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3853be1b-3804-4afd-8ff7-bf1e44f7c5bd | unsupervised-person-re-identification-a | 2109.06057 | null | https://arxiv.org/abs/2109.06057v2 | https://arxiv.org/pdf/2109.06057v2.pdf | Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions | Person re-identification (Re-ID) has been a significant research topic in the past decade due to its real-world applications and research significance. While supervised person Re-ID methods achieve superior performance over unsupervised counterparts, they can not scale to large unlabelled datasets and new domains due t... | ['Xiaojun Chang', 'Andy Song', 'Lina Yao', 'Chung-Hsing Yeh', 'Pengzhen Ren', 'Xiangtan Lin'] | 2021-09-01 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.90606201e-01 -1.20141976e-01 -9.76853669e-02 -5.97979426e-01
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1.57767043e-01 8.68242383e-01 -2.30738476e-01 9.14646238... | [14.66651439666748, 1.0248562097549438] |
daa4fdab-5767-44c9-a903-ded0f3f3204a | pressim-an-end-to-end-framework-for-dynamic | 2302.00391 | null | https://arxiv.org/abs/2302.00391v1 | https://arxiv.org/pdf/2302.00391v1.pdf | PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation | Ground pressure exerted by the human body is a valuable source of information for human activity recognition (HAR) in unobtrusive pervasive sensing. While data collection from pressure sensors to develop HAR solutions requires significant resources and effort, we present a novel end-to-end framework, PresSim, to synthe... | ['Paul Lukowicz', 'Sungho Suh', 'Bo Zhou', 'Lala Shakti Swarup Ray'] | 2023-02-01 | null | null | null | null | ['human-activity-recognition', 'pgtask', 'human-activity-recognition'] | ['computer-vision', 'natural-language-processing', 'time-series'] | [ 4.55082297e-01 4.66688238e-02 1.45210803e-01 -9.35362875e-02
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-2.77344733e-01 4.80681300e-01 -2.84160636e-02 -1.10306427... | [7.043891429901123, -1.0702954530715942] |
e907d6b2-eea4-4d86-bc0a-310e28ec592d | a-generalized-framework-for-edge-preserving-1 | 2107.07058 | null | https://arxiv.org/abs/2107.07058v4 | https://arxiv.org/pdf/2107.07058v4.pdf | A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing | Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various require... | ['Michael Ng', 'Jie Yang', 'Xiaolin Huang', 'Yinjie Lei', 'Pingping Zhang', 'Wei Liu'] | 2021-07-15 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 2.75314689e-01 -1.15001872e-01 2.67855395e-02 -7.56918490e-02
-5.50384939e-01 -1.71241552e-01 5.64484000e-01 -3.40318717e-02
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1.40797213e-01 -2.26723239e-01 5.87080657e-01 -2.88193643... | [11.220465660095215, -2.5587985515594482] |
0bda4210-e1ee-46c9-94d1-e56c2ef2fba6 | low-rank-and-sparse-nmf-for-joint-endmembers | 1703.05785 | null | http://arxiv.org/abs/1703.05785v1 | http://arxiv.org/pdf/1703.05785v1.pdf | Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images | Estimation of the number of endmembers existing in a scene constitutes a
critical task in the hyperspectral unmixing process. The accuracy of this
estimate plays a crucial role in subsequent unsupervised unmixing steps i.e.,
the derivation of the spectral signatures of the endmembers (endmembers'
extraction) and the es... | ['Konstantinos D. Koutroumbas', 'Paris V. Giampouras', 'Athanasios A. Rontogiannis'] | 2017-03-16 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.84445322e-01 -3.44409049e-01 1.21845961e-01 1.61924493e-02
-5.77609479e-01 -5.03734350e-01 5.01936197e-01 8.80872086e-02
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-5.28051257e-01 -5.36629677e-01 -3.77090305e-01 -1.38292241e+00
2.76502222e-01 2.60096908e-01 -5.52368343e-01 1.20473139... | [10.106128692626953, -2.0797221660614014] |
d931f791-5106-418b-96ad-fb5a97548182 | unsupervised-image-representation-learning | 2205.15821 | null | https://arxiv.org/abs/2205.15821v2 | https://arxiv.org/pdf/2205.15821v2.pdf | Unsupervised Image Representation Learning with Deep Latent Particles | We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into low-dimensional latent ``particles'', where each particle is described by its spatial location and features of its surrounding region. To dri... | ['Aviv Tamar', 'Tal Daniel'] | 2022-05-31 | null | null | null | null | ['unsupervised-facial-landmark-detection', 'image-manipulation', 'video-prediction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.64159052e-02 -1.53678458e-03 -2.78223127e-01 -4.41355318e-01
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-9.68486667e-02 7.15645909e-01 3.56889725e-01 3.45613569... | [10.063597679138184, -0.04620067775249481] |
78395f0f-9d52-4356-9bc6-9811218cca80 | fault-detection-in-ball-bearings | 2209.11041 | null | https://arxiv.org/abs/2209.11041v1 | https://arxiv.org/pdf/2209.11041v1.pdf | Fault Detection in Ball Bearings | Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machine... | ['Sarah Moll', 'Joshua Pickard'] | 2022-09-19 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.58194434e-02 -5.16916974e-04 1.65944219e-01 1.65582210e-01
-1.27619013e-01 -1.55351330e-02 3.31962109e-01 -3.60378802e-01
-6.87909573e-02 2.90456027e-01 -2.59739518e-01 -4.39750820e-01
-3.75896811e-01 -6.77048504e-01 -6.52627528e-01 -7.53701746e-01
-4.98933077e-01 5.17215431e-01 5.46618819e-01 -3.62778634... | [6.880308151245117, 2.302938461303711] |
21958354-d8f4-4098-abd8-447e94481502 | skin-lesion-classification-using-ensembles-of | 1910.03910 | null | https://arxiv.org/abs/1910.03910v1 | https://arxiv.org/pdf/1910.03910v1.pdf | Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data | In this paper, we describe our method for the ISIC 2019 Skin Lesion Classification Challenge. The challenge comes with two tasks. For task 1, skin lesions have to be classified based on dermoscopic images. For task 2, dermoscopic images and additional patient meta data have to be used. A diverse dataset of 25000 images... | ['René Werner', 'Mohsin Shaikh', 'Nils Gessert', 'Maximilian Nielsen', 'Alexander Schlaefer'] | 2019-10-09 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.53636944e-01 1.45803511e-01 -2.19484940e-01 -3.47536236e-01
-8.88077796e-01 -3.93482834e-01 4.61759657e-01 4.04430002e-01
-6.80872977e-01 7.73217976e-01 -2.06937790e-01 -8.22414737e-03
-1.05830565e-01 -7.54645586e-01 -5.14483154e-01 -7.94856787e-01
2.62810290e-01 3.10181379e-01 4.41414237e-01 5.92133999... | [15.554583549499512, -2.8000922203063965] |
c4dc890c-1899-4e0f-abfe-f88ad079cb3f | veil-vetting-extracted-image-labels-from-in | 2303.09608 | null | https://arxiv.org/abs/2303.09608v1 | https://arxiv.org/pdf/2303.09608v1.pdf | VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object Detection | The use of large-scale vision-language datasets is limited for object detection due to the negative impact of label noise on localization. Prior methods have shown how such large-scale datasets can be used for pretraining, which can provide initial signal for localization, but is insufficient without clean bounding-box... | ['Adriana Kovashka', 'Arushi Rai'] | 2023-03-16 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 3.91871095e-01 5.61880358e-02 -1.06673978e-01 -7.39781260e-01
-1.44846201e+00 -1.01106858e+00 5.39357185e-01 9.29654390e-02
-9.26093459e-01 8.50745857e-01 6.14489876e-02 -2.72115767e-01
5.78779101e-01 -3.39792460e-01 -1.07104611e+00 -6.30634904e-01
1.70677617e-01 2.56264240e-01 5.79070747e-01 2.64563829... | [9.416007995605469, 1.3021571636199951] |
b9b4aab9-572e-4b18-a8fd-3d51f8fa169f | allenact-a-framework-for-embodied-ai-research | 2008.12760 | null | https://arxiv.org/abs/2008.12760v1 | https://arxiv.org/pdf/2008.12760v1.pdf | AllenAct: A Framework for Embodied AI Research | The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics communities. This growth has be... | ['Aniruddha Kembhavi', 'Kuo-Hao Zeng', 'Luca Weihs', 'Roozbeh Mottaghi', 'Klemen Kotar', 'Jordi Salvador', 'Unnat Jain'] | 2020-08-28 | null | null | null | null | ['embodied-question-answering'] | ['computer-vision'] | [-1.03925928e-01 2.89141864e-01 6.96985945e-02 -2.20438540e-01
-2.39437088e-01 -7.46179402e-01 8.25163007e-01 1.40727665e-02
-5.63502729e-01 7.19664335e-01 3.42850953e-01 -2.84693003e-01
-1.20858192e-01 -7.53040731e-01 -7.76751041e-01 -5.88698208e-01
-4.71735686e-01 3.82871568e-01 3.74604948e-02 -6.89568639... | [4.351856231689453, 0.932873547077179] |
02db9e3d-9a9b-44a1-83ed-a4c9e26d454a | ide-3d-interactive-disentangled-editing-for | 2205.15517 | null | https://arxiv.org/abs/2205.15517v1 | https://arxiv.org/pdf/2205.15517v1.pdf | IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis | Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editing flexibility. In this work, we propose a new approach that brings the best of both worlds together. Our system consists of three major com... | ['Yebin Liu', 'Jue Wang', 'Lizhen Wang', 'Yichun Shi', 'Xuan Wang', 'Jingxiang Sun'] | 2022-05-31 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 2.92669475e-01 4.37583625e-01 3.36369336e-01 -2.73426563e-01
-4.32973564e-01 -5.32862723e-01 7.88858175e-01 -6.45306408e-01
2.92391121e-01 6.12647116e-01 3.29401463e-01 1.58617064e-01
8.76732171e-02 -1.01359665e+00 -5.73120713e-01 -5.26769698e-01
5.90476096e-01 5.66975176e-01 -1.28062321e-02 -4.24787760... | [12.553464889526367, -0.4049391746520996] |
25986cab-241b-4643-ba89-4e401192be67 | infoseg-unsupervised-semantic-image | 2110.03477 | null | https://arxiv.org/abs/2110.03477v1 | https://arxiv.org/pdf/2110.03477v1.pdf | InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization | We propose a novel method for unsupervised semantic image segmentation based on mutual information maximization between local and global high-level image features. The core idea of our work is to leverage recent progress in self-supervised image representation learning. Representation learning methods compute a single ... | ['Patrick Knöbelreiter', 'Robert Harb'] | 2021-10-07 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 6.83538973e-01 3.32877040e-01 -3.79499286e-01 -5.23854554e-01
-1.15531671e+00 -4.68890041e-01 5.29860079e-01 4.17036116e-01
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-8.69110972e-03 -8.26158822e-01 -6.76129878e-01 -7.79515386e-01
1.42927289e-01 4.95894909e-01 1.60944924e-01 3.31630975... | [9.616400718688965, 0.7456880211830139] |
4c6aaa67-81b1-441b-a6e7-c8ba95e3ec19 | neural-pitch-shifting-and-time-stretching | 2110.02360 | null | https://arxiv.org/abs/2110.02360v1 | https://arxiv.org/pdf/2110.02360v1.pdf | Neural Pitch-Shifting and Time-Stretching with Controllable LPCNet | Modifying the pitch and timing of an audio signal are fundamental audio editing operations with applications in speech manipulation, audio-visual synchronization, and singing voice editing and synthesis. Thus far, methods for pitch-shifting and time-stretching that use digital signal processing (DSP) have been favored ... | ['Bryan Pardo', 'Juan-Pablo Caceres', 'Nicholas J. Bryan', 'Zeyu Jin', 'Max Morrison'] | 2021-10-05 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 2.63829499e-01 -2.31854454e-01 -4.48125824e-02 -5.15159443e-02
-7.71527946e-01 -5.90692818e-01 3.33046883e-01 1.40910484e-02
-3.14531744e-01 5.52459002e-01 2.31293753e-01 -4.11515757e-02
-3.36104222e-02 -2.04191759e-01 -5.72994292e-01 -6.77217603e-01
-2.07030937e-01 -1.83440700e-01 2.38023803e-01 -2.03668192... | [15.50790786743164, 5.973680019378662] |
996308d8-a66a-461f-b6cd-7c9b6322628c | nonlinear-intensity-scale-and-rotation | 2302.14239 | null | https://arxiv.org/abs/2302.14239v1 | https://arxiv.org/pdf/2302.14239v1.pdf | Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images | We present an effective method for the matching of multimodal images. Accurate image matching is the basis of various applications, such as image registration and structure from motion. Conventional matching methods fail when handling noisy multimodal image pairs with severe scale change, rotation, and nonlinear intens... | ['Yuxuan Liu', 'Li Zhang', 'Zhongli Fan'] | 2023-02-28 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 1.28646001e-01 -5.87528050e-01 -1.14164636e-01 -4.86500144e-01
-9.33150887e-01 -5.87967336e-01 4.11709458e-01 -3.31184827e-02
-4.39659536e-01 1.28980428e-01 3.94742399e-01 2.49321684e-01
-8.44915137e-02 -5.77566385e-01 -4.00022179e-01 -6.09179676e-01
3.43629956e-01 3.29666249e-02 3.14104915e-01 -3.98612112... | [10.311230659484863, -1.7898311614990234] |
c8033768-bbbb-45c7-8a8a-8b8d6c03dcc2 | the-brain-tumor-segmentation-brats-mets | 2306.00838 | null | https://arxiv.org/abs/2306.00838v1 | https://arxiv.org/pdf/2306.00838v1.pdf | The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI | Clinical monitoring of metastatic disease to the brain can be a laborious and time-consuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest dia... | ['Mariam Aboian', 'Jeffrey Rudie', 'Spyridon Bakas', 'Gian Marco Conte', 'Fatima Memon', 'Umber Shafique', 'Ichiro Ikuta', 'Veronica Chiang', 'Sanjay Aneja', 'Evan Calabrese', 'Florian Kofler', 'Anahita Fathi Kazerooni', 'Ariana Familiar', 'Zeke Meier', 'Elaine Johanson', 'Ivan Ezhov', 'Marie Piraud', 'Koen van Leemput... | 2023-06-01 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 2.13330895e-01 -1.57920256e-01 -2.38434896e-01 -4.84640487e-02
-1.09695208e+00 -5.32515705e-01 4.68533099e-01 6.23425543e-01
-7.15237379e-01 6.89618528e-01 3.78387898e-01 -7.34320223e-01
3.65983509e-02 -3.86788875e-01 8.50636959e-02 -9.20561671e-01
-5.70214763e-02 8.70652199e-01 3.90579551e-01 1.29382517... | [14.653213500976562, -2.4922115802764893] |
