paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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109c18cc-6532-4c8e-9b1d-7e2b12ea0203 | nnformer-interleaved-transformer-for | 2109.03201 | null | https://arxiv.org/abs/2109.03201v6 | https://arxiv.org/pdf/2109.03201v6.pdf | nnFormer: Interleaved Transformer for Volumetric Segmentation | Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks to overcome their inherent shortcomings of spatial inductive bias... | ['Yizhou Yu', 'Liansheng Wang', 'Lequan Yu', 'Yinghao Zhang', 'Jiansen Guo', 'Hong-Yu Zhou'] | 2021-09-07 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 3.15313905e-01 5.60570061e-01 6.59946306e-03 -6.36377990e-01
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1aa562c8-6cd3-4306-adc2-b13b9ec39967 | non-stationary-dynamic-pricing-via-actor | 2208.09372 | null | https://arxiv.org/abs/2208.09372v3 | https://arxiv.org/pdf/2208.09372v3.pdf | Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing | This paper presents a novel non-stationary dynamic pricing algorithm design, where pricing agents face incomplete demand information and market environment shifts. The agents run price experiments to learn about each product's demand curve and the profit-maximizing price, while being aware of market environment shifts ... | ['Henghsiu Tsai', 'Chi-Hua Wang', 'Po-Yi Liu'] | 2022-08-19 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.67789923e-02 2.80568004e-01 -9.75531518e-01 -1.71792299e-01
-8.33639145e-01 -7.01457322e-01 2.35396683e-01 -1.86153099e-01
-6.73436940e-01 1.28315210e+00 -3.35784793e-01 -6.98271155e-01
-6.17251337e-01 -8.29948604e-01 -6.82854891e-01 -6.78746462e-01
-4.19166416e-01 1.13727796e+00 -1.19389892e-01 3.54136527... | [4.49793815612793, 3.2678756713867188] |
deee8450-4c17-482a-9488-ce81547a75a1 | pixelrnn-in-pixel-recurrent-neural-networks | 2304.05440 | null | https://arxiv.org/abs/2304.05440v1 | https://arxiv.org/pdf/2304.05440v1.pdf | PixelRNN: In-pixel Recurrent Neural Networks for End-to-end-optimized Perception with Neural Sensors | Conventional image sensors digitize high-resolution images at fast frame rates, producing a large amount of data that needs to be transmitted off the sensor for further processing. This is challenging for perception systems operating on edge devices, because communication is power inefficient and induces latency. Fuele... | ['Gordon Wetzstein', 'Piotr Dudek', 'Laurie Bose', 'Haley M. So'] | 2023-04-11 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.10614264e+00 -1.45481199e-01 -2.33428031e-01 -2.12850496e-01
-5.95161021e-01 -1.59309328e-01 2.45235965e-01 1.48704322e-02
-8.12239528e-01 9.83731449e-02 9.20988247e-02 -2.42700204e-01
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1.17138498e-01 -1.60622969e-01 5.86051106e-01 2.17397630... | [8.298930168151855, 2.4460160732269287] |
4d2e6912-aeb0-48aa-b4d0-da73a98fba37 | contcommrtd-a-distributed-content-based | 2301.12984 | null | https://arxiv.org/abs/2301.12984v1 | https://arxiv.org/pdf/2301.12984v1.pdf | ContCommRTD: A Distributed Content-based Misinformation-aware Community Detection System for Real-Time Disaster Reporting | Real-time social media data can provide useful information on evolving hazards. Alongside traditional methods of disaster detection, the integration of social media data can considerably enhance disaster management. In this paper, we investigate the problem of detecting geolocation-content communities on Twitter and pr... | ['Adrian Paschke', 'Ciprian-Octavian Truică', 'Elena-Simona Apostol'] | 2023-01-30 | null | null | null | null | ['community-detection'] | ['graphs'] | [-5.50235569e-01 1.30732954e-01 2.22898081e-01 -1.26861632e-01
-6.17707610e-01 -3.24336916e-01 8.71335924e-01 1.46751916e+00
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-9.31341112e-01 5.23152709e-01 7.28957474e-01 -8.11098933... | [8.454626083374023, 9.755800247192383] |
e92e5068-3d00-468a-b854-4cacb000eb7d | learning-rate-free-bayesian-inference-in | 2305.14943 | null | https://arxiv.org/abs/2305.14943v1 | https://arxiv.org/pdf/2305.14943v1.pdf | Learning Rate Free Bayesian Inference in Constrained Domains | We introduce a suite of new particle-based algorithms for sampling on constrained domains which are entirely learning rate free. Our approach leverages coin betting ideas from convex optimisation, and the viewpoint of constrained sampling as a mirrored optimisation problem on the space of probability measures. Based on... | ['Christopher Nemeth', 'Lester Mackey', 'Louis Sharrock'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.32895434e-01 -2.85414439e-02 -5.71149409e-01 -2.82290518e-01
-1.03633988e+00 -4.54144746e-01 7.61396229e-01 -4.94303793e-01
-6.75393283e-01 1.47723353e+00 1.40216798e-01 -3.17600578e-01
-3.28120917e-01 -7.01387346e-01 -7.46818721e-01 -6.60580516e-01
-7.95597658e-02 1.07404375e+00 -1.19774707e-01 1.71823457... | [6.793126106262207, 4.052186965942383] |
ac1afe61-d738-4d71-bfea-74f3d162d44b | combining-recurrent-convolutional-and | 2110.13985 | null | https://arxiv.org/abs/2110.13985v1 | https://arxiv.org/pdf/2110.13985v1.pdf | Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers | Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency. We introduce a simple sequence model inspired by control systems t... | ['Christopher Ré', 'Atri Rudra', 'Tri Dao', 'Khaled Saab', 'Karan Goel', 'Isys Johnson', 'Albert Gu'] | 2021-10-26 | combining-recurrent-convolutional-and-1 | http://proceedings.neurips.cc/paper/2021/hash/05546b0e38ab9175cd905eebcc6ebb76-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/05546b0e38ab9175cd905eebcc6ebb76-Paper.pdf | neurips-2021-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 2.60353208e-01 -3.89547199e-01 -2.61033595e-01 -2.56067425e-01
-3.28732044e-01 -3.80906552e-01 6.82340026e-01 -1.51662037e-01
-6.22079492e-01 6.41236544e-01 3.52501608e-02 -8.84536922e-01
-4.05537814e-01 -5.67086577e-01 -1.01356578e+00 -7.52119005e-01
-6.30942941e-01 2.21527386e-02 -3.36285025e-01 -4.76461738... | [7.432005405426025, 3.3980491161346436] |
