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2954b4d8-08b6-40f7-9d9b-169e684eea1e | suds-scalable-urban-dynamic-scenes | 2303.14536 | null | https://arxiv.org/abs/2303.14536v1 | https://arxiv.org/pdf/2303.14536v1.pdf | SUDS: Scalable Urban Dynamic Scenes | We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons are that such methods (a) tend to scale linearly with the number of moving objects and input videos because a separate model is built for ea... | ['Deva Ramanan', 'Francesco Ferroni', 'Jason Y. Zhang', 'Haithem Turki'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Turki_SUDS_Scalable_Urban_Dynamic_Scenes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Turki_SUDS_Scalable_Urban_Dynamic_Scenes_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 6.07305691e-02 -3.30806047e-01 7.53619801e-03 -2.13401958e-01
-1.15534198e+00 -9.07854795e-01 7.61591434e-01 -4.32816535e-01
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2.00745553e-01 -1.04614246e+00 -8.71593356e-01 -5.19451737e-01
-2.69699723e-01 8.15166831e-01 3.88188481e-01 -3.73628974... | [8.489541053771973, -2.1005303859710693] |
a7bf589c-9dbb-4a4c-aeef-67720d1ee1ef | extracting-multi-valued-relations-from | 2307.03122 | null | https://arxiv.org/abs/2307.03122v2 | https://arxiv.org/pdf/2307.03122v2.pdf | Extracting Multi-valued Relations from Language Models | The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge. However, existing methods focus only on a single object per subject-relation pair, even though often multiple objects are correct. To overcome this limitation,... | ['Gerhard Weikum', 'Simon Razniewski', 'Sneha Singhania'] | 2023-07-06 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 3.41625512e-01 7.24563539e-01 -1.14507008e+00 -4.77796048e-01
-1.16999590e+00 -4.51941907e-01 8.96808267e-01 6.05462134e-01
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5.85754178e-02 8.74363422e-01 3.20849776e-01 -8.22427794... | [9.53054428100586, 8.662474632263184] |
73e407ea-a611-428e-8d04-aab0ac28a912 | movese-movable-and-moving-lidar-scene | 2306.14812 | null | https://arxiv.org/abs/2306.14812v1 | https://arxiv.org/pdf/2306.14812v1.pdf | MOVESe: MOVablE and Moving LiDAR Scene Segmentation with Improved Navigation in Seg-label free settings | Accurate detection of movable and moving objects in LiDAR is of vital importance for navigation. Most existing works focus on extracting and removing moving objects during navigation. Movable objects like pedestrians, parked vehicles, etc. although static may move in the future. This leads to erroneous navigation and a... | ['Prem Kumar Kalra', 'Anurag Mittal', 'Dhruv Makwana', 'Onkar Susladkar', 'Prashant Kumar'] | 2023-06-26 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.86750722e-01 4.89216447e-02 1.91221207e-01 -2.79039681e-01
-7.13136375e-01 -7.78025091e-01 3.35169971e-01 8.61289501e-02
-6.57426298e-01 7.48110831e-01 -5.22830188e-01 -5.89392126e-01
2.97580119e-02 -9.49100137e-01 -8.57414365e-01 -4.80678767e-01
-1.28985643e-01 8.76503944e-01 1.14710677e+00 -3.11723560... | [7.948953628540039, -2.4138131141662598] |
563e8e82-752f-4292-8f02-d147e568f091 | bayesian-optimisation-for-sequential | 2107.12809 | null | https://arxiv.org/abs/2107.12809v3 | https://arxiv.org/pdf/2107.12809v3.pdf | Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing | Bayesian optimization (BO) is an approach to globally optimizing black-box objective functions that are expensive to evaluate. BO-powered experimental design has found wide application in materials science, chemistry, experimental physics, drug development, etc. This work aims to bring attention to the benefits of appl... | ['Alessio Benavoli', 'Dermot Brabazon', 'Andrew Parnell', 'Mimi Zhang'] | 2021-07-27 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.60501504e-01 -1.24112040e-01 -1.76836908e-01 -1.54771626e-01
-5.71283221e-01 -4.02782321e-01 -1.01205949e-02 7.11957365e-02
-9.85163962e-04 1.02530360e+00 -1.33190945e-01 -4.29898202e-01
-5.90049624e-01 -7.54226983e-01 -9.30691659e-01 -1.05051947e+00
-1.22167863e-01 5.56332290e-01 5.77020086e-02 -1.08302450... | [6.204687595367432, 3.6834359169006348] |
a23aaaf8-d732-40ea-bf41-aaba1eeb326b | data-augmentation-vision-transformer-for-fine | 2211.12879 | null | https://arxiv.org/abs/2211.12879v2 | https://arxiv.org/pdf/2211.12879v2.pdf | Data Augmentation Vision Transformer for Fine-grained Image Classification | Recently, the vision transformer (ViT) has made breakthroughs in image recognition. Its self-attention mechanism (MSA) can extract discriminative labeling information of different pixel blocks to improve image classification accuracy. However, the classification marks in their deep layers tend to ignore local features ... | ['Weijie Wu', 'Weibin Qiu', 'Liqiang Zhu', 'Chao Hu'] | 2022-11-23 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 3.41338068e-01 9.21494793e-03 -1.70750409e-01 -3.40591908e-01
-2.67315090e-01 6.72130436e-02 2.54169375e-01 -1.78551912e-01
-5.43654859e-01 4.18338150e-01 7.77090341e-02 4.09997851e-02
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2.33697191e-01 1.42083550e-02 4.87343103e-01 -9.87350866... | [9.590507507324219, 1.8881902694702148] |
53039bdf-677f-459a-a98e-58342185c9e3 | improving-contextual-spelling-correction-by | 2302.11192 | null | https://arxiv.org/abs/2302.11192v1 | https://arxiv.org/pdf/2302.11192v1.pdf | Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation | We previously proposed contextual spelling correction (CSC) to correct the output of end-to-end (E2E) automatic speech recognition (ASR) models with contextual information such as name, place, etc. Although CSC has achieved reasonable improvement in the biasing problem, there are still two drawbacks for further accurac... | ['Sheng Zhao', 'Jinyu Li', 'Yanqing Liu', 'Xiaoqiang Wang'] | 2023-02-22 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 4.58429545e-01 1.19727597e-01 -6.80435076e-02 -5.70752800e-01
-9.93030488e-01 -3.48241806e-01 4.65666652e-01 -8.83429646e-02
-7.68784523e-01 6.22614205e-01 7.10784197e-01 -4.92849141e-01
1.51250079e-01 -4.97675538e-01 -7.96822667e-01 -3.82253826e-01
7.34442711e-01 5.19325972e-01 4.31395411e-01 -7.23734915... | [14.354682922363281, 6.712670803070068] |
ace1e298-9b84-4c18-9082-6d3dcdf126f6 | machine-learning-for-detecting-data | 2012.09344 | null | https://arxiv.org/abs/2012.09344v2 | https://arxiv.org/pdf/2012.09344v2.pdf | Machine Learning for Detecting Data Exfiltration: A Review | Context: Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks. It is important to systematically review and synthesize the ML-based data exfiltration cou... | ['Raj Gaire', 'M. Ali Babar', 'Faheem Ullah', 'Bushra Sabir'] | 2020-12-17 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 1.90209895e-01 -5.23920298e-01 -4.83859986e-01 -1.70315821e-02
-4.08365071e-01 -1.00740111e+00 8.66837502e-01 7.95761526e-01
-3.33293647e-01 1.67419553e-01 2.36855119e-01 -1.05178976e+00
-6.62985623e-01 -8.54559600e-01 -4.71603245e-01 -4.06212449e-01
-4.36384618e-01 -3.34846675e-01 1.06840007e-01 -2.27669209... | [5.35377311706543, 7.217463970184326] |
a10b6339-bd50-4555-9d9e-eccfda14be86 | bump-hunting-through-density-curvature | 2208.00174 | null | https://arxiv.org/abs/2208.00174v2 | https://arxiv.org/pdf/2208.00174v2.pdf | Bump hunting through density curvature features | Bump hunting deals with finding in sample spaces meaningful data subsets known as bumps. These have traditionally been conceived as modal or concave regions in the graph of the underlying density function. We define an abstract bump construct based on curvature functionals of the probability density. Then, we explore s... | ['Javier Fernández Serrano', 'José E. Chacón'] | 2022-07-30 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-5.16943812e-01 3.24833184e-01 -1.21145435e-01 -2.99878150e-01
-5.65993965e-01 -4.29548621e-01 3.55410993e-01 6.42649710e-01
-1.93462908e-01 7.43464828e-01 7.42523968e-02 -3.45706791e-01
-7.34373033e-01 -7.14486063e-01 -7.40420818e-01 -5.37396550e-01
-7.86200047e-01 5.22481024e-01 2.34567195e-01 -2.51709342... | [7.404913902282715, 4.216629981994629] |
4115182c-89a3-44b1-9206-06efc41b2625 | single-unit-status-in-deep-convolutional | 2002.06274 | null | https://arxiv.org/abs/2002.06274v2 | https://arxiv.org/pdf/2002.06274v2.pdf | Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined | Deep convolutional neural networks (DCNNs) trained for face identification develop representations that generalize over variable images, while retaining subject (e.g., gender) and image (e.g., viewpoint) information. Identity, gender, and viewpoint codes were studied at the "neural unit" and ensemble levels of a face-i... | ["Alice J. O'Toole", 'Y. Ivette Colón', 'Matthew Q. Hill', 'Connor J. Parde', 'Carlos D. Castillo', 'Prithviraj Dhar'] | 2020-02-14 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 1.76845178e-01 -1.26328632e-01 -1.10415593e-01 -7.89640069e-01
-2.48646706e-01 -1.06547749e+00 3.20808023e-01 -2.28529423e-01
-2.60487407e-01 4.62406307e-01 1.18633367e-01 1.38408151e-02
3.10826674e-02 -5.76396465e-01 -5.58811426e-01 -8.36359978e-01
-8.14884081e-02 3.48698139e-01 -6.26099467e-01 3.14416796... | [12.956074714660645, 1.2669785022735596] |
5e66b86e-1e02-4c68-8328-e69a95e089e9 | guiding-attention-in-sequence-to-sequence | 2002.08801 | null | https://arxiv.org/abs/2002.08801v2 | https://arxiv.org/pdf/2002.08801v2.pdf | Guiding attention in Sequence-to-sequence models for Dialogue Act prediction | The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translati... | ['Pierre Colombo', 'Matteo Manica', 'Emmanuel Vignon', 'Emile Chapuis', 'Giovanna Varni', 'Chloe Clavel'] | 2020-02-20 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 1.74348027e-01 5.25561631e-01 -8.11116770e-02 -8.10737550e-01
-7.23432541e-01 -5.64893842e-01 9.80029881e-01 -2.54953086e-01
-4.31799114e-01 1.00758684e+00 5.37925661e-01 -2.95170426e-01
2.93317735e-01 -4.80681121e-01 -3.06822270e-01 -3.65494162e-01
3.74623351e-02 1.28061545e+00 1.76326796e-01 -4.36101854... | [12.769728660583496, 7.723872661590576] |
2efcc01b-b5ce-4ffe-86f6-6b2638027522 | text-and-style-conditioned-gan-for-generation | 2009.00678 | null | https://arxiv.org/abs/2009.00678v1 | https://arxiv.org/pdf/2009.00678v1.pdf | Text and Style Conditioned GAN for Generation of Offline Handwriting Lines | This paper presents a GAN for generating images of handwritten lines conditioned on arbitrary text and latent style vectors. Unlike prior work, which produce stroke points or single-word images, this model generates entire lines of offline handwriting. The model produces variable-sized images by using style vectors to ... | ['Bryan Morse', 'Curtis Wigington', 'Brian Price', 'Chris Tensmeyer', 'Brian Davis', 'Rajiv Jain'] | 2020-09-01 | null | null | null | null | ['handwriting-generation'] | ['computer-vision'] | [ 7.17269957e-01 3.78504932e-01 -7.74844438e-02 -3.50860238e-01
-3.69558603e-01 -1.13297272e+00 5.82640350e-01 -7.30589092e-01
3.50843184e-02 8.00673306e-01 6.19225129e-02 -3.01642388e-01
6.01918638e-01 -1.02635026e+00 -7.59012938e-01 -5.66645503e-01
6.64647281e-01 6.98833108e-01 -3.00625712e-01 -8.79694670... | [11.657032012939453, -0.13475199043750763] |
5f0f2cd0-33a5-489f-ba66-06e3d23cc56a | modeling-inter-class-and-intra-class | 2210.03591 | null | https://arxiv.org/abs/2210.03591v3 | https://arxiv.org/pdf/2210.03591v3.pdf | Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery | Novel class discovery (NCD) aims at learning a model that transfers the common knowledge from a class-disjoint labelled dataset to another unlabelled dataset and discovers new classes (clusters) within it. Many methods, as well as elaborate training pipelines and appropriate objectives, have been proposed and considera... | ['Yang Gao', 'Jing Huo', 'Zhichen Fan', 'Wenbin Li'] | 2022-10-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Modeling_Inter-Class_and_Intra-Class_Constraints_in_Novel_Class_Discovery_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Modeling_Inter-Class_and_Intra-Class_Constraints_in_Novel_Class_Discovery_CVPR_2023_paper.pdf | cvpr-2023-1 | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 2.46836208e-02 -1.60390660e-01 -3.06359679e-01 -6.40526652e-01
-6.91072583e-01 -5.35375655e-01 7.98279464e-01 1.72868550e-01
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-3.49283606e-01 -4.97147322e-01 -4.52542394e-01 -7.58457303e-01
-8.81066024e-02 4.06512022e-01 1.94857910e-01 2.95913249... | [9.52513313293457, 3.173762321472168] |
6c87fcdf-48c6-4c62-979f-b8ee80e9e1ca | anisotropic-multi-scale-graph-convolutional | 2210.09466 | null | https://arxiv.org/abs/2210.09466v2 | https://arxiv.org/pdf/2210.09466v2.pdf | Anisotropic Multi-Scale Graph Convolutional Network for Dense Shape Correspondence | This paper studies 3D dense shape correspondence, a key shape analysis application in computer vision and graphics. We introduce a novel hybrid geometric deep learning-based model that learns geometrically meaningful and discretization-independent features with a U-Net model as the primary node feature extraction modul... | ['Yalin Wang', 'Zhangsihao Yang', 'Wenhui Zhu', 'Mohammad Farazi'] | 2022-10-17 | null | null | null | null | ['3d-dense-shape-correspondence'] | ['computer-vision'] | [-1.67999174e-02 1.70952901e-01 1.31827816e-01 -2.96530902e-01