639a0968-75f3-4d71-87d9-251a6792fa6a | data-driven-geophysics-from-dictionary | 2007.06183 | null | https://arxiv.org/abs/2007.06183v2 | https://arxiv.org/pdf/2007.06183v2.pdf | Data-driven geophysics: from dictionary learning to deep learning | Understanding the principles of geophysical phenomena is an essential and challenging task. "Model-driven" approaches have supported the development of geophysics for a long time; however, such methods suffer from the curse of dimensionality and may inaccurately model the subsurface. "Data-driven" techniques may overco... | ['Siwei Yu', 'Jianwei Ma'] | 2020-07-13 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-2.91610241e-01 -2.06120819e-01 1.49385497e-01 -6.87560320e-01
-9.41425204e-01 -1.99373722e-01 5.66837132e-01 1.88588366e-01
-2.39277616e-01 7.89203167e-01 3.28320473e-01 -8.45465541e-01
-4.38373089e-01 -1.10136235e+00 -4.88042384e-01 -9.81073320e-01
-6.58200622e-01 7.80748904e-01 -3.33764434e-01 -3.59223604... | [6.838844299316406, 2.526124954223633] |
07c2eb27-7e1c-4ee9-b880-13ec6af9300a | emora-stdm-a-versatile-framework-for | 2006.06143 | null | https://arxiv.org/abs/2006.06143v1 | https://arxiv.org/pdf/2006.06143v1.pdf | Emora STDM: A Versatile Framework for Innovative Dialogue System Development | This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions. Our framework caters to a wide range of expertise levels by s... | ['Jinho D. Choi', 'James D. Finch'] | 2020-06-11 | null | https://aclanthology.org/2020.sigdial-1.32 | https://aclanthology.org/2020.sigdial-1.32.pdf | sigdial-acl-2020-7 | ['dialogue-management'] | ['natural-language-processing'] | [-2.18633264e-01 5.61157465e-01 1.59933522e-01 -4.61279601e-01
-3.52616370e-01 -1.04623425e+00 8.74953032e-01 4.10747856e-01
-7.62691498e-02 6.68551862e-01 2.89483964e-01 -7.80327618e-01
-1.43077001e-01 -5.78128278e-01 5.58047831e-01 2.62728631e-01
2.69580513e-01 5.75775385e-01 5.87169528e-01 -9.78087544... | [12.809829711914062, 7.992326736450195] |
9484a13b-f1f2-4ba1-ad61-fd78ed690b76 | transfer-learning-with-ensembles-of-deep | 2103.12068 | null | https://arxiv.org/abs/2103.12068v4 | https://arxiv.org/pdf/2103.12068v4.pdf | Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets | Several machine learning techniques for accurate detection of skin cancer from medical images have been reported. Many of these techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based on limited amounts of training data. However, the classification accuracy of th... | ['Teemu Roos', 'Aqsa Saeed Qureshi'] | 2021-03-22 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 4.44585443e-01 -7.02845678e-03 -3.20378900e-01 -3.38827968e-01
-5.54564714e-01 -1.60641059e-01 4.88425821e-01 5.82844913e-01
-9.20688450e-01 6.79610729e-01 -6.89153746e-02 -1.73290163e-01
-3.47064942e-01 -8.42756629e-01 -4.14150923e-01 -8.85539174e-01
7.42535219e-02 1.58073269e-02 1.24866389e-01 1.03496693... | [15.602160453796387, -2.9268903732299805] |
920a996c-85e1-49dc-98a0-710581471583 | speecht5-unified-modal-encoder-decoder-pre | 2110.07205 | null | https://arxiv.org/abs/2110.07205v3 | https://arxiv.org/pdf/2110.07205v3.pdf | SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing | Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-... | ['Furu Wei', 'Jinyu Li', 'Shujie Liu', 'Chengyi Wang', 'Yao Qian', 'Zhihua Wei', 'Yu Zhang', 'Qing Li', 'Tom Ko', 'Yu Wu', 'Shuo Ren', 'Long Zhou', 'Rui Wang', 'Junyi Ao'] | 2021-10-14 | null | https://aclanthology.org/2022.acl-long.393 | https://aclanthology.org/2022.acl-long.393.pdf | acl-2022-5 | ['speaker-identification'] | ['speech'] | [ 5.59501946e-01 2.83855975e-01 -3.83074045e-01 -6.74707770e-01
-1.18463981e+00 -4.30987775e-01 6.57577217e-01 -2.79168457e-01
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5.27272642e-01 5.55564940e-01 -2.14586750e-01 -1.51794642... | [14.522651672363281, 7.075060844421387] |
4d1cbaf4-43c7-4078-b043-95e38e4ce227 | hcam-hierarchical-cross-attention-model-for | 2304.06910 | null | https://arxiv.org/abs/2304.06910v1 | https://arxiv.org/pdf/2304.06910v1.pdf | HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition | Emotion recognition in conversations is challenging due to the multi-modal nature of the emotion expression. We propose a hierarchical cross-attention model (HCAM) approach to multi-modal emotion recognition using a combination of recurrent and co-attention neural network models. The input to the model consists of two ... | ['Sriram Ganapathy', 'Soumya Dutta'] | 2023-04-14 | null | null | null | null | ['multimodal-emotion-recognition', 'emotion-classification', 'emotion-recognition-in-conversation', 'emotion-classification', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.12042472e-01 -6.39943630e-02 3.25021237e-01 -5.40122986e-01
-9.80786920e-01 -8.64197910e-02 4.98526394e-01 -1.32930055e-02
-4.57359791e-01 3.11254710e-01 8.24613452e-01 1.33279145e-01
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-3.81332748e-02 1.15063608e-01 -5.08847773e-01 -3.65681380... | [13.358830451965332, 5.525308609008789] |
29f294d6-d9c4-4ff7-823b-60fac4aede98 | explorable-tone-mapping-operators | 2010.10000 | null | https://arxiv.org/abs/2010.10000v1 | https://arxiv.org/pdf/2010.10000v1.pdf | Explorable Tone Mapping Operators | Tone-mapping plays an essential role in high dynamic range (HDR) imaging. It aims to preserve visual information of HDR images in a medium with a limited dynamic range. Although many works have been proposed to provide tone-mapped results from HDR images, most of them can only perform tone-mapping in a single pre-desig... | ['Soo-Chang Pei', 'Yu-Lin Chang', 'Chia-Ping Chen', 'Yu-Lun Liu', 'Hung-Jin Lin', 'Ren Wang', 'Chien-Chuan Su'] | 2020-10-20 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 5.40187657e-01 -4.19787586e-01 -2.28136390e-01 -9.68534425e-02
-8.15774977e-01 -4.03521717e-01 5.05845964e-01 -6.75753951e-01
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-3.09968460e-02 -9.43827212e-01 -3.31917942e-01 -9.13446486e-01
3.05878878e-01 -1.01559445e-01 3.71134788e-01 -7.14774907... | [10.960424423217773, -2.247119665145874] |
f7cdbd16-61c9-468e-bd86-80ee64b74133 | on-using-the-ua-speech-and-torgo-databases-to | 2211.08833 | null | https://arxiv.org/abs/2211.08833v1 | https://arxiv.org/pdf/2211.08833v1.pdf | On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches | Although the UA-Speech and TORGO databases of control and dysarthric speech are invaluable resources made available to the research community with the objective of developing robust automatic speech recognition systems, they have also been used to validate a considerable number of automatic dysarthric speech classifica... | ['Ina Kodrasi', 'Parvaneh Janbakhshi', 'Guilherme Schu'] | 2022-11-16 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-5.21898177e-03 -5.49181625e-02 2.04795107e-01 -3.27154011e-01
-9.95803773e-01 -5.13649940e-01 6.33024216e-01 -5.22301435e-01
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-1.50586441e-01 -2.60259926e-01 -2.11504295e-01 -7.36490905e-01
1.62365392e-01 5.08654356e-01 2.19670877e-01 -4.31509078... | [14.51404857635498, 6.331265926361084] |
ae88c69b-d531-4619-8b1a-c2b40b89cf27 | wwfedcbmir-world-wide-federated-content-based | 2305.03383 | null | https://arxiv.org/abs/2305.03383v1 | https://arxiv.org/pdf/2305.03383v1.pdf | WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval | The paper proposes a Federated Content-Based Medical Image Retrieval (FedCBMIR) platform that utilizes Federated Learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR assists pathologists in diagnosing breast cancer more rapidly by identifying similar medical i... | ['Valery Naranjo', 'Zhiming Zhao', 'Javier Oliver Moll', 'Adrián Colomer', 'Yuandou Wang', 'Zahra Tabatabaei'] | 2023-05-05 | null | null | null | null | ['whole-slide-images', 'medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'computer-vision', 'medical'] | [-1.92649826e-01 -4.35262844e-02 -4.27403361e-01 4.73918319e-02
-1.49366868e+00 -5.82042575e-01 1.10703275e-01 4.20983166e-01
-5.98804593e-01 5.24417818e-01 1.08359277e-01 -6.34382427e-01
-2.59174675e-01 -7.19723582e-01 -5.11414766e-01 -1.12654173e+00
1.46672964e-01 3.57075363e-01 5.02728298e-02 7.75716901... | [15.142382621765137, -2.9214584827423096] |
e6d13c01-2715-4921-9d28-b20bb524c7f3 | sequence-aware-item-recommendations-for | 2304.00578 | null | https://arxiv.org/abs/2304.00578v1 | https://arxiv.org/pdf/2304.00578v1.pdf | Sequence-aware item recommendations for multiply repeated user-item interactions | Recommender systems are one of the most successful applications of machine learning and data science. They are successful in a wide variety of application domains, including e-commerce, media streaming content, email marketing, and virtually every industry where personalisation facilitates better user experience or boo... | ['Berthold Lausen', 'Henrik Nordmark', 'Maged Ali', 'Juan Pablo Equihua'] | 2023-04-02 | null | null | null | null | ['matrix-completion', 'marketing', 'collaborative-filtering'] | ['methodology', 'miscellaneous', 'miscellaneous'] | [ 2.59476513e-01 -3.68076712e-01 -3.19601387e-01 -5.43674588e-01
-1.85097545e-01 -5.26000202e-01 5.77934921e-01 6.80446148e-01
-7.12040246e-01 3.20011199e-01 4.14614588e-01 -4.33114409e-01
-4.91789430e-01 -6.97291493e-01 -3.09434950e-01 -3.78786445e-01
-3.77124488e-01 5.36910832e-01 8.17507133e-03 -6.05418921... | [10.067075729370117, 5.804570198059082] |
31dcf81a-0395-4aab-91bd-3bf0242849b2 | neural-sign-language-translation-based-on | 1811.11436 | null | https://arxiv.org/abs/1811.11436v2 | https://arxiv.org/pdf/1811.11436v2.pdf | Neural Sign Language Translation based on Human Keypoint Estimation | We propose a sign language translation system based on human keypoint estimation. It is well-known that many problems in the field of computer vision require a massive amount of dataset to train deep neural network models. The situation is even worse when it comes to the sign language translation problem as it is far m... | ['Sang-Ki Ko', 'Choongsang Cho', 'Hyedong Jung', 'Chang Jo Kim'] | 2018-11-28 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 1.66990891e-01 -5.51288545e-01 -3.15941513e-01 -3.28399599e-01
-7.55663216e-01 -3.61425728e-01 5.03263474e-01 -9.79738355e-01
-7.66760707e-01 6.39117718e-01 4.75090802e-01 -1.65112510e-01
2.20096424e-01 -5.49785972e-01 -7.29244828e-01 -7.58977830e-01
4.39945728e-01 3.87313962e-01 -4.25412841e-02 -2.40658179... | [9.155017852783203, -6.467704772949219] |
6809937e-4fb9-49b9-a85b-79b7cb25ec6c | energy-disaggregation-using-variational | 2103.12177 | null | https://arxiv.org/abs/2103.12177v2 | https://arxiv.org/pdf/2103.12177v2.pdf | Energy Disaggregation using Variational Autoencoders | Non-intrusive load monitoring (NILM) is a technique that uses a single sensor to measure the total power consumption of a building. Using an energy disaggregation method, the consumption of individual appliances can be estimated from the aggregate measurement. Recent disaggregation algorithms have significantly improve... | ['Ghyslain Gagnon', 'Mohamed Cheriet', 'Marc-André Carbonneau', 'Antoine Langevin'] | 2021-03-22 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-6.99132308e-02 -1.78744644e-01 5.02270535e-02 -2.83602893e-01
-1.04117894e+00 -2.91370392e-01 4.67899561e-01 -1.74706317e-02
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-1.29641324e-01 -9.76622581e-01 -5.93925953e-01 -1.24503732e+00
2.60426432e-01 4.05343711e-01 -3.40007126e-01 2.02055186... | [16.06534194946289, 7.581466197967529] |
9acae079-eae2-4996-8e91-a9184d12b967 | multi-modal-transformer-path-prediction-for | 2208.07256 | null | https://arxiv.org/abs/2208.07256v1 | https://arxiv.org/pdf/2208.07256v1.pdf | Multi-modal Transformer Path Prediction for Autonomous Vehicle | Reasoning about vehicle path prediction is an essential and challenging problem for the safe operation of autonomous driving systems. There exist many research works for path prediction. However, most of them do not use lane information and are not based on the Transformer architecture. By utilizing different types of ... | ['Wei-Shinn Ku', 'Kazuya Sakai', 'Min-Te Sun', 'Jie Zhang', 'Chia Hong Tseng'] | 2022-08-15 | null | null | null | null | ['trajectory-forecasting'] | ['computer-vision'] | [-2.71226913e-01 3.00087556e-02 -5.18771470e-01 -5.52464068e-01
2.26405989e-02 -2.79147536e-01 5.72621346e-01 2.32637823e-02
-2.32205242e-01 6.91476762e-01 2.63063312e-01 -7.94288278e-01
-3.16285431e-01 -1.24652767e+00 -4.83472198e-01 -6.58085883e-01
1.56624373e-02 3.07287097e-01 9.20113862e-01 -5.71831286... | [5.774857521057129, 1.1552883386611938] |
19004410-3e16-49ec-bbae-452ffcf5bfbd | learning-based-defect-recognitions-for | 2302.06093 | null | https://arxiv.org/abs/2302.06093v1 | https://arxiv.org/pdf/2302.06093v1.pdf | Learning-Based Defect Recognitions for Autonomous UAV Inspections | Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG, and Resnet. Moreover, inspired by the feature p... | ['Kangcheng Liu'] | 2023-02-13 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [-2.01596677e-01 1.82277486e-02 2.46446967e-01 -1.29904121e-01
-3.53732854e-01 -1.55499633e-02 -3.66541356e-01 -1.49628625e-03
-2.85843551e-01 2.16709971e-01 -4.47032005e-01 -4.36643630e-01
1.13208428e-01 -1.29375172e+00 -5.25575936e-01 -6.94762588e-01
2.29445449e-03 -8.42151493e-02 6.50853097e-01 -5.29744864... | [7.4806742668151855, 1.5075167417526245] |