cd901cc0-9670-4e41-a35f-4e4cb9333c61 | self-supervised-learning-across-domains | 2007.12368 | null | https://arxiv.org/abs/2007.12368v2 | https://arxiv.org/pdf/2007.12368v2.pdf | Self-Supervised Learning Across Domains | Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly effective, because supervised learning can never be exhaustive and thus learning auto... | ["Antonio D'Innocente", 'Yujun Liao', 'Tatiana Tommasi', 'Barbara Caputo', 'Silvia Bucci', 'Fabio Maria Carlucci'] | 2020-07-24 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 3.05795521e-01 7.17455521e-02 -4.43214595e-01 -6.47850275e-01
7.13793263e-02 -6.15949333e-01 5.22538245e-01 4.15168047e-01
-4.32214528e-01 8.21115613e-01 9.17502306e-03 2.96294242e-01
-4.99833494e-01 -8.18702161e-01 -5.51711798e-01 -8.58158469e-01
-1.37909368e-01 8.87134135e-01 5.84622264e-01 -3.95602167... | [9.773608207702637, 2.646498441696167] |
2cd911e0-720a-4cf7-b680-f9de38accebc | revisiting-prototypical-network-for-cross | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Revisiting_Prototypical_Network_for_Cross_Domain_Few-Shot_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Revisiting_Prototypical_Network_for_Cross_Domain_Few-Shot_Learning_CVPR_2023_paper.pdf | Revisiting Prototypical Network for Cross Domain Few-Shot Learning | Prototypical Network is a popular few-shot solver that aims at establishing a feature metric generalizable to novel few-shot classification (FSC) tasks using deep neural networks. However, its performance drops dramatically when generalizing to the FSC tasks in new domains. In this study, we revisit this problem an... | ['Yanning Zhang', 'Wei Wei', 'Lei Zhang', 'Peng Wang', 'Fei Zhou'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 3.25655013e-01 -1.17966942e-01 -2.78744489e-01 -5.68182588e-01
-5.30043423e-01 -4.16685820e-01 5.35836220e-01 5.80360368e-03
-4.81157720e-01 8.68221998e-01 -1.06241882e-01 1.76693708e-01
-5.57381868e-01 -1.09485459e+00 -7.33409822e-01 -7.32613802e-01
1.59823343e-01 2.30006114e-01 4.41585779e-01 -4.44147378... | [9.963224411010742, 2.9332547187805176] |
6b8de838-7371-4c32-99ae-4c458827dbf6 | deep-learning-provides-rapid-screen-for | 2301.05938 | null | https://arxiv.org/abs/2301.05938v1 | https://arxiv.org/pdf/2301.05938v1.pdf | Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes | Deep learning has been shown to be useful to detect breast cancer metastases by analyzing whole slide images of sentinel lymph nodes. However, it requires extensive scanning and analysis of all the lymph nodes slides for each case. Our deep learning study focuses on breast cancer screening with only a small set of imag... | ['Andy N. D. Nguyen', 'Hongxia Sun', 'Amer Wahed', 'Karan Saluja', 'Kevin Chiu', 'Jianmin Ding', 'Songlin Zhang', 'Xiaohong Iris Wang', 'Kareem Allam'] | 2023-01-14 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.78760284e-01 1.73413932e-01 -2.43029729e-01 -1.52859464e-01
-1.06259823e+00 -7.28261888e-01 -2.83054076e-03 6.24562621e-01
-5.62724352e-01 2.60803312e-01 4.27922700e-03 -1.01431084e+00
1.98496968e-01 -9.09388959e-01 -4.81481761e-01 -1.01755011e+00
-2.24084370e-02 5.91325462e-01 5.48956156e-01 -6.40183315... | [15.132198333740234, -3.106757164001465] |
7af6a8b3-b2b9-4564-b548-b5585fbee7df | smaclite-a-lightweight-environment-for-multi | 2305.05566 | null | https://arxiv.org/abs/2305.05566v1 | https://arxiv.org/pdf/2305.05566v1.pdf | SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning | There is a lack of standard benchmarks for Multi-Agent Reinforcement Learning (MARL) algorithms. The Starcraft Multi-Agent Challenge (SMAC) has been widely used in MARL research, but is built on top of a heavy, closed-source computer game, StarCraft II. Thus, SMAC is computationally expensive and requires knowledge and... | ['Stefano V. Albrecht', 'Filippos Christianos', 'Adam Michalski'] | 2023-05-09 | null | null | null | null | ['multi-agent-reinforcement-learning', 'starcraft-ii', 'smac-1', 'starcraft', 'smac'] | ['methodology', 'playing-games', 'playing-games', 'playing-games', 'playing-games'] | [-6.43100977e-01 -2.06422716e-01 -2.02984229e-01 3.59988242e-01
-6.51829243e-01 -7.54693329e-01 8.09691131e-01 4.06389415e-01
-6.73852265e-01 1.05546594e+00 -5.49594648e-02 -4.33632344e-01
-1.80222392e-01 -8.81501734e-01 -7.36499727e-01 -4.97853249e-01
-3.74861002e-01 9.22265947e-01 6.73978806e-01 -9.19179499... | [3.8120429515838623, 1.7160718441009521] |
a0789f57-c15f-424a-84f6-3a74bc271d60 | semantic-hierarchical-priors-for-intrinsic | 1902.03830 | null | https://arxiv.org/abs/1902.03830v2 | https://arxiv.org/pdf/1902.03830v2.pdf | Semantic Hierarchical Priors for Intrinsic Image Decomposition | Intrinsic Image Decomposition (IID) is a challenging and interesting computer vision problem with various applications in several fields. We present novel semantic priors and an integrated approach for single image IID that involves analyzing image at three hierarchical context levels. Local context priors capture scen... | ['P. J. Narayanan', 'Saurabh Saini'] | 2019-02-11 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 6.51780725e-01 1.68943942e-01 9.59933698e-02 -5.11976063e-01
-3.81895900e-01 -2.05915362e-01 5.38649619e-01 -1.16493486e-01
-1.02988444e-01 4.17018682e-01 5.02991259e-01 2.13532388e-01
-2.62731403e-01 -6.17662430e-01 -5.39295197e-01 -8.14150631e-01
1.24665964e-02 2.39531342e-02 5.28136969e-01 -7.19298497... | [9.588708877563477, -2.7665090560913086] |
7c01f305-6d2c-471a-9f51-4c2167312b60 | continuous-adaptation-of-multi-camera-person | 1607.00417 | null | http://arxiv.org/abs/1607.00417v1 | http://arxiv.org/pdf/1607.00417v1.pdf | Continuous Adaptation of Multi-Camera Person Identification Models through Sparse Non-redundant Representative Selection | The problem of image-base person identification/recognition is to provide an
identity to the image of an individual based on learned models that describe
his/her appearance. Most traditional person identification systems rely on
learning a static model on tediously labeled training data. Though labeling