-2.60992140e-01 -5.00801861e-01 7.35222280e-01 2.96055764e-01
-1.15295410e-01 3.01209152e-01 5.57571501e-02 -2.39104792e-01
-1.38868466e-01 -1.35801065e+00 -8.42351735e-01 -5.11205673e-01
-4.61522877e-01 4.92432058e-01 5.28402686e-01 -2.98934102... | [8.323112487792969, -3.6938765048980713] |
4708ec23-211a-4848-bbc3-f7b4df906092 | correction-of-out-of-focus-microscopic-images | null | null | https://www.sciencedirect.com/science/article/pii/S2001037022001192 | https://doi.org/10.1016/j.csbj.2022.04.003 | Correction of out-of-focus microscopic images by deep learning | Motivation
Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of-focus, resulting in poor performance in research and ... | ['Yang Zhang.', 'Mario Juhas', 'Shiming Tang', 'Junyi Li', 'Weihuang Liu', 'Hao Jiang', 'Chi Zhang'] | 2022-04-26 | null | null | null | computational-and-structural-biotechnology | ['medical-image-generation'] | ['medical'] | [ 2.56776720e-01 -4.64030832e-01 3.68400991e-01 -2.66810544e-02
-7.20644057e-01 -5.25594413e-01 2.53222644e-01 -2.59751081e-01
-7.37397909e-01 1.07050347e+00 -5.12081087e-01 -2.35752881e-01
3.40342253e-01 -7.10687339e-01 -7.13660836e-01 -1.16592908e+00
1.29168183e-01 2.76987493e-01 7.47654215e-02 3.06875497... | [12.961817741394043, -2.691622495651245] |
86663ef6-489f-4fd7-8ede-4dd1f99c4eba | a-survey-of-federated-learning-for-connected | 2303.10677 | null | https://arxiv.org/abs/2303.10677v1 | https://arxiv.org/pdf/2303.10677v1.pdf | A Survey of Federated Learning for Connected and Automated Vehicles | Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system. Machine learning-based methods are widely u... | ['Ziran Wang', 'Stanislaw H /. Zak', 'Liangqi Yuan', 'Vishnu Pandi Chellapandi'] | 2023-03-19 | null | null | null | null | ['motion-planning'] | ['robots'] | [-1.88907042e-01 -3.85350771e-02 -4.51392084e-01 -4.05361891e-01
-6.32504046e-01 -2.92997986e-01 6.64555967e-01 -4.31288518e-02
-2.72356719e-01 5.75597227e-01 -2.40489885e-01 -3.70168835e-01
-3.42768840e-02 -9.74043787e-01 -6.29086375e-01 -7.83857226e-01
1.89067096e-01 5.57298139e-02 5.64367592e-01 -2.12270945... | [5.823068618774414, 1.1546225547790527] |
91347090-c95f-4b87-aeb4-f71b66b54c70 | conditioned-u-net-introducing-a-control | 1907.01277 | null | https://arxiv.org/abs/1907.01277v3 | https://arxiv.org/pdf/1907.01277v3.pdf | Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source Separations | Data-driven models for audio source separation such as U-Net or Wave-U-Net are usually models dedicated to and specifically trained for a single task, e.g. a particular instrument isolation. Training them for various tasks at once commonly results in worse performances than training them for a single specialized task. ... | ['Gabriel Meseguer-Brocal', 'Geoffroy Peeters'] | 2019-07-02 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 5.27446628e-01 -2.62277909e-02 -1.85102925e-01 -2.24472046e-01
-1.00242615e+00 -5.41933417e-01 3.59086692e-01 2.79578753e-03
-2.79324085e-01 2.81542331e-01 4.42215151e-06 -8.14945847e-02
-3.22379261e-01 -5.46979308e-01 -8.31480682e-01 -9.20700014e-01
4.10702527e-02 1.88194349e-01 1.61555573e-01 -2.61551619... | [15.402838706970215, 5.578395366668701] |
db3c3c11-295d-41ca-8034-16c182e103e8 | efficient-chemical-space-exploration-using | 2209.00514 | null | https://arxiv.org/abs/2209.00514v1 | https://arxiv.org/pdf/2209.00514v1.pdf | Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation | We introduce an explorative active learning (AL) algorithm based on Gaussian process regression and marginalized graph kernel (GPR-MGK) to explore chemical space with minimum cost. Using high-throughput molecular dynamics simulation to generate data and graph neural network (GNN) to predict, we constructed an active le... | ['Huai Sun', 'Guang Lin', 'Liang Wu', 'Hongyi Liu', 'Zheng Gong', 'Yu-Hang Tang', 'Yan Xiang'] | 2022-09-01 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 9.93016958e-02 3.01790684e-01 -3.24439526e-01 -1.65958956e-01
-6.99100316e-01 -4.06544864e-01 5.02440751e-01 9.00130808e-01
-6.45512700e-01 1.47897780e+00 -5.28900266e-01 -4.58312690e-01
-3.41886818e-01 -1.01256704e+00 -7.20490396e-01 -1.03568339e+00
-7.76757240e-01 6.92248762e-01 1.82814151e-02 2.47632802... | [5.139789581298828, 5.441060543060303] |
e6d6b835-8b87-4bfb-9220-2ce9bf625fc4 | multi-projection-fusion-for-real-time | 2011.01974 | null | https://arxiv.org/abs/2011.01974v2 | https://arxiv.org/pdf/2011.01974v2.pdf | Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds | Semantic segmentation of 3D point cloud data is essential for enhanced high-level perception in autonomous platforms. Furthermore, given the increasing deployment of LiDAR sensors onboard of cars and drones, a special emphasis is also placed on non-computationally intensive algorithms that operate on mobile GPUs. Previ... | ['Yara Ali Alnaggar', 'Mohamed ElHelw', 'Karim Amer', 'Mohamed Afifi'] | 2020-11-03 | multi-projection-fusion-for-real-time-1 | https://openaccess.thecvf.com/content/WACV2021/html/Alnaggar_Multi_Projection_Fusion_for_Real-Time_Semantic_Segmentation_of_3D_LiDAR_WACV_2021_paper.html | https://openaccess.thecvf.com/content/WACV2021/papers/Alnaggar_Multi_Projection_Fusion_for_Real-Time_Semantic_Segmentation_of_3D_LiDAR_WACV_2021_paper.pdf | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-4.99738473e-03 -5.15466966e-02 3.63683611e-01 -5.34085095e-01
-4.84083176e-01 -5.23471534e-01 6.18933439e-01 -7.91986883e-02
-8.30399096e-01 1.14649966e-01 -6.38829947e-01 -6.29670739e-01
7.54434317e-02 -1.00950897e+00 -9.13198531e-01 -2.99215049e-01
2.35809907e-01 1.01112151e+00 7.87571132e-01 -4.74447370... | [8.071134567260742, -2.733649492263794] |
20cbf827-3a13-49d9-8d7e-6e1f83bcbd13 | gradskip-communication-accelerated-local | 2210.16402 | null | https://arxiv.org/abs/2210.16402v2 | https://arxiv.org/pdf/2210.16402v2.pdf | GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity | We study a class of distributed optimization algorithms that aim to alleviate high communication costs by allowing the clients to perform multiple local gradient-type training steps prior to communication. While methods of this type have been studied for about a decade, the empirically observed acceleration properties ... | ['Peter Richtárik', 'Mher Safaryan', 'Artavazd Maranjyan'] | 2022-10-28 | null | null | null | null | ['distributed-optimization', 'common-sense-reasoning'] | ['methodology', 'reasoning'] | [ 2.08132818e-01 2.51215070e-01 -2.27541625e-01 -2.14428291e-01
-1.09329557e+00 -7.66141295e-01 3.74029160e-01 3.73793125e-01
-7.71106064e-01 8.85670006e-01 2.41057828e-01 -4.29870337e-01
-5.06989002e-01 -9.64603007e-01 -1.14509273e+00 -1.17934704e+00
-2.32657284e-01 6.95239663e-01 6.18137084e-02 -1.50668100... | [6.340595722198486, 4.85524845123291] |
85019e63-870e-4166-9e17-b29f392d0e0e | a-computational-efficient-pumped-storage | 2304.03821 | null | https://arxiv.org/abs/2304.03821v1 | https://arxiv.org/pdf/2304.03821v1.pdf | A Computational Efficient Pumped Storage Hydro Optimization in the Look-ahead Unit Commitment and Real-time Market Dispatch Under Uncertainty | Pumped storage hydro units (PSHU) are great sources of flexibility in power systems. This is especially valuable in modern systems with increasing shares of intermittent renewable resources. However, the flexibility from PSHUs, particularly in the real-time market, has not been thoroughly studied. The storage optimizat... | ['Ross Baldick', 'Yonghong Chen', 'Arezou Ghesmati', 'Bing Huang'] | 2023-04-07 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-6.63867474e-01 -1.21201307e-01 1.02386646e-01 2.65853822e-01
-3.80835444e-01 -8.65210235e-01 4.71229136e-01 1.23868920e-01
1.84297532e-01 1.39081001e+00 -8.90443400e-02 -5.34597576e-01
-5.20754993e-01 -1.23206007e+00 -3.57227772e-01 -1.04038584e+00
-4.17163074e-01 3.92674387e-01 -1.07299484e-01 -5.21400750... | [5.664921283721924, 2.531590461730957] |
5501e785-dd48-45a6-8521-d2a3d89989ed | forensicability-assessment-of-questioned | 2209.01935 | null | https://arxiv.org/abs/2209.01935v1 | https://arxiv.org/pdf/2209.01935v1.pdf | Forensicability Assessment of Questioned Images in Recapturing Detection | Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensi... | ['Alex C. Kot', 'Jiwu Huang', 'Zitong Yu', 'Rizhao Cai', 'Lin Zhao', 'Changsheng chen'] | 2022-09-05 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 1.21606894e-01 -3.21362644e-01 1.36330768e-01 -1.15500286e-01
-7.27419615e-01 -4.65732425e-01 5.19153416e-01 1.56990588e-02
-4.98022825e-01 4.68223393e-01 -3.24673295e-01 8.76095146e-02
-2.92857975e-01 -6.12309158e-01 -4.10794586e-01 -9.21909988e-01
-1.14399649e-01 3.07736725e-01 4.06469144e-02 9.84539613... | [12.42020320892334, 0.9948204159736633] |
21b27a05-42b6-428a-b09e-ea4a8ac4706e | estimation-of-mitral-valve-hinge-point | 2301.08782 | null | https://arxiv.org/abs/2301.08782v1 | https://arxiv.org/pdf/2301.08782v1.pdf | Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation | Cardiac image segmentation is a powerful tool in regard to diagnostics and treatment of cardiovascular diseases. Purely feature-based detection of anatomical structures like the mitral valve is a laborious task due to specifically required feature engineering and is especially challenging in echocardiograms, because of... | ['Heinrich Martin Overhoff', 'Christian Schmidt'] | 2023-01-20 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 5.63201234e-02 2.55149752e-01 3.21041167e-01 -3.53311300e-02
-5.24381757e-01 -7.89634287e-01 6.53841812e-03 4.57445174e-01
-4.86335576e-01 4.96711493e-01 -2.16487572e-01 -4.65789199e-01
-2.22357973e-01 -5.28640330e-01 -2.08905399e-01 -5.49569488e-01
-4.34084743e-01 6.43356502e-01 2.84184247e-01 1.76104888... | [14.152054786682129, -2.5199429988861084] |
7cdf21f7-6382-41a3-be8a-21529ca16383 | a-capsule-network-for-hierarchical-multi | 2209.05723 | null | https://arxiv.org/abs/2209.05723v1 | https://arxiv.org/pdf/2209.05723v1.pdf | A Capsule Network for Hierarchical Multi-Label Image Classification | Image classification is one of the most important areas in computer vision. Hierarchical multi-label classification applies when a multi-class image classification problem is arranged into smaller ones based upon a hierarchy or taxonomy. Thus, hierarchical classification modes generally provide multiple class predictio... | ['Brano Kusy', 'Antonio Robles-Kelly', 'Khondaker Tasrif Noor'] | 2022-09-13 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.62701559e-01 2.24052742e-01 -4.00116116e-01 -6.40149236e-01
-5.20323992e-01 -4.54789817e-01 5.41226983e-01 4.24465060e-01
-2.92384356e-01 4.18975025e-01 -2.58773416e-02 -3.61193158e-02
-1.76266551e-01 -7.05134392e-01 -3.43926638e-01 -6.45785987e-01
2.40906447e-01 4.98038232e-01 4.65742648e-01 2.89431363... | [9.64946460723877, 4.273163318634033] |
8c335215-f568-4f52-aad3-cad45e5b492b | identification-of-substation-configurations | 2207.05603 | null | https://arxiv.org/abs/2207.05603v2 | https://arxiv.org/pdf/2207.05603v2.pdf | Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence | Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable na... | ['Ganesh K. Venayagamoorthy', 'Dulip Madurasinghe'] | 2022-07-12 | null | null | null | null | ['energy-management'] | ['time-series'] | [-2.00929940e-01 -6.97606921e-01 2.01545849e-01 1.20002970e-01
2.64979661e-01 -1.22345746e+00 5.76144934e-01 4.76031780e-01
4.96997893e-01 1.20487106e+00 -4.83757108e-01 -4.71987784e-01
-7.80479312e-01 -9.02395427e-01 1.92312956e-01 -8.50002646e-01
-2.41875917e-01 5.27651787e-01 -2.30060980e-01 -4.73354429... | [5.787928104400635, 2.5691099166870117] |
b356c617-1e61-4f9b-9897-9fbc7ccfeca8 | modern-talking-in-key-point-analysis-key | null | null | https://aclanthology.org/2021.argmining-1.18 | https://aclanthology.org/2021.argmining-1.18.pdf | Modern Talking in Key Point Analysis: Key Point Matching using Pretrained Encoders | We contribute to the ArgMining 2021 shared task on Quantitative Summarization and Key Point Analysis with two approaches for argument key point matching. For key point matching the task is to decide if a short key point matches the content of an argument with the same topic and stance towards the topic. We approach thi... | ['Yamen Ajjour', 'Max Henze', 'Thi Kim Hanh Luu', 'Jan Heinrich Reimer'] | null | null | null | null | emnlp-argmining-2021-11 | ['key-point-matching'] | ['natural-language-processing'] | [ 2.00828552e-01 4.31355387e-01 -7.83870935e-01 -2.30928779e-01
-1.69538653e+00 -1.07655156e+00 1.18004644e+00 1.19347858e+00
-7.25809693e-01 6.52530849e-01 8.91334414e-01 -3.79902512e-01
-2.44328573e-01 -4.94617045e-01 -7.69325316e-01 -1.59435809e-01
2.75564730e-01 6.00898623e-01 3.43215108e-01 -3.51812184... | [10.466540336608887, 9.206056594848633] |
