paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f1bdd34b-5e15-475a-b2b2-0a3bde94771b | agreement-among-human-and-automated | null | null | https://openreview.net/forum?id=WL_MC7HgGQ | https://openreview.net/pdf?id=WL_MC7HgGQ | AGREEMENT AMONG HUMAN AND AUTOMATED TRANSCRIPTIONS OF GLOBAL SONGS | Cross-cultural musical analysis requires standardized symbolic representation of sounds such as score notation. However, transcription into notation is usually conducted manually by ear, which is time-consuming and subjective. Our aim is to evaluate the reliability of existing methods for transcribing songs from divers... | ['Anonymous'] | 2021-05-24 | null | null | null | null | ['music-transcription'] | ['music'] | [ 3.91580880e-01 -8.87339637e-02 3.98346782e-01 -1.19305998e-01
-1.38349009e+00 -1.37800848e+00 1.29851848e-01 -6.48287237e-02
-3.18837196e-01 7.57327855e-01 5.35780787e-01 -1.29307210e-01
-3.39159667e-01 -2.16363683e-01 -3.81304741e-01 -3.48504692e-01
1.65265389e-02 6.74598157e-01 3.64939123e-02 -3.27503115... | [15.92697525024414, 5.371687889099121] |
18e73d3b-2f00-49e1-9bd3-518e04bcd4de | locate-this-not-that-class-conditioned-sound | 2203.04197 | null | https://arxiv.org/abs/2203.04197v1 | https://arxiv.org/pdf/2203.04197v1.pdf | Locate This, Not That: Class-Conditioned Sound Event DOA Estimation | Existing systems for sound event localization and detection (SELD) typically operate by estimating a source location for all classes at every time instant. In this paper, we propose an alternative class-conditioned SELD model for situations where we may not be interested in localizing all classes all of the time. This ... | ['Jonathan Le Roux', 'Zhong-Qiu Wang', 'Gordon Wichern', 'Olga Slizovskaia'] | 2022-03-08 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 1.67828634e-01 -4.76213634e-01 1.62748396e-01 -3.13038439e-01
-1.75333881e+00 -7.02409446e-01 5.56310534e-01 3.85228485e-01
-4.98925567e-01 3.45587403e-01 2.79412895e-01 -8.66451189e-02
-1.54106557e-01 -4.36726362e-01 -8.04579556e-01 -6.72872007e-01
-4.90499675e-01 2.61656433e-01 5.99185884e-01 1.00396387... | [15.198359489440918, 5.198291778564453] |
b72b4250-d82f-447e-a9eb-70f8cabb08e1 | leveraging-multi-view-image-sets-for | 1911.07262 | null | https://arxiv.org/abs/1911.07262v1 | https://arxiv.org/pdf/1911.07262v1.pdf | Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation | We present an unsupervised approach for factorizing object appearance into highlight, shading, and albedo layers, trained by multi-view real images. To do so, we construct a multi-view dataset by collecting numerous customer product photos online, which exhibit large illumination variations that make them suitable for ... | ['Stephen Lin', 'Renjiao Yi', 'Ping Tan'] | 2019-11-17 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.30127740e-01 -4.58802938e-01 -1.93670020e-02 -3.94759089e-01
-4.19129044e-01 -8.49180341e-01 4.28655714e-01 -3.38890225e-01
1.78384602e-01 1.70139685e-01 -7.93874916e-03 2.31880188e-01
-1.61663201e-02 -5.60838640e-01 -6.97946489e-01 -9.23902631e-01
4.51295614e-01 -5.56911975e-02 1.65629819e-01 -3.09285045... | [9.92387580871582, -2.8187363147735596] |
084ce3a1-db39-40ce-89a7-5cfdd50ef071 | distributed-layer-partitioned-training-for | 1904.06049 | null | http://arxiv.org/abs/1904.06049v1 | http://arxiv.org/pdf/1904.06049v1.pdf | Distributed Layer-Partitioned Training for Privacy-Preserved Deep Learning | Deep Learning techniques have achieved remarkable results in many domains.
Often, training deep learning models requires large datasets, which may require
sensitive information to be uploaded to the cloud to accelerate training. To
adequately protect sensitive information, we propose distributed
layer-partitioned train... | ['Chun-Nan Chou', 'Chun-Hsien Yu', 'Emily Chang'] | 2019-04-12 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-1.56509221e-01 -1.45478174e-01 -4.08421814e-01 -1.00936842e+00
-6.52558446e-01 -5.75101614e-01 2.71324050e-02 -3.62785570e-02
-7.57010341e-01 9.56492841e-01 -9.46231857e-02 -3.80411029e-01
-4.95191254e-02 -8.50349128e-01 -8.77890825e-01 -9.09447730e-01
-2.21534535e-01 1.51862174e-01 -6.46793982e-03 2.15581104... | [5.87939977645874, 6.853515148162842] |
f2778e4b-11bc-47a4-afa3-3fee95144cce | real-time-monocular-visual-odometry-for | 1806.05842 | null | https://arxiv.org/abs/1806.05842v3 | https://arxiv.org/pdf/1806.05842v3.pdf | Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments | In the context of robotic underwater operations, the visual degradations induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, most localization methods are based on expensive navigational sensors associated with acoustic positioning. On the other hand, visual odo... | ['Pauline Trouvé-Peloux', 'Julien Moras', 'Maxime Ferrera', 'Vincent Creuze'] | 2018-06-15 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-1.25596568e-01 -2.02148240e-02 4.37228233e-01 -2.25687638e-01
-2.72837222e-01 -4.62029308e-01 5.20506203e-01 2.30521917e-01
-1.38300288e+00 7.49053121e-01 -3.82551342e-01 9.88638178e-02
-3.31308961e-01 -6.45920753e-01 -5.92829168e-01 -8.73582900e-01
-2.81849205e-01 6.09980524e-01 6.42473221e-01 -5.34110904... | [7.515535354614258, -1.800980567932129] |
a86a7bb0-50bf-437d-95ee-acd92da71d37 | pmvos-pixel-level-matching-based-video-object | 2009.08855 | null | https://arxiv.org/abs/2009.08855v1 | https://arxiv.org/pdf/2009.08855v1.pdf | PMVOS: Pixel-Level Matching-Based Video Object Segmentation | Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided. Due to this limitation of using prior knowledge about the target object, feature matching, which compares template features representing the target ... | ['Suhwan Cho', 'Sungjun Jang', 'Sungmin Woo', 'Sangyoun Lee', 'Heansung Lee'] | 2020-09-18 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 3.21613789e-01 -2.85136819e-01 -3.02802473e-01 -4.99172419e-01
-8.68743956e-01 -3.04153889e-01 3.56853008e-01 -5.91498427e-02
-5.47484934e-01 3.51261288e-01 -1.67946264e-01 3.76596749e-01
1.90740556e-01 -5.71205735e-01 -8.03552866e-01 -5.69682300e-01
-1.37119424e-02 8.00049528e-02 8.57210040e-01 2.19968893... | [9.13656997680664, -0.09598333388566971] |
b9a5b4c6-6a96-45ee-93c0-46db5a3194be | blind-video-deflickering-by-neural-filtering | 2303.08120 | null | https://arxiv.org/abs/2303.08120v1 | https://arxiv.org/pdf/2303.08120v1.pdf | Blind Video Deflickering by Neural Filtering with a Flawed Atlas | Many videos contain flickering artifacts. Common causes of flicker include video processing algorithms, video generation algorithms, and capturing videos under specific situations. Prior work usually requires specific guidance such as the flickering frequency, manual annotations, or extra consistent videos to remove th... | ['Qifeng Chen', 'Zhaoxiang Zhang', 'Xuanchi Ren', 'Chenyang Lei'] | 2023-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lei_Blind_Video_Deflickering_by_Neural_Filtering_With_a_Flawed_Atlas_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lei_Blind_Video_Deflickering_by_Neural_Filtering_With_a_Flawed_Atlas_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation', 'video-temporal-consistency'] | ['computer-vision', 'computer-vision'] | [ 4.00022268e-01 -6.55616522e-01 3.09531223e-02 -2.17126295e-01
-6.91305220e-01 -8.04640830e-01 2.63493598e-01 -6.67252183e-01
-8.27241018e-02 3.10006052e-01 3.52716595e-01 -2.92301048e-02
2.00534210e-01 -2.92167991e-01 -1.06213272e+00 -8.31998944e-01
-3.62542830e-02 -7.17961371e-01 3.06208521e-01 6.25131205... | [10.725076675415039, -1.5294805765151978] |
d66289a5-20be-4b06-afeb-d36e5d9128c8 | hla-class-i-binding-prediction-via | 1701.00593 | null | http://arxiv.org/abs/1701.00593v2 | http://arxiv.org/pdf/1701.00593v2.pdf | HLA class I binding prediction via convolutional neural networks | Many biological processes are governed by protein-ligand interactions. One
such example is the recognition of self and nonself cells by the immune system.
This immune response process is regulated by the major histocompatibility
complex (MHC) protein which is encoded by the human leukocyte antigen (HLA)
complex. Unders... | ['Yeeleng Scott Vang', 'Xiaohui Xie'] | 2017-01-03 | null | null | null | null | ['mhc-presentation-prediction'] | ['medical'] | [ 4.72133011e-01 -4.05373991e-01 -4.36159283e-01 -6.30247235e-01
-7.77726531e-01 -6.73325181e-01 1.31694078e-01 4.56629783e-01
-6.36653125e-01 1.14086890e+00 2.24985421e-01 -4.49764997e-01
4.67562266e-02 -9.44528997e-01 -7.62909353e-01 -1.00197828e+00
-1.58947036e-02 1.09431875e+00 1.05970822e-01 -3.40279520... | [4.733050346374512, 5.612176418304443] |
706aed2b-15fe-468c-af43-1870867518f8 | autotaskformer-searching-vision-transformers | 2304.08756 | null | https://arxiv.org/abs/2304.08756v2 | https://arxiv.org/pdf/2304.08756v2.pdf | AutoTaskFormer: Searching Vision Transformers for Multi-task Learning | Vision Transformers have shown great performance in single tasks such as classification and segmentation. However, real-world problems are not isolated, which calls for vision transformers that can perform multiple tasks concurrently. Existing multi-task vision transformers are handcrafted and heavily rely on human exp... | ['Mi Zhang', 'Deng Cai', 'Zebin Ren', 'Quanlu Zhang', 'Kan Ren', 'Yuge Zhang', 'Shen Yan', 'Yang Liu'] | 2023-04-18 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 2.99784467e-02 -3.62192392e-01 2.27902755e-01 -3.27585340e-01
-8.79662573e-01 -6.38917029e-01 4.51264322e-01 -6.01255178e-01
-7.18591213e-01 3.65937710e-01 -1.11976318e-01 -1.14442997e-01
-7.48721436e-02 -2.32081473e-01 -4.55763698e-01 -7.08082497e-01
5.16618252e-01 7.99320161e-01 7.21017957e-01 3.93497273... | [9.741081237792969, 1.5901906490325928] |
c6f3e0d9-58ee-4fcd-8dad-503ae991cd5b | trust-aware-resilient-control-and | 2305.16818 | null | https://arxiv.org/abs/2305.16818v2 | https://arxiv.org/pdf/2305.16818v2.pdf | Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles | We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs can also indu... | ['Wenchao Li', 'Christos G. Cassandras', 'Wei Xiao', 'Ehsan Sabouni', 'H M Sabbir Ahmad'] | 2023-05-26 | null | null | null | null | ['navigate'] | ['reasoning'] | [-0.56626046 0.2917245 0.06731325 -0.10059398 -0.18381959 -1.0001694
0.5588755 -0.11051565 -0.37606773 0.95283943 -0.4878059 -0.6376267
0.10025848 -1.086412 -0.71504223 -0.64098656 -0.6472055 0.32367536
0.8336023 -0.8016122 -0.17199557 0.6720342 -0.91107434 -0.64404845
0.9643093 0.8284102 -0.52... | [5.347891807556152, 7.2378458976745605] |
a4edfd43-70b0-4600-bbb5-abaf7025119b | study-of-robust-sparsity-aware-rls-algorithms | 2204.08990 | null | https://arxiv.org/abs/2204.08990v1 | https://arxiv.org/pdf/2204.08990v1.pdf | Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments | This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise. The proposed algorithm generalizes multiple algorithms only by replacing the specified criterion of robustness and sparsity-aware penalty. Furthermore, by jo... | ['B. Chen', 'R. C. de Lamare', 'Y. Zakharov', 'L. Lu', 'Y. Yu'] | 2022-04-09 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.25493497e-01 -4.80903447e-01 8.97863656e-02 8.94065108e-03
-9.77427781e-01 -2.81156301e-01 5.66535778e-02 -3.08011055e-01
9.83643830e-02 7.83093870e-01 -2.22396408e-03 -5.43107092e-02
-6.00328028e-01 -1.70444936e-01 -6.20281816e-01 -8.42008591e-01
-4.16676044e-01 -1.43152222e-01 2.60129541e-01 -3.15935344... | [6.560156345367432, 1.5944578647613525] |
fbd28611-1cd1-4214-974a-167ad02fae8b | numerical-approximation-in-cfd-problems-using | 2111.02987 | null | https://arxiv.org/abs/2111.02987v1 | https://arxiv.org/pdf/2111.02987v1.pdf | Numerical Approximation in CFD Problems Using Physics Informed Machine Learning | The thesis focuses on various techniques to find an alternate approximation method that could be universally used for a wide range of CFD problems but with low computational cost and low runtime. Various techniques have been explored within the field of machine learning to gauge the utility in fulfilling the core ambit... | ['Balaji Srinivasan', 'Vikas Dwivedi', 'Siddharth Rout'] | 2021-11-01 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-1.89467862e-01 -9.22486559e-02 1.35867164e-01 -3.15153711e-02
-1.59138188e-01 -3.28527302e-01 6.83251858e-01 7.30157946e-04
-2.86164671e-01 1.08295739e+00 -2.95941472e-01 -2.95014381e-01
-6.41091883e-01 -7.78158665e-01 -2.04639763e-01 -1.13377810e+00
-2.63210267e-01 7.27408648e-01 -1.24239065e-01 -4.54718590... | [6.392177581787109, 3.3687777519226074] |
9074b068-8a0e-4f8e-9b41-ed020259cf0a | winners-at-w-nut-2020-shared-task-3 | null | null | https://aclanthology.org/2020.wnut-1.79 | https://aclanthology.org/2020.wnut-1.79.pdf | Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets | Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19. In this paper, we describe our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individua... | ['Tejas Vaidhya', 'Ayush Kaushal'] | null | null | null | null | emnlp-wnut-2020-11 | ['sentence-classification'] | ['natural-language-processing'] | [ 1.41663909e-01 1.87379330e-01 -2.55612642e-01 -1.06146201e-01
-1.01904023e+00 -2.14131415e-01 6.95524871e-01 8.13396275e-01
-7.68778980e-01 1.04683471e+00 7.97628343e-01 -2.45200306e-01
6.90945908e-02 -6.65966630e-01 -1.79443404e-01 -3.10828030e-01
-2.75411218e-01 7.64039755e-01 1.30031377e-01 -4.95967358... | [8.554841995239258, 9.403800010681152] |
c6118dc7-7ab8-4cb5-b277-909411a7e0af | mimo-sar-a-hierarchical-high-resolution | 2101.09293 | null | https://arxiv.org/abs/2101.09293v2 | https://arxiv.org/pdf/2101.09293v2.pdf | MIMO-SAR: A Hierarchical High-resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving | Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this ... | ['Guanbin Xing', 'Sumit Roy', 'Xiangyu Gao'] | 2021-01-22 | null | null | null | null | ['radar-odometry'] | ['robots'] | [ 4.58670825e-01 -1.33768544e-01 4.03730303e-01 -6.81045771e-01
-7.30358601e-01 -5.55635631e-01 7.79811382e-01 -7.30113983e-01
-4.37997580e-01 6.64568305e-01 -6.89594224e-02 -6.43271208e-01
-4.73689824e-01 -8.50414872e-01 -6.53963983e-02 -6.41091943e-01
