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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
daa2fbe1-ba62-4131-8e31-473ea9bf9d8f | memguard-defending-against-black-box | 1909.10594 | null | https://arxiv.org/abs/1909.10594v3 | https://arxiv.org/pdf/1909.10594v3.pdf | MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples | In a membership inference attack, an attacker aims to infer whether a data sample is in a target classifier's training dataset or not. Specifically, given a black-box access to the target classifier, the attacker trains a binary classifier, which takes a data sample's confidence score vector predicted by the target cla... | ['Neil Zhenqiang Gong', 'Ahmed Salem', 'Jinyuan Jia', 'Yang Zhang', 'Michael Backes'] | 2019-09-23 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 4.34222519e-01 2.55597979e-01 -4.31125075e-01 -4.13173854e-01
-6.52625501e-01 -1.26040006e+00 4.41730618e-01 1.57628134e-01
-2.58050054e-01 5.64121306e-01 -4.28298324e-01 -8.72939587e-01
6.79814517e-02 -1.26271045e+00 -8.68375421e-01 -7.69704044e-01
-2.67436981e-01 2.10150704e-01 1.35466665e-01 1.39160275... | [5.89677095413208, 7.20881986618042] |
b4e0673b-07c2-4610-b5a1-1cf0f3f434a5 | yelan-event-camera-based-3d-human-pose | 2301.06648 | null | https://arxiv.org/abs/2301.06648v2 | https://arxiv.org/pdf/2301.06648v2.pdf | Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment | As a beloved sport worldwide, dancing is getting integrated into traditional and virtual reality-based gaming platforms nowadays. It opens up new opportunities in the technology-mediated dancing space. These platforms primarily rely on passive and continuous human pose estimation as an input capture mechanism. Existing... | ['Tauhidur Rahman', 'Donghyun Kim', 'Hava Siegelmann', 'Upal Mahbub', 'Edward Wang', 'Francesca Walsh', 'Ramzi Majaj', 'Haowen Yu', 'Kaidong Chai', 'Zhongyang Zhang'] | 2023-01-17 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-2.83318490e-01 -6.67081892e-01 1.51723683e-01 3.91491205e-02
-4.98710811e-01 -5.05785584e-01 7.26090148e-02 -3.49481553e-01
-7.60727286e-01 4.01162684e-01 8.81786123e-02 2.03709841e-01
2.73752570e-01 -8.02526116e-01 -5.27785778e-01 -4.67315465e-01
1.64364874e-01 1.13377124e-01 8.89484882e-01 -4.72711235... | [7.230047702789307, -0.9601946473121643] |
639ae6d1-4d8f-4058-8b0e-d43d4d536e7a | facemap-towards-unsupervised-face-clustering | 2203.10090 | null | https://arxiv.org/abs/2203.10090v1 | https://arxiv.org/pdf/2203.10090v1.pdf | FaceMap: Towards Unsupervised Face Clustering via Map Equation | Face clustering is an essential task in computer vision due to the explosion of related applications such as augmented reality or photo album management. The main challenge of this task lies in the imperfectness of similarities among image feature representations. Given an existing feature extraction model, it is still... | ['Xiaoyu Wang', 'Guangming Lu', 'Hanling Yi', 'Ling Xing', 'Aibo Wang', 'Yifan Yang', 'Xiaotian Yu'] | 2022-03-21 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 1.98855087e-01 6.51236176e-02 1.21652864e-01 -5.07209957e-01
-3.68306458e-01 -2.98847407e-01 3.76131147e-01 -1.86812043e-01
-1.21188559e-01 2.09265992e-01 9.27889347e-02 2.03484312e-01
-5.25633574e-01 -3.69603872e-01 -6.38195693e-01 -7.69525290e-01
-3.39193165e-01 3.51408809e-01 -1.18952021e-01 4.64386046... | [13.464273452758789, 1.0651777982711792] |
0bfb27e3-0d27-4f31-8668-9654386ec6f9 | natural-language-guided-visual-relationship | 1711.06032 | null | http://arxiv.org/abs/1711.06032v2 | http://arxiv.org/pdf/1711.06032v2.pdf | Natural Language Guided Visual Relationship Detection | Reasoning about the relationships between object pairs in images is a crucial
task for holistic scene understanding. Most of the existing works treat this
task as a pure visual classification task: each type of relationship or phrase
is classified as a relation category based on the extracted visual features.
However, ... | ['Bodo Rosenhahn', 'Wentong Liao', 'Lin Shuai', 'Michael Ying Yang'] | 2017-11-16 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.40003800e-01 -9.09669548e-02 -2.33005673e-01 -4.41447526e-01
-2.05648780e-01 -3.10346216e-01 9.55422103e-01 2.03693867e-01
-1.08966872e-01 3.27286154e-01 1.20069496e-01 -2.41681829e-01
-7.35965967e-02 -9.34727728e-01 -5.83517313e-01 -4.61066842e-01
2.01918527e-01 3.96081358e-01 3.98355484e-01 -3.50564063... | [10.32104778289795, 1.6516989469528198] |
4cd0ac3e-a5ee-4947-965d-44340d621a6e | unsupervised-hdr-imaging-what-can-be-learned | 2202.05522 | null | https://arxiv.org/abs/2202.05522v1 | https://arxiv.org/pdf/2202.05522v1.pdf | Unsupervised HDR Imaging: What Can Be Learned from a Single 8-bit Video? | Recently, Deep Learning-based methods for inverse tone-mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. Typically, these methods, to be effective, need ... | ['Thomas Bashford-Rogers', 'Kurt Debattista', 'Demetris Marnerides', 'Francesco Banterle'] | 2022-02-11 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 5.90582132e-01 -6.49471655e-02 1.37432560e-01 -1.94546118e-01
-6.47841513e-01 -2.81235814e-01 4.71141577e-01 -4.36649650e-01
-2.93762684e-01 1.05283439e+00 -4.27463055e-02 2.50379313e-02
-1.71154290e-01 -9.47849691e-01 -8.94113004e-01 -6.86208069e-01
1.11434437e-01 3.57662410e-01 5.43853760e-01 -5.41297913... | [11.086297988891602, -2.164604663848877] |
5bacaa20-43dd-4caf-8052-809a27270df0 | swahbert-language-model-of-swahili | null | null | https://aclanthology.org/2022.naacl-main.23 | https://aclanthology.org/2022.naacl-main.23.pdf | SwahBERT: Language Model of Swahili | The rapid development of social networks, electronic commerce, mobile Internet, and other technologies, has influenced the growth of Web data.Social media and Internet forums are valuable sources of citizens’ opinions, which can be analyzed for community development and user behavior analysis.Unfortunately, the scarcit... | ['Jeong Young-Seob', 'Young-Seob Jeong', 'Medard Edmund Mswahili', 'Gati Martin'] | null | null | null | null | naacl-2022-7 | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-6.10873103e-01 -2.36250520e-01 -3.23386431e-01 -4.67270523e-01
-5.76610029e-01 -7.55746543e-01 4.55122590e-01 4.83243436e-01
-9.62197304e-01 8.45551372e-01 3.04210812e-01 -4.51321423e-01
5.17158449e-01 -7.41237521e-01 -3.64183076e-02 -4.07110155e-01
-1.22300230e-01 4.36840415e-01 -2.19505310e-01 -6.09843552... | [11.171838760375977, 7.037832260131836] |
5e491994-cb8b-47db-a298-38d3171b3f05 | comparing-cross-correlation-based | 2111.08513 | null | https://arxiv.org/abs/2111.08513v2 | https://arxiv.org/pdf/2111.08513v2.pdf | Comparing Cross Correlation-Based Similarities | The real-valued Jaccard and coincidence indices, in addition to their conceptual and computational simplicity, have been verified to be able to provide promising results in tasks such as template matching, tending to yield peaks that are sharper and narrower than those typically obtained by standard cross-correlation, ... | ['Luciano da F. Costa'] | 2021-11-08 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.15262049e-01 -1.71968356e-01 2.76976436e-01 -7.82300383e-02
-6.90766871e-01 -4.53223169e-01 1.07678807e+00 8.70857954e-01
-7.52727032e-01 8.23559701e-01 -3.11398834e-01 1.88998252e-01
-1.08163822e+00 -9.40547168e-01 -4.31912750e-01 -1.13177216e+00
-3.32424998e-01 5.45105159e-01 1.28067225e-01 -3.30251634... | [8.104124069213867, 3.8519415855407715] |
e09c079a-0e82-4a5f-9d46-bde37d19410e | inpaintnerf360-text-guided-3d-inpainting-on | 2305.15094 | null | https://arxiv.org/abs/2305.15094v1 | https://arxiv.org/pdf/2305.15094v1.pdf | InpaintNeRF360: Text-Guided 3D Inpainting on Unbounded Neural Radiance Fields | Neural Radiance Fields (NeRF) can generate highly realistic novel views. However, editing 3D scenes represented by NeRF across 360-degree views, particularly removing objects while preserving geometric and photometric consistency, remains a challenging problem due to NeRF's implicit scene representation. In this paper,... | ['Sabine Süsstrunk', 'Alaa Abboud', 'Tong Zhang', 'Dongqing Wang'] | 2023-05-24 | null | null | null | null | ['3d-inpainting'] | ['computer-vision'] | [ 7.68643498e-01 3.01365823e-01 4.27483052e-01 -5.09029329e-01
-7.67869711e-01 -8.46256971e-01 4.79550779e-01 -3.00824642e-01
1.23025887e-01 5.40197253e-01 2.19413236e-01 -1.25533752e-02
5.87256551e-02 -7.55397141e-01 -1.19888616e+00 -2.14331552e-01
5.79060555e-01 1.99452415e-01 9.34737176e-02 -2.45924756... | [9.300774574279785, -3.069485664367676] |
48773e43-cf0d-4c0e-818f-01e36aa72765 | shrinkml-end-to-end-asr-model-compression | 1907.03540 | null | https://arxiv.org/abs/1907.03540v2 | https://arxiv.org/pdf/1907.03540v2.pdf | ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning | End-to-end automatic speech recognition (ASR) models are increasingly large and complex to achieve the best possible accuracy. In this paper, we build an AutoML system that uses reinforcement learning (RL) to optimize the per-layer compression ratios when applied to a state-of-the-art attention based end-to-end ASR mod... | ['Łukasz Dudziak', 'Nicholas D. Lane', 'Mohamed S. Abdelfattah', 'Stefanos Laskaridis', 'Ravichander Vipperla'] | 2019-07-08 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.25565857e-01 -6.13111211e-03 -6.49619475e-02 -1.99718788e-01
-1.53198481e+00 -2.69193858e-01 4.92290407e-01 -3.60342525e-02
-1.00948608e+00 5.06664574e-01 4.44012523e-01 -6.09575450e-01
-1.71737865e-01 -1.62177369e-01 -7.52888680e-01 -4.30441797e-01
9.07959640e-02 9.56368864e-01 -7.02502578e-02 -2.61620909... | [14.35792350769043, 6.817792892456055] |
3a8824eb-609f-45c4-bc81-50ccb93a2f6d | towards-a-flexible-deep-learning-method-for | 1905.08059 | null | https://arxiv.org/abs/1905.08059v1 | https://arxiv.org/pdf/1905.08059v1.pdf | Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram | Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation ... | ['Stanislas Chambon', 'Poul Jennum', 'Emmanuel Mignot', 'Valentin Thorey', 'Helge B. D. Sorensen', 'Alexander Neergaard Olesen'] | 2019-05-16 | null | null | null | null | ['multimodal-sleep-stage-detection', 'sleep-staging'] | ['medical', 'medical'] | [ 1.06959455e-01 3.12712491e-02 -1.58468876e-02 -6.22621238e-01
-1.21581994e-01 -2.74060845e-01 3.59580964e-01 3.08753490e-01
-7.83487439e-01 7.21957862e-01 2.53682107e-01 -9.29810479e-02
3.41983065e-02 -3.75379354e-01 5.80448732e-02 -6.50380671e-01
-3.61547977e-01 3.09472889e-01 4.20393229e-01 2.17083804... | [13.5518217086792, 3.4874937534332275] |
1cf43314-cf1a-49d4-b740-bb559a1b9b54 | best-of-three-worlds-analysis-for-linear | 2303.06825 | null | https://arxiv.org/abs/2303.06825v1 | https://arxiv.org/pdf/2303.06825v1.pdf | Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm | The linear bandit problem has been studied for many years in both stochastic and adversarial settings. Designing an algorithm that can optimize the environment without knowing the loss type attracts lots of interest. \citet{LeeLWZ021} propose an algorithm that actively detects the loss type and then switches between di... | ['Shuai Li', 'Canzhe Zhao', 'Fang Kong'] | 2023-03-13 | null | null | null | null | ['type'] | ['speech'] | [ 4.04565372e-02 1.02284133e-01 -7.88365245e-01 -3.35200578e-01
-8.72364819e-01 -8.32810640e-01 1.01724572e-01 -1.68348268e-01
-4.14713591e-01 1.12063694e+00 -1.04854189e-01 -6.03444099e-01
-8.43732297e-01 -6.49939775e-01 -9.50571597e-01 -1.25871694e+00
-4.89964560e-02 5.72902441e-01 -1.11831374e-01 -2.71298379... | [4.536576747894287, 3.370358943939209] |
0044a742-bfb6-4c77-9536-68da4e275e68 | robot-task-planning-based-on-large-language | 2306.05171 | null | https://arxiv.org/abs/2306.05171v1 | https://arxiv.org/pdf/2306.05171v1.pdf | Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures | Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks. We propose a task planning method that combines human expertise with an LLM and have designed an LLM prompt template, Think_Net_Prompt, with stronger expressive power to represent structured pro... | ['Fang Yi-shu', 'Chen Zi-rui', 'Shi Hai-peng', 'Pan Wei-qin', 'Lu Xing-tong', 'Sheng Bi', 'Yue Zhen'] | 2023-06-08 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 7.49200955e-02 4.81632531e-01 1.69603094e-01 -4.55861568e-01
-3.48920971e-01 -8.64980102e-01 2.52214789e-01 -9.87757817e-02
-1.00531831e-01 5.57246327e-01 4.19157356e-01 -2.57274568e-01
-3.59927088e-01 -4.41709906e-01 -3.38887662e-01 -3.39568742e-02
2.59387523e-01 7.14013636e-01 2.73937196e-01 -4.14622009... | [4.461702823638916, 0.9454271197319031] |
7b5e005b-4392-45af-b089-a4eea2ee09ee | automatic-gesture-recognition-in-robot | 2002.08718 | null | https://arxiv.org/abs/2002.08718v1 | https://arxiv.org/pdf/2002.08718v1.pdf | Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search | Automatic surgical gesture recognition is fundamental for improving intelligence in robot-assisted surgery, such as conducting complicated tasks of surgery surveillance and skill evaluation. However, current methods treat each frame individually and produce the outcomes without effective consideration on future informa... | ['Pheng-Ann Heng', 'Xiaojie Gao', 'Qi Dou', 'Yueming Jin'] | 2020-02-20 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 5.88014901e-01 4.24989998e-01 -4.48312074e-01 -4.07749563e-01