e72ed504-2763-4761-9776-6bcb3ed50d66 | large-language-models-as-annotators-enhancing | 2306.15766 | null | https://arxiv.org/abs/2306.15766v1 | https://arxiv.org/pdf/2306.15766v1.pdf | Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost | State-of-the-art supervised NLP models achieve high accuracy but are also susceptible to failures on inputs from low-data regimes, such as domains that are not represented in training data. As an approximation to collecting ground-truth labels for the specific domain, we study the use of large language models (LLMs) fo... | ['Amit Sharma', 'Parikshit Bansal'] | 2023-06-27 | null | null | null | null | ['active-learning', 'active-learning', 'semantic-textual-similarity', 'semantic-similarity'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.64971280e-01 7.30083287e-01 -1.00908530e+00 -6.80281997e-01
-1.27479672e+00 -8.00199628e-01 6.39208019e-01 5.01024246e-01
-5.88163674e-01 1.01333666e+00 9.16927308e-02 -1.72448754e-01
-3.89584392e-01 -6.66689098e-01 -1.06960809e+00 -2.78704911e-01
3.33937347e-01 1.33301222e+00 5.24229705e-01 1.98496029... | [10.476170539855957, 7.879371643066406] |
36edad20-afd4-49d2-9de7-486f3c29ccd8 | dinasti-dialogues-with-a-negotiating | null | null | https://aclanthology.org/L14-1466 | https://aclanthology.org/L14-1466.pdf | DINASTI: Dialogues with a Negotiating Appointment Setting Interface | This paper describes the DINASTI (DIalogues with a Negotiating Appointment SeTting Interface) corpus, which is composed of 1734 dialogues with the French spoken dialogue system NASTIA (Negotiating Appointment SeTting InterfAce). NASTIA is a reinforcement learning-based system. The DINASTI corpus was collected while the... | ['Romain Laroche', 'Layla El Asri', 'Olivier Pietquin'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['dialogue-management'] | ['natural-language-processing'] | [-1.33115515e-01 8.14872205e-01 1.68330908e-01 -8.03213298e-01
-9.55154061e-01 -9.04589951e-01 6.02514863e-01 3.56906861e-01
-6.31029963e-01 1.21686161e+00 4.46326107e-01 -4.28392500e-01
-2.44983226e-01 -4.31437731e-01 2.43327003e-02 -1.92341194e-01
5.45315556e-02 1.21147346e+00 -1.83970481e-02 -9.44013655... | [13.053573608398438, 7.921948432922363] |
08caf8aa-401c-4450-9f61-95e45419d185 | fusevis-interpreting-neural-networks-for | 2012.08932 | null | https://arxiv.org/abs/2012.08932v1 | https://arxiv.org/pdf/2012.08932v1.pdf | FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization | Image fusion helps in merging two or more images to construct a more informative single fused image. Recently, unsupervised learning based convolutional neural networks (CNN) have been utilized for different types of image fusion tasks such as medical image fusion, infrared-visible image fusion for autonomous driving a... | ['Stefan Gumhold', 'Nishant Kumar'] | 2020-12-06 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 4.46012765e-01 3.09462249e-01 4.03326541e-01 -3.18087846e-01
-3.51697147e-01 -2.15762779e-01 3.45373869e-01 4.03534621e-01
-2.99825847e-01 6.06082380e-01 -5.67986406e-02 -5.41759133e-01
-1.31430417e-01 -6.80700839e-01 -6.58806205e-01 -7.75432229e-01
1.32707611e-01 7.85934925e-02 2.34198704e-01 -4.11476851... | [14.704549789428711, -2.4476938247680664] |
a790e311-4552-46ae-90f5-4c2d35a71b35 | synthetic-data-generation-for-a-longitudinal | 2305.07685 | null | https://arxiv.org/abs/2305.07685v1 | https://arxiv.org/pdf/2305.07685v1.pdf | Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis results | Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns.... | ['Juliane Fluck', 'Holger Fröhlich', 'Ute Nöthlings', 'Fabian Prasser', 'Tim Adams', 'Ines Perrar', 'Julian Schneider', 'Lisa Kühnel'] | 2023-05-12 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 5.68574429e-01 5.62194288e-01 -8.32519680e-02 -3.45668793e-01
-9.30544138e-01 -4.30658281e-01 5.17673910e-01 1.00649786e+00
-4.95876253e-01 9.68096495e-01 4.98268187e-01 -3.08727652e-01
-4.38833654e-01 -1.14468944e+00 -8.68601203e-01 -4.42411929e-01
-2.38594916e-02 6.71343744e-01 -3.32029581e-01 -4.09916602... | [6.444545745849609, 6.6034932136535645] |
ab978b24-7246-4460-880c-676fd2f89c65 | target-confusion-in-end-to-end-speaker | 2204.01355 | null | https://arxiv.org/abs/2204.01355v1 | https://arxiv.org/pdf/2204.01355v1.pdf | Target Confusion in End-to-end Speaker Extraction: Analysis and Approaches | Recently, end-to-end speaker extraction has attracted increasing attention and shown promising results. However, its performance is often inferior to that of a blind source separation (BSS) counterpart with a similar network architecture, due to the auxiliary speaker encoder may sometimes generate ambiguous speaker emb... | ['Yuexian Zou', 'Haoran Zhang', 'Rongzhi Gu', 'Dongchao Yang', 'Zifeng Zhao'] | 2022-04-04 | null | null | null | null | ['speech-separation', 'speaker-separation'] | ['speech', 'speech'] | [ 3.65701169e-01 -2.95106899e-02 1.66879967e-01 -3.25354069e-01
-9.47997451e-01 -4.51974839e-01 4.18012977e-01 -1.92161754e-01
-2.04403758e-01 5.97671211e-01 4.23335701e-01 -2.82721609e-01
-1.52415320e-01 -5.36397584e-02 -3.39507043e-01 -1.04963195e+00
2.15348333e-01 -2.06372254e-02 8.44101682e-02 1.20276242... | [14.696296691894531, 5.939779758453369] |
950b0a90-f11f-4fd5-8c2a-56955b90ce9b | improving-deep-embedded-clustering-via | null | null | https://aclanthology.org/2022.coling-1.195 | https://aclanthology.org/2022.coling-1.195.pdf | Improving Deep Embedded Clustering via Learning Cluster-level Representations | Driven by recent advances in neural networks, various Deep Embedding Clustering (DEC) based short text clustering models are being developed. In these works, latent representation learning and text clustering are performed simultaneously. Although these methods are becoming increasingly popular, they use pure cluster-o... | ['Xian Yang', 'Yike Guo', 'Liang Bai', 'Shuai Niu', 'Yida Xu', 'Yunya Song', 'Zhihua Wang', 'Qing Yin'] | null | null | null | null | coling-2022-10 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-1.68562889e-01 6.03708159e-03 -1.84246987e-01 -4.26401556e-01
-5.85425198e-01 -6.76366538e-02 7.12924957e-01 4.19860363e-01
-1.32764250e-01 -8.21772069e-02 4.23450708e-01 1.11599334e-01
-3.78076360e-02 -6.23865962e-01 -3.24566245e-01 -8.30784976e-01
2.73971111e-01 4.66625422e-01 -5.92011586e-02 2.57955492... | [10.36952018737793, 6.690972328186035] |
2dcffe9e-71b9-4411-84aa-d55549dff751 | read-like-humans-autonomous-bidirectional-and | 2103.06495 | null | https://arxiv.org/abs/2103.06495v1 | https://arxiv.org/pdf/2103.06495v1.pdf | Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition | Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively model linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from: 1) implicitly language modeling; 2) unidirectional feature rep... | ['Yongdong Zhang', 'Zhendong Mao', 'Yuxin Wang', 'Hongtao Xie', 'Shancheng Fang'] | 2021-03-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-text-recognition'] | ['computer-vision'] | [ 6.28198907e-02 -4.74976867e-01 -3.70285004e-01 -5.37253380e-01
-2.37626866e-01 -1.10982344e-01 7.88682818e-01 -5.66359580e-01
-4.92668480e-01 3.65609616e-01 3.91799837e-01 -5.37305295e-01
2.59310097e-01 -7.42907643e-01 -8.15944612e-01 -5.03011823e-01
7.51190662e-01 1.35891706e-01 1.42961428e-01 -1.88498393... | [11.837982177734375, 2.14511775970459] |
e20af336-2dfd-4f7d-96b2-ccc27bd00098 | multilingual-name-entity-recognition-and | 2211.02415 | null | https://arxiv.org/abs/2211.02415v1 | https://arxiv.org/pdf/2211.02415v1.pdf | Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures | Named Entity Recognition and Intent Classification are among the most important subfields of the field of Natural Language Processing. Recent research has lead to the development of faster, more sophisticated and efficient models to tackle the problems posed by those two tasks. In this work we explore the effectiveness... | ['Konstantinos Ch. Chatzisavvas', 'George Sarigiannidis', 'Athena Vakali', 'Angelos Theofilatos', 'Antonia Paflioti', 'Sofia Rizou'] | 2022-11-04 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [-2.39341781e-01 -1.44923985e-01 -2.29194969e-01 -6.13918602e-01
-5.88488162e-01 -4.14684981e-01 9.71898854e-01 5.06970137e-02
-1.01353025e+00 8.31531942e-01 5.69757700e-01 -6.73553884e-01
-2.87146345e-02 -7.27556288e-01 -1.52799338e-01 -1.33747697e-01
-2.49231711e-01 5.72792232e-01 2.27035135e-01 -4.35496181... | [10.122786521911621, 9.375977516174316] |
adbbc7b9-b546-48bd-ab95-f2eda19c4aaa | global-and-local-consistent-wavelet-domain | 1809.07764 | null | http://arxiv.org/abs/1809.07764v2 | http://arxiv.org/pdf/1809.07764v2.pdf | Global and Local Consistent Wavelet-domain Age Synthesis | Age synthesis is a challenging task due to the complicated and non-linear
transformation in human aging process. Aging information is usually reflected
in local facial parts, such as wrinkles at the eye corners. However, these
local facial parts contribute less in previous GAN based methods for age
synthesis. To addres... | ['Pei-Pei Li', 'Yibo Hu', 'Zhenan Sun', 'Ran He'] | 2018-09-20 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [ 7.26509243e-02 3.57231237e-02 1.32994667e-01 -2.27144897e-01
-2.19854176e-01 3.22382748e-02 2.70604193e-01 -5.01614451e-01
3.25607695e-02 9.67998803e-01 2.41345733e-01 4.01576906e-01
2.01200485e-01 -9.94209349e-01 -5.79286993e-01 -1.07779443e+00
5.66996820e-02 -1.06646508e-01 -2.46937916e-01 -2.30882898... | [13.033914566040039, 0.29294002056121826] |
e6bc2f77-77c3-4779-b20c-ba69d94d2e17 | explainable-artificial-intelligence-for | 2009.02098 | null | https://arxiv.org/abs/2009.02098v2 | https://arxiv.org/pdf/2009.02098v2.pdf | Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring | The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine-granular data, process mining has recently emerged as a promising research discipline. As an important branch of process mining, predictive... | ['Nijat Mehdiyev', 'Peter Fettke'] | 2020-09-04 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 4.86044616e-01 6.44634247e-01 -2.12154817e-02 -1.51180074e-01
-2.55693253e-02 5.12719620e-03 9.31110322e-01 7.93859720e-01
4.52846894e-03 5.76972842e-01 3.43332916e-01 -4.67645615e-01
-1.06489635e+00 -7.80378044e-01 -3.04918647e-01 -7.45661438e-01
-2.49854326e-01 7.36417592e-01 -6.21548057e-01 1.31920755... | [8.645628929138184, 6.001407146453857] |
4c19f561-bf02-4657-86b7-2401c91334c6 | a-robust-and-efficient-video-representation | 1504.05524 | null | http://arxiv.org/abs/1504.05524v1 | http://arxiv.org/pdf/1504.05524v1.pdf | A robust and efficient video representation for action recognition | This paper introduces a state-of-the-art video representation and applies it
to efficient action recognition and detection. We first propose to improve the
popular dense trajectory features by explicit camera motion estimation. More
specifically, we extract feature point matches between frames using SURF
descriptors an... | ['Cordelia Schmid', 'Heng Wang', 'Jakob Verbeek', 'Dan Oneata'] | 2015-04-21 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.26183981e-01 -7.09889174e-01 -3.14095765e-01 -5.30112348e-02
-8.17554533e-01 -5.70466518e-01 6.60364687e-01 -1.88642275e-02
-4.98293638e-01 4.83688146e-01 6.65718138e-01 3.27410638e-01
-5.44671603e-02 -4.50819254e-01 -6.65969729e-01 -6.43294096e-01
-3.05320531e-01 1.98548228e-01 6.23451829e-01 -4.41880934... | [8.115589141845703, 0.3066141605377197] |
69a107ef-b706-447c-aba1-d10a8cd1e754 | arcnn-a-semantic-enhanced-relation-detection | null | null | https://openreview.net/forum?id=_TLu-3ucBI | https://openreview.net/pdf?id=_TLu-3ucBI | ARCNN: A Semantic Enhanced Relation Detection Model for Knowledge Base Question Answering | Relation detection plays an important role in knowledge base question answering (KBQA), and it is critical for the final performance of KBQA systems. The previous works mainly focused on enriching the information representations of questions and relations, and neglected the interaction information of questions and rela... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.36833775e-01 3.36173922e-01 -1.65572971e-01 -2.86200911e-01
-7.80703843e-01 -4.22617704e-01 3.60241383e-01 2.28315189e-01
-3.91066611e-01 7.19260871e-01 2.63630927e-01 -4.35203105e-01
-1.68504015e-01 -1.28302097e+00 -6.72829568e-01 -1.99705899e-01
4.48596776e-01 5.58280468e-01 9.69918728e-01 -8.28882933... | [10.580270767211914, 8.00596809387207] |
f17790e5-5b80-4121-ac53-ee3becfe50e8 | cose-co-text-conditioned-generative-1 | 2206.05706 | null | https://arxiv.org/abs/2206.05706v2 | https://arxiv.org/pdf/2206.05706v2.pdf | CoSe-Co: Text Conditioned Generative CommonSense Contextualizer | Pre-trained Language Models (PTLMs) have been shown to perform well on natural language tasks. Many prior works have leveraged structured commonsense present in the form of entities linked through labeled relations in Knowledge Graphs (KGs) to assist PTLMs. Retrieval approaches use KG as a separate static module which ... | ['Balaji Krishnamurthy', 'Jivat Neet Kaur', 'Sumit Bhatia', 'Milan Aggarwal', 'Rachit Bansal'] | 2022-06-12 | cose-co-text-conditioned-generative | https://aclanthology.org/2022.naacl-main.83 | https://aclanthology.org/2022.naacl-main.83.pdf | naacl-2022-7 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.76390731e-01 8.50263476e-01 -9.57444683e-02 -2.39312589e-01