-6.86422512e-02 4.50267851e-01 3.92545387e-02 -3.18128437... | [6.765275478363037, 0.9258217811584473] |
81f911c9-f2e3-4903-9a72-3212face78a6 | improving-neural-text-summarization-using | null | null | https://openreview.net/forum?id=9nBQn6hjJmg | https://openreview.net/pdf?id=9nBQn6hjJmg | Improving Neural Text Summarization using Knowledge Graphs | In this paper, we propose a method for extractive text summarization using auto-regressive transformers. For better learning procedure we adopt the knowledge graph method to convert our textual data to more informative text and unsupervised training methods for wide use. We feed the informative text to our pre-trained ... | ['Sumit Kumar', 'Raj Ratn Pranesh', 'Ambesh Shekhar'] | 2020-10-24 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.17049319e-01 7.13903248e-01 -4.39049155e-01 -4.28127736e-01
-8.39315057e-01 -3.40411246e-01 8.32415581e-01 4.21919316e-01
-1.42894611e-01 1.21109295e+00 1.13314211e+00 -2.14374274e-01
6.30908832e-02 -8.63079965e-01 -5.36789000e-01 -2.45700717e-01
3.13457757e-01 8.11745584e-01 -6.42112643e-02 -3.32507044... | [12.539624214172363, 9.54670524597168] |
9a079358-ecb0-4be4-8753-00c6e1b9f977 | volumetric-super-resolution-of-multispectral | 1705.05745 | null | http://arxiv.org/abs/1705.05745v1 | http://arxiv.org/pdf/1705.05745v1.pdf | Volumetric Super-Resolution of Multispectral Data | Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
order to reconstruct a high-spatial/high-spectral resolution multispectral
image volume, either... | ['Vildan Atalay Aydin', 'Hassan Foroosh'] | 2017-05-14 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 9.81114507e-01 -1.03054631e+00 -1.09006464e-01 -9.82350409e-02
-1.07637203e+00 -6.61881208e-01 3.12733501e-01 -4.58772600e-01
-5.24582863e-01 8.19522917e-01 -5.98100238e-02 -1.30834088e-01
-6.36235893e-01 -1.25395155e+00 -1.56745061e-01 -9.33197320e-01
5.90355545e-02 -2.00900406e-01 2.52011478e-01 -4.84624386... | [10.114933967590332, -2.0743682384490967] |
4f02cf89-2b52-4709-99b2-d99b3b43318e | scalable-logo-recognition-using-proxies | 1811.08009 | null | http://arxiv.org/abs/1811.08009v1 | http://arxiv.org/pdf/1811.08009v1.pdf | Scalable Logo Recognition using Proxies | Logo recognition is the task of identifying and classifying logos. Logo
recognition is a challenging problem as there is no clear definition of a logo
and there are huge variations of logos, brands and re-training to cover every
variation is impractical. In this paper, we formulate logo recognition as a
few-shot object... | ['Srikar Appalaraju', 'Istvan Fehervari'] | 2018-11-19 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [-1.02010253e-03 -4.06659126e-01 -5.85576534e-01 -4.33035553e-01
-9.52126145e-01 -8.40420783e-01 4.09719795e-01 3.39826420e-02
1.06227875e-01 2.72929911e-02 -6.09711558e-02 3.33718449e-01
-8.11159983e-02 -7.83232927e-01 -1.13336611e+00 -4.37439442e-01
-1.28065675e-01 9.96849239e-01 4.57477897e-01 6.11266345... | [9.31908893585205, 1.306208610534668] |
745bc38b-4504-401b-868c-b3ad66151f8b | cross-domain-review-generation-for-aspect | null | null | https://aclanthology.org/2021.findings-acl.421 | https://aclanthology.org/2021.findings-acl.421.pdf | Cross-Domain Review Generation for Aspect-Based Sentiment Analysis | null | ['Rui Xia', 'Chenggong Gong', 'Jianfei Yu'] | null | null | null | null | findings-acl-2021-8 | ['review-generation'] | ['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... | [-7.399898529052734, 3.6167614459991455] |
f0b9aa49-4cdf-4652-9d52-b16ade87fc74 | t-recx-tiny-resource-efficient-convolutional | 2207.06613 | null | https://arxiv.org/abs/2207.06613v2 | https://arxiv.org/pdf/2207.06613v2.pdf | T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit | Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is gaining popularity due to recent breakthroughs in ML and Internet of Things (IoT). Most tinyML research focuses on model compression techniques that trade accuracy (and model capacity) for compact models to fit into the KB-sized tiny-edge devic... | ['Steve Wilton', 'Nikhil P Ghanathe'] | 2022-07-14 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.33100241e-01 5.61865605e-02 -7.17589915e-01 -4.95024502e-01
-5.05333900e-01 -2.42376328e-01 2.15912104e-01 4.64710928e-02
-7.39326596e-01 4.23139125e-01 4.29351777e-02 -7.98643172e-01
1.82834595e-01 -6.50369585e-01 -1.03126287e+00 -1.09817691e-01
-2.75671724e-02 2.20160782e-01 3.09172213e-01 4.43316847... | [8.591816902160645, 3.043600559234619] |
17cf3457-24a3-4dc1-89ef-d929d016ca2a | towards-safe-autonomous-driving-policies | 2307.01316 | null | https://arxiv.org/abs/2307.01316v1 | https://arxiv.org/pdf/2307.01316v1.pdf | Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach | The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving. Deep reinforcement learning (DRL) has emerged as a popular approach to tackle this problem. However, the application of existing DRL solutions is mainly confined to si... | ['Saber Fallah', 'Mustafa Yıldırım', 'Iman Sharifi'] | 2023-07-03 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-8.58821347e-02 2.53771335e-01 -2.75692016e-01 -3.46825033e-01
-3.76675248e-01 -4.94320959e-01 7.53435194e-01 -2.49880850e-01
-4.76327240e-01 1.02076864e+00 -2.83233911e-01 -7.22914815e-01
-5.51566958e-01 -9.81550395e-01 -8.91230464e-01 -4.40276176e-01
-2.51931399e-01 3.03314030e-01 4.74027008e-01 -6.92640245... | [5.244971752166748, 1.2481963634490967] |
3d0d5eda-e03b-4d13-9f97-9c0c4d995783 | st-detr-spatio-temporal-object-traces | 2107.05887 | null | https://arxiv.org/abs/2107.05887v2 | https://arxiv.org/pdf/2107.05887v2.pdf | ST-DETR: Spatio-Temporal Object Traces Attention Detection Transformer | We propose ST-DETR, a Spatio-Temporal Transformer-based architecture for object detection from a sequence of temporal frames. We treat the temporal frames as sequences in both space and time and employ the full attention mechanisms to take advantage of the features correlations over both dimensions. This treatment enab... | ['Ahmad El-Sallab', 'Eslam Mohamed'] | 2021-07-13 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 6.00896962e-02 -4.45968598e-01 -8.52421746e-02 -2.87461549e-01
-8.90125692e-01 -6.03641570e-01 1.00594413e+00 -6.97944639e-03
-8.22543263e-01 4.32728469e-01 4.61718678e-01 1.54745197e-02
-2.22341269e-01 -6.11169338e-01 -7.06694484e-01 -7.81707168e-01
-6.32671297e-01 7.10339621e-02 8.01712215e-01 6.67956248... | [8.695175170898438, 0.46207454800605774] |
023cf483-5f6b-4725-9302-46ef16a7f917 | a-probabilistic-translation-method-for | 1411.1006 | null | http://arxiv.org/abs/1411.1006v2 | http://arxiv.org/pdf/1411.1006v2.pdf | A Probabilistic Translation Method for Dictionary-based Cross-lingual Information Retrieval in Agglutinative Languages | Translation ambiguity, out of vocabulary words and missing some translations
in bilingual dictionaries make dictionary-based Cross-language Information
Retrieval (CLIR) a challenging task. Moreover, in agglutinative languages which
do not have reliable stemmers, missing various lexical formations in bilingual
dictionar... | ['Azadeh Shakery', 'Javid Dadashkarimi', 'Heshaam Faili'] | 2014-11-04 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-3.63131374e-01 -4.94735271e-01 -6.84336603e-01 -1.96372449e-01
-9.86342549e-01 -1.11965692e+00 7.10396826e-01 5.21649957e-01
-7.82294333e-01 1.05331063e+00 3.53841811e-01 -7.71117568e-01
-4.46577482e-02 -7.31067717e-01 -3.78338307e-01 -1.95525557e-01
5.88210642e-01 1.12135530e+00 2.59285986e-01 -9.14114118... | [11.101367950439453, 9.998368263244629] |
7508d7fe-292d-47d6-bb53-529dbdb0471f | a-hierarchical-structured-self-attentive | 1805.07799 | null | http://arxiv.org/abs/1805.07799v1 | http://arxiv.org/pdf/1805.07799v1.pdf | A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS) | The recent advance in neural network architecture and training algorithms
have shown the effectiveness of representation learning. The neural
network-based models generate better representation than the traditional ones.
They have the ability to automatically learn the distributed representation for
sentences and docum... | ['Kamal Al-Sabahi', 'Mohammed Nadher', 'Zhang Zuping'] | 2018-05-20 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 1.00105830e-01 2.10802406e-01 -2.06429780e-01 -4.31398183e-01
-5.84262848e-01 -2.00220704e-01 8.68941307e-01 5.66647828e-01
-4.53906208e-01 7.95164704e-01 1.08491898e+00 1.19826376e-01
-6.09722659e-02 -6.93161666e-01 -5.22426069e-01 -5.56636572e-01
-5.96634969e-02 1.93576992e-01 2.13024184e-01 -2.74983048... | [12.287875175476074, 9.24690055847168] |
afaf4b2e-2438-4cd0-aea6-5a89b866ce59 | deep-learning-for-real-time-gravitational | 1711.07966 | null | http://arxiv.org/abs/1711.07966v2 | http://arxiv.org/pdf/1711.07966v2.pdf | Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data | The recent Nobel-prize-winning detections of gravitational waves from merging
black holes and the subsequent detection of the collision of two neutron stars
in coincidence with electromagnetic observations have inaugurated a new era of
multimessenger astrophysics. To enhance the scope of this emergent science, we
propo... | ['E. A. Huerta', 'Daniel George'] | 2017-11-21 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-3.71502548e-01 -1.65404961e-01 5.67757010e-01 2.07923651e-02
-5.00450253e-01 -5.31166673e-01 1.12547517e+00 -3.29815656e-01
-5.23880839e-01 3.46068650e-01 -9.62824151e-02 -6.74986124e-01
-2.90450543e-01 -1.04878533e+00 -3.39155823e-01 -8.84345174e-01
-5.96004307e-01 8.41975808e-01 3.00822169e-01 -1.57712817... | [7.555856704711914, 3.1289854049682617] |
c54462e8-29af-4591-a2ec-762347604889 | large-margin-convex-polytope-machine | null | null | http://papers.nips.cc/paper/5511-large-margin-convex-polytope-machine | http://papers.nips.cc/paper/5511-large-margin-convex-polytope-machine.pdf | Large-Margin Convex Polytope Machine | We present the Convex Polytope Machine (CPM), a novel non-linear learning algorithm for large-scale binary classification tasks. The CPM finds a large margin convex polytope separator which encloses one class. We develop a stochastic gradient descent based algorithm that is amenable to massive datasets, and augment it ... | ['Anthony D. Joseph', 'J. D. Tygar', 'Alex Kantchelian', 'Ling Huang', 'Peter L. Bartlett', 'Michael C. Tschantz'] | 2014-12-01 | null | null | null | neurips-2014-12 | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 8.21283981e-02 -1.28100961e-01 -6.55189455e-01 -3.77934843e-01
-1.26708817e+00 -6.99874699e-01 3.86986077e-01 3.01714838e-01
-2.86467701e-01 9.14299667e-01 -3.26710075e-01 -7.53215432e-01
-3.36245120e-01 -5.54260433e-01 -8.38124454e-01 -7.01295435e-01
-3.47547859e-01 8.98067713e-01 3.15294027e-01 1.60590738... | [8.334735870361328, 4.040724277496338] |
7cfa7237-6a5d-4cd1-885e-64cff8677f7e | film-ensemble-probabilistic-deep-learning-via | 2206.00050 | null | https://arxiv.org/abs/2206.00050v4 | https://arxiv.org/pdf/2206.00050v4.pdf | FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation | The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to quantify epistemic uncertainty, usable across a wide class of prediction models, is to train a mode... | ['Konrad Schindler', 'Jan Dirk Wegner', "Stefano D'Aronco", 'Rodrigo Caye Daudt', 'Bernd Bischl', 'Mina Rezaei', 'Hüseyin Anil Gündüz', 'Alexander Becker', 'Mehmet Ozgur Turkoglu'] | 2022-05-31 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 7.52334222e-02 4.84852880e-01 4.09533650e-01 -3.43341142e-01
-8.91417742e-01 -7.10982144e-01 9.63296592e-01 1.21025257e-01
-5.74534118e-01 1.15717411e+00 -2.11675861e-03 -1.70185238e-01
-5.17744899e-01 -7.14490175e-01 -9.39328253e-01 -9.21543837e-01
-3.88434343e-02 6.14146411e-01 1.68920457e-02 -2.33102217... | [7.354802131652832, 3.8062994480133057] |
b528aee1-6cce-4f25-990e-da095ec05581 | classification-of-primitive-manufacturing | 2303.09558 | null | https://arxiv.org/abs/2303.09558v1 | https://arxiv.org/pdf/2303.09558v1.pdf | Classification of Primitive Manufacturing Tasks from Filtered Event Data | Collaborative robots are increasingly present in industry to support human activities. However, to make the human-robot collaborative process more effective, there are several challenges to be addressed. Collaborative robotic systems need to be aware of the human activities to (1) anticipate collaborative/assistive act... | ['Pedro Neto', 'Laura Duarte'] | 2023-03-15 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 4.89606827e-01 4.83780392e-02 3.49303126e-01 -4.21114534e-01
-3.25079232e-01 -2.24466443e-01 4.62782174e-01 -5.28727472e-02
-4.36600119e-01 4.84435499e-01 5.50078861e-02 2.83833891e-01
-4.23945814e-01 -3.64466012e-01 -6.01762414e-01 -4.62478340e-01
-3.06484282e-01 3.83606374e-01 2.45681241e-01 -1.80979997... | [4.990148544311523, 0.7335023880004883] |
e15e9cf0-59e8-4ee8-914f-426a71d105b3 | dual-stream-transformer-for-generic-event | 2207.03038 | null | https://arxiv.org/abs/2207.03038v3 | https://arxiv.org/pdf/2207.03038v3.pdf | Dual-Stream Transformer for Generic Event Boundary Captioning | This paper describes our champion solution for the CVPR2022 Generic Event Boundary Captioning (GEBC) competition. GEBC requires the captioning model to have a comprehension of instantaneous status changes around the given video boundary, which makes it much more challenging than conventional video captioning task. In t... | ['Longyin Wen', 'Libo Zhang', 'YuFei Wang', 'Guang Chen', 'Hanhua Ye', 'Xin Gu'] | 2022-07-07 | null | null | null | null | ['boundary-captioning'] | ['computer-vision'] | [ 5.15681028e-01 1.57017097e-01 -2.41384178e-01 -3.28687876e-01
-1.21735620e+00 -5.31820297e-01 7.04462886e-01 -1.69211701e-02
-1.58312216e-01 7.58658648e-01 8.30537736e-01 -9.04442817e-02
4.17920411e-01 -4.34903890e-01 -1.16420949e+00 -3.32084805e-01
-3.59818116e-02 3.98844540e-01 2.94286221e-01 -1.62411615... | [10.500754356384277, 0.6569319367408752] |
