 -1.59486830e-01 4.89211857e-01
-7.79547811e-01 3.66699189e-01 6.33910745e-02 -4.69584525e-01
-2.99508423e-01 -5.38065016e-01 -2.36752689e-01 -9.60978448e-01
-9.21008408e-01 1.93670556e-01 3.68144065e-01 -1.12029657... | [8.59467601776123, -1.1181389093399048] |
d3e0de8f-aa75-43cf-aaf2-2b58473f7ce9 | custom-structure-preservation-in-face-aging | 2207.11025 | null | https://arxiv.org/abs/2207.11025v1 | https://arxiv.org/pdf/2207.11025v1.pdf | Custom Structure Preservation in Face Aging | In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image. We disentangle the style and content of the input image and propose a new decoder network that adopts a style-based strategy to combine the style... | ['Óscar Cordón', 'Pablo Mesejo', 'Stéphane Lathuilière', 'Guillermo Gomez-Trenado'] | 2022-07-22 | null | null | null | null | ['face-age-editing'] | ['computer-vision'] | [ 3.40897232e-01 3.68354023e-01 -3.22244465e-02 -5.53140044e-01
-5.80160990e-02 -5.37484527e-01 5.95715702e-01 -1.49685696e-01
-5.04653037e-01 4.79423732e-01 3.49689066e-01 -1.06782377e-01
3.61150980e-01 -6.33674502e-01 -7.53673196e-01 -5.00953794e-01
3.84473562e-01 4.07512859e-03 4.02424671e-02 -9.31617469... | [12.462071418762207, -0.27507761120796204] |
514b8faf-d8ef-488f-b0c1-68ced2c46043 | marked-personas-using-natural-language | 2305.18189 | null | https://arxiv.org/abs/2305.18189v1 | https://arxiv.org/pdf/2305.18189v1.pdf | Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models | To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marked Personas, a prompt-based method to measure stereotypes in LLMs for intersectional demographic groups without any lexicon or data labeling.... | ['Dan Jurafsky', 'Esin Durmus', 'Myra Cheng'] | 2023-05-29 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 2.94950843e-01 7.20315516e-01 -3.30214262e-01 -4.13216025e-01
-3.57033581e-01 -8.21089864e-01 1.39208937e+00 6.15734458e-01
-2.24469885e-01 4.27160233e-01 1.40874100e+00 -2.95062274e-01
1.46510586e-01 -9.79928732e-01 -3.55837971e-01 -3.53393912e-01
1.23821728e-01 5.22851110e-01 -5.85615754e-01 -5.68554759... | [9.252728462219238, 10.214604377746582] |
156472c7-c642-4ce3-97ec-e1dde0df2465 | unis-mmc-multimodal-classification-via | 2305.09299 | null | https://arxiv.org/abs/2305.09299v1 | https://arxiv.org/pdf/2305.09299v1.pdf | UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning | Multimodal learning aims to imitate human beings to acquire complementary information from multiple modalities for various downstream tasks. However, traditional aggregation-based multimodal fusion methods ignore the inter-modality relationship, treat each modality equally, suffer sensor noise, and thus reduce multimod... | ['Eng Siong Chng', 'Deepu Rajan', 'Yuchen Hu', 'Chen Chen', 'Meng Shen', 'Heqing Zou'] | 2023-05-16 | null | null | null | null | ['image-text-classification'] | ['miscellaneous'] | [ 5.57159901e-01 1.02695443e-01 -3.87629569e-01 -3.60601097e-01
-1.29779780e+00 -3.23484659e-01 8.76437366e-01 1.59281462e-01
-1.96513444e-01 8.03485453e-01 4.63359773e-01 1.73643142e-01
-9.26521420e-02 -2.31143862e-01 -7.80071676e-01 -1.12481570e+00
3.33473176e-01 3.28265935e-01 -3.29534858e-01 -2.55794197... | [13.124393463134766, 5.020175933837891] |
d3d7af0c-fb5f-4c96-89cf-41b10cd7a747 | mutual-information-maximization-for-effective | 2003.06439 | null | https://arxiv.org/abs/2003.06439v1 | https://arxiv.org/pdf/2003.06439v1.pdf | Mutual Information Maximization for Effective Lip Reading | Lip reading has received an increasing research interest in recent years due to the rapid development of deep learning and its widespread potential applications. One key point to obtain good performance for the lip reading task depends heavily on how effective the representation can be to capture the lip movement infor... | ['Shiguang Shan', 'Xilin Chen', 'Xing Zhao', 'Shuang Yang'] | 2020-03-13 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.49930799e-02 -3.66959721e-01 -2.23410144e-01 -1.79996029e-01
-6.14737093e-01 -1.31562948e-01 5.32630324e-01 -1.10870279e-01
-3.42736334e-01 4.75061297e-01 5.26326239e-01 1.54046670e-01
-1.36680469e-01 -3.23453516e-01 -4.89054114e-01 -9.64943171e-01
1.62444741e-01 -3.54845136e-01 4.17126387e-01 -8.78602788... | [14.278482437133789, 4.89946174621582] |
95671e9c-2f22-4cb9-bf00-2b134ff177f4 | mind-your-language-abuse-and-offense | 1809.08652 | null | http://arxiv.org/abs/1809.08652v1 | http://arxiv.org/pdf/1809.08652v1.pdf | Mind Your Language: Abuse and Offense Detection for Code-Switched Languages | In multilingual societies like the Indian subcontinent, use of code-switched
languages is much popular and convenient for the users. In this paper, we study
offense and abuse detection in the code-switched pair of Hindi and English
(i.e. Hinglish), the pair that is the most spoken. The task is made difficult
due to non... | ['Rajiv Ratn Shah', 'Roger Zimmermann', 'Kshitij Rajput', 'Yaman Kumar', 'Raghav Kapoor', 'Ponnurangam Kumaraguru'] | 2018-09-23 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-6.06746137e-01 -2.33892903e-01 -2.39363447e-01 -1.31065786e-01
-6.46764934e-01 -7.55818129e-01 7.60088444e-01 -9.05725956e-02
-6.05283499e-01 7.04901278e-01 2.55430073e-01 -4.87597346e-01
2.85982102e-01 -2.99547613e-01 -4.33768660e-01 -5.87334156e-01
-2.21575171e-01 3.70070457e-01 -8.50413442e-02 -5.01956284... | [8.835644721984863, 10.571843147277832] |
d68beaef-e26d-475c-a9d7-955a5db87dbe | neural-field-conditioning-strategies-for-2d | 2304.14371 | null | https://arxiv.org/abs/2304.14371v1 | https://arxiv.org/pdf/2304.14371v1.pdf | Neural Field Conditioning Strategies for 2D Semantic Segmentation | Neural fields are neural networks which map coordinates to a desired signal. When a neural field should jointly model multiple signals, and not memorize only one, it needs to be conditioned on a latent code which describes the signal at hand. Despite being an important aspect, there has been little research on conditio... | ['Stefan Wermter', 'Sven Magg', 'Martin Gromniak'] | 2023-04-12 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 7.11406469e-01 2.59719610e-01 -5.02137616e-02 -4.51918364e-01
-7.31839299e-01 -3.28584909e-01 7.95494497e-01 -6.34001642e-02
-4.32846069e-01 6.62636280e-01 2.29743049e-01 -1.33202523e-01
1.46663800e-01 -7.28425384e-01 -9.47128475e-01 -7.43302882e-01
-1.26700914e-02 3.12572151e-01 2.58056432e-01 6.44972175... | [9.727343559265137, 0.7147372364997864] |
dd0d8a3c-5346-4066-9345-c15609766a83 | degenerative-adversarial-neuroimage-nets | 1907.02787 | null | https://arxiv.org/abs/1907.02787v2 | https://arxiv.org/pdf/1907.02787v2.pdf | Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression | Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This capability is valuable for secondary prevention clinical trials where outcomes and screening criteria involve neuroimaging. Traditional comp... | ['Neil P. Oxtoby', 'Daniele Ravi', 'Daniel C. Alexander'] | 2019-07-05 | null | null | null | null | ['predicting-patient-outcomes'] | ['medical'] | [ 3.12543184e-01 -1.06853463e-01 3.31286043e-01 -5.43605864e-01
-6.52382076e-01 -2.71134406e-01 5.99664927e-01 -3.37719023e-01
-4.63308096e-01 8.33170474e-01 3.17603260e-01 -3.41026783e-01
-8.70771147e-03 -5.56686044e-01 -7.19064772e-01 -6.20327830e-01
-8.43688190e-01 7.81671941e-01 1.61103323e-01 -1.32007480... | [14.103503227233887, -1.9327499866485596] |
ce8acb4d-afc1-4664-a073-54565d2ca603 | ridcp-revitalizing-real-image-dehazing-via | 2304.03994 | null | https://arxiv.org/abs/2304.03994v1 | https://arxiv.org/pdf/2304.03994v1.pdf | RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors | Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data and introducing more robust priors into the network. Specificall... | ['Chong-Yi Li', 'Zhi Chai', 'Chun-Le Guo', 'Zheng-Peng Duan', 'Rui-Qi Wu'] | 2023-04-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_RIDCP_Revitalizing_Real_Image_Dehazing_via_High-Quality_Codebook_Priors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_RIDCP_Revitalizing_Real_Image_Dehazing_via_High-Quality_Codebook_Priors_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-dehazing'] | ['computer-vision'] | [ 3.12293202e-01 -4.55430383e-03 5.15433371e-01 -2.19898656e-01
-7.44030833e-01 -4.10035342e-01 5.99782646e-01 -3.14258724e-01
-2.33916551e-01 3.79236579e-01 3.54553550e-01 5.68291219e-03
-7.95606822e-02 -9.36260343e-01 -8.20823252e-01 -1.22688067e+00
2.17615306e-01 -7.61519149e-02 3.43529224e-01 -4.71204758... | [10.936593055725098, -3.1161489486694336] |
4957b07b-245e-464b-a0ce-8bfaab7afaff | vader-video-alignment-differencing-and | 2303.13193 | null | https://arxiv.org/abs/2303.13193v2 | https://arxiv.org/pdf/2303.13193v2.pdf | VADER: Video Alignment Differencing and Retrieval | We propose VADER, a spatio-temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos. VADER matches and coarsely aligns partial video fragments to candidate videos using a robust visual descriptor and scalable search over adaptively chunked video content. A... | ['John Collomosse', 'Viswanathan Swaminathan', 'Ritwik Sinha', 'Stefano Petrangeli', 'Md. Mehrab Tanjim', 'Tu Bui', 'Simon Jenni', 'Alexander Black'] | 2023-03-23 | null | null | null | null | ['video-alignment', 'misinformation'] | ['computer-vision', 'miscellaneous'] | [ 1.84391648e-01 -2.88838506e-01 -6.89942956e-01 -2.63412409e-02
-6.22666180e-01 -1.11871040e+00 6.36224508e-01 7.12285101e-01
-2.83061564e-01 3.31407398e-01 7.40462422e-01 1.12277701e-01
-1.89635858e-01 -3.03492010e-01 -7.68907607e-01 -1.68355227e-01
-7.69895732e-01 -2.02146154e-02 8.09397280e-01 2.38236383... | [12.305644035339355, 1.0001720190048218] |
52a46cf9-8e7d-4e9d-9080-c487bffeca97 | disassembling-the-dataset-a-camera-alignment | 2001.08680 | null | https://arxiv.org/abs/2001.08680v3 | https://arxiv.org/pdf/2001.08680v3.pdf | Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization | The fundamental difficulty in person re-identification (ReID) lies in learning the correspondence among individual cameras. It strongly demands costly inter-camera annotations, yet the trained models are not guaranteed to transfer well to previously unseen cameras. These problems significantly limit the application of ... | ['Zijie Zhuang', 'Tianyu Zhang', 'Longhui Wei', 'Haizhou Ai', 'Qi Tian', 'Lingxi Xie', 'Hengheng Zhang', 'Haozhe Wu'] | 2020-01-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1493_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570137.pdf | eccv-2020-8 | ['direct-transfer-person-re-identification'] | ['computer-vision'] | [-1.33217171e-01 -2.93281287e-01 4.39535007e-02 -6.06973171e-01
-6.14797890e-01 -8.98248851e-01 4.92500752e-01 -1.62063807e-01
-6.24233961e-01 5.56883872e-01 2.03165814e-01 1.47364259e-01
9.47068483e-02 -2.34204620e-01 -7.44598389e-01 -6.69842482e-01
2.86051035e-01 2.35036016e-01 -4.63436432e-02 6.89757690... | [14.698918342590332, 0.9948827624320984] |
00a307a7-5015-4cde-8145-b9a4383e3fc1 | inducing-early-neural-collapse-in-deep-neural | 2209.08378 | null | https://arxiv.org/abs/2209.08378v3 | https://arxiv.org/pdf/2209.08378v3.pdf | Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks | We propose a simple modification to standard ResNet architectures--L2 normalization over feature space--that substantially improves out-of-distribution (OoD) performance on the previously proposed Deep Deterministic Uncertainty (DDU) benchmark. We show that this change also induces early Neural Collapse (NC), an effect... | ['Bernhard Rabus', 'William Yolland', 'Jarrod Haas'] | 2022-09-17 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-1.13445930e-01 2.56472945e-01 8.24549794e-02 -4.02697027e-01
-4.30343539e-01 -5.80197752e-01 1.07907832e+00 8.28704089e-02
-6.44712031e-01 7.37781703e-01 2.75967181e-01 -5.20080745e-01
-2.16498375e-01 -7.76794255e-01 -8.90457988e-01 -7.36494541e-01
-3.46933156e-01 2.74453491e-01 5.04342437e-01 7.44338194... | [7.567513942718506, 3.70713472366333] |
1f3b6f5b-5b39-4db6-8e9d-18ca25d507aa | learning-iterative-neural-optimizers-for | 2303.16206 | null | https://arxiv.org/abs/2303.16206v1 | https://arxiv.org/pdf/2303.16206v1.pdf | Learning Iterative Neural Optimizers for Image Steganography | Image steganography is the process of concealing secret information in images through imperceptible changes. Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natural images, and propo... | ['Kilian Q Weinberger', 'Varsha Kishore', 'Xiangyu Chen'] | 2023-03-27 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 1.02552485e+00 5.19402564e-01 4.59577031e-02 1.50778778e-02
-4.54778105e-01 -1.72583327e-01 5.37083030e-01 -4.08863187e-01
-5.30451596e-01 5.22889316e-01 6.64921254e-02 -6.47430480e-01
3.59532386e-01 -5.86826563e-01 -1.15161240e+00 -9.03239489e-01