-1.08371186e+00 -8.37775826e-01 9.49135423e-01 2.54313290e-01
-2.70136625e-01 1.18619418e+00 7.22282946e-01 -4.96355653e-01
-2.22278722e-02 -1.25322580e+00 -1.17237663e+00 -8.14241469e-02
4.95521337e-01 7.52776444e-01 2.23073006e-01 -7.67984927... | [10.561161994934082, 8.067534446716309] |
23fb64c9-d12a-496e-ab99-0ee01883a943 | towards-generalized-and-distributed-privacy | 2010.01792 | null | https://arxiv.org/abs/2010.01792v5 | https://arxiv.org/pdf/2010.01792v5.pdf | Can we Generalize and Distribute Private Representation Learning? | We study the problem of learning representations that are private yet informative, i.e., provide information about intended "ally" targets while hiding sensitive "adversary" attributes. We propose Exclusion-Inclusion Generative Adversarial Network (EIGAN), a generalized private representation learning (PRL) architectur... | ['Carlee Joe-Wong', 'Saurabh Bagchi', 'Christopher Brinton', 'Seyyedali Hosseinalipour', 'Taejin Kim', 'Sheikh Shams Azam'] | 2020-10-05 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 8.67679119e-02 5.25362551e-01 -2.63203055e-01 -2.74594128e-01
-1.20126951e+00 -1.03768563e+00 6.79614246e-01 -6.03513187e-03
-1.36719570e-01 9.18289959e-01 3.05309445e-01 -2.94920474e-01
-1.95779726e-01 -9.73194718e-01 -1.05150485e+00 -1.00697351e+00
-3.53887498e-01 5.55759370e-01 -2.11154997e-01 -2.07027346... | [5.952830791473389, 6.983017921447754] |
22f6c05a-c412-4b48-b245-f997e03c51fd | hesitate-in-portuguese | null | null | https://aclanthology.org/L14-1473 | https://aclanthology.org/L14-1473.pdf | HESITA(te) in Portuguese | Hesitations, so-called disfluencies, are a characteristic of spontaneous speech, playing a primary role in its structure, reflecting aspects of the language production and the management of inter-communication. In this paper we intend to present a database of hesitations in European Portuguese speech - HESITA - as a re... | ['o', 'Fern Perdig{\\~a}o', 'Carla Lopes', 'Jorge Proen{\\c{c}}a', 'C', 'Dirce Celorico', 'Sara eias', 'Arlindo Veiga'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['acoustic-modelling'] | ['speech'] | [-1.08755767e-01 2.25126669e-01 3.27743083e-01 -1.99944258e-01
-4.64687169e-01 -3.84410590e-01 6.58600807e-01 1.93628088e-01
-3.42648208e-01 6.21948779e-01 5.64535081e-01 -9.63682383e-02
-8.19784403e-02 -5.94426036e-01 -3.37824821e-01 -5.56993783e-01
2.45987430e-01 4.96160775e-01 3.60608339e-01 -6.05244815... | [14.73733901977539, 6.570913791656494] |
d9d0e1aa-504a-499a-9340-371a846d5e41 | fine-grained-visual-categorization-using-meta | 1807.10916 | null | http://arxiv.org/abs/1807.10916v1 | http://arxiv.org/pdf/1807.10916v1.pdf | Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data | Fine-grained visual categorization (FGVC) is challenging due in part to the
fact that it is often difficult to acquire an enough number of training
samples. To employ large models for FGVC without suffering from overfitting,
existing methods usually adopt a strategy of pre-training the models using a
rich set of auxili... | ['Kui Jia', 'Yabin Zhang', 'Hui Tang'] | 2018-07-28 | fine-grained-visual-categorization-using-meta-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper.pdf | eccv-2018-9 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 2.07875311e-01 -2.06320137e-01 5.89610264e-02 -5.14333427e-01
-5.00256896e-01 -3.24990511e-01 5.28517723e-01 1.52143657e-01
-5.03832221e-01 7.47401416e-01 3.17530520e-02 2.21611559e-03
-1.88643739e-01 -7.78867364e-01 -5.27103961e-01 -8.21536720e-01
4.14940298e-01 3.26702178e-01 2.66013205e-01 5.00080362... | [9.466415405273438, 2.945391893386841] |
bed71632-30fd-45a0-b89e-d6a3fd49373e | egotaskqa-understanding-human-tasks-in | 2210.03929 | null | https://arxiv.org/abs/2210.03929v1 | https://arxiv.org/pdf/2210.03929v1.pdf | EgoTaskQA: Understanding Human Tasks in Egocentric Videos | Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (i.e., state changes), and their causal dependencies. These challenges... | ['Siyuan Huang', 'Song-Chun Zhu', 'Ting Lei', 'Baoxiong Jia'] | 2022-10-08 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 5.78093007e-02 1.32711917e-01 -3.04197431e-01 -4.15781170e-01
-1.17093615e-01 -6.39632940e-01 1.16911423e+00 -1.10854596e-01
-2.48883590e-01 7.44462907e-01 1.03434753e+00 -1.12797171e-01
-3.78602505e-01 -4.15498674e-01 -6.76868558e-01 -4.77361619e-01
-4.72128361e-01 4.19424653e-01 2.34631032e-01 -4.31497455... | [8.4756441116333, 0.654166042804718] |
3012ff17-d447-4627-a4e8-081a802f66d6 | undercover-deepfakes-detecting-fake-segments | 2305.06564 | null | https://arxiv.org/abs/2305.06564v2 | https://arxiv.org/pdf/2305.06564v2.pdf | Undercover Deepfakes: Detecting Fake Segments in Videos | The recent renaissance in generative models, driven primarily by the advent of diffusion models and iterative improvement in GAN methods, has enabled many creative applications. However, each advancement is also accompanied by a rise in the potential for misuse. In the arena of deepfake generation this is a key societa... | ['Saman Halgamuge', 'Terence Sim', 'Deshani Geethika', 'Sanka Rasnayaka', 'Tamasha Malepathirana', 'Sachith Seneviratne', 'Rashindrie Perera', 'Sanjay Saha'] | 2023-05-11 | null | null | null | null | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 1.50313377e-01 4.70200069e-02 3.09895948e-02 1.43001154e-02
-4.76907432e-01 -8.08496118e-01 9.10216451e-01 -7.23581731e-01
7.88874775e-02 7.31478631e-01 2.29769871e-01 -1.92938030e-01
1.54638112e-01 -8.15171480e-01 -9.84403372e-01 -7.68724561e-01
1.80510655e-01 1.99017331e-01 1.99611232e-01 -2.77657807... | [12.415035247802734, 1.0273890495300293] |
2d7a2d64-cc73-46fd-b49d-f5cbec4bb8ec | visfis-visual-feature-importance-supervision | 2206.11212 | null | https://arxiv.org/abs/2206.11212v2 | https://arxiv.org/pdf/2206.11212v2.pdf | VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives | Many past works aim to improve visual reasoning in models by supervising feature importance (estimated by model explanation techniques) with human annotations such as highlights of important image regions. However, recent work has shown that performance gains from feature importance (FI) supervision for Visual Question... | ['Mohit Bansal', 'Peter Hase', 'Zhuofan Ying'] | 2022-06-22 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.72433609e-01 5.38817525e-01 -4.82616276e-01 -5.88583171e-01
-6.01292074e-01 -6.30163074e-01 1.00103343e+00 2.28113815e-01
-1.90630093e-01 6.01679623e-01 5.63775659e-01 -4.84162718e-01
-2.58718878e-01 -4.85930353e-01 -1.06431293e+00 -2.39974886e-01
3.24179709e-01 5.64805925e-01 3.13967377e-01 -5.75249083... | [10.839553833007812, 1.934008240699768] |
cf622fff-db6b-4b24-b0d2-f624335b89fa | global-adaptation-meets-local-generalization | 2303.16456 | null | https://arxiv.org/abs/2303.16456v1 | https://arxiv.org/pdf/2303.16456v1.pdf | Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation | When applying a pre-trained 2D-to-3D human pose lifting model to a target unseen dataset, large performance degradation is commonly encountered due to domain shift issues. We observe that the degradation is caused by two factors: 1) the large distribution gap over global positions of poses between the source and target... | ['Gaoang Wang', 'Jenq-Neng Hwang', 'Zhongyu Jiang', 'Wenhao Chai'] | 2023-03-29 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 2.62999266e-01 1.39199838e-01 1.35661960e-01 -2.29032084e-01
-1.22210634e+00 -6.78437293e-01 3.85207981e-01 -3.79787952e-01
-4.05120492e-01 7.10334599e-01 2.52621233e-01 2.53289104e-01
5.49650230e-02 -6.09073699e-01 -1.09224927e+00 -6.81894779e-01
-6.88460981e-03 8.54710639e-01 2.90615380e-01 -5.61785281... | [6.969570159912109, -1.0548936128616333] |
d2df0614-3fa3-47da-8fef-c80c740103c4 | mammograms-classification-a-review | 2203.03618 | null | https://arxiv.org/abs/2203.03618v1 | https://arxiv.org/pdf/2203.03618v1.pdf | Mammograms Classification: A Review | An advanced reliable low-cost form of screening method, Digital mammography has been used as an effective imaging method for breast cancer detection. With an increased focus on technologies to aid healthcare, Mammogram images have been utilized in developing computer-aided diagnosis systems that will potentially help i... | ['Marawan Elbatel'] | 2022-03-04 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 6.74266338e-01 4.37600315e-01 -4.00185466e-01 -5.50314963e-01
-7.42869198e-01 5.22203445e-02 4.18057412e-01 5.12836397e-01
-5.35459995e-01 3.99500579e-01 -7.63534531e-02 -6.63656116e-01
2.92783733e-02 -9.23895419e-01 -4.20465738e-01 -7.89669514e-01
-2.93338865e-01 5.47790289e-01 3.30782115e-01 2.14459896... | [15.21530818939209, -2.50240421295166] |
fea4006f-b2e7-4088-9556-84333d4a06d8 | top1-solution-of-qq-browser-2021-ai-algorithm | 2111.01677 | null | https://arxiv.org/abs/2111.01677v1 | https://arxiv.org/pdf/2111.01677v1.pdf | Top1 Solution of QQ Browser 2021 Ai Algorithm Competition Track 1 : Multimodal Video Similarity | In this paper, we describe the solution to the QQ Browser 2021 Ai Algorithm Competition (AIAC) Track 1. We use the multi-modal transformer model for the video embedding extraction. In the pretrain phase, we train the model with three tasks, (1) Video Tag Classification (VTC), (2) Mask Language Modeling (MLM) and (3) Ma... | ['Xuan Ouyang', 'Majing Lou', 'Zhuoran Ma'] | 2021-10-30 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [ 3.38320374e-01 1.17443070e-01 -1.99559808e-01 -1.63119495e-01
-1.16400576e+00 -5.94826639e-01 8.60388100e-01 -2.65495718e-01
-8.00526083e-01 2.85858482e-01 2.94168711e-01 -3.40071440e-01
3.07002872e-01 -1.04622178e-01 -8.81634235e-01 -3.88678342e-01
-3.70838344e-02 5.74051082e-01 5.01913786e-01 8.89893696... | [10.180978775024414, 0.9148262739181519] |
92c7f85b-6976-46f6-bad5-80b62b2e1e4d | impact-of-channel-variation-on-one-class | 2109.14900 | null | https://arxiv.org/abs/2109.14900v3 | https://arxiv.org/pdf/2109.14900v3.pdf | Impact of Channel Variation on One-Class Learning for Spoof Detection | Margin-based losses, especially one-class classification loss, have improved the generalization capabilities of countermeasure systems (CMs), but their reliability is not tested with spoofing attacks degraded with channel variation. Our experiments aim to tackle this in two ways: first, by investigating the impact of v... | ['Rohit Singh Rathore', 'Anmol Arora', 'Rohit Arora'] | 2021-09-30 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 6.36585951e-02 -3.07414293e-01 -2.64934838e-01 -1.86261442e-02
-6.70937300e-01 -5.67621052e-01 5.24814367e-01 3.12341928e-01
-6.64059281e-01 3.85510981e-01 -1.61695600e-01 -9.79521453e-01
-1.64533764e-01 -6.86228395e-01 -5.15884340e-01 -9.51354206e-01
-7.68368065e-01 -3.58143181e-01 5.85340381e-01 -3.07813585... | [13.992761611938477, 5.862380504608154] |
e94c4696-aa5f-432c-91af-60481630b386 | mudiff-unified-diffusion-for-complete | 2304.14621 | null | https://arxiv.org/abs/2304.14621v2 | https://arxiv.org/pdf/2304.14621v2.pdf | MUDiff: Unified Diffusion for Complete Molecule Generation | Molecule generation is a very important practical problem, with uses in drug discovery and material design, and AI methods promise to provide useful solutions. However, existing methods for molecule generation focus either on 2D graph structure or on 3D geometric structure, which is not sufficient to represent a comple... | ['Doina Precup', 'Stefano Ermon', 'Jie Fu', 'Rex Ying', 'Minkai Xu', 'Sitao Luan', 'Chenqing Hua'] | 2023-04-28 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.46792072e-01 -1.05074376e-01 -4.84355301e-01 -4.42904346e-02
-2.05581218e-01 -7.20611632e-01 8.34464490e-01 4.77455407e-01
-1.11597283e-02 8.42181087e-01 1.41236395e-01 -3.58624399e-01
-1.35463670e-01 -1.26976979e+00 -8.40862215e-01 -9.51202869e-01
-1.28640682e-01 6.55524552e-01 -1.48244640e-02 -3.50823164... | [5.103027820587158, 5.733699798583984] |
3b7b9d05-ca88-4804-9b90-8fa3a1228a2d | 1st-place-solution-to-eccv-2022-challenge-on-1 | 2209.00224 | null | https://arxiv.org/abs/2209.00224v1 | https://arxiv.org/pdf/2209.00224v1.pdf | 1st Place Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: End-to-End Recognition of Out of Vocabulary Words | Scene text recognition has attracted increasing interest in recent years due to its wide range of applications in multilingual translation, autonomous driving, etc. In this report, we describe our solution to the Out of Vocabulary Scene Text Understanding (OOV-ST) Challenge, which aims to extract out-of-vocabulary (OOV... | ['Song Bai', 'Wenqing Zhang', 'Yu Hao', 'Chuhui Xue', 'Zhangzi Zhu'] | 2022-09-01 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.50928390e-01 -8.20691884e-02 -3.96631390e-01 -5.83799422e-01
-9.31312561e-01 -6.38974905e-01 1.15634084e+00 -8.20285976e-02
-4.34433907e-01 2.98890442e-01 6.56925917e-01 -3.72185707e-01