86afeb37-6bc3-45a4-b13e-3a967aca0169 | estimating-uncertainty-in-pet-image | 2306.04664 | null | https://arxiv.org/abs/2306.04664v1 | https://arxiv.org/pdf/2306.04664v1.pdf | Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling | Positron emission tomography (PET) is an important functional medical imaging technique often used in the evaluation of certain brain disorders, whose reconstruction problem is ill-posed. The vast majority of reconstruction methods in PET imaging, both iterative and deep learning, return a single estimate without quant... | ['Damir Seršić', 'Tomislav Matulić', 'Tin Vlašić'] | 2023-06-07 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 2.93009460e-01 3.47730100e-01 -2.09506974e-02 -5.98701358e-01
-1.47514737e+00 2.20540706e-02 2.80762464e-01 -2.31873706e-01
-5.74860334e-01 1.32805872e+00 1.13159418e-01 -2.08176881e-01
-1.06249340e-01 -7.02391684e-01 -9.57294106e-01 -9.79309916e-01
1.46434829e-01 1.02673614e+00 4.09317873e-02 7.40409851... | [13.559350967407227, -2.295027494430542] |
552534ef-f985-4502-bbad-52d8665a45d5 | disco-efficient-unsupervised-decoding-for | 2107.05380 | null | https://arxiv.org/abs/2107.05380v2 | https://arxiv.org/pdf/2107.05380v2.pdf | DISCO : efficient unsupervised decoding for discrete natural language problems via convex relaxation | In this paper we study test time decoding; an ubiquitous step in almost all sequential text generation task spanning across a wide array of natural language processing (NLP) problems. Our main contribution is to develop a continuous relaxation framework for the combinatorial NP-hard decoding problem and propose Disco -... | ['Rudrajit Das', 'Anish Acharya'] | 2021-07-07 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 7.11245537e-01 3.06722045e-01 1.95495725e-01 -2.43588611e-01
-1.59871924e+00 -9.71291006e-01 3.16816688e-01 1.07330099e-01
-4.22123760e-01 1.40221751e+00 1.19230285e-01 -6.68190837e-01
-9.55660269e-02 -6.21742547e-01 -9.99825776e-01 -5.59454322e-01
-6.28895685e-02 8.86463583e-01 -4.95795608e-02 -5.04991293... | [11.972678184509277, 9.098523139953613] |
63936c75-3194-46a9-a75b-7cd650d2edbb | patch-netvlad-learned-patch-descriptor-and | 2202.05738 | null | https://arxiv.org/abs/2202.05738v1 | https://arxiv.org/pdf/2202.05738v1.pdf | Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition | Visual Place Recognition (VPR) in areas with similar scenes such as urban or indoor scenarios is a major challenge. Existing VPR methods using global descriptors have difficulty capturing local specific regions (LSR) in the scene and are therefore prone to localization confusion in such scenarios. As a result, finding ... | ['Tiantian Feng', 'Chen Ye', 'Fenglin Zhang', 'Jiafeng Cui', 'Junqiao Zhao', 'Yingfeng Cai'] | 2022-02-11 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-1.25815123e-01 -6.15481317e-01 -2.52616137e-01 -2.35739633e-01
-9.12050009e-01 -4.78010058e-01 6.04680836e-01 4.50045854e-01
-3.95297736e-01 6.01481378e-01 1.73636436e-01 -5.49339280e-02
-1.98577061e-01 -1.03542995e+00 -6.68866873e-01 -6.07164323e-01
-3.95925529e-02 2.85284463e-02 7.09432662e-01 -2.57313460... | [7.626209735870361, -1.868905782699585] |
04ba02e1-27a3-4069-9c71-a235e31532af | comparative-study-of-subset-selection-methods | 2306.17551 | null | https://arxiv.org/abs/2306.17551v1 | https://arxiv.org/pdf/2306.17551v1.pdf | Comparative study of subset selection methods for rapid prototyping of 3D object detection algorithms | Object detection in 3D is a crucial aspect in the context of autonomous vehicles and drones. However, prototyping detection algorithms is time-consuming and costly in terms of energy and environmental impact. To address these challenges, one can check the effectiveness of different models by training on a subset of the... | ['Tomasz Kryjak', 'Konrad Lis'] | 2023-06-30 | null | null | null | null | ['3d-object-detection', 'object-detection', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.49729860e-02 -2.08816484e-01 -5.86147718e-02 -1.01609729e-01
-4.19599831e-01 -7.51562893e-01 7.48504281e-01 -7.61235952e-02
-6.17773294e-01 6.35381460e-01 -4.75846708e-01 -2.00257823e-01
1.33457839e-01 -9.76625919e-01 -7.95509815e-01 -7.19305456e-01
-1.66444197e-01 5.73554575e-01 7.09810495e-01 1.70040399... | [8.102227210998535, -1.2249318361282349] |
c9b66b9c-c35c-4c96-af6e-9087ba4aecba | grantrel-grant-information-extraction-via | null | null | https://aclanthology.org/2021.findings-acl.236 | https://aclanthology.org/2021.findings-acl.236.pdf | GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction | null | ['Shanfeng Zhu', 'Hong Zhou', 'Xiaodi Huang', 'Li Huang', 'Junyi Bian'] | null | null | null | null | findings-acl-2021-8 | ['joint-entity-and-relation-extraction'] | ['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... | [-7.276376724243164, 3.630176067352295] |
ebcbb8e3-0cf5-4fdd-94c6-c93c4febf13b | a-local-temporal-difference-code-for | null | null | http://proceedings.neurips.cc/paper/2020/hash/9dd16e049becf4d5087c90a83fea403b-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/9dd16e049becf4d5087c90a83fea403b-Paper.pdf | A Local Temporal Difference Code for Distributional Reinforcement Learning | Recent theoretical and experimental results suggest that the dopamine system implements distributional temporal difference backups, allowing learning of the entire distributions of the long-run values of states rather than just their expected values. However, the distributional codes explored so far rely on a complex i... | ['Alexandre Pouget', 'Peter Dayan', 'Pablo Tano'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['distributional-reinforcement-learning'] | ['methodology'] | [-8.64445046e-03 -9.38683525e-02 -1.88406065e-01 -4.65959221e-01
-5.14540255e-01 -8.47672641e-01 6.44184649e-01 4.51093853e-01
-9.69801843e-01 1.08346891e+00 2.85428703e-01 -3.38338882e-01
-1.48093387e-01 -7.66560316e-01 -4.55161422e-01 -9.19517756e-01
-5.02031803e-01 4.48989809e-01 2.06121832e-01 -1.27521038... | [4.104117393493652, 1.8107656240463257] |
01d0823b-d852-4d83-a195-6641ffb7b416 | sentence-structure-and-word-relationship | 2108.12750 | null | https://arxiv.org/abs/2108.12750v1 | https://arxiv.org/pdf/2108.12750v1.pdf | Sentence Structure and Word Relationship Modeling for Emphasis Selection | Emphasis Selection is a newly proposed task which focuses on choosing words for emphasis in short sentences. Traditional methods only consider the sequence information of a sentence while ignoring the rich sentence structure and word relationship information. In this paper, we propose a new framework that considers sen... | ['Wai Lam', 'Haoran Yang'] | 2021-08-29 | null | https://aclanthology.org/2021.ranlp-1.175 | https://aclanthology.org/2021.ranlp-1.175.pdf | ranlp-2021-9 | ['word-similarity'] | ['natural-language-processing'] | [ 2.78546333e-01 8.95092413e-02 -4.78939712e-01 -6.16662204e-01
7.72985667e-02 -1.58963129e-01 2.16500871e-02 7.43495166e-01
-5.29341757e-01 6.24782264e-01 7.09392309e-01 -3.26565951e-01
-6.02129996e-02 -8.83131087e-01 -6.26838440e-03 -4.90032405e-01
-3.35704931e-03 -1.02712333e-01 2.17820778e-01 -5.54560423... | [11.159334182739258, 8.769057273864746] |
de41dc2a-c21e-444b-905d-176aa2b53266 | timexplain-a-framework-for-explaining-the | 2007.07606 | null | https://arxiv.org/abs/2007.07606v1 | https://arxiv.org/pdf/2007.07606v1.pdf | timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers | Modern time series classifiers display impressive predictive capabilities, yet their decision-making processes mostly remain black boxes to the user. At the same time, model-agnostic explainers, such as the recently proposed SHAP, promise to make the predictions of machine learning models interpretable, provided there ... | ['Patrick Schäfer', 'Vanja Doskoč', 'Martin Schirneck', 'Felix Mujkanovic', 'Tobias Friedrich'] | 2020-07-15 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 1.59471691e-01 2.09615275e-01 -3.84112656e-01 -4.97827828e-01
-1.31719381e-01 -8.56423259e-01 8.03435802e-01 -6.01725914e-02
2.78743386e-01 7.27814078e-01 1.09068610e-01 -7.25186527e-01
-7.73221135e-01 -5.71508825e-01 -4.98833567e-01 -5.69812298e-01
-5.26874602e-01 6.13896906e-01 -1.89807877e-01 -5.69465518... | [7.0882463455200195, 3.175719976425171] |
ff47bc4f-a301-4acd-a2ad-c1b2acc916f4 | object-detection-and-pose-estimation-from-rgb | 2101.07347 | null | https://arxiv.org/abs/2101.07347v1 | https://arxiv.org/pdf/2101.07347v1.pdf | Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic Grasping | In recent times, object detection and pose estimation have gained significant attention in the context of robotic vision applications. Both the identification of objects of interest as well as the estimation of their pose remain important capabilities in order for robots to provide effective assistance for numerous rob... | ['M. Nicolescu', 'M. T. Chowdhury', 'S. K. Paul'] | 2021-01-18 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 2.59269059e-01 -1.03610419e-01 -1.17883310e-02 3.42279486e-02
-1.75584808e-01 -6.43888474e-01 1.22251861e-01 1.30745873e-01
-3.95205766e-01 1.59352690e-01 -7.60228217e-01 3.00131321e-01
-4.12373573e-01 -4.45031613e-01 -7.49582350e-01 -8.62846494e-01
-2.25783631e-01 1.20849979e+00 5.25961220e-01 6.05107322... | [5.933801651000977, -0.8847728967666626] |
68fefa1a-14f3-4dee-bf19-52193b4d16c3 | robust-vision-using-retro-reflective-markers | 2007.12514 | null | http://arxiv.org/abs/2007.12514v2 | http://arxiv.org/pdf/2007.12514v2.pdf | Robust Vision Using Retro Reflective Markers for Remote Handling in ITER | The International Thermonuclear Experimental Reactor (ITER)'s working
environment is characterized by extreme conditions, that deem maintenance and
inspection tasks to be carried out through remote handling. 3D Node is a
hardware/software module that extracts critical information from the remote
environment during fine... | [] | 2020-07-27 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-1.17463090e-01 -2.78013609e-02 2.24160358e-01 1.27597451e-01
-7.04017460e-01 -5.95639825e-01 6.45995140e-01 -1.13170743e-01
-3.71090591e-01 3.43060881e-01 -1.73000902e-01 -3.87531728e-01
-2.13509411e-01 -2.85392433e-01 -3.97068352e-01 -5.97583354e-01
1.27030447e-01 9.67549920e-01 7.06842780e-01 -3.95339817... | [7.818589210510254, -1.8117672204971313] |
1eb7b7af-128e-4635-8df7-7f30f38c38d6 | time-series-forecasting-with-ensembled | 2111.13164 | null | https://arxiv.org/abs/2111.13164v6 | https://arxiv.org/pdf/2111.13164v6.pdf | Neural network stochastic differential equation models with applications to financial data forecasting | In this article, we employ a collection of stochastic differential equations with drift and diffusion coefficients approximated by neural networks to predict the trend of chaotic time series which has big jump properties. Our contributions are, first, we propose a model called L\'evy induced stochastic differential equ... | ['Tao Liu', 'Jinqiao Duan', 'Yubin Lu', 'Ting Gao', 'Luxuan Yang'] | 2021-11-25 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-3.76836956e-01 -3.97760361e-01 3.62310171e-01 -9.62223951e-03
7.47949211e-03 -5.09407461e-01 3.02379429e-01 -2.98857868e-01
-4.86139119e-01 1.03403580e+00 -2.40766734e-01 -3.14175665e-01
-4.78410095e-01 -6.89393580e-01 -3.35624069e-01 -9.69281256e-01
-6.32356703e-01 4.96203423e-01 9.00261402e-02 -5.58928847... | [6.719962120056152, 3.453284502029419] |
cbb20fab-cd02-4cce-9b04-be7e50bdd301 | potential-auto-driving-threat-universal-rain | 2211.09959 | null | https://arxiv.org/abs/2211.09959v1 | https://arxiv.org/pdf/2211.09959v1.pdf | Potential Auto-driving Threat: Universal Rain-removal Attack | The problem of robustness in adverse weather conditions is considered a significant challenge for computer vision algorithms in the applicants of autonomous driving. Image rain removal algorithms are a general solution to this problem. They find a deep connection between raindrops/rain-streaks and images by mining the ... | ['Yuanjian Zhang', 'Cunjia Liu', 'Jingjing Jiang', 'Zhuoran Hou', 'Jihao Li', 'Jinchegn Hu'] | 2022-11-18 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 4.03428495e-01 -2.17406765e-01 6.02156162e-01 -1.95054337e-01
-1.73037067e-01 -5.12783527e-01 3.48110735e-01 -2.24085942e-01
-3.74748111e-01 6.11999810e-01 -4.92393494e-01 -6.61762297e-01
-8.31137877e-03 -9.40915227e-01 -8.03044736e-01 -1.22983742e+00
-2.06539482e-01 -2.92039990e-01 4.62148815e-01 -7.26433694... | [10.915060043334961, -3.263864517211914] |
957b1ef9-cd68-4971-b652-4ba3b734db3a | persistent-dirac-for-molecular-representation | 2302.02386 | null | https://arxiv.org/abs/2302.02386v1 | https://arxiv.org/pdf/2302.02386v1.pdf | Persistent Dirac for molecular representation | Molecular representations are of fundamental importance for the modeling and analysis of molecular systems. Representation models and in general approaches based on topological data analysis (TDA) have demonstrated great success in various steps of drug design and materials discovery. Here we develop a mathematically r... | ['Kelin Xia', 'Ginestra Bianconi', 'JunJie Wee'] | 2023-02-05 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 2.83046782e-01 -3.73213410e-01 -1.63497329e-01 -9.29235965e-02
-1.64858624e-01 -4.46240693e-01 6.33700311e-01 3.88725907e-01
-1.31246090e-01 7.68718839e-01 1.21597402e-01 -5.47339559e-01
-6.30238235e-01 -9.17849362e-01 -4.02224332e-01 -1.26434302e+00
-6.92664862e-01 3.31872195e-01 5.82514517e-02 -3.92846376... | [5.2396087646484375, 5.347587585449219] |
7d6f0afa-08fb-46e1-b438-dd9941d017e8 | transmrsr-transformer-based-self-distilled | 2306.06669 | null | https://arxiv.org/abs/2306.06669v1 | https://arxiv.org/pdf/2306.06669v1.pdf | TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution | Magnetic resonance images (MRI) acquired with low through-plane resolution compromise time and cost. The poor resolution in one orientation is insufficient to meet the requirement of high resolution for early diagnosis of brain disease and morphometric study. The common Single image super-resolution (SISR) solutions fa... | ['Bin Sheng', 'TingLi Chen', 'Xiaoer Wei', 'Menghan Hu', 'Tao Tan', 'Xiaohong Liu', 'Shan Huang'] | 2023-06-11 | null | null | null | null | ['image-super-resolution', 'image-reconstruction', 'super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.56414109e-01 2.54911603e-03 1.33788332e-01 -4.53018755e-01