-4.62037921e-01 -1.93031989e-02 -1.99414164e-01 -3.99525672... | [4.342766761779785, 8.038812637329102] |
2873f4d2-ed31-4d1d-8516-ec06dec24468 | exploiting-the-textual-potential-from-vision | 2303.04497 | null | https://arxiv.org/abs/2303.04497v1 | https://arxiv.org/pdf/2303.04497v1.pdf | Exploiting the Textual Potential from Vision-Language Pre-training for Text-based Person Search | Text-based Person Search (TPS), is targeted on retrieving pedestrians to match text descriptions instead of query images. Recent Vision-Language Pre-training (VLP) models can bring transferable knowledge to downstream TPS tasks, resulting in more efficient performance gains. However, existing TPS methods improved by VL... | ['Shouhong Ding', 'Qiong Jia', 'Junjie Li', 'Fufu Yu', 'Guanshuo Wang'] | 2023-03-08 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 2.10053220e-01 -6.38467595e-02 -4.26560789e-01 -4.96953905e-01
-1.02465832e+00 -4.41461593e-01 1.03429997e+00 -1.18569963e-01
-7.54553795e-01 5.93245804e-01 4.52525020e-01 -3.39868404e-02
1.42486900e-01 -5.74783444e-01 -9.42158103e-01 -5.49290776e-01
4.32362676e-01 5.00499308e-01 4.41543370e-01 -7.81794116... | [14.57864761352539, 0.8904938101768494] |
3374a5b8-e5ea-4221-b6de-0a9603ccdb51 | weight-poisoning-attacks-on-pretrained-models | null | null | https://aclanthology.org/2020.acl-main.249 | https://aclanthology.org/2020.acl-main.249.pdf | Weight Poisoning Attacks on Pretrained Models | Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of their choice. This raises the question of whether downloading untrusted pre-trained weights can pose a security threat. In this paper, we show ... | ['Keita Kurita', 'Paul Michel', 'Graham Neubig'] | 2020-07-01 | null | null | null | acl-2020-6 | ['spam-detection'] | ['natural-language-processing'] | [ 4.27272290e-01 1.85889736e-01 2.32352167e-02 -8.00854117e-02
-7.71210074e-01 -1.44414127e+00 4.27674145e-01 1.83809698e-01
-7.49235868e-01 6.13587260e-01 -2.63315886e-01 -6.86111450e-01
1.97372884e-01 -7.85266161e-01 -1.15355182e+00 -8.30351293e-01
-1.97726816e-01 1.79345623e-01 2.95838505e-01 -8.10749307... | [5.861743450164795, 7.680727481842041] |
4624fe2a-653a-4975-9dab-3a29b8de5bca | generalizable-one-shot-neural-head-avatar | 2306.08768 | null | https://arxiv.org/abs/2306.08768v1 | https://arxiv.org/pdf/2306.08768v1.pdf | Generalizable One-shot Neural Head Avatar | We present a method that reconstructs and animates a 3D head avatar from a single-view portrait image. Existing methods either involve time-consuming optimization for a specific person with multiple images, or they struggle to synthesize intricate appearance details beyond the facial region. To address these limitation... | ['Jan Kautz', 'Umar Iqbal', 'Koki Nagano', 'Sifei Liu', 'Shalini De Mello', 'Xueting Li'] | 2023-06-14 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 2.50437587e-01 3.39246958e-01 3.36810797e-01 -4.30865794e-01
-5.45918107e-01 -5.73964179e-01 5.06674111e-01 -4.92620379e-01
1.03477277e-01 5.40753663e-01 2.11181313e-01 3.56781751e-01
5.36312640e-01 -6.76652014e-01 -7.22927511e-01 -5.57919383e-01
3.75222355e-01 7.18926132e-01 1.79631524e-02 -3.16559434... | [12.789714813232422, -0.3517579138278961] |
fefe8a75-7d62-4455-8141-ccf3ccc318a7 | deltar-depth-estimation-from-a-light-weight | 2209.13362 | null | https://arxiv.org/abs/2209.13362v1 | https://arxiv.org/pdf/2209.13362v1.pdf | DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image | Light-weight time-of-flight (ToF) depth sensors are small, cheap, low-energy and have been massively deployed on mobile devices for the purposes like autofocus, obstacle detection, etc. However, due to their specific measurements (depth distribution in a region instead of the depth value at a certain pixel) and extreme... | ['Zhaopeng Cui', 'yinda zhang', 'Guofeng Zhang', 'Hujun Bao', 'Han Zhou', 'Wenqi Dong', 'Xinyang Liu', 'Yijin Li'] | 2022-09-27 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 3.01711291e-01 -2.21593723e-01 -1.86401624e-02 -4.43072885e-01
-7.13046312e-01 -1.83924884e-01 1.69328526e-01 -3.54232490e-01
-4.53791827e-01 4.41503644e-01 6.46328256e-02 -1.52656138e-01
1.35721490e-01 -9.46041524e-01 -5.38085282e-01 -5.98996699e-01
4.12584841e-01 2.05438629e-01 4.60012048e-01 -1.11223578... | [9.043135643005371, -2.4657716751098633] |
ba97f202-94cf-4d77-849a-22f56a3dd65c | findings-of-the-vardial-evaluation-campaign-3 | 2305.20080 | null | https://arxiv.org/abs/2305.20080v1 | https://arxiv.org/pdf/2305.20080v1.pdf | Findings of the VarDial Evaluation Campaign 2023 | This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with EACL 2023. Three separate shared tasks were included th... | ['Marcos Zampieri', 'Yves Scherrer', 'Barbara Plank', 'Kai North', 'Nikola Ljubešić', 'Mourhaf Kazzaz', 'Tommi Jauhiainen', 'Rob van der Goot', 'Çağrı Çöltekin', 'Noëmi Aepli'] | 2023-05-31 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [-3.87755513e-01 6.73766062e-02 -2.92122483e-01 -6.61924720e-01
-1.27923703e+00 -9.01062369e-01 8.03919554e-01 4.48857635e-01
-5.80367923e-01 4.83859777e-01 5.75139165e-01 -4.37292993e-01
2.99356043e-01 -2.16586590e-01 -2.93170232e-02 2.93175448e-02
-7.15209171e-02 7.54646897e-01 2.05774769e-01 -1.98685586... | [10.235885620117188, 10.701041221618652] |
e58b18a7-ef4f-4de8-acfa-016caa66d29f | audio-visual-video-face-hallucination-with | 2211.10883 | null | https://arxiv.org/abs/2211.10883v1 | https://arxiv.org/pdf/2211.10883v1.pdf | Audio-visual video face hallucination with frequency supervision and cross modality support by speech based lip reading loss | Recently, there has been numerous breakthroughs in face hallucination tasks. However, the task remains rather challenging in videos in comparison to the images due to inherent consistency issues. The presence of extra temporal dimension in video face hallucination makes it non-trivial to learn the facial motion through... | ['Vivek Singh Bawa', 'Vinay Kumar', 'Abhinav Dhall', 'Shailza Sharma'] | 2022-11-20 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 1.00812286e-01 2.37495840e-01 8.02044570e-02 -6.95394203e-02
-6.01428926e-01 -1.26026839e-01 5.60708344e-01 -7.87332654e-01
1.45620942e-01 8.87559056e-01 6.46778166e-01 5.88138163e-01
-8.47803354e-02 -5.59239566e-01 -8.91074657e-01 -9.94237781e-01
5.73582985e-02 -1.55366853e-01 -2.33271588e-02 -2.98963815... | [13.107150077819824, -0.31075215339660645] |
9b1020ff-3696-45c5-946a-f98dab312c5e | stochastic-multi-person-3d-motion-forecasting | 2306.05421 | null | https://arxiv.org/abs/2306.05421v1 | https://arxiv.org/pdf/2306.05421v1.pdf | Stochastic Multi-Person 3D Motion Forecasting | This paper aims to deal with the ignored real-world complexities in prior work on human motion forecasting, emphasizing the social properties of multi-person motion, the diversity of motion and social interactions, and the complexity of articulated motion. To this end, we introduce a novel task of stochastic multi-pers... | ['Liang-Yan Gui', 'Yu-Xiong Wang', 'Sirui Xu'] | 2023-06-08 | null | null | null | null | ['motion-forecasting'] | ['computer-vision'] | [-2.79255271e-01 8.76841396e-02 4.69213650e-02 -1.70989960e-01
-3.53841037e-01 -4.53868926e-01 1.08447349e+00 -6.22462511e-01
4.75613438e-02 5.41007400e-01 9.37538445e-01 5.10545969e-02
1.90966100e-01 -8.04720283e-01 -5.90476573e-01 -8.10792625e-01
-9.39060450e-02 6.74573243e-01 3.25966328e-01 -3.66090328... | [7.235209941864014, -0.08526485413312912] |
38eeb920-4f6e-49f6-be2f-08b9bf45f833 | camembert-bio-a-tasty-french-language-model | 2306.15550 | null | https://arxiv.org/abs/2306.15550v1 | https://arxiv.org/pdf/2306.15550v1.pdf | CamemBERT-bio: a Tasty French Language Model Better for your Health | Clinical data in hospitals are increasingly accessible for research through clinical data warehouses, however these documents are unstructured. It is therefore necessary to extract information from medical reports to conduct clinical studies. Transfer learning with BERT-like models such as CamemBERT has allowed major a... | ['Eric de la Clergerie', 'Laurent Romary', 'Rian Touchent'] | 2023-06-27 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [-3.71689588e-01 3.31659973e-01 -4.32875872e-01 -5.57369232e-01
-1.02008247e+00 -2.33030066e-01 2.27722630e-01 8.43202889e-01
-8.76980305e-01 1.34196115e+00 1.99230447e-01 -4.27736342e-01
1.02411546e-01 -8.36205423e-01 -7.53031909e-01 -4.38097805e-01
-4.51933555e-02 8.53528082e-01 -9.07235146e-02 3.03090475... | [8.465446472167969, 8.73388957977295] |
e1069af5-131d-4be6-b840-cfe17923d187 | fine-grained-visual-entailment | 2203.15704 | null | https://arxiv.org/abs/2203.15704v1 | https://arxiv.org/pdf/2203.15704v1.pdf | Fine-Grained Visual Entailment | Visual entailment is a recently proposed multimodal reasoning task where the goal is to predict the logical relationship of a piece of text to an image. In this paper, we propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to... | ['Shih-Fu Chang', 'YiPeng Zhang', 'Christopher Thomas'] | 2022-03-29 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 2.77751476e-01 5.43397605e-01 -5.00690401e-01 -7.18150973e-01
-9.75832999e-01 -6.62520945e-01 8.51496398e-01 1.06285304e-01
4.45271693e-02 6.58942759e-01 4.32080448e-01 -2.51275897e-01
3.90843004e-02 -5.29690325e-01 -1.30286288e+00 -2.71033328e-02
5.03566504e-01 5.95718920e-01 2.56424993e-01 2.50332747... | [10.653221130371094, 1.5937604904174805] |
562b073d-7bd6-4a76-af81-e4f6becc958d | lifted-message-passing-for-the-generalized | 1610.01525 | null | http://arxiv.org/abs/1610.01525v1 | http://arxiv.org/pdf/1610.01525v1.pdf | Lifted Message Passing for the Generalized Belief Propagation | We introduce the lifted Generalized Belief Propagation (GBP) message passing
algorithm, for the computation of sum-product queries in Probabilistic
Relational Models (e.g. Markov logic network). The algorithm forms a compact
region graph and establishes a modified version of message passing, which
mimics the GBP behavi... | ['Udi Apsel'] | 2016-10-05 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [-1.59155354e-01 6.22540951e-01 -2.71248490e-01 -3.69837433e-01
-7.42964745e-01 -6.39787376e-01 9.86135244e-01 6.12173021e-01
-4.46177460e-02 3.04806739e-01 -2.26207256e-01 -4.70569313e-01
-6.97168350e-01 -1.40799773e+00 -8.40984166e-01 -5.20978868e-01
-7.17124164e-01 1.23722124e+00 9.55446959e-01 -1.53949410... | [8.400256156921387, 6.523624420166016] |
50d54c6d-d7b5-4400-be3a-d4804306e99b | a-real-time-fire-segmentation-method-based-on | null | null | https://www.sciencedirect.com/science/article/pii/S2405896322005055 | https://pdf.sciencedirectassets.com/313346/1-s2.0-S2405896322X00074/1-s2.0-S2405896322005055/main.pdf | A Real-time Fire Segmentation Method Based on A Deep Learning Approach | As a kind of the forest “fault”, fire is highly destructive and difficult to rescue. Fire segmentation is helpful for firefighters to understand the fire scale and formulate a reasonable fire-fighting plan. Therefore, this paper proposes a real-time fire segmentation method based on deep learning. This method is an imp... | ['Yi Yingmin', 'Guo Xie', 'Han Liu', 'Shangbin Jiao', 'Ziquan Yu', 'Jing Xin', 'Lingxia Mu', 'Youmin Zhang', 'Mengna Li'] | 2022-07-22 | null | null | null | ifac-papersonline-2022-7 | ['real-time-semantic-segmentation'] | ['computer-vision'] | [-1.06388830e-01 -4.67617571e-01 3.53874895e-03 -1.05931982e-01
2.51330525e-01 -8.21091607e-02 3.53507064e-02 -4.57520813e-01
-8.33639443e-01 6.29435718e-01 1.69248790e-01 -4.28598970e-01
3.93144153e-02 -1.44744861e+00 -4.10886824e-01 -5.97282588e-01
3.02134603e-01 9.11096390e-03 6.57663286e-01 -3.54415953... | [9.348976135253906, -0.8326335549354553] |
a9a9e1f8-cbab-42e1-ad11-142974acd9b9 | wordrank-learning-word-embeddings-via-robust | 1506.02761 | null | http://arxiv.org/abs/1506.02761v4 | http://arxiv.org/pdf/1506.02761v4.pdf | WordRank: Learning Word Embeddings via Robust Ranking | Embedding words in a vector space has gained a lot of attention in recent
years. While state-of-the-art methods provide efficient computation of word
similarities via a low-dimensional matrix embedding, their motivation is often
left unclear. In this paper, we argue that word embedding can be naturally
viewed as a rank... | ['S. V. N. Vishwanathan', 'Shin Matsushima', 'Hyokun Yun', 'Shihao Ji', 'Pinar Yanardag'] | 2015-06-09 | wordrank-learning-word-embeddings-via-robust-1 | https://aclanthology.org/D16-1063 | https://aclanthology.org/D16-1063.pdf | emnlp-2016-11 | ['learning-word-embeddings'] | ['methodology'] | [-1.72712162e-01 -1.94577530e-01 -5.72690189e-01 -1.36510357e-01
-9.80644524e-01 -5.30451417e-01 8.82511079e-01 6.46183074e-01
-9.10527468e-01 2.51932353e-01 7.92520523e-01 -2.74016887e-01
-1.51317954e-01 -7.79144645e-01 -3.20733219e-01 -4.95599300e-01
-1.64916828e-01 5.61398447e-01 1.20521933e-01 -5.93090534... | [10.581413269042969, 8.583974838256836] |