5.66576838e-01 -1.41023248e-01 -7.86131501e-01 -1.36286467e-01
4.82983440e-01 6.10591829e-01 2.92676479e-01 -3.11168253... | [11.801094055175781, 2.1210763454437256] |
e4db41b2-9ddf-451c-bef2-183430c4e10c | knowledge-graph-transfer-network-for-few-shot | 1911.09579 | null | https://arxiv.org/abs/1911.09579v2 | https://arxiv.org/pdf/1911.09579v2.pdf | Knowledge Graph Transfer Network for Few-Shot Recognition | Few-shot learning aims to learn novel categories from very few samples given some base categories with sufficient training samples. The main challenge of this task is the novel categories are prone to dominated by color, texture, shape of the object or background context (namely specificity), which are distinct for the... | ['Riquan Chen', 'Liang Lin', 'Tianshui Chen', 'Hefeng Wu', 'Guanbin Li', 'Xiaolu Hui'] | 2019-11-21 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.37280226e-01 1.81305185e-01 -2.41077706e-01 -4.00929362e-01
-1.09002106e-02 -3.18181187e-01 4.41917926e-01 1.78895056e-01
-2.27165371e-01 6.71447575e-01 1.35463700e-02 1.73213154e-01
-1.38683885e-01 -1.07850778e+00 -7.17109680e-01 -8.37652981e-01
-1.89771116e-01 1.64717123e-01 6.41231954e-01 -3.03750426... | [9.866225242614746, 2.811307430267334] |
7732df62-386a-4151-af58-07396f0ef4d1 | lip-to-speech-synthesis-in-the-wild-with | 2302.08841 | null | https://arxiv.org/abs/2302.08841v1 | https://arxiv.org/pdf/2302.08841v1.pdf | Lip-to-Speech Synthesis in the Wild with Multi-task Learning | Recent studies have shown impressive performance in Lip-to-speech synthesis that aims to reconstruct speech from visual information alone. However, they have been suffering from synthesizing accurate speech in the wild, due to insufficient supervision for guiding the model to infer the correct content. Distinct from th... | ['Yong Man Ro', 'Joanna Hong', 'Minsu Kim'] | 2023-02-17 | null | null | null | null | ['lip-to-speech-synthesis', 'speech-synthesis'] | ['computer-vision', 'speech'] | [ 2.43885234e-01 2.95389891e-01 -1.85866222e-01 -1.53528482e-01
-1.20666385e+00 -1.57809407e-01 5.15951753e-01 -5.92030108e-01
1.51308263e-02 7.58565128e-01 6.45963550e-01 -1.95690393e-01
3.58897865e-01 -1.82177663e-01 -7.99198687e-01 -7.87612140e-01
8.91911626e-01 1.11144297e-01 -4.02255431e-02 -9.82540697... | [14.350533485412598, 5.051478385925293] |
77b8665d-c0d4-41e9-91d0-957d31d9a16d | taking-a-closer-look-at-visual-relation | 2303.13209 | null | https://arxiv.org/abs/2303.13209v1 | https://arxiv.org/pdf/2303.13209v1.pdf | Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label Learning | Current video-based scene graph generation (VidSGG) methods have been found to perform poorly on predicting predicates that are less represented due to the inherent biased distribution in the training data. In this paper, we take a closer look at the predicates and identify that most visual relations (e.g. sit_above) i... | ['Jun Xiao', 'Yi Yang', 'Lei Chen', 'Tao Jiang', 'Zhiqing Chen', 'Yawei Luo', 'Wenqing Wang'] | 2023-03-23 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 4.20044363e-01 2.17438072e-01 -5.09167790e-01 -3.48602802e-01
-4.88288015e-01 -6.73348427e-01 7.99877167e-01 9.14128721e-02
2.46362910e-01 6.00544095e-01 1.08406290e-01 -3.37200135e-01
-1.90447554e-01 -7.56653070e-01 -9.95422661e-01 -7.64560223e-01
3.32514085e-02 6.66157901e-01 6.22127295e-01 -1.53593663... | [10.28366470336914, 1.7208770513534546] |
c39a40f8-291a-4894-8bfc-3d21e93ec735 | mvkt-ecg-efficient-single-lead-ecg | 2301.12178 | null | https://arxiv.org/abs/2301.12178v1 | https://arxiv.org/pdf/2301.12178v1.pdf | MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring | The widespread emergence of smart devices for ECG has sparked demand for intelligent single-lead ECG-based diagnostic systems. However, it is challenging to develop a single-lead-based ECG interpretation model for multiple diseases diagnosis due to the lack of some key disease information. In this work, we propose inte... | ['Guijin Wang', 'Jintao Fei', 'Wenming Yang', 'Wei-Qiang Zhang', 'Hui Chen', 'Li Sun', 'Yuzhen Qin'] | 2023-01-28 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 4.61262763e-01 4.78620790e-02 -8.63810629e-02 -4.41973865e-01
-9.29256499e-01 -6.12041652e-01 -3.59299183e-01 -9.20737311e-02
8.40325058e-02 7.46788085e-01 -2.25827359e-02 -4.70322788e-01
-6.57261133e-01 -7.55646646e-01 -4.13934439e-01 -8.49600434e-01
2.72136927e-01 6.36166811e-01 -2.30245128e-01 1.74292356... | [14.230962753295898, 3.174302577972412] |
77412d42-db9b-4cf3-bd56-485b7f4a257a | deep-bingham-networks-dealing-with | 2012.11002 | null | https://arxiv.org/abs/2012.11002v1 | https://arxiv.org/pdf/2012.11002v1.pdf | Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation | In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can naturally handle pose-related uncertainties and ambiguities arising in almost all real life applications concerning 3D data. While existing works strive to find a single solution to the pose estimation problem, we make peace with the a... | ['Tolga Birdal', 'Slobodan Ilic', 'Leonidas Guibas', 'Nassir Navab', 'Mai Bui', 'Haowen Deng'] | 2020-12-20 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 1.02843247e-01 1.40564442e-01 -5.85828349e-02 -2.69922853e-01
-8.38517666e-01 -6.28032446e-01 6.80438221e-01 -2.89351076e-01
-4.61028308e-01 7.00791240e-01 -2.02165574e-01 -2.73388624e-01
-5.53567410e-01 -4.50000018e-01 -1.01289785e+00 -7.70124853e-01
3.13119479e-02 1.08949447e+00 1.03587210e-01 -1.71530366... | [7.561233043670654, -2.0786848068237305] |
6ffe0125-3345-408b-adc1-5b66c35576e7 | scope-safe-exploration-for-dynamic-computer | 2204.10451 | null | https://arxiv.org/abs/2204.10451v1 | https://arxiv.org/pdf/2204.10451v1.pdf | SCOPE: Safe Exploration for Dynamic Computer Systems Optimization | Modern computer systems need to execute under strict safety constraints (e.g., a power limit), but doing so often conflicts with their ability to deliver high performance (i.e. minimal latency). Prior work uses machine learning to automatically tune hardware resources such that the system execution meets safety constra... | ['Yi Ding', 'Michael Carbin', 'Henry Hoffmann', 'Ahsan Pervaiz', 'Hyunji Kim'] | 2022-04-22 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-6.68620691e-02 -3.47051710e-01 -7.78233707e-01 -3.37218523e-01
-5.81489146e-01 -6.41962349e-01 2.23532349e-01 3.36427301e-01
-2.25185752e-01 2.09622443e-01 -2.98723076e-02 -7.46729970e-01
1.87829018e-01 -6.79798007e-01 -6.56678200e-01 -4.84874338e-01
-3.23582202e-01 1.27960473e-01 3.76067191e-01 6.84976131... | [5.804508209228516, 3.1500155925750732] |
2f26f82c-6085-4f16-88fd-49af5e85ef64 | global-inference-to-chinese-temporal-relation | null | null | https://aclanthology.org/C16-1137 | https://aclanthology.org/C16-1137.pdf | Global Inference to Chinese Temporal Relation Extraction | Previous studies on temporal relation extraction focus on mining sentence-level information or enforcing coherence on different temporal relation types among various event mentions in the same sentence or neighboring sentences, largely ignoring those discourse-level temporal relations in nonadjacent sentences. In this ... | ['Qiaoming Zhu', 'Peifeng Li', 'Guodong Zhou', 'Hongling Wang'] | 2016-12-01 | global-inference-to-chinese-temporal-relation-1 | https://aclanthology.org/C16-1137 | https://aclanthology.org/C16-1137.pdf | coling-2016-12 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 8.46641138e-02 3.29854935e-01 -7.68030226e-01 -4.40949172e-01
-6.53968573e-01 -5.83735108e-01 9.47902143e-01 6.20379210e-01
-4.44216251e-01 1.15045202e+00 1.04616296e+00 -5.16148925e-01
-2.99393505e-01 -1.09813631e+00 -3.98764312e-01 -2.84027636e-01
-5.53207636e-01 5.38795209e-03 8.28891575e-01 -5.01485705... | [9.093050956726074, 9.230123519897461] |
10cd9a61-8f8d-4c76-ba3e-6c74b5a89988 | implicit-warping-for-animation-with-image | 2210.01794 | null | https://arxiv.org/abs/2210.01794v1 | https://arxiv.org/pdf/2210.01794v1.pdf | Implicit Warping for Animation with Image Sets | We present a new implicit warping framework for image animation using sets of source images through the transfer of the motion of a driving video. A single cross- modal attention layer is used to find correspondences between the source images and the driving image, choose the most appropriate features from different so... | ['Ming-Yu Liu', 'Ting-Chun Wang', 'Arun Mallya'] | 2022-10-04 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 1.07100524e-01 -3.21612746e-01 -2.79204398e-01 -1.58130631e-01
-5.79126596e-01 -6.34478629e-01 9.62771714e-01 -6.16973221e-01
-5.39770663e-01 3.02247941e-01 2.86095619e-01 5.38303778e-02
5.69227450e-02 -6.15270615e-01 -7.73501694e-01 -6.37128055e-01
-8.85479301e-02 2.98944801e-01 3.85108560e-01 -5.73418319... | [10.87016487121582, -0.8746203184127808] |
4a9aeb87-7c15-40d5-b206-0c69fcf8e32f | bayesian-neural-networks-via-mcmc-a-python | 2304.02595 | null | https://arxiv.org/abs/2304.02595v1 | https://arxiv.org/pdf/2304.02595v1.pdf | Bayesian neural networks via MCMC: a Python-based tutorial | Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling techniques are used to implement Bayesian inference. In the past three decades, MCMC methods have faced a nu... | ['Joshua Simmons', 'Royce Chen', 'Rohitash Chandra'] | 2023-04-02 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.41639426e-01 -3.34829465e-02 -3.62848490e-02 -6.23036623e-01
-7.90548027e-01 -3.07457775e-01 8.09284747e-01 -3.88229609e-01
-6.54157281e-01 9.75984931e-01 -1.73257664e-02 -4.18839633e-01
-2.01259717e-01 -7.81755388e-01 -6.78087533e-01 -1.09207141e+00
-3.76428366e-02 7.07973182e-01 1.48861647e-01 4.12947297... | [7.065767765045166, 3.845533609390259] |
98dc42bf-601c-4f74-817b-a1fbc8905b11 | probing-cross-modal-representations-in-multi | null | null | https://aclanthology.org/2021.repl4nlp-1.16 | https://aclanthology.org/2021.repl4nlp-1.16.pdf | Probing Cross-Modal Representations in Multi-Step Relational Reasoning | We investigate the representations learned by vision and language models in tasks that require relational reasoning. Focusing on the problem of assessing the relative size of objects in abstract visual contexts, we analyse both one-step and two-step reasoning. For the latter, we construct a new dataset of three-image s... | ['Sandro Pezzelle', 'Raquel Fernández', 'Desmond Elliott', 'Iuliia Parfenova'] | null | null | null | null | acl-repl4nlp-2021-8 | ['relational-reasoning'] | ['natural-language-processing'] | [ 4.74733263e-01 5.27679026e-01 3.36256534e-01 -4.89973217e-01
-4.18583870e-01 -7.27362692e-01 1.08242273e+00 4.46354061e-01
-3.82339537e-01 1.80534735e-01 3.40593725e-01 -2.77077317e-01
-3.69679511e-01 -8.51198912e-01 -8.79434168e-01 -5.66009820e-01
1.32117420e-01 9.39882517e-01 4.66171890e-01 -2.19129995... | [10.638761520385742, 2.120687484741211] |
ef2375e8-2f41-489b-af71-358ba3096188 | a-semi-supervised-learning-approach-for-b | 2211.14050 | null | https://arxiv.org/abs/2211.14050v3 | https://arxiv.org/pdf/2211.14050v3.pdf | A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images | Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment. Manual inspection of B-lines requires experts and is time-consuming, whilst modelling automation methods is currently problem... | ['Alin Achim', 'Marco Allinovi', 'Oktay Karakuş', 'Nantheera Anantrasirichai', 'Tianqi Yang'] | 2022-11-25 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 3.63948166e-01 2.89369702e-01 5.27572148e-02 -4.22492117e-01
-8.72394979e-01 -2.18175352e-01 3.76358658e-01 4.00482178e-01
-4.85070199e-01 7.99988449e-01 -1.89353511e-01 -3.06281507e-01
-3.17635626e-01 -6.14390910e-01 -5.02538860e-01 -8.36760461e-01
7.03349411e-02 4.32074487e-01 6.69691980e-01 5.05450487... | [14.844454765319824, -2.207166910171509] |
1c52a4fe-e7f0-47a4-b8a8-8729a635df6a | prediction-of-cellular-burden-with-host | 2004.00995 | null | https://arxiv.org/abs/2004.00995v2 | https://arxiv.org/pdf/2004.00995v2.pdf | Prediction of cellular burden with host-circuit models | Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognised bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene ... | ['Diego A. Oyarzún', 'Andrea Y. Weiße', 'Evangelos-Marios Nikolados'] | 2020-04-02 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 4.09916937e-01 -2.13650763e-01 1.73609808e-01 4.70185548e-01
1.36375189e-01 -1.03396666e+00 3.08015615e-01 3.48946452e-01
1.00208689e-02 1.00953853e+00 -1.25126868e-01 -5.19930840e-01
-1.80741817e-01 -7.94475973e-01 -7.46436417e-01 -7.71556616e-01
-6.19324781e-02 1.51004180e-01 -4.26942036e-02 -5.12757063... | [5.716117858886719, 4.332364082336426] |
aff490c0-4246-4669-8ff9-58d15905dbdb | toward-safe-and-accelerated-deep | 2209.13532 | null | https://arxiv.org/abs/2209.13532v1 | https://arxiv.org/pdf/2209.13532v1.pdf | Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks | Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio resource management (RRM) problems in next-generation networks. Given their capabilities to build an approximate and continuously updated m... | ['Hossam S. Hassanein', 'Hatem Abou-zeid', 'Ahmad M. Nagib'] | 2022-09-16 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.60783410e-01 3.10478685e-03 -6.79540873e-01 -3.79629806e-02