-1.35211265e+00 5.77706620e-02 3.37268740e-01 -6.25558913e-01
-3.88010651e-01 7.20000386e-01 4.95768666e-01 -2.15097163e-02
-1.57050818e-01 -6.62971318e-01 -4.62068588e-01 -1.08780992e+00
1.35029897e-01 2.93440193e-01 4.17467594e-01 1.13869449... | [13.629505157470703, -2.4012317657470703] |
0228a668-1113-4a0e-8865-49dbaa535772 | automatic-sexism-detection-with-multilingual | 2106.04908 | null | https://arxiv.org/abs/2106.04908v2 | https://arxiv.org/pdf/2106.04908v2.pdf | Automatic Sexism Detection with Multilingual Transformer Models | Sexism has become an increasingly major problem on social networks during the last years. The first shared task on sEXism Identification in Social neTworks (EXIST) at IberLEF 2021 is an international competition in the field of Natural Language Processing (NLP) with the aim to automatically identify sexism in social me... | ['Matthias Zeppelzauer', 'Alexander Schindler', 'Sven Schlarb', 'Johannes Bogensperger', 'Manuel Hecht', 'Armin Kirchknopf', 'Djordje Slijepčević', 'Daria Liakhovets', 'Jaqueline Boeck', 'Mina Schütz'] | 2021-06-09 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 3.17776859e-01 5.38348615e-01 -2.19251633e-01 -5.43529809e-01
-4.50125605e-01 -5.35057724e-01 1.31107056e+00 6.31140947e-01
-6.78451359e-01 7.36637533e-01 1.37485847e-01 -3.19793940e-01
-5.52647054e-01 -8.64006996e-01 -4.71315295e-01 -4.01484638e-01
9.14913416e-03 1.24647689e+00 1.13707013e-01 -4.64734375... | [9.372026443481445, 10.365900993347168] |
1456ccaa-cef4-41b9-973e-d70647d2d082 | stubborn-lexical-bias-in-data-and-models | 2306.02190 | null | https://arxiv.org/abs/2306.02190v1 | https://arxiv.org/pdf/2306.02190v1.pdf | Stubborn Lexical Bias in Data and Models | In NLP, recent work has seen increased focus on spurious correlations between various features and labels in training data, and how these influence model behavior. However, the presence and effect of such correlations are typically examined feature by feature. We investigate the cumulative impact on a model of many suc... | ['Noah A. Smith', 'Jesse Dodge', 'Sofia Serrano'] | 2023-06-03 | null | null | null | null | ['natural-language-inference'] | ['natural-language-processing'] | [ 2.97390819e-01 2.03767031e-01 -2.43585110e-01 -7.42257118e-01
-9.75570560e-01 -7.50673473e-01 7.33986855e-01 3.81065875e-01
-7.96296597e-01 7.84029841e-01 5.87033093e-01 -7.18200207e-01
-8.99362490e-02 -6.28047526e-01 -8.93739223e-01 -5.03174543e-01
2.99675196e-01 2.06363365e-01 1.95630863e-01 -1.20883598... | [10.802263259887695, 9.223336219787598] |
b04c9152-54e8-4148-8c0f-35458273386f | a-multi-dimensional-deep-structured-state | 2306.00331 | null | https://arxiv.org/abs/2306.00331v1 | https://arxiv.org/pdf/2306.00331v1.pdf | A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models | We propose a multi-dimensional structured state space (S4) approach to speech enhancement. To better capture the spectral dependencies across the frequency axis, we focus on modifying the multi-dimensional S4 layer with whitening transformation to build new small-footprint models that also achieve good performance. We ... | ['Chin-Hui Lee', 'Sabato Marco Siniscalchi', 'Chao-Han Huck Yang', 'Pin-Jui Ku'] | 2023-06-01 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 1.25777237e-02 4.27436568e-02 -1.97173804e-02 -2.13842481e-01
-7.14885592e-01 -1.78055823e-01 1.99336156e-01 -3.58235896e-01
-5.06012678e-01 1.88076198e-01 4.88972962e-01 -6.16540551e-01
1.97161973e-01 -4.02217358e-01 -3.75326604e-01 -4.44678992e-01
-2.50063002e-01 -5.27826667e-01 1.85164154e-01 -4.93436426... | [14.89173698425293, 5.957709312438965] |
3c64de12-26b9-495f-abad-120282fc7362 | m6doc-a-large-scale-multi-format-multi-type | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cheng_M6Doc_A_Large-Scale_Multi-Format_Multi-Type_Multi-Layout_Multi-Language_Multi-Annotation_Category_Dataset_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cheng_M6Doc_A_Large-Scale_Multi-Format_Multi-Type_Multi-Layout_Multi-Language_Multi-Annotation_Category_Dataset_CVPR_2023_paper.pdf | M6Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis | Document layout analysis is a crucial prerequisite for document understanding, including document retrieval and conversion. Most public datasets currently contain only PDF documents and lack realistic documents. Models trained on these datasets may not generalize well to real-world scenarios. Therefore, this paper ... | ['Lianwen Jin', 'Kai Ding', 'Jing Li', 'Zecheng Xie', 'Qiyuan Zhu', 'Jiaxin Zhang', 'Sihang Wu', 'Peirong Zhang', 'Hiuyi Cheng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['document-layout-analysis'] | ['computer-vision'] | [-1.04418606e-01 -4.06369567e-01 -3.57886165e-01 -1.47964627e-01
-1.04064047e+00 -1.04317260e+00 6.96394622e-01 3.41510862e-01
-9.19019505e-02 4.64353621e-01 3.81617725e-01 -7.01539218e-01
-4.71862793e-01 -9.09884453e-01 -6.45511329e-01 -3.45527798e-01
8.03525075e-02 6.66131079e-01 2.45796695e-01 7.20634088... | [11.685769081115723, 2.5379719734191895] |
95d6b910-e26b-462c-aa41-94a23272b47e | learning-to-classify-intents-and-slot-labels | 2004.10793 | null | https://arxiv.org/abs/2004.10793v1 | https://arxiv.org/pdf/2004.10793v1.pdf | Learning to Classify Intents and Slot Labels Given a Handful of Examples | Intent classification (IC) and slot filling (SF) are core components in most goal-oriented dialogue systems. Current IC/SF models perform poorly when the number of training examples per class is small. We propose a new few-shot learning task, few-shot IC/SF, to study and improve the performance of IC and SF models on c... | ['Jason Krone', 'Mona Diab', 'Yi Zhang'] | 2020-04-22 | learning-to-classify-intents-and-slot-labels-1 | https://aclanthology.org/2020.nlp4convai-1.12 | https://aclanthology.org/2020.nlp4convai-1.12.pdf | ws-2020-7 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 1.22677952e-01 3.19628298e-01 -6.18696332e-01 -3.24673712e-01
-6.92557216e-01 -2.50920206e-01 1.02037597e+00 8.02980810e-02
-6.75250649e-01 8.28273535e-01 6.00592077e-01 -7.51559213e-02
-1.62639856e-01 -6.95378184e-01 -6.95007518e-02 -1.86792105e-01
8.91395733e-02 9.72976863e-01 5.97807527e-01 -8.89394164... | [11.977683067321777, 7.682446479797363] |
3479e427-0ed3-4fb6-9563-57497664fc8c | just-a-glimpse-rethinking-temporal | 2305.18418 | null | https://arxiv.org/abs/2305.18418v2 | https://arxiv.org/pdf/2305.18418v2.pdf | Just a Glimpse: Rethinking Temporal Information for Video Continual Learning | Class-incremental learning is one of the most important settings for the study of Continual Learning, as it closely resembles real-world application scenarios. With constrained memory sizes, catastrophic forgetting arises as the number of classes/tasks increases. Studying continual learning in the video domain poses ev... | ['Bernard Ghanem', 'Chen Zhao', 'Merey Ramazanova', 'Juan Leon Alcazar', 'Lama Alssum'] | 2023-05-28 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 7.66217187e-02 -7.16548145e-01 -5.24404168e-01 5.02133705e-02
-4.90173608e-01 -2.42084652e-01 4.73519921e-01 1.65108442e-01
-7.70003498e-01 9.36111629e-01 -1.88419838e-02 -1.77780733e-01
-1.21485889e-02 -3.18122685e-01 -1.15767312e+00 -8.30305398e-01
-5.35190880e-01 6.35913163e-02 7.23972917e-01 1.06187105... | [8.605113983154297, 0.7086646556854248] |
19136360-54bc-4192-a958-6afdd20e59e0 | deep-learned-svt-unrolling-singular-value | 2105.06934 | null | https://arxiv.org/abs/2105.06934v1 | https://arxiv.org/pdf/2105.06934v1.pdf | Deep learned SVT: Unrolling singular value thresholding to obtain better MSE | Affine rank minimization problem is the generalized version of low rank matrix completion problem where linear combinations of the entries of a low rank matrix are observed and the matrix is estimated from these measurements. We propose a trainable deep neural network by unrolling a popular iterative algorithm called t... | ['Sheetal Kalyani', 'Siva Shanmugam'] | 2021-05-14 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [ 3.40795308e-01 1.32724226e-01 2.75857717e-01 -4.25073504e-01
-9.00186956e-01 -7.20228910e-01 4.81788844e-01 -4.07018304e-01
-6.05123758e-01 6.93624616e-01 4.24500525e-01 -1.87967643e-01
-6.48294091e-01 -2.27565587e-01 -1.24508154e+00 -7.42448092e-01
-2.67091930e-01 6.22634828e-01 -3.94358367e-01 -1.38615757... | [6.990872383117676, 4.590592861175537] |
1caec31e-4c6f-4392-b12e-35084f6c9736 | generalized-rectifier-wavelet-covariance-1 | 2203.07902 | null | https://arxiv.org/abs/2203.07902v1 | https://arxiv.org/pdf/2203.07902v1.pdf | Generalized Rectifier Wavelet Covariance Models For Texture Synthesis | State-of-the-art maximum entropy models for texture synthesis are built from statistics relying on image representations defined by convolutional neural networks (CNN). Such representations capture rich structures in texture images, outperforming wavelet-based representations in this regard. However, conversely to neur... | ['Stéphane Mallat', 'Sixin Zhang', 'Antoine Brochard'] | 2022-03-14 | generalized-rectifier-wavelet-covariance | https://openreview.net/forum?id=ziRLU3Y2PN_ | https://openreview.net/pdf?id=ziRLU3Y2PN_ | iclr-2022-4 | ['texture-synthesis'] | ['computer-vision'] | [ 3.36427033e-01 1.45299971e-01 -3.50833088e-01 -1.17756158e-01
-6.16877913e-01 -1.78831682e-01 9.12585258e-01 2.59896576e-01
-1.60124794e-01 7.39924908e-01 2.59289891e-01 1.53716087e-01
-2.46315226e-01 -1.38847697e+00 -7.89095700e-01 -9.86516654e-01
-3.70022744e-01 5.96834570e-02 2.19061926e-01 -5.61867416... | [11.301395416259766, -0.6428686380386353] |
e412f938-8b1d-4352-be9b-b60efc13040e | what-if-we-do-not-have-multiple-videos-of-the | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Sultani_What_If_We_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Sultani_What_If_We_CVPR_2016_paper.pdf | What If We Do Not Have Multiple Videos of the Same Action? -- Video Action Localization Using Web Images | This paper tackles the problem of spatio-temporal action localization in a video without assuming the availability of multiple videos or any prior annotations. Action is localized by employing images downloaded from internet using action name. Given web images, we first mitigate image noise using random walk framewor... | ['Waqas Sultani', 'Mubarak Shah'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 4.10319299e-01 -1.16217211e-01 -2.77665913e-01 1.57720502e-02
-5.82205951e-01 -5.18527210e-01 3.11474770e-01 -2.87016094e-01
-5.27303040e-01 7.02544332e-01 5.43735385e-01 2.16754094e-01
-1.20477512e-01 -2.33573943e-01 -8.58401299e-01 -8.32814813e-01
6.07113615e-02 -4.44776535e-01 7.69961715e-01 3.50497663... | [9.269017219543457, -0.25957173109054565] |
b67fe4fa-beba-4d9c-8e17-cb9beece79b2 | polyglot-distributed-word-representations-for | 1307.1662 | null | http://arxiv.org/abs/1307.1662v2 | http://arxiv.org/pdf/1307.1662v2.pdf | Polyglot: Distributed Word Representations for Multilingual NLP | Distributed word representations (word embeddings) have recently contributed
to competitive performance in language modeling and several NLP tasks. In this
work, we train word embeddings for more than 100 languages using their
corresponding Wikipedias. We quantitatively demonstrate the utility of our word
embeddings by... | ['Rami Al-Rfou', 'Steven Skiena', 'Bryan Perozzi'] | 2013-07-05 | polyglot-distributed-word-representations-for-1 | https://aclanthology.org/W13-3520 | https://aclanthology.org/W13-3520.pdf | ws-2013-8 | ['multilingual-nlp'] | ['natural-language-processing'] | [-5.75676799e-01 -5.35756499e-02 -7.25799918e-01 -3.99762839e-01
-9.29694533e-01 -7.21320331e-01 8.60815763e-01 4.90137637e-01
-9.01584387e-01 5.01303673e-01 9.36637402e-01 -4.45055425e-01
4.35143150e-02 -7.31428146e-01 -1.52410045e-01 -2.12196678e-01
-7.47406781e-02 5.85958123e-01 -4.48355405e-03 -4.70576584... | [10.77316951751709, 9.65013599395752] |
c30f3811-b153-473f-b8de-3011247badfa | can-deep-neural-networks-learn-process-model | 2202.11985 | null | https://arxiv.org/abs/2202.11985v1 | https://arxiv.org/pdf/2202.11985v1.pdf | Can deep neural networks learn process model structure? An assessment framework and analysis | Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning havebeen proposed for these tasks in recent years. Especiall... | ['Jochen De Weerdt', 'Seppe vanden Broucke', 'Jari Peeperkorn'] | 2022-02-24 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 4.53434408e-01 3.95962119e-01 -9.76897329e-02 -2.90878147e-01
-1.35229826e-01 -2.59292185e-01 1.06727719e+00 4.83445257e-01
-4.54711653e-02 4.17137951e-01 3.11284542e-01 -5.24436295e-01
-6.83007598e-01 -8.73681128e-01 -4.31892961e-01 -4.16160047e-01
-2.89921463e-01 5.93646288e-01 7.88411573e-02 1.38883799... | [8.583480834960938, 5.935131549835205] |
fa1b16ae-9d29-4bd5-9605-269b351ccba6 | exploration-in-nethack-with-secret-discovery | 1711.03087 | null | http://arxiv.org/abs/1711.03087v2 | http://arxiv.org/pdf/1711.03087v2.pdf | Exploration in NetHack With Secret Discovery | Roguelike games generally feature exploration problems as a critical, yet
often repetitive element of gameplay. Automated approaches, however, face
challenges in terms of optimality, as well as due to incomplete information,
such as from the presence of secret doors. This paper presents an algorithmic
approach to explo... | ['Jonathan C. Campbell', 'Clark Verbrugge'] | 2017-11-08 | null | null | null | null | ['nethack'] | ['playing-games'] | [ 1.36630148e-01 2.97061205e-01 1.37714684e-01 1.93427905e-01
-4.95264739e-01 -1.03008032e+00 4.48032796e-01 2.11727962e-01
-8.02289307e-01 1.15157318e+00 -2.28028819e-02 -4.85176831e-01
-5.83746493e-01 -9.53263998e-01 -5.04034340e-01 -4.16971624e-01
-6.64430857e-01 6.89825892e-01 5.19429862e-01 -5.29214323... | [3.777085781097412, 1.5532044172286987] |
f22ad567-228d-4e50-9458-ffa2ac66ad5f | toward-negotiable-reinforcement-learning | 1701.01302 | null | http://arxiv.org/abs/1701.01302v3 | http://arxiv.org/pdf/1701.01302v3.pdf | Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making | Existing multi-objective reinforcement learning (MORL) algorithms do not
account for objectives that arise from players with differing beliefs.