7fba8e13-3d55-4202-b624-b7172ff0bd51 | dory-automatic-end-to-end-deployment-of-real | 2008.07127 | null | https://arxiv.org/abs/2008.07127v3 | https://arxiv.org/pdf/2008.07127v3.pdf | DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs | The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applications. Low-Cost MCU-based end-nodes have limited on-chip memory and often replace caches with scratchpads, to reduce area overheads and increa... | ['Francesco Conti', 'Davide Rossi', 'Nazareno Bruschi', 'Alessio Burrello', 'Angelo Garofalo', 'Giuseppe Tagliavini'] | 2020-08-17 | null | null | null | null | ['tiling-deployment'] | ['computer-code'] | [-2.12549001e-01 1.16138935e-01 -5.46776533e-01 -1.11257486e-01
-1.85562804e-01 -1.99519128e-01 -4.49905880e-02 -3.03497523e-01
-6.48141742e-01 7.75517166e-01 -4.27325904e-01 -9.31059241e-01
-1.27938807e-01 -9.84427273e-01 -8.96547258e-01 -6.02701068e-01
-7.89538771e-02 4.71128911e-01 5.11181712e-01 1.58300325... | [8.338179588317871, 2.750638484954834] |
5b758b0e-9de1-44cf-8560-6bae260d4296 | pedestrian-crossing-action-recognition-and | 2306.01075 | null | https://arxiv.org/abs/2306.01075v1 | https://arxiv.org/pdf/2306.01075v1.pdf | Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints | Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas. In this work, we aim at identifying crossing pedestrians and predicting their future trajectories. To achieve th... | ['CongCong Li', 'Eugene Ie', 'Weilong Yang', 'Khaled S. Refaat', 'Jeonhyung Kang', 'Junhua Mao', 'Tian Lan', 'Zhishuai Zhang', 'Jonathan Stroud', 'Feiyu Chen', 'Xinwei Shi', 'Jiachen Li'] | 2023-06-01 | null | null | null | null | ['trajectory-prediction', 'autonomous-vehicles', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-6.90090433e-02 -4.77481395e-01 -2.50423223e-01 -5.74645579e-01
-8.57043505e-01 -4.40042228e-01 6.83929384e-01 2.08234012e-01
-6.88286066e-01 5.72140932e-01 3.75058383e-01 -1.32498875e-01
9.81291533e-02 -8.36276472e-01 -7.85736620e-01 -4.66251910e-01
-1.42542452e-01 3.25151592e-01 8.60093117e-01 -3.14248741... | [6.250101089477539, 0.6500959992408752] |
5e1f3841-e509-43d4-b58b-97f8db42a657 | prompt-based-time-series-forecasting-a-new | 2210.08964 | null | https://arxiv.org/abs/2210.08964v4 | https://arxiv.org/pdf/2210.08964v4.pdf | PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting | This paper presents a new perspective on time series forecasting. In existing time series forecasting methods, the models take a sequence of numerical values as input and yield numerical values as output. The existing SOTA models are largely based on the Transformer architecture, modified with multiple encoding mechani... | ['Flora D. Salim', 'Hao Xue'] | 2022-09-20 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 1.93556339e-01 -3.00947666e-01 -4.11075912e-02 -7.40217030e-01
-4.80686039e-01 -6.98101938e-01 1.15972745e+00 -1.33414741e-03
1.48441583e-01 6.96675599e-01 5.62543631e-01 -7.69306839e-01
-5.84984161e-02 -1.07510817e+00 -3.49762589e-01 -6.28878117e-01
-1.63671076e-01 1.95720971e-01 -3.04082543e-01 -8.21233809... | [6.8344645500183105, 2.990361452102661] |
1168ee85-7198-47e0-b313-dc41bf5e9d3e | sod-mtgan-small-object-detection-via-multi | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Yongqiang_Zhang_SOD-MTGAN_Small_Object_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yongqiang_Zhang_SOD-MTGAN_Small_Object_ECCV_2018_paper.pdf | SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network | Object detection is a fundamental and important problem in computer vision. Although impressive results have been achieved on large/medium sized objects on large-scale detection benchmarks (e.g. the COCO dataset), the performance on small objects is far from satisfaction. The reason is that small objects lack sufficien... | ['Yancheng Bai', 'Yongqiang Zhang', 'Mingli Ding', 'Bernard Ghanem'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['small-object-detection'] | ['computer-vision'] | [ 4.17114019e-01 -5.20278104e-02 2.28840739e-01 2.36751549e-02
-8.16242158e-01 -4.10146743e-01 3.47518325e-01 -5.46379387e-01
-2.93222100e-01 7.23082542e-01 -1.04960315e-01 2.84599215e-01
3.51241231e-01 -8.82359982e-01 -9.68715787e-01 -9.44512188e-01
1.12138525e-01 3.16016793e-01 7.15003848e-01 -1.34536222... | [10.079021453857422, -0.8884159326553345] |
671a1de4-14eb-417c-a614-79f1ef029fff | mathqa-towards-interpretable-math-word | 1905.13319 | null | https://arxiv.org/abs/1905.13319v1 | https://arxiv.org/pdf/1905.13319v1.pdf | MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms | We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs. Due to annotation challenges, current datasets in this domain have been either relatively small in scale or did not offer precise operational annotations over diver... | ['Hannaneh Hajishirzi', 'Rik Koncel-Kedziorski', 'Yejin Choi', 'Saadia Gabriel', 'Peter Lin', 'Aida Amini'] | 2019-05-30 | mathqa-towards-interpretable-math-word-1 | https://aclanthology.org/N19-1245 | https://aclanthology.org/N19-1245.pdf | naacl-2019-6 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 1.19245671e-01 2.61126250e-01 -2.24715292e-01 -7.93730736e-01
-1.00463724e+00 -9.95748460e-01 9.81654748e-02 3.93211007e-01
-1.75670952e-01 3.51495296e-01 2.61426419e-01 -6.85476005e-01
-3.55666243e-02 -1.06726301e+00 -1.11407208e+00 1.28339350e-01
2.13570431e-01 8.70815277e-01 -1.15008041e-01 -3.60958666... | [9.565014839172363, 7.45976448059082] |
42760db1-cddc-4231-98bb-a12f9eb17789 | multi-view-bangla-sign-language-mv-bsl | 2302.11559 | null | https://arxiv.org/abs/2302.11559v2 | https://arxiv.org/pdf/2302.11559v2.pdf | Word level Bangla Sign Language Dataset for Continuous BSL Recognition | An robust sign language recognition system can greatly alleviate communication barriers, particularly for people who struggle with verbal communication. This is crucial for human growth and progress as it enables the expression of thoughts, feelings, and ideas. However, sign recognition is a complex task that faces num... | ['Ibrahim Elwarfalli', 'Sohaib Abdullah', 'Md Mahedi Hasan', 'Md Nur Hossain', 'A. J. M. Akhtarujjaman Joha', 'Md Shamimul Islam'] | 2023-02-22 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-1.60626695e-01 -5.82906783e-01 5.15642986e-02 -2.97380865e-01
-1.66746974e-01 -2.27728069e-01 4.26836491e-01 -7.62464046e-01
-4.28642482e-01 4.62602913e-01 5.52062511e-01 1.10395432e-01
-5.89965796e-03 -2.54583806e-01 -1.87879935e-01 -7.51506686e-01
1.64279819e-01 -3.96337286e-02 4.58356217e-02 -2.61784196... | [9.110786437988281, -6.412359714508057] |
08dc308f-1046-46fa-9d33-bc6e3de38838 | deep-template-matching-for-pedestrian | 2011.06798 | null | https://arxiv.org/abs/2011.06798v1 | https://arxiv.org/pdf/2011.06798v1.pdf | Deep Template Matching for Pedestrian Attribute Recognition with the Auxiliary Supervision of Attribute-wise Keypoints | Pedestrian Attribute Recognition (PAR) has aroused extensive attention due to its important role in video surveillance scenarios. In most cases, the existence of a particular attribute is strongly related to a partial region. Recent works design complicated modules, e.g., attention mechanism and proposal of body parts ... | ['Jianmin Li', 'Pengyuan Ren', 'Jiajun Zhang'] | 2020-11-13 | null | null | null | null | ['template-matching', 'pedestrian-attribute-recognition'] | ['computer-vision', 'computer-vision'] | [-2.07159638e-01 -2.18434244e-01 -1.30792439e-01 -6.88623667e-01
-4.48768675e-01 -1.89323872e-01 5.38292408e-01 1.30924091e-01
-3.26237112e-01 7.57874370e-01 2.25905493e-01 2.40127012e-01
1.61075532e-01 -8.68136764e-01 -8.68593514e-01 -7.70242453e-01
2.10565835e-01 5.39629638e-01 6.23362601e-01 -1.56184703... | [14.423880577087402, 0.9708338975906372] |
051f3e2c-3491-405a-93af-489c011d509f | accurate-method-of-temporal-shift-estimation | null | null | https://ieeexplore.ieee.org/abstract/document/8478431 | https://www.researchgate.net/profile/Aleksandr-Ploshkin-2/publication/328082698_ACCURATE_METHOD_OF_TEMPORAL-SHIFT_ESTIMATION_FOR_3D_VIDEO/links/5bc20f40458515a7a9e71cf2/ACCURATE-METHOD-OF-TEMPORAL-SHIFT-ESTIMATION-FOR-3D-VIDEO.pdf | ACCURATE METHOD OF TEMPORAL-SHIFT ESTIMATION FOR 3D VIDEO | Video synchronization is a fundamental computer-vision task that is necessary for a wide range of applications. A 3D video involves two streams, which show the scene from different angles concurrently, but many cases exhibit desynchronization between them. This paper investigates the problem of synchronizing the left a... | ['Dmitriy Vatolin', 'Aleksandr Ploshkin'] | 2018-06-03 | null | null | null | 3dtv-conference-the-true-vision-capture | ['video-synchronization', 'video-alignment'] | ['computer-vision', 'computer-vision'] | [ 3.90754938e-01 -6.41651034e-01 -1.56132663e-02 -1.55268550e-01
-6.16776310e-02 -6.68963015e-01 7.10670710e-01 -4.33621377e-01
-2.67221302e-01 3.92369092e-01 -7.15443864e-02 -1.20760456e-01
3.30359012e-01 -2.87215650e-01 -5.90068877e-01 -6.91145420e-01
-4.16751504e-02 -2.49806512e-03 9.16169524e-01 -9.86808315... | [9.135103225708008, -2.3803653717041016] |
4f1f926f-7d7c-45d4-9f9f-5fdadb6cd60c | minimal-solutions-for-panoramic-stitching | 2012.00465 | null | https://arxiv.org/abs/2012.00465v1 | https://arxiv.org/pdf/2012.00465v1.pdf | Minimal Solutions for Panoramic Stitching Given Gravity Prior | When capturing panoramas, people tend to align their cameras with the vertical axis, i.e., the direction of gravity. Moreover, modern devices, such as smartphones and tablets, are equipped with an IMU (Inertial Measurement Unit) that can measure the gravity vector accurately. Using this prior, the y-axes of the cameras... | ['Zuzana Kukelova', 'Daniel Barath', 'Yaqing Ding'] | 2020-12-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ding_Minimal_Solutions_for_Panoramic_Stitching_Given_Gravity_Prior_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ding_Minimal_Solutions_for_Panoramic_Stitching_Given_Gravity_Prior_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-stitching'] | ['computer-vision'] | [ 3.92518759e-01 -1.80210084e-01 -5.32503501e-02 6.95060790e-02
-7.57499039e-02 -8.16897869e-01 6.82148457e-01 -5.66279590e-01
-5.08905470e-01 3.45703334e-01 -2.62412410e-02 -1.32011753e-02
2.56519198e-01 -4.43521976e-01 -8.82220030e-01 -5.71127117e-01
4.65813577e-01 4.20427114e-01 -1.22933030e-01 1.08300589... | [8.063968658447266, -2.2695741653442383] |
24367a40-1eff-4812-aa48-a03cfa4adffd | interactiveie-towards-assessing-the-strength | 2305.14659 | null | https://arxiv.org/abs/2305.14659v1 | https://arxiv.org/pdf/2305.14659v1.pdf | InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction | Learning template based information extraction from documents is a crucial yet difficult task. Prior template-based IE approaches assume foreknowledge of the domain templates; however, real-world IE do not have pre-defined schemas and it is a figure-out-as you go phenomena. To quickly bootstrap templates in a real-worl... | ['Jordan Boyd-Graber', 'Benjamin Van Durme', 'Andrew Blair-Stanek', 'Francis Ferraro', 'Aparna Garimella', 'Anandhavelu N', 'Michelle Yuan', 'Ishani Mondal'] | 2023-05-24 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 6.69187367e-01 8.58274817e-01 -3.26019108e-01 -5.00285804e-01
-1.35059130e+00 -7.02182531e-01 8.47426474e-01 7.38223940e-02
-4.29515094e-01 9.65582490e-01 3.41329634e-01 -5.95299482e-01
-1.17386103e-01 -7.98944235e-01 -9.64863718e-01 -1.64763872e-02
4.75969791e-01 1.03538322e+00 4.35181946e-01 -4.91814584... | [10.136137962341309, 8.528116226196289] |
65a9b470-6dc5-40eb-824c-0e26c262d5ea | qmrnet-quality-metric-regression-for-eo-image | 2210.06618 | null | https://arxiv.org/abs/2210.06618v2 | https://arxiv.org/pdf/2210.06618v2.pdf | QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution | Latest advances in Super-Resolution (SR) have been tested with general purpose images such as faces, landscapes and objects, mainly unused for the task of super-resolving Earth Observation (EO) images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both Full-Reference... | ['Katalin Takáts', 'Javier Marín', 'David Vilaseca', 'Clara Garcia-Moll', 'Laura Riordan-Chen', 'Eva Mohedano', 'Pau Gallés', 'David Berga'] | 2022-10-12 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 2.96281189e-01 -3.17562968e-01 3.48563939e-01 -3.96688551e-01
-6.31689012e-01 -4.14354980e-01 5.60505331e-01 -1.36948943e-01
-3.99056971e-01 8.46698344e-01 1.61978081e-01 -6.42105117e-02
-7.30397403e-01 -9.58717942e-01 -2.20692098e-01 -6.47997618e-01
-6.39631212e-01 1.79678112e-01 3.85497361e-01 -4.76368725... | [10.730290412902832, -1.9642661809921265] |
5d3a93b3-92ba-489c-86e3-0ac23254a7fc | large-language-models-fail-on-trivial | 2302.08399 | null | https://arxiv.org/abs/2302.08399v5 | https://arxiv.org/pdf/2302.08399v5.pdf | Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks | Intuitive psychology is a pillar of common-sense reasoning. The replication of this reasoning in machine intelligence is an important stepping-stone on the way to human-like artificial intelligence. Several recent tasks and benchmarks for examining this reasoning in Large-Large Models have focused in particular on beli... | ['Tomer Ullman'] | 2023-02-16 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-1.13696866e-01 6.79314792e-01 -2.50396460e-01 -4.18209016e-01
4.10841359e-03 -4.60895300e-02 6.05186522e-01 4.48728055e-01
-5.15141547e-01 3.64766300e-01 3.15807730e-01 -8.46316874e-01
-4.24864024e-01 -8.22004735e-01 -3.45052063e-01 -2.02113420e-01
1.88684881e-01 8.08533430e-01 -1.25290789e-02 -4.73917007... | [9.653044700622559, 7.308392524719238] |