-5.57901621e-01 -3.09170663e-01 1.53292730e-01 -5.74848413e-01
-3.35599422e-01 1.50079703e+00 -4.06711429e-01 -8.50972652e-01
-9.93048012e-01 -8.60180020e-01 -4.01839405e-01 -5.62267184e-01
-7.17791855e-01 4.12457108e-01 -1.73254982e-02 -6.12093210... | [5.9025163650512695, 1.6576881408691406] |
4ee4a1eb-4506-42c8-b250-581d2a9a50e5 | overfit-neural-networks-as-a-compact-shape | 2009.09808 | null | https://arxiv.org/abs/2009.09808v3 | https://arxiv.org/pdf/2009.09808v3.pdf | On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes | A neural implicit outputs a number indicating whether the given query point in space is inside, outside, or on a surface. Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input. While affording latent-space interpolation, this comes at... | ['Derek Nowrouzezahrai', 'Alec Jacobson', 'Thomas Davies'] | 2020-09-17 | on-the-effectiveness-of-weight-encoded-neural | https://openreview.net/forum?id=_QnwcbR-GG | https://openreview.net/pdf?id=_QnwcbR-GG | null | ['3d-shape-representation'] | ['computer-vision'] | [ 5.47859490e-01 2.88384974e-01 -1.16545409e-01 -3.77009243e-01
-7.89505064e-01 -2.07672849e-01 4.00437444e-01 1.57404676e-01
-1.66016400e-01 3.60864222e-01 1.75916225e-01 -4.28054303e-01
1.75484475e-02 -1.22975624e+00 -1.08301222e+00 -5.38454473e-01
-1.92938060e-01 6.75144792e-01 9.38349366e-02 -6.10615052... | [8.570436477661133, -3.6546154022216797] |
a8e39d75-11e2-4c66-a7af-584dd5cc9031 | the-professional-go-annotation-dataset-page | 2211.01559 | null | https://arxiv.org/abs/2211.01559v1 | https://arxiv.org/pdf/2211.01559v1.pdf | The ProfessionAl Go annotation datasEt (PAGE) | The game of Go has been highly under-researched due to the lack of game records and analysis tools. In recent years, the increasing number of professional competitions and the advent of AlphaZero-based algorithms provide an excellent opportunity for analyzing human Go games on a large scale. In this paper, we present t... | ['Haoyue Li', 'Danni Zhang', 'Yifan Gao'] | 2022-11-03 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 1.12209721e-02 4.26672995e-02 -5.07733412e-02 9.95561779e-02
-8.04465473e-01 -6.73013031e-01 2.78362453e-01 2.39488527e-01
-6.69686496e-01 6.17570341e-01 2.67908573e-01 7.68556073e-02
-3.95398825e-01 -7.28505313e-01 -4.41062003e-01 -2.24714443e-01
-1.34653136e-01 5.49123704e-01 4.99643713e-01 -6.06924117... | [6.517930030822754, 0.3476470410823822] |
31a23f74-a38d-409a-85b7-c6ef50b5e513 | alphablock-embodied-finetuning-for-vision | 2305.18898 | null | https://arxiv.org/abs/2305.18898v1 | https://arxiv.org/pdf/2305.18898v1.pdf | AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation | We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks. These tasks often involve complex multi-step reasoning, presenting significant challenges due to the limited paired data connecting human instructions (e.g., makin... | ['Jianlong Fu', 'LiMin Wang', 'Ruihua Song', 'Bei Liu', 'Jiange Yang', 'Wenhui Tan', 'Chuhao Jin'] | 2023-05-30 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.78392753e-01 3.27653795e-01 -7.11939633e-02 -2.15690196e-01
-7.01341987e-01 -4.84779775e-01 6.40844345e-01 -2.14914620e-01
-3.77800107e-01 5.78061759e-01 3.00502360e-01 -4.79847878e-01
-2.40445286e-01 -5.16776621e-01 -1.09112227e+00 -2.82851398e-01
-6.94524869e-02 5.94367921e-01 1.32708520e-01 -5.54274499... | [4.512800693511963, 0.7413591742515564] |
f6d3fbf8-931f-4344-b7c8-17a3fcce2052 | on-faking-a-nash-equilibrium | 2306.08041 | null | https://arxiv.org/abs/2306.08041v1 | https://arxiv.org/pdf/2306.08041v1.pdf | On Faking a Nash Equilibrium | We characterize offline data poisoning attacks on Multi-Agent Reinforcement Learning (MARL), where an attacker may change a data set in an attempt to install a (potentially fictitious) unique Markov-perfect Nash equilibrium. We propose the unique Nash set, namely the set of games, specified by their Q functions, with a... | ['Qiaomin Xie', 'Xiaojin Zhu', 'Jeremy McMahan', 'Young Wu'] | 2023-06-13 | null | null | null | null | ['data-poisoning', 'multi-agent-reinforcement-learning'] | ['adversarial', 'methodology'] | [-3.29853833e-01 2.21303955e-01 -2.45894998e-01 5.47638297e-01
-9.39418912e-01 -1.38371420e+00 4.58202809e-01 1.30841762e-01
-9.95785832e-01 7.94964194e-01 1.21057719e-01 -4.62760359e-01
-4.56300884e-01 -1.10275233e+00 -1.04176736e+00 -1.00884736e+00
-7.07246006e-01 8.21027458e-01 1.23348951e-01 -3.79490465... | [4.038307189941406, 2.3955671787261963] |
2a1335f0-5e7d-4e58-ab5b-1d16170de05f | machine-learning-aided-precise-indoor | 2204.03990 | null | https://arxiv.org/abs/2204.03990v1 | https://arxiv.org/pdf/2204.03990v1.pdf | Machine Learning aided Precise Indoor Positioning | This study describes a UWB and Machine Learning (ML)-based indoor positioning system. We propose a simple mathematical strategy to create data to reduce the job of measurements for fingerprint-based indoor localization systems. A considerable number of measurements can be avoided this way. The paper compares and contra... | ['Zihuai Lin', 'Anqi Yin'] | 2022-04-08 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.45190164e-01 9.85705480e-02 -8.18271488e-02 -7.69207895e-01
-7.53972888e-01 -3.34910929e-01 4.42384183e-01 1.09244343e-02
-7.04138041e-01 1.40891182e+00 -3.50470126e-01 -7.07727969e-01
-6.95778728e-01 -9.55701530e-01 -3.48487645e-01 -6.71102226e-01
-2.42232800e-01 2.31155798e-01 8.24642777e-02 9.90620330... | [6.396177291870117, 0.9741840958595276] |
127582eb-c9df-438e-a91f-8dbd7563df5f | a-similarity-measure-for-material-appearance | 1905.01562 | null | https://arxiv.org/abs/1905.01562v1 | https://arxiv.org/pdf/1905.01562v1.pdf | A Similarity Measure for Material Appearance | We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced ex... | ['Belen Masia', 'Elena Garces', 'Manuel Lagunas', 'Ana Serrano', 'Sandra Malpica', 'Diego Gutierrez'] | 2019-05-04 | null | null | null | null | ['image-similarity-search'] | ['computer-vision'] | [ 7.84781650e-02 -3.11271846e-01 3.66994351e-01 -6.75102472e-01
-5.88351130e-01 -8.34606349e-01 8.01436365e-01 5.61013103e-01
-1.26044929e-01 8.76503587e-02 5.13242900e-01 1.16337530e-01
-1.45316288e-01 -4.87931013e-01 -6.63853645e-01 -2.28087261e-01
1.17118977e-01 3.63762826e-01 2.37033833e-02 -1.46082461... | [11.4469633102417, -0.8713866472244263] |
782ca496-8f30-44e9-a7a8-edc1d5b127b8 | privacy-preserving-record-linkage-for | 2301.04000 | null | https://arxiv.org/abs/2301.04000v1 | https://arxiv.org/pdf/2301.04000v1.pdf | Privacy-Preserving Record Linkage for Cardinality Counting | Several applications require counting the number of distinct items in the data, which is known as the cardinality counting problem. Example applications include health applications such as rare disease patients counting for adequate awareness and funding, and counting the number of cases of a new disease for outbreak d... | ['Sanath Kumar Ramesh', 'Mohamed Ali Kaafar', 'Dinusha Vatsalan', 'Nan Wu'] | 2023-01-09 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 4.39138226e-02 -1.32207781e-01 -2.31046334e-01 -5.51908791e-01
-3.91860247e-01 -6.05909169e-01 1.20449208e-01 1.10016549e+00
-6.15136147e-01 8.21128070e-01 -2.87217766e-01 5.28558269e-02
-4.48714316e-01 -1.17994738e+00 -5.59374452e-01 -6.22137070e-01
-1.69873431e-01 7.34699488e-01 1.53297618e-01 2.66190559... | [6.171946048736572, 6.637572765350342] |
3410b928-b08b-49f4-a01c-ad8efa4eebfb | real-time-sentiment-change-detection-of | 1804.00482 | null | http://arxiv.org/abs/1804.00482v1 | http://arxiv.org/pdf/1804.00482v1.pdf | Real Time Sentiment Change Detection of Twitter Data Streams | In the past few years, there has been a huge growth in Twitter sentiment
analysis having already provided a fair amount of research on sentiment
detection of public opinion among Twitter users. Given the fact that Twitter
messages are generated constantly with dizzying rates, a huge volume of
streaming data is created,... | ['Vassilis P. Plagianakos', 'Aristidis G. Vrahatis', 'Spiros V. Georgakopoulos', 'Sotiris K. Tasoulis'] | 2018-04-02 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 2.01361522e-01 -1.75221682e-01 -1.82503745e-01 -2.77152896e-01
-3.99631023e-01 -7.39444733e-01 9.30805624e-01 1.11204517e+00
-6.45242572e-01 5.07709503e-01 1.10467188e-01 -2.77973503e-01
1.14578560e-01 -1.11964977e+00 -1.96575120e-01 -6.41437292e-01
-1.66348264e-01 2.10735977e-01 4.58518028e-01 -6.97021961... | [10.843572616577148, 6.962477207183838] |
f9fc6caa-bd09-4e45-97f1-572930ecdc71 | simultaneously-optimizing-perturbations-and | 2212.12995 | null | https://arxiv.org/abs/2212.12995v1 | https://arxiv.org/pdf/2212.12995v1.pdf | Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks | Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial patches by either optimizing their perturbation values while fixing the pasting position or manipulating the position while fixing the patch'... | ['Bo Zhang', 'Jie Yu', 'Ying Guo', 'Xingxing Wei'] | 2022-12-26 | null | null | null | null | ['real-world-adversarial-attack', 'traffic-sign-recognition'] | ['adversarial', 'computer-vision'] | [ 1.80329546e-01 -1.48077354e-01 2.35483218e-02 -5.88384084e-02
-8.59678388e-01 -8.40426862e-01 1.92326158e-01 -6.52560294e-01
-3.92726630e-01 5.38309634e-01 -3.68143469e-01 -3.57312083e-01
-1.48676172e-01 -8.59351158e-01 -9.73698318e-01 -1.11153400e+00
-5.54755367e-02 -4.66693453e-02 2.34731421e-01 -4.36688513... | [5.48932409286499, 7.92644739151001] |
c2e0ccac-4ca6-44ed-a94a-9b4570ada8d4 | exploring-rich-and-efficient-spatial-temporal | 2008.02973 | null | https://arxiv.org/abs/2008.02973v1 | https://arxiv.org/pdf/2008.02973v1.pdf | Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection | The current main stream methods formulate their video saliency mainly from two independent venues, i.e., the spatial and temporal branches. As a complementary component, the main task for the temporal branch is to intermittently focus the spatial branch on those regions with salient movements. In this way, even though ... | ['Yuming Fang', 'Dingwen Zhang', 'Chenglizhao Chen', 'Hong Qin', 'Chong Peng', 'Guotao Wang'] | 2020-08-07 | null | null | null | null | ['video-salient-object-detection', 'video-saliency-detection'] | ['computer-vision', 'computer-vision'] | [-5.51891662e-02 -2.18695953e-01 -4.39892292e-01 -4.12344038e-02
-2.65701354e-01 -2.95705348e-01 1.90302223e-01 9.11423862e-02
-1.47417441e-01 4.91347522e-01 4.52711850e-01 1.04180530e-01
-6.87258765e-02 -8.78280103e-01 -5.64216554e-01 -6.99427724e-01
1.83704212e-01 -3.88633519e-01 1.26088023e+00 -3.96871120... | [9.618141174316406, -0.34975045919418335] |
ca96252d-b1f6-436a-8a65-8e802c6e286d | byte-pair-encoding-for-symbolic-music | 2301.11975 | null | https://arxiv.org/abs/2301.11975v1 | https://arxiv.org/pdf/2301.11975v1.pdf | Byte Pair Encoding for Symbolic Music | The symbolic music modality is nowadays mostly represented as discrete and used with sequential models such as Transformers, for deep learning tasks. Recent research put efforts on the tokenization, i.e. the conversion of data into sequences of integers intelligible to such models. This can be achieved by many ways as ... | ['Nicolas Gutowski', 'Amal El Fallah Seghrouchni', 'Fabien Chhel', 'Jean-Pierre Briot', 'Nathan Fradet'] | 2023-01-27 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.07052609e-01 -1.37380153e-01 -1.23039536e-01 -1.27473876e-01
-1.71526894e-01 -9.21097398e-01 7.13395178e-01 3.30395460e-01
-4.91192251e-01 5.44330597e-01 4.70187038e-01 -1.16200790e-01
-1.21502675e-01 -9.90543664e-01 -8.18478882e-01 -6.51840150e-01
-1.58703893e-01 4.13828820e-01 1.81680117e-02 -3.04851010... | [15.90690803527832, 5.40526008605957] |
70cf72fb-b4c1-4864-97a0-e7ce5e14a827 | functional-integrative-bayesian-analysis-of | 2212.14165 | null | https://arxiv.org/abs/2212.14165v1 | https://arxiv.org/pdf/2212.14165v1.pdf | Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data | Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and treat human diseases. While significant improvements have been made in multi-omic data integration methods to discover biolo... | ['Veerabhadran Baladandayuthapani', 'Nicholas Henderson', 'Rupam Bhattacharyya'] | 2022-12-29 | null | null | null | null | ['data-integration', 'variable-selection'] | ['knowledge-base', 'methodology'] | [ 4.54288274e-01 -4.31186080e-01 -4.20660406e-01 -3.64210457e-01
-9.73948240e-01 -3.99612010e-01 2.82333970e-01 7.10635602e-01
-2.73887664e-01 8.44549835e-01 4.15387571e-01 -2.15852097e-01
-7.00457335e-01 -6.99702561e-01 -4.14546549e-01 -1.10045755e+00
-2.06136391e-01 6.80754781e-01 -2.33634822e-02 3.58233005... | [6.563896179199219, 5.415600776672363] |