-7.19601095e-01 -4.68476862e-01 4.24770683e-01 6.42606243e-02
-9.57568407e-01 6.26681149e-01 1.48612544e-01 -4.30268943e-01
-3.08931410e-01 -3.33969265e-01 -5.02944887e-01 -9.30899799e-01
9.54535455e-02 4.93039548e-01 8.66933614e-02 -1.00680985... | [14.07989501953125, -3.315211772918701] |
2f5483e0-a35b-4795-ab13-e248346a557e | comparing-deep-learning-models-for-multi-cell | 1910.00722 | null | https://arxiv.org/abs/1910.00722v1 | https://arxiv.org/pdf/1910.00722v1.pdf | Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images | Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for collecting a majority of the cervical cells and aiding cytopathologists in locating abnormal cells. Most methods published in the research literature rely on accurate cell s... | ['Sudhir Sornapudi', 'Lisa Allen', 'Zhiyun Xue', 'Sameer Antani', 'G. T. Brown', 'Rodney Long'] | 2019-10-02 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.37818116e-01 1.21872880e-01 -1.73323229e-01 -8.30597207e-02
-7.12304354e-01 -7.97841251e-01 2.93558300e-01 1.04436684e+00
-2.89304286e-01 6.18260980e-01 -3.75341624e-01 -8.77453983e-01
5.98275550e-02 -1.08818126e+00 -3.61481279e-01 -1.10179818e+00
-3.82634555e-03 8.42970610e-01 4.83742982e-01 1.96918413... | [15.030685424804688, -3.1179134845733643] |
f44d4da9-2cf8-42ad-af7c-832e3554e64b | scenegraphnet-neural-message-passing-for-3d | 1907.11308 | null | https://arxiv.org/abs/1907.11308v1 | https://arxiv.org/pdf/1907.11308v1.pdf | SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation | In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings. Given an input, potentially incomplete, 3D scene and a query location, our method predicts a probability distribution over object types that fit well in that location. Our distrib... | ['Evangelos Kalogerakis', 'Yang Zhou', 'Zachary While'] | 2019-07-25 | scenegraphnet-neural-message-passing-for-3d-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_SceneGraphNet_Neural_Message_Passing_for_3D_Indoor_Scene_Augmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_SceneGraphNet_Neural_Message_Passing_for_3D_Indoor_Scene_Augmentation_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-object-recognition', 'scene-generation'] | ['computer-vision', 'computer-vision'] | [ 0.5689176 0.05201081 0.15315624 -0.8021307 -0.5889449 -0.35642007
0.6958231 0.51752794 -0.24299783 0.4073267 0.30021694 -0.05985869
-0.03582001 -1.1660492 -1.1979598 -0.13106082 -0.47485423 0.66658086
0.44099608 0.20181161 0.34809974 0.8563183 -1.923015 0.4564458
0.27202648 0.9044424 0.8... | [8.326822280883789, -3.078261137008667] |
da7d69c5-df16-49cc-873c-581a33f2e0a3 | a-generalist-framework-for-panoptic | 2210.06366 | null | https://arxiv.org/abs/2210.06366v2 | https://arxiv.org/pdf/2210.06366v2.pdf | A Generalist Framework for Panoptic Segmentation of Images and Videos | Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image. As permutations of instance IDs are also valid solutions, the task requires learning of high-dimensional one-to-many mapping. As a result, state-of-the-art approaches use customized architectures and task-specific loss functions. ... | ['David J. Fleet', 'Geoffrey Hinton', 'Saurabh Saxena', 'Lala Li', 'Ting Chen'] | 2022-10-12 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 8.96170855e-01 2.23274902e-01 -2.77204126e-01 -4.52979416e-01
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-3.48994248e-02 -9.05324996e-01 -1.10253954e+00 -6.44203663e-01
-6.23387806e-02 7.20015585e-01 2.58227646e-01 4.09226626... | [9.691221237182617, 0.7077986598014832] |
303c734c-c146-42ec-85eb-84183c8cef30 | understanding-text-driven-motion-synthesis | 2305.13773 | null | https://arxiv.org/abs/2305.13773v1 | https://arxiv.org/pdf/2305.13773v1.pdf | Understanding Text-driven Motion Synthesis with Keyframe Collaboration via Diffusion Models | The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently. However, in most cases, textual expressions only contain general and qualitative motion descriptions, while lack fine depiction and sufficient intensity, leading to the synthesized motions that either ... | ['Jianfeng Lu', 'Weiqing Li', 'Shengxiang Hu', 'Bin Li', 'Huaijiang Sun', 'Xiaoning Sun', 'Dong Wei'] | 2023-05-23 | null | null | null | null | ['motion-synthesis'] | ['computer-vision'] | [-1.74354911e-01 1.49717554e-01 -4.46951296e-03 -6.83691129e-02
-3.49115372e-01 -5.64473867e-01 8.89438868e-01 -6.71566248e-01
-1.01239689e-01 6.24526441e-01 5.29497862e-01 6.46761134e-02
-4.37877979e-03 -6.72226608e-01 -5.59207737e-01 -7.27360427e-01
1.65178236e-02 3.19729060e-01 2.69722641e-01 -5.51716924... | [10.870596885681152, -0.6900227665901184] |
bab2957c-368b-4ef0-97af-00755c5efa1c | five-a-network-you-only-need-9k-parameters | 2305.08824 | null | https://arxiv.org/abs/2305.08824v1 | https://arxiv.org/pdf/2305.08824v1.pdf | Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement | A lightweight underwater image enhancement network is of great significance for resource-constrained platforms, but balancing model size, computational efficiency, and enhancement performance has proven difficult for previous approaches. In this work, we propose the Five A$^{+}$ Network (FA$^{+}$Net), a highly efficien... | ['ErKang Chen', 'Yun Liu', 'Shi Jun', 'Wenhao Chai', 'Sixiang Chen', 'Jinbin Bai', 'Tian Ye', 'Jingxia Jiang'] | 2023-05-15 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 2.43136540e-01 5.22127235e-03 7.12892115e-01 -2.37860009e-01
-6.36848748e-01 -2.35253200e-01 -2.58998036e-01 4.90912013e-02
-9.30204213e-01 6.30792737e-01 -9.12296027e-02 -3.69889140e-01
-2.97863215e-01 -1.03070843e+00 -7.80088067e-01 -9.34520006e-01
-7.04771757e-01 -6.32990718e-01 3.61445695e-01 -4.56032962... | [10.694324493408203, -3.527733087539673] |
cb5937d9-aea5-4e8b-a5ab-40bda044cf3c | a-act-action-anticipation-through-cycle | 2204.00942 | null | https://arxiv.org/abs/2204.00942v1 | https://arxiv.org/pdf/2204.00942v1.pdf | A-ACT: Action Anticipation through Cycle Transformations | While action anticipation has garnered a lot of research interest recently, most of the works focus on anticipating future action directly through observed visual cues only. In this work, we take a step back to analyze how the human capability to anticipate the future can be transferred to machine learning algorithms. ... | ['Tao Mei', 'Amit K. Roy-Chowdhury', 'Liefeng Bo', 'Jingen Liu', 'Akash Gupta'] | 2022-04-02 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 5.87469459e-01 3.51924062e-01 -1.41288489e-01 -6.72192574e-01
1.53306827e-01 -2.31274590e-01 9.35530722e-01 2.20037252e-01
-4.39749420e-01 2.77754605e-01 5.71781576e-01 -2.21647054e-01
-2.21487820e-01 -6.07654631e-01 -4.94593620e-01 -3.05675685e-01
-1.19174927e-01 2.05071270e-01 1.49662241e-01 -3.45033526... | [8.112422943115234, 0.569298267364502] |
a222ae41-57b0-4db0-bfbd-80acccb7daf4 | entity-resolution-in-open-domain | null | null | https://aclanthology.org/2021.naacl-industry.4 | https://aclanthology.org/2021.naacl-industry.4.pdf | Entity Resolution in Open-domain Conversations | In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest. To improve the relevancy of retrieved knowledge, we propose a neural entity linking (NEL) approach. Different from formal documents, such as news, conversational utterances are inf... | ['Dilek Hakkani-Tur', 'Yang Liu', 'Yue Liu', 'Han Wang', 'Akshay Grewal', 'Tiantong Deng', 'Matthew Welch', 'Jiyang Wang', 'Jiangning Chen', 'Mihail Eric', 'Tong Wang', 'Mingyue Shang'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['entity-resolution'] | ['natural-language-processing'] | [-1.11929357e-01 5.86266935e-01 -1.56213403e-01 -4.44683701e-01
-9.29737568e-01 -7.30142415e-01 8.61773849e-01 2.71791164e-02
-7.01745868e-01 1.41413534e+00 8.71111512e-01 -2.19297707e-01
2.38269523e-01 -8.00589442e-01 -4.95664448e-01 -9.80895460e-02
3.32989842e-01 6.59822464e-01 2.47629270e-01 -7.96574473... | [12.417398452758789, 8.092597007751465] |
75b93b4a-13e4-4bbc-95b4-97150aa4405f | double-doubly-robust-thompson-sampling-for | 2209.06983 | null | https://arxiv.org/abs/2209.06983v2 | https://arxiv.org/pdf/2209.06983v2.pdf | Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits | We propose a novel contextual bandit algorithm for generalized linear rewards with an $\tilde{O}(\sqrt{\kappa^{-1} \phi T})$ regret over $T$ rounds where $\phi$ is the minimum eigenvalue of the covariance of contexts and $\kappa$ is a lower bound of the variance of rewards. In several practical cases where $\phi=O(d)$,... | ['Myunghee Cho Paik', 'Kyungbok Lee', 'Wonyoung Kim'] | 2022-09-15 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.99510321e-01 2.34248698e-01 -8.22451293e-01 -3.67912561e-01
-1.40484273e+00 -9.11892056e-01 1.52143184e-02 -1.01133082e-02
-7.08877444e-01 1.11288095e+00 -5.22557236e-02 -8.82401407e-01
-8.96970809e-01 -6.71628654e-01 -1.09956634e+00 -8.37857425e-01
-4.74607915e-01 3.96023303e-01 -2.71136463e-01 -7.82586038... | [4.5486741065979, 3.336412191390991] |
382b34de-7ca9-4ef8-a295-48aee6ee4f97 | neuro-symbolic-reinforcement-learning-with | 2110.10963 | null | https://arxiv.org/abs/2110.10963v1 | https://arxiv.org/pdf/2110.10963v1.pdf | Neuro-Symbolic Reinforcement Learning with First-Order Logic | Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro-symbolic framework ... | ['Alexander Gray', 'Asim Munawar', 'Michiaki Tatsubori', 'Don Joven Agravante', 'Akifumi Wachi', 'Ryosuke Kohita', 'Subhajit Chaudhury', 'Masaki Ono', 'Daiki Kimura'] | 2021-10-21 | null | https://aclanthology.org/2021.emnlp-main.283 | https://aclanthology.org/2021.emnlp-main.283.pdf | emnlp-2021-11 | ['text-based-games'] | ['playing-games'] | [ 9.79884192e-02 7.24770606e-01 -2.86653399e-01 -3.73211950e-01
-3.49208713e-02 -3.99355710e-01 6.43058538e-01 -1.80706620e-01
-4.70724881e-01 1.16198432e+00 7.24019110e-02 -7.15967000e-01
-1.88022226e-01 -1.08105981e+00 -9.28858757e-01 -2.46913388e-01
1.23002708e-01 9.77959633e-01 -2.86681298e-02 -5.13154685... | [3.851123094558716, 1.3111730813980103] |
7e108c15-0144-49bf-9aa1-806f8299efdc | a-weak-self-supervision-with-transition-based | null | null | https://openreview.net/forum?id=zO6zGKsaex | https://openreview.net/pdf?id=zO6zGKsaex | A Weak Self-supervision with Transition-Based Modeling for Reference Resolution | The reference resolution is a task to find the link between an entity and its source action in the same recipe. In this study, we introduce a weak self-supervision method with a transition-based model for reference resolution tasks for recipes, where the aim of the task is to make the syntax of the instructions used fo... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['entity-resolution'] | ['natural-language-processing'] | [ 2.82577872e-01 6.28111780e-01 -6.34654820e-01 -5.03221273e-01
-8.44078422e-01 -2.96529055e-01 6.25852585e-01 2.17746824e-01
-2.39761442e-01 8.55800450e-01 6.51336372e-01 4.68615890e-01
-3.63814533e-02 -5.80051720e-01 -9.42124367e-01 -1.14390634e-01
3.23015690e-01 4.59189892e-01 3.15289080e-01 -7.58158445... | [9.211990356445312, 9.378533363342285] |
106de69f-ebea-48d5-a134-dba6c1e2a7af | cloud-removal-for-remote-sensing-imagery-via | 2009.13015 | null | https://arxiv.org/abs/2009.13015v2 | https://arxiv.org/pdf/2009.13015v2.pdf | Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network | Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties. However, remote sensing imagery is inevitably affected by climate, especially clouds. Removing the cloud in the high-resolution remote sensing satellite image is an indispensable pre-processing... | ['Heng Pan'] | 2020-09-28 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 3.96994174e-01 -4.00674760e-01 3.23179901e-01 1.15145100e-02
-5.11276066e-01 -4.44009006e-01 3.03365260e-01 -5.94312131e-01
-2.03271210e-01 7.22245514e-01 9.72439870e-02 -3.58533323e-01
-5.04546203e-02 -1.27945316e+00 -4.10501420e-01 -1.12044060e+00
2.51149029e-01 1.40863895e-01 -9.47757140e-02 -1.92552015... | [9.982512474060059, -1.8662077188491821] |
7771b73a-01c4-409d-8eae-fb3e8a381639 | causal-semantic-communication-for-digital | 2304.12502 | null | https://arxiv.org/abs/2304.12502v1 | https://arxiv.org/pdf/2304.12502v1.pdf | Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach | A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing (e.g., edge computing), and artificial intelligence (AI) technologies to enable many connected intelligence services. In order to handle the large amounts of network data based on digital twins (... | ['Yong Xiao', 'Walid Saad', 'Christo Kurisummoottil Thomas'] | 2023-04-25 | null | null | null | null | ['causal-inference', 'causal-inference', 'edge-computing'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 3.36262286e-01 8.09308469e-01 -5.42946935e-01 -1.87477350e-01
-1.75216690e-01 -2.45819271e-01 5.65726757e-01 -5.55820823e-01
2.91017264e-01 1.02169240e+00 4.45038140e-01 -3.00917506e-01
-5.82685351e-01 -1.11970842e+00 -7.55883574e-01 -7.92074203e-01
-3.78158599e-01 2.63534278e-01 -1.06476061e-01 2.30653975... | [6.354032516479492, 1.7642735242843628] |