8bbe27bc-0baf-437a-94e0-79ce6390a90b | scanpath-prediction-in-panoramic-videos-via | 2305.02536 | null | https://arxiv.org/abs/2305.02536v2 | https://arxiv.org/pdf/2305.02536v2.pdf | Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization | Predicting human scanpaths when exploring panoramic videos is a challenging task due to the spherical geometry and the multimodality of the input, and the inherent uncertainty and diversity of the output. Most previous methods fail to give a complete treatment of these characteristics, and thus are prone to errors. In ... | ['Kede Ma', 'Kanglong Fan', 'Mu Li'] | 2023-05-04 | null | null | null | null | ['scanpath-prediction', 'data-compression'] | ['computer-vision', 'time-series'] | [ 4.09770161e-01 2.29113385e-01 -3.21951747e-01 -1.65850133e-01
-7.13302493e-01 -4.85378027e-01 5.67032456e-01 -5.98186851e-01
1.38975844e-01 4.67898965e-01 -4.80655488e-03 -2.54834089e-02
-3.89723659e-01 -5.62215209e-01 -1.04664743e+00 -5.91675878e-01
-2.23056540e-01 6.07488930e-01 1.57783434e-01 1.20079694... | [9.005108833312988, -2.6192009449005127] |
3275ad05-6550-48e8-9310-f646031f91b0 | sjtu-nlp-at-semeval-2018-task-9-neural | 1805.10465 | null | http://arxiv.org/abs/1805.10465v1 | http://arxiv.org/pdf/1805.10465v1.pdf | SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings | This paper describes a hypernym discovery system for our participation in the
SemEval-2018 Task 9, which aims to discover the best (set of) candidate
hypernyms for input concepts or entities, given the search space of a
pre-defined vocabulary. We introduce a neural network architecture for the
concerned task and empiri... | ['Jiangtong Li', 'Bingjie Tang', 'Zhuosheng Zhang', 'Hai Zhao'] | 2018-05-26 | sjtu-nlp-at-semeval-2018-task-9-neural-1 | https://aclanthology.org/S18-1147 | https://aclanthology.org/S18-1147.pdf | semeval-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [-4.09291908e-02 4.52270806e-01 -4.74101454e-01 -1.47793695e-01
-1.28809392e-01 -1.37009680e-01 5.78756094e-01 4.40151721e-01
-8.67549539e-01 5.01543820e-01 7.18683183e-01 -3.53263736e-01
-2.65008062e-01 -1.08145976e+00 -1.47918448e-01 -2.60044128e-01
-2.21464321e-01 9.05344665e-01 -1.67150244e-01 -5.73096991... | [10.0923433303833, 8.75131893157959] |
2520446b-2c17-4951-a384-a820590ec5bb | robust-model-training-and-generalisation-with | 2006.06599 | null | https://arxiv.org/abs/2006.06599v2 | https://arxiv.org/pdf/2006.06599v2.pdf | Robust model training and generalisation with Studentising flows | Normalising flows are tractable probabilistic models that leverage the power of deep learning to describe a wide parametric family of distributions, all while remaining trainable using maximum likelihood. We discuss how these methods can be further improved based on insights from robust (in particular, resistant) stati... | ['Gustav Eje Henter', 'Simon Alexanderson'] | 2020-06-11 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [-3.31681073e-01 4.71231863e-02 -2.26113573e-01 -3.05205911e-01
-6.43559933e-01 -8.29008341e-01 7.72441983e-01 -2.12415427e-01
-3.35217029e-01 1.07810438e+00 2.99368322e-01 -5.49685717e-01
-6.10261679e-01 -8.51943791e-01 -6.00941420e-01 -7.61149228e-01
-3.97768110e-01 3.58617097e-01 2.63537139e-01 1.65858418... | [7.141428470611572, 3.8503663539886475] |
dfed1901-667e-4056-b48e-04ef24a05cba | from-neural-re-ranking-to-neural-ranking | null | null | https://dl.acm.org/citation.cfm?id=3271800 | https://ciir-publications.cs.umass.edu/getpdf.php?id=1302 | From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing | The availability of massive data and computing power allowing for effective data driven neural approaches is having a major impact
on machine learning and information retrieval research, but these models have a basic problem with efficiency. Current neural ranking models are implemented as multistage rankers: for effi... | ['Erik Learned-Miller', 'W. Bruce Croft', 'Mostafa Dehghani', 'Hamed Zamani', 'and Jaap Kamps'] | 2018-10-22 | null | null | null | 27th-acm-international-conference-on | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 3.49009156e-01 -1.55922174e-01 -6.08325124e-01 -2.94828564e-01
-1.08223212e+00 -6.39290214e-01 7.50512779e-01 1.99481517e-01
-3.34004343e-01 3.64249915e-01 6.96841359e-01 -1.69677183e-01
-6.25817895e-01 -8.16523910e-01 -7.78953075e-01 -4.18369740e-01
-2.44923756e-01 8.39029908e-01 1.47003978e-01 -3.90641838... | [11.440435409545898, 7.579082489013672] |
5e16e900-6547-43e8-b073-70c9a83deee3 | imitating-task-and-motion-planning-with | 2305.16309 | null | https://arxiv.org/abs/2305.16309v1 | https://arxiv.org/pdf/2305.16309v1.pdf | Imitating Task and Motion Planning with Visuomotor Transformers | Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. However, common methods of data collection, such as human supervision, scale poorly, as they are time-consuming and labor-intensive. In contras... | ['Dieter Fox', 'Ruslan Salakhutdinov', 'Ankur Handa', 'Caelan Garrett', 'Ajay Mandlekar', 'Murtaza Dalal'] | 2023-05-25 | null | null | null | null | ['robot-manipulation', 'motion-planning'] | ['robots', 'robots'] | [-2.05358073e-01 -1.26731303e-03 -2.36254916e-01 -1.15186602e-01
-6.91199064e-01 -7.67411649e-01 5.80940902e-01 -3.79949510e-01
-4.46073681e-01 8.22934628e-01 -2.73940355e-01 -3.53262812e-01
4.13070954e-02 -4.00230914e-01 -1.26441324e+00 -5.00121176e-01
-5.28765880e-02 1.00693500e+00 3.80188704e-01 -3.73598188... | [4.572278022766113, 0.8057027459144592] |
5e486e28-8c28-4147-ae6b-147f6dc208ee | location-aware-single-image-reflection | 2012.07131 | null | https://arxiv.org/abs/2012.07131v2 | https://arxiv.org/pdf/2012.07131v2.pdf | Location-aware Single Image Reflection Removal | This paper proposes a novel location-aware deep-learning-based single image reflection removal method. Our network has a reflection detection module to regress a probabilistic reflection confidence map, taking multi-scale Laplacian features as inputs. This probabilistic map tells if a region is reflection-dominated or ... | ['Rynson W. H. Lau', 'Weiwei Xu', 'Hujun Bao', 'Yin Yang', 'Ke Xu', 'Zheng Dong'] | 2020-12-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Dong_Location-Aware_Single_Image_Reflection_Removal_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Dong_Location-Aware_Single_Image_Reflection_Removal_ICCV_2021_paper.pdf | iccv-2021-1 | ['reflection-removal'] | ['computer-vision'] | [ 2.42396042e-01 9.09599140e-02 1.07112683e-01 -1.48477823e-01
-7.06827939e-01 7.40283867e-03 4.91424650e-01 -3.89936537e-01
-7.14874268e-02 2.38545880e-01 5.02294004e-01 -4.32539493e-01
9.20386165e-02 -9.33032155e-01 -5.87829053e-01 -9.18946981e-01
1.55275449e-01 -1.73848301e-01 4.21045542e-01 -1.92042395... | [10.682731628417969, -2.9113402366638184] |
1e0c3e92-3745-42bd-97a8-ec6246685418 | asfm-net-asymmetrical-siamese-feature | 2104.09587 | null | https://arxiv.org/abs/2104.09587v3 | https://arxiv.org/pdf/2104.09587v3.pdf | ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion | We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net. Specifically, the Siamese auto-encoder neural network is adopted to map the partial and complete input point cloud into a share... | ['Uwe Stilla', 'Kailang Cao', 'Rui Song', 'Wei Li', 'Yan Xia', 'Yaqi Xia'] | 2021-04-19 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.94946036e-01 3.06041986e-02 6.15292341e-02 -3.07681948e-01
-9.10287201e-01 -2.93104321e-01 6.47919416e-01 -4.80538815e-01
-5.27773835e-02 3.21635336e-01 1.19539164e-01 1.18432216e-01
-2.97439657e-02 -6.83664799e-01 -1.03685582e+00 -4.43454295e-01
2.83839643e-01 7.98948586e-01 -5.98841347e-03 4.45364751... | [8.31541919708252, -3.5319738388061523] |
42f0334b-827e-49a3-bef2-b1b5b2833ec9 | kernel-embedding-of-maps-for-sequential | 1805.11380 | null | http://arxiv.org/abs/1805.11380v1 | http://arxiv.org/pdf/1805.11380v1.pdf | Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter | In this work, a novel sequential Monte Carlo filter is introduced which aims
at efficient sampling of high-dimensional state spaces with a limited number of
particles. Particles are pushed forward from the prior to the posterior density
using a sequence of mappings that minimizes the Kullback-Leibler divergence
between... | ['Peter Jan vanLeeuwen', 'Manuel Pulido'] | 2018-05-29 | null | null | null | null | ['sequential-bayesian-inference'] | ['time-series'] | [-3.49163532e-01 -4.23047543e-01 5.09967029e-01 -6.64132833e-02
-6.51159212e-02 -2.33215272e-01 9.40734446e-01 8.87164250e-02
-9.20563638e-01 1.10778260e+00 -1.32676259e-01 -1.58776209e-01
-1.62202835e-01 -1.04514754e+00 -4.43804592e-01 -8.03757906e-01
-6.64463162e-01 6.39431953e-01 4.82417226e-01 -2.67049491... | [6.518038272857666, 3.6991515159606934] |
c267105c-a40d-4222-9f42-f2066cfcb71a | data-free-quantization-through-weight | 1906.04721 | null | https://arxiv.org/abs/1906.04721v3 | https://arxiv.org/pdf/1906.04721v3.pdf | Data-Free Quantization Through Weight Equalization and Bias Correction | We introduce a data-free quantization method for deep neural networks that does not require fine-tuning or hyperparameter selection. It achieves near-original model performance on common computer vision architectures and tasks. 8-bit fixed-point quantization is essential for efficient inference on modern deep learning ... | ['Max Welling', 'Mart van Baalen', 'Tijmen Blankevoort', 'Markus Nagel'] | 2019-06-11 | data-free-quantization-through-weight-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Nagel_Data-Free_Quantization_Through_Weight_Equalization_and_Bias_Correction_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Nagel_Data-Free_Quantization_Through_Weight_Equalization_and_Bias_Correction_ICCV_2019_paper.pdf | iccv-2019-10 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 3.16802800e-01 6.20621592e-02 -2.03980282e-01 -6.97885513e-01
-7.28863895e-01 -6.10515237e-01 4.71808434e-01 1.75390705e-01
-9.78789687e-01 3.17026228e-01 -3.95544618e-01 -7.15383530e-01
2.23444894e-01 -7.00515985e-01 -9.01244104e-01 -5.45021832e-01
2.24332158e-02 4.51294243e-01 5.90790749e-01 -1.01686850... | [8.622089385986328, 3.0249826908111572] |
6c07e8d7-16fb-466b-aa98-17b1286daadb | nanonet-real-time-polyp-segmentation-in-video | 2104.11138 | null | https://arxiv.org/abs/2104.11138v1 | https://arxiv.org/pdf/2104.11138v1.pdf | NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy | Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions... | ['Pål Halvorsen', 'Thomas de Lange', 'Dag Johansen', 'Håvard D. Johansen', 'Michael A. Riegler', 'Sharib Ali', 'Nikhil Kumar Tomar', 'Debesh Jha'] | 2021-04-22 | null | null | null | null | ['instrument-recognition'] | ['audio'] | [-1.66190177e-01 1.60578396e-02 -1.43281907e-01 -1.22901157e-01
-5.53339124e-01 -8.01852047e-01 -2.44566262e-01 5.11282980e-01
-4.43319201e-01 1.44848034e-01 -2.85246164e-01 -7.04638898e-01
-3.74044552e-02 -8.49645674e-01 -5.70062160e-01 -6.72611892e-01
-4.06542808e-01 3.13806355e-01 3.90806049e-01 2.78782308... | [14.438115119934082, -2.934734582901001] |
4c173503-1d80-4473-9ab0-b9350373e7f2 | using-offline-data-to-speed-up-reinforcement | 2304.09825 | null | https://arxiv.org/abs/2304.09825v1 | https://arxiv.org/pdf/2304.09825v1.pdf | Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments | One of the key challenges of Reinforcement Learning (RL) is the ability of agents to generalise their learned policy to unseen settings. Moreover, training RL agents requires large numbers of interactions with the environment. Motivated by the recent success of Offline RL and Imitation Learning (IL), we conduct a study... | ['Javier Del Ser', 'Stefano V. Albrecht', 'Esther Villar-Rodriguez', 'Lukas Schäfer', 'Alain Andres'] | 2023-04-18 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.38444790e-01 -1.50609594e-02 -1.21495627e-01 1.13312483e-01
-7.05712378e-01 -1.03768539e+00 8.51415098e-01 3.98051649e-01
-9.18763578e-01 1.01388705e+00 8.14314261e-02 -5.90841711e-01
-1.04266062e-01 -7.71033585e-01 -1.03428614e+00 -6.97540760e-01
-6.49166346e-01 6.98085666e-01 1.40769929e-01 -1.15078084... | [4.095732688903809, 1.8401966094970703] |
099b07eb-64a1-4e31-95b0-db7ea7ea6a69 | hold-on-honey-men-at-work-a-semi-supervised | null | null | https://aclanthology.org/2021.acl-srw.19 | https://aclanthology.org/2021.acl-srw.19.pdf | ``Hold on honey, men at work'': A semi-supervised approach to detecting sexism in sitcoms | Television shows play an important role inpropagating societal norms. Owing to the popularity of the situational comedy (sitcom) genre, it contributes significantly to the over-all development of society. In an effort to analyze the content of television shows belong-ing to this genre, we present a dataset of dialogue ... | ['Zeerak Waseem', 'Arijit Ghosh Chowdhury', 'Tanvi Anand', 'Smriti Singh'] | 2021-08-01 | null | null | null | acl-2021-5 | ['sentence-classification'] | ['natural-language-processing'] | [ 1.30804449e-01 4.70237255e-01 -3.00989211e-01 -9.02557373e-01
-7.59372354e-01 -6.92771554e-01 1.16854310e+00 5.83161175e-01
-3.08956891e-01 7.39496291e-01 6.23545647e-01 -3.07334840e-01
7.02511147e-02 -8.21884811e-01 -8.15476418e-01 -5.36317050e-01
2.09455475e-01 7.88596928e-01 -1.84063271e-01 -8.21286380... | [8.785465240478516, 10.351993560791016] |
e564c319-e6c8-4f6f-8f12-e44cbded7dab | an-optimization-based-deep-equilibrium-model | 2306.06378 | null | https://arxiv.org/abs/2306.06378v1 | https://arxiv.org/pdf/2306.06378v1.pdf | An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees | In this paper, we propose a novel methodology for addressing the hyperspectral image deconvolution problem. This problem is highly ill-posed, and thus, requires proper priors (regularizers) to model the inherent spectral-spatial correlations of the HSI signals. To this end, a new optimization problem is formulated, lev... | ['Kostas Berberidis', 'Dimitris Ampeliotis', 'Alexandros Gkillas'] | 2023-06-10 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 4.51510429e-01 -1.83250234e-01 3.59618694e-01 -7.24490033e-03