manually is an ... | ['Amit K. Roy-Chowdhury', 'Abir Das', 'Rameswar Panda'] | 2016-07-01 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 3.82238835e-01 -4.07158256e-01 -1.93879813e-01 -7.52570868e-01
-6.59973502e-01 -6.09891236e-01 2.52714366e-01 2.49290988e-01
-6.18306339e-01 6.43081546e-01 -8.42135325e-02 1.92344338e-01
6.61744103e-02 -5.27884483e-01 -6.09268606e-01 -6.32556379e-01
3.13633859e-01 7.78520644e-01 -1.59799412e-01 2.03667700... | [14.739654541015625, 1.0266761779785156] |
36a820ba-7f03-40b5-a0ae-dfe3ec1f8f2a | learning-ergodic-averages-in-chaotic-systems | 2001.04027 | null | https://arxiv.org/abs/2001.04027v2 | https://arxiv.org/pdf/2001.04027v2.pdf | Learning ergodic averages in chaotic systems | We propose a physics-informed machine learning method to predict the time average of a chaotic attractor. The method is based on the hybrid echo state network (hESN). We assume that the system is ergodic, so the time average is equal to the ergodic average. Compared to conventional echo state networks (ESN) (purely dat... | ['Francisco Huhn', 'Luca Magri'] | 2020-01-09 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.09683469e-01 5.20121939e-02 4.68681455e-01 1.46105438e-01
-5.14431775e-01 -3.54650170e-01 9.34706509e-01 9.57970843e-02
-5.95397234e-01 7.68956900e-01 -1.27777785e-01 -4.23001975e-01
-2.04969302e-01 -6.89635277e-01 -2.91194081e-01 -1.24840367e+00
-4.21604127e-01 5.01797736e-01 2.85032660e-01 -3.85494113... | [6.57594633102417, 3.4478542804718018] |
031c4e65-c0f1-48c7-85bc-4819fc73710c | multilingual-event-extraction-from-historical | 2305.10928 | null | https://arxiv.org/abs/2305.10928v1 | https://arxiv.org/pdf/2305.10928v1.pdf | Multilingual Event Extraction from Historical Newspaper Adverts | NLP methods can aid historians in analyzing textual materials in greater volumes than manually feasible. Developing such methods poses substantial challenges though. First, acquiring large, annotated historical datasets is difficult, as only domain experts can reliably label them. Second, most available off-the-shelf N... | ['Isabelle Augenstein', 'Natalia da Silva Perez', 'Nadav Borenstein'] | 2023-05-18 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 0.03319117 0.01425107 -0.35121462 -0.00620659 -1.4682889 -1.2385551
1.1500462 0.40698755 -0.9672983 1.2559034 0.4773311 -0.44050166
0.13023174 -0.7242053 -0.75044745 -0.51761407 -0.07582855 0.9433783
-0.05734904 -0.4263887 0.24925889 0.46469927 -1.0918185 0.18703364
0.81891644 0.5343131 0.22... | [10.595136642456055, 10.059042930603027] |
b25bcacc-848a-41cc-8e2d-51a7b274b059 | coherent-reconstruction-of-multiple-humans-1 | 2006.08586 | null | https://arxiv.org/abs/2006.08586v1 | https://arxiv.org/pdf/2006.08586v1.pdf | Coherent Reconstruction of Multiple Humans from a Single Image | In this work, we address the problem of multi-person 3D pose estimation from a single image. A typical regression approach in the top-down setting of this problem would first detect all humans and then reconstruct each one of them independently. However, this type of prediction suffers from incoherent results, e.g., in... | ['Georgios Pavlakos', 'Wen Jiang', 'Xiaowei Zhou', 'Kostas Daniilidis', 'Nikos Kolotouros'] | 2020-06-15 | coherent-reconstruction-of-multiple-humans | http://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Coherent_Reconstruction_of_Multiple_Humans_From_a_Single_Image_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Coherent_Reconstruction_of_Multiple_Humans_From_a_Single_Image_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-depth-estimation', '3d-human-reconstruction'] | ['computer-vision', 'computer-vision'] | [-9.84511804e-03 2.86125660e-01 1.30775183e-01 -4.13827002e-01
-5.48774660e-01 -2.19212547e-01 3.72499496e-01 -2.41580922e-02
-4.78405029e-01 5.33886909e-01 1.48724839e-01 3.24265152e-01
1.40945911e-01 -6.65050685e-01 -8.51435959e-01 -7.00491369e-01
1.99192554e-01 1.15550709e+00 3.91236633e-01 -1.32252216... | [7.108514308929443, -1.0335441827774048] |
41403fe0-aff7-41a3-a115-9a551b8c6486 | feature-compatible-progressive-learning-for | 2304.10305 | null | https://arxiv.org/abs/2304.10305v2 | https://arxiv.org/pdf/2304.10305v2.pdf | Feature-compatible Progressive Learning for Video Copy Detection | Video Copy Detection (VCD) has been developed to identify instances of unauthorized or duplicated video content. This paper presents our second place solutions to the Meta AI Video Similarity Challenge (VSC22), CVPR 2023. In order to compete in this challenge, we propose Feature-Compatible Progressive Learning (FCPL) f... | ['Yi Yang', 'Yifan Sun', 'Wenhao Wang'] | 2023-04-20 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [-3.86039056e-02 -7.14849055e-01 -2.76578486e-01 -6.62725493e-02
-1.26584756e+00 -4.22907829e-01 6.56682491e-01 -8.43580142e-02
-1.30449906e-01 3.92460734e-01 3.74882609e-01 3.89523320e-02
-1.98021770e-01 -2.18568608e-01 -7.58696973e-01 -2.67966062e-01
-5.30217588e-01 1.69745490e-01 4.09033298e-01 -1.06144466... | [10.264575958251953, 0.736552357673645] |
89dac179-e3bf-4b0f-a653-cda50302593b | ranking-distance-calibration-for-cross-domain | 2112.00260 | null | https://arxiv.org/abs/2112.00260v2 | https://arxiv.org/pdf/2112.00260v2.pdf | Ranking Distance Calibration for Cross-Domain Few-Shot Learning | Recent progress in few-shot learning promotes a more realistic cross-domain setting, where the source and target datasets are from different domains. Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited. This encourages us to explore more inform... | ['Chengjie Wang', 'Yanwei Fu', 'Shaogang Gong', 'Pan Li'] | 2021-12-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Ranking_Distance_Calibration_for_Cross-Domain_Few-Shot_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Ranking_Distance_Calibration_for_Cross-Domain_Few-Shot_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 4.15448844e-01 2.06863657e-02 -3.55218351e-01 -4.29893821e-01
-1.16271698e+00 -4.92643058e-01 8.49640071e-01 5.07751144e-02
-4.83153105e-01 5.66904843e-01 2.94694334e-01 2.97045916e-01