cb4eda21-6b00-4a11-b019-23e82eac5483 | terrain-analysis-in-starcraft-1-and-2-as | 2205.08683 | null | https://arxiv.org/abs/2205.08683v1 | https://arxiv.org/pdf/2205.08683v1.pdf | Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization | Terrain analysis in Real-Time Strategy games is a necessary step to allow spacial reasoning. The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial representation. On StarCraft games, all previous works on terrain analysis propose a crisp analysis ... | ['Florian Richoux'] | 2022-05-18 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-4.23661977e-01 1.77798107e-01 5.55344336e-02 3.33291851e-03
6.84194714e-02 -9.65749025e-01 6.82631552e-01 4.34250206e-01
-5.17367482e-01 8.08291137e-01 -3.28898966e-01 -6.43099964e-01
-4.87136692e-01 -1.71635115e+00 -2.44590208e-01 -3.83601874e-01
-4.97897029e-01 1.21415401e+00 1.26483095e+00 -1.22546875... | [3.4765446186065674, 1.4654655456542969] |
97fda81b-885c-4238-ab2c-c7c76d58e21b | weighted-histogram-equalization-using-entropy | 2111.08578 | null | https://arxiv.org/abs/2111.08578v3 | https://arxiv.org/pdf/2111.08578v3.pdf | Weighted Histogram Equalization Using Entropy of Probability Density Function | Low-contrast image enhancement is essential for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to increase the visibility of an image while maintaining its naturalness. In this paper, the weighted histogram equalization using the entropy of the... | ['Sos Agaian', 'Thaweesak Trongtirakul'] | 2021-11-16 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.57154751e-01 -7.44831920e-01 3.24204803e-01 -2.75860995e-01
-5.09076536e-01 -2.14984179e-01 2.89922565e-01 3.00879866e-01
-4.47207183e-01 8.12942147e-01 1.05298236e-01 -2.34207794e-01
-4.02573287e-01 -9.33775544e-01 -2.50118285e-01 -1.05554783e+00
-3.92658621e-01 -6.48669422e-01 4.93153453e-01 -2.56508321... | [11.041389465332031, -2.3362462520599365] |
eb184e6c-16c0-4e8a-906d-874e62aec354 | vihealthbert-pre-trained-language-models-for | null | null | http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.35.pdf | http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.35.pdf | ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining | Pre-trained language models have become crucial to achieving competitive results across many Natural Language Processing (NLP) problems. For monolingual pre-trained models in low-resource languages, the quantity has been significantly increased. However, most of them relate to the general domain, and there are limited ... | ['Steven Quoc Hung', 'Trung Huu and Truong', 'Huy Duc and Bui', 'Vu and Ta', 'Vu Hoang and Hoang', 'Nguyen and Tran', 'Minh'] | 2022-06-01 | null | null | null | lrec-2022-6 | ['word-sense-disambiguation', 'medical-named-entity-recognition', 'named-entity-recognition-in-vietnamese', 'vietnamese-datasets'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.32171556e-01 3.24008435e-01 -6.46611691e-01 -3.79180819e-01
-1.55956781e+00 -4.92849469e-01 4.49778914e-01 7.78914690e-01
-9.01726663e-01 1.11064208e+00 1.10058737e+00 -3.47107649e-01
-1.56771746e-02 -4.04766589e-01 -2.03520164e-01 -2.83213794e-01
1.78587154e-01 1.02332509e+00 -3.02014738e-01 -6.46819353... | [8.835389137268066, 8.899136543273926] |
2a3046bd-acd5-44f9-8d23-f7ac179d8285 | syntax-ignorant-n-gram-embeddings-for | null | null | https://aclanthology.org/W19-4604 | https://aclanthology.org/W19-4604.pdf | Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects | Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content. These embeddings are usually composed via ordered, syntax-aware composition functions and learned within deep neural frameworks. With the free word order and the varying syntax nature across the di... | ['Ismail Babao{\\u{g}}lu', 'Mourad Gridach', 'Hatem Haddad', 'Hala Mulki'] | 2019-08-01 | null | null | null | ws-2019-8 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-4.34347719e-01 -1.51850179e-01 4.27480012e-01 -7.02786863e-01
-1.48977980e-01 -9.73019719e-01 9.38507736e-01 3.21154833e-01
-6.46897554e-01 2.06599295e-01 6.06442094e-01 -3.38494003e-01
2.14226097e-01 -8.76825929e-01 -2.68286735e-01 -7.45909572e-01
-1.23869166e-01 3.72835368e-01 -2.59854525e-01 -1.23606682... | [11.15727424621582, 7.212823867797852] |
075bf644-bdd6-4736-89a4-9d5659235c96 | more-for-less-compact-convolutional | 2307.00213 | null | https://arxiv.org/abs/2307.00213v1 | https://arxiv.org/pdf/2307.00213v1.pdf | More for Less: Compact Convolutional Transformers Enable Robust Medical Image Classification with Limited Data | Transformers are very powerful tools for a variety of tasks across domains, from text generation to image captioning. However, transformers require substantial amounts of training data, which is often a challenge in biomedical settings, where high quality labeled data can be challenging or expensive to obtain. This stu... | ['Andrew Kean Gao'] | 2023-07-01 | null | null | null | null | ['image-captioning', 'medical-image-classification', 'text-generation'] | ['computer-vision', 'medical', 'natural-language-processing'] | [ 4.97163445e-01 3.18862535e-02 -6.29124194e-02 -3.13344032e-01
-1.19150460e+00 -5.93438089e-01 4.28432494e-01 1.53664470e-01
-5.84718645e-01 8.74691844e-01 9.76397991e-02 -5.74368179e-01
8.96513090e-03 -4.81846750e-01 -6.14172995e-01 -7.31331706e-01
1.54151976e-01 3.73456687e-01 3.14152129e-02 2.63694495... | [14.871862411499023, -2.4614195823669434] |
1c851b13-72b7-4d66-be4f-30505cb59234 | research-on-multi-agent-communication-and | 2305.17141 | null | https://arxiv.org/abs/2305.17141v1 | https://arxiv.org/pdf/2305.17141v1.pdf | Research on Multi-Agent Communication and Collaborative Decision-Making Based on Deep Reinforcement Learning | In a multi-agent environment, In order to overcome and alleviate the non-stationarity of the multi-agent environment, the mainstream method is to adopt the framework of Centralized Training Decentralized Execution (CTDE). This thesis is based on the framework of CTDE, and studies the cooperative decision-making of mult... | ['Zeng Da'] | 2023-05-23 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-5.71788192e-01 -6.48116767e-02 -3.38781834e-01 1.01203537e-02
-9.49087217e-02 2.74471462e-01 3.63993049e-01 1.28060296e-01
-6.74186885e-01 6.93673670e-01 3.81853729e-01 -1.20893858e-01
-2.98369557e-01 -8.29302669e-01 -1.64592922e-01 -1.15697944e+00
-3.47938985e-01 8.34474921e-01 2.01895431e-01 -4.86499369... | [3.7771575450897217, 2.0258948802948] |
a3ba69af-3400-4f94-bbda-60d9b09cfaa5 | kqgc-knowledge-graph-embedding-with-smoothing | 2205.12102 | null | https://arxiv.org/abs/2205.12102v1 | https://arxiv.org/pdf/2205.12102v1.pdf | KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation | Leveraging graphs on recommender systems has gained popularity with the development of graph representation learning (GRL). In particular, knowledge graph embedding (KGE) and graph neural networks (GNNs) are representative GRL approaches, which have achieved the state-of-the-art performance on several recommendation ta... | ['Pablo Loyola', 'Takuma Ebisu', 'Manoj Kondapaka', 'Satyen Abrol', 'Yu Hirate', 'Md Mostafizur Rahman', 'Toyotaro Suzumura', 'Daisuke Kikuta'] | 2022-05-23 | null | null | null | null | ['entity-embeddings'] | ['methodology'] | [-3.27067733e-01 2.57973254e-01 -4.11249757e-01 -1.77338824e-01
4.86304201e-02 -3.48862350e-01 5.80139697e-01 2.71620035e-01
1.22536927e-01 2.26761162e-01 6.28818095e-01 -4.78591591e-01
-5.29858053e-01 -1.27331126e+00 -6.87013030e-01 -5.56426406e-01
-1.64917395e-01 -7.10958838e-02 1.34276196e-01 -5.70851386... | [10.203763008117676, 5.648401737213135] |
44b47356-38a4-4984-a9f3-6c0f06e08cd4 | conformalized-semi-supervised-random-forest | 2302.02237 | null | https://arxiv.org/abs/2302.02237v1 | https://arxiv.org/pdf/2302.02237v1.pdf | Conformalized semi-supervised random forest for classification and abnormality detection | Traditional classifiers infer labels under the premise that the training and test samples are generated from the same distribution. This assumption can be problematic for safety-critical applications such as medical diagnosis and network attack detection. In this paper, we consider the multi-class classification proble... | ['Leying Guan', 'Mingwenchan Xu', 'Yujin Han'] | 2023-02-04 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 3.83179694e-01 4.45509590e-02 -3.28565717e-01 -6.95376754e-01
-6.23614907e-01 -3.77677083e-01 2.34833121e-01 6.01266563e-01
-1.31428704e-01 1.16255236e+00 -6.24299645e-01 -6.44856572e-01
-2.43557259e-01 -8.73648643e-01 -6.53131425e-01 -8.17518234e-01
-2.31374055e-01 6.45809531e-01 1.63501292e-01 4.40497190... | [8.653070449829102, 4.027576446533203] |
1452dc18-e27e-4e47-826b-9ebe69e5a3c6 | efficient-automation-of-neural-network-design | 2304.05405 | null | https://arxiv.org/abs/2304.05405v2 | https://arxiv.org/pdf/2304.05405v2.pdf | Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search | In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures. This rise is mainly due to the popularity of DARTS, one of the first major DNAS methods. In contrast with previous works based on Reinfo... | ['Hedi Tabia', 'Hichem Arioui', 'Ahmad Nasser', 'Alexandre Heuillet'] | 2023-04-11 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.91793498e-02 4.47800830e-02 -2.15220407e-01 -2.76354074e-01
-1.41461343e-01 -6.16421223e-01 6.46900833e-01 -9.08275247e-02
-6.94583893e-01 7.55932927e-01 -5.65534607e-02 -4.30923641e-01
-2.59425104e-01 -7.83703625e-01 -5.94071031e-01 -7.44354069e-01
1.39550656e-01 4.39219475e-01 9.36953202e-02 -4.77738470... | [8.395282745361328, 3.237260341644287] |
313872fd-9fed-4397-b0b6-759d5b21f818 | exploring-unknown-states-with-action-balance | 2003.04518 | null | https://arxiv.org/abs/2003.04518v2 | https://arxiv.org/pdf/2003.04518v2.pdf | Exploring Unknown States with Action Balance | Exploration is a key problem in reinforcement learning. Recently bonus-based methods have achieved considerable successes in environments where exploration is difficult such as Montezuma's Revenge, which assign additional bonuses (e.g., intrinsic rewards) to guide the agent to rarely visited states. Since the bonus is ... | ['Yujing Hu', 'Yan Song', 'Changjie Fan', 'Yingfeng Chen'] | 2020-03-10 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-3.56096715e-01 2.82245427e-01 -5.73441088e-01 8.87712985e-02
-2.62967229e-01 -3.62721682e-01 5.47899485e-01 -1.43732846e-01
-8.88892949e-01 1.38448870e+00 7.60638192e-02 -4.94100213e-01
-4.53393698e-01 -1.00989795e+00 -6.60953641e-01 -1.03364336e+00
-3.82292330e-01 3.70485246e-01 2.51599103e-01 -4.63442445... | [3.9158782958984375, 1.8177975416183472] |
f85df74a-8713-402a-b1bd-899b0a68fe7f | back-to-the-future-unsupervised-backprop | 2010.05906 | null | https://arxiv.org/abs/2010.05906v4 | https://arxiv.org/pdf/2010.05906v4.pdf | Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning | Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future. However, simultaneous incorporation of past and future contexts using generative language models (LMs... | ['Yejin Choi', 'Antoine Bosselut', 'Ronan Le Bras', 'Jena Hwang', 'Chandra Bhagavatula', 'Peter West', 'Vered Shwartz', 'Lianhui Qin'] | 2020-10-12 | null | https://aclanthology.org/2020.emnlp-main.58 | https://aclanthology.org/2020.emnlp-main.58.pdf | emnlp-2020-11 | ['text-infilling'] | ['natural-language-processing'] | [ 5.79832852e-01 5.27078569e-01 -2.00239062e-01 -5.78665078e-01
-4.06433791e-01 -6.47428632e-01 1.19119310e+00 1.42376840e-01
-5.73250592e-01 1.11875749e+00 6.69173837e-01 -5.65415740e-01
4.87321503e-02 -1.01867151e+00 -9.92280066e-01 -2.99623936e-01
1.21493317e-01 7.25092769e-01 -5.36543168e-02 -4.09837991... | [11.293642044067383, 8.887675285339355] |
6ca3d8c0-e3bf-4e78-8590-d165eb93570a | estimating-parameters-of-the-tree-root-in | 2112.13494 | null | https://arxiv.org/abs/2112.13494v1 | https://arxiv.org/pdf/2112.13494v1.pdf | Estimating Parameters of the Tree Root in Heterogeneous Soil Environments via Mask-Guided Multi-Polarimetric Integration Neural Network | Ground-penetrating radar (GPR) has been used as a non-destructive tool for tree root inspection. Estimating root-related parameters from GPR radargrams greatly facilitates root health monitoring and imaging. However, the task of estimating root-related parameters is challenging as the root reflection is a complex funct... | ['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Genevieve Ow', 'Chongyi Li', 'Qiqi Dai', 'Yee Hui Lee', 'Hai-Han Sun'] | 2021-12-27 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.80140692e-01 -9.17717740e-02 2.39170790e-01 -1.63444728e-01
-5.74181497e-01 -8.30501020e-02 -2.37072676e-01 2.88981676e-01
1.69810474e-01 5.35727620e-01 -2.77569830e-01 -4.87470716e-01