9319bb70-2383-4e67-a00c-fe3bb0fe2637 | bidirectional-gaitnet-a-bidirectional | 2306.04161 | null | https://arxiv.org/abs/2306.04161v1 | https://arxiv.org/pdf/2306.04161v1.pdf | Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions | We present a novel generative model, called Bidirectional GaitNet, that learns the relationship between human anatomy and its gait. The simulation model of human anatomy is a comprehensive, full-body, simulation-ready, musculoskeletal model with 304 Hill-type musculotendon units. The Bidirectional GaitNet consists of f... | ['Jungdam Won', 'Jehee Lee', 'Moon Seok Park', 'Jungnam Park'] | 2023-06-07 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-3.00997317e-01 1.75176203e-01 -4.14049402e-02 -6.32942915e-02
-4.06738371e-01 7.70145953e-02 1.00646079e-01 -3.40332866e-01
-1.11050233e-01 7.41547763e-01 5.11219740e-01 -7.80883506e-02
-7.12614506e-02 -8.82059515e-01 -9.36547041e-01 -7.18179703e-01
-4.84043092e-01 1.11979282e+00 2.03702599e-01 -5.24980843... | [7.108688831329346, -0.08530712127685547] |
f72058c6-bc46-41eb-872a-766501e46fcf | a-framework-for-event-based-computer-vision | 2205.06836 | null | https://arxiv.org/abs/2205.06836v1 | https://arxiv.org/pdf/2205.06836v1.pdf | A Framework for Event-based Computer Vision on a Mobile Device | We present the first publicly available Android framework to stream data from an event camera directly to a mobile phone. Today's mobile devices handle a wider range of workloads than ever before and they incorporate a growing gamut of sensors that make devices smarter, more user friendly and secure. Conventional camer... | ['Sio-Hoi Ieng', 'Serge Picaud', 'Gregor Lenz'] | 2022-05-13 | null | null | null | null | ['face-detection', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.59541225e-01 -5.39757073e-01 -8.64439160e-02 -2.34179899e-01
-3.17169189e-01 -6.31734490e-01 4.80454594e-01 -5.14643006e-02
-8.13750684e-01 5.22832692e-01 -2.32105032e-02 -6.95968568e-02
2.95432031e-01 -5.34231424e-01 -6.11290216e-01 -6.56447828e-01
9.58103035e-03 -7.28309900e-02 4.28304017e-01 1.83713481... | [8.677674293518066, -1.2649062871932983] |
4ca4a299-6a37-49d3-80a3-f925f9959378 | incrementer-transformer-for-class-incremental | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shang_Incrementer_Transformer_for_Class-Incremental_Semantic_Segmentation_With_Knowledge_Distillation_Focusing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shang_Incrementer_Transformer_for_Class-Incremental_Semantic_Segmentation_With_Knowledge_Distillation_Focusing_CVPR_2023_paper.pdf | Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class | Class-incremental semantic segmentation aims to incrementally learn new classes while maintaining the capability to segment old ones, and suffers catastrophic forgetting since the old-class labels are unavailable. Most existing methods are based on convolutional networks and prevent forgetting through knowledge dis... | ['Lanxiao Wang', 'Heqian Qiu', 'Qingbo Wu', 'Fanman Meng', 'Hongliang Li', 'Chao Shang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 3.30175370e-01 3.23225081e-01 -2.63573974e-01 -5.55449903e-01
-3.74522507e-01 -8.07477057e-01 3.31568927e-01 1.26064211e-01
-6.49324596e-01 7.97206342e-01 -2.41843313e-01 -2.29009703e-01
2.24225774e-01 -1.09473932e+00 -9.27880883e-01 -5.97698808e-01
5.12719035e-01 4.91234303e-01 9.82267022e-01 2.99283798... | [9.405097961425781, 2.1140520572662354] |
3e439039-f968-4369-8ef7-2ab306f097cf | fake-face-detection-via-adaptive-residuals | 2005.04945 | null | https://arxiv.org/abs/2005.04945v2 | https://arxiv.org/pdf/2005.04945v2.pdf | Fake face detection via adaptive manipulation traces extraction network | With the proliferation of face image manipulation (FIM) techniques such as Face2Face and Deepfake, more fake face images are spreading over the internet, which brings serious challenges to public confidence. Face image forgery detection has made considerable progresses in exposing specific FIM, but it is still in scarc... | ['Gaobo Yang', 'Zhiqing Guo', 'Xingming Sun', 'Jiyou Chen'] | 2020-05-11 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.86233896e-01 -2.35797718e-01 2.05899730e-01 -2.77958632e-01
-1.81721821e-02 -2.22329482e-01 4.90870416e-01 -5.13654172e-01
-3.30257230e-02 1.64262995e-01 -3.77841175e-01 -2.30694741e-01
2.19124258e-01 -8.52178037e-01 -8.13909292e-01 -6.26874924e-01
-1.80473626e-02 -2.44905829e-01 3.20009477e-02 -1.41060159... | [12.616007804870605, 1.019174337387085] |
790b9cfa-9d43-4d24-877e-09cab19c2668 | confident-neural-network-regression-with | 2202.10903 | null | https://arxiv.org/abs/2202.10903v1 | https://arxiv.org/pdf/2202.10903v1.pdf | Confident Neural Network Regression with Bootstrapped Deep Ensembles | With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential. In this paper we present a computationally cheap extension of Deep Ensembles for a regression setting called Bootstrapped Deep Ensembles that explicitly takes the effect of finite data in... | ['Tom Heskes', 'Eric Cator', 'Laurens Sluijterman'] | 2022-02-22 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-5.72307229e-01 -1.30279884e-02 1.22177489e-01 -5.59501648e-01
-8.29534948e-01 -4.54177469e-01 6.86118364e-01 -5.89407459e-02
-3.27336699e-01 1.45001483e+00 -1.07212201e-01 -5.97276390e-01
-1.78351834e-01 -8.54288459e-01 -9.85056579e-01 -4.75572646e-01
-1.13764107e-01 5.99481702e-01 -1.74408425e-02 -3.46689224... | [7.327704429626465, 3.765185594558716] |
13efc19c-acbe-4feb-a6ac-0d67656e13a5 | prolango-protein-function-prediction-using | 1710.07016 | null | http://arxiv.org/abs/1710.07016v1 | http://arxiv.org/pdf/1710.07016v1.pdf | ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network | With the development of next generation sequencing techniques, it is fast and
cheap to determine protein sequences but relatively slow and expensive to
extract useful information from protein sequences because of limitations of
traditional biological experimental techniques. Protein function prediction has
been a long ... | ['Haiqing Jiang', 'Renzhi Cao', 'Leong Chan', 'Colton Freitas', 'Zhangxin Chen', 'Miao Sun'] | 2017-10-19 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 7.27511406e-01 -2.75682032e-01 -4.88621965e-02 -4.26908195e-01
-7.29256511e-01 -6.04295135e-01 3.40931416e-02 1.06113411e-01
-2.98657060e-01 1.36680198e+00 -5.15451953e-02 -6.63785338e-01
3.05150658e-01 -6.07515991e-01 -1.01105344e+00 -7.30678022e-01
3.96873862e-01 6.46454394e-01 1.72170594e-01 -2.72627294... | [4.651221752166748, 5.587434768676758] |
efb69c22-1296-4439-b5d6-0e1c48d8eccb | jointly-localizing-and-describing-events-for | 1804.08274 | null | http://arxiv.org/abs/1804.08274v1 | http://arxiv.org/pdf/1804.08274v1.pdf | Jointly Localizing and Describing Events for Dense Video Captioning | Automatically describing a video with natural language is regarded as a
fundamental challenge in computer vision. The problem nevertheless is not
trivial especially when a video contains multiple events to be worthy of
mention, which often happens in real videos. A valid question is how to
temporally localize and then ... | ['Yingwei Pan', 'Ting Yao', 'Yehao Li', 'Hongyang Chao', 'Tao Mei'] | 2018-04-23 | jointly-localizing-and-describing-events-for-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Jointly_Localizing_and_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Jointly_Localizing_and_CVPR_2018_paper.pdf | cvpr-2018-6 | ['dense-video-captioning'] | ['computer-vision'] | [ 3.27858150e-01 -1.12666180e-02 -1.17246374e-01 -3.07934880e-01
-9.52699780e-01 -5.57979345e-01 7.59362876e-01 6.07910752e-02
-4.55550671e-01 6.85994506e-01 5.78464091e-01 1.18128851e-01
3.02629858e-01 -3.42132926e-01 -1.05920541e+00 -6.01192772e-01
-4.17005420e-02 5.62584639e-01 3.28722656e-01 -3.81084830... | [10.32767105102539, 0.6586288213729858] |
7b49b6cd-9ec3-4680-a9f6-7a04723cacfa | parsimonious-hmms-for-offline-handwritten | 1808.04138 | null | http://arxiv.org/abs/1808.04138v1 | http://arxiv.org/pdf/1808.04138v1.pdf | Parsimonious HMMs for Offline Handwritten Chinese Text Recognition | Recently, hidden Markov models (HMMs) have achieved promising results for
offline handwritten Chinese text recognition. However, due to the large
vocabulary of Chinese characters with each modeled by a uniform and fixed
number of hidden states, a high demand of memory and computation is required.
In this study, to addr... | ['Zi-Rui Wang', 'Wenchao Wang', 'Jun Du'] | 2018-08-13 | null | null | null | null | ['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 1.50075987e-01 -3.03540915e-01 -9.45835412e-02 -2.77223349e-01
-5.98205030e-01 -2.26108178e-01 5.68785608e-01 -3.67826074e-01
-5.50334454e-01 7.36203790e-01 1.36514470e-01 -5.98874271e-01
2.25347534e-01 -4.72745836e-01 -3.32880229e-01 -1.13361967e+00
4.57337707e-01 6.25459611e-01 3.27100694e-01 2.64254242... | [12.032342910766602, 2.4379310607910156] |
fcc294e2-4d79-4708-95a5-90326cf58497 | deformation-flow-based-two-stream-network-for | 2003.05709 | null | https://arxiv.org/abs/2003.05709v2 | https://arxiv.org/pdf/2003.05709v2.pdf | Deformation Flow Based Two-Stream Network for Lip Reading | Lip reading is the task of recognizing the speech content by analyzing movements in the lip region when people are speaking. Observing on the continuity in adjacent frames in the speaking process, and the consistency of the motion patterns among different speakers when they pronounce the same phoneme, we model the lip ... | ['Yuan-Hang Zhang', 'Jing-Yun Xiao', 'Xilin Chen', 'Shuang Yang', 'Shiguang Shan'] | 2020-03-12 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.30016291e-01 3.54801752e-02 -6.36851430e-01 -2.71825463e-01
-8.29833329e-01 -4.60591823e-01 5.95885217e-01 -5.45018256e-01
-2.61147767e-01 2.98865497e-01 7.16580987e-01 5.35064749e-03
3.81164938e-01 -2.24518791e-01 -7.84879029e-01 -8.15624654e-01
2.55061507e-01 -1.80954169e-02 1.74159572e-01 2.56631792... | [14.275574684143066, 4.942604064941406] |
f8ad1b93-d1fe-4263-b2de-2147505d7195 | mathematics-word-problems-common-sense-and | 2301.09723 | null | https://arxiv.org/abs/2301.09723v2 | https://arxiv.org/pdf/2301.09723v2.pdf | Mathematics, word problems, common sense, and artificial intelligence | The paper discusses the capacities and limitations of current artificial intelligence (AI) technology to solve word problems that combine elementary knowledge with commonsense reasoning. No existing AI systems can solve these reliably. We review three approaches that have been developed, using AI natural language techn... | ['Ernest Davis'] | 2023-01-23 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 3.87731791e-01 3.84759843e-01 6.73139617e-02 -3.29886854e-01
-2.94906437e-01 -9.11845684e-01 6.64427638e-01 3.03067535e-01
-2.84433186e-01 1.03329539e+00 -2.82088697e-01 -1.29750240e+00
-2.81433880e-01 -1.37608159e+00 -5.49726248e-01 6.07688949e-02
-8.43018070e-02 7.02660322e-01 3.06780577e-01 -6.03653550... | [9.103619575500488, 7.07271146774292] |
e917ca48-90fe-4339-9225-7e4e8077b612 | scenesketcher-fine-grained-image-retrieval | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3324_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640698.pdf | SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches | Sketch-based image retrieval (SBIR) has been a popular research topic in recent years. Existing works concentrate on mapping the visual information of sketches and images to a semantic space at the object level. In this paper, for the first time, we study the fine-grained scene-level SBIR problem which aims at retrievi... | ['Yong-Jin Liu', 'Yu-Kun Lai', 'Ran Zuo', 'Hongan Wang', 'Cuixia Ma', 'Changqing Zou', 'Xiaoming Deng', 'Fang Liu'] | null | null | null | null | eccv-2020-8 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.04042926e-01 -7.25343049e-01 -1.38762057e-01 -2.91763127e-01
-5.55983245e-01 -6.70201719e-01 8.08089852e-01 2.65095174e-01
-5.06889969e-02 9.26519781e-02 1.82935581e-01 2.32008696e-01
-6.23767614e-01 -9.88190889e-01 -3.37577641e-01 -3.60205293e-01
2.64747590e-01 3.12232524e-01 3.37128282e-01 -2.76147872... | [11.642533302307129, 0.6211288571357727] |
3e713b87-bf0b-47e7-adc5-5de4864950ed | restoring-and-attributing-ancient-texts-using | null | null | https://www.nature.com/articles/s41586-022-04448-z | https://www.nature.com/articles/s41586-022-04448-z.pdf | Restoring and attributing ancient texts using deep neural networks | Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original... | ['Nando de Freitas', 'Jonathan Prag', 'Ion Androutsopoulos', 'Marita Chatzipanagiotou', 'John Pavlopoulos', 'Mahyar Bordbar', 'Brendan Shillingford', 'Thea Sommerschield', 'Yannis Assael'] | 2022-03-09 | null | null | null | nature-2022-3 | ['ancient-tex-restoration'] | ['miscellaneous'] | [-1.97024465e-01 3.93749446e-01 -2.27064773e-01 7.12629110e-02
-4.85764951e-01 -8.77220154e-01 9.93686914e-01 8.13311338e-02
-6.05725586e-01 6.55638158e-01 7.88650453e-01 -5.71853638e-01
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2.08605602e-01 9.37109947e-01 -2.73226142e-01 -4.62728977... | [10.493780136108398, 10.103754043579102] |