-5.29718459e-01 -1.31891906e-01 1.58272654e-01 -2.78552115e-01
-2.11821333e-01 1.07028341e+00 -5.69158196e-02 2.09366786e-03
-6.64302886e-01 -4.53015983e-01 -4.55568463e-01 -1.29758871e+00
6.31374121e-02 -1.45912796e-01 -3.43221843e-01 -1.32776052... | [10.52873706817627, -2.176647186279297] |
135f9817-4be5-4065-abd9-aa725cee69a6 | adaptive-video-highlight-detection-by | 2007.09598 | null | https://arxiv.org/abs/2007.09598v1 | https://arxiv.org/pdf/2007.09598v1.pdf | Adaptive Video Highlight Detection by Learning from User History | Recently, there is an increasing interest in highlight detection research where the goal is to create a short duration video from a longer video by extracting its interesting moments. However, most existing methods ignore the fact that the definition of video highlight is highly subjective. Different users may have dif... | ['Yang Wang', 'Mahesh Kumar Krishna Reddy', 'Mrigank Rochan', 'Linwei Ye'] | 2020-07-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3702_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660256.pdf | eccv-2020-8 | ['highlight-detection'] | ['computer-vision'] | [ 2.03020945e-01 -3.44364822e-01 -9.30229127e-02 -5.75287521e-01
-5.59889555e-01 -4.30691242e-01 2.33663604e-01 4.48717969e-03
-5.07893145e-01 2.73268640e-01 1.90152630e-01 1.06350094e-01
3.07050824e-01 -6.44160032e-01 -8.22757900e-01 -5.83612502e-01
-5.32433093e-01 -5.34311473e-01 5.32720268e-01 -1.84261822... | [10.117919921875, 0.4479760527610779] |
0601c22a-3cf9-41db-a94a-d3382e2d618e | intrinsic-image-transfer-for-illumination | 2107.00704 | null | https://arxiv.org/abs/2107.00704v2 | https://arxiv.org/pdf/2107.00704v2.pdf | Intrinsic Image Transfer for Illumination Manipulation | This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework consisting of three photo-realistic losses defined on the sub-layers factorized by an intr... | ['Haihui Wang', 'Qianying Zhang', 'Michael Ruzhansky', 'Junqing Huang'] | 2021-07-01 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 1.23551106e+00 -1.19675383e-01 1.45959765e-01 -3.26403618e-01
-4.11962062e-01 -2.83417851e-01 5.49670875e-01 -4.63088959e-01
-3.94342273e-01 6.88018441e-01 -8.29816461e-02 1.19081654e-01
-3.25547814e-01 -7.36577690e-01 -8.75911653e-01 -1.22206688e+00
5.09479225e-01 -2.04789206e-01 -2.59255528e-01 -3.12512249... | [10.353979110717773, -2.7442164421081543] |
884806a2-c84a-4f32-8455-9a4e85e349ae | an-empirical-study-on-relation-extraction-in | 2112.05910 | null | https://arxiv.org/abs/2112.05910v1 | https://arxiv.org/pdf/2112.05910v1.pdf | An Empirical Study on Relation Extraction in the Biomedical Domain | Relation extraction is a fundamental problem in natural language processing. Most existing models are defined for relation extraction in the general domain. However, their performance on specific domains (e.g., biomedicine) is yet unclear. To fill this gap, this paper carries out an empirical study on relation extracti... | ['Yongkang Li'] | 2021-12-11 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 4.41115648e-01 4.18336123e-01 -7.03067839e-01 -3.64856541e-01
-6.94357634e-01 -2.92033792e-01 4.80815440e-01 9.10338759e-01
-4.02472198e-01 1.10879910e+00 1.49480060e-01 -6.96417212e-01
-1.34419248e-01 -9.66213763e-01 -3.27052146e-01 -3.69678885e-01
-1.20520659e-01 4.56952333e-01 1.66273013e-01 -1.66466057... | [8.732399940490723, 8.686895370483398] |
6e8ab5bf-864b-4e1a-9029-d50ad38c4379 | probing-script-knowledge-from-pre-trained | 2204.10176 | null | https://arxiv.org/abs/2204.10176v1 | https://arxiv.org/pdf/2204.10176v1.pdf | Probing Script Knowledge from Pre-Trained Models | Script knowledge is critical for humans to understand the broad daily tasks and routine activities in the world. Recently researchers have explored the large-scale pre-trained language models (PLMs) to perform various script related tasks, such as story generation, temporal ordering of event, future event prediction an... | ['Lifu Huang', 'Mo Yu', 'Xingyu Zhang', 'Zijian Jin'] | 2022-04-16 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 1.90818071e-01 4.16905619e-02 -1.88680097e-01 -6.14719808e-01
-3.05303872e-01 -8.45935524e-01 1.13230538e+00 2.96707571e-01
-1.62942082e-01 7.84286141e-01 7.41411328e-01 -3.33877325e-01
-2.13843569e-01 -6.64552748e-01 -6.15259767e-01 -1.85011953e-01
-1.97069407e-01 7.64875233e-01 5.66996634e-01 -1.43252730... | [11.18035888671875, 8.829509735107422] |
add04f0a-2b5d-46b4-85b1-69e69f0b4aaf | lidar-in-the-loop-hyperparameter-optimization | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Goudreault_LiDAR-in-the-Loop_Hyperparameter_Optimization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Goudreault_LiDAR-in-the-Loop_Hyperparameter_Optimization_CVPR_2023_paper.pdf | LiDAR-in-the-Loop Hyperparameter Optimization | LiDAR has become a cornerstone sensing modality for 3D vision. LiDAR systems emit pulses of light into the scene, take measurements of the returned signal, and rely on hardware digital signal processing (DSP) pipelines to construct 3D point clouds from these measurements. The resulting point clouds output by these ... | ['Felix Heide', 'Nicolas Robidoux', 'Mario Bijelic', 'Dominik Scheuble', 'Félix Goudreault'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['hyperparameter-optimization'] | ['methodology'] | [ 2.42344141e-01 -3.61076891e-01 3.07289660e-01 -6.18846714e-01
-8.12308669e-01 -7.47147262e-01 3.94162744e-01 1.07872277e-01
-5.06967485e-01 2.37032667e-01 -7.16739535e-01 -5.53892612e-01
4.61556576e-02 -8.49978089e-01 -7.80585110e-01 -4.34605271e-01
1.30857944e-01 7.35458374e-01 4.30826873e-01 2.27066964... | [7.784411430358887, -2.6574208736419678] |
cad4a705-a4a1-48e7-9ba6-7ab1148bfee8 | task-oriented-feature-distillation | null | null | http://proceedings.neurips.cc/paper/2020/hash/a96b65a721e561e1e3de768ac819ffbb-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/a96b65a721e561e1e3de768ac819ffbb-Paper.pdf | Task-Oriented Feature Distillation | Feature distillation, a primary method in knowledge distillation, always leads to significant accuracy improvements. Most existing methods distill features in the teacher network through a manually designed transformation. In this paper, we propose a novel distillation method named task-oriented feature distillation (T... | ['Chenglong Bao', 'Kaisheng Ma', 'Zuoqiang Shi', 'Yukang Shi', 'Linfeng Zhang'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['3d-classification'] | ['computer-vision'] | [ 3.07188910e-02 1.89904884e-01 -8.35578069e-02 -5.94073117e-01
-4.78261024e-01 -4.85979438e-01 7.12004185e-01 7.93097392e-02
-5.22416115e-01 6.84929252e-01 1.43538713e-01 -2.47800097e-01
-4.43593003e-02 -8.79453123e-01 -8.39145422e-01 -7.18157828e-01
5.29341698e-01 7.90967271e-02 1.49341106e-01 -8.66186917... | [9.443382263183594, 3.4066965579986572] |
d2846b4a-def2-4eaf-aa83-9857dc6b2d00 | tsa-inf-at-semeval-2017-task-4-an-ensemble-of | null | null | https://aclanthology.org/S17-2135 | https://aclanthology.org/S17-2135.pdf | TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis | This paper describes the submission of team TSA-INF to SemEval-2017 Task 4 Subtask A. The submitted system is an ensemble of three varying deep learning architectures for sentiment analysis. The core of the architecture is a convolutional neural network that performs well on text classification as is. The second subsys... | ['Jasper Friedrichs', 'Amit Ajit Deshmane'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-9.63199809e-02 1.32466525e-01 1.66358098e-01 -6.53859138e-01
-6.19774878e-01 -5.02669632e-01 6.72642052e-01 2.60489970e-01
-7.25781143e-01 4.31156516e-01 3.58282715e-01 -6.25451505e-01
2.85471588e-01 -6.08595312e-01 -4.43648845e-01 -4.61059451e-01
2.62844592e-01 6.10393584e-01 -1.76106356e-02 -9.45912123... | [10.866456031799316, 7.519080638885498] |
b0b0f9ad-0f4e-46ad-bddf-cdfbcff1df60 | alfred-a-system-for-prompted-weak-supervision | 2305.18623 | null | https://arxiv.org/abs/2305.18623v1 | https://arxiv.org/pdf/2305.18623v1.pdf | Alfred: A System for Prompted Weak Supervision | Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. In contrast to typical PWS systems where weak supervision sources are programs coded by experts, Alfred enables users to encode their subject matter expertise via natural language prompts for... | ['Stephen Bach', 'Peilin Yu'] | 2023-05-29 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-4.61210638e-01 5.34595214e-02 -4.27019298e-01 -7.90243268e-01
-8.08350027e-01 -7.44470477e-01 5.06524861e-01 3.47918034e-01
-4.27265793e-01 4.38161463e-01 3.44579786e-01 -5.64694583e-01
2.98071325e-01 -5.01579523e-01 -6.25703931e-01 -2.22968459e-01
4.06796038e-01 7.51885712e-01 5.52726090e-01 -3.64602447... | [11.751314163208008, 8.049894332885742] |
1c647844-5385-4c57-9fb2-371ca8e30280 | universal-sentence-encoder | 1803.11175 | null | http://arxiv.org/abs/1803.11175v2 | http://arxiv.org/pdf/1803.11175v2.pdf | Universal Sentence Encoder | We present models for encoding sentences into embedding vectors that
specifically target transfer learning to other NLP tasks. The models are
efficient and result in accurate performance on diverse transfer tasks. Two
variants of the encoding models allow for trade-offs between accuracy and
compute resources. For both ... | ['Yun-Hsuan Sung', 'Mario Guajardo-Cespedes', 'Sheng-yi Kong', 'Yinfei Yang', 'Brian Strope', 'Nan Hua', 'Daniel Cer', 'Steve Yuan', 'Rhomni St. John', 'Ray Kurzweil', 'Noah Constant', 'Nicole Limtiaco', 'Chris Tar'] | 2018-03-29 | null | null | null | null | ['conversational-response-selection', 'subjectivity-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.44707763e-01 6.57740980e-02 -4.27247137e-01 -5.85673273e-01
-1.26268375e+00 -6.21517837e-01 7.06057370e-01 3.03496778e-01
-8.94126475e-01 8.28462481e-01 5.43167651e-01 -6.23981714e-01
3.55169773e-02 -8.42823029e-01 -8.10370207e-01 -1.89422995e-01
-2.32947111e-01 6.04056120e-01 1.35440558e-01 -2.96356201... | [10.698678016662598, 8.590311050415039] |
ec9b2bbd-1726-4fa6-bf3e-0825c995d6c9 | learn-to-decompose-cascaded-decomposition | 2207.07973 | null | https://arxiv.org/abs/2207.07973v1 | https://arxiv.org/pdf/2207.07973v1.pdf | Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition | Most existing compound facial expression recognition (FER) methods rely on large-scale labeled compound expression data for training. However, collecting such data is labor-intensive and time-consuming. In this paper, we address the compound FER task in the cross-domain few-shot learning (FSL) setting, which requires o... | ['Hanzi Wang', 'Si Chen', 'Jing-Hao Xue', 'Yan Yan', 'Xinyi Zou'] | 2022-07-16 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.72762915e-03 -3.67342204e-01 -1.46442920e-01 -7.88929999e-01
-9.23707664e-01 -2.47306257e-01 8.00028518e-02 -5.19823194e-01
-2.18102142e-01 6.75987124e-01 -1.56195745e-01 3.44568372e-01
1.35550365e-01 -3.71224284e-01 -4.59215820e-01 -8.99228871e-01
-8.23511044e-04 7.06598014e-02 -4.04073834e-01 -4.12337154... | [13.612257957458496, 1.6500401496887207] |
f09fb7a4-c29b-45ae-b9aa-884afaf94007 | anomaly-detection-for-an-e-commerce-pricing | 1902.09566 | null | https://arxiv.org/abs/1902.09566v5 | https://arxiv.org/pdf/1902.09566v5.pdf | Anomaly Detection for an E-commerce Pricing System | Online retailers execute a very large number of price updates when compared to brick-and-mortar stores. Even a few mis-priced items can have a significant business impact and result in a loss of customer trust. Early detection of anomalies in an automated real-time fashion is an important part of such a pricing system.... | ['Mátyás A. Sustik', 'Jagdish Ramakrishnan', 'Elham Shaabani', 'Chao Li'] | 2019-02-25 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-3.33034366e-01 -3.30161601e-01 1.72088876e-01 -5.67957222e-01
-6.22031808e-01 -6.95602775e-01 9.61782485e-02 1.09379745e+00
-3.65204424e-01 8.89683068e-02 -2.06297100e-01 -3.92200530e-01
-3.55542570e-01 -1.00033987e+00 -7.12585330e-01 -2.97182322e-01
-7.74715245e-01 8.97901118e-01 4.70077664e-01 -5.07507205... | [7.306511402130127, 2.845252275466919] |
c60ba4a9-71d7-4b2e-b00f-2c26bff6f705 | probabilistic-human-mesh-recovery-in-3d | 2304.06024 | null | https://arxiv.org/abs/2304.06024v1 | https://arxiv.org/pdf/2304.06024v1.pdf | Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric Views | Automatic perception of human behaviors during social interactions is crucial for AR/VR applications, and an essential component is estimation of plausible 3D human pose and shape of our social partners from the egocentric view. One of the biggest challenges of this task is severe body truncation due to close social di... | ['Siyu Tang', 'Darren Cosker', 'Sadegh Aliakbarian', 'Yan Zhang', 'Qianli Ma', 'Siwei Zhang'] | 2023-04-12 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [-1.29924580e-01 3.82465124e-01 3.20433766e-01 -2.43259400e-01
-3.06502581e-01 -2.82862961e-01 3.65108877e-01 -4.38498527e-01
-1.24686860e-01 3.55802000e-01 4.79590476e-01 4.98476118e-01
-1.63019210e-01 -6.87107325e-01 -5.36135912e-01 -4.80136782e-01
-2.39981804e-02 1.00305140e+00 4.04330790e-01 -6.22478902... | [7.0572004318237305, -0.9999073147773743] |