-4.72369999e-01 -1.12574995e+00 -1.84942782e-01 -9.68162596e-01
-4.68320698e-01 3.91664833e-01 3.04355741e-01 -2.70815194... | [6.844038963317871, 1.4016773700714111] |
0b8e36d3-02f1-41a2-a3c3-97d4595a41c1 | non-parametric-cumulants-approach-for-outlier | 2305.10911 | null | https://arxiv.org/abs/2305.10911v1 | https://arxiv.org/pdf/2305.10911v1.pdf | Non-parametric cumulants approach for outlier detection of multivariate financial data | In this paper, we propose an outlier detection algorithm for multivariate data based on their projections on the directions that maximize the Cumulant Generating Function (CGF). We prove that CGF is a convex function, and we characterize the CGF maximization problem on the unit n-circle as a concave minimization proble... | ['Jacopo Maria Ricci', 'Rosella Giacometti', 'Francesco Cesarone'] | 2023-05-18 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-2.00978145e-01 -9.17121470e-02 4.64776486e-01 -2.15798393e-01
-3.69022220e-01 -5.39472759e-01 5.67096710e-01 8.40720981e-02
-4.59214389e-01 4.57894951e-01 1.05832517e-01 -5.26140988e-01
-1.61130339e-01 -5.85018277e-01 -5.72099209e-01 -7.23149121e-01
-2.60091901e-01 3.81154746e-01 -2.69572679e-02 1.46499574... | [7.2955217361450195, 4.090228080749512] |
b33ffdfc-45e8-4063-b5a2-a88471ad6e49 | blind-universal-bayesian-image-denoising-with | 1907.03029 | null | https://arxiv.org/abs/1907.03029v2 | https://arxiv.org/pdf/1907.03029v2.pdf | Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning | Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for addi... | ['Sabine Süsstrunk', 'Majed El Helou'] | 2019-07-05 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 5.72174549e-01 -5.20620942e-01 6.85239673e-01 -2.16649085e-01
-8.78190100e-01 -3.95393461e-01 2.51607716e-01 -3.12210590e-01
-5.98353922e-01 5.17346919e-01 7.12122954e-03 -2.84443885e-01
-4.95698266e-02 -8.50634158e-01 -8.27778995e-01 -1.09613132e+00
1.01949577e-03 -5.75112514e-02 -1.87677182e-02 -4.85693932... | [11.346796989440918, -2.3420865535736084] |
f592131d-1ed1-4562-9404-2641457bc278 | co-evolving-real-time-strategy-game-micro | 1803.10314 | null | http://arxiv.org/abs/1803.10314v1 | http://arxiv.org/pdf/1803.10314v1.pdf | Co-evolving Real-Time Strategy Game Micro | We investigate competitive co-evolution of unit micromanagement in real-time
strategy games. Although good long-term macro-strategy and good short-term unit
micromanagement both impact real-time strategy games performance, this paper
focuses on generating quality micro. Better micro, for example, can help
players win s... | ['Siming Liu', 'Sushil J. Louis', 'Navin K Adhikari', 'Walker Spurgeon'] | 2018-03-27 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-3.87805365e-02 -5.88578805e-02 5.05832434e-01 2.91794538e-01
-5.60242534e-01 -6.00128293e-01 3.98049206e-01 -1.19030468e-01
-9.15682018e-01 1.14371717e+00 -1.97831944e-01 -5.39890155e-02
-4.09408391e-01 -1.14497650e+00 -4.97947246e-01 -6.29104078e-01
-2.23491743e-01 6.97506428e-01 2.84973800e-01 -9.09794688... | [3.488337516784668, 1.6006461381912231] |
7d8e5cd7-5712-4663-87cd-4b00a9ebaa39 | illumination-controllable-dehazing-network | 2306.05675 | null | https://arxiv.org/abs/2306.05675v1 | https://arxiv.org/pdf/2306.05675v1.pdf | Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding | On the one hand, the dehazing task is an illposedness problem, which means that no unique solution exists. On the other hand, the dehazing task should take into account the subjective factor, which is to give the user selectable dehazed images rather than a single result. Therefore, this paper proposes a multi-output d... | ['James Tin-Yau Kwok', 'Yuan Yan Tang', 'Lei He', 'Xiaofeng Cong', 'Jie Gui'] | 2023-06-09 | null | null | null | null | ['image-dehazing', 'image-restoration'] | ['computer-vision', 'computer-vision'] | [ 4.03053820e-01 -3.00115012e-02 1.30845204e-01 -3.60155195e-01
-2.86224693e-01 -1.35869309e-02 8.45320523e-02 -5.10990798e-01
-4.19484615e-01 3.21664631e-01 -5.08302869e-03 -1.99171558e-01
-2.48977408e-01 -9.33365464e-01 -8.06404352e-01 -1.23153746e+00
7.51769006e-01 -2.91975915e-01 2.64457792e-01 -1.72255784... | [10.909843444824219, -3.059062957763672] |
3b2b9b21-e3dc-4c46-b127-bb90e4237d96 | diffusion-models-in-bioinformatics-a-new-wave | 2302.10907 | null | https://arxiv.org/abs/2302.10907v1 | https://arxiv.org/pdf/2302.10907v1.pdf | Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action | Denoising diffusion models have emerged as one of the most powerful generative models in recent years. They have achieved remarkable success in many fields, such as computer vision, natural language processing (NLP), and bioinformatics. Although there are a few excellent reviews on diffusion models and their applicatio... | ['Jianlin Cheng', 'Dong Xu', 'Duolin Wang', 'Mengrui Chen', 'Yanli Wang', 'Jian Liu', 'Zhiye Guo'] | 2023-02-13 | null | null | null | null | ['protein-design'] | ['medical'] | [ 3.33249509e-01 -4.17370796e-01 9.87721309e-02 -1.70497671e-01
-4.79615748e-01 -4.55583960e-01 4.19334441e-01 4.43774939e-01
-7.87940741e-01 7.71484077e-01 2.62108713e-01 -2.26129651e-01
-3.29840034e-01 -5.35431683e-01 -2.69222140e-01 -1.55087650e+00
-8.66530538e-02 8.84805202e-01 9.40082893e-02 -6.93424046... | [11.153564453125, -0.0023050648160278797] |
508b2cb5-7b58-43e5-b166-b2313b9a396b | taming-small-sample-bias-in-low-budget-active | 2306.11056 | null | https://arxiv.org/abs/2306.11056v1 | https://arxiv.org/pdf/2306.11056v1.pdf | Taming Small-sample Bias in Low-budget Active Learning | Active learning (AL) aims to minimize the annotation cost by only querying a few informative examples for each model training stage. However, training a model on a few queried examples suffers from the small-sample bias. In this paper, we address this small-sample bias issue in low-budget AL by exploring a regularizer ... | ['Tianyi Zhou', 'Xiaotian Lu', 'Jieyu Zhang', 'Linxin Song'] | 2023-06-19 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 2.87278116e-01 4.74026829e-01 -7.36163616e-01 -7.82829106e-01
-1.28040004e+00 -5.52856684e-01 1.96908936e-01 2.36510977e-01
-9.97328281e-01 8.46235335e-01 -2.11597756e-01 -2.48195097e-01
-1.48083061e-01 -8.24700058e-01 -8.63913357e-01 -6.70588493e-01
2.15409756e-01 7.96699464e-01 4.90763575e-01 2.35138997... | [9.064910888671875, 3.868990182876587] |
5c58184c-a0df-4704-a82e-e2f8948f61c1 | micronnet-a-highly-compact-deep-convolutional | 1804.00497 | null | http://arxiv.org/abs/1804.00497v3 | http://arxiv.org/pdf/1804.00497v3.pdf | MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification | Traffic sign recognition is a very important computer vision task for a
number of real-world applications such as intelligent transportation
surveillance and analysis. While deep neural networks have been demonstrated in
recent years to provide state-of-the-art performance traffic sign recognition,
a key challenge for ... | ['Michael St. Jules', 'Alexander Wong', 'Mohammad Javad Shafiee'] | 2018-03-28 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 9.29684564e-02 -3.61466557e-01 -2.97974885e-01 -3.08327258e-01
6.98697418e-02 1.49426283e-02 2.83321977e-01 -5.94326138e-01
-8.38577569e-01 3.53903621e-01 -6.11750782e-01 -9.26615894e-01
-1.16217751e-02 -6.49432421e-01 -4.99310821e-01 -8.63663018e-01
6.64620548e-02 2.00997248e-01 6.01263821e-01 -1.35982811... | [8.000544548034668, -0.6611939668655396] |
c251f533-3900-4b29-8ea6-02d2a0327ef3 | cross-modal-self-attention-network-for | 1904.04745 | null | http://arxiv.org/abs/1904.04745v1 | http://arxiv.org/pdf/1904.04745v1.pdf | Cross-Modal Self-Attention Network for Referring Image Segmentation | We consider the problem of referring image segmentation. Given an input image
and a natural language expression, the goal is to segment the object referred
by the language expression in the image. Existing works in this area treat the
language expression and the input image separately in their representations.
They do ... | ['Mrigank Rochan', 'Yang Wang', 'Linwei Ye', 'Zhi Liu'] | 2019-04-09 | cross-modal-self-attention-network-for-2 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ye_Cross-Modal_Self-Attention_Network_for_Referring_Image_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ye_Cross-Modal_Self-Attention_Network_for_Referring_Image_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['referring-expression-segmentation', 'referring-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.37256426e-01 9.14282128e-02 -4.73185301e-01 -6.33948326e-01
-8.25307846e-01 -4.76283193e-01 5.49066067e-01 2.47796372e-01
-5.87998748e-01 3.31655949e-01 2.00108200e-01 2.01995611e-01
3.74865644e-02 -6.12160802e-01 -6.40500486e-01 -5.52300274e-01
3.11800748e-01 1.73396990e-01 4.68360633e-01 -9.43265036... | [10.270099639892578, 1.159226655960083] |
a94c1e69-3f77-4545-8885-2f919cd1c133 | neural-natural-language-processing-for-long | 2305.16259 | null | https://arxiv.org/abs/2305.16259v4 | https://arxiv.org/pdf/2305.16259v4.pdf | Neural Natural Language Processing for Long Texts: A Survey of the State-of-the-Art | The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural Language Processing (NLP) during the past decade. However, the demands of long document analysis are quite different from those of shorter texts, while the ever increasing size of documents uploaded on-line renders automated understanding of long... | ['Ioannis Mademlis', 'Ioannis Gkionis', 'Dimitrios Tsirmpas'] | 2023-05-25 | null | null | null | null | ['sentiment-analysis', 'document-classification', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.42081150e-01 1.49227232e-01 -5.27273536e-01 -3.65975380e-01
-7.62700140e-01 -8.39082658e-01 7.14643896e-01 6.20709956e-01
-6.15410507e-01 7.62123585e-01 6.14616513e-01 -2.34189391e-01
-2.10344523e-01 -5.22547603e-01 -3.19481909e-01 -6.90450132e-01
1.63900971e-01 4.51716304e-01 -6.86960578e-01 -1.81360155... | [12.451088905334473, 9.467552185058594] |
8a2377ed-e82e-4263-818e-af378be6037a | axiou-an-axiomatically-justified-measure-for | 2203.16062 | null | https://arxiv.org/abs/2203.16062v1 | https://arxiv.org/pdf/2203.16062v1.pdf | AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval | Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applications where conventional measures are not well suited. Video Moment Retrieval (VMR) is one such application, and the current practice is to... | ['Tetsuya Sakai', 'Janne Heikkila', 'Esa Rahtu', 'Yuta Nakashima', 'Mayu Otani', 'Riku Togashi'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Togashi_AxIoU_An_Axiomatically_Justified_Measure_for_Video_Moment_Retrieval_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Togashi_AxIoU_An_Axiomatically_Justified_Measure_for_Video_Moment_Retrieval_CVPR_2022_paper.pdf | cvpr-2022-1 | ['moment-retrieval'] | ['computer-vision'] | [-7.69197419e-02 -4.89448696e-01 -2.23954588e-01 -1.29672170e-01
-9.04480815e-01 -6.59004211e-01 4.08408821e-01 1.56368867e-01
-6.29166603e-01 4.71621484e-01 -1.37840286e-01 -8.27195644e-02
-9.07642007e-01 -6.29188418e-01 -3.87890458e-01 -6.32620394e-01
-4.64178264e-01 1.81754485e-01 5.58964193e-01 -2.46090144... | [10.28726863861084, 0.6694245338439941] |
533a8da4-ba54-42fa-bbf3-894684423bf8 | editing-commonsense-knowledge-in-gpt | 2305.14956 | null | https://arxiv.org/abs/2305.14956v1 | https://arxiv.org/pdf/2305.14956v1.pdf | Editing Commonsense Knowledge in GPT | Memory editing methods for updating encyclopedic knowledge in transformers have received increasing attention for their efficacy, specificity, and generalization advantages. However, it remains unclear if such methods can be adapted for the more nuanced domain of commonsense knowledge. We propose $MEMIT_{CSK}$, an adap... | ['Niket Tandon', 'Sarah Wiegreffe', 'Xiang Lorraine Li', 'Wenlong Zhao', 'Akshay Krishna Sheshadri', 'Debanjan Mondal', 'Anshita Gupta'] | 2023-05-24 | null | null | null | null | ['specificity', 'model-editing'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.67202580e-01 2.70685345e-01 -1.25071555e-02 -3.36036384e-01
-7.54086137e-01 -8.30378950e-01 4.17614520e-01 3.31211746e-01
-7.78035164e-01 9.42177951e-01 4.64780480e-01 -1.70930728e-01
-2.08331808e-01 -7.42376089e-01 -8.97283673e-01 3.69746536e-02
4.66261744e-01 4.50359881e-01 3.40969622e-01 -5.46519578... | [10.506449699401855, 8.164589881896973] |
48fdaa44-1231-46e8-959b-a5a4296126e4 | high-resolution-semantic-labeling-with | 1611.01962 | null | http://arxiv.org/abs/1611.01962v1 | http://arxiv.org/pdf/1611.01962v1.pdf | High-Resolution Semantic Labeling with Convolutional Neural Networks | Convolutional neural networks (CNNs) have received increasing attention over
the last few years. They were initially conceived for image categorization,
i.e., the problem of assigning a semantic label to an entire input image.