Concretely, consider two players with different beliefs and utility functions
who may cooperate to build a machine that takes actions on their behalf. A
representation is neede... | ['Andrew Critch'] | 2017-01-05 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.44514605e-01 6.30950034e-01 -5.29168010e-01 -1.03063777e-01
-6.90828979e-01 -6.17411554e-01 4.58199769e-01 3.04400563e-01
-9.82490540e-01 1.11284816e+00 4.18113261e-01 -5.18602848e-01
-5.36299467e-01 -9.55060780e-01 -1.99200250e-02 -8.45481336e-01
7.31570572e-02 9.98983085e-01 1.02378972e-01 -2.02277631... | [4.16765832901001, 2.6879467964172363] |
067dbff5-6b14-4b31-a715-fda3d2b7f680 | structured-vision-language-pretraining-for | 2212.04267 | null | https://arxiv.org/abs/2212.04267v2 | https://arxiv.org/pdf/2212.04267v2.pdf | Vision and Structured-Language Pretraining for Cross-Modal Food Retrieval | Vision-Language Pretraining (VLP) and Foundation models have been the go-to recipe for achieving SoTA performance on general benchmarks. However, leveraging these powerful techniques for more complex vision-language tasks, such as cooking applications, with more structured input data, is still little investigated. In t... | ['Matthieu Cord', 'Nicolas Thome', 'Mustafa Shukor'] | 2022-12-08 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 3.8126844e-01 -8.9261524e-02 -2.2779524e-01 -3.9452615e-01
-8.4416741e-01 -7.1249437e-01 6.3662642e-01 3.0353433e-01
-5.9125149e-01 1.5612085e-01 4.1309187e-01 -1.3910040e-01
2.4094589e-01 -6.0710996e-01 -1.2651625e+00 -5.0868177e-01
1.6478612e-01 3.2013464e-01 -1.6550277e-01 -1.6487253e-01
-1.2994841e-01... | [10.8156156539917, 1.6325703859329224] |
40e6ee3b-2ff9-411a-bbc9-7c166c6f06cf | variational-sequential-labelers-for-semi-1 | 1906.09535 | null | https://arxiv.org/abs/1906.09535v1 | https://arxiv.org/pdf/1906.09535v1.pdf | Variational Sequential Labelers for Semi-Supervised Learning | We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration fr... | ['Karen Livescu', 'Mingda Chen', 'Kevin Gimpel', 'Qingming Tang'] | 2019-06-23 | variational-sequential-labelers-for-semi | https://aclanthology.org/D18-1020 | https://aclanthology.org/D18-1020.pdf | emnlp-2018-10 | ['learning-word-embeddings'] | ['methodology'] | [ 3.97829980e-01 -1.16120661e-02 -9.37607110e-01 -5.65068185e-01
-9.36137974e-01 -8.46512973e-01 8.14058661e-01 -1.06457509e-01
-4.80151683e-01 7.36805260e-01 6.00946844e-01 -2.62513280e-01
4.89196658e-01 -3.55586737e-01 -3.90847027e-01 -9.63575006e-01
1.42345011e-01 8.10328543e-01 -1.08814411e-01 2.72832751... | [11.614514350891113, 9.22871208190918] |
3bad05d8-68a2-43e3-b289-db2a2f5c58ab | exploiting-context-information-for-generic | 2207.01050 | null | https://arxiv.org/abs/2207.01050v1 | https://arxiv.org/pdf/2207.01050v1.pdf | Exploiting Context Information for Generic Event Boundary Captioning | Generic Event Boundary Captioning (GEBC) aims to generate three sentences describing the status change for a given time boundary. Previous methods only process the information of a single boundary at a time, which lacks utilization of video context information. To tackle this issue, we design a model that directly take... | ['Ping Luo', 'Ran Cheng', 'Feng Zheng', 'Teng Wang', 'Jinrui Zhang'] | 2022-07-03 | null | null | null | null | ['boundary-captioning'] | ['computer-vision'] | [ 2.75017262e-01 3.28176990e-02 -1.51703998e-01 -4.75960761e-01
-9.83876109e-01 -4.32927668e-01 3.98110986e-01 -1.20543800e-02
-2.64176637e-01 8.61204922e-01 5.19856811e-01 -1.97879791e-01
4.47280794e-01 -5.73209405e-01 -9.05068636e-01 -3.98271114e-01
3.53812799e-02 1.55714989e-01 4.75114703e-01 -1.47601053... | [10.386734962463379, 0.7050961256027222] |
a45806f4-8254-4418-903b-1ea1d9695208 | raidionics-an-open-software-for-pre-and | 2305.14351 | null | https://arxiv.org/abs/2305.14351v1 | https://arxiv.org/pdf/2305.14351v1.pdf | Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting | For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation and generation of clinical reports... | ['Ingerid Reinertsen', 'Ole Solheim', 'André Pedersen', 'Ragnhild Holden Helland', 'Valeria Gaitan', 'Demah Alsinan', 'David Bouget'] | 2023-04-28 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [-6.08306937e-02 3.69297087e-01 -1.28216997e-01 -2.61803597e-01
-1.01841879e+00 -2.58145422e-01 3.78843129e-01 8.36398125e-01
-7.42137432e-01 8.69594812e-01 4.55287099e-02 -5.13125896e-01
-2.12000847e-01 -6.60558939e-01 7.51851425e-02 -8.18947077e-01
-1.71852320e-01 9.02516127e-01 3.01845104e-01 1.40719548... | [14.68093490600586, -2.5449600219726562] |
7e69260f-5feb-4a6e-aca0-f4e23d92fc3a | attack-on-practical-speaker-verification | 2105.09022 | null | https://arxiv.org/abs/2105.09022v1 | https://arxiv.org/pdf/2105.09022v1.pdf | Attack on practical speaker verification system using universal adversarial perturbations | In authentication scenarios, applications of practical speaker verification systems usually require a person to read a dynamic authentication text. Previous studies played an audio adversarial example as a digital signal to perform physical attacks, which would be easily rejected by audio replay detection modules. This... | ['Xiaolin Hu', 'Thomas Fang Zheng', 'Xingliang Cheng', 'Jianmin Li', 'Le Liu', 'Shuning Zhao', 'Weiyi Zhang'] | 2021-05-19 | null | null | null | null | ['real-world-adversarial-attack', 'room-impulse-response'] | ['adversarial', 'audio'] | [ 5.31135142e-01 2.74427980e-01 5.37296653e-01 7.82058612e-02
-1.23412931e+00 -9.52686012e-01 2.99360335e-01 -1.27498120e-01
-2.88582146e-01 4.15463686e-01 2.07843930e-02 -6.35193884e-01
3.13775450e-01 -3.95816982e-01 -6.57620132e-01 -9.17669713e-01
-2.68542558e-01 -1.93562478e-01 1.13376349e-01 -1.83083817... | [13.987298965454102, 5.809381484985352] |
effeb4fb-e6c5-4907-a495-7a6cac7a5a1b | machine-and-deep-learning-methods-with-manual | 2210.10903 | null | https://arxiv.org/abs/2210.10903v1 | https://arxiv.org/pdf/2210.10903v1.pdf | Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language | Research in Natural Language Processing (NLP) has increasingly become important due to applications such as text classification, text mining, sentiment analysis, POS tagging, named entity recognition, textual entailment, and many others. This paper introduces several machine and deep learning methods with manual and au... | ['Rashid Mehmood', 'Fahad AlQurashi', 'Istiak Ahmad'] | 2022-10-19 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-4.10844058e-01 -7.95978904e-02 -4.82531697e-01 -3.89188796e-01
-5.98884583e-01 -6.30224705e-01 7.84983575e-01 4.67300057e-01
-9.07506764e-01 8.81047904e-01 7.49990165e-01 -7.48631299e-01
2.30098516e-01 -1.00166965e+00 -2.73075819e-01 -8.44165802e-01
9.59089771e-02 5.54784834e-01 -2.47167632e-01 -7.58868977... | [10.272985458374023, 9.699134826660156] |
58a8fcd7-bb16-4214-b771-97a5924156e3 | integrating-nearest-neighbors-on-neural | 2305.06789 | null | https://arxiv.org/abs/2305.06789v2 | https://arxiv.org/pdf/2305.06789v2.pdf | Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation | Treatment effect estimation is of high-importance for both researchers and practitioners across many scientific and industrial domains. The abundance of observational data makes them increasingly used by researchers for the estimation of causal effects. However, these data suffer from biases, from several weaknesses, l... | ['Christos Diou', 'Niki Kiriakidou'] | 2023-05-11 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 4.84799802e-01 -1.65691838e-01 -1.10313404e+00 -3.23772609e-01
-5.58983386e-01 -8.28276128e-02 6.15860105e-01 5.51788509e-01
-2.42553473e-01 1.18041646e+00 7.33787119e-01 -5.38587928e-01
-8.99100125e-01 -9.50049162e-01 -8.41553450e-01 -9.82814372e-01
-3.39892119e-01 2.22099945e-01 -4.00441855e-01 -1.24695860... | [8.045384407043457, 5.414320945739746] |
e9215038-a20d-4c45-8d08-a4407f18f766 | hitpr-hierarchical-transformer-for-place | 2204.05481 | null | https://arxiv.org/abs/2204.05481v1 | https://arxiv.org/pdf/2204.05481v1.pdf | HiTPR: Hierarchical Transformer for Place Recognition in Point Cloud | Place recognition or loop closure detection is one of the core components in a full SLAM system. In this paper, aiming at strengthening the relevancy of local neighboring points and the contextual dependency among global points simultaneously, we investigate the exploitation of transformer-based network for feature ext... | ['Hui Kong', 'Chengzhong Xu', 'Yan Yan', 'Zhixing Hou'] | 2022-04-12 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.11300563e-02 -1.76138371e-01 5.68163930e-04 -2.60547072e-01
-6.72033787e-01 -4.03066188e-01 7.53523529e-01 6.55235350e-01
-5.71774125e-01 5.79498410e-01 -2.55859308e-02 -3.26781720e-02
-4.21311945e-01 -1.07082748e+00 -9.18224871e-01 -7.34747112e-01
-1.94550663e-01 4.57971811e-01 6.19440019e-01 -1.79918692... | [7.588040828704834, -2.1698057651519775] |
98374afd-5fa0-4c34-806f-eb2e41b24176 | continuous-human-activity-recognition-using-a | 2304.06173 | null | https://arxiv.org/abs/2304.06173v1 | https://arxiv.org/pdf/2304.06173v1.pdf | Continuous Human Activity Recognition using a MIMO Radar for Transitional Motion Analysis | The prompt and accurate recognition of Continuous Human Activity (CHAR) is critical in identifying and responding to health events, particularly fall risk assessment. In this paper, we examine a multi-antenna radar system that can process radar data returns for multiple individuals in an indoor setting, enabling CHAR f... | ['Syed A. Hamza', 'LaJuan Washington Jr.', 'Bennett J. Richman', 'John Kobak'] | 2023-04-12 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 7.59628832e-01 -6.25401199e-01 -3.98967750e-02 -1.85942322e-01
-1.00866592e+00 -3.73482019e-01 2.76015162e-01 -7.26807788e-02
-4.49291945e-01 5.18988013e-01 3.97199839e-01 -1.59267947e-01
-3.31863165e-01 -7.75629103e-01 -5.93228154e-02 -6.51622355e-01
-7.17097700e-01 2.08174065e-01 3.00380498e-01 1.72423482... | [6.775967597961426, 0.516135036945343] |
da883953-16ac-41a5-9f78-f3f65e7dce4f | active-sequential-two-sample-testing | 2301.12616 | null | https://arxiv.org/abs/2301.12616v3 | https://arxiv.org/pdf/2301.12616v3.pdf | Active Sequential Two-Sample Testing | Two-sample testing tests whether the distributions generating two samples are identical. We pose the two-sample testing problem in a new scenario where the sample measurements (or sample features) are inexpensive to access, but their group memberships (or labels) are costly. We devise the first \emph{active sequential ... | ['Visar Berisha', 'Gautam Dasarathy', 'Pouria Saidi', 'Prad Kadambi', 'Karthikeyan Natesan Ramamurthy', 'Weizhi Li'] | 2023-01-30 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 7.08671272e-01 5.99764520e-03 -5.62788367e-01 -7.09625483e-01
-1.45187676e+00 -7.58093059e-01 2.77667612e-01 7.10715428e-02
-3.88308942e-01 1.00341487e+00 -6.40341759e-01 -5.16542435e-01
-6.03699803e-01 -9.39058244e-01 -6.05240703e-01 -9.20193791e-01
-7.79310167e-02 9.51894462e-01 4.91034925e-01 3.71752620... | [7.659017562866211, 4.383215427398682] |
62671cbd-0880-4048-9ff3-adb3894241b2 | crosel-cross-selection-of-confident-pseudo | 2303.10365 | null | https://arxiv.org/abs/2303.10365v2 | https://arxiv.org/pdf/2303.10365v2.pdf | CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning | Partial-label learning (PLL) is an important weakly supervised learning problem, which allows each training example to have a candidate label set instead of a single ground-truth label. Identification-based methods have been widely explored to tackle label ambiguity issues in PLL, which regard the true label as a laten... | ['Lei Feng', 'Yiqun Wang', 'Hongxin Wei', 'Shiyu Tian'] | 2023-03-18 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 1.81374043e-01 -2.61710547e-02 -6.68314755e-01 -7.22014844e-01
-1.31232488e+00 -7.20649958e-01 3.86025459e-01 -2.52251215e-02
-3.44536930e-01 9.81568694e-01 -4.02759850e-01 7.09213614e-02
-8.46960172e-02 -4.04494524e-01 -5.88307738e-01 -1.02158856e+00
3.27719808e-01 6.44465804e-01 -3.84583138e-02 5.86147130... | [9.446141242980957, 4.0109968185424805] |