fc63aede-3ea5-4cb9-96b9-d7a94c32c8de | a-cloud-based-machine-learning-pipeline-for | 2306.07786 | null | https://arxiv.org/abs/2306.07786v2 | https://arxiv.org/pdf/2306.07786v2.pdf | A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews | The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. In this paper, we present a cloud-based system that can extract insigh... | ['Andras Hajdu', 'Marianna Szabo', 'Janos Toth', 'Attila Tiba', 'Istvan Lakatos', 'Balazs Harangi', 'Gergo Bogacsovics', 'Robert Lakatos'] | 2023-06-13 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [-2.69157588e-01 1.87446237e-01 -3.17076951e-01 -4.06140089e-01
-7.70460963e-01 -3.46408278e-01 6.33959949e-01 7.29109585e-01
-4.75723803e-01 2.82255054e-01 3.31448972e-01 -4.39598620e-01
-1.18385926e-01 -9.85673785e-01 -2.34040156e-01 -7.38700181e-02
1.73091650e-01 7.59497702e-01 1.68643799e-02 -1.49656668... | [10.464959144592285, 7.913849353790283] |
a8e809ea-021f-466b-88da-a354bc09a14f | cleansing-jewel-a-neural-spelling-correction | 2304.03427 | null | https://arxiv.org/abs/2304.03427v1 | https://arxiv.org/pdf/2304.03427v1.pdf | Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts | Scholars in the humanities rely heavily on ancient manuscripts to study history, religion, and socio-political structures in the past. Many efforts have been devoted to digitizing these precious manuscripts using OCR technology, but most manuscripts were blemished over the centuries so that an Optical Character Recogni... | ['Yung-Sung Chuang', 'Queenie Luo'] | 2023-04-07 | null | null | null | null | ['optical-character-recognition', 'spelling-correction'] | ['computer-vision', 'natural-language-processing'] | [ 3.02651614e-01 -9.29059312e-02 4.22019243e-01 -1.54269710e-01
-5.65735936e-01 -4.25911039e-01 6.86837196e-01 1.42718777e-02
-4.41485852e-01 8.13642204e-01 1.10118993e-01 -3.65820169e-01
1.73489600e-01 -7.98221052e-01 -8.72810304e-01 -2.94823140e-01
4.23971489e-02 4.25194412e-01 1.71441481e-01 -3.90977085... | [10.681577682495117, 10.203104972839355] |
207e1d94-3c5a-4a5b-a642-760c72ea61af | 3d-human-mesh-estimation-from-virtual-markers-1 | 2303.11726 | null | https://arxiv.org/abs/2303.11726v2 | https://arxiv.org/pdf/2303.11726v2.pdf | 3D Human Mesh Estimation from Virtual Markers | Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocr... | ['Yizhou Wang', 'Wentao Zhu', 'Chunyu Wang', 'Jiajun Su', 'Xiaoxuan Ma'] | 2023-03-21 | 3d-human-mesh-estimation-from-virtual-markers | http://openaccess.thecvf.com//content/CVPR2023/html/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.02754742e-01 2.04339251e-01 -2.14653179e-01 1.31223977e-01
-5.75591683e-01 -3.60535979e-01 4.70478535e-01 -1.87041193e-01
-2.31970206e-01 6.38716936e-01 1.01588421e-01 4.74682838e-01
1.66396365e-01 -7.76164770e-01 -1.02063835e+00 -4.32970762e-01
-5.15321828e-02 8.16309988e-01 4.19874102e-01 -1.39901951... | [7.073263168334961, -1.2452845573425293] |
fb0244d4-93fa-4703-ba26-7fa1605642a2 | fdsnet-finger-dorsal-image-spoof-detection | 1812.07444 | null | http://arxiv.org/abs/1812.07444v1 | http://arxiv.org/pdf/1812.07444v1.pdf | FDSNet: Finger dorsal image spoof detection network using light field camera | At present spoofing attacks via which biometric system is potentially
vulnerable against a fake biometric characteristic, introduces a great
challenge to recognition performance. Despite the availability of a broad range
of presentation attack detection (PAD) or liveness detection algorithms,
fingerprint sensors are vu... | ['Aditya Nigam', 'Avantika Singh', 'Gaurav Jaswal'] | 2018-12-18 | null | null | null | null | ['finger-dorsal-image-spoof-detection'] | ['computer-vision'] | [ 8.20384145e-01 -5.06571114e-01 -4.41060634e-03 -2.95571461e-02
-9.09011438e-02 -7.65868425e-01 6.97204769e-01 -3.70249152e-01
-3.33526820e-01 5.00198483e-01 -1.50629893e-01 -4.78377938e-01
-1.12555169e-01 -7.40117311e-01 -5.68426669e-01 -4.72187102e-01
-1.99712306e-01 -6.36791810e-03 1.05869129e-01 -3.38118076... | [12.999394416809082, 1.0862905979156494] |
44354cd7-4a03-4924-94f7-a3212c8ee92a | p2p-tuning-pre-trained-image-models-for-point | 2208.02812 | null | https://arxiv.org/abs/2208.02812v2 | https://arxiv.org/pdf/2208.02812v2.pdf | P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting | Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning. The pre-trained models with high representation ability and transferability achieve a great success and dominate many downstream tasks in natural language processing and 2D vision. However, it is non-trivial to promot... | ['Jiwen Lu', 'Jie zhou', 'Yongming Rao', 'Xumin Yu', 'Ziyi Wang'] | 2022-08-04 | null | null | null | null | ['3d-point-cloud-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.86528592e-02 1.25615671e-02 -7.70327635e-03 -5.85003734e-01
-8.35022628e-01 -6.25037789e-01 4.91923720e-01 -2.03998491e-01
-4.03471261e-01 7.36527741e-02 -5.22291541e-01 -5.00010133e-01
5.20697311e-02 -8.27067435e-01 -1.17338359e+00 -5.70960879e-01
3.17692161e-01 6.83393002e-01 2.10358009e-01 -2.23092109... | [8.135237693786621, -3.281085968017578] |
efc077b4-031f-4e6d-aef1-807662ee90f7 | contrastive-enhanced-slide-filter-mixer-for | 2305.04322 | null | https://arxiv.org/abs/2305.04322v1 | https://arxiv.org/pdf/2305.04322v1.pdf | Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation | Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data. Most existing methods model user preference in the time domain, omitting the fact that users' behaviors are also influenced by various frequency patterns that are difficult to separa... | ['Xiaofang Zhou', 'Victor S. Sheng', 'Yanchi Liu', 'Guanfeng Liu', 'Junhua Fang', 'Pengpeng Zhao', 'Huanhuan Yuan', 'Xinyu Du'] | 2023-05-07 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 7.78385848e-02 -6.74043238e-01 -7.05837011e-01 -5.01985490e-01
-2.14228675e-01 -5.41346848e-01 3.90953332e-01 9.39385593e-02
-4.31744963e-01 3.30570281e-01 5.44532657e-01 -2.21890360e-01
-4.80626911e-01 -7.27659523e-01 -4.88748223e-01 -7.43832231e-01
-1.50063023e-01 -6.45132139e-02 5.76327406e-02 -3.76406193... | [10.139878273010254, 5.565402507781982] |
5571b2ec-0770-43b8-a000-f9b635623fa7 | neural-time-warping-for-multiple-sequence | 2006.15753 | null | https://arxiv.org/abs/2006.15753v1 | https://arxiv.org/pdf/2006.15753v1.pdf | Neural Time Warping For Multiple Sequence Alignment | Multiple sequences alignment (MSA) is a traditional and challenging task for time-series analyses. The MSA problem is formulated as a discrete optimization problem and is typically solved by dynamic programming. However, the computational complexity increases exponentially with respect to the number of input sequences.... | ['Keisuke Kawano', 'Takuro Kutsuna', 'Satoshi Koide'] | 2020-06-29 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 6.35993719e-01 -4.41371799e-01 -2.58713037e-01 -3.02442729e-01
-8.39902043e-01 -6.86199844e-01 1.96592063e-01 1.08034974e-02
-4.38994765e-01 7.22747147e-01 -3.44648585e-02 -5.10270655e-01
-2.90598840e-01 -4.81565535e-01 -7.44199634e-01 -9.15748775e-01
-4.25701380e-01 4.88632619e-01 4.66542207e-02 -4.60626960... | [7.356597900390625, 3.378730297088623] |
ac75ca6b-557c-4330-81ad-1951bbd74836 | algorithms-incentives-and-democracy | 2307.02319 | null | https://arxiv.org/abs/2307.02319v1 | https://arxiv.org/pdf/2307.02319v1.pdf | Algorithms, Incentives, and Democracy | Classification algorithms are increasingly used in areas such as housing, credit, and law enforcement in order to make decisions affecting peoples' lives. These algorithms can change individual behavior deliberately (a fraud prediction algorithm deterring fraud) or inadvertently (content sorting algorithms spreading mi... | ['John W. Patty', 'Elizabeth Maggie Penn'] | 2023-07-05 | null | null | null | null | ['fairness', 'classification-1', 'fairness', 'misinformation'] | ['computer-vision', 'methodology', 'miscellaneous', 'miscellaneous'] | [ 3.81393492e-01 1.66652590e-01 -4.25673336e-01 -5.66785932e-01
-1.13723643e-01 -5.80906034e-01 4.09485012e-01 6.27367854e-01
-9.46523845e-01 9.65220451e-01 3.27288300e-01 -5.97158611e-01
-8.80356282e-02 -9.08228338e-01 -3.55339170e-01 -4.84045863e-01
3.45442921e-01 2.47729346e-01 -2.57823139e-01 1.25470579... | [8.86113166809082, 5.453906536102295] |
0388c80a-4907-4cd3-84c1-ed7bcd440e8d | qaid-question-answering-inspired-few-shot | 2303.01593 | null | https://arxiv.org/abs/2303.01593v2 | https://arxiv.org/pdf/2303.01593v2.pdf | QAID: Question Answering Inspired Few-shot Intent Detection | Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. To that end, we utilize a question-answering retrieval architecture and adopt a t... | ['Boaz Carmeli', 'Doron Cohen', 'Koren Lazar', 'Yosi Mass', 'Matan Vetzler', 'Asaf Yehudai'] | 2023-03-02 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [ 2.27470726e-01 -3.45723689e-01 -3.38413268e-01 -6.87526584e-01
-1.23851144e+00 -3.83213490e-01 7.94635236e-01 3.25825870e-01
-6.51923895e-01 3.13875496e-01 7.75986850e-01 -5.44562712e-02
7.49447122e-02 -6.11436844e-01 -2.16680780e-01 -3.97437923e-02
1.69221580e-01 4.59304124e-01 4.94704187e-01 -4.93011594... | [11.83538818359375, 7.7255096435546875] |
7f879c18-2812-4981-ba62-ea626c9eb08c | sign-language-recognition-via-skeleton-aware | 2110.06161 | null | https://arxiv.org/abs/2110.06161v1 | https://arxiv.org/pdf/2110.06161v1.pdf | Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble | Sign language is commonly used by deaf or mute people to communicate but requires extensive effort to master. It is usually performed with the fast yet delicate movement of hand gestures, body posture, and even facial expressions. Current Sign Language Recognition (SLR) methods usually extract features via deep neural ... | ['Yun Fu', 'Kunpeng Li', 'Yue Bai', 'Lichen Wang', 'Bin Sun', 'Songyao Jiang'] | 2021-10-12 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 1.09722130e-01 -4.72645789e-01 -2.85942346e-01 -2.40517244e-01
-9.53973114e-01 -1.63374484e-01 4.91726726e-01 -8.89071643e-01
-3.96745980e-01 3.79355401e-01 5.01891494e-01 8.91465619e-02
-1.35956123e-01 -3.87835473e-01 -3.25231105e-01 -9.34788525e-01
2.63990998e-01 1.07216887e-01 3.07039231e-01 -3.18937510... | [9.152165412902832, -6.451597213745117] |
01b19d6f-ddcc-4853-a60d-7f3ea620d1c0 | context-aware-retail-product-recommendation | 2109.08561 | null | https://arxiv.org/abs/2109.08561v1 | https://arxiv.org/pdf/2109.08561v1.pdf | Context-aware Retail Product Recommendation with Regularized Gradient Boosting | In the FARFETCH Fashion Recommendation challenge, the participants needed to predict the order in which various products would be shown to a user in a recommendation impression. The data was provided in two phases - a validation phase and a test phase. The validation phase had a labelled training set that contained a b... | ['Ayan Basak', 'Sourya Dipta Das'] | 2021-09-17 | null | null | null | null | ['product-recommendation', 'context-aware-product-recommendation'] | ['miscellaneous', 'miscellaneous'] | [ 8.23047087e-02 5.83095178e-02 1.65606178e-02 -8.81411850e-01
-2.82277524e-01 -1.00333667e+00 6.77593052e-01 2.16273993e-01
-4.92724299e-01 5.02221227e-01 2.84938693e-01 -1.41543418e-01
-2.43292123e-01 -7.85603762e-01 -5.63309491e-01 -2.64265478e-01
-2.65202671e-01 6.81701720e-01 1.92236692e-01 2.80216653... | [10.14369010925293, 5.757058143615723] |
e2ba8662-c75e-45fa-b48f-126a4c4f5ff6 | mrsn-multi-relation-support-network-for-video | 2304.11975 | null | https://arxiv.org/abs/2304.11975v1 | https://arxiv.org/pdf/2304.11975v1.pdf | MRSN: Multi-Relation Support Network for Video Action Detection | Action detection is a challenging video understanding task, requiring modeling spatio-temporal and interaction relations. Current methods usually model actor-actor and actor-context relations separately, ignoring their complementarity and mutual support. To solve this problem, we propose a novel network called Multi-Re... | ['Tong Lu', 'Minglei Yuan', 'Guo Chen', 'Yin-Dong Zheng'] | 2023-04-24 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 2.90938437e-01 2.86048412e-01 -7.51062930e-01 -6.53047383e-01
-1.27045929e-01 -2.44897947e-01 9.35698986e-01 8.10645074e-02
-1.21463582e-01 5.73412657e-01 7.83269346e-01 -1.17962502e-01
-4.37296748e-01 -6.79470420e-01 -6.96978986e-01 -1.26920408e-04
-4.43148434e-01 3.23144227e-01 6.74282610e-01 -2.47526675... | [8.604284286499023, 0.7236707806587219] |
5b52b578-6da2-4c7a-a17c-1c02dff2f6b9 | variational-laws-of-visual-attention-for | null | null | http://papers.nips.cc/paper/6972-variational-laws-of-visual-attention-for-dynamic-scenes | http://papers.nips.cc/paper/6972-variational-laws-of-visual-attention-for-dynamic-scenes.pdf | Variational Laws of Visual Attention for Dynamic Scenes | Computational models of visual attention are at the crossroad of disciplines like cognitive science, computational neuroscience, and computer vision. This paper proposes a model of attentional scanpath that is based on the principle that there are foundational laws that drive the emergence of visual attention. We devis... | ['Marco Gori', 'Dario Zanca'] | 2017-12-01 | null | null | null | neurips-2017-12 | ['scanpath-prediction'] | ['computer-vision'] | [ 9.58290994e-02 4.23515961e-03 -9.35798287e-02 -5.64670339e-02
1.41645685e-01 -2.15137646e-01 5.11124790e-01 -1.01766795e-01