-5.22220373e-01 -6.99828625e-01 -3.99990082e-01 -9.45706964e-01
3.18933055e-02 6.52899861e-01 4.34592128e-01 -2.11492449... | [10.01702880859375, 2.8964693546295166] |
76dc5399-b629-4c6e-91fa-553cf8bce504 | complementary-bi-directional-feature | 2207.02437 | null | https://arxiv.org/abs/2207.02437v1 | https://arxiv.org/pdf/2207.02437v1.pdf | Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation | Recently, horizontal representation-based panoramic semantic segmentation approaches outperform projection-based solutions, because the distortions can be effectively removed by compressing the spherical data in the vertical direction. However, these methods ignore the distortion distribution prior and are limited to u... | ['Yao Zhao', 'Zhijie Shen', 'Kang Liao', 'Lang Nie', 'Chunyu Lin', 'Zishuo Zheng'] | 2022-07-06 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 6.61911488e-01 1.30067766e-01 -1.03949390e-01 -2.83419788e-01
-4.54683751e-01 -2.51139045e-01 3.82578254e-01 -2.24755079e-01
-3.60336870e-01 2.67003775e-01 3.69469285e-01 8.10785592e-02
-5.27626835e-02 -1.03240407e+00 -6.77489996e-01 -1.02664173e+00
4.48321313e-01 3.66559066e-02 6.35034323e-01 -2.31641144... | [10.942795753479004, -1.6325080394744873] |
64e54f6e-6783-41b2-aafe-4aa57b34f459 | wganvo-monocular-visual-odometry-based-on | 2007.13704 | null | https://arxiv.org/abs/2007.13704v1 | https://arxiv.org/pdf/2007.13704v1.pdf | WGANVO: Monocular Visual Odometry based on Generative Adversarial Networks | In this work we present WGANVO, a Deep Learning based monocular Visual Odometry method. In particular, a neural network is trained to regress a pose estimate from an image pair. The training is performed using a semi-supervised approach. Unlike geometry based monocular methods, the proposed method can recover the absol... | ['Taihú Pire', 'Javier Cremona', 'Lucas Uzal'] | 2020-07-27 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-3.42919618e-01 2.36655086e-01 -1.10791415e-01 -5.03641367e-01
-5.38989343e-03 -3.51856470e-01 7.93819904e-01 -5.37141144e-01
-5.12092650e-01 7.78863430e-01 -1.42498568e-01 1.86571702e-02
2.93768764e-01 -5.01501620e-01 -7.47025847e-01 -4.02716488e-01
1.96354032e-01 9.94416833e-01 1.27433375e-01 -1.14316337... | [7.987481117248535, -2.2041714191436768] |
9d9a9df3-65a2-4e9f-9a22-cdcae8c3a835 | deep-face-quality-assessment | 1811.04346 | null | http://arxiv.org/abs/1811.04346v1 | http://arxiv.org/pdf/1811.04346v1.pdf | Deep Face Quality Assessment | Face image quality is an important factor in facial recognition systems as
its verification and recognition accuracy is highly dependent on the quality of
image presented. Rejecting low quality images can significantly increase the
accuracy of any facial recognition system. In this project, a simple approach
is present... | ['Vishal Agarwal'] | 2018-11-11 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 4.06022608e-01 2.09462214e-02 2.88231879e-01 -9.71339405e-01
-3.20019215e-01 -1.92406952e-01 3.18333000e-01 -2.15443015e-01
-4.14859653e-01 3.47583473e-01 -9.60790887e-02 -3.88494916e-02
-1.82559386e-01 -9.85322893e-01 -2.85221964e-01 -5.68318963e-01
1.58568308e-01 3.52981299e-01 -1.74649894e-01 -3.26455310... | [13.067625045776367, 0.8382455110549927] |
10cd535a-5285-47cf-96a5-2f52ffad7d15 | domain-adversarial-training-for-accented | 1806.02786 | null | http://arxiv.org/abs/1806.02786v1 | http://arxiv.org/pdf/1806.02786v1.pdf | Domain Adversarial Training for Accented Speech Recognition | In this paper, we propose a domain adversarial training (DAT) algorithm to
alleviate the accented speech recognition problem. In order to reduce the
mismatch between labeled source domain data ("standard" accent) and unlabeled
target domain data (with heavy accents), we augment the learning objective for
a Kaldi TDNN n... | ['Mei-Yuh Hwang', 'Ching-Feng Yeh', 'Mari Ostendorf', 'Sining Sun', 'Lei Xie'] | 2018-06-07 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [ 4.42621201e-01 5.19314051e-01 2.00137243e-01 -7.25937426e-01
-1.14580655e+00 -9.53662217e-01 5.57296991e-01 -4.81033921e-01
-6.30941033e-01 9.40488875e-01 5.75522482e-01 -5.76716185e-01
3.50835681e-01 -3.07609856e-01 -7.09792256e-01 -6.94171250e-01
3.83360147e-01 7.91998744e-01 -2.44817644e-01 -3.90417546... | [14.354082107543945, 6.769536972045898] |
7f223160-c9f9-4351-b092-995ba59f9e7a | the-syn-series-corpora-of-written-czech | null | null | https://aclanthology.org/L14-1267 | https://aclanthology.org/L14-1267.pdf | The SYN-series corpora of written Czech | The paper overviews the SYN series of synchronic corpora of written Czech compiled within the framework of the Czech National Corpus project. It describes their design and processing with a focus on the annotation, i.e. lemmatization and morphological tagging. The paper also introduces SYN2013PUB, a new 935-million new... | ["Hana Skoumalov{\\'a}", "Pavel Proch{\\'a}zka", 'Michal K{\\v{r}}en', "Milena Hn{\\'a}tkov{\\'a}"] | 2014-05-01 | null | null | null | lrec-2014-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-1.98894903e-01 3.66068602e-01 -3.18153113e-01 1.94214821e-01
-8.50123107e-01 -1.14724708e+00 1.14375365e+00 7.71627545e-01
-1.06643355e+00 7.39618897e-01 7.15220273e-01 -3.04328501e-01
-1.30447736e-02 -3.70638311e-01 -3.37054640e-01 -2.32191414e-01
4.11967844e-01 7.25087464e-01 1.88698739e-01 -3.68709028... | [10.33865737915039, 10.212453842163086] |
4b116dd2-3bb5-471b-8f83-6ee9757e70da | conditional-graphical-lasso-for-multi-label | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Li_Conditional_Graphical_Lasso_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Conditional_Graphical_Lasso_CVPR_2016_paper.pdf | Conditional Graphical Lasso for Multi-Label Image Classification | Multi-label image classification aims to predict multiple labels for a single image which contains diverse content. By utilizing label correlations, various techniques have been developed to improve classification performance. However, current existing methods either neglect image features when exploiting label correla... | ['DaCheng Tao', 'Qiang Li', 'Wei Bian', 'Maoying Qiao'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['multi-label-image-classification'] | ['computer-vision'] | [ 0.5712614 -0.44724947 -0.44571272 -0.7419278 -1.2119291 -0.38855907