d478f2fe-8ce2-45ba-9b89-ab1a88546de0 | non-local-latent-relation-distillation-for-1 | 2204.01971 | null | https://arxiv.org/abs/2204.01971v2 | https://arxiv.org/pdf/2204.01971v2.pdf | Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation | Available 3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring synthetic or in-studio domains, acquiring such supervision for each new target environment is highly inconvenient. To this end, we cast 3D pose learning as a self-super... | ['R. Venkatesh Babu', 'Anirban Chakraborty', 'Varun Jampani', 'Pradyumna YM', 'Anirudh Jamkhandi', 'Siddharth Seth', 'Jogendra Nath Kundu'] | 2022-04-05 | non-local-latent-relation-distillation-for | http://proceedings.neurips.cc/paper/2021/hash/018b59ce1fd616d874afad0f44ba338d-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/018b59ce1fd616d874afad0f44ba338d-Paper.pdf | neurips-2021-12 | ['unsupervised-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.56288350e-01 2.77039111e-01 -1.32161127e-02 -5.55937409e-01
-1.04203999e+00 -6.80616796e-01 7.59443581e-01 -4.46431071e-01
-3.26889426e-01 6.46954954e-01 2.44863048e-01 3.42537105e-01
1.57123268e-01 -5.13195038e-01 -1.30146217e+00 -6.28372133e-01
6.02692552e-02 8.15633535e-01 -4.03574854e-02 -3.36514264... | [7.018738269805908, -1.0290406942367554] |
ee22648c-e58c-4712-addc-42edaa5fe317 | learning-spatiotemporal-frequency-transformer-1 | 2212.14046 | null | https://arxiv.org/abs/2212.14046v1 | https://arxiv.org/pdf/2212.14046v1.pdf | Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution | Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges are remained to effectively ext... | ['Dongmei Fu', 'Chang Xu', 'Daochang Liu', 'Jianlong Fu', 'Huan Yang', 'Zhongwei Qiu'] | 2022-12-27 | null | null | null | null | ['video-super-resolution', 'video-enhancement'] | ['computer-vision', 'computer-vision'] | [ 4.22850490e-01 -5.30907571e-01 -2.82130450e-01 -3.27801406e-02
-1.08903825e+00 -1.70767531e-01 2.51869678e-01 -5.61556697e-01
2.77354002e-01 7.38572657e-01 6.81080341e-01 2.62291789e-01
-1.83424801e-01 -5.35140038e-01 -8.25804353e-01 -8.38488162e-01
-5.15596122e-02 -6.11578107e-01 1.26334578e-01 -4.41344023... | [11.116957664489746, -2.0198380947113037] |
645e12c1-2413-43ea-a5ef-b90ed4aa1935 | two-stage-is-enough-a-concise-deep-unfolding | 2201.05810 | null | https://arxiv.org/abs/2201.05810v2 | https://arxiv.org/pdf/2201.05810v2.pdf | Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive Sensing | We consider the reconstruction problem of video compressive sensing (VCS) under the deep unfolding/rolling structure. Yet, we aim to build a flexible and concise model using minimum stages. Different from existing deep unfolding networks used for inverse problems, where more stages are used for higher performance but w... | ['Xin Yuan', 'Xiaoyu Yang', 'Siming Zheng'] | 2022-01-15 | null | null | null | null | ['video-compressive-sensing'] | ['computer-vision'] | [ 4.26642329e-01 2.54235119e-02 2.55405694e-01 -8.70315582e-02
-7.13448167e-01 -1.81487530e-01 2.87736148e-01 -7.36944020e-01
-2.64587134e-01 3.89463693e-01 1.35674030e-01 -4.70253021e-01
-1.53781176e-02 -4.77661014e-01 -1.03213215e+00 -6.35748029e-01
-2.60138780e-01 -1.92581892e-01 2.48796299e-01 -2.65295535... | [11.191635131835938, -2.009080410003662] |
16d5b81b-8268-4e7d-8774-4cccb1dff5f0 | cabm-content-aware-bit-mapping-for-single | 2304.06454 | null | https://arxiv.org/abs/2304.06454v1 | https://arxiv.org/pdf/2304.06454v1.pdf | CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input | With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patc... | ['Shunli Zhang', 'Yurong Chen', 'Yandong Guo', 'Jiaming Liu', 'Ming Lu', 'Senmao Tian'] | 2023-04-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tian_CABM_Content-Aware_Bit_Mapping_for_Single_Image_Super-Resolution_Network_With_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tian_CABM_Content-Aware_Bit_Mapping_for_Single_Image_Super-Resolution_Network_With_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-super-resolution'] | ['computer-vision'] | [ 2.43258983e-01 -2.35667884e-01 -5.44722617e-01 -4.91461933e-01
-5.71682453e-01 -3.36036712e-01 -9.32222456e-02 -2.19480768e-01
-4.12190795e-01 7.01868951e-01 3.24403793e-02 -2.04723537e-01
-1.86814278e-01 -1.17180681e+00 -6.85366869e-01 -7.68295169e-01
3.09513420e-01 -2.76327543e-02 6.56738520e-01 -2.52629421... | [8.654312133789062, 3.0258989334106445] |
a628746f-ce9b-4ecd-a2cc-cbdfa3e4f092 | sharingan-combining-synthetic-and-real-data-1 | 2006.04026 | null | https://arxiv.org/abs/2006.04026v1 | https://arxiv.org/pdf/2006.04026v1.pdf | SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation | We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images ... | ['Hao Zhou', 'Koutilya PNVR', 'David Jacobs'] | 2020-06-07 | sharingan-combining-synthetic-and-real-data | http://openaccess.thecvf.com/content_CVPR_2020/html/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/PNVR_SharinGAN_Combining_Synthetic_and_Real_Data_for_Unsupervised_Geometry_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['surface-normals-estimation'] | ['computer-vision'] | [ 5.44112325e-01 3.98821324e-01 1.23825073e-01 -8.98223817e-01
-7.45329201e-01 -4.40209895e-01 5.70966125e-01 -7.81436920e-01
-3.99000078e-01 7.63369083e-01 -1.80871129e-01 -2.50681918e-02
3.06743562e-01 -8.05027366e-01 -1.04357016e+00 -3.93380553e-01
1.18645534e-01 4.91187304e-01 2.39212200e-01 -6.32529706... | [8.613385200500488, -2.508945941925049] |
4c0376b9-5781-49b0-aaf0-af2a6f361535 | what-if-we-enrich-day-ahead-solar-irradiance | 2306.01112 | null | https://arxiv.org/abs/2306.01112v1 | https://arxiv.org/pdf/2306.01112v1.pdf | What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context? | Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_{2}$ emissions. Nonetheless, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. While the majority of prior research has centered on e... | ['Yoshua Bengio', 'Loubna Benabbou', 'Tianle Yuan', 'Stefano Massaroli', 'Dan Assouline', 'Ghait Boukachab', 'Oussama Boussif'] | 2023-06-01 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [ 5.45779802e-02 -5.18990576e-01 9.28666070e-02 -2.68463939e-01
-6.53354526e-01 -8.73833954e-01 7.89662361e-01 2.10142527e-02
1.75172061e-01 1.18433249e+00 3.08290403e-02 -6.82705045e-01
-4.69189942e-01 -1.23593152e+00 -5.73061109e-01 -1.36899948e+00
-4.45488235e-03 -8.20686296e-02 -4.89122480e-01 -3.01560074... | [6.356087684631348, 2.768005847930908] |
1b8c6626-ada0-4099-bc65-ae8fffda6850 | ecg-signal-super-resolution-by-considering | 2012.03803 | null | https://arxiv.org/abs/2012.03803v2 | https://arxiv.org/pdf/2012.03803v2.pdf | SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification | A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limita... | ['Kai-Chun Liu', 'Yu Tsao', 'Chun-Yen Shen', 'Guo-Yuan Li', 'Chih-Han Huang', 'Jhih-Yu Chen', 'Huan-Hsin Tseng', 'Yuan-Hong Tsai', 'Tsai-Min Chen'] | 2020-12-07 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 2.36560106e-01 -6.28216088e-01 1.80637762e-01 -9.87065881e-02
-1.07826626e+00 -1.86426565e-01 -2.04267889e-01 1.29882768e-01
-3.60577971e-01 8.63509119e-01 -1.81526378e-01 -9.00344551e-02
-5.16641498e-01 -6.73303902e-01 -2.86360234e-01 -9.19932187e-01
-2.34719202e-01 -2.14247271e-01 -1.77151471e-01 4.70751561... | [14.209052085876465, 3.314535140991211] |
f6c30f72-6a3e-4503-9a86-6262050f8370 | scalable-distributed-ai-frameworks-leveraging | 2304.13738 | null | https://arxiv.org/abs/2304.13738v1 | https://arxiv.org/pdf/2304.13738v1.pdf | Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency | In recent years, the integration of artificial intelligence (AI) and cloud computing has emerged as a promising avenue for addressing the growing computational demands of AI applications. This paper presents a comprehensive study of scalable, distributed AI frameworks leveraging cloud computing for enhanced deep learni... | ['Neelesh Mungoli'] | 2023-04-26 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-3.24403018e-01 -5.81224024e-01 7.99840242e-02 -4.14251298e-01
-2.87573248e-01 -6.28438234e-01 1.22502171e-01 2.02193484e-01
-3.76793772e-01 3.81031305e-01 -1.85797781e-01 -5.32705151e-02
-4.96467412e-01 -9.12260890e-01 -3.37103426e-01 -8.39701474e-01
-4.61020738e-01 1.13927197e+00 -4.52722669e-01 1.41731724... | [8.42861270904541, 2.9593558311462402] |
e93c2a61-c220-45bb-9c3f-c0d821fa0501 | parcel3d-shape-reconstruction-from-single-rgb | 2304.08994 | null | https://arxiv.org/abs/2304.08994v1 | https://arxiv.org/pdf/2304.08994v1.pdf | Parcel3D: Shape Reconstruction from Single RGB Images for Applications in Transportation Logistics | We focus on enabling damage and tampering detection in logistics and tackle the problem of 3D shape reconstruction of potentially damaged parcels. As input we utilize single RGB images, which corresponds to use-cases where only simple handheld devices are available, e.g. for postmen during delivery or clients on delive... | ['Kai Furmans', 'Laura Dörr', 'Felix Hertlein', 'Alexander Naumann'] | 2023-04-18 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 3.91522832e-02 9.62826163e-02 3.70997041e-01 1.26030028e-01
-6.60252988e-01 -9.89169538e-01 4.12896842e-01 3.12057883e-01
7.80887390e-03 3.35544020e-01 -3.05665225e-01 -3.18088502e-01
1.30197525e-01 -1.34908164e+00 -1.26982474e+00 -3.71886104e-01
-3.44043881e-01 7.75906980e-01 1.28022924e-01 -3.66541415... | [7.606844425201416, -2.7240161895751953] |
87227949-4c8d-4846-9e55-5a00e73afb9b | multivariate-time-series-imputation-with-1 | 1907.04155 | null | https://arxiv.org/abs/1907.04155v5 | https://arxiv.org/pdf/1907.04155v5.pdf | GP-VAE: Deep Probabilistic Time Series Imputation | Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years. This raises the question whether deep learning methodologies can outperform classical data imputation methods in this domain. However, naive applications of deep learn... | ['Gunnar Rätsch', 'Dmitry Baranchuk', 'Vincent Fortuin', 'Stephan Mandt'] | 2019-07-09 | null | null | null | null | ['multivariate-time-series-imputation'] | ['time-series'] | [ 9.98777524e-02 2.20598504e-01 -1.04870729e-01 -6.54840648e-01
-1.00463986e+00 -1.36188507e-01 5.61909854e-01 4.81281988e-02
-2.46486604e-01 1.08423197e+00 5.33493698e-01 -1.25647232e-01
-5.39737403e-01 -6.33354127e-01 -8.95341218e-01 -8.95697653e-01
1.07065403e-04 8.73687327e-01 -8.49407732e-01 2.56957293... | [7.1881232261657715, 3.671808958053589] |
b3ed94f9-4004-43ec-b221-7e3013c95c8d | bear-physics-principled-building-environment | 2211.14744 | null | https://arxiv.org/abs/2211.14744v1 | https://arxiv.org/pdf/2211.14744v1.pdf | BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning | Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously. However, the lack of an easily configurable building dynamical model and energy management task simulation and evaluation platform has arguably slowed the p... | ['Yize Chen', 'Yuanyuan Shi', 'Chi Zhang'] | 2022-11-27 | null | null | null | null | ['energy-management'] | ['time-series'] | [-4.09648269e-01 -2.52711356e-01 -1.27469927e-01 2.62790889e-01
-3.95298541e-01 -5.10095060e-01 3.96597117e-01 9.15561244e-02
1.16399966e-01 1.16723764e+00 -2.78574318e-01 -2.92097569e-01
-3.87645215e-01 -1.25475991e+00 -5.17816544e-01 -1.00144875e+00
-3.72883111e-01 5.28920054e-01 1.05119534e-01 -6.57212913... | [5.217106342315674, 2.306025743484497] |
e9944785-38e9-434d-9632-e4dce3f90b6c | mitigating-approximate-memorization-in | 2305.01550 | null | https://arxiv.org/abs/2305.01550v1 | https://arxiv.org/pdf/2305.01550v1.pdf | Mitigating Approximate Memorization in Language Models via Dissimilarity Learned Policy | Large Language models (LLMs) are trained on large amounts of data, which can include sensitive information that may compromise per- sonal privacy. LLMs showed to memorize parts of the training data and emit those data verbatim when an adversary prompts appropriately. Previous research has primarily focused on data prep... | ['Aly M. Kassem'] | 2023-05-02 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.34374449e-01 2.18083024e-01 -1.98041216e-01 -5.04679918e-01
-7.43327737e-01 -6.52047455e-01 5.62463105e-01 4.60501820e-01
-8.99212360e-01 8.48723829e-01 1.01537257e-01 -3.40896875e-01
2.42191702e-01 -8.85882020e-01 -9.73608553e-01 -4.94434237e-01
1.54612735e-01 -1.68594196e-01 -3.42221141e-01 -7.47779384... | [6.044522762298584, 7.087235450744629] |
f75c7bfc-3eb5-496f-885a-028cbf4ea89b | cascading-multiway-attentions-for-document | null | null | https://aclanthology.org/I17-1064 | https://aclanthology.org/I17-1064.pdf | Cascading Multiway Attentions for Document-level Sentiment Classification | Document-level sentiment classification aims to assign the user reviews a sentiment polarity. Previous methods either just utilized the document content without consideration of user and product information, or did not comprehensively consider what roles the three kinds of information play in text modeling. In this pap... | ['Xu sun', 'Dehong Ma', 'Houfeng Wang', 'Xiaodong Zhang', 'Sujian Li'] | 2017-11-01 | cascading-multiway-attentions-for-document-1 | https://aclanthology.org/I17-1064 | https://aclanthology.org/I17-1064.pdf | ijcnlp-2017-11 | ['product-recommendation'] | ['miscellaneous'] | [-2.85513327e-02 -1.60395011e-01 -3.63349229e-01 -7.65235066e-01
-1.79771990e-01 -4.17208374e-01 7.61390388e-01 2.28390723e-01
-2.58063078e-01 3.55152875e-01 7.21662879e-01 -4.75528121e-01
3.58533531e-01 -9.47939992e-01 -4.79372114e-01 -4.80982840e-01
5.19475341e-01 1.06064603e-02 -1.09546773e-01 -6.84496224... | [11.417582511901855, 6.699455738067627] |
a1e8cc0b-f97c-44c7-bcaf-b4e0b63c244c | evaluating-generatively-synthesized-diabetic | 2208.05593 | null | https://arxiv.org/abs/2208.05593v2 | https://arxiv.org/pdf/2208.05593v2.pdf | Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery | Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine learning classifiers. The impact of this imbalance is exacerbated as the severity of the DR stage increases, affecting the classifiers' diag... | ["Ruairi O'Reilly", 'Mubashir Husain Rehmani', 'Muhammad Muneeb Saad', 'Cristina-Madalina Dragan'] | 2022-08-10 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 6.21785581e-01 2.05239937e-01 -5.36173508e-02 -2.92924404e-01