b8a68b55-8486-4073-bcb6-e277ff65b1f9 | perceptual-loss-for-robust-unsupervised | 2104.10011 | null | https://arxiv.org/abs/2104.10011v1 | https://arxiv.org/pdf/2104.10011v1.pdf | Perceptual Loss for Robust Unsupervised Homography Estimation | Homography estimation is often an indispensable step in many computer vision tasks. The existing approaches, however, are not robust to illumination and/or larger viewpoint changes. In this paper, we propose bidirectional implicit Homography Estimation (biHomE) loss for unsupervised homography estimation. biHomE minimi... | ['Bahram Zonooz', 'Elahe Arani', 'Daniel Koguciuk'] | 2021-04-20 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.36734107e-01 -1.13077894e-01 8.49707276e-02 -1.40109658e-01
-5.85559487e-01 -5.59741676e-01 7.59071290e-01 -6.07300818e-01
-2.53783446e-02 6.08058691e-01 2.60723531e-02 3.40571851e-01
-3.48767964e-03 -5.53921998e-01 -9.95376587e-01 -8.06496024e-01
4.86250609e-01 3.84495348e-01 4.71206615e-04 -7.16894865... | [8.68472671508789, -2.333843946456909] |
aa3024af-e302-48a5-9079-c06cf690935c | clothing-change-feature-augmentation-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Han_Clothing-Change_Feature_Augmentation_for_Person_Re-Identification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Han_Clothing-Change_Feature_Augmentation_for_Person_Re-Identification_CVPR_2023_paper.pdf | Clothing-Change Feature Augmentation for Person Re-Identification | Clothing-change person re-identification (CC Re-ID) aims to match the same person who changes clothes across cameras. Current methods are usually limited by the insufficient number and variation of clothing in training data, e.g. each person only has 2 outfits in the PRCC dataset. In this work, we propose a novel C... | ['Tieniu Tan', 'Liang Wang', 'Yan Huang', 'Shaogang Gong', 'Ke Han'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification'] | ['computer-vision'] | [ 4.43715721e-01 -2.64980912e-01 2.51256585e-01 -6.07369363e-01
-3.02677333e-01 -7.13320255e-01 4.89239544e-01 -3.70684266e-01
-1.60110623e-01 5.20635009e-01 3.05685967e-01 4.76741225e-01
1.64125517e-01 -6.71511471e-01 -9.15546238e-01 -6.23574734e-01
1.14276223e-01 1.44549429e-01 -3.63304824e-01 -2.39874125... | [12.029805183410645, -0.837383508682251] |
aa17a5eb-01c5-45c6-b2dc-b32af2f5431e | dynamic-portfolio-optimization-with-inverse | 2112.15499 | null | https://arxiv.org/abs/2112.15499v2 | https://arxiv.org/pdf/2112.15499v2.pdf | Dynamic Portfolio Optimization with Inverse Covariance Clustering | Market conditions change continuously. However, in portfolio's investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into ... | ['Tomaso Aste', 'Yuanrong Wang'] | 2021-12-31 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-3.68615091e-01 -5.03973126e-01 1.64282937e-02 -2.56506111e-02
-4.14286137e-01 -1.16861534e+00 8.27960312e-01 -2.26498336e-01
2.27280855e-02 5.78945756e-01 1.15270950e-02 -6.40870929e-01
-9.14117873e-01 -1.00227058e+00 -2.26760492e-01 -7.44895995e-01
-2.24818960e-01 6.49702728e-01 1.74457416e-01 -1.15303427... | [4.927839279174805, 4.069913864135742] |
54c32506-243c-4633-a626-ff932cc5838b | language-based-audio-retrieval-task-in-dcase | 2209.09967 | null | https://arxiv.org/abs/2209.09967v3 | https://arxiv.org/pdf/2209.09967v3.pdf | Language-based Audio Retrieval Task in DCASE 2022 Challenge | Language-based audio retrieval is a task, where natural language textual captions are used as queries to retrieve audio signals from a dataset. It has been first introduced into DCASE 2022 Challenge as Subtask 6B of task 6, which aims at developing computational systems to model relationships between audio signals and ... | ['Tuomas Virtanen', 'Samuel Lipping', 'Huang Xie'] | 2022-09-20 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 5.26318312e-01 3.50779714e-03 3.26706320e-01 -1.52614221e-01
-2.22903609e+00 -7.69491971e-01 6.94716454e-01 3.31521034e-01
-1.24502957e-01 6.25048339e-01 7.93061554e-01 1.52375758e-01
-1.24251775e-01 -8.87225196e-02 -8.34304810e-01 5.88714750e-03
-5.26182234e-01 4.86790329e-01 2.84027904e-01 -3.71710271... | [15.279250144958496, 4.890787601470947] |
ffbe97e9-6327-419f-ab91-38af4c775940 | unsupervised-learning-of-compositional-energy | 2111.03042 | null | https://arxiv.org/abs/2111.03042v1 | https://arxiv.org/pdf/2111.03042v1.pdf | Unsupervised Learning of Compositional Energy Concepts | Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience. Such concepts are diverse, and include global scene descriptors, such as the weather or lighting, as well as local scene descriptors, such as the color or size of a particular object. So far, unsupervised discovery of co... | ['Igor Mordatch', 'Joshua B. Tenenbaum', 'Yash Sharma', 'Shuang Li', 'Yilun Du'] | 2021-11-04 | null | http://proceedings.neurips.cc/paper/2021/hash/838aac83e00e8c5ca0f839c96d6cb3be-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/838aac83e00e8c5ca0f839c96d6cb3be-Paper.pdf | neurips-2021-12 | ['unsupervised-image-decomposition'] | ['computer-vision'] | [ 1.56713836e-02 -3.62818658e-01 1.84201941e-01 -6.01826251e-01
-3.70623171e-01 -8.40987980e-01 7.18355060e-01 5.30920684e-01
-2.47222424e-01 2.74793804e-01 1.61674023e-01 3.59303206e-01
-1.07213981e-01 -8.42195392e-01 -8.90393496e-01 -8.13239634e-01
-3.85507792e-02 3.22467059e-01 -3.42343710e-02 6.40351176... | [9.837468147277832, 1.0989406108856201] |
24825579-01d8-40dc-9f9e-32bc5090ceab | describing-a-knowledge-base | 1809.01797 | null | http://arxiv.org/abs/1809.01797v2 | http://arxiv.org/pdf/1809.01797v2.pdf | Describing a Knowledge Base | We aim to automatically generate natural language descriptions about an input
structured knowledge base (KB). We build our generation framework based on a
pointer network which can copy facts from the input KB, and add two attention
mechanisms: (i) slot-aware attention to capture the association between a slot
type and... | ['Lifu Huang', 'Heng Ji', 'Boliang Zhang', 'Qingyun Wang', 'Zhiying Jiang', 'Kevin Knight', 'Xiaoman Pan'] | 2018-09-06 | describing-a-knowledge-base-1 | https://aclanthology.org/W18-6502 | https://aclanthology.org/W18-6502.pdf | ws-2018-11 | ['kb-to-language-generation', 'table-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [-1.82020620e-01 8.94967556e-01 -4.35505301e-01 -3.59943002e-01
-1.19803917e+00 -4.19960499e-01 5.20162046e-01 3.73840809e-01
-5.28878212e-01 1.50015557e+00 6.78470433e-01 -2.06032917e-01
2.38335773e-01 -1.30905461e+00 -1.06796157e+00 -1.30102649e-01
1.70303866e-01 1.01057434e+00 4.75423664e-01 -5.01077831... | [9.722877502441406, 8.58381462097168] |
dc06250e-051d-46dc-8b33-0517043655a2 | convolutional-attention-networks-for | 1805.06606 | null | http://arxiv.org/abs/1805.06606v2 | http://arxiv.org/pdf/1805.06606v2.pdf | Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data | Emotion recognition has become a popular topic of interest, especially in the
field of human computer interaction. Previous works involve unimodal analysis
of emotion, while recent efforts focus on multi-modal emotion recognition from
vision and speech. In this paper, we propose a new method of learning about the
hidde... | ['Ji-Hoon Jeong', 'Chan Woo Lee', 'Woo Yong Choi', 'Kyu Ye Song'] | 2018-05-17 | convolutional-attention-networks-for-1 | https://aclanthology.org/W18-3304 | https://aclanthology.org/W18-3304.pdf | ws-2018-7 | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-4.85744961e-02 -9.67679769e-02 1.27284825e-01 -5.86413205e-01
-6.12813115e-01 -2.10998327e-01 5.91934860e-01 1.75404012e-01
-5.91298163e-01 3.98867369e-01 4.48057264e-01 -3.29443328e-02
3.73470336e-01 -2.22090170e-01 -2.00780302e-01 -5.70731580e-01
1.22501716e-01 1.32692203e-01 -5.85963249e-01 -1.34120017... | [13.297905921936035, 5.377673625946045] |
a4920f9b-ac0a-414b-8695-d889dc62521c | evaluation-of-audio-visual-alignments-in | 2108.02562 | null | https://arxiv.org/abs/2108.02562v1 | https://arxiv.org/pdf/2108.02562v1.pdf | Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models | Systems that can find correspondences between multiple modalities, such as between speech and images, have great potential to solve different recognition and data analysis tasks in an unsupervised manner. This work studies multimodal learning in the context of visually grounded speech (VGS) models, and focuses on their... | ['Okko Räsänen', 'Khazar Khorrami'] | 2021-07-05 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.72869354e-01 -6.08773753e-02 -8.91976729e-02 -4.09504026e-01
-1.35268188e+00 -3.91081184e-01 9.97315466e-01 9.30426195e-02
-5.28811991e-01 1.00195043e-01 3.91660213e-01 2.88751721e-02
-5.75457141e-03 -1.36857539e-01 -7.39256561e-01 -5.57270527e-01
2.64459729e-01 6.18398070e-01 7.20261261e-02 -1.87901139... | [10.922431945800781, 1.4856189489364624] |
d7a9670b-53b8-4e02-bb90-20b533ec3075 | internvideo-general-video-foundation-models | 2212.03191 | null | https://arxiv.org/abs/2212.03191v2 | https://arxiv.org/pdf/2212.03191v2.pdf | InternVideo: General Video Foundation Models via Generative and Discriminative Learning | The foundation models have recently shown excellent performance on a variety of downstream tasks in computer vision. However, most existing vision foundation models simply focus on image-level pretraining and adpation, which are limited for dynamic and complex video-level understanding tasks. To fill the gap, we presen... | ['Yu Qiao', 'LiMin Wang', 'Yali Wang', 'Jiashuo Yu', 'Junting Pan', 'Guo Chen', 'Sen Xing', 'Zun Wang', 'Yi Liu', 'Jilan Xu', 'Hongjie Zhang', 'Zhiyu Zhao', 'Bingkun Huang', 'Yinan He', 'Yizhuo Li', 'Kunchang Li', 'Yi Wang'] | 2022-12-06 | null | null | null | null | ['action-classification', 'video-question-answering', 'open-set-action-recognition', 'video-understanding', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.56218819e-02 -3.43398362e-01 -6.88717604e-01 -2.81103313e-01
-7.38007367e-01 -3.71262223e-01 5.25517642e-01 -6.51868224e-01
-2.62152582e-01 3.33370119e-01 2.07209185e-01 -2.72870958e-01
5.48543155e-01 -2.95533270e-01 -1.11510539e+00 -5.95559597e-01
7.90674388e-02 -1.08969316e-01 3.20933759e-01 -8.97541791... | [9.722247123718262, 0.8124778866767883] |
438b4efa-24f8-4cfd-aa73-81d6652be46f | self-supervised-learning-for-robust-voice | 2204.03421 | null | https://arxiv.org/abs/2204.03421v2 | https://arxiv.org/pdf/2204.03421v2.pdf | Self-supervised learning for robust voice cloning | Voice cloning is a difficult task which requires robust and informative features incorporated in a high quality TTS system in order to effectively copy an unseen speaker's voice. In our work, we utilize features learned in a self-supervised framework via the Bootstrap Your Own Latent (BYOL) method, which is shown to pr... | ['Pirros Tsiakoulis', 'Aimilios Chalamandaris', 'Gunu Jho', 'June Sig Sung', 'Spyros Raptis', 'Konstantinos Markopoulos', 'Panos Kakoulidis', 'Georgios Vamvoukakis', 'Karolos Nikitaras', 'Nikolaos Ellinas', 'Konstantinos Klapsas'] | 2022-04-07 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 3.69116187e-01 3.39930296e-01 1.92464262e-01 -3.02225173e-01
-1.07605910e+00 -6.52673721e-01 6.33200049e-01 -1.59462348e-01
-1.91022396e-01 5.92990160e-01 5.01600444e-01 -9.62185860e-02
1.88781768e-01 -2.66339391e-01 -5.87604403e-01 -8.07623327e-01
3.43994856e-01 2.84164995e-01 2.56751053e-04 -1.29144177... | [14.868396759033203, 6.540005207061768] |
ae950962-070d-4233-b57d-e3c0e6a54b76 | task-specific-normalization-for-continual | 2107.13429 | null | https://arxiv.org/abs/2107.13429v2 | https://arxiv.org/pdf/2107.13429v2.pdf | Task-Specific Normalization for Continual Learning of Blind Image Quality Models | The computational vision community has recently paid attention to continual learning for blind image quality assessment (BIQA). The primary challenge is to combat catastrophic forgetting of previously-seen IQA datasets (i.e., tasks). In this paper, we present a simple yet effective continual learning method for BIQA wi... | ['Xiaokang Yang', 'Guangtao Zhai', 'Kede Ma', 'Weixia Zhang'] | 2021-07-28 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 3.05483013e-01 -2.73231447e-01 4.62096065e-01 -3.71429741e-01