0.5944479 0.18325472 -0.37646657 0.6854227 -0.2613044 0.16681673
-0.10913214 -0.27591825 -0.6295596 -1.0325112 0.6209314 0.44438055
-0.07112507 0.67382216 0.41581172 0.07776556 -1.6026481 0.42669755
0.67567414 1.1070812 0.... | [9.507450103759766, 4.1321563720703125] |
fd910602-ab01-4597-9b2e-390ec55b5c2e | memory-efficient-episodic-control | 1911.09560 | null | https://arxiv.org/abs/1911.09560v1 | https://arxiv.org/pdf/1911.09560v1.pdf | Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means | Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches. Using non-/semi-parametric models to estimate the value function, they learn rapidly, retrieving cached values from similar past states. In realistic scenarios... | ['Anil Anthony Bharath', 'Pierre Richemond', 'Marta Sarrico', 'Kai Arulkumaran', 'Andrea Agostinelli'] | 2019-11-21 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-3.69731963e-01 -3.08324665e-01 -1.12322234e-01 1.44341774e-03
-5.19011855e-01 -4.36593503e-01 4.92348373e-01 2.87583202e-01
-8.43848348e-01 1.27492440e+00 1.09881256e-02 1.55564649e-02
-7.59939849e-01 -9.44247007e-01 -6.98037088e-01 -8.48955154e-01
-4.28216726e-01 8.02381158e-01 4.91401255e-01 -2.71994084... | [4.078391075134277, 1.8987270593643188] |
0d052a32-2707-4aa2-819d-14e541983fd6 | can-label-noise-transition-matrix-help-to | null | null | https://openreview.net/forum?id=c0AD3ll9Wyv | https://openreview.net/pdf?id=c0AD3ll9Wyv | Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction? | Existing methods for learning with noisy labels can be generally divided into two categories: (1) sample selection and label correction based on the memorization effect of neural networks; (2) loss correction with the transition matrix. So far, the two categories of methods have been studied independently because they ... | ['Masashi Sugiyama', 'Gang Niu', 'Bo Han', 'Mingming Gong', 'Alan Blair', 'Tongliang Liu', 'Xuefeng Li', 'Yu Yao'] | 2021-09-29 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 5.03779471e-01 2.73098927e-02 -1.67286262e-01 -5.95664322e-01
-7.28997409e-01 -3.71924609e-01 5.90146005e-01 3.90373528e-01
-5.82142353e-01 8.62570345e-01 6.56744912e-02 4.77372073e-02
-1.27310187e-01 -7.80088961e-01 -7.49240577e-01 -1.18944895e+00
3.67210627e-01 1.82916984e-01 1.75203666e-01 2.14305803... | [9.31523609161377, 3.885422945022583] |
98109f88-ef06-4f5d-849c-4eb7ad342c1d | opi-at-semeval-2023-task-1-image-text | 2304.07127 | null | https://arxiv.org/abs/2304.07127v1 | https://arxiv.org/pdf/2304.07127v1.pdf | OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation | The goal of visual word sense disambiguation is to find the image that best matches the provided description of the word's meaning. It is a challenging problem, requiring approaches that combine language and image understanding. In this paper, we present our submission to SemEval 2023 visual word sense disambiguation s... | ['Sławomir Dadas'] | 2023-04-14 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-3.01459078e-02 -1.65152803e-01 -4.38971013e-01 -9.56162438e-02
-8.19441855e-01 -7.05637276e-01 8.90294790e-01 6.82329953e-01
-1.02287674e+00 6.89882576e-01 5.79440355e-01 6.58241585e-02
1.16008662e-01 -2.84251750e-01 -2.12966219e-01 -3.74057651e-01
2.70820767e-01 5.59827745e-01 2.24399075e-01 -4.21389908... | [10.79671859741211, 1.5213834047317505] |
e8fa6e5c-c508-4342-91cc-55afc446d8ce | generalizing-to-unseen-domains-with | 2207.04913 | null | https://arxiv.org/abs/2207.04913v1 | https://arxiv.org/pdf/2207.04913v1.pdf | Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge | Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains. In this research, we consider the scenario where different domain shifts occur among conditional distributions of different classes across domains. When labeled sam... | ['Yang Li', 'Shao-Lun Huang', 'Yao Xie', 'Liyan Xie', 'Jingge Wang'] | 2022-07-11 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 2.31027469e-01 -1.69599354e-01 -1.87684044e-01 -8.26990247e-01
-1.08849061e+00 -8.78516078e-01 5.15679717e-01 3.19557078e-02
-3.45686793e-01 1.09828520e+00 -9.66348127e-02 -4.66040000e-02
-5.90946376e-01 -8.11228812e-01 -5.82985640e-01 -8.41822028e-01
1.43302873e-01 6.24399781e-01 3.40117931e-01 2.30184887... | [10.339449882507324, 3.1254024505615234] |
5eb15c14-8310-4f47-a5ee-d0de42d0b796 | pnen-pyramid-non-local-enhanced-networks | 2008.09742 | null | https://arxiv.org/abs/2008.09742v1 | https://arxiv.org/pdf/2008.09742v1.pdf | PNEN: Pyramid Non-Local Enhanced Networks | Existing neural networks proposed for low-level image processing tasks are usually implemented by stacking convolution layers with limited kernel size. Every convolution layer merely involves in context information from a small local neighborhood. More contextual features can be explored as more convolution layers are ... | ['Kai-Kuang Ma', 'Feida Zhu', 'Chaowei Fang'] | 2020-08-22 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 4.78183866e-01 -1.91071421e-01 2.35510275e-01 -5.74921787e-01
-7.14470983e-01 1.00989491e-01 4.61073995e-01 4.85243201e-02
-8.70554745e-01 6.57765865e-01 1.53524280e-01 1.66440353e-01
-2.45414793e-01 -1.11190140e+00 -8.08803082e-01 -9.28384423e-01
-1.06490679e-01 -6.09611869e-01 6.69461846e-01 -3.29972863... | [10.942111015319824, -1.8173872232437134] |
5b4806eb-37b7-4513-98f2-13fd3d23ea74 | discovering-the-real-association-multimodal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zang_Discovering_the_Real_Association_Multimodal_Causal_Reasoning_in_Video_Question_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zang_Discovering_the_Real_Association_Multimodal_Causal_Reasoning_in_Video_Question_CVPR_2023_paper.pdf | Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering | Video Question Answering (VideoQA) is challenging as it requires capturing accurate correlations between modalities from redundant information. Recent methods focus on the explicit challenges of the task, e.g. multimodal feature extraction, video-text alignment and fusion. Their frameworks reason the answer relying... | ['Wei Liang', 'Mingtao Pei', 'Hanqing Wang', 'Chuanqi Zang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-question-answering'] | ['computer-vision'] | [ 2.17532605e-01 -1.37956157e-01 -2.19461784e-01 -4.97728527e-01