-7.16101468e-01 -3.50951970e-01 5.54463267e-01 -7.09335180e-03
-3.00519437e-01 8.82024944e-01 3.48356575e-01 -1.37648270e-01
-2.19662994e-01 -7.56233096e-01 -3.95358086e-01 -8.39219928e-01
1.28414586e-01 2.17922822e-01 -3.59388322e-01 -1.65155232... | [14.355939865112305, -2.0093917846679688] |
8c73ef8c-7176-4b18-94aa-9eac68749dbd | distribution-regularized-self-supervised | 2206.09683 | null | https://arxiv.org/abs/2206.09683v1 | https://arxiv.org/pdf/2206.09683v1.pdf | Distribution Regularized Self-Supervised Learning for Domain Adaptation of Semantic Segmentation | This paper proposes a novel pixel-level distribution regularization scheme (DRSL) for self-supervised domain adaptation of semantic segmentation. In a typical setting, the classification loss forces the semantic segmentation model to greedily learn the representations that capture inter-class variations in order to det... | ['Mohsen Ali', 'Yu-Tseh Chi', 'Rehan Hafiz', 'Hamza Rawal', 'Javed Iqbal'] | 2022-06-20 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.55875719e-01 2.00696155e-01 -4.58701849e-01 -4.87887740e-01
-1.08513737e+00 -7.15907753e-01 3.20635319e-01 -5.71776778e-02
-5.09529710e-01 7.16468990e-01 -9.74052176e-02 1.97872251e-01
2.47466937e-02 -7.10240722e-01 -6.83467746e-01 -1.20625365e+00
3.15109491e-01 5.56643069e-01 3.30796629e-01 1.11653954... | [9.673096656799316, 1.379259705543518] |
2e3392cf-58ee-4b99-b544-c583db7a3f4d | mau-a-motion-aware-unit-for-video-prediction | null | null | http://proceedings.neurips.cc/paper/2021/hash/e25cfa90f04351958216f97e3efdabe9-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/e25cfa90f04351958216f97e3efdabe9-Paper.pdf | MAU: A Motion-Aware Unit for Video Prediction and Beyond | Accurately predicting inter-frame motion information plays a key role in video prediction tasks. In this paper, we propose a Motion-Aware Unit (MAU) to capture reliable inter-frame motion information by broadening the temporal receptive field of the predictive units. The MAU consists of two modules, the attention modul... | ['Wen Gao', 'Xiang Xinguang', 'Yan Ye', 'Siwei Ma', 'Shanshe Wang', 'Xinfeng Zhang', 'Zheng Chang'] | 2021-12-01 | null | https://openreview.net/forum?id=qwtfY-3ibt7 | https://openreview.net/pdf?id=qwtfY-3ibt7 | neurips-2021-12 | ['video-prediction'] | ['computer-vision'] | [ 3.15246463e-01 -1.61547825e-01 -3.33142698e-01 -2.56427705e-01
-2.74510354e-01 2.05152318e-01 5.47814965e-01 -2.09329024e-01
-2.64334142e-01 5.44845164e-01 4.81443942e-01 1.81364626e-01
2.50677228e-01 -6.61644995e-01 -6.58368587e-01 -9.49130177e-01
-4.82454710e-02 -2.51175076e-01 8.74287963e-01 1.61888659... | [8.72280216217041, 0.38664552569389343] |
91148b81-e450-4ca3-9fbe-f2ad692eca7a | semi-supervised-clustering-for-short-text-via | 1602.06797 | null | http://arxiv.org/abs/1602.06797v2 | http://arxiv.org/pdf/1602.06797v2.pdf | Semi-supervised Clustering for Short Text via Deep Representation Learning | In this work, we propose a semi-supervised method for short text clustering,
where we represent texts as distributed vectors with neural networks, and use a
small amount of labeled data to specify our intention for clustering. We design
a novel objective to combine the representation learning process and the
k-means cl... | ['Abraham Ittycheriah', 'Zhiguo Wang', 'Haitao Mi'] | 2016-02-22 | semi-supervised-clustering-for-short-text-via-1 | https://aclanthology.org/K16-1004 | https://aclanthology.org/K16-1004.pdf | conll-2016-8 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-6.52268827e-02 -1.74222440e-01 -4.72403377e-01 -8.26261461e-01
-5.20299196e-01 -6.71604276e-01 2.83133060e-01 3.43436778e-01
-6.03984177e-01 2.75070041e-01 4.31112349e-01 -1.36238545e-01
-1.22656167e-01 -5.27543128e-01 -2.37327769e-01 -7.64045298e-01
2.88582504e-01 1.05228710e+00 -1.25354121e-03 3.37978601... | [10.38455867767334, 6.711939334869385] |
a05a63cd-a4e3-4fa6-8aba-28a0d519bb5b | robust-reference-based-super-resolution-via | 2106.01863 | null | https://arxiv.org/abs/2106.01863v1 | https://arxiv.org/pdf/2106.01863v1.pdf | Robust Reference-based Super-Resolution via C2-Matching | Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to co... | ['Ziwei Liu', 'Chen Change Loy', 'Xintao Wang', 'Kelvin C. K. Chan', 'Yuming Jiang'] | 2021-06-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Jiang_Robust_Reference-Based_Super-Resolution_via_C2-Matching_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Jiang_Robust_Reference-Based_Super-Resolution_via_C2-Matching_CVPR_2021_paper.pdf | cvpr-2021-1 | ['reference-based-super-resolution'] | ['computer-vision'] | [ 5.07177114e-01 -1.78708658e-01 -1.02736175e-01 -2.52751112e-01
-1.27365255e+00 -2.88189232e-01 6.46271706e-01 -5.00327528e-01
-1.93763614e-01 5.76464117e-01 4.49221164e-01 1.16225533e-01
-1.09533988e-01 -7.61016309e-01 -9.09171700e-01 -7.78477013e-01
3.71321231e-01 -4.45905030e-02 3.63609165e-01 -5.22461832... | [10.909940719604492, -2.101102113723755] |
942bad4b-c30b-499b-b5bd-2baecf657fc4 | multi-image-steganography-using-deep-neural | 2101.00350 | null | https://arxiv.org/abs/2101.00350v1 | https://arxiv.org/pdf/2101.00350v1.pdf | Multi-Image Steganography Using Deep Neural Networks | Steganography is the science of hiding a secret message within an ordinary public message. Over the years, steganography has been used to encode a lower resolution image into a higher resolution image by simple methods like LSB manipulation. We aim to utilize deep neural networks for the encoding and decoding of multip... | ['Yugant Rana', 'Mansi Anand', 'Japsimar Singh Wahi', 'Abhishek Das'] | 2021-01-02 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 1.33080792e+00 3.45213771e-01 1.01009332e-01 -2.15061858e-01
-4.12689418e-01 -3.21230978e-01 5.01589000e-01 -5.01306713e-01
-3.09543729e-01 7.14565575e-01 -7.20570832e-02 -4.21873569e-01
5.59524655e-01 -1.36222816e+00 -7.96947300e-01 -7.89512038e-01
-4.18076187e-01 -3.39477301e-01 2.61324316e-01 -6.48849905... | [4.327792644500732, 8.045413970947266] |
81677405-05cb-449b-b9f8-0fdc8b3faa5b | adversarial-training-for-low-resource | 2306.06384 | null | https://arxiv.org/abs/2306.06384v1 | https://arxiv.org/pdf/2306.06384v1.pdf | Adversarial Training For Low-Resource Disfluency Correction | Disfluencies commonly occur in conversational speech. Speech with disfluencies can result in noisy Automatic Speech Recognition (ASR) transcripts, which affects downstream tasks like machine translation. In this paper, we propose an adversarially-trained sequence-tagging model for Disfluency Correction (DC) that utiliz... | ['Pushpak Bhattacharyya', 'Preethi Jyothi', 'Vineet Bhat'] | 2023-06-10 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 1.91818580e-01 1.12590738e-01 2.39180267e-01 -2.94942081e-01
-1.15614963e+00 -8.89741004e-01 3.63318980e-01 -5.83108842e-01
-3.42308939e-01 1.04486775e+00 6.11684620e-01 -6.30058467e-01
8.72412145e-01 -2.20047142e-02 -7.29078293e-01 -4.05022204e-01
9.52164605e-02 4.89315718e-01 9.93293896e-03 -5.00487328... | [14.414555549621582, 6.879691123962402] |
038c3421-0123-435a-a8ea-8cd032ee60c1 | soft-language-clustering-for-multilingual | 2306.07610 | null | https://arxiv.org/abs/2306.07610v1 | https://arxiv.org/pdf/2306.07610v1.pdf | Soft Language Clustering for Multilingual Model Pre-training | Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size. In this paper, we propose XLM-P, which contextually... | ['Jie zhou', 'Yunbo Cao', 'Binghuai Lin', 'Fandong Meng', 'Yi Jing', 'Yongjing Yin', 'Yufan Jiang', 'Jiali Zeng'] | 2023-06-13 | null | null | null | null | ['clustering', 'zero-shot-cross-lingual-transfer', 'text-classification', 'cross-lingual-transfer'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 9.82724726e-02 -2.71759421e-01 -4.94006783e-01 -3.49069953e-01
-1.68442237e+00 -8.80004227e-01 5.82372069e-01 3.96073192e-01
-9.77655649e-01 8.60287189e-01 3.54581743e-01 -7.53934026e-01
2.86306620e-01 -5.05996644e-01 -8.44245315e-01 -9.48680639e-02
1.27713367e-01 6.68348849e-01 1.19831786e-01 -4.21772420... | [11.301629066467285, 9.73676872253418] |
4b1c6f28-ce25-4be5-93fe-72538041e0fe | neural-abstructions-abstractions-that-support | 2107.09285 | null | https://arxiv.org/abs/2107.09285v1 | https://arxiv.org/pdf/2107.09285v1.pdf | Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning | Although virtual agents are increasingly situated in environments where natural language is the most effective mode of interaction with humans, these exchanges are rarely used as an opportunity for learning. Leveraging language interactions effectively requires addressing limitations in the two most common approaches t... | ['Li Fei-Fei', 'Christopher D. Manning', 'Kaylee Burns'] | 2021-07-20 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 8.34822431e-02 8.09481144e-01 2.41182446e-02 -6.46884680e-01
-2.28619978e-01 -9.03022587e-01 1.04591656e+00 1.59255862e-01
-5.14476299e-01 6.83678389e-01 4.53339100e-01 -3.67537707e-01
2.42502004e-01 -1.02392972e+00 -7.37317085e-01 -8.79015401e-02
-5.96844852e-02 7.35212922e-01 1.97450846e-01 -7.03601062... | [4.131354808807373, 1.1748504638671875] |
b02389f6-d6bd-4dda-9c5a-22ce9c886f06 | shift-of-perspective-identification-within | 1906.02430 | null | https://arxiv.org/abs/1906.02430v4 | https://arxiv.org/pdf/1906.02430v4.pdf | Shift-of-Perspective Identification Within Legal Cases | Arguments, counter-arguments, facts, and evidence obtained via documents related to previous court cases are of essential need for legal professionals. Therefore, the process of automatic information extraction from documents containing legal opinions related to court cases can be considered to be of significant import... | ['Thejan Rupasinghe', 'Amal Shehan Perera', 'Nisansa de Silva', 'Gathika Ratnayaka', 'Viraj Salaka Gamage', 'Menuka Warushavithana'] | 2019-06-06 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 5.41859269e-01 1.86276853e-01 -1.70349717e-01 -4.25384343e-01
-1.08811247e+00 -8.93758833e-01 7.89865017e-01 8.39755416e-01
-5.12338042e-01 1.04714036e+00 5.06151378e-01 -8.17116439e-01
-4.66741979e-01 -6.47044659e-01 -2.54358053e-01 -5.38874328e-01
5.85253119e-01 3.40599805e-01 4.27770615e-01 -2.64658898... | [9.581527709960938, 9.477852821350098] |
4a01a872-578f-419a-a192-8a5f53f48710 | variance-preserving-based-interpolation | 2306.08527 | null | https://arxiv.org/abs/2306.08527v1 | https://arxiv.org/pdf/2306.08527v1.pdf | Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement | The goal of this study is to implement diffusion models for speech enhancement (SE). The first step is to emphasize the theoretical foundation of variance-preserving (VP)-based interpolation diffusion under continuous conditions. Subsequently, we present a more concise framework that encapsulates both the VP- and varia... | ['Wenbin Zhang', 'Yu Gao', 'Chin-Hui Lee', 'Jun Du', 'Zilu Guo'] | 2023-06-14 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 7.13033229e-02 4.95227473e-03 4.07166779e-02 -1.46694947e-03
-8.87132287e-01 -1.49934785e-02 6.84216976e-01 -1.60221875e-01
-4.23992932e-01 6.78584993e-01 4.77710754e-01 -3.88578266e-01
-2.55575866e-01 -4.35417682e-01 -3.85844439e-01 -8.62079918e-01
-3.55700642e-01 -3.76449645e-01 2.44090185e-01 -3.85642856... | [15.0833158493042, 6.0243611335754395] |
a14f04c1-fba6-4b4a-ac53-99d1a7df5c5e | generation-of-a-spanish-artificial | null | null | https://aclanthology.org/L18-1400 | https://aclanthology.org/L18-1400.pdf | Generation of a Spanish Artificial Collocation Error Corpus | null | ["Sara Rodr{\\'\\i}guez-Fern{\\'a}ndez", 'Leo Wanner', 'Roberto Carlini'] | 2018-05-01 | generation-of-a-spanish-artificial-1 | https://aclanthology.org/L18-1400 | https://aclanthology.org/L18-1400.pdf | lrec-2018-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.300709247589111, 3.6335134506225586] |
e5f3eedd-c9ad-424c-8b15-da272a4aaba2 | unsupervised-dependency-parsing-lets-use | 1504.04666 | null | http://arxiv.org/abs/1504.04666v1 | http://arxiv.org/pdf/1504.04666v1.pdf | Unsupervised Dependency Parsing: Let's Use Supervised Parsers | We present a self-training approach to unsupervised dependency parsing that
reuses existing supervised and unsupervised parsing algorithms. Our approach,
called `iterated reranking' (IR), starts with dependency trees generated by an
unsupervised parser, and iteratively improves these trees using the richer
probability ... | ['Phong Le', 'Willem Zuidema'] | 2015-04-18 | unsupervised-dependency-parsing-lets-use-1 | https://aclanthology.info/papers/N15-1067/n15-1067 | https://www.aclweb.org/anthology/N15-1067 | hlt-2015-5 | ['unsupervised-dependency-parsing'] | ['natural-language-processing'] | [ 2.68945962e-01 9.69552875e-01 -5.12502730e-01 -8.94912422e-01
-1.18097603e+00 -7.86531866e-01 4.16243225e-01 5.16405940e-01
-4.58319277e-01 8.79438579e-01 6.34717464e-01 -5.75666070e-01
2.08470702e-01 -6.64860070e-01 -4.85551357e-01 -2.37334877e-01
-2.59182483e-01 9.28263366e-01 6.83800817e-01 -3.06134403... | [10.361613273620605, 9.748448371887207] |
c9ad6221-f882-44ae-b450-bf5a6bacb1c8 | how-to-evaluate-the-quality-of-unsupervised | 1607.01152 | null | http://arxiv.org/abs/1607.01152v1 | http://arxiv.org/pdf/1607.01152v1.pdf | How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms? | When sufficient labeled data are available, classical criteria based on
Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be
used to compare the performance of un-supervised anomaly detection algorithms.