In this paper we address the problem of dense semantic labeling, which
consists in assignin... | ['Guillaume Charpiat', 'Pierre Alliez', 'Emmanuel Maggiori', 'Yuliya Tarabalka'] | 2016-11-07 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 5.82951486e-01 8.29263106e-02 -3.20416689e-01 -5.74674785e-01
-4.94543433e-01 -8.02259624e-01 5.48924088e-01 2.54941851e-01
-5.78997672e-01 4.43084210e-01 -5.10229021e-02 -3.42767648e-02
-3.86797488e-01 -1.22717810e+00 -7.20714271e-01 -5.85989714e-01
1.70335367e-01 4.29303080e-01 3.19730878e-01 -2.60675013... | [9.733377456665039, 0.7361385822296143] |
8b7227c4-e056-4d8c-9920-435d6a4ed3b3 | a-log-linear-model-for-unsupervised-text | null | null | https://aclanthology.org/D13-1007 | https://aclanthology.org/D13-1007.pdf | A Log-Linear Model for Unsupervised Text Normalization | null | ['Yi Yang', 'Jacob Eisenstein'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['lexical-normalization'] | ['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... | [-7.391500473022461, 3.7005531787872314] |
30b1c6da-4c69-4135-95a6-976e9ee7841c | prnu-based-source-camera-identification-for | 2107.01885 | null | https://arxiv.org/abs/2107.01885v1 | https://arxiv.org/pdf/2107.01885v1.pdf | PRNU Based Source Camera Identification for Webcam Videos | This communication is about an application of image forensics where we use camera sensor fingerprints to identify source camera (SCI: Source Camera Identification) in webcam videos. Sensor or camera fingerprints are based on computing the intrinsic noise that is always present in this kind of sensors due to manufacturi... | ['Fernando Martin-Rodriguez'] | 2021-07-05 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 6.03548169e-01 -6.31384909e-01 -8.14960301e-02 1.60197750e-01
-4.23238128e-01 -9.50548589e-01 5.22486389e-01 -1.83900014e-01
-3.57025415e-01 4.29792047e-01 -1.20457694e-01 -1.36973098e-01
-2.58591890e-01 -6.05634332e-01 -7.90865362e-01 -6.01220310e-01
2.20436603e-01 -1.58198327e-02 4.25431609e-01 2.88565874... | [12.38310432434082, 0.9978600144386292] |
8585eb57-0942-4e2b-a306-85519838f207 | eulernet-adaptive-feature-interaction | 2304.10711 | null | https://arxiv.org/abs/2304.10711v1 | https://arxiv.org/pdf/2304.10711v1.pdf | EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction | Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless in... | ['Zhao Cao', 'Ji-Rong Wen', 'Wayne Xin Zhao', 'Ting Bai', 'Zhen Tian'] | 2023-04-21 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-3.20161879e-01 -4.72057790e-01 -1.47586063e-01 -4.33221281e-01
-9.03528109e-02 -5.26083708e-01 3.03161323e-01 -5.10555916e-02
-3.84373844e-01 3.84242088e-01 -3.85522880e-02 -2.51253575e-01
-5.29979646e-01 -8.48443389e-01 -5.63071609e-01 -8.30578089e-01
-1.55534774e-01 1.19082771e-01 2.12739035e-01 -5.30776441... | [10.101983070373535, 5.4144792556762695] |
772a6237-e27f-41ba-9194-b090c507ce73 | two-birds-one-stone-a-unified-framework-for | 2304.11335 | null | https://arxiv.org/abs/2304.11335v1 | https://arxiv.org/pdf/2304.11335v1.pdf | Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers | Current arbitrary style transfer models are limited to either image or video domains. In order to achieve satisfying image and video style transfers, two different models are inevitably required with separate training processes on image and video domains, respectively. In this paper, we show that this can be precluded ... | ['Libo Zhang', 'Heng Fan', 'Bohai Gu'] | 2023-04-22 | null | null | null | null | ['style-transfer', 'video-style-transfer'] | ['computer-vision', 'computer-vision'] | [ 3.71228814e-01 -1.86286584e-01 -1.18951261e-01 -3.12745124e-01
-6.87550306e-01 -5.16016781e-01 6.38917685e-01 -6.30806506e-01
-2.79621661e-01 8.20929229e-01 8.13897625e-02 -1.59202158e-01
1.99106008e-01 -6.01008654e-01 -8.99986923e-01 -7.79180646e-01
4.79994953e-01 1.03463091e-01 3.63042831e-01 -3.43764424... | [11.317412376403809, -0.7088479399681091] |
a1f80398-b779-4c84-9e6d-b5d51ce132e5 | depth-estimation-in-nighttime-using-stereo | 1909.13701 | null | https://arxiv.org/abs/1909.13701v2 | https://arxiv.org/pdf/1909.13701v2.pdf | Nighttime Stereo Depth Estimation using Joint Translation-Stereo Learning: Light Effects and Uninformative Regions | Nighttime stereo depth estimation is still challenging, as assumptions associated with daytime lighting conditions do not hold any longer. Nighttime is not only about low-light and dense noise, but also about glow/glare, flares, non-uniform distribution of light, etc. One of the possible solutions is to train a network... | ['Lionel Heng', 'Loong-Fah Cheong', 'Robby T. Tan', 'Aashish Sharma'] | 2019-09-30 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 3.06441605e-01 -1.28827721e-01 4.07110810e-01 -6.02016032e-01
-4.96042997e-01 -6.84343755e-01 4.62303311e-01 -6.53547585e-01
-2.49716058e-01 1.11222064e+00 2.20345676e-01 -2.11090177e-01
3.28826249e-01 -9.01757777e-01 -8.12704921e-01 -1.01449084e+00
3.92775834e-01 1.77106142e-01 2.43793219e-01 -4.50876683... | [9.81088924407959, -2.9204325675964355] |
f4b9577f-e7ee-4109-96a9-d810bc545d12 | bayesian-optimisation-for-robust-model | 2203.00551 | null | https://arxiv.org/abs/2203.00551v3 | https://arxiv.org/pdf/2203.00551v3.pdf | Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty | We propose an adaptive optimisation approach for tuning stochastic model predictive control (MPC) hyper-parameters while jointly estimating probability distributions of the transition model parameters based on performance rewards. In particular, we develop a Bayesian optimisation (BO) algorithm with a heteroscedastic n... | ['Fabio Ramos', 'Rafael Oliveira', 'Rel Guzman'] | 2022-03-01 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.77495414e-01 3.58145684e-02 -1.18466884e-01 1.46823883e-01
-6.44254148e-01 -5.05384505e-01 6.66221857e-01 -2.95245111e-01
-4.90026385e-01 1.01346052e+00 -3.40893082e-02 -3.99457477e-02
-8.16435695e-01 -6.00366414e-01 -7.70631611e-01 -1.04857278e+00
-1.26610473e-01 7.86375463e-01 2.09707722e-01 1.08320273... | [4.906641483306885, 2.3784332275390625] |
e0f91631-79c9-401a-a95c-58c29479dc4c | neural-networks-for-stock-price-prediction | 1805.11317 | null | http://arxiv.org/abs/1805.11317v1 | http://arxiv.org/pdf/1805.11317v1.pdf | Neural networks for stock price prediction | Due to the extremely volatile nature of financial markets, it is commonly
accepted that stock price prediction is a task full of challenge. However in
order to make profits or understand the essence of equity market, numerous
market participants or researchers try to forecast stock price using various
statistical, econ... | ['Ren-Jie Han', 'Yu-Long Zhou', 'Yue-Gang Song'] | 2018-05-29 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-6.72613084e-01 -4.16966945e-01 -2.54177392e-01 -1.78113297e-01
1.01631284e-01 -4.79707837e-01 4.80391234e-01 -3.07984620e-01
-2.63826311e-01 1.18199909e+00 -3.22697163e-02 -8.98041725e-01
-3.25756490e-01 -1.02058899e+00 -1.27001852e-01 -5.23392737e-01
-1.34160504e-01 2.57396460e-01 5.06760031e-02 -5.31600237... | [4.570211887359619, 4.168581485748291] |
66ab6fd1-13ce-44b7-9012-68ae6efa507d | pages-iai-uni-bonn-de-https-pages-iai-uni | null | null | https://pages.iai.uni-bonn.de/gall_juergen/download/jgall_forecastintention_3dv21.pdf | https://pages.iai.uni-bonn.de/gall_juergen/download/jgall_forecastintention_3dv21.pdf | Intention-based Long-Term Human Motion Anticipation | Recently, a few works have been proposed to model the
uncertainty of the future human motion. These works do
not forecast a single sequence but multiple sequences for
the same observation. While these works focused on in-
creasing the diversity, this work focuses on keeping a high
quality of the forecast sequences... | ['Juergen Gall', 'Chintan Zaveri', 'Julian Tanke'] | 2021-12-01 | null | null | null | 3dv-2021-12 | ['human-pose-forecasting'] | ['computer-vision'] | [-1.27669588e-01 2.31267229e-01 5.57228662e-02 -3.34538192e-01
-3.54724139e-01 -1.52785361e-01 9.38021302e-01 1.12198897e-01
-3.90811503e-01 8.78335059e-01 4.63510901e-01 9.69669670e-02
1.84896782e-01 -6.29234672e-01 -3.13405693e-01 -2.80230552e-01
-8.18795115e-02 5.78524888e-01 8.60978186e-01 -3.44855398... | [7.340800762176514, -0.05529000237584114] |
d4b31ec3-df03-4d1e-a3d1-401e4d7b15aa | the-kfiou-loss-for-rotated-object-detection-1 | 2201.12558 | null | https://arxiv.org/abs/2201.12558v6 | https://arxiv.org/pdf/2201.12558v6.pdf | The KFIoU Loss for Rotated Object Detection | Differing from the well-developed horizontal object detection area whereby the computing-friendly IoU based loss is readily adopted and well fits with the detection metrics. In contrast, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. In this pap... | ['Jirui Yang', 'Qi Tian', 'Xiaopeng Zhang', 'Junchi Yan', 'Wentao Wang', 'Gefan Zhang', 'Yue Zhou', 'Xue Yang'] | 2022-01-29 | the-kfiou-loss-for-rotated-object-detection | https://openreview.net/forum?id=B9LUI0pZFGc | https://openreview.net/pdf?id=B9LUI0pZFGc | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-1.49493501e-01 4.65242518e-03 -5.02277119e-03 -1.97034195e-01
-5.02587199e-01 -4.25711423e-01 5.20142674e-01 1.13864444e-01
-5.82735538e-01 3.23572069e-01 -3.18082899e-01 -1.41828150e-01
-7.66038597e-02 -6.93323135e-01 -6.45482063e-01 -8.04636836e-01
-4.07790532e-03 2.02160060e-01 6.07890129e-01 -2.16171011... | [8.586236000061035, -0.8714494705200195] |
aef0b466-1b32-401f-95af-8788705d95ce | suggesting-relevant-questions-for-a-query | 2204.12069 | null | https://arxiv.org/abs/2204.12069v1 | https://arxiv.org/pdf/2204.12069v1.pdf | Suggesting Relevant Questions for a Query Using Statistical Natural Language Processing Technique | Suggesting similar questions for a user query has many applications ranging from reducing search time of users on e-commerce websites, training of employees in companies to holistic learning for students. The use of Natural Language Processing techniques for suggesting similar questions is prevalent over the existing a... | ['Onkar Litake', 'Sheetal Sonawane', 'Archana Ghotkar', 'Hrushabh Hirudkar', 'Anuj Kanetkar', 'Shriniwas Nayak'] | 2022-04-26 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 1.31874084e-01 -4.37922515e-02 -1.15162559e-01 -5.58351099e-01
-8.22978854e-01 -5.64103127e-01 6.45482898e-01 9.07074273e-01
-4.89849538e-01 3.77901077e-01 3.86773169e-01 -2.49976918e-01
-1.03564978e+00 -6.37766123e-01 -1.15521505e-01 -8.09306279e-02
1.77762821e-01 5.02045870e-01 6.95356786e-01 -4.98996526... | [10.54991626739502, 7.612081527709961] |
b779652e-83cb-4c9e-bfa2-7694b3e250ea | self-chained-image-language-model-for-video | 2305.06988 | null | https://arxiv.org/abs/2305.06988v1 | https://arxiv.org/pdf/2305.06988v1.pdf | Self-Chained Image-Language Model for Video Localization and Question Answering | Recent studies have shown promising results on utilizing pre-trained image-language models for video question answering. While these image-language models can efficiently bootstrap the representation learning of video-language models, they typically concatenate uniformly sampled video frames as visual inputs without ex... | ['Mohit Bansal', 'Prateek Yadav', 'Jaemin Cho', 'Shoubin Yu'] | 2023-05-11 | null | null | null | null | ['moment-retrieval', 'video-question-answering'] | ['computer-vision', 'computer-vision'] | [-9.31785032e-02 -3.56666803e-01 -4.42656040e-01 -2.25184575e-01
-1.28261375e+00 -7.87599385e-01 4.06169444e-01 4.01309580e-02
-5.68200171e-01 3.81747991e-01 2.99977124e-01 -1.90290064e-01
3.93035382e-01 -5.04761815e-01 -1.11220539e+00 -3.97069633e-01
5.69905266e-02 2.93760210e-01 7.00135946e-01 1.35428295... | [10.152024269104004, 0.8215510249137878] |