-5.60997486e-01 -4.89762366e-01 8.00019026e-01 1.74387380e-01
-2.16449827e-01 4.69882041e-01 9.01470542e-01 1.61601994e-02
-3.65689546e-01 -4.50957417e-01 -7.69952595e-01 -6.07131660e-01
3.16688687e-01 3.20847780e-02 4.98726100e-01 -2.13555157... | [10.374366760253906, 1.1180083751678467] |
146ea12e-68e8-400f-ba99-b3e053fa9535 | a-review-of-driver-gaze-estimation-and | 2307.01470 | null | https://arxiv.org/abs/2307.01470v1 | https://arxiv.org/pdf/2307.01470v1.pdf | A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding | Driver gaze plays an important role in different gaze-based applications such as driver attentiveness detection, visual distraction detection, gaze behavior understanding, and building driver assistance system. The main objective of this study is to perform a comprehensive summary of driver gaze fundamentals, methods t... | ['Pranamesh Chakraborty', 'Pavan Kumar Sharma'] | 2023-07-04 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-1.81455016e-01 -2.30512731e-02 -4.12275225e-01 -6.91398501e-01
-1.30707189e-01 -2.01185688e-01 -1.15545660e-01 -3.39215249e-01
-3.83680671e-01 4.30967838e-01 -5.53699434e-02 -7.23751783e-01
-2.78160185e-01 9.03426334e-02 -1.63531274e-01 -8.72411311e-01
3.04872364e-01 -4.44976896e-01 1.46737084e-01 -6.06085420... | [14.075566291809082, 0.11102213710546494] |
d909c40d-ac27-4b76-a2ff-df2975eae3c7 | exploring-sentence-community-for-document | null | null | https://aclanthology.org/2021.findings-emnlp.32 | https://aclanthology.org/2021.findings-emnlp.32.pdf | Exploring Sentence Community for Document-Level Event Extraction | Document-level event extraction is critical to various natural language processing tasks for providing structured information. Existing approaches by sequential modeling neglect the complex logic structures for long texts. In this paper, we leverage the entity interactions and sentence interactions within long document... | ['Weijia Jia', 'Yusheng Huang'] | null | null | null | null | findings-emnlp-2021-11 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 3.71403813e-01 6.91415310e-01 -5.74904025e-01 -5.53057075e-01
-4.95696604e-01 -6.21388495e-01 8.72835696e-01 1.07423151e+00
-3.52689564e-01 9.14168239e-01 9.32952225e-01 -4.27438676e-01
-1.43814251e-01 -1.21749532e+00 -7.82136798e-01 -7.87591189e-02
-4.74093229e-01 3.35257202e-01 4.67629761e-01 -9.18368548... | [9.06202507019043, 9.082265853881836] |
cec12f63-4886-403b-8c8d-53a5dc580344 | learning-based-dequantization-for-image | 1803.01532 | null | http://arxiv.org/abs/1803.01532v2 | http://arxiv.org/pdf/1803.01532v2.pdf | Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination | All existing image enhancement methods, such as HDR tone mapping, cannot
recover A/D quantization losses due to insufficient or excessive lighting,
(underflow and overflow problems). The loss of image details due to A/D
quantization is complete and it cannot be recovered by traditional image
processing methods, but the... | ['Xiao Shu', 'Chang Liu', 'Xiaolin Wu'] | 2018-03-05 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 8.67443085e-01 -2.13715270e-01 2.51443863e-01 -1.04067922e-01
-5.32124937e-01 -1.35612741e-01 3.15775156e-01 -2.14339226e-01
-1.20221950e-01 1.04622591e+00 2.73794532e-01 1.21955136e-02
1.48075446e-01 -8.54393899e-01 -5.85402012e-01 -8.92304897e-01
4.22600001e-01 -2.99203992e-01 2.37281825e-02 -5.15302122... | [10.920714378356934, -2.284127950668335] |
70f4060c-cad8-46cb-a413-4f78ba1b2fe7 | margin-optimal-classification-trees | 2210.10567 | null | https://arxiv.org/abs/2210.10567v4 | https://arxiv.org/pdf/2210.10567v4.pdf | Margin Optimal Classification Trees | In recent years there has been growing attention to interpretable machine learning models which can give explanatory insights on their behavior. Thanks to their interpretability, decision trees have been intensively studied for classification tasks, and due to the remarkable advances in mixed-integer programming (MIP),... | ['Laura Palagi', 'Marta Monaci', 'Giorgio Grani', "Federico D'Onofrio"] | 2022-10-19 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.95484799e-01 4.97319490e-01 -7.51927972e-01 -5.77628076e-01
-3.52942288e-01 -1.67472586e-01 3.15512925e-01 2.91872293e-01
-4.08236831e-02 1.01735961e+00 -1.45685300e-01 -6.12686574e-01
-9.06823397e-01 -4.40811515e-01 -4.00309533e-01 -7.94227540e-01
-3.17138225e-01 3.98849696e-01 -4.00839776e-01 1.55268162... | [8.463187217712402, 4.176764011383057] |
1d775824-42df-4550-9ec6-328c401d4c46 | aerial-scene-parsing-from-tile-level-scene | 2201.01953 | null | https://arxiv.org/abs/2201.01953v2 | https://arxiv.org/pdf/2201.01953v2.pdf | Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling | Given an aerial image, aerial scene parsing (ASP) targets to interpret the semantic structure of the image content, e.g., by assigning a semantic label to every pixel of the image. With the popularization of data-driven methods, the past decades have witnessed promising progress on ASP by approaching the problem with t... | ['Deren Li', 'Gong Cheng', 'Liangpei Zhang', 'Gui-Song Xia', 'Yang Long'] | 2022-01-06 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 9.12599981e-01 4.76647764e-02 -1.94767505e-01 -4.90351856e-01
-7.98015237e-01 -7.74476826e-01 2.80486673e-01 4.15500313e-01
-2.31675625e-01 5.00281930e-01 -2.05025211e-01 -3.77242774e-01
-1.63196936e-01 -1.26686943e+00 -1.10353684e+00 -6.51401877e-01
4.86830249e-02 3.69480222e-01 3.87413830e-01 -2.68184334... | [9.642585754394531, 0.3259478211402893] |