69b9d20e-ce94-4c1e-84fe-855123f44e10 | oriole-thwarting-privacy-against-trustworthy | 2102.11502 | null | https://arxiv.org/abs/2102.11502v2 | https://arxiv.org/pdf/2102.11502v2.pdf | Oriole: Thwarting Privacy against Trustworthy Deep Learning Models | Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their explicit permission for the purpose of training high-precision face recognition models, creating a serious violation of privacy. Recently, a wel... | ['Haifeng Qian', 'Minhui Xue', 'Benjamin Zi Hao Zhao', 'Hu Wang', 'Liuqiao Chen'] | 2021-02-23 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 3.45250443e-02 8.78595859e-02 1.28664985e-01 -1.82712063e-01
-2.34793231e-01 -8.33234668e-01 3.81524563e-01 -2.87378758e-01
-5.16041636e-01 5.86932838e-01 -2.23552436e-01 -6.07232451e-01
-2.40446925e-01 -8.91685486e-01 -7.86626518e-01 -9.70675647e-01
-2.77617514e-01 -3.64740044e-01 -8.09177384e-02 -1.66401163... | [12.841224670410156, 1.064206600189209] |
0686bb6e-18b8-4f59-b009-3edc1cfe19a5 | improved-instruction-ordering-in-recipe | 2305.17280 | null | https://arxiv.org/abs/2305.17280v1 | https://arxiv.org/pdf/2305.17280v1.pdf | Improved Instruction Ordering in Recipe-Grounded Conversation | In this paper, we study the task of instructional dialogue and focus on the cooking domain. Analyzing the generated output of the GPT-J model, we reveal that the primary challenge for a recipe-grounded dialog system is how to provide the instructions in the correct order. We hypothesize that this is due to the model's ... | ['Alan Ritter', 'Wei Xu', 'Ruohao Guo', 'Duong Minh Le'] | 2023-05-26 | null | null | null | null | ['response-generation', 'intent-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.01872258e-01 3.75417590e-01 -3.14251473e-03 -3.92811209e-01
-5.75559378e-01 -9.78260338e-01 3.25024784e-01 2.71261722e-01
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1.69998109e-01 -6.14754498e-01 -4.71179783e-01 -4.43687998e-02
4.64190751e-01 3.61256868e-01 4.80630137e-02 -8.88836920... | [12.708710670471191, 8.06861400604248] |
2e161e18-ca1d-4118-b2dc-403ad760c24f | polyu-bpcoma-a-dataset-and-benchmark-towards | 2206.07468 | null | https://arxiv.org/abs/2206.07468v2 | https://arxiv.org/pdf/2206.07468v2.pdf | PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System | Constructing colorized point clouds from mobile laser scanning and images is a fundamental work in surveying and mapping. It is also an essential prerequisite for building digital twins for smart cities. However, existing public datasets are either in relatively small scales or lack accurate geometrical and color groun... | ['Daping Yang', 'Yue Yu', 'Haodong Xiang', 'Sheng Bao', 'Muyang Wang', 'Pengxin Chen', 'Wenzhong Shi'] | 2022-06-15 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-1.70837060e-01 -7.23362863e-01 2.76065201e-01 -4.15061891e-01
-8.96933317e-01 -8.14446688e-01 5.26647925e-01 -2.94945747e-01
-2.84369022e-01 5.72367787e-01 -7.39005387e-01 -5.87815344e-01
1.69046432e-01 -1.39939988e+00 -6.28460765e-01 -5.93278408e-01
3.48713905e-01 9.04169917e-01 4.11749542e-01 -4.29367632... | [8.271885871887207, -2.606301784515381] |
5cb4c76d-c56a-436b-b8ff-8333ef6ca345 | three-stream-fusion-network-for-first-person | 2002.08219 | null | https://arxiv.org/abs/2002.08219v1 | https://arxiv.org/pdf/2002.08219v1.pdf | Three-Stream Fusion Network for First-Person Interaction Recognition | First-person interaction recognition is a challenging task because of unstable video conditions resulting from the camera wearer's movement. For human interaction recognition from a first-person viewpoint, this paper proposes a three-stream fusion network with two main parts: three-stream architecture and three-stream ... | ['Seong-Whan Lee', 'Dong-Gyu Lee', 'Ye-Ji Kim'] | 2020-02-19 | null | null | null | null | ['human-interaction-recognition'] | ['computer-vision'] | [ 2.46734068e-01 -8.50427330e-01 -1.26922637e-01 -1.45092621e-01
-4.43589866e-01 1.93902031e-02 7.44761109e-01 -4.20072049e-01
-3.84991705e-01 3.24347168e-01 5.97480416e-01 6.68254316e-01
1.03114553e-01 -1.33435771e-01 -5.16200304e-01 -7.55723119e-01
-1.68528467e-01 -2.76127964e-01 1.25516847e-01 8.18009749... | [8.021780967712402, 0.5585549473762512] |
e9a40fe5-4ca4-4954-8896-1c39da63e559 | pairwise-instance-relation-augmentation-for | 2211.10685 | null | https://arxiv.org/abs/2211.10685v1 | https://arxiv.org/pdf/2211.10685v1.pdf | Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification | Multi-label text classification (MLTC) is one of the key tasks in natural language processing. It aims to assign multiple target labels to one document. Due to the uneven popularity of labels, the number of documents per label follows a long-tailed distribution in most cases. It is much more challenging to learn classi... | ['Xiangliang Zhang', 'Liping Jing', 'Pengyu Xu', 'Lin Xiao'] | 2022-11-19 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 3.19773197e-01 2.09046677e-01 -5.53136051e-01 -6.61992192e-01
-8.30632269e-01 -5.94552100e-01 6.68402135e-01 2.58496761e-01
-1.01732813e-01 7.77184963e-01 1.30242556e-01 -3.78302597e-02
-2.34097451e-01 -5.45666337e-01 -3.28221530e-01 -1.10899580e+00
2.10483715e-01 1.17477977e+00 5.39174639e-02 -2.32631527... | [9.59089469909668, 4.134714603424072] |
d5ba5339-c1ed-4fdf-8c3c-80d43ff207a8 | ensemble-multi-quantile-adaptively-flexible | 2211.14545 | null | https://arxiv.org/abs/2211.14545v3 | https://arxiv.org/pdf/2211.14545v3.pdf | Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification | We propose a novel, succinct, and effective approach for distribution prediction to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution prediction of $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$ in regression tasks. This conditional distribution's quantiles of probability levels spreadin... | ['Wenxuan Ma', 'Yonghua Su', 'Xing Yan'] | 2022-11-26 | null | null | null | null | ['additive-models'] | ['methodology'] | [-3.86263609e-01 1.30413055e-01 -1.33621335e-01 -8.33395243e-01
-1.14822829e+00 -6.46453321e-01 4.30759370e-01 2.94604540e-01
-2.63651103e-01 1.18107402e+00 -6.86228052e-02 -5.27999640e-01
-8.39392781e-01 -9.01332736e-01 -9.88934100e-01 -9.88822341e-01
-8.42657834e-02 6.54946089e-01 -1.00813903e-01 -7.45439553... | [7.674149513244629, 4.080683708190918] |
d6c4ec55-d48d-4223-a3e3-2f34cd4b29eb | structure-based-approach-to-identifying-small | 2111.11558 | null | https://arxiv.org/abs/2111.11558v1 | https://arxiv.org/pdf/2111.11558v1.pdf | Structure-based approach to identifying small sets of driver nodes in biological networks | In network control theory, driving all the nodes in the Feedback Vertex Set (FVS) forces the network into one of its attractors (long-term dynamic behaviors). The FVS is often composed of more nodes than can be realistically manipulated in a system; for example, only up to three nodes can be controlled in intracellular... | ['Réka Albert', 'Jorge Gómez Tejda Zañudo', 'Eli Newby'] | 2021-11-22 | null | null | null | null | ['feedback-vertex-set-fvs'] | ['graphs'] | [ 2.45390400e-01 2.20743120e-01 -8.56274813e-02 2.45409161e-01
4.16502446e-01 -1.07189572e+00 8.50752413e-01 6.09979451e-01
-1.50719225e-01 1.04816175e+00 -1.21438488e-01 -4.68409687e-01
-6.26519978e-01 -1.04865634e+00 -5.10205388e-01 -9.02171791e-01
-6.05207741e-01 3.82670254e-01 7.81350076e-01 -5.20128250... | [6.322310924530029, 4.658834457397461] |
142fc693-78e8-46f7-8975-f316d6fafb2b | end-to-end-dense-video-grounding-via-parallel | 2109.11265 | null | https://arxiv.org/abs/2109.11265v4 | https://arxiv.org/pdf/2109.11265v4.pdf | End-to-End Dense Video Grounding via Parallel Regression | Video grounding aims to localize the corresponding video moment in an untrimmed video given a language query. Existing methods often address this task in an indirect way, by casting it as a proposal-and-match or fusion-and-detection problem. Solving these surrogate problems often requires sophisticated label assignment... | ['Weilin Huang', 'LiMin Wang', 'Fengyuan Shi'] | 2021-09-23 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 3.30063879e-01 -1.67559758e-01 -4.33104664e-01 -2.97004879e-01
-1.18733931e+00 -4.20560151e-01 5.52300334e-01 2.72839442e-02
-4.84741122e-01 4.49701458e-01 1.01987027e-01 -2.86252975e-01
2.97064811e-01 -4.98141795e-01 -1.22929335e+00 -5.73758721e-01
1.50958508e-01 2.04204589e-01 2.51634836e-01 -5.92129724... | [10.043506622314453, 0.7654090523719788] |
379338c7-d516-4d76-b9bc-3bd577a99042 | a-survey-on-heterogeneous-face-recognition | 1409.5114 | null | https://arxiv.org/abs/1409.5114v2 | https://arxiv.org/pdf/1409.5114v2.pdf | A Survey on Heterogeneous Face Recognition: Sketch, Infra-red, 3D and Low-resolution | Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets being ... | ['Yi-Zhe Song', 'Xueming Li', 'Timothy Hospedales', 'Shuxin Ouyang'] | 2014-09-17 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 5.03324330e-01 -4.55437094e-01 -5.03511071e-01 -5.90660751e-01
-7.18528390e-01 -3.71160805e-01 6.01644278e-01 -4.79530811e-01
-5.05085550e-02 4.80640650e-01 3.96553159e-01 2.94672340e-01
-4.78251159e-01 -5.99100769e-01 -1.58820108e-01 -7.32691884e-01
-3.26141477e-01 2.13607222e-01 -2.89541364e-01 -3.46950710... | [13.180286407470703, 0.6217048764228821] |
4a155347-f99c-4503-8522-09893ba0a542 | hyperbox-a-supervised-approach-for-hypernym | 2204.02058 | null | https://arxiv.org/abs/2204.02058v2 | https://arxiv.org/pdf/2204.02058v2.pdf | HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings | Hypernymy plays a fundamental role in many AI tasks like taxonomy learning, ontology learning, etc. This has motivated the development of many automatic identification methods for extracting this relation, most of which rely on word distribution. We present a novel model HyperBox to learn box embeddings for hypernym di... | ['Dr. Apurva Narayan', 'Maulik Parmar'] | 2022-04-05 | null | https://aclanthology.org/2022.lrec-1.652 | https://aclanthology.org/2022.lrec-1.652.pdf | lrec-2022-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 1.43425658e-01 3.11441123e-01 -5.00867069e-01 -9.53415632e-02
-1.48358876e-02 -4.22940373e-01 7.02919543e-01 6.32145405e-01
-9.38501179e-01 6.18710637e-01 2.59990573e-01 -1.38170332e-01
-5.83357036e-01 -1.09865618e+00 -1.97290152e-01 -3.61545771e-01
-6.56718463e-02 1.04064143e+00 2.77259320e-01 -3.86423141... | [9.85006046295166, 8.735739707946777] |
f057a422-8729-4c55-a6dc-0a6a3efd9e7f | efficient-task-oriented-dialogue-systems-with | 2208.07097 | null | https://arxiv.org/abs/2208.07097v2 | https://arxiv.org/pdf/2208.07097v2.pdf | Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task | The adoption of pre-trained language models in task-oriented dialogue systems has resulted in significant enhancements of their text generation abilities. However, these architectures are slow to use because of the large number of trainable parameters and can sometimes fail to generate diverse responses. To address the... | ['Todor Kolev', 'Radostin Cholakov'] | 2022-08-15 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 3.44460040e-01 3.72112989e-01 -4.48653810e-02 -5.90205729e-01
-1.54584730e+00 -7.40432441e-01 8.91533077e-01 -5.33268005e-02
-4.42003727e-01 1.13957000e+00 4.81961876e-01 -4.10917461e-01
2.22399265e-01 -4.18975979e-01 -1.37978017e-01 -2.59731442e-01
3.25504988e-01 1.03038752e+00 4.18795496e-02 -8.52865100... | [12.663553237915039, 8.1770658493042] |
95ddba90-eb09-4ece-8d02-f6d6188fd46d | zone-based-federated-learning-for-mobile | 2303.06246 | null | https://arxiv.org/abs/2303.06246v1 | https://arxiv.org/pdf/2303.06246v1.pdf | Zone-based Federated Learning for Mobile Sensing Data | Mobile apps, such as mHealth and wellness applications, can benefit from deep learning (DL) models trained with mobile sensing data collected by smart phones or wearable devices. However, currently there is no mobile sensing DL system that simultaneously achieves good model accuracy while adapting to user mobility beha... | ['Cristian Borcea', 'Ruoming Jin', 'An Chen', 'Vijaya Datta Mayyuri', 'Hessamaldin Mohammadi', 'NhatHai Phan', 'Thinh On', 'Xiaopeng Jiang'] | 2023-03-10 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [-1.28997624e-01 -5.91586307e-02 -9.31444943e-01 -4.44237113e-01
-4.53998744e-01 -1.49967298e-01 3.65567282e-02 -1.26488730e-02
-2.31517762e-01 6.66171134e-01 2.10256889e-01 -7.16962397e-01
-2.71647900e-01 -1.03918350e+00 -4.93016124e-01 -4.63593662e-01
-3.10564667e-01 7.02988803e-02 6.70417100e-02 2.49792457... | [5.999951362609863, 6.159941673278809] |
a14ed4a6-0820-4bbc-97db-046b7f400975 | multi-view-subspace-adaptive-learning-via | 2201.00171 | null | https://arxiv.org/abs/2201.00171v1 | https://arxiv.org/pdf/2201.00171v1.pdf | Multi-view Subspace Adaptive Learning via Autoencoder and Attention | Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods, such as sparse subspace clustering (SSC) and low-ranking subspace clustering (LRSC), cluster the affinity matrix for a single view, thus ig... | ['Xiong-lin Luo', 'Run-kun Lu', 'Hao-jie Xie', 'Jian-wei Liu'] | 2022-01-01 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-5.27308345e-01 -5.81571996e-01 -3.38046789e-01 -3.22193742e-01