85e3e0bb-72fe-4e75-9486-c1d033a213c3 | unsupervised-extractive-summarization-by | 2104.08392 | null | https://arxiv.org/abs/2104.08392v1 | https://arxiv.org/pdf/2104.08392v1.pdf | Unsupervised Extractive Summarization by Human Memory Simulation | Summarization systems face the core challenge of identifying and selecting important information. In this paper, we tackle the problem of content selection in unsupervised extractive summarization of long, structured documents. We introduce a wide range of heuristics that leverage cognitive representations of content u... | ['Shay B. Cohen', 'Matthias Galle', 'Ronald Cardenas'] | 2021-04-16 | null | null | null | null | ['unsupervised-extractive-summarization'] | ['natural-language-processing'] | [ 5.54282367e-01 6.20905280e-01 -5.40911973e-01 -1.27940504e-02
-8.60331893e-01 -7.07074404e-01 5.14185786e-01 1.12204480e+00
-3.51180762e-01 1.10848534e+00 1.19137096e+00 -2.14807652e-02
-3.46419930e-01 -5.88113189e-01 -5.80737352e-01 -2.54835397e-01
-1.10405728e-01 3.89580578e-01 8.04369152e-02 -5.64083271... | [12.516242027282715, 9.496461868286133] |
4c4c83b7-46e1-4f2d-8145-319c2a9fb4e5 | deblurgan-v2-deblurring-orders-of-magnitude | 1908.03826 | null | https://arxiv.org/abs/1908.03826v1 | https://arxiv.org/pdf/1908.03826v1.pdf | DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better | We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility. DeblurGAN-v2 is based on a relativistic conditional GAN with a double-scale discriminator. For the first ti... | ['Junru Wu', 'Tetiana Martyniuk', 'Zhangyang Wang', 'Orest Kupyn'] | 2019-08-10 | deblurgan-v2-deblurring-orders-of-magnitude-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Kupyn_DeblurGAN-v2_Deblurring_Orders-of-Magnitude_Faster_and_Better_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Kupyn_DeblurGAN-v2_Deblurring_Orders-of-Magnitude_Faster_and_Better_ICCV_2019_paper.pdf | iccv-2019-10 | ['single-image-blind-deblurring', 'blind-face-restoration'] | ['computer-vision', 'computer-vision'] | [-8.37612674e-02 -2.85911292e-01 -1.71974152e-01 2.35580772e-01
-8.50447237e-01 -6.11884534e-01 5.54983795e-01 -1.07788181e+00
-3.17064002e-02 6.57021284e-01 5.66176355e-01 -2.81255305e-01
1.90463290e-01 -6.77335441e-01 -7.64654994e-01 -9.05801833e-01
1.59452781e-01 2.26679686e-02 1.63685337e-01 -3.58116746... | [11.43323802947998, -2.300601005554199] |
74db058d-1bc4-4dd7-9400-c9ac8247a213 | initiative-defense-against-facial | 2112.10098 | null | https://arxiv.org/abs/2112.10098v1 | https://arxiv.org/pdf/2112.10098v1.pdf | Initiative Defense against Facial Manipulation | Benefiting from the development of generative adversarial networks (GAN), facial manipulation has achieved significant progress in both academia and industry recently. It inspires an increasing number of entertainment applications but also incurs severe threats to individual privacy and even political security meanwhil... | ['Nenghai Yu', 'WeimingZhang', 'Wenbo Zhou', 'Jie Zhang', 'Qidong Huang'] | 2021-12-19 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 5.96494973e-01 1.12137787e-01 -1.23882130e-01 -9.78611857e-02
-3.77547622e-01 -1.02094889e+00 6.29656017e-01 -5.48276246e-01
-4.79990244e-02 5.99614620e-01 -1.47524089e-01 -1.13146998e-01
2.63801962e-01 -9.85852063e-01 -6.97698414e-01 -9.91902471e-01
2.48499230e-01 -7.52166882e-02 -7.09608793e-02 -1.72189146... | [12.78032112121582, 0.9016807079315186] |
f4b49d63-177b-468d-8858-8174a05823b8 | topics-to-avoid-demoting-latent-confounds-in | 1909.00453 | null | https://arxiv.org/abs/1909.00453v2 | https://arxiv.org/pdf/1909.00453v2.pdf | Topics to Avoid: Demoting Latent Confounds in Text Classification | Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. In this work, we observe this limitation with respect to the task of native language identification. We find that s... | ['Noah A. Smith', 'Yulia Tsvetkov', 'Shuly Wintner', 'Sachin Kumar'] | 2019-09-01 | topics-to-avoid-demoting-latent-confounds-in-1 | https://aclanthology.org/D19-1425 | https://aclanthology.org/D19-1425.pdf | ijcnlp-2019-11 | ['native-language-identification'] | ['natural-language-processing'] | [ 3.81238729e-01 2.10150495e-01 -4.99726772e-01 -6.75667822e-01
-5.43617010e-01 -8.94187212e-01 9.15797353e-01 1.41228110e-01
-4.85135823e-01 7.57743001e-01 5.60027719e-01 -4.78624851e-01
2.37808615e-01 -7.20129907e-01 -7.45345891e-01 -5.23021281e-01
2.78161198e-01 6.29876316e-01 -3.99145484e-01 1.61310315... | [10.548357963562012, 10.137763023376465] |
8809ac9a-2b0e-47f4-b2e8-ada2a279d684 | switchhit-a-probabilistic-complementarity | 2203.00591 | null | https://arxiv.org/abs/2203.00591v1 | https://arxiv.org/pdf/2203.00591v1.pdf | SwitchHit: A Probabilistic, Complementarity-Based Switching System for Improved Visual Place Recognition in Changing Environments | Visual place recognition (VPR), a fundamental task in computer vision and robotics, is the problem of identifying a place mainly based on visual information. Viewpoint and appearance changes, such as due to weather and seasonal variations, make this task challenging. Currently, there is no universal VPR technique that ... | ['Shoaib Ehsan', 'Klaus McDonald-Maier', 'Michael Milford', 'Maria Waheed'] | 2022-03-01 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 7.79349580e-02 -3.92906129e-01 -1.50911257e-01 -1.55411452e-01
-4.65607941e-01 -6.71555340e-01 5.85060537e-01 2.64535099e-01
-4.85057920e-01 6.52000189e-01 -6.25083983e-01 -3.95510584e-01
-1.92262501e-01 -8.72396350e-01 -7.84112513e-01 -6.58667445e-01
-5.41014224e-02 7.22956002e-01 9.60451126e-01 -3.29842746... | [7.459722995758057, -1.91754949092865] |
7fb71003-6586-414d-98df-c5663623f75d | mplug-docowl-modularized-multimodal-large | 2307.02499 | null | https://arxiv.org/abs/2307.02499v1 | https://arxiv.org/pdf/2307.02499v1.pdf | mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding | Document understanding refers to automatically extract, analyze and comprehend information from various types of digital documents, such as a web page. Existing Multi-model Large Language Models (MLLMs), including mPLUG-Owl, have demonstrated promising zero-shot capabilities in shallow OCR-free text recognition, indica... | ['Fei Huang', 'Ji Zhang', 'Qian Qi', 'Junfeng Tian', 'Chenliang Li', 'Guohai Xu', 'Chenlin Zhao', 'Yuhao Dan', 'Ming Yan', 'Qinghao Ye', 'Haiyang Xu', 'Anwen Hu', 'Jiabo Ye'] | 2023-07-04 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 1.65450305e-01 -3.46559733e-01 -4.29147691e-01 -4.36954379e-01
-8.66242051e-01 -6.92311287e-01 5.65450609e-01 2.60646850e-01
-2.86331266e-01 2.22173110e-01 1.70690179e-01 -8.15343380e-01
8.21822602e-03 -5.25705278e-01 -8.58545184e-01 -1.97074801e-01
6.17866993e-01 3.56742531e-01 2.31741846e-01 -3.05832893... | [11.445191383361816, 2.158355712890625] |
f4c9c4b4-517a-4c19-830d-23c687f63606 | bubblerank-safe-online-learning-to-rerank | 1806.05819 | null | https://arxiv.org/abs/1806.05819v2 | https://arxiv.org/pdf/1806.05819v2.pdf | BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback | In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists. Learning to rank has traditionally been studied in two settings. In the offline setting, rankers are typically learned from relevance labels created by judges. This approach has... | ['Maarten de Rijke', 'Csaba Szepesvari', 'Branislav Kveton', 'Masrour Zoghi', 'Ilya Markov', 'Tor Lattimore', 'Chang Li'] | 2018-06-15 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.11381337e-01 -1.93648580e-02 -7.70577371e-01 -3.76174331e-01
-1.24382043e+00 -9.97730970e-01 9.15901661e-02 4.25781786e-01
-6.10851228e-01 9.22798574e-01 1.03644200e-01 -4.72357512e-01
-6.99203432e-01 -6.27303839e-01 -9.82618213e-01 -5.56269407e-01
-3.58867735e-01 8.96756470e-01 1.70130551e-01 -1.91821098... | [4.638023376464844, 3.3294436931610107] |
d3a7ac7b-bb90-4da0-ad3d-ad0fc6d2ba71 | sharp-deviations-bounds-for-dirichlet | 2304.03056 | null | https://arxiv.org/abs/2304.03056v1 | https://arxiv.org/pdf/2304.03056v1.pdf | Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms | In this work, we derive sharp non-asymptotic deviation bounds for weighted sums of Dirichlet random variables. These bounds are based on a novel integral representation of the density of a weighted Dirichlet sum. This representation allows us to obtain a Gaussian-like approximation for the sum distribution using geomet... | ['Michal Valko', 'Daniil Tiapkin', 'Alexey Naumov', 'Pierre Menard', 'Denis Belomestny'] | 2023-04-06 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 1.09264374e-01 3.72290969e-01 -3.69563580e-01 -1.86107680e-01
-8.18790853e-01 -6.95927203e-01 2.69217253e-01 1.43685371e-01
-3.20879072e-01 1.17032325e+00 8.97012055e-02 -5.02570450e-01
-5.40935636e-01 -8.89620960e-01 -7.28041708e-01 -9.23893929e-01
1.63162246e-01 8.71711493e-01 1.03985250e-01 -5.71813583... | [4.603140354156494, 3.324242353439331] |
aab7b9d9-3f82-4258-9177-317fdcfdbb43 | ecg-beats-classification-via-online-sparse | 2008.06672 | null | https://arxiv.org/abs/2008.06672v1 | https://arxiv.org/pdf/2008.06672v1.pdf | ECG beats classification via online sparse dictionary and time pyramid matching | Recently, the Bag-Of-Word (BOW) algorithm provides efficient features and promotes the accuracy of the ECG classification system. However, BOW algorithm has two shortcomings: (1). it has large quantization errors and poor reconstruction performance; (2). it loses heart beat's time information, and may provide confusing... | ['Nanyu Li', 'Duo Deng', 'Chunyu Yuan', 'Yujuan Si'] | 2020-08-15 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 7.83929499e-05 -8.84738922e-01 -1.19590893e-01 -1.41183302e-01
-4.42127347e-01 -2.40366030e-02 -3.38653088e-01 4.75621670e-01
-3.04602921e-01 2.67980963e-01 2.40379289e-01 8.17087516e-02
-2.27480873e-01 -1.05268896e+00 2.17354849e-01 -1.01422918e+00
1.95594244e-02 9.02952105e-02 3.78018796e-01 -1.49855554... | [14.183272361755371, 3.2294209003448486] |
2068f1ee-e1c8-44a3-b012-04b197b0f4ee | emotion-experiencer-recognition-as-a | 2305.16731 | null | https://arxiv.org/abs/2305.16731v2 | https://arxiv.org/pdf/2305.16731v2.pdf | Automatic Emotion Experiencer Recognition | The most prominent subtask in emotion analysis is emotion classification; to assign a category to a textual unit, for instance a social media post. Many research questions from the social sciences do, however, not only require the detection of the emotion of an author of a post but to understand who is ascribed an emot... | ['Roman Klinger', 'Maximilian Wegge'] | 2023-05-26 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 8.36932138e-02 2.75586337e-01 1.74242958e-01 -4.69764233e-01
-4.66981441e-01 -7.85819054e-01 6.82671010e-01 6.41502202e-01
-6.45715773e-01 5.37476122e-01 4.12211508e-01 -6.20602779e-02
2.61286438e-01 -5.16103983e-01 -1.59811988e-01 -4.65301722e-01
2.32894421e-01 2.43722752e-01 -3.44117880e-01 -1.69398814... | [12.662830352783203, 6.340439319610596] |
8a1251e6-3e65-4910-8fbf-35dbc785870c | multilingual-named-entity-recognition-using-1 | 1906.09978 | null | https://arxiv.org/abs/1906.09978v1 | https://arxiv.org/pdf/1906.09978v1.pdf | Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF | In this paper we tackle multilingual named entity recognition task. We use the BERT Language Model as embeddings with bidirectional recurrent network, attention, and NCRF on the top. We apply multilingual BERT only as embedder without any fine-tuning. We test out model on the dataset of the BSNLP shared task, which con... | ['Ekaterina Artemova', 'Anton A. Emelyanov'] | 2019-06-21 | multilingual-named-entity-recognition-using-2 | https://aclanthology.org/W19-3713 | https://aclanthology.org/W19-3713.pdf | ws-2019-8 | ['multilingual-text-classification', 'joint-ner-and-classification', 'multilingual-named-entity-recognition'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [-7.16090679e-01 1.29895866e-01 -1.86724126e-01 -2.89328873e-01
-6.31783843e-01 -7.34163523e-01 7.21949637e-01 1.93463769e-02
-1.21675241e+00 9.64164019e-01 7.07473218e-01 -7.90665627e-01
3.30448329e-01 -4.64029968e-01 -7.41449058e-01 -3.27353209e-01
3.40918377e-02 7.06949115e-01 7.53250718e-02 -1.95308506... | [9.891218185424805, 9.775591850280762] |