-8.73786390e-01 -3.48280519e-01 3.69435549e-01 -1.81132659e-01
-6.75639987e-01 7.21945941e-01 2.27724880e-01 -3.07400644e-01
-5.02487898e-01 -4.23655778e-01 -7.23716855e-01 -1.00701141e+00
1.38716504e-01 2.50464827e-02 3.88849169e-01 1.06055714... | [11.87967300415039, -1.800777554512024] |
2e195d75-7398-4df4-812e-15be28ab226e | stockemotions-discover-investor-emotions-for | 2301.09279 | null | https://arxiv.org/abs/2301.09279v2 | https://arxiv.org/pdf/2301.09279v2.pdf | StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series | There has been growing interest in applying NLP techniques in the financial domain, however, resources are extremely limited. This paper introduces StockEmotions, a new dataset for detecting emotions in the stock market that consists of 10,000 English comments collected from StockTwits, a financial social media platfor... | ['Soyeon Caren Han', 'Josiah Poon', 'Hoyoul Luis Youn', 'Jean Lee'] | 2023-01-23 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-8.98605168e-01 -3.71899486e-01 -2.40420029e-01 -6.87439322e-01
-2.48602912e-01 -6.95546627e-01 6.74696982e-01 1.57488808e-02
-4.17151093e-01 5.33786654e-01 5.34205258e-01 -3.54821414e-01
3.52825582e-01 -7.43951857e-01 -1.78529024e-01 -2.21006572e-01
-2.60927707e-01 -1.28361508e-01 -7.46228695e-02 -5.33720434... | [4.440478324890137, 4.2959771156311035] |
d6d94415-74b2-4a57-99bf-09909af9da24 | transition-based-dependency-parsing-with | null | null | https://aclanthology.org/P16-2001 | https://aclanthology.org/P16-2001.pdf | Transition-based dependency parsing with topological fields | null | ['Dani{\\"e}l de Kok', 'Erhard Hinrichs'] | 2016-08-01 | null | null | null | acl-2016-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.355665683746338, 3.693446636199951] |
21b77cf0-06e2-46a1-ad42-0bdf1c4a7fe3 | assigning-confidence-to-molecular-property | 2102.11439 | null | https://arxiv.org/abs/2102.11439v1 | https://arxiv.org/pdf/2102.11439v1.pdf | Assigning Confidence to Molecular Property Prediction | Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design. Recently, machine learning techniques have emerged as a powerful and cost-effective strategy to learn from existing datasets and perform prediction... | ['Alán Aspuru-Guzik', 'Vincent A. Voelz', 'Seyone Chithrananda', 'Naruki Yoshikawa', 'Matteo Aldeghi', 'Riley J. Hickman', 'Matthew F. D. Hurley', 'Robert Pollice', 'AkshatKumar Nigam'] | 2021-02-23 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 4.76557165e-01 -1.90584272e-01 -3.35737973e-01 -1.52918652e-01
-8.96854758e-01 -8.11027706e-01 4.71869290e-01 8.70468974e-01
-3.03449422e-01 1.52237856e+00 -1.32132590e-01 -7.03538537e-01
-5.30642092e-01 -5.82783043e-01 -7.36283779e-01 -9.10648406e-01
-5.24912365e-02 6.41219139e-01 -1.07125103e-01 7.75697827... | [5.126319885253906, 5.51729679107666] |
944ff6d5-4dff-4372-8951-7734f55e0028 | crackle-detection-in-lung-sounds-using | 2104.14921 | null | https://arxiv.org/abs/2104.14921v1 | https://arxiv.org/pdf/2104.14921v1.pdf | Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks | Large annotated lung sound databases are publicly available and might be used to train algorithms for diagnosis systems. However, it might be a challenge to develop a well-performing algorithm for small non-public data, which have only a few subjects and show differences in recording devices and setup. In this paper, w... | ['Franz Pernkopf', 'Truc Nguyen'] | 2021-04-30 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 1.74179107e-01 -1.29703134e-01 4.75489907e-02 -1.42831355e-01
-1.21619153e+00 -5.17039001e-01 -1.46427780e-01 -1.22671381e-01
-1.53638110e-01 2.44415939e-01 9.45956483e-02 -4.28561091e-01
1.39065906e-01 -9.35108721e-01 -7.71423876e-01 -6.62016392e-01
1.52129248e-01 4.60287571e-01 7.60495305e-01 2.48122200... | [14.5599365234375, 3.906963586807251] |
320493e7-0ec5-4aea-9102-300da1addd4d | real-time-convolutional-neural-networks-for | 1710.07557 | null | http://arxiv.org/abs/1710.07557v1 | http://arxiv.org/pdf/1710.07557v1.pdf | Real-time Convolutional Neural Networks for Emotion and Gender Classification | In this paper we propose an implement a general convolutional neural network
(CNN) building framework for designing real-time CNNs. We validate our models
by creating a real-time vision system which accomplishes the tasks of face
detection, gender classification and emotion classification simultaneously in
one blended ... | ['Paul Plöger', 'Matias Valdenegro-Toro', 'Octavio Arriaga'] | 2017-10-20 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-2.89709747e-01 2.37874061e-01 4.23006296e-01 -7.61140764e-01
1.81270957e-01 -1.53613970e-01 6.62653565e-01 -1.37313500e-01
-7.99350023e-01 3.67107809e-01 -2.08914727e-01 -6.77581728e-02
-4.88556214e-02 -3.21068913e-01 -5.77798843e-01 -5.80057144e-01
-6.07731044e-01 5.41064024e-01 -1.90755352e-01 -5.99466980... | [13.522012710571289, 1.8426072597503662] |
25657a42-8cda-4da9-a8d6-530660b44872 | compressed-heterogeneous-graph-for | 2303.06565 | null | https://arxiv.org/abs/2303.06565v1 | https://arxiv.org/pdf/2303.06565v1.pdf | Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization | Multi-document summarization (MDS) aims to generate a summary for a number of related documents. We propose HGSUM, an MDS model that extends an encoder-decoder architecture, to incorporate a heterogeneous graph to represent different semantic units (e.g., words and sentences) of the documents. This contrasts with exist... | ['Jey Han Lau', 'Jianzhong Qi', 'Miao Li'] | 2023-03-12 | null | null | null | null | ['graph-similarity', 'multi-document-summarization', 'document-summarization'] | ['graphs', 'natural-language-processing', 'natural-language-processing'] | [ 1.82246447e-01 7.55575597e-01 -2.94219106e-01 -1.71419472e-01
-9.96653020e-01 -5.66582859e-01 7.92727530e-01 6.60441279e-01
1.56327069e-01 6.77898049e-01 1.24463296e+00 1.95398688e-01
-1.10316455e-01 -8.82467151e-01 -9.13238585e-01 -3.01793605e-01
-9.60880145e-02 5.55420160e-01 8.51992965e-02 -1.03919476... | [12.437117576599121, 9.437346458435059] |
dcd0e829-8568-40e8-af69-961860e1bce9 | dreamidentity-improved-editability-for | 2307.00300 | null | https://arxiv.org/abs/2307.00300v1 | https://arxiv.org/pdf/2307.00300v1.pdf | DreamIdentity: Improved Editability for Efficient Face-identity Preserved Image Generation | While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centric images, an intractable problem is how to preserve the face identity for conditioned face images. Existing methods either require time-consuming optimization for each face-identity or learning an efficient encoder at... | ['Zhendong Mao', 'Yongdong Zhang', 'Mengqi Huang', 'Qian He', 'Wei Liu', 'Shancheng Fang', 'Zhuowei Chen'] | 2023-07-01 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 2.34184310e-01 2.70168353e-02 2.57138252e-01 -7.98409581e-01
-6.86398029e-01 -4.34445590e-01 5.26082397e-01 -8.60094309e-01
-2.21157849e-01 5.28286994e-01 2.19212368e-01 3.39073449e-01
2.63249129e-01 -6.87390983e-01 -1.02449358e+00 -5.13508022e-01
4.94203746e-01 4.19412702e-01 -5.01686871e-01 -8.43934119... | [12.569753646850586, -0.08234184980392456] |
ed078b14-99ad-4d70-baea-48fe67df75cc | on-the-use-of-arxiv-as-a-dataset | 1905.00075 | null | http://arxiv.org/abs/1905.00075v1 | http://arxiv.org/pdf/1905.00075v1.pdf | On the Use of ArXiv as a Dataset | The arXiv has collected 1.5 million pre-print articles over 28 years, hosting
literature from scientific fields including Physics, Mathematics, and Computer
Science. Each pre-print features text, figures, authors, citations, categories,
and other metadata. These rich, multi-modal features, combined with the natural
gra... | ["Kevin P. O'Keeffe", 'Alexander A. Alemi', 'Matthew Bierbaum', 'Colin B. Clement'] | 2019-04-30 | null | null | null | null | ['author-attribution', 'text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-2.35952288e-01 2.05023661e-01 -4.26758438e-01 1.65187642e-02
-6.55364990e-01 -1.13859808e+00 1.16942227e+00 6.22443080e-01
-1.68702111e-01 7.43597984e-01 5.20218968e-01 -7.63285995e-01
-2.69935399e-01 -1.04132581e+00 -7.12796330e-01 -8.66088420e-02
-2.38068312e-01 6.10452414e-01 -4.70370799e-02 2.29643002... | [9.561223983764648, 8.159749031066895] |
8f839d65-f2d8-44e2-94c5-34603db306f7 | what-makes-a-good-dataset-for-symbol | 2304.08352 | null | https://arxiv.org/abs/2304.08352v1 | https://arxiv.org/pdf/2304.08352v1.pdf | What Makes a Good Dataset for Symbol Description Reading? | The usage of mathematical formulas as concise representations of a document's key ideas is common practice. Correctly interpreting these formulas, by identifying mathematical symbols and extracting their descriptions, is an important task in document understanding. This paper makes the following contributions to the ma... | ['Bradley Eck', 'Joern Ploennigs', 'Karol Lynch'] | 2023-04-17 | null | null | null | null | ['phrase-ranking'] | ['natural-language-processing'] | [ 3.75384331e-01 1.24300756e-01 -3.20010424e-01 -6.08002007e-01
-1.26733685e+00 -8.54209721e-01 9.13865566e-01 7.71036625e-01
-1.71571881e-01 5.17748415e-01 3.96347612e-01 -8.89676273e-01
-5.66494524e-01 -8.47607970e-01 -7.65549541e-01 3.33631933e-01
1.09910265e-01 8.50573778e-01 7.90416449e-02 -7.96047986... | [9.59323787689209, 7.50109338760376] |
f3343604-3707-4b30-a4f7-26e2de041cbe | shuffle-and-learn-unsupervised-learning-using | 1603.08561 | null | http://arxiv.org/abs/1603.08561v2 | http://arxiv.org/pdf/1603.08561v2.pdf | Shuffle and Learn: Unsupervised Learning using Temporal Order Verification | In this paper, we present an approach for learning a visual representation
from the raw spatiotemporal signals in videos. Our representation is learned
without supervision from semantic labels. We formulate our method as an
unsupervised sequential verification task, i.e., we determine whether a
sequence of frames from ... | ['Martial Hebert', 'C. Lawrence Zitnick', 'Ishan Misra'] | 2016-03-28 | null | null | null | null | ['self-supervised-action-recognition', 'video-alignment'] | ['computer-vision', 'computer-vision'] | [ 4.44443256e-01 -1.06184445e-02 -3.51998180e-01 -6.45513892e-01
-7.52312005e-01 -7.10951447e-01 6.48951411e-01 -1.52623683e-01
-5.64091206e-01 6.69784307e-01 4.04469818e-01 2.62729019e-01
2.91727453e-01 -1.80008993e-01 -1.13040102e+00 -5.19798696e-01
-3.26484352e-01 2.95982361e-01 3.85119051e-01 -7.13272393... | [8.298137664794922, 0.4740368723869324] |
6d396de9-f603-4f08-9acc-a0f28932767c | a-sequence-to-sequence-model-for-user | 1607.00070 | null | http://arxiv.org/abs/1607.00070v1 | http://arxiv.org/pdf/1607.00070v1.pdf | A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems | User simulation is essential for generating enough data to train a
statistical spoken dialogue system. Previous models for user simulation suffer
from several drawbacks, such as the inability to take dialogue history into
account, the need of rigid structure to ensure coherent user behaviour, heavy
dependence on a spec... | ['Kaheer Suleman', 'Jing He', 'Layla El Asri'] | 2016-06-30 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [ 4.64426637e-01 5.79218447e-01 1.27702102e-01 -5.24475992e-01
-7.92025924e-01 -6.85685754e-01 1.03560007e+00 -9.08148661e-02
-4.34295654e-01 7.94314742e-01 8.47206950e-01 -5.74014425e-01
3.20717186e-01 -4.37044024e-01 6.09864946e-03 -1.56324625e-01
7.51494318e-02 8.71696353e-01 1.76216751e-01 -8.82862747... | [12.935517311096191, 7.996414661407471] |
efda7080-252b-4a06-b43a-241a59cf01f8 | perception-based-energy-functions-in-seam | 1701.06141 | null | http://arxiv.org/abs/1701.06141v1 | http://arxiv.org/pdf/1701.06141v1.pdf | Perception-based energy functions in seam-cutting | Image stitching is challenging in consumer-level photography, due to
alignment difficulties in unconstrained shooting environment. Recent studies
show that seam-cutting approaches can effectively relieve artifacts generated
by local misalignment. Normally, seam-cutting is described in terms of energy
minimization, howe... | ['Tianli Liao', 'Chao Wang', 'Nan Li'] | 2017-01-22 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.17873859e-01 -1.17455177e-01 1.65726170e-01 -3.57670844e-01