-7.18705535e-01 -6.02470696e-01 5.34255564e-01 -5.56723297e-01
1.65597394e-01 1.07523426e-01 9.20862556e-01 4.44341958e-01
-2.38741606e-01 -1.99839383e-01 -4.84644920e-01 -1.04657066e+00
2.18712419e-01 4.85864848e-01 -1.33490711e-01 1.96411178... | [8.324095726013184, 4.573714256286621] |
08b70c72-bbfd-46f2-83c4-976e926b0f9b | unisar-a-unified-structure-aware | null | null | https://openreview.net/forum?id=QWqNTJUZzss | https://openreview.net/pdf?id=QWqNTJUZzss | UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL | Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffective when applied to different settings. We present UniSAr (Unified Structure-Aware Autoregressive Language Model), which benefits from direct... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-to-sql'] | ['computer-code'] | [-5.17782420e-02 3.03989112e-01 -3.65592420e-01 -9.24207330e-01
-1.47624445e+00 -8.53852093e-01 2.65893281e-01 1.79999784e-01
6.66582733e-02 2.33590692e-01 6.70743585e-01 -8.75759900e-01
1.27327293e-01 -1.05274570e+00 -1.05675089e+00 2.41824880e-01
1.72099337e-01 1.05037355e+00 2.76638448e-01 -4.85266656... | [9.931329727172852, 7.826891899108887] |
69356342-dbd0-4ac9-8872-99582094e32a | learning-dynamics-and-generalization-in | 2206.02126 | null | https://arxiv.org/abs/2206.02126v1 | https://arxiv.org/pdf/2206.02126v1.pdf | Learning Dynamics and Generalization in Reinforcement Learning | Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a potentially discontinuous value function, and generalizing well to new observations. In this paper, we analyze the learning dynamics of temporal difference algorithms to gain novel insight into the tension between these two objectiv... | ['Yarin Gal', 'Marta Kwiatkowska', 'Will Dabney', 'Mark Rowland', 'Clare Lyle'] | 2022-06-05 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 1.75475970e-01 2.02118218e-01 -1.07760049e-01 -5.64570315e-02
-6.48412645e-01 -8.43043864e-01 6.99838042e-01 9.14075002e-02
-1.04100037e+00 1.13954771e+00 2.93660369e-02 -4.23236936e-01
-3.14058840e-01 -5.45208037e-01 -8.58580172e-01 -8.87959182e-01
-5.50063550e-01 4.63968962e-01 1.27336696e-01 -3.55707198... | [4.0955305099487305, 1.9221323728561401] |
12a046b5-139c-47f4-9db6-9580f1680caf | when-adversarial-attacks-become-interpretable | 2206.06854 | null | https://arxiv.org/abs/2206.06854v2 | https://arxiv.org/pdf/2206.06854v2.pdf | On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective | Input gradients have a pivotal role in a variety of applications, including adversarial attack algorithms for evaluating model robustness, explainable AI techniques for generating Saliency Maps, and counterfactual explanations. However, Saliency Maps generated by traditional neural networks are often noisy and provide ... | ['Thibaut Boissin', 'Louis Béthune', 'Thomas Fel', 'Franck Mamalet', 'Mathieu Serrurier'] | 2022-06-14 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.69680929e-01 7.69386053e-01 -3.43023866e-01 -3.52318823e-01
-5.75890064e-01 -7.10133553e-01 8.43471169e-01 -3.27943601e-02
-9.93740782e-02 7.71717608e-01 3.49394262e-01 -4.78851616e-01
-3.96149188e-01 -5.70797682e-01 -1.36508918e+00 -6.53273761e-01
-2.41069540e-01 3.95649463e-01 -8.04596841e-02 -4.14335877... | [8.764872550964355, 5.1019206047058105] |
8c8178ff-e7bf-4fe2-aee7-6e0dc9bd5b5f | robust-graph-representation-learning-via | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2611-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2611-Paper.pdf | Robust Graph Representation Learning via Neural Sparsification | Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection. Real-life graphs usually have complex information in the local neighborhood, where each node is described by a rich set of features and connects to dozens or even hundreds of neighbors... | ['Wei Cheng', 'Dongjin Song', 'Wenchao Yu', 'Bo Zong', 'Jingchao Ni', 'Wei Wang', 'Haifeng Chen', 'Cheng Zheng'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/2611-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/2611-Paper.pdf | icml-2020-1 | ['product-recommendation'] | ['miscellaneous'] | [ 2.16293111e-01 5.68971157e-01 -5.66780746e-01 -5.44376314e-01
-8.61576796e-02 -3.16188961e-01 9.93647277e-02 4.42792147e-01
9.98655632e-02 4.45420057e-01 1.16617531e-01 -4.77129221e-01
-2.32709900e-01 -1.11096299e+00 -7.24634171e-01 -5.30595124e-01
-4.12047714e-01 5.07517755e-01 6.59469962e-02 -2.97687292... | [7.154940128326416, 6.21339750289917] |
a6ebe738-60fe-4e3d-8123-d5caf2d18add | a-sparsity-algorithm-with-applications-to | 2107.10306 | null | https://arxiv.org/abs/2107.10306v1 | https://arxiv.org/pdf/2107.10306v1.pdf | A Sparsity Algorithm with Applications to Corporate Credit Rating | In Artificial Intelligence, interpreting the results of a Machine Learning technique often termed as a black box is a difficult task. A counterfactual explanation of a particular "black box" attempts to find the smallest change to the input values that modifies the prediction to a particular output, other than the orig... | ['Ionut Florescu', 'Zhi Chen', 'Dan Wang'] | 2021-07-21 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.54578441e-01 7.15684712e-01 -6.08192742e-01 -5.50212622e-01
-1.12910725e-01 -4.65226263e-01 7.05913067e-01 8.89277235e-02
-1.57045498e-01 1.14472532e+00 6.66546106e-01 -7.67161727e-01
-1.17757685e-01 -7.67057836e-01 -9.50006485e-01 -3.93187404e-01
1.75523400e-01 1.76271461e-02 -6.37891352e-01 -9.60126519... | [8.667208671569824, 5.63020658493042] |
ea58c4d7-4397-49a0-9730-a75a8bd0fe61 | a-hitchhikers-guide-on-distributed-training | 1810.11787 | null | http://arxiv.org/abs/1810.11787v1 | http://arxiv.org/pdf/1810.11787v1.pdf | A Hitchhiker's Guide On Distributed Training of Deep Neural Networks | Deep learning has led to tremendous advancements in the field of Artificial
Intelligence. One caveat however is the substantial amount of compute needed to
train these deep learning models. Training a benchmark dataset like ImageNet on
a single machine with a modern GPU can take upto a week, distributing training
on mu... | ['Kuntal Dey', 'Manraj Singh Grover', 'Karanbir Chahal'] | 2018-10-28 | null | null | null | null | ['2048'] | ['playing-games'] | [-3.53599280e-01 -4.44774747e-01 1.96112052e-01 -7.98025548e-01
-5.17537713e-01 -2.90097982e-01 4.43028718e-01 2.12104440e-01
-1.12558436e+00 5.71870923e-01 -5.01638830e-01 -6.99623883e-01
1.93199903e-01 -9.62568641e-01 -6.11427903e-01 -7.18579888e-01
-6.11754283e-02 6.94275796e-01 3.09604168e-01 7.64928609... | [8.431069374084473, 3.202775478363037] |
f34372bf-27e9-4f36-a251-21331d92be17 | skeleton-based-human-action-evaluation-using | null | null | https://www.sciencedirect.com/science/article/pii/S003132032100282X | https://www.sciencedirect.com/science/article/pii/S003132032100282X | Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression | Human action evaluation (HAE) involves judgments about the abnormality and quality of human actions. If performed effectively, HAE based on skeleton data can be used to monitor the outcomes of behavioral therapies for Alzheimer's disease (AD). In this paper, we propose a two-task graph convolutional network (2T-GCN) to... | ['Xiaoying Wang', 'Qintai Yang', 'Keith C. C. Chan', 'Yan Liu', 'Bruce X. B. Yu'] | 2021-06-21 | null | null | null | pattern-recognition-2021-6 | ['action-assessment'] | ['computer-vision'] | [ 2.92296082e-01 1.68401495e-01 -7.84408599e-02 -4.57993627e-01
-5.33881068e-01 6.57623261e-02 1.06158361e-01 2.76465476e-01
-5.35629988e-01 7.34487832e-01 4.14047837e-01 9.30090472e-02
-2.24108174e-01 -9.04639959e-01 -4.71228033e-01 -1.53895393e-01
-4.53171343e-01 4.44056958e-01 3.03459048e-01 -6.21565171... | [7.181716442108154, 0.3493252396583557] |
80edc1b9-f11e-4dae-9fc4-adf7b385bdd5 | probabilistic-spatial-analysis-in | 2102.11865 | null | https://arxiv.org/abs/2102.11865v1 | https://arxiv.org/pdf/2102.11865v1.pdf | Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps | 3D microscopy is key in the investigation of diverse biological systems, and the ever increasing availability of large datasets demands automatic cell identification methods that not only are accurate, but also can imply the uncertainty in their predictions to inform about potential errors and hence confidence in concl... | ['Orcun Goksel', 'César Nombela-Arrieta', 'Tiziano Portenier', 'Alvaro Gomariz'] | 2021-02-23 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.60538989e-01 -1.21510901e-01 2.29234308e-01 -2.85778254e-01
-1.16704059e+00 -5.41141987e-01 5.98542452e-01 6.81215525e-01
-5.71081817e-01 1.02047300e+00 -1.72502816e-01 -2.76084393e-01
-1.70192927e-01 -8.80694389e-01 -8.86003315e-01 -1.19333637e+00
-2.31448375e-02 1.02544653e+00 5.57995915e-01 5.52630186... | [14.586431503295898, -3.002011775970459] |
9208cc08-0dad-4d0e-923b-574f1dfeeed1 | where-is-my-hand-deep-hand-segmentation-for | 2102.04750 | null | https://arxiv.org/abs/2102.04750v1 | https://arxiv.org/pdf/2102.04750v1.pdf | Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots | The ability to distinguish between the self and the background is of paramount importance for robotic tasks. The particular case of hands, as the end effectors of a robotic system that more often enter into contact with other elements of the environment, must be perceived and tracked with precision to execute the inten... | ['Alexandre Bernardino', 'Pedro Vicente', 'Alexandre Almeida'] | 2021-02-09 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 2.49796346e-01 2.31243044e-01 1.41844690e-01 -1.14078656e-01
8.49410594e-02 -6.22433305e-01 4.46512908e-01 -5.15616477e-01
-7.63386488e-01 6.20795608e-01 -5.84989667e-01 -1.38151526e-01
-6.23745695e-02 -4.86781448e-01 -9.75636005e-01 -6.19794965e-01
-7.09792376e-02 7.81788170e-01 3.07218522e-01 -3.02383125... | [6.068423271179199, -0.7039772868156433] |
1626a0e1-e255-4289-a9d8-84090e0906f9 | few-shot-single-view-3d-reconstruction-with | 2208.00183 | null | https://arxiv.org/abs/2208.00183v1 | https://arxiv.org/pdf/2208.00183v1.pdf | Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network | 3D reconstruction of novel categories based on few-shot learning is appealing in real-world applications and attracts increasing research interests. Previous approaches mainly focus on how to design shape prior models for different categories. Their performance on unseen categories is not very competitive. In this pape... | ['Yu Xiang', 'Xiangdong Zhou', 'Zhixin Ling', 'Yijiang Chen', 'Zhen Xing'] | 2022-07-30 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-9.45197791e-02 -1.55577615e-01 -1.40018180e-01 -5.94823956e-01
-7.79884040e-01 -2.81437218e-01 8.18832159e-01 -1.63105875e-02
-2.32044205e-01 -3.66748520e-03 4.27346021e-01 1.93359137e-01
-3.40323597e-02 -9.40812230e-01 -7.37793386e-01 -5.45321345e-01
6.06643438e-01 7.22998977e-01 9.05916929e-01 -9.53720510... | [8.027339935302734, -3.180663585662842] |
cc8527df-1c15-4add-a2e2-24c6fd6d9af4 | breaking-with-fixed-set-pathology-recognition | 2205.07139 | null | https://arxiv.org/abs/2205.07139v1 | https://arxiv.org/pdf/2205.07139v1.pdf | Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training | When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from these medical reports for training. This form of supervision limits the potential usage of models as ... | ['Jens Kleesiek', 'Rainer Stiefelhagen', 'M. Saquib Sarfraz', 'Simon Reiß', 'Constantin Seibold'] | 2022-05-14 | null | null | null | null | ['thoracic-disease-classification', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 7.83962011e-01 7.93818951e-01 -3.00433874e-01 -6.45365000e-01
-1.38098383e+00 -5.31961203e-01 5.51488757e-01 6.82325304e-01
-5.94565034e-01 8.00114334e-01 2.57692695e-01 -6.91485703e-01
-2.91574806e-01 -6.83146119e-01 -8.22773635e-01 -7.01848328e-01
1.03807442e-01 7.16761291e-01 1.83108389e-01 2.18953267... | [14.773320198059082, -2.2004334926605225] |
ab5f977b-d281-4175-9946-3edafb5e3798 | grid-anchor-based-image-cropping-a-new | 1909.08989 | null | https://arxiv.org/abs/1909.08989v1 | https://arxiv.org/pdf/1909.08989v1.pdf | Grid Anchor based Image Cropping: A New Benchmark and An Efficient Model | Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Most of the existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruths, which can hardly reflect the non-uniqueness and flexibility of ima... | ['Hui Zeng', 'Lida Li', 'Lei Zhang', 'Zisheng Cao'] | 2019-09-18 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 1.52211130e-01 -2.83215106e-01 -1.28351510e-01 7.74809495e-02
-5.43870032e-01 -7.68235564e-01 8.61734971e-02 3.46769482e-01
-1.06558852e-01 3.88639301e-01 -3.89162481e-01 -4.07053351e-01
1.30972296e-01 -1.12878442e+00 -8.11288834e-01 -7.52371967e-01
8.09182525e-02 -2.57420659e-01 4.80918109e-01 -1.91311270... | [11.285538673400879, -1.1966967582702637] |
6b7ab0de-19f7-4bf7-8f4c-3aafb5b5551c | learning-fine-grained-expressions-to-solve | null | null | https://aclanthology.org/D17-1084 | https://aclanthology.org/D17-1084.pdf | Learning Fine-Grained Expressions to Solve Math Word Problems | This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from v... | ['Chin-Yew Lin', 'Shuming Shi', 'Jian Yin', 'Danqing Huang'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 3.42108190e-01 2.06269383e-01 1.08638108e-01 -5.04171968e-01