9153510e-f19b-4399-9667-f079c8102303 | towards-offensive-language-identification-for | null | null | https://aclanthology.org/2021.dravidianlangtech-1.3 | https://aclanthology.org/2021.dravidianlangtech-1.3.pdf | Towards Offensive Language Identification for Dravidian Languages | Offensive speech identification in countries like India poses several challenges due to the usage of code-mixed and romanized variants of multiple languages by the users in their posts on social media. The challenge of offensive language identification on social media for Dravidian languages is harder, considering the ... | ['Yashvardhan Sharma', 'Siva Sai'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['transliteration'] | ['natural-language-processing'] | [-1.68366060e-01 -2.63682336e-01 -2.96123058e-01 -7.74507001e-02
-1.37276697e+00 -9.48036790e-01 5.88805079e-01 -3.22044700e-01
-5.06009400e-01 3.21705371e-01 4.86792326e-01 -7.58162856e-01
4.07985486e-02 -2.34562844e-01 -1.89533979e-01 -3.47289234e-01
-3.68932746e-02 4.76917744e-01 -2.51182407e-01 -6.16442680... | [9.2388916015625, 10.532865524291992] |
7711ecf3-1652-4691-be25-e52d4238617b | sttracker-spatio-temporal-tracker-for-3d | 2306.17440 | null | https://arxiv.org/abs/2306.17440v1 | https://arxiv.org/pdf/2306.17440v1.pdf | STTracker: Spatio-Temporal Tracker for 3D Single Object Tracking | 3D single object tracking with point clouds is a critical task in 3D computer vision. Previous methods usually input the last two frames and use the predicted box to get the template point cloud in previous frame and the search area point cloud in the current frame respectively, then use similarity-based or motion-base... | ['Zheng Fang', 'Zhiheng Li', 'Yubo Cui'] | 2023-06-30 | null | null | null | null | ['object-tracking', '3d-single-object-tracking'] | ['computer-vision', 'computer-vision'] | [-5.42871617e-02 -8.35105002e-01 -6.87445924e-02 -2.88108103e-02
-5.35194278e-01 -4.79818612e-01 4.78092223e-01 -9.95077118e-02
-3.79538119e-01 2.36566827e-01 -2.24177614e-01 1.41953155e-01
1.74926743e-01 -6.91790760e-01 -7.54166722e-01 -8.60164523e-01
-5.33576347e-02 3.81955594e-01 9.87742841e-01 -4.53100093... | [6.602785110473633, -2.336766004562378] |
354ca205-df2d-40ac-b806-a200067ab008 | em-pre-training-for-multi-party-dialogue | 2305.12412 | null | https://arxiv.org/abs/2305.12412v1 | https://arxiv.org/pdf/2305.12412v1.pdf | EM Pre-training for Multi-party Dialogue Response Generation | Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time. Different from two-party dialogues where each response is a direct reply to i... | ['Hai Zhao', 'Yiyang Li'] | 2023-05-21 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 4.73765761e-01 6.93218291e-01 1.74799293e-01 -6.53448403e-01
-9.95935202e-01 -6.56966686e-01 9.86090183e-01 -3.74823213e-02
-3.11736524e-01 9.85953987e-01 6.83745086e-01 -3.28946531e-01
2.44680524e-01 -8.53111207e-01 2.16817409e-01 -4.76086706e-01
4.90283459e-01 8.49837542e-01 2.93482449e-02 -7.78736174... | [12.786247253417969, 8.0309476852417] |
41577c19-1ebf-42fa-8ca5-eb68e6cc63d2 | layered-stereo-by-cooperative-grouping-with | 2006.16094 | null | https://arxiv.org/abs/2006.16094v3 | https://arxiv.org/pdf/2006.16094v3.pdf | Level Set Stereo for Cooperative Grouping with Occlusion | Localizing stereo boundaries is difficult because matching cues are absent in the occluded regions that are adjacent to them. We introduce an energy and level-set optimizer that improves boundaries by encoding the essential geometry of occlusions: The spatial extent of an occlusion must equal the amplitude of the dispa... | ['Todd Zickler', 'Jialiang Wang'] | 2020-06-29 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 2.34630302e-01 -4.05559093e-02 -2.68767446e-01 -5.45523822e-01
-5.39939404e-01 -4.71533895e-01 1.65816650e-01 1.06276326e-01
-2.35779896e-01 8.97769153e-01 7.27552831e-01 -1.17439047e-01
2.02709034e-01 -6.61626577e-01 -7.80184269e-01 -2.06904262e-01
-8.14128518e-02 2.54208803e-01 6.36848092e-01 -3.06728870... | [8.864907264709473, -2.4267330169677734] |
91cd6695-f378-44eb-bfa7-0377cf7211fe | multilingual-unsupervised-sentence | 2005.00352 | null | https://arxiv.org/abs/2005.00352v2 | https://arxiv.org/pdf/2005.00352v2.pdf | MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases | Progress in sentence simplification has been hindered by a lack of labeled parallel simplification data, particularly in languages other than English. We introduce MUSS, a Multilingual Unsupervised Sentence Simplification system that does not require labeled simplification data. MUSS uses a novel approach to sentence s... | ['Benoît Sagot', 'Éric de la Clergerie', 'Angela Fan', 'Louis Martin', 'Antoine Bordes'] | 2020-05-01 | null | https://aclanthology.org/2022.lrec-1.176 | https://aclanthology.org/2022.lrec-1.176.pdf | lrec-2022-6 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 3.59047055e-02 2.56495953e-01 -1.74776822e-01 -6.70125902e-01
-9.93118465e-01 -6.26985312e-01 5.60912371e-01 7.06716657e-01
-7.74965167e-01 8.41104388e-01 8.92201126e-01 -3.29652727e-01
1.16764039e-01 -6.86847031e-01 -6.32245958e-01 4.92204390e-02
5.07525682e-01 8.01153481e-01 -3.86608511e-01 -9.76709008... | [11.07859992980957, 10.303961753845215] |
e74e92ec-b2cf-40ed-8171-5fd5f915c58b | scale-guided-hypernetwork-for-blind-super | 2306.02398 | null | https://arxiv.org/abs/2306.02398v1 | https://arxiv.org/pdf/2306.02398v1.pdf | Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment | With the emergence of image super-resolution (SR) algorithm, how to blindly evaluate the quality of super-resolution images has become an urgent task. However, existing blind SR image quality assessment (IQA) metrics merely focus on visual characteristics of super-resolution images, ignoring the available scale informa... | ['Jun Fu'] | 2023-06-04 | null | null | null | null | ['image-super-resolution', 'image-quality-assessment', 'super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.75483599e-01 -4.12828565e-01 4.46524285e-02 -2.71819174e-01
-7.32347906e-01 -3.87740344e-01 2.16909885e-01 -2.58870244e-01
-2.19367206e-01 4.51526821e-01 5.33204973e-01 -1.78407416e-01
-4.49276865e-01 -8.37560713e-01 -1.72696739e-01 -6.53440237e-01
1.99644774e-01 -2.64719218e-01 3.61313403e-01 -2.55004048... | [11.753117561340332, -1.939570665359497] |
6095fec7-28e6-4de0-b544-8286f111e4bd | large-scale-long-tailed-recognition-in-an | 1904.05160 | null | http://arxiv.org/abs/1904.05160v2 | http://arxiv.org/pdf/1904.05160v2.pdf | Large-Scale Long-Tailed Recognition in an Open World | Real world data often have a long-tailed and open-ended distribution. A
practical recognition system must classify among majority and minority classes,
generalize from a few known instances, and acknowledge novelty upon a never
seen instance. We define Open Long-Tailed Recognition (OLTR) as learning from
such naturally... | ['Zhongqi Miao', 'Jiayun Wang', 'Boqing Gong', 'Ziwei Liu', 'Xiaohang Zhan', 'Stella X. Yu'] | 2019-04-10 | large-scale-long-tailed-recognition-in-an-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Large-Scale_Long-Tailed_Recognition_in_an_Open_World_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Large-Scale_Long-Tailed_Recognition_in_an_Open_World_CVPR_2019_paper.pdf | cvpr-2019-6 | ['long-tail-learning-with-class-descriptors'] | ['methodology'] | [ 2.64514443e-02 -2.64867514e-01 -4.23379272e-01 -6.57070756e-01
-7.44302690e-01 -5.51470637e-01 6.04851305e-01 8.49636123e-02
-2.57569075e-01 3.05431843e-01 1.95693672e-01 1.39656335e-01
-2.55462110e-01 -6.29083395e-01 -5.25242805e-01 -5.73995411e-01
-9.49223712e-03 5.58949888e-01 -1.67995781e-01 1.02515921... | [9.632003784179688, 2.7644848823547363] |
168daf59-ec3c-4e87-ab14-213025d66c63 | compressing-video-calls-using-synthetic | 2210.03692 | null | https://arxiv.org/abs/2210.03692v1 | https://arxiv.org/pdf/2210.03692v1.pdf | Compressing Video Calls using Synthetic Talking Heads | We leverage the modern advancements in talking head generation to propose an end-to-end system for talking head video compression. Our algorithm transmits pivot frames intermittently while the rest of the talking head video is generated by animating them. We use a state-of-the-art face reenactment network to detect key... | ['C V Jawahar', 'Vinay P. Namboodiri', 'Rudrabha Mukhopadhyay', 'Anchit Gupta', 'Madhav Agarwal'] | 2022-10-07 | null | null | null | null | ['talking-head-generation', 'face-reenactment'] | ['computer-vision', 'computer-vision'] | [ 8.55101496e-02 5.36898114e-02 -1.39751792e-01 -3.68208408e-01
-7.12628782e-01 -2.30628848e-01 3.09972703e-01 -3.41866940e-01
-6.46232218e-02 4.41306472e-01 8.18541348e-01 1.37976453e-01
1.04819819e-01 -6.73906982e-01 -6.12156749e-01 -8.28326881e-01
-1.39037207e-01 3.55036616e-01 2.17213020e-01 -1.88533887... | [13.207052230834961, -0.4774235785007477] |
fdecc07f-0453-4aa3-afae-493417a19b88 | transferrable-end-to-end-learning-for-protein-1 | null | null | https://openreview.net/forum?id=SkgToo0qFm | https://openreview.net/pdf?id=SkgToo0qFm | Transferrable End-to-End Learning for Protein Interface Prediction | While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question. In this work we demonstrate that representations learned from raw atomic coordinates can outperfor... | ['Ron O. Dror', 'Rishi Bedi', 'Raphael J. L. Townshend'] | 2018-09-27 | null | null | null | null | ['protein-interface-prediction'] | ['miscellaneous'] | [ 4.97024655e-01 2.21780241e-01 3.69877182e-02 -3.79711270e-01
-8.60734344e-01 -5.61247885e-01 4.97135252e-01 3.82775605e-01
-5.29585779e-01 1.17813301e+00 -2.06796220e-03 -4.11857724e-01
1.94015920e-01 -6.85054302e-01 -1.27714407e+00 -8.45078111e-01
-2.70343333e-01 8.52888644e-01 4.84958172e-01 -4.32441264... | [4.853334426879883, 5.645125389099121] |
556bb972-7620-4637-ab2a-83e20f492a2c | editing-text-in-the-wild | 1908.03047 | null | https://arxiv.org/abs/1908.03047v1 | https://arxiv.org/pdf/1908.03047v1.pdf | Editing Text in the Wild | In this paper, we are interested in editing text in natural images, which aims to replace or modify a word in the source image with another one while maintaining its realistic look. This task is challenging, as the styles of both background and text need to be preserved so that the edited image is visually indistinguis... | ['Xiang Bai', 'Chengquan Zhang', 'Junyu Han', 'Errui Ding', 'Liang Wu', 'Jingtuo Liu', 'Jiaming Liu'] | 2019-08-08 | null | null | null | null | ['scene-text-editing'] | ['computer-vision'] | [ 9.51839328e-01 -5.44122458e-02 2.26020172e-01 -1.33821398e-01
-2.57363886e-01 -4.03417677e-01 4.68435317e-01 -1.17748275e-01
-3.62836510e-01 8.05806458e-01 1.31509408e-01 -1.62476093e-01
4.25591081e-01 -7.72414267e-01 -8.71236682e-01 -7.21032023e-01
7.23129213e-01 2.87727583e-02 2.06373870e-01 -2.64026880... | [11.542257308959961, -0.6181567311286926] |
ec308bb9-5324-44bd-bef5-8e7b7a2e603d | placenta-segmentation-in-ultrasound-imaging | 2206.14746 | null | https://arxiv.org/abs/2206.14746v1 | https://arxiv.org/pdf/2206.14746v1.pdf | Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View | Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In... | ['Julia A. Schnabel', 'Joseph V. Hajnal', 'Daniel Rueckert', 'Bernhard Kainz', 'Jacqueline Matthew', 'Karen Lloyd', 'Nooshin Ghavami', 'Shujie Deng', 'Gavin Wheeler', 'Robert Wright', 'Emily Skelton', 'Alberto Gomez', 'Veronika A. Zimmer'] | 2022-06-29 | null | null | null | null | ['placenta-segmentation'] | ['medical'] | [ 3.66511136e-01 4.39803243e-01 3.77720088e-01 -5.56312263e-01
-1.18752456e+00 -1.17950344e+00 7.38971010e-02 4.48828757e-01
-2.90651709e-01 2.49592692e-01 -1.17651843e-01 -4.52500612e-01
-9.39173400e-02 -5.90946853e-01 -8.94002318e-01 -6.83326185e-01
-4.55954522e-02 6.02306724e-01 4.01471734e-01 3.58824819... | [14.254617691040039, -2.4278461933135986] |