bb13bdd5-0979-4167-a4b5-0ddd0aaa1f11 | zero-shot-long-form-voice-cloning-with | 2201.10375 | null | https://arxiv.org/abs/2201.10375v2 | https://arxiv.org/pdf/2201.10375v2.pdf | Zero-Shot Long-Form Voice Cloning with Dynamic Convolution Attention | With recent advancements in voice cloning, the performance of speech synthesis for a target speaker has been rendered similar to the human level. However, autoregressive voice cloning systems still suffer from text alignment failures, resulting in an inability to synthesize long sentences. In this work, we propose a va... | ['Ivan Ozhiganov', 'Artem Gorodetskii'] | 2022-01-25 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 2.38956839e-01 3.79954934e-01 2.72726119e-01 -4.06203032e-01
-9.34854209e-01 -3.77291203e-01 6.26473188e-01 -4.87585723e-01
-5.00655621e-02 5.73261201e-01 5.05225003e-01 -3.04368258e-01
4.04234976e-01 -3.02409530e-01 -6.25135303e-01 -6.55445576e-01
4.91705358e-01 4.11131114e-01 7.62111023e-02 -2.19131127... | [14.903270721435547, 6.5775628089904785] |
9a64cd79-ecd3-4f84-b96e-a46baf77d83a | scalable-variable-selection-for-two-view | 2307.01558 | null | https://arxiv.org/abs/2307.01558v1 | https://arxiv.org/pdf/2307.01558v1.pdf | Scalable variable selection for two-view learning tasks with projection operators | In this paper we propose a novel variable selection method for two-view settings, or for vector-valued supervised learning problems. Our framework is able to handle extremely large scale selection tasks, where number of data samples could be even millions. In a nutshell, our method performs variable selection by iterat... | ['Juho Rousu', 'Tat Hong Duong Le', 'Riikka Huusari', 'Sandor Szedmak'] | 2023-07-04 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 2.58893460e-01 -9.85521898e-02 -2.87845671e-01 -4.44648951e-01
-4.96644616e-01 -5.78020036e-01 4.95420635e-01 2.02757880e-01
-3.17349672e-01 1.04915118e+00 -8.97892192e-02 1.84652552e-01
-4.97227132e-01 -1.00916147e+00 -7.52675310e-02 -9.22623813e-01
-4.02935654e-01 7.11952090e-01 -2.32811317e-01 -2.17137024... | [7.914144992828369, 4.37347412109375] |
aebbed39-723e-462d-a2e8-88c51b7dfcd0 | implementing-a-portable-clinical-nlp-system | 1811.06179 | null | http://arxiv.org/abs/1811.06179v1 | http://arxiv.org/pdf/1811.06179v1.pdf | Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective | This paper presents a Lisp architecture for a portable NLP system, termed
LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard,
customized and in-house developed NLP tools. Our system facilitates portability
across different institutions and data systems by incorporating an enriched
Common Data Mo... | ['Yuan Luo', 'Peter Szolovits'] | 2018-11-15 | null | null | null | null | ['computational-phenotyping'] | ['medical'] | [ 1.85610741e-01 5.02415955e-01 -4.40649152e-01 -6.96966767e-01
-1.07156289e+00 -6.48658276e-01 3.55347358e-02 8.82071972e-01
-2.42575228e-01 1.17097127e+00 4.29915696e-01 -5.66881120e-01
-7.16870964e-01 -6.29288733e-01 -1.66791864e-02 -2.37458527e-01
1.55711830e-01 1.14824378e+00 4.16261584e-01 2.76179105... | [8.530489921569824, 8.672755241394043] |
3d63e5dd-9ed8-492d-aad0-423e4a4f00fa | il-mcam-an-interactive-learning-and-multi | 2206.03368 | null | https://arxiv.org/abs/2206.03368v1 | https://arxiv.org/pdf/2206.03368v1.pdf | IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach | In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. However, existing approaches focus more on end-to-end automatic classification using computers rathe... | ['Marcin Grzegorzek', 'Xinyu Huang', 'Hongzan Sun', 'Changhao Sun', 'Wanli Liu', 'Yixin Li', 'Weiming Hu', 'Md Mamunur Rahaman', 'Xiaoyan Li', 'Chen Li', 'HaoYuan Chen'] | 2022-06-07 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 8.85097757e-02 1.16922997e-01 -2.53456272e-02 -2.76336998e-01
-6.91573560e-01 -1.41938493e-01 3.52922857e-01 3.66563201e-01
-8.65931630e-01 3.70230079e-01 -9.93117839e-02 -6.02132559e-01
-3.25443409e-02 -5.22428751e-01 -5.44213951e-01 -8.82168770e-01
-3.60876471e-02 1.20242164e-01 2.32998028e-01 8.44790190... | [14.962089538574219, -2.806582450866699] |
dfe1a34e-8c0a-4939-bc14-daa1e8c0111d | an-interpretable-classifier-for-high | 2002.07613 | null | https://arxiv.org/abs/2002.07613v1 | https://arxiv.org/pdf/2002.07613v1.pdf | An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization | Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analysis. In this work, we extend the globally-aware multiple instance clas... | ['Nan Wu', 'Laura Heacock', 'Kyunghyun Cho', 'Krzysztof J. Geras', 'Kangning Liu', 'Jungkyu Park', 'Sudarshini Tyagi', 'Linda Moy', 'S. Gene Kim', 'Yiqiu Shen', 'Jason Phang'] | 2020-02-13 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 6.84409201e-01 4.98741537e-01 -5.78655005e-01 -4.36285734e-01
-1.19033182e+00 -6.12373352e-02 1.31974250e-01 5.62372029e-01
-4.49866563e-01 4.54160273e-01 1.80207044e-01 -6.71545625e-01
3.96931581e-02 -9.39781904e-01 -9.84628856e-01 -4.75923747e-01
-5.50194643e-02 2.15581074e-01 3.91737193e-01 1.19335949... | [15.072592735290527, -2.5372424125671387] |
78637963-195e-46d9-a832-399452ad271e | individualized-and-global-feature | 2211.04409 | null | https://arxiv.org/abs/2211.04409v1 | https://arxiv.org/pdf/2211.04409v1.pdf | Individualized and Global Feature Attributions for Gradient Boosted Trees in the Presence of $\ell_2$ Regularization | While $\ell_2$ regularization is widely used in training gradient boosted trees, popular individualized feature attribution methods for trees such as Saabas and TreeSHAP overlook the training procedure. We propose Prediction Decomposition Attribution (PreDecomp), a novel individualized feature attribution for gradient ... | ['Qingyao Sun'] | 2022-11-08 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 3.35483879e-01 3.57209533e-01 -5.80611944e-01 -7.77620912e-01
-7.08416402e-01 -1.99838310e-01 2.42549628e-01 1.26843795e-01
1.45353064e-01 7.60190487e-01 -7.44689554e-02 -1.92593887e-01
-3.33884865e-01 -7.88736582e-01 -7.76388526e-01 -9.45815802e-01
-1.62237510e-01 4.46044713e-01 -1.86917171e-01 -1.01644158... | [8.136127471923828, 4.692221641540527] |