-1.23775101e+00 -9.41730857e-01 4.29066479e-01 3.00431490e-01
-4.44693536e-01 8.28214526e-01 -1.94381624e-02 -3.44663322e-01
-3.97820771e-01 -1.31720865e+00 -8.12454164e-01 -1.33200765e-01
3.60818744e-01 1.14129770e+00 8.66925716e-02 -5.54970980... | [9.704614639282227, 7.41464900970459] |
b380d8fc-df97-4097-8a5b-6c1eeb4def85 | cross-attentional-audio-visual-fusion-for-1 | 2111.05222 | null | https://arxiv.org/abs/2111.05222v1 | https://arxiv.org/pdf/2111.05222v1.pdf | Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition | Multimodal analysis has recently drawn much interest in affective computing, since it can improve the overall accuracy of emotion recognition over isolated uni-modal approaches. The most effective techniques for multimodal emotion recognition efficiently leverage diverse and complimentary sources of information, such a... | ['Patrick Cardinal', 'Eric Granger', 'Gnana Praveen R'] | 2021-11-09 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-3.34579162e-02 -5.30305326e-01 -1.33713320e-01 -4.43967253e-01
-8.41596305e-01 -5.21067142e-01 4.71475214e-01 1.69768050e-01
-2.98323065e-01 4.14555609e-01 4.79868680e-01 3.78788173e-01
-1.62350103e-01 -3.20889235e-01 -2.82970130e-01 -7.30616212e-01
-7.19473734e-02 -5.13024807e-01 -3.68064076e-01 -3.93944740... | [13.314620971679688, 5.079830646514893] |
71b60706-5d77-43ab-8043-f4c5f653327b | fully-automatic-mitral-valve-4d-shape | 2305.00627 | null | https://arxiv.org/abs/2305.00627v2 | https://arxiv.org/pdf/2305.00627v2.pdf | CNN-based fully automatic mitral valve extraction using CT images and existence probability maps | Accurate extraction of mitral valve shape from clinical tomographic images acquired in patients has proven useful for planning surgical and interventional mitral valve treatments. However, manual extraction of the mitral valve shape is laborious, and the existing automatic extraction methods have not been sufficiently ... | ['Kiyohide Satoh', 'Takuya Sakaguchi', 'Klaus Fuglsang Kofoed', 'Michael Huy Cuong Pham', 'Masahiko Asami', 'James V. Chapman', 'Keitaro Kawashima', 'Gakuto Aoyama', 'Toru Tanaka', 'Ryo Ishikawa', 'Yukiteru Masuda'] | 2023-05-01 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-1.44607276e-01 2.95612305e-01 8.28368142e-02 -3.77366617e-02
-5.18866360e-01 -5.81459463e-01 -7.71222711e-02 -1.14620114e-02
-4.05138671e-01 9.00316775e-01 6.13901317e-02 -4.86408353e-01
-3.23343784e-01 -6.84976339e-01 -7.23207071e-02 -6.12650812e-01
-5.45202374e-01 1.08938539e+00 3.95608425e-01 2.90145099... | [14.159425735473633, -2.5196282863616943] |
1834cf8c-4fe9-4e64-b06b-10b3d1d22f88 | unsupervised-learning-of-discourse-structures | 2012.09446 | null | https://arxiv.org/abs/2012.09446v1 | https://arxiv.org/pdf/2012.09446v1.pdf | Unsupervised Learning of Discourse Structures using a Tree Autoencoder | Discourse information, as postulated by popular discourse theories, such as RST and PDTB, has been shown to improve an increasing number of downstream NLP tasks, showing positive effects and synergies of discourse with important real-world applications. While methods for incorporating discourse become more and more sop... | ['Giuseppe Carenini', 'Patrick Huber'] | 2020-12-17 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 5.78298569e-01 9.06648517e-01 -1.92257971e-01 -3.94224703e-01
-9.66015756e-01 -6.72463179e-01 1.07528067e+00 3.12612325e-01
-1.69252560e-01 1.33630919e+00 6.51974678e-01 -4.26982909e-01
-3.21948193e-02 -8.81717086e-01 -5.17868221e-01 -6.69970334e-01
1.46629974e-01 6.79231107e-01 4.13363129e-01 -3.67567003... | [10.780993461608887, 9.37536907196045] |
e9f31be9-9997-46ff-915a-ed32a8a6af6c | inference-for-two-stage-experiments-under | 2301.09016 | null | https://arxiv.org/abs/2301.09016v3 | https://arxiv.org/pdf/2301.09016v3.pdf | Inference for Two-stage Experiments under Covariate-Adaptive Randomization | This paper studies inference in two-stage randomized experiments under covariate-adaptive randomization. In the initial stage of this experimental design, clusters (e.g., households, schools, or graph partitions) are stratified and randomly assigned to control or treatment groups based on cluster-level covariates. Subs... | ['Jizhou Liu'] | 2023-01-21 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 2.48072580e-01 -5.65244965e-02 -8.24386299e-01 -4.38759476e-01
-4.78302151e-01 -3.64634663e-01 1.41505271e-01 2.48787090e-01
-3.94256651e-01 9.40307140e-01 3.41200411e-01 -8.22318435e-01
-6.09777689e-01 -8.92425418e-01 -6.95279419e-01 -6.27382457e-01
-2.01017842e-01 7.93663561e-02 -3.09454769e-01 6.39467478... | [7.968296527862549, 5.218391418457031] |
1d8abc24-917b-451a-b000-f4f296c767b4 | representation-learning-via-global-temporal | 2105.05217 | null | https://arxiv.org/abs/2105.05217v1 | https://arxiv.org/pdf/2105.05217v1.pdf | Representation Learning via Global Temporal Alignment and Cycle-Consistency | We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal. In particular, we propose a loss... | ['Allan D. Jepson', 'Konstantinos G. Derpanis', 'Isma Hadji'] | 2021-05-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hadji_Representation_Learning_via_Global_Temporal_Alignment_and_Cycle-Consistency_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-synchronization'] | ['computer-vision'] | [ 3.40950221e-01 -2.28355527e-01 -5.93468130e-01 -3.27435523e-01
-8.64413738e-01 -7.13198423e-01 8.40530396e-01 5.36874272e-02
-1.96934670e-01 3.99000913e-01 8.76530945e-01 2.41886422e-01
-3.63311768e-01 -3.14849436e-01 -8.30919802e-01 -7.72856116e-01
-5.77442586e-01 3.40473711e-01 2.86279500e-01 -1.31608993... | [8.526937484741211, 0.694337010383606] |
257e8a58-1f14-46e6-94ba-12e3d703e35a | wip-abstract-robust-out-of-distribution | 2107.11736 | null | https://arxiv.org/abs/2107.11736v1 | https://arxiv.org/pdf/2107.11736v1.pdf | WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS | Highly complex deep learning models are increasingly integrated into modern cyber-physical systems (CPS), many of which have strict safety requirements. One problem arising from this is that deep learning lacks interpretability, operating as a black box. The reliability of deep learning is heavily impacted by how well ... | ['Arvind Easwaran', 'Yeli Feng'] | 2021-07-25 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [-9.51652303e-02 -1.32645339e-01 -1.28656358e-01 5.37957251e-03
-3.04525048e-01 -4.96220797e-01 6.41336203e-01 -2.53989458e-01
-2.57098287e-01 5.22533238e-01 -2.05174610e-02 -4.45216209e-01
-2.52759248e-01 -6.28858685e-01 -9.35474098e-01 -6.61047459e-01
-3.95308346e-01 1.58792511e-01 4.26373601e-01 -1.53312668... | [5.5203633308410645, 7.3547844886779785] |
1e1622b8-b7e6-41f4-aa57-5af8de57ad9c | symbolic-regression-on-fpgas-for-fast-machine | 2305.04099 | null | https://arxiv.org/abs/2305.04099v1 | https://arxiv.org/pdf/2305.04099v1.pdf | Symbolic Regression on FPGAs for Fast Machine Learning Inference | The high-energy physics community is investigating the feasibility of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to improve physics sensitivity while meeting data processing latency limitations. In this contribution, we introduce a novel end-to-end procedure that utilizes a mac... | ['Maurizio Pierini', 'Isobel Ojalvo', 'Philip Harris', 'Peter Elmer', 'Sridhara Dasu', 'Miles Cranmer', 'Ekaterina Govorkova', 'Vladimir Loncar', 'Adrian Alan Pol', 'Ho Fung Tsoi'] | 2023-05-06 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.50263429e-01 7.86738768e-02 -2.24813253e-01 -6.11114860e-01
-4.33078825e-01 -5.71646094e-01 3.04782122e-01 2.04756781e-01
-4.25098449e-01 7.28583157e-01 -6.69822335e-01 -9.01465893e-01
-6.26315296e-01 -8.39954734e-01 -8.07877541e-01 -5.85254014e-01
-2.50729650e-01 7.36624241e-01 4.06505028e-03 -6.54850155... | [8.279984474182129, 2.9892995357513428] |
88938dc1-b7ce-4b8a-8626-67d4fa8b1223 | building-a-data-collection-for-deception | null | null | https://aclanthology.org/W12-0405 | https://aclanthology.org/W12-0405.pdf | Building a Data Collection for Deception Research | null | ['Joan Bachenko', 'Eileen Fitzpatrick'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.399774551391602, 3.6110270023345947] |
31a8e456-a4e0-4379-a142-fc9b90875031 | ad-net-training-a-shadow-detector-with | 1712.01361 | null | http://arxiv.org/abs/1712.01361v2 | http://arxiv.org/pdf/1712.01361v2.pdf | A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation | We propose a novel GAN-based framework for detecting shadows in images, in
which a shadow detection network (D-Net) is trained together with a shadow
attenuation network (A-Net) that generates adversarial training examples. The
A-Net modifies the original training images constrained by a simplified
physical shadow mode... | ['Dimitris Samaras', 'Minh Hoai', 'Vu Nguyen', 'Tomas F. Yago Vicente', 'Hieu Le'] | 2017-12-04 | ad-net-training-a-shadow-detector-with-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Hieu_Le_AD_Net_Training_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Hieu_Le_AD_Net_Training_ECCV_2018_paper.pdf | eccv-2018-9 | ['shadow-detection', 'detecting-shadows'] | ['computer-vision', 'computer-vision'] | [ 8.67328525e-01 5.71569085e-01 3.58568519e-01 -3.27548176e-01
-4.61012661e-01 -2.01290295e-01 4.50563669e-01 -8.88764560e-01
2.50700163e-03 8.12204540e-01 -8.27986598e-02 -5.89286566e-01
8.82369339e-01 -7.93368340e-01 -1.04345608e+00 -8.65982473e-01
4.38341405e-03 2.36754656e-01 1.03363407e+00 -1.09507389... | [10.848518371582031, -4.111350059509277] |
2d64836f-c775-497b-aa66-7c980b090afc | effective-document-image-enhancement-using | null | null | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4354038 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4354038 | Effective Document Image Enhancement Using tokens-to-token Transformer Network | Document image enhancement is a fundamental and important stage for attaining the best performance in any document analysis assignment because there are many degradation situations that could harm document images, making it more difficult to recognize and analyze them. In this paper, we propose to employ a Tokens-to-To... | ['Umapada Pal', 'Swalpa Kumar Roy', 'Risab Biswas'] | 2023-02-10 | null | null | null | preprint-2023-2 | ['image-enhancement'] | ['computer-vision'] | [ 4.78673309e-01 -4.82918173e-01 -2.94299182e-02 -2.07976669e-01
-4.32278931e-01 -2.52487034e-01 5.99476755e-01 8.22932273e-02
-4.59227473e-01 4.81760293e-01 6.80246875e-02 -4.45722759e-01
7.35999048e-02 -7.15499282e-01 -4.47436750e-01 -1.11795700e+00
2.63962924e-01 -1.40349478e-01 1.88120991e-01 -2.72519857... | [11.330297470092773, -2.0526554584503174] |
3fa3d444-7908-4808-b2ac-51735e5e0fd7 | almeria-boosting-pairwise-molecular-contrasts | 2305.13254 | null | https://arxiv.org/abs/2305.13254v1 | https://arxiv.org/pdf/2305.13254v1.pdf | ALMERIA: Boosting pairwise molecular contrasts with scalable methods | Searching for potential active compounds in large databases is a necessary step to reduce time and costs in modern drug discovery pipelines. Such virtual screening methods seek to provide predictions that allow the search space to be narrowed down. Although cheminformatics has made great progress in exploiting the pote... | ['Pilar M. Ortigosa', 'Horacio Pérez-Sánchez', 'Juana L. Redondo', 'Rafael Mena-Yedra'] | 2023-04-28 | null | null | null | null | ['activity-prediction', 'drug-discovery', 'hyperparameter-optimization', 'activity-prediction'] | ['computer-vision', 'medical', 'methodology', 'time-series'] | [ 1.66279763e-01 -1.07115939e-01 -3.36728245e-01 -3.06044400e-01
-7.08827853e-01 -6.89975619e-01 3.41626942e-01 7.20430911e-01
-3.70471209e-01 1.33143210e+00 -3.73359501e-01 -6.38764679e-01
-3.79938185e-01 -6.24223471e-01 -3.80962789e-01 -7.58306026e-01
-4.27584320e-01 9.72719371e-01 1.28976837e-01 -7.40375966... | [5.0498456954956055, 5.534786701202393] |
4cbc6a52-b034-4b3a-9a1d-4e0ad729a4e9 | wheat-head-counting-by-estimating-a-density | 2303.10542 | null | https://arxiv.org/abs/2303.10542v1 | https://arxiv.org/pdf/2303.10542v1.pdf | Wheat Head Counting by Estimating a Density Map with Convolutional Neural Networks | Wheat is one of the most significant crop species with an annual worldwide grain production of 700 million tonnes. Assessing the production of wheat spikes can help us measure the grain production. Thus, detecting and characterizing spikes from images of wheat fields is an essential component in a wheat breeding proces... | ['Hongyu Guo'] | 2023-03-19 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-4.70576547e-02 -2.02762589e-01 1.29126728e-01 -3.12050998e-01
-3.56990695e-01 -6.00963295e-01 1.26378119e-01 3.80142033e-01
-2.65330166e-01 5.05881667e-01 -2.92570710e-01 -3.11924666e-01
-1.12455711e-01 -1.46332812e+00 -7.48234451e-01 -8.01449955e-01
-1.10195324e-01 2.77506620e-01 4.91689563e-01 -3.47950369... | [9.164143562316895, -1.5139051675796509] |
f47f7bf5-50df-48bf-8a10-e93f320e4493 | contextual-attention-for-hand-detection-in | 1904.04882 | null | http://arxiv.org/abs/1904.04882v1 | http://arxiv.org/pdf/1904.04882v1.pdf | Contextual Attention for Hand Detection in the Wild | We present Hand-CNN, a novel convolutional network architecture for detecting