9db3b3b1-18f9-48c9-895f-aade9b6c3ec7 | smoothed-multi-view-subspace-clustering | 2106.09875 | null | https://arxiv.org/abs/2106.09875v1 | https://arxiv.org/pdf/2106.09875v1.pdf | Smoothed Multi-View Subspace Clustering | In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation across multiple views. However, multi-view data can be very complicated and are not easy to cluster in real-world applications. Most existing methods operate on raw data and may not o... | ['Zhao Kang', 'Zhengrui Ma', 'Liang Liu', 'Peng Chen'] | 2021-06-18 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.62310058e-01 -5.26560485e-01 -7.22565576e-02 -1.60890386e-01
-5.00822663e-01 -7.41883755e-01 4.15394098e-01 1.73395798e-02
1.53064325e-01 1.63284525e-01 3.22257847e-01 8.28919932e-02
-2.89603770e-01 -5.92204928e-01 -2.16493741e-01 -1.11401916e+00
2.65906572e-01 4.02054600e-02 2.15844333e-01 9.06965509... | [8.17601490020752, 4.613504409790039] |
a2d9ff5d-9ca6-41ba-9a2a-5c6013bd5503 | phenaki-variable-length-video-generation-from | 2210.02399 | null | https://arxiv.org/abs/2210.02399v1 | https://arxiv.org/pdf/2210.02399v1.pdf | Phenaki: Variable Length Video Generation From Open Domain Textual Description | We present Phenaki, a model capable of realistic video synthesis, given a sequence of textual prompts. Generating videos from text is particularly challenging due to the computational cost, limited quantities of high quality text-video data and variable length of videos. To address these issues, we introduce a new mode... | ['Dumitru Erhan', 'Julius Kunze', 'Santiago Castro', 'Mohammad Taghi Saffar', 'Han Zhang', 'Hernan Moraldo', 'Pieter-Jan Kindermans', 'Mohammad Babaeizadeh', 'Ruben Villegas'] | 2022-10-05 | null | null | null | null | ['video-generation', 'video-prediction'] | ['computer-vision', 'computer-vision'] | [ 6.53858066e-01 1.53274417e-01 -2.80615836e-01 -1.36545852e-01
-9.69962358e-01 -6.20611846e-01 8.30669343e-01 -3.57539803e-01
-2.84080923e-01 9.30028915e-01 4.27381068e-01 -1.91913754e-01
3.22866797e-01 -5.87300181e-01 -1.26045728e+00 -5.51874042e-01
-1.06536753e-01 1.96637183e-01 1.68957189e-01 2.68655479... | [10.788999557495117, -0.27343907952308655] |
ffe8f7ab-c07c-4c31-9d21-f50eea9aa2ef | a-melody-unsupervision-model-for-singing | 2110.06546 | null | https://arxiv.org/abs/2110.06546v2 | https://arxiv.org/pdf/2110.06546v2.pdf | A Melody-Unsupervision Model for Singing Voice Synthesis | Recent studies in singing voice synthesis have achieved high-quality results leveraging advances in text-to-speech models based on deep neural networks. One of the main issues in training singing voice synthesis models is that they require melody and lyric labels to be temporally aligned with audio data. The temporal a... | ['Juhan Nam', 'Soonbeom Choi'] | 2021-10-13 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 1.08057044e-01 -2.58846898e-02 -5.10878190e-02 -4.00314927e-01
-1.14207506e+00 -7.84642816e-01 3.25158775e-01 -5.07237673e-01
8.16012674e-04 4.15170670e-01 3.20097089e-01 -2.76984483e-01
1.31037101e-01 -4.57738817e-01 -6.42380059e-01 -5.54803312e-01
1.80817574e-01 5.20597816e-01 1.00678034e-01 -2.19974115... | [15.45610237121582, 6.193839073181152] |
d480aa9c-1bf9-47a0-9437-bf27919ab486 | minimal-learning-machine-for-multi-label | 2305.05518 | null | https://arxiv.org/abs/2305.05518v1 | https://arxiv.org/pdf/2305.05518v1.pdf | Minimal Learning Machine for Multi-Label Learning | Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose methods and evaluate how this technique and its core component, the distance mapping, can be adapted to multi-label learning... | ['Tommi Kärkkäinen', 'João P. P. Gomes', 'César L. C. Mattos', 'Amauri Souza', 'Joonas Hämäläinen'] | 2023-05-09 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 3.24102879e-01 2.44650200e-01 -3.86272162e-01 -8.42689872e-01
-1.31342745e+00 -6.53257549e-01 5.71013212e-01 7.53312230e-01
-4.12764519e-01 6.33901417e-01 -4.38355833e-01 -3.24118972e-01
-7.42873669e-01 -6.03341639e-01 -3.59386981e-01 -8.14029455e-01
4.42439988e-02 8.36022794e-01 2.40228072e-01 1.62302062... | [9.312506675720215, 4.325052261352539] |
03d5d8f0-7c67-42c3-8eae-0dc10c68c7ce | robust-machine-learning-segmentation-for | 2209.02032 | null | https://arxiv.org/abs/2209.02032v2 | https://arxiv.org/pdf/2209.02032v2.pdf | Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets | Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyse such scans could transform neuroimaging research. Yet, their potential remains untapped, since no automated algorithm is robust enough to cope... | ['Juan. E. Iglesias', 'Sudeshna Das', 'Steven E. Arnold', 'You Cheng', 'Colin Magdamo', 'Benjamin Billot'] | 2022-09-05 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [ 2.98641592e-01 1.91598490e-01 3.98189604e-01 -5.95729291e-01
-8.54113817e-01 -3.20187569e-01 3.59968364e-01 3.12072545e-01
-7.40467250e-01 7.10891247e-01 1.34049922e-01 -2.06845105e-01
-1.72463134e-01 -4.79867637e-01 -5.17939806e-01 -5.06652772e-01
-4.76044178e-01 1.06777442e+00 6.30920231e-01 1.09674126... | [14.112432479858398, -2.2738490104675293] |
529de9f7-a3af-4121-a717-a1ca4d073c98 | nonparanormal-information-estimation | 1702.07803 | null | http://arxiv.org/abs/1702.07803v1 | http://arxiv.org/pdf/1702.07803v1.pdf | Nonparanormal Information Estimation | We study the problem of using i.i.d. samples from an unknown multivariate
probability distribution $p$ to estimate the mutual information of $p$. This
problem has recently received attention in two settings: (1) where $p$ is
assumed to be Gaussian and (2) where $p$ is assumed only to lie in a large
nonparametric smooth... | ['Barnabás Pøczos', 'Shashank Singh'] | 2017-02-24 | nonparanormal-information-estimation-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=805 | http://proceedings.mlr.press/v70/singh17a/singh17a.pdf | icml-2017-8 | ['mutual-information-estimation'] | ['methodology'] | [-2.94216275e-01 9.43967849e-02 -4.62447964e-02 -3.30980837e-01
-9.59113598e-01 -6.48498833e-01 2.86004335e-01 -1.93498194e-01
-2.37514257e-01 9.21542466e-01 -3.46032947e-01 -1.73047677e-01
-5.64268589e-01 -6.94372892e-01 -8.21936190e-01 -8.53217065e-01
-3.31192374e-01 6.67580843e-01 -1.23780243e-01 2.91693002... | [7.2349853515625, 4.178459644317627] |
d06593dd-5677-47f1-9b8e-b5d160dc0bd0 | ror-read-over-read-for-long-document-machine | 2109.04780 | null | https://arxiv.org/abs/2109.04780v2 | https://arxiv.org/pdf/2109.04780v2.pdf | RoR: Read-over-Read for Long Document Machine Reading Comprehension | Transformer-based pre-trained models, such as BERT, have achieved remarkable results on machine reading comprehension. However, due to the constraint of encoding length (e.g., 512 WordPiece tokens), a long document is usually split into multiple chunks that are independently read. It results in the reading field being ... | ['BoWen Zhou', 'Xiaodong He', 'Youzheng Wu', 'Yongwei Zhou', 'Yifan Wang', 'Junwei Bao', 'Jing Zhao'] | 2021-09-10 | null | https://aclanthology.org/2021.findings-emnlp.160 | https://aclanthology.org/2021.findings-emnlp.160.pdf | findings-emnlp-2021-11 | ['triviaqa'] | ['miscellaneous'] | [ 4.23857778e-01 5.68728030e-01 -2.70329297e-01 -1.53607965e-01
-1.19006681e+00 -5.33379912e-01 5.32082915e-01 5.83452106e-01
-3.46152395e-01 7.90783942e-01 7.79518723e-01 -3.80945355e-01
5.99643029e-03 -8.73919070e-01 -7.79663146e-01 -5.99445581e-01
4.79885191e-01 6.73373461e-01 2.65091896e-01 -2.61512876... | [11.437163352966309, 8.114126205444336] |
0cb0638c-a072-43fa-9882-2da810a2e27d | pst-plant-segmentation-transformer-enhanced | 2206.13082 | null | https://arxiv.org/abs/2206.13082v2 | https://arxiv.org/pdf/2206.13082v2.pdf | PST: Plant Segmentation Transformer for 3D Point Clouds of rapeseed plants at the podding stage | Segmentation of plant point clouds to obtain high-precise morphological traits is essential for plant phenotyping. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, previous studies mainly focus on the hard voxelization-based or down-sampling-based methods, ... | ['Haiyan Cen', 'Yong He', 'Pengyao Xie', 'Zhihong Ma', 'Ruiming Du'] | 2022-06-27 | null | null | null | null | ['plant-phenotyping', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision'] | [-7.13900179e-02 -1.60526589e-01 2.06152108e-02 -1.32188722e-01
-4.69945103e-01 -5.61265111e-01 5.78731187e-02 2.59762347e-01
1.94427408e-02 3.35033745e-01 -9.67272639e-01 -2.62213469e-01
-3.27208579e-01 -1.32358849e+00 -6.76528335e-01 -8.54757428e-01
-6.40399754e-02 8.19313228e-01 4.03074682e-01 -1.68035865... | [9.096100807189941, -1.6478341817855835] |
9bcc8580-d256-447b-9a76-d91f59a4073f | manipulating-the-distributions-of-experience | 2006.00283 | null | https://arxiv.org/abs/2006.00283v1 | https://arxiv.org/pdf/2006.00283v1.pdf | Manipulating the Distributions of Experience used for Self-Play Learning in Expert Iteration | Expert Iteration (ExIt) is an effective framework for learning game-playing policies from self-play. ExIt involves training a policy to mimic the search behaviour of a tree search algorithm - such as Monte-Carlo tree search - and using the trained policy to guide it. The policy and the tree search can then iteratively ... | ['Éric Piette', 'Dennis J. N. J. Soemers', 'Cameron Browne', 'Matthew Stephenson'] | 2020-05-30 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 6.70615062e-02 1.22770742e-02 -3.01714689e-01 -1.72172280e-04
-5.90018392e-01 -7.31123030e-01 3.82705480e-01 1.07958829e-02
-8.03361893e-01 8.53951752e-01 2.34134406e-01 -5.10629475e-01
-3.64542693e-01 -7.67354429e-01 -5.49379766e-01 -6.97600365e-01
-2.45679960e-01 5.29991925e-01 4.58645612e-01 -3.47282946... | [3.7298192977905273, 1.6371930837631226] |
9a6570a9-1aae-464f-b791-7a7d0bdef7d7 | analyzing-and-diagnosing-pose-estimation-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/He_Analyzing_and_Diagnosing_Pose_Estimation_With_Attributions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/He_Analyzing_and_Diagnosing_Pose_Estimation_With_Attributions_CVPR_2023_paper.pdf | Analyzing and Diagnosing Pose Estimation With Attributions | We present Pose Integrated Gradient (PoseIG), the first interpretability technique designed for pose estimation. We extend the concept of integrated gradients for pose estimation to generate pixel-level attribution maps. To enable comparison across different pose frameworks, we unify different pose outputs into a c... | ['Angela Yao', 'Qiuxia Lin', 'Kerui Gu', 'Linlin Yang', 'Qiyuan He'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['hand-pose-estimation'] | ['computer-vision'] | [-3.26755643e-02 2.27049053e-01 -3.60575020e-01 -3.24568093e-01
-7.37299919e-01 -7.54323542e-01 3.83665830e-01 -3.65037173e-01
-1.64850727e-01 5.70397198e-01 7.58350790e-01 -1.88995525e-02
-3.17693442e-01 -4.21064079e-01 -7.33668864e-01 -1.30995616e-01
-1.34081140e-01 4.21857953e-01 -4.19724584e-02 -1.91568837... | [7.00507926940918, -0.9483263492584229] |
bc82a5f0-d3c7-4fbf-9298-cbdee8139887 | architecture-representations-for-quantum | 2210.15073 | null | https://arxiv.org/abs/2210.15073v3 | https://arxiv.org/pdf/2210.15073v3.pdf | Hierarchical quantum circuit representations for neural architecture search | Machine learning with hierarchical quantum circuits, usually referred to as Quantum Convolutional Neural Networks (QCNNs), is a promising prospect for near-term quantum computing. The QCNN is a circuit model inspired by the architecture of Convolutional Neural Networks (CNNs). CNNs are successful because they do not ne... | ['Francesco Petruccione', 'Carsten Blank', 'Daniel K. Park', 'Ilya Sinayskiy', 'Matt Lourens'] | 2022-10-26 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 4.73833144e-01 -1.47186518e-01 -1.58140317e-01 -1.54953733e-01
-5.28233469e-01 -6.96768761e-01 3.29390943e-01 2.75468919e-02
-2.90324390e-01 5.50928056e-01 -4.26384658e-01 -5.89571953e-01
-4.64442104e-01 -1.32726014e+00 -7.93016255e-01 -7.89974868e-01
6.62274659e-02 2.99356252e-01 9.04171839e-02 -5.28571069... | [5.5676398277282715, 4.9798665046691895] |
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