-4.53065723e-01 2.89282590e-01 8.95512700e-02 -2.76155919e-01
-3.02739382e-01 -3.87499422e-01 -5.65039635e-01 -8.13141227e-01
2.10932434e-01 -4.88848761e-02 4.29756641e-01 -3.82602066... | [9.964715003967285, 1.5788871049880981] |
f99c26ab-5c48-4ffe-b601-fcb003fe00ce | 3d-compositional-zero-shot-learning-with | 2111.14673 | null | https://arxiv.org/abs/2111.14673v2 | https://arxiv.org/pdf/2111.14673v2.pdf | 3D Compositional Zero-shot Learning with DeCompositional Consensus | Parts represent a basic unit of geometric and semantic similarity across different objects. We argue that part knowledge should be composable beyond the observed object classes. Towards this, we present 3D Compositional Zero-shot Learning as a problem of part generalization from seen to unseen object classes for semant... | ['Federico Tombari', 'Luc van Gool', 'Yongqin Xian', 'Evin Pınar Örnek', 'Muhammad Ferjad Naeem'] | 2021-11-29 | null | null | null | null | ['zero-shot-segmentation', 'compositional-zero-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 5.64495683e-01 5.17948508e-01 -1.77271068e-01 -3.90708178e-01
-7.56141961e-01 -7.72831261e-01 4.39873546e-01 7.03173205e-02
1.85033128e-01 1.38944928e-02 -2.87347175e-02 3.33665609e-01
-1.79188728e-01 -7.60156035e-01 -9.69533443e-01 -6.80146813e-01
2.40365118e-01 9.81529951e-01 8.99943948e-01 -1.20276459... | [9.293172836303711, 0.6440396904945374] |
e01c6c17-68af-4b2c-ac75-d978a7944db7 | deep-learning-based-bio-medical-image | 2305.14841 | null | https://arxiv.org/abs/2305.14841v1 | https://arxiv.org/pdf/2305.14841v1.pdf | Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning | Image segmentation is a branch of computer vision that is widely used in real world applications including biomedical image processing. With recent advancement of deep learning, image segmentation has achieved at a very high level performance. Recently, UNet architecture is found as the core of novel deep learning segm... | ['Abouzar Ghavami', 'Nima Hassanpour'] | 2023-05-24 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 3.81631285e-01 8.57387707e-02 -3.40448059e-02 -5.63672245e-01
-7.00619161e-01 -2.52098680e-01 1.08419545e-01 -2.26041395e-02
-9.22357082e-01 4.40196276e-01 -3.85056257e-01 -4.70082045e-01
3.55501562e-01 -7.62135684e-01 -7.83767939e-01 -5.32737613e-01
1.09419726e-01 7.03149974e-01 7.26739705e-01 -6.17410243... | [14.576254844665527, -2.4951319694519043] |
eef6b20c-b889-4e25-aec5-d62719ee7689 | mahalo-unifying-offline-reinforcement | 2303.17156 | null | https://arxiv.org/abs/2303.17156v1 | https://arxiv.org/pdf/2303.17156v1.pdf | MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations | We study a new paradigm for sequential decision making, called offline Policy Learning from Observation (PLfO). Offline PLfO aims to learn policies using datasets with substandard qualities: 1) only a subset of trajectories is labeled with rewards, 2) labeled trajectories may not contain actions, 3) labeled trajectorie... | ['Ching-An Cheng', 'Byron Boots', 'Anqi Li'] | 2023-03-30 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-1.08985290e-01 4.71639574e-01 -7.39066541e-01 6.42026886e-02
-9.69893754e-01 -8.90029728e-01 5.13693929e-01 9.40959305e-02
-3.96086663e-01 1.19250560e+00 6.41715229e-02 -4.79790121e-01
-1.86829463e-01 -5.22158206e-01 -1.21300507e+00 -7.45718241e-01
-2.34831139e-01 7.39890099e-01 -5.58684058e-02 6.81658799... | [4.124475479125977, 2.282212257385254] |
abc3bbd3-650a-47e2-b8c1-98b7902235ed | a-multi-stream-convolutional-neural-network | 1812.10328 | null | http://arxiv.org/abs/1812.10328v1 | http://arxiv.org/pdf/1812.10328v1.pdf | A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition | In this work, we present a framework based on multi-stream convolutional
neural networks (CNNs) for group activity recognition. Streams of CNNs are
separately trained on different modalities and their predictions are fused at
the end. Each stream has two branches to predict the group activity based on
person and scene ... | ['Mina Ghadimi Atigh', 'Ahmad Nickabadi', 'Sina Mokhtarzadeh Azar'] | 2018-12-26 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 1.77885190e-01 -1.27579838e-01 -3.53891492e-01 -3.16510528e-01
-3.62766027e-01 -1.64316699e-01 9.14208531e-01 -4.23146747e-02
-7.67605007e-01 6.15853250e-01 4.21812177e-01 4.35515642e-01
1.31604522e-01 -8.25616181e-01 -7.79900670e-01 -3.95167619e-01
-1.19816333e-01 2.25173488e-01 4.31882411e-01 -5.89578822... | [8.043997764587402, 0.4782930910587311] |
732ad4c4-d35e-4509-88a7-7e60c1194fd5 | cmr3d-contextualized-multi-stage-refinement | 2209.06641 | null | https://arxiv.org/abs/2209.06641v1 | https://arxiv.org/pdf/2209.06641v1.pdf | CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection | Existing deep learning-based 3D object detectors typically rely on the appearance of individual objects and do not explicitly pay attention to the rich contextual information of the scene. In this work, we propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework, which takes a 3D scene as... | ['Hisham Cholakkal', 'Rao Muhammad Anwer', 'Fahad Shahbaz Khan', 'Jean Lahoud', 'Dhanalaxmi Gaddam'] | 2022-09-13 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.13099672e-01 -1.58082202e-01 -3.22928168e-02 -5.86801171e-01
-4.87170249e-01 -4.56206620e-01 6.98587298e-01 4.54269350e-01
-5.57286620e-01 9.45192855e-03 4.26629893e-02 -2.35301912e-01
2.92842895e-01 -6.48780286e-01 -8.23509455e-01 -3.29152584e-01
3.00399866e-02 4.62509662e-01 1.00249922e+00 7.79634416... | [9.173436164855957, 0.5126264095306396] |
17e482a5-f394-49c6-ba74-3802ce02a7f6 | multi-task-instruction-tuning-of-llama-for | 2305.13225 | null | https://arxiv.org/abs/2305.13225v1 | https://arxiv.org/pdf/2305.13225v1.pdf | Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance | ChatGPT and GPT-4 have attracted substantial interest from both academic and industrial circles, owing to their remarkable few-shot (or even zero-shot) ability to handle various tasks. Recent work shows that, after being fine-tuned with a few sets of instruction-driven data, the recently proposed LLM, LLaMa, exhibits a... | ['Wei Bi', 'Tao Fang', 'Xinting Huang', 'Deng Cai', 'Leyang Cui', 'Yue Zhang'] | 2023-05-22 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 7.20238760e-02 -3.24729621e-01 -5.63194871e-01 -4.94141638e-01
-8.21915448e-01 -3.47134441e-01 6.73934221e-01 -1.34412020e-01
-2.62770891e-01 5.90797067e-01 1.28860220e-01 -7.55012810e-01
-1.02044933e-01 -4.81033325e-01 -5.87893605e-01 -2.85753369e-01
8.88777375e-02 4.00880635e-01 5.15026569e-01 -5.90117931... | [10.654145240783691, 8.31674861907959] |
1372390f-f2be-48f7-8e3b-aae9810950ed | falrr-a-fast-low-rank-representation-solver | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Xiao_FaLRR_A_Fast_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Xiao_FaLRR_A_Fast_2015_CVPR_paper.pdf | FaLRR: A Fast Low Rank Representation Solver | Low rank representation (LRR) has shown promising performance for various computer vision applications such as face clustering. Existing algorithms for solving LRR usually depend on its two-variable formulation which contains the original data matrix. In this paper, we develop a fast LRR solver called FaLRR, by reformu... | ['DaCheng Tao', 'Shijie Xiao', 'Wen Li', 'Dong Xu'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['face-clustering'] | ['computer-vision'] | [ 1.60350457e-01 -2.42767945e-01 -2.41504878e-01 -2.25873917e-01
-8.44549537e-01 -4.44834590e-01 4.75562178e-02 -3.83542925e-01
-1.09154426e-01 5.27026355e-01 1.63542554e-01 -4.11725134e-01
-4.38874781e-01 -4.80963647e-01 -6.56616092e-01 -8.98456693e-01
-3.09231039e-02 2.54711151e-01 -3.34845573e-01 -7.55613670... | [7.454444408416748, 4.4902729988098145] |
83eacd85-cd54-4037-ba8b-46d387635bd7 | intel-tau-a-color-constancy-dataset | 1910.10404 | null | https://arxiv.org/abs/1910.10404v5 | https://arxiv.org/pdf/1910.10404v5.pdf | INTEL-TAU: A Color Constancy Dataset | In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5... | ['Alexandros Iosifidis', 'Moncef Gabbouj', 'Jenni Raitoharju', 'Jarno Nikkanen', 'Firas Laakom'] | 2019-10-23 | null | null | null | null | ['image-declipping', 'color-constancy', 'few-shot-camera-adaptive-color-constancy', 'few-shot-camera-adaptive-color-constancy', 'multi-target-regression'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 6.41994178e-02 -6.66837692e-01 2.09142324e-02 -4.92168605e-01
-1.76420778e-01 -6.21343732e-01 1.61917105e-01 -4.91752893e-01
-6.16264820e-01 7.50131786e-01 -1.54437497e-01 2.13619713e-02
2.51161039e-01 -5.79719961e-01 -5.84489703e-01 -7.57695556e-01
4.63209718e-01 -3.61894101e-01 -4.61385474e-02 3.24183442... | [10.47726058959961, -2.48588228225708] |
7dc7c68e-707c-4def-9d68-e99a2595666f | boosting-theory-of-mind-performance-in-large | 2304.11490 | null | https://arxiv.org/abs/2304.11490v3 | https://arxiv.org/pdf/2304.11490v3.pdf | Boosting Theory-of-Mind Performance in Large Language Models via Prompting | Large language models (LLMs) excel in many tasks in 2023, but they still face challenges in complex reasoning. Theory-of-mind (ToM) tasks, which require understanding agents' beliefs, goals, and mental states, are essential for common-sense reasoning involving humans, making it crucial to enhance LLM performance in thi... | ['Christopher J. Honey', 'Shima Rahimi Moghaddam'] | 2023-04-22 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 7.84439594e-02 4.27731782e-01 1.13949506e-02 -3.39563750e-02
-5.21491170e-01 -2.45674461e-01 7.77903259e-01 4.97297198e-01
-5.61605990e-01 4.45344001e-01 3.44439805e-01 -8.85674894e-01
-3.61030251e-01 -7.79423058e-01 -3.75676870e-01 6.40846882e-03
1.15430333e-01 7.38345563e-01 1.42017230e-01 -6.39661074... | [9.601364135742188, 7.310346603393555] |
8277a68e-45c9-4ac0-b8ea-e9d72fa4b93e | latent-ransac | 1802.07045 | null | http://arxiv.org/abs/1802.07045v2 | http://arxiv.org/pdf/1802.07045v2.pdf | Latent RANSAC | We present a method that can evaluate a RANSAC hypothesis in constant time,
i.e. independent of the size of the data. A key observation here is that
correct hypotheses are tightly clustered together in the latent parameter
domain. In a manner similar to the generalized Hough transform we seek to find
this cluster, only... | ['Simon Korman', 'Roee Litman'] | 2018-02-20 | latent-ransac-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Korman_Latent_RANSAC_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Korman_Latent_RANSAC_CVPR_2018_paper.pdf | cvpr-2018-6 | ['robust-face-alignment', '3d-plane-detection', 'camera-localization', 'homography-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.78165555e-01 -2.10446581e-01 8.76150355e-02 -1.26775816e-01
-1.17650914e+00 -1.05814242e+00 8.75670254e-01 3.57575476e-01
-4.81988966e-01 5.20606458e-01 -1.74952656e-01 -1.93298012e-01
-1.07320873e-02 -3.04570258e-01 -8.76976490e-01 -6.53501213e-01
-4.53801490e-02 8.20979476e-01 4.64267880e-01 2.08298992... | [7.870306015014648, -2.2389233112335205] |
ecfadcdd-d0c2-4504-9928-7c175923a1eb | probabilistic-querying-of-continuous-time | 2211.08499 | null | https://arxiv.org/abs/2211.08499v1 | https://arxiv.org/pdf/2211.08499v1.pdf | Probabilistic Querying of Continuous-Time Event Sequences | Continuous-time event sequences, i.e., sequences consisting of continuous time stamps and associated event types ("marks"), are an important type of sequential data with many applications, e.g., in clinical medicine or user behavior modeling. Since these data are typically modeled autoregressively (e.g., using neural H... | ['Padhraic Smyth', 'Stephan Mandt', 'Yuxin Chang', 'Alex Boyd'] | 2022-11-15 | null | null | null | null | ['type'] | ['speech'] | [ 1.87124074e-01 -3.66744012e-01 -2.97390431e-01 -3.51719737e-01
-7.46265352e-01 -4.33843225e-01 3.81905317e-01 8.28590810e-01
-6.26689970e-01 1.03742075e+00 2.74208337e-02 -9.34113622e-01
-1.96671575e-01 -1.04661810e+00 -9.56777275e-01 -5.93004107e-01
-5.49278259e-01 5.62742770e-01 3.07304934e-02 6.05820864... | [7.75428581237793, 5.037208080291748] |
ef139e2e-3c58-45cb-8ba7-89271102030e | vqnet-2-0-a-new-generation-machine-learning | 2301.03251 | null | https://arxiv.org/abs/2301.03251v1 | https://arxiv.org/pdf/2301.03251v1.pdf | VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum | With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generatio... | ['Guoping Guo', 'Zhaoyun Chen', 'Nenghai Yu', 'Weiming Zhang', 'Yang Yang', 'Ye Li', 'Wenyu Zhu', 'Wei Wang', 'Zhaohui Zhou', 'Hanchao Wang', 'Yiming Zhao', 'Lei LI', 'Yuan Fang', 'Menghan Dou', 'Zhilong Jia', 'Huanyu Bian'] | 2023-01-09 | null | null | null | null | ['unity'] | ['computer-vision'] | [-3.60235989e-01 -4.21662003e-01 4.79847454e-02 -1.92385301e-01
-3.17500472e-01 -3.10628980e-01 2.57546693e-01 -1.68416515e-01
-5.87363303e-01 6.91015959e-01 -6.03654802e-01 -4.86654431e-01
1.50014699e-01 -1.37905395e+00 -5.61439574e-01 -9.28627551e-01
9.73869264e-02 7.89739713e-02 2.44302019e-01 -7.46465325... | [5.57726526260376, 4.978218078613281] |
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