hand masks and predicting hand orientations in unconstrained images. Hand-CNN
extends MaskRCNN with a novel attention mechanism to incorporate contextual
cues in the detection process. This attention mechanism can be implemented as
an efficie... | ['Yang Wang', 'Zhengwei Wei', 'Minh Hoai', 'Justin Zhang', 'Supreeth Narasimhaswamy'] | 2019-04-09 | contextual-attention-for-hand-detection-in-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Narasimhaswamy_Contextual_Attention_for_Hand_Detection_in_the_Wild_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Narasimhaswamy_Contextual_Attention_for_Hand_Detection_in_the_Wild_ICCV_2019_paper.pdf | iccv-2019-10 | ['hand-detection'] | ['computer-vision'] | [-1.16220146e-01 -3.42534661e-01 -3.01460028e-01 -3.00082207e-01
-2.07022205e-01 -8.19783151e-01 1.84991762e-01 -6.01063669e-01
-6.40646875e-01 3.57667953e-01 2.92340219e-01 -2.34755933e-01
3.70606422e-01 -4.19472784e-01 -5.90369403e-01 -4.00467575e-01
4.89884242e-03 3.25009018e-01 7.05484569e-01 6.22490533... | [6.600776195526123, -0.6692930459976196] |
b32a7950-8e08-4ec4-ac85-eeb11ef25d75 | meteorologists-and-students-a-resource-for | 1809.02494 | null | http://arxiv.org/abs/1809.02494v1 | http://arxiv.org/pdf/1809.02494v1.pdf | Meteorologists and Students: A resource for language grounding of geographical descriptors | We present a data resource which can be useful for research purposes on
language grounding tasks in the context of geographical referring expression
generation. The resource is composed of two data sets that encompass 25
different geographical descriptors and a set of associated graphical
representations, drawn as poly... | ['Kees Van Deemter', 'Alejandro Ramos-Soto', 'Jose M. Alonso', 'Ehud Reiter', 'Albert Gatt'] | 2018-09-07 | meteorologists-and-students-a-resource-for-1 | https://aclanthology.org/W18-6551 | https://aclanthology.org/W18-6551.pdf | ws-2018-11 | ['referring-expression-generation'] | ['computer-vision'] | [-1.84754923e-03 6.04569852e-01 -1.39027566e-01 -4.65616673e-01
-4.93569016e-01 -7.53974736e-01 1.12340152e+00 7.66999483e-01
-4.23033357e-01 9.44435239e-01 6.78215683e-01 -5.10955930e-01
-2.57744372e-01 -9.88696456e-01 -1.56895563e-01 -5.55046797e-02
-3.21137667e-01 2.98403203e-01 3.15029532e-01 -7.49066114... | [9.555692672729492, 9.166899681091309] |
93cbf800-2f5e-4323-bae4-86914604befe | unsupervised-temporal-feature-aggregation-for | 2002.08097 | null | https://arxiv.org/abs/2002.08097v1 | https://arxiv.org/pdf/2002.08097v1.pdf | Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos | Image-based sports analytics enable automatic retrieval of key events in a game to speed up the analytics process for human experts. However, most existing methods focus on structured television broadcast video datasets with a straight and fixed camera having minimum variability in the capturing pose. In this paper, we... | ['Phongtharin Vinayavekhin', 'Asim Munawar', 'Koji Ito', 'Yuki Inaba', 'Subhajit Chaudhury', 'Shuji Kidokoro', 'Daiki Kimura', 'Ryuki Tachibana', 'Minoru Matsumoto', 'Hiroki Ozaki'] | 2020-02-19 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 2.59722114e-01 -2.88437068e-01 1.29815715e-03 -9.15420875e-02
-1.00696075e+00 -8.87877107e-01 3.93675596e-01 2.21426889e-01
-5.58909833e-01 2.42126361e-01 2.53005505e-01 4.70077068e-01
-3.08871090e-01 -5.46990335e-01 -8.85675669e-01 -5.20360887e-01
-1.55380778e-02 4.31752473e-01 4.62124974e-01 -3.58625948... | [7.832108497619629, 0.161538764834404] |
8a018863-e248-4f10-84c9-24b7e356be6a | meta-learning-for-code-summarization | null | null | https://openreview.net/forum?id=Sm8-5PzKW0 | https://openreview.net/pdf?id=Sm8-5PzKW0 | Meta Learning for Code Summarization | Source code summarization is the task of generating a high-level natural language description for a segment of programming language
code. Current neural models for the task differ in their architecture and the aspects of code they consider. In this paper, we show that three
SOTA models for code summarization work well ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['code-summarization'] | ['computer-code'] | [ 3.15919340e-01 5.75180531e-01 -8.04494321e-01 -3.09689254e-01
-1.30952144e+00 -6.23213768e-01 3.34865063e-01 5.83316088e-01
4.51209173e-02 3.93534243e-01 7.88545847e-01 -4.07811821e-01
2.77215540e-01 -4.03543919e-01 -8.32596481e-01 -1.06737785e-01
-4.22045663e-02 1.27737850e-01 1.60510302e-01 -3.58716846... | [7.606393814086914, 7.948085308074951] |
7b74cd77-753f-41f7-984e-094217ece2fc | 3d-fcn-feature-driven-regression-forest-based | 1806.03019 | null | http://arxiv.org/abs/1806.03019v1 | http://arxiv.org/pdf/1806.03019v1.pdf | 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation | This paper presents a fully automated atlas-based pancreas segmentation
method from CT volumes utilizing 3D fully convolutional network (FCN)
feature-based pancreas localization. Segmentation of the pancreas is difficult
because it has larger inter-patient spatial variations than other organs.
Previous pancreas segment... | ['Kensaku MORI', 'Daniel Rueckert', "Ken'ichi Karasawa", 'Takayuki Kitasaka', 'Michitaka Fujiwara', 'Holger R. Roth', 'Masahiro Oda', 'Kazunari Misawa', 'Natsuki Shimizu'] | 2018-06-08 | null | null | null | null | ['pancreas-segmentation', 'automated-pancreas-segmentation'] | ['medical', 'medical'] | [-6.17192626e-01 6.96275532e-02 -2.08871588e-01 -5.85268497e-01
-6.34743750e-01 -8.38334143e-01 1.74012128e-02 4.78819549e-01
-3.48055214e-01 5.19810617e-01 3.99316460e-01 2.04067677e-02
-4.46657911e-02 -7.73798406e-01 -7.50883043e-01 -1.02843833e+00
-5.85373878e-01 9.21090901e-01 2.82353401e-01 4.41245198... | [14.470680236816406, -2.6815993785858154] |
2bba509b-2424-4ebb-9ee1-8553fe7bd5d4 | rafare-learning-robust-and-accurate-non | 2302.05486 | null | https://arxiv.org/abs/2302.05486v1 | https://arxiv.org/pdf/2302.05486v1.pdf | RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs | We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR). While tremendous efforts have been devoted to parametric SVFR, a visible gap still lies between the result 3D shape and the ground truth. We believe there are two major obstacles: 1) the representation of the parametri... | ['Xun Cao', 'Menghua Wu', 'Yuanxun Lu', 'Hao Zhu', 'Longwei Guo'] | 2023-02-10 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.20870791e-01 -9.86199677e-02 1.17381297e-01 -7.46965170e-01
-7.01928675e-01 -4.76790726e-01 3.85567993e-01 -7.04918742e-01
1.42140195e-01 4.22162592e-01 8.24333876e-02 1.41468838e-01
1.18559264e-02 -5.37850797e-01 -6.13955021e-01 -6.06246293e-01
5.91945127e-02 5.41798353e-01 -1.08419314e-01 -1.86389640... | [13.199761390686035, 0.06367260962724686] |
4a0a4cab-24ac-4072-83b4-67e3e3f9a55b | region-aware-video-object-segmentation-with | 2207.10258 | null | https://arxiv.org/abs/2207.10258v1 | https://arxiv.org/pdf/2207.10258v1.pdf | Region Aware Video Object Segmentation with Deep Motion Modeling | Current semi-supervised video object segmentation (VOS) methods usually leverage the entire features of one frame to predict object masks and update memory. This introduces significant redundant computations. To reduce redundancy, we present a Region Aware Video Object Segmentation (RAVOS) approach that predicts region... | ['Ajmal Mian', 'Yongsheng Gao', 'Mohammed Bennamoun', 'Bo Miao'] | 2022-07-21 | null | null | null | null | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [ 2.74319369e-02 -2.05915093e-01 -6.92364573e-01 -4.35124606e-01
-5.70216417e-01 -3.13326836e-01 4.48554121e-02 -8.31770375e-02
-5.37696242e-01 3.88820529e-01 -1.44660667e-01 -3.92826498e-02
5.29087901e-01 -5.49838960e-01 -1.06369448e+00 -3.37233514e-01
8.91063139e-02 1.44966722e-01 1.35791349e+00 4.83766496... | [9.17347526550293, -0.07909996062517166] |
281c650b-b52e-4904-931f-35f016cbd124 | semantic-graph-based-place-recognition-for-3d | 2008.11459 | null | https://arxiv.org/abs/2008.11459v1 | https://arxiv.org/pdf/2008.11459v1.pdf | Semantic Graph Based Place Recognition for 3D Point Clouds | Due to the difficulty in generating the effective descriptors which are robust to occlusion and viewpoint changes, place recognition for 3D point cloud remains an open issue. Unlike most of the existing methods that focus on extracting local, global, and statistical features of raw point clouds, our method aims at the ... | ['Yong liu', 'Xiangrui Zhao', 'Feng Wen', 'Xuemeng Yang', 'Xianfang Zeng', 'Mengmeng Wang', 'Guangyao Zhai', 'Xin Kong', 'Wanlong Li'] | 2020-08-26 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-1.02960296e-01 -3.12288612e-01 -1.13770589e-01 -3.98848057e-01
-4.50769156e-01 -5.38050234e-01 6.37323499e-01 4.00203168e-01
-8.77043977e-02 2.40950942e-01 -2.26070270e-01 -4.50786278e-02
-3.31625730e-01 -1.10096288e+00 -5.52759111e-01 -4.15802032e-01
-6.89659640e-02 4.56694305e-01 6.19635284e-01 -1.70931473... | [7.652292251586914, -2.626012086868286] |
14b2ca36-bd96-4620-bbb9-eeb47517966a | self-monitoring-navigation-agent-via | 1901.03035 | null | http://arxiv.org/abs/1901.03035v1 | http://arxiv.org/pdf/1901.03035v1.pdf | Self-Monitoring Navigation Agent via Auxiliary Progress Estimation | The Vision-and-Language Navigation (VLN) task entails an agent following
navigational instruction in photo-realistic unknown environments. This
challenging task demands that the agent be aware of which instruction was
completed, which instruction is needed next, which way to go, and its
navigation progress towards the ... | ['Ghassan AlRegib', 'Chih-Yao Ma', 'Richard Socher', 'Zsolt Kira', 'Jiasen Lu', 'Caiming Xiong', 'Zuxuan Wu'] | 2019-01-10 | self-monitoring-navigation-agent-via-1 | https://openreview.net/forum?id=r1GAsjC5Fm | https://openreview.net/pdf?id=r1GAsjC5Fm | iclr-2019-5 | ['vision-language-navigation', 'natural-language-visual-grounding'] | ['computer-vision', 'reasoning'] | [ 2.12377042e-01 -3.76645513e-02 -8.13731477e-02 -2.70465076e-01
-6.96115136e-01 -5.51383376e-01 7.70170867e-01 2.33417735e-01
-8.50606561e-01 4.41192269e-01 1.87635973e-01 -5.57356775e-01
6.10333309e-02 -4.80066180e-01 -6.95625186e-01 -4.99364227e-01
8.65142327e-03 4.22345400e-01 5.28512239e-01 -3.98488432... | [4.529544830322266, 0.4992086589336395] |
c94c69e7-d5d4-45c4-b8dd-ca517b97c11c | is-robustbench-autoattack-a-suitable | 2112.01601 | null | https://arxiv.org/abs/2112.01601v2 | https://arxiv.org/pdf/2112.01601v2.pdf | Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? | Recently, RobustBench (Croce et al. 2020) has become a widely recognized benchmark for the adversarial robustness of image classification networks. In its most commonly reported sub-task, RobustBench evaluates and ranks the adversarial robustness of trained neural networks on CIFAR10 under AutoAttack (Croce and Hein 20... | ['Janis Keuper', 'Margret Keuper', 'Dominik Strassel', 'Peter Lorenz'] | 2021-12-02 | null | https://openreview.net/forum?id=aLB3FaqoMBs | https://openreview.net/pdf?id=aLB3FaqoMBs | aaai-workshop-advml-2022-2 | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 1.58625066e-01 1.52593926e-01 2.49595895e-01 3.23222913e-02
-7.72811353e-01 -1.07628644e+00 9.05598700e-01 3.60624120e-02
-8.33367586e-01 8.30599546e-01 -3.67430747e-02 -4.75114375e-01
-3.05836320e-01 -5.25250971e-01 -7.58514404e-01 -7.29080617e-01
-4.04809952e-01 -4.05145735e-02 4.89518583e-01 -3.67059886... | [5.630026817321777, 7.898019313812256] |
82524d6a-0696-4f46-b2bc-2dc44d0fc15a | crossloc3d-aerial-ground-cross-source-3d | 2303.17778 | null | https://arxiv.org/abs/2303.17778v1 | https://arxiv.org/pdf/2303.17778v1.pdf | CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition | We present CrossLoc3D, a novel 3D place recognition method that solves a large-scale point matching problem in a cross-source setting. Cross-source point cloud data corresponds to point sets captured by depth sensors with different accuracies or from different distances and perspectives. We address the challenges in te... | ['Dinesh Manocha', 'Damon Conover', 'Adarsh Jagan Sathyamoorthy', 'Jing Liang', 'Xijun Wang', 'Montana Hoover', 'Aswath Muthuselvam', 'Tianrui Guan'] | 2023-03-31 | null | null | null | null | ['metric-learning', '3d-place-recognition', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [-2.68793643e-01 -4.22643363e-01 7.00454647e-03 -2.28430778e-01
-9.36593175e-01 -8.00092041e-01 5.89157581e-01 3.64938468e-01
-3.21807414e-01 9.11286175e-02 -8.54932368e-02 -1.52941912e-01
-3.59719664e-01 -1.06034184e+00 -9.22986805e-01 -3.96649152e-01
-2.97720402e-01 7.95758367e-01 2.52114534e-01 -1.87230825... | [7.510725498199463, -2.429027557373047] |
41d89ac2-34c0-4853-aff7-47e1feef0946 | explosive-proofs-of-mathematical-truths | 2004.00055 | null | https://arxiv.org/abs/2004.00055v2 | https://arxiv.org/pdf/2004.00055v2.pdf | Epistemic Phase Transitions in Mathematical Proofs | Mathematical proofs are both paradigms of certainty and some of the most explicitly-justified arguments that we have in the cultural record. Their very explicitness, however, leads to a paradox, because the probability of error grows exponentially as the argument expands. When a mathematician encounters a proof, how do... | ['Scott Viteri', 'Simon DeDeo'] | 2020-03-31 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 7.85316676e-02 8.31404328e-01 1.10109514e-02 -1.15793474e-01