-2.14364395e-01 -4.02856499e-01 2.56610274e-01 -1.93157405e-01
-2.72911876e-01 2.91516721e-01 2.56831437e-01 -1.18688755e-01
2.96322078e-01 -4.76228625e-01 -7.39192963e-01 -3.85999590e-01
5.02928019e-01 -3.57448786e-01 5.43348789e-01 -4.33717668... | [11.165724754333496, -1.2078474760055542] |
48d866ce-37d2-4a98-ac78-c4c319a203e3 | attribute2font-creating-fonts-you-want-from | 2005.07865 | null | https://arxiv.org/abs/2005.07865v1 | https://arxiv.org/pdf/2005.07865v1.pdf | Attribute2Font: Creating Fonts You Want From Attributes | Font design is now still considered as an exclusive privilege of professional designers, whose creativity is not possessed by existing software systems. Nevertheless, we also notice that most commercial font products are in fact manually designed by following specific requirements on some attributes of glyphs, such as ... | ['Yue Gao', 'Zhouhui Lian', 'Yizhi Wang'] | 2020-05-16 | null | null | null | null | ['font-style-transfer'] | ['computer-vision'] | [ 4.65327203e-01 -6.33349195e-02 1.27680972e-01 -5.70948303e-01
-2.89422646e-02 -9.36152637e-01 5.12495935e-01 -6.28269315e-02
-3.25051621e-02 6.88108027e-01 -1.66564226e-01 -3.86722118e-01
1.77526623e-01 -8.77995014e-01 -6.74208403e-01 -5.30343831e-01
6.61545098e-01 3.81750017e-01 -4.09007892e-02 -3.50399375... | [11.67818546295166, -0.32473182678222656] |
187cfff9-ea30-4041-972c-41869a365081 | automatically-identifying-words-that-can | 2010.13641 | null | https://arxiv.org/abs/2010.13641v1 | https://arxiv.org/pdf/2010.13641v1.pdf | Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification | A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels. Manually defining this mapping between words and labels requires both domain expertise and a... | ['Hinrich Schütze', 'Helmut Schmid', 'Timo Schick'] | 2020-10-26 | null | https://aclanthology.org/2020.coling-main.488 | https://aclanthology.org/2020.coling-main.488.pdf | coling-2020-8 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.95742863e-01 8.91340524e-02 -4.22181517e-01 -8.33871126e-01
-1.11868930e+00 -7.89099336e-01 7.31453955e-01 4.72443342e-01
-5.69820523e-01 5.17999232e-01 2.74668872e-01 -5.08995771e-01
4.55237664e-02 -6.74580276e-01 -1.75206453e-01 -3.26670446e-02
4.71508831e-01 6.48245454e-01 2.50216573e-01 -4.27600056... | [10.702798843383789, 7.943620681762695] |
21416845-a1f0-4195-833d-18dbd7ddcd33 | synbols-probing-learning-algorithms-with | 2009.06415 | null | https://arxiv.org/abs/2009.06415v2 | https://arxiv.org/pdf/2009.06415v2.pdf | Synbols: Probing Learning Algorithms with Synthetic Datasets | Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to test specific properties and failure modes of learning algorithms is thus a problem of high interest, as it has a direct impact on innovation ... | ['David Vázquez', 'Matt Craddock', 'Frédéric Branchaud-Charron', 'Pau Rodríguez', 'Issam Laradji', 'Parmida Atighehchian', 'Massimo Caccia', 'Laurent Charlin', 'Alexandre Lacoste', 'Alexandre Drouin'] | 2020-09-14 | null | http://proceedings.neurips.cc/paper/2020/hash/0169cf885f882efd795951253db5cdfb-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/0169cf885f882efd795951253db5cdfb-Paper.pdf | neurips-2020-12 | ['object-counting'] | ['computer-vision'] | [ 3.82118225e-01 -1.24268100e-01 -1.25476450e-01 -4.93300945e-01
-6.23845220e-01 -7.97950804e-01 9.28430498e-01 2.86474645e-01
-3.39263767e-01 8.03837538e-01 3.10164923e-03 -3.21038991e-01
-3.81706357e-01 -7.30860353e-01 -5.87585509e-01 -6.93148673e-01
-3.80132079e-01 4.40989554e-01 2.07434803e-01 -6.56804517... | [9.655993461608887, 2.635838747024536] |
36cc2597-1ca2-4a98-988f-927a5b1a9665 | self-supervised-deep-subspace-clustering-with | 2206.04958 | null | https://arxiv.org/abs/2206.04958v1 | https://arxiv.org/pdf/2206.04958v1.pdf | Self-Supervised Deep Subspace Clustering with Entropy-norm | Auto-Encoder based deep subspace clustering (DSC) is widely used in computer vision, motion segmentation and image processing. However, it suffers from the following three issues in the self-expressive matrix learning process: the first one is less useful information for learning self-expressive weights due to the simp... | ['Xuesong Yin', 'Simin Kou', 'Guangyi Zhao'] | 2022-06-10 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-8.94108638e-02 -1.94984570e-01 -5.75162172e-02 -2.42908344e-01
-3.67169559e-01 1.03870086e-01 9.51119885e-02 -2.24627405e-01
-4.19257015e-01 3.66017133e-01 2.47889206e-01 4.19838220e-01
-4.62062210e-01 -6.38467610e-01 -5.50781906e-01 -1.11315596e+00
-2.62997627e-01 1.24417275e-01 1.42202720e-01 -1.73343405... | [8.483704566955566, 4.023707866668701] |
9202691e-9d20-48ff-8393-9515bf21757c | non-intrusive-load-monitoring-with-fully | 1812.03915 | null | http://arxiv.org/abs/1812.03915v1 | http://arxiv.org/pdf/1812.03915v1.pdf | Non-Intrusive Load Monitoring with Fully Convolutional Networks | Non-intrusive load monitoring or energy disaggregation involves estimating
the power consumption of individual appliances from measurements of the total
power consumption of a home. Deep neural networks have been shown to be
effective for energy disaggregation. In this work, we present a deep neural
network architectur... | ['Cillian Brewitt', 'Nigel Goddard'] | 2018-12-10 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-1.03061825e-01 6.80293664e-02 -1.30834971e-02 -4.83131230e-01
-4.82149422e-01 -5.11663795e-01 3.60560119e-01 -7.75902122e-02
-1.72002494e-01 9.98640120e-01 2.38959581e-01 -3.53669554e-01
1.67122439e-01 -1.12993240e+00 -2.94071257e-01 -1.05152178e+00
5.79999313e-02 4.56078321e-01 -5.32944322e-01 2.39765003... | [16.064964294433594, 7.5791754722595215] |
9e8e2a77-6639-4833-a29e-2233eca20b60 | genplot-increasing-the-scale-and-diversity-of | 2306.11699 | null | https://arxiv.org/abs/2306.11699v1 | https://arxiv.org/pdf/2306.11699v1.pdf | GenPlot: Increasing the Scale and Diversity of Chart Derendering Data | Vertical bars, horizontal bars, dot, scatter, and line plots provide a diverse set of visualizations to represent data. To understand these plots, one must be able to recognize textual components, locate data points in a plot, and process diverse visual contexts to extract information. In recent works such as Pix2Struc... | ['Brendan Artley'] | 2023-06-20 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 1.94628403e-01 -1.51201561e-01 -5.76275848e-02 -3.32964778e-01
-6.68570518e-01 -1.11310768e+00 8.66420627e-01 3.27394992e-01
3.96717817e-01 4.23806995e-01 2.71902859e-01 -8.42471659e-01
1.99290246e-01 -5.41404665e-01 -4.89560544e-01 -2.24314700e-03
3.35136987e-02 2.06883937e-01 -8.83542076e-02 -1.92430347... | [11.315768241882324, 1.962185263633728] |
00675df2-596b-453c-9a89-2296d6c37f11 | end-to-end-integration-of-speech-recognition | 2204.00540 | null | https://arxiv.org/abs/2204.00540v1 | https://arxiv.org/pdf/2204.00540v1.pdf | End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation | This work presents our end-to-end (E2E) automatic speech recognition (ASR) model targetting at robust speech recognition, called Integraded speech Recognition with enhanced speech Input for Self-supervised learning representation (IRIS). Compared with conventional E2E ASR models, the proposed E2E model integrates two i... | ['Shinji Watanabe', 'Yuya Fujita', 'Takashi Maekaku', 'Xuankai Chang'] | 2022-04-01 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 4.61563855e-01 1.86967656e-01 3.12307596e-01 -4.31155324e-01
-1.53666246e+00 -2.00987890e-01 6.68185353e-01 -2.14848742e-01
-4.71586347e-01 2.32307598e-01 3.92166287e-01 -5.16633928e-01
1.26347184e-01 -7.82687217e-02 -6.00532413e-01 -7.07432866e-01
4.27296758e-02 3.64071578e-02 1.58990815e-01 -4.57070172... | [14.688508987426758, 6.214816570281982] |
6c20ff67-bd32-45dd-ab8b-652f47984cb3 | a-framework-for-differentiable-discovery-of | null | null | https://openreview.net/forum?id=ueiBFzt7CiK | https://openreview.net/pdf?id=ueiBFzt7CiK | A Framework For Differentiable Discovery Of Graph Algorithms | Recently there is a surge of interests in using graph neural networks (GNNs) to learn algorithms. However, these works focus more on imitating existing algorithms, and are limited in two important aspects: the search space for algorithms is too small and the learned GNN models are not interpretable. To address these is... | ['Le Song', 'Xin Gao', 'Yu Li', 'Xinshi Chen', 'Hanjun Dai'] | 2021-01-01 | null | https://openreview.net/forum?id=5UvvKsBTDcR | https://openreview.net/pdf?id=5UvvKsBTDcR | neurips-workshop-lmca-2020-12 | ['tree-decomposition'] | ['graphs'] | [ 3.04781139e-01 5.25871098e-01 -5.18219471e-01 4.69786488e-02
2.67181635e-01 -7.06813693e-01 3.50483626e-01 2.26697236e-01
1.86869964e-01 7.22447932e-01 -1.24757521e-01 -7.05114543e-01
-6.36391103e-01 -1.40632617e+00 -9.93756950e-01 -3.22719038e-01
-1.71580225e-01 7.84933329e-01 2.27394640e-01 -8.75157490... | [7.313413143157959, 6.139611721038818] |
6bdfbce2-b6e0-482f-a1b0-dc4fd5f3af3d | game-plan-what-ai-can-do-for-football-and | 2011.09192 | null | https://arxiv.org/abs/2011.09192v1 | https://arxiv.org/pdf/2011.09192v1.pdf | Game Plan: What AI can do for Football, and What Football can do for AI | The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professiona... | ['Demis Hassabis', 'Thore Graepel', 'Jackson Broshear', 'Nathalie Beauguerlange', 'Simon Bouton', 'Razia Ahamed', 'Trevor Back', 'Remi Munos', 'Andrew Jaegle', 'Mark Rowland', 'Ali Eslami', 'Bart De Vylder', 'Julien Perolat', 'Alex Bridgland', 'Nicolas Heess', 'Michal Valko', 'Praneet Dutta', 'Marta Garnelo', 'Kris Cao... | 2020-11-18 | null | null | null | null | ['game-of-football'] | ['playing-games'] | [ 9.46837142e-02 -4.01947312e-02 -4.97688711e-01 1.67594045e-01
-5.65730035e-01 -5.29934108e-01 3.65139097e-01 5.35428882e-01
-6.55568957e-01 5.95799148e-01 3.97013932e-01 -2.55112857e-01
-7.96109021e-01 -8.51568580e-01 -4.36649382e-01 -5.71812987e-01
-5.37740767e-01 6.62352204e-01 5.37193790e-02 -7.56632090... | [6.585975646972656, 0.3760640621185303] |
e0106137-0c8c-43d5-926d-f40213f6b91b | on-the-sample-complexity-of-vanilla-model | 2303.04268 | null | https://arxiv.org/abs/2303.04268v1 | https://arxiv.org/pdf/2303.04268v1.pdf | On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples | Offline reinforcement learning (offline RL) considers problems where learning is performed using only previously collected samples and is helpful for the settings in which collecting new data is costly or risky. In model-based offline RL, the learner performs estimation (or optimization) using a model constructed accor... | ['Ufuk Topcu', 'Mustafa O. Karabag'] | 2023-03-07 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.94026639e-02 4.34508651e-01 -8.51766706e-01 -7.83703849e-03
-1.28207898e+00 -6.04269624e-01 3.57231677e-01 3.57311398e-01
-9.08217669e-01 1.38793385e+00 -1.65966094e-01 -6.62923336e-01
-2.26572409e-01 -7.78957665e-01 -1.09404922e+00 -6.82775497e-01
-5.20783544e-01 6.74798906e-01 -4.55295891e-02 9.70708430... | [4.28427267074585, 2.6892616748809814] |
8bf81ae6-4e90-4d47-b925-86f0c22fa7b5 | interpretable-amr-based-question | 2206.08486 | null | https://arxiv.org/abs/2206.08486v1 | https://arxiv.org/pdf/2206.08486v1.pdf | Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering | Effective multi-hop question answering (QA) requires reasoning over multiple scattered paragraphs and providing explanations for answers. Most existing approaches cannot provide an interpretable reasoning process to illustrate how these models arrive at an answer. In this paper, we propose a Question Decomposition meth... | ['Patricia Riddle', 'Michael Witbrock', 'Yang Chen', 'Yonghua Zhu', 'Zhenyun Deng'] | 2022-06-16 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.85395315e-01 1.23674166e+00 2.80871063e-01 -5.82215607e-01
-1.71125340e+00 -1.01703358e+00 2.64120936e-01 3.11350375e-01
2.44176894e-01 6.59043610e-01 4.39121664e-01 -1.12820375e+00
-2.51471132e-01 -1.17984855e+00 -7.61278868e-01 2.63497084e-01
4.74092185e-01 1.14481068e+00 5.76829374e-01 -7.16373146... | [10.984414100646973, 7.9216437339782715] |
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