However , in many situations, few or no data are labeled. This calls for
alternative crite... | ['Nicolas Goix'] | 2016-07-05 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 5.20619750e-02 -1.36556774e-01 -1.86040401e-01 -6.37539387e-01
-5.65631211e-01 -5.25406480e-01 6.12902701e-01 8.25410724e-01
-5.51623642e-01 7.50135660e-01 -4.60245907e-01 -4.86418277e-01
-4.99921888e-01 -7.58492112e-01 4.84880209e-02 -6.04584694e-01
-2.69978821e-01 6.83976293e-01 4.54864800e-01 1.97896525... | [8.106179237365723, 4.114152908325195] |
7a045705-6517-4034-9eef-6c01df28a1b9 | two-heads-are-better-than-one-towards-better | 2305.17528 | null | https://arxiv.org/abs/2305.17528v1 | https://arxiv.org/pdf/2305.17528v1.pdf | Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection | Both transduction and rejection have emerged as important techniques for defending against adversarial perturbations. A recent work by Tram\`er showed that, in the rejection-only case (no transduction), a strong rejection-solution can be turned into a strong (but computationally inefficient) non-rejection solution. Thi... | ['Somesh Jha', 'YIngyu Liang', 'Jiefeng Chen', 'Xi Wu', 'Yang Guo', 'Nils Palumbo'] | 2023-05-27 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [ 5.52940547e-01 1.71123818e-01 -8.51315353e-03 -2.46787500e-02
-1.24083281e+00 -1.02960324e+00 5.69762051e-01 -2.09476352e-02
-4.47681040e-01 7.31847227e-01 -3.24070305e-01 -7.82489061e-01
-7.15038106e-02 -9.03615832e-01 -1.04335034e+00 -1.10286474e+00
-1.36947840e-01 2.24178940e-01 4.07962501e-01 -5.31515539... | [5.774572849273682, 7.656317710876465] |
4facf4e9-2569-4b98-a01f-804eb1285c61 | joint-multi-scale-tone-mapping-and-denoising | 2303.09071 | null | https://arxiv.org/abs/2303.09071v2 | https://arxiv.org/pdf/2303.09071v2.pdf | Joint Multi-Scale Tone Mapping and Denoising for HDR Image Enhancement | An image processing unit (IPU), or image signal processor (ISP) for high dynamic range (HDR) imaging usually consists of demosaicing, white balancing, lens shading correction, color correction, denoising, and tone-mapping. Besides noise from the imaging sensors, almost every step in the ISP introduces or amplifies nois... | ['Jan P. Allebach', 'Huaijin Chen', 'Litao Hu'] | 2023-03-16 | null | null | null | null | ['demosaicking', 'image-enhancement', 'tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.34296298e-01 -3.97318423e-01 5.31508446e-01 -4.14444596e-01
-6.00862920e-01 -3.54800105e-01 4.53565381e-02 -3.40302616e-01
-4.25609767e-01 1.81220040e-01 3.46727878e-01 -1.68060198e-01
1.86984271e-01 -7.92743146e-01 -7.38623917e-01 -8.04206491e-01
2.61177421e-01 -1.19186617e-01 2.27724835e-01 -3.59756291... | [10.768959999084473, -2.3701319694519043] |
72f7ffd0-582f-45bc-adbd-f281b1b8e4ed | vector-space-model-as-cognitive-space-for | 1708.06068 | null | http://arxiv.org/abs/1708.06068v1 | http://arxiv.org/pdf/1708.06068v1.pdf | Vector Space Model as Cognitive Space for Text Classification | In this era of digitization, knowing the user's sociolect aspects have become
essential features to build the user specific recommendation systems. These
sociolect aspects could be found by mining the user's language sharing in the
form of text in social media and reviews. This paper describes about the
experiment that... | ['Soman Kp', 'Barathi Ganesh HB', 'Anand Kumar M'] | 2017-08-21 | null | null | null | null | ['gender-prediction', 'native-language-identification'] | ['computer-vision', 'natural-language-processing'] | [-2.70248502e-01 -6.22127345e-03 -5.21913886e-01 -5.92980385e-01
-2.41596118e-01 -5.80512106e-01 1.07321906e+00 5.27816296e-01
-7.41043687e-01 5.49865663e-01 4.92685705e-01 -3.37302119e-01
-2.11426038e-02 -7.27771640e-01 2.52368599e-02 -5.64788878e-01
2.16404468e-01 6.42821848e-01 -2.99428433e-01 -3.61068726... | [9.459895133972168, 10.335882186889648] |
f292a505-b7a4-4b60-92a7-1c34b6b5a6e3 | cartoongan-generative-adversarial-networks | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf | CartoonGAN: Generative Adversarial Networks for Photo Cartoonization | In this paper, we propose a solution to transforming photos of real-world scenes into cartoon style images, which is valuable and challenging in computer vision and computer graphics. Our solution belongs to learning based methods, which have recently become popular to stylize images in artistic forms such as painting.... | ['Yong-Jin Liu', 'Yu-Kun Lai', 'Yang Chen'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['real-to-cartoon-translation'] | ['computer-vision'] | [ 3.32455814e-01 -7.44335726e-02 2.16849908e-01 -1.07100628e-01
-3.61012936e-01 -5.06787062e-01 5.64929426e-01 -6.04350507e-01
-5.85396402e-02 8.48930717e-01 -1.53636321e-01 3.18502113e-02
2.51506299e-01 -1.02044237e+00 -9.60598469e-01 -7.01543987e-01
2.83478707e-01 3.18600446e-01 8.82996768e-02 -2.51525491... | [11.640449523925781, -0.6584645509719849] |
7b8ab5ac-6b27-404b-a7e6-c2e59cfcbb9d | qtran-learning-to-factorize-with | 1905.05408 | null | https://arxiv.org/abs/1905.05408v1 | https://arxiv.org/pdf/1905.05408v1.pdf | QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning | We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. However, VDN and QMIX are representative examples that use the idea of factorization of the joint action-value function into individual ones f... | ['Yung Yi', 'David Earl Hostallero', 'Wan Ju Kang', 'Daewoo Kim', 'Kyunghwan Son'] | 2019-05-14 | null | null | null | null | ['smac-1'] | ['playing-games'] | [-4.80285674e-01 1.42423600e-01 -4.26199764e-01 1.69061601e-01
-9.04649258e-01 -7.13128626e-01 3.13093543e-01 -1.09274589e-01
-7.41925359e-01 1.37241685e+00 6.94748461e-02 -5.68858981e-01
-7.46090591e-01 -4.94242281e-01 -7.96939790e-01 -1.03840733e+00
-7.55474329e-01 6.09026790e-01 -3.54613247e-03 -6.60133958... | [3.7636032104492188, 2.0688273906707764] |
3412bf40-ff2e-4829-bb8a-5d8096b4135d | auto-clustering-output-layer-automatic | 1702.08648 | null | http://arxiv.org/abs/1702.08648v2 | http://arxiv.org/pdf/1702.08648v2.pdf | Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks | In this paper, we discuss a different type of semi-supervised setting: a
coarse level of labeling is available for all observations but the model has to
learn a fine level of latent annotation for each one of them. Problems in this
setting are likely to be encountered in many domains such as text
categorization, protei... | ['Ozsel Kilinc', 'Ismail Uysal'] | 2017-02-28 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 6.56388342e-01 4.71023679e-01 -4.32527959e-01 -6.96215570e-01
-5.84689915e-01 -4.62712198e-01 5.92104018e-01 6.05700672e-01
-5.49319446e-01 7.32708693e-01 -7.24992082e-02 -1.99607402e-01
-2.38360614e-01 -5.45824885e-01 -9.34881032e-01 -9.91353154e-01
-3.01358663e-02 5.71333230e-01 2.24692389e-01 4.55486536... | [9.406658172607422, 3.200993061065674] |
17cad912-3685-4386-938d-fd06204266b6 | a-novel-system-for-extractive-clinical-note | null | null | https://aclanthology.org/W19-1906 | https://aclanthology.org/W19-1906.pdf | A Novel System for Extractive Clinical Note Summarization using EHR Data | While much data within a patient{'}s electronic health record (EHR) is coded, crucial information concerning the patient{'}s care and management remain buried in unstructured clinical notes, making it difficult and time-consuming for physicians to review during their usual clinical workflow. In this paper, we present o... | ['Ching-Huei Tsou', 'Jennifer Liang', 'Ananya Poddar'] | 2019-06-01 | null | null | null | ws-2019-6 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 6.58308625e-01 6.91842318e-01 2.40252152e-01 -4.57994372e-01
-1.22459424e+00 -6.79927707e-01 1.77826136e-02 1.63245893e+00
-3.11747372e-01 5.73682666e-01 1.11689377e+00 -5.76070070e-01
-3.45203668e-01 -3.70435804e-01 -4.23990972e-02 -1.88240871e-01
-2.56274015e-01 7.94101059e-01 -2.68487692e-01 2.69155025... | [8.518792152404785, 8.601919174194336] |
ae533e44-94b7-400d-ab6c-c2561ce39a78 | bidirectional-semi-supervised-dual-branch-cnn | 2210.08291 | null | https://arxiv.org/abs/2210.08291v5 | https://arxiv.org/pdf/2210.08291v5.pdf | Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions | Semi-supervised learning via teacher-student network can train a model effectively on a few labeled samples. It enables a student model to distill knowledge from the teacher's predictions of extra unlabeled data. However, such knowledge flow is typically unidirectional, having the performance vulnerable to the quality ... | ['Qiang Li', 'Xin Yang', 'Dun Li', 'Ying Zhou', 'Zhiwei Wang', 'Hongkuan Shi'] | 2022-10-15 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 1.14244424e-01 3.66322339e-01 -4.70690191e-01 -6.49041772e-01
-5.11237204e-01 -3.53009224e-01 2.23224431e-01 3.32363509e-02
-3.88897926e-01 6.82420909e-01 4.79706489e-02 -4.13481116e-01
8.24150890e-02 -9.67705667e-01 -8.74001384e-01 -1.08997977e+00
4.64445829e-01 3.00402850e-01 6.62995458e-01 5.05845696... | [9.31240177154541, 1.3928433656692505] |
c80758ee-23f8-499f-b383-bdd814619b80 | tablesense-spreadsheet-table-detection-with | 2106.13500 | null | https://arxiv.org/abs/2106.13500v1 | https://arxiv.org/pdf/2106.13500v1.pdf | TableSense: Spreadsheet Table Detection with Convolutional Neural Networks | Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layo... | ['Dongmei Zhang', 'Zhouyu Fu', 'Shi Han', 'Shijie Liu', 'Haoyu Dong'] | 2021-06-25 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-2.11354792e-01 -2.76211977e-01 -1.74500227e-01 -1.98550642e-01
-9.92061198e-01 -9.18329239e-01 3.29476386e-01 6.43994451e-01
-7.36217201e-02 4.31843609e-01 1.19501874e-01 -4.23744291e-01
-5.83950765e-02 -1.16574776e+00 -8.47335339e-01 -1.84510484e-01
1.16080111e-02 6.06954277e-01 2.40006119e-01 -2.49474674... | [11.649930000305176, 3.0408682823181152] |
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