-6.20318890e-01 -9.32982385e-01 9.61301982e-01 5.27033925e-01
-1.14238314e-01 1.01160240e+00 4.80893850e-02 -1.41431940e+00
-6.54407203e-01 -1.00543940e+00 -9.76654530e-01 -1.98031738e-01
-1.84175223e-01 5.66722631e-01 1.25750929e-01 -3.50736648... | [8.813677787780762, 6.738065242767334] |
5d0310e4-c81a-456b-a16d-1a6aa58ac09b | catena-causal-and-temporal-relation | null | null | https://aclanthology.org/C16-1007 | https://aclanthology.org/C16-1007.pdf | CATENA: CAusal and TEmporal relation extraction from NAtural language texts | We present CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. We evaluate the performance of each sieve, showing that the rule-based, the machine-learned and the reasoning components... | ['Paramita Mirza', 'Sara Tonelli'] | 2016-12-01 | catena-causal-and-temporal-relation-1 | https://aclanthology.org/C16-1007 | https://aclanthology.org/C16-1007.pdf | coling-2016-12 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-3.22437406e-01 4.25836712e-01 -7.11868644e-01 -3.33575249e-01
-2.12779254e-01 -4.77411300e-01 1.49057257e+00 1.66267022e-01
-2.61328876e-01 7.32702017e-01 6.90053463e-01 -4.20871347e-01
-5.97020864e-01 -6.91748142e-01 -5.52423179e-01 -4.74875301e-01
-7.31526971e-01 7.14184999e-01 5.49191415e-01 -6.65036380... | [9.071165084838867, 9.194928169250488] |
7aa28665-ec88-4b25-87d0-f6534a3c1904 | a-lossless-intra-reference-block | 2104.01846 | null | https://arxiv.org/abs/2104.01846v1 | https://arxiv.org/pdf/2104.01846v1.pdf | A Lossless Intra Reference Block Recompression Scheme for Bandwidth Reduction in HEVC-IBC | The reference frame memory accesses in inter prediction result in high DRAM bandwidth requirement and power consumption. This problem is more intensive by the adoption of intra block copy (IBC), a new coding tool in the screen content coding (SCC) extension to High Efficiency Video Coding (HEVC). In this paper, we prop... | ['Fan Liang', 'Ren Mao', 'Jian Cao', 'Guangyu Zhong', 'Jun Wang', 'Jiyuan Hu'] | 2021-04-05 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 3.9655420e-01 -1.5377097e-01 -5.2636796e-01 8.1991352e-02
-2.2910337e-01 7.5662062e-02 4.2704618e-01 1.3535912e-02
-1.3512400e-01 6.3108122e-01 5.8713549e-01 -3.6065382e-01
4.7396746e-01 -1.0925356e+00 -5.8464473e-01 -7.1316117e-01
2.2535758e-01 -2.6462999e-01 1.0986463e+00 4.1275326e-02
7.8944886e-01... | [11.277226448059082, -1.7382723093032837] |
b0c531cf-c668-4803-865a-27e0e708d0d1 | towards-fine-dining-recipe-generation-with | 2209.12774 | null | https://arxiv.org/abs/2209.12774v1 | https://arxiv.org/pdf/2209.12774v1.pdf | Towards Fine-Dining Recipe Generation with Generative Pre-trained Transformers | Food is essential to human survival. So much so that we have developed different recipes to suit our taste needs. In this work, we propose a novel way of creating new, fine-dining recipes from scratch using Transformers, specifically auto-regressive language models. Given a small dataset of food recipes, we try to trai... | ['Konstantinos Skianis', 'Konstantinos Katserelis'] | 2022-09-26 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [-4.17102784e-01 -2.61345744e-01 -1.53030530e-02 -5.25600255e-01
-2.93634772e-01 -5.81752896e-01 2.01098904e-01 4.21840638e-01
-2.68567801e-01 3.54005218e-01 6.03108943e-01 -1.32328853e-01
1.59271032e-01 -1.20353520e+00 -6.54295504e-01 -2.99349517e-01
5.75272329e-02 2.48177394e-01 -2.06818819e-01 -8.40976655... | [11.520524978637695, 4.536914348602295] |
3209b32c-aa5c-4076-bbd4-8d704e93e75d | false-target-detection-in-ofdm-based-joint | 2303.14782 | null | https://arxiv.org/abs/2303.14782v1 | https://arxiv.org/pdf/2303.14782v1.pdf | False Target Detection in OFDM-based Joint RADAR-Communication Systems | Joint RADAR communication (JRC) systems that use orthogonal frequency division multiplexing (OFDM) can be compromised by an adversary that re-produces the received OFDM signal creating thus false RADAR targets. This paper presents a set of algorithms that can be deployed at the JRC system and can detect the presence of... | ['Antonios Argyriou'] | 2023-03-26 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 6.77792370e-01 -2.33151894e-02 1.50755167e-01 -9.14913788e-02
-4.05146450e-01 -6.73464358e-01 6.81551278e-01 -9.90127996e-02
-4.46532428e-01 1.07290781e+00 -4.18603688e-01 -8.42861652e-01
-3.51329416e-01 -8.70068192e-01 -2.23765627e-01 -5.16690910e-01
-1.20333982e+00 -9.60277840e-02 7.04829320e-02 -2.93173995... | [6.349600791931152, 1.2289947271347046] |
80b3f692-b8c7-4369-87fc-0461254042a9 | modeling-time-series-and-spatial-data-for | 2212.13259 | null | https://arxiv.org/abs/2212.13259v1 | https://arxiv.org/pdf/2212.13259v1.pdf | Modeling Time-Series and Spatial Data for Recommendations and Other Applications | With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-time event sequences. We follow a ground-up approach, i.e., first, we address the problems that may arise due to the poor quality of CTES data... | ['Vinayak Gupta'] | 2022-12-25 | null | null | null | null | ['activity-prediction', 'point-processes', 'temporal-sequences', 'activity-prediction'] | ['computer-vision', 'methodology', 'reasoning', 'time-series'] | [ 4.26794402e-02 -1.55335620e-01 -2.29152724e-01 -1.31732553e-01
-3.57006520e-01 -3.18569511e-01 6.38483405e-01 -1.90898385e-02
-1.38609692e-01 6.05299771e-01 5.41875482e-01 -5.01200676e-01
-7.90592432e-01 -1.10675859e+00 -6.22222364e-01 -5.46070099e-01
-4.24877405e-01 6.33145988e-01 5.48305586e-02 -6.75654650... | [6.983304977416992, 3.1634199619293213] |
2405e473-6697-4f71-b514-5fac095ac801 | diabetic-retinopathy-grading-system-based-on | 2012.12515 | null | https://arxiv.org/abs/2012.12515v1 | https://arxiv.org/pdf/2012.12515v1.pdf | Diabetic Retinopathy Grading System Based on Transfer Learning | Much effort is being made by the researchers in order to detect and diagnose diabetic retinopathy (DR) accurately automatically. The disease is very dangerous as it can cause blindness suddenly if it is not continuously screened. Therefore, many computers aided diagnosis (CAD) systems have been developed to diagnose th... | ['Mohammed Elmogy', 'Sherif Barakat', 'Eman AbdelMaksoud'] | 2020-12-23 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-2.10340112e-01 -3.94504726e-01 -1.67621933e-02 -7.69610941e-01
-3.44452351e-01 -1.29039973e-01 1.97848037e-01 -1.85141683e-01
-2.67011255e-01 8.41668546e-01 1.19538195e-01 -1.08514406e-01
-1.30233154e-01 -7.41233349e-01 2.79572546e-01 -7.04578400e-01
1.41633227e-01 5.63485742e-01 1.98689818e-01 1.49489507... | [15.831741333007812, -3.982593297958374] |
eabce8b2-3504-48a6-a38e-aac5a1ce7040 | non-aligned-supervision-for-real-image | 2303.04940 | null | https://arxiv.org/abs/2303.04940v3 | https://arxiv.org/pdf/2303.04940v3.pdf | Non-aligned supervision for Real Image Dehazing | Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in misaligned hazy and clear image pairs. In this paper, we propose a non-aligned supervision framework that consists of three networks - dehazing, airlight, and transmission. In particular, we explore a non-alignment... | ['Jian Yang', 'Jun Li', 'Xiang Li', 'Jianjun Qian', 'Fei Guo', 'Junkai Fan'] | 2023-03-08 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 4.51605767e-01 -1.74641147e-01 5.71906805e-01 -4.35664505e-01
-5.53065121e-01 -1.16156988e-01 2.94941425e-01 -7.12405682e-01
-2.86500484e-01 7.43489683e-01 1.06179237e-01 -2.80717999e-01
1.22963734e-01 -6.74384236e-01 -8.72390270e-01 -1.19570756e+00
1.05934232e-01 -1.68574974e-01 1.42968476e-01 -4.71801877... | [10.913405418395996, -3.1710596084594727] |
0499ff77-da4a-46dd-b00e-20322b28a042 | contfv-a-contrastive-learning-framework-for | null | null | https://openreview.net/forum?id=SMEv6k1Nb2 | https://openreview.net/pdf?id=SMEv6k1Nb2 | ConTFV: A Contrastive Learning Framework for Table-based Fact Verification | Table-based fact verification is a binary classification task where the challenging part lies in the table's structural parsing and symbolic reasoning. Jointly pre-training on abundant textual and tabular data has been conducted for table semantic parsing recently. However, these models are designed for the table's gen... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 1.60495818e-01 7.81367958e-01 -9.43911970e-01 -5.03995478e-01
-1.53523910e+00 -8.23009670e-01 7.18020558e-01 6.15696013e-01
2.41575375e-01 7.81377077e-01 6.45714164e-01 -6.91398740e-01
3.15919816e-01 -1.13064170e+00 -1.01250815e+00 2.22540110e-01
1.51480258e-01 4.85598505e-01 3.66134703e-01 -4.16641802... | [9.613146781921387, 7.800009727478027] |
127cca7a-06f0-4974-a915-f92a855968d0 | relphormer-relational-graph-transformer-for | 2205.10852 | null | https://arxiv.org/abs/2205.10852v5 | https://arxiv.org/pdf/2205.10852v5.pdf | Relphormer: Relational Graph Transformer for Knowledge Graph Representations | Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer architectures have not yielded promising improvements in the Knowledge Graph (KG) representations, where the translational distance paradigm domin... | ['Huajun Chen', 'Wei Guo', 'Qiang Chen', 'Jing Chen', 'Feiyu Xiong', 'Xiaozhuan Liang', 'Ningyu Zhang', 'Siyuan Cheng', 'Zhen Bi'] | 2022-05-22 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-3.70522141e-02 2.79983699e-01 -4.78117377e-01 -3.52589399e-01
-5.62936902e-01 -6.64794147e-01 2.64390349e-01 2.34895781e-01
-1.74459100e-01 4.44636256e-01 4.24716979e-01 -5.02423644e-01
-3.49857539e-01 -1.12704098e+00 -9.03041005e-01 -4.03070241e-01
1.33351102e-01 4.49847341e-01 1.85438842e-01 -2.51086920... | [8.872431755065918, 7.968909740447998] |
ff549c35-3d2b-4cd3-8f57-d4de243a171d | cips-3d-a-3d-aware-generator-of-gans-based-on | 2110.09788 | null | https://arxiv.org/abs/2110.09788v1 | https://arxiv.org/pdf/2110.09788v1.pdf | CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis | The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses. The recently proposed NeRF-based GANs made great progress towards 3D-aware generators, but they are unable to generate high-quality images yet. T... | ['Qi Tian', 'Bingbing Ni', 'Lingxi Xie', 'Peng Zhou'] | 2021-10-19 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 2.34667838e-01 4.29731131e-01 8.40696096e-02 -2.56504536e-01
-9.22888577e-01 -6.77035034e-01 6.87313080e-01 -1.00441885e+00
1.42504603e-01 6.89040065e-01 2.27712840e-01 -3.63776743e-01
4.66407180e-01 -7.45326877e-01 -9.56802130e-01 -6.71588123e-01
2.28134379e-01 2.43299082e-01 -3.67271781e-01 -2.32810974... | [11.76325511932373, -0.4302081763744354] |
d1f3c7de-e27a-40a1-9254-497dc0c69f71 | what-matters-in-training-a-gpt4-style | 2307.02469 | null | https://arxiv.org/abs/2307.02469v1 | https://arxiv.org/pdf/2307.02469v1.pdf | What Matters in Training a GPT4-Style Language Model with Multimodal Inputs? | Recent advancements in Large Language Models (LLMs) such as GPT4 have displayed exceptional multi-modal capabilities in following open-ended instructions given images. However, the performance of these models heavily relies on design choices such as network structures, training data, and training strategies, and these ... | ['Tao Kong', 'Yuchen Zhang', 'Yang Wei', 'Guoqiang Wei', 'Jiangnan Xia', 'Jiani Zheng', 'Hanbo Zhang', 'Yan Zeng'] | 2023-07-05 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 1.17636539e-01 1.74219280e-01 -3.35116744e-01 -3.10731173e-01
-7.20382631e-01 -6.48872495e-01 6.73216343e-01 -1.50157437e-01
-6.08171940e-01 4.82723892e-01 4.06748652e-01 -6.67462468e-01
1.74749345e-02 -3.87643754e-01 -9.54272985e-01 -2.20643893e-01
1.67740241e-01 5.57813287e-01 1.60232082e-01 -3.15941334... | [10.754571914672852, 1.5239101648330688] |
00c399e3-e2ff-45f6-bcbe-0d648e94a23e | boun-isik-participation-an-unsupervised | null | null | https://aclanthology.org/D19-5722 | https://aclanthology.org/D19-5722.pdf | BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes | This paper presents our participation to the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-re... | ['Arzucan {\\"O}zg{\\"u}r', '{\\"O}mer Faruk Tuna', '{\\.I}lknur Karadeniz'] | 2019-11-01 | null | null | null | ws-2019-11 | ['medical-concept-normalization'] | ['medical'] | [ 1.71830013e-01 3.53430450e-01 1.06479116e-01 -3.87839198e-01
-2.25322098e-01 -1.94254413e-01 7.99214125e-01 1.05732834e+00
-9.49858785e-01 8.93354297e-01 2.38208801e-01 -1.99211583e-01
-2.43130922e-01 -9.15710747e-01 -7.65872955e-01 -8.31189573e-01
-1.16742320e-01 5.09869516e-01 -7.80324861e-02 -2.68203229... | [8.490765571594238, 8.726394653320312] |
e2ec10ff-5711-46dc-bc8a-400475d68e3a | sriubc-simple-similarity-features-for | null | null | https://aclanthology.org/S12-1091 | https://aclanthology.org/S12-1091.pdf | SRIUBC: Simple Similarity Features for Semantic Textual Similarity | null | ['Eneko Agirre', 'Eric Yeh'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['video-description'] | ['computer-vision'] | [-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... | [-7.359133243560791, 3.728935480117798] |
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