c4ee9e64-9414-4291-8b1f-bd5e70f33790 | geowine-geolocation-based-wiki-image-news-and | 2104.14994 | null | https://arxiv.org/abs/2104.14994v2 | https://arxiv.org/pdf/2104.14994v2.pdf | GeoWINE: Geolocation based Wiki, Image,News and Event Retrieval | In the context of social media, geolocation inference on news or events has become a very important task. In this paper, we present the GeoWINE (Geolocation-based Wiki-Image-News-Event retrieval) demonstrator, an effective modular system for multimodal retrieval which expects only a single image as input. The GeoWINE s... | ['Ralph Ewerth', 'Jens Lehmann', 'Sherzod Hakimov', 'Eric Müller-Budack', 'Endri Kacupaj', 'Golsa Tahmasebzadeh'] | 2021-04-30 | null | null | null | null | ['photo-geolocation-estimation'] | ['computer-vision'] | [-5.56122303e-01 1.03622086e-01 -1.88049972e-01 -2.29892448e-01
-9.83268142e-01 -5.31590819e-01 9.98513460e-01 6.35009646e-01
-8.02261591e-01 4.41184729e-01 4.19224828e-01 1.07591301e-01
-4.11013216e-01 -1.02833617e+00 -6.04453146e-01 -4.93741453e-01
-3.47442508e-01 5.60448945e-01 4.45790708e-01 -2.64340669... | [7.6894354820251465, -1.8071354627609253] |
2e7d57cf-b7cd-40c3-afe1-c49ce6391f7b | stratified-graphical-models-context-specific | 1309.6415 | null | http://arxiv.org/abs/1309.6415v2 | http://arxiv.org/pdf/1309.6415v2.pdf | Stratified Graphical Models - Context-Specific Independence in Graphical Models | Theory of graphical models has matured over more than three decades to
provide the backbone for several classes of models that are used in a myriad of
applications such as genetic mapping of diseases, credit risk evaluation,
reliability and computer security, etc. Despite of their generic applicability
and wide adoptan... | ['Timo Koski', 'Jukka Corander', 'Henrik Nyman', 'Johan Pensar'] | 2013-09-25 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 5.61899900e-01 3.55471522e-02 -2.78419614e-01 -6.90319896e-01
-2.30063781e-01 -3.88544977e-01 8.70280683e-01 1.63678974e-01
-2.81228900e-01 9.58509326e-01 -8.88633355e-02 -6.19039953e-01
-8.07612836e-01 -9.26643968e-01 -3.23814243e-01 -8.23494375e-01
-4.11972493e-01 6.98899865e-01 4.20252055e-01 1.93628967... | [7.1421332359313965, 4.692811489105225] |
4872d780-93ef-43d0-bef2-3bafaad6b485 | the-sound-of-silence-efficiency-of-first | 2210.02746 | null | https://arxiv.org/abs/2210.02746v1 | https://arxiv.org/pdf/2210.02746v1.pdf | The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection | The recent integration of generative neural strategies and audio processing techniques have fostered the widespread of synthetic speech synthesis or transformation algorithms. This capability proves to be harmful in many legal and informative processes (news, biometric authentication, audio evidence in courts, etc.). T... | ['Simone Milani', 'Federica Latora', 'Daniele Mari'] | 2022-10-06 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 5.47040641e-01 -1.15748927e-01 2.29609028e-01 9.44839343e-02
-9.64879930e-01 -6.55611753e-01 8.83265436e-01 1.34633273e-01
-4.06715125e-01 8.58588219e-01 -5.37294038e-02 -3.94064635e-01
-1.70119151e-01 -4.60108370e-01 -1.14059031e-01 -8.91683400e-01
1.66030765e-01 6.75060004e-02 3.60919178e-01 -2.11945385... | [14.809823989868164, 5.776573181152344] |
8ef8bee9-3066-4a72-99f6-b16eae03503f | self-supervised-augmentation-consistency-for | 2105.00097 | null | https://arxiv.org/abs/2105.00097v1 | https://arxiv.org/pdf/2105.00097v1.pdf | Self-supervised Augmentation Consistency for Adapting Semantic Segmentation | We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. In contrast to previous work, we abandon the use of computationally involved adversarial objectives, network ensembles and style transfer. Instead, we employ standard data augmentation techniques $-$ photom... | ['Stefan Roth', 'Nikita Araslanov'] | 2021-04-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Araslanov_Self-Supervised_Augmentation_Consistency_for_Adapting_Semantic_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Araslanov_Self-Supervised_Augmentation_Consistency_for_Adapting_Semantic_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 6.42476439e-01 3.75615507e-01 1.22814618e-01 -5.90917289e-01
-1.07454324e+00 -8.61945510e-01 6.21771872e-01 -4.14863318e-01
-6.59067154e-01 8.02877426e-01 -2.25909233e-01 -2.97320366e-01
1.09399192e-01 -5.13116896e-01 -8.83951068e-01 -5.74016392e-01
3.70940924e-01 5.50615907e-01 2.34143257e-01 -3.56461257... | [9.906726837158203, 1.0942490100860596] |
00bd5404-0517-4558-8713-7a1eafc69216 | train-smarter-not-harder-learning-deep | 2211.15717 | null | https://arxiv.org/abs/2211.15717v3 | https://arxiv.org/pdf/2211.15717v3.pdf | Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation | Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging. Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates ar... | ['Frank Lindseth', 'Ole-Jakob Elle', 'Thomas Langø', 'Shanmugapriya Survarachakan', 'David Bouget', 'Egidijus Pelanis', 'André Pedersen', 'Javier Pérez de Frutos'] | 2022-11-28 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 4.61143851e-01 4.29454952e-01 -5.22336289e-02 -9.01973069e-01
-8.45144629e-01 -2.65799224e-01 4.72754180e-01 2.57421374e-01
-9.87453997e-01 5.53938150e-01 1.25340139e-02 -2.60026157e-01
-8.34562257e-02 -8.38354051e-01 -7.93983638e-01 -5.58430672e-01
-3.84718060e-01 6.65620685e-01 3.89801770e-01 -1.41489774... | [14.25632381439209, -2.4935178756713867] |
e80fe562-75d6-4c1a-ba4a-71d743f39f82 | combining-multiscale-features-for | 1606.04985 | null | http://arxiv.org/abs/1606.04985v1 | http://arxiv.org/pdf/1606.04985v1.pdf | Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach | Nowadays, hyperspectral image classification widely copes with spatial
information to improve accuracy. One of the most popular way to integrate such
information is to extract hierarchical features from a multiscale segmentation.
In the classification context, the extracted features are commonly concatenated
into a lon... | ['Sébastien Lefèvre', 'Yanwei Cui', 'Laetitia Chapel'] | 2016-06-15 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 7.06710398e-01 -4.16448683e-01 1.16862031e-02 -3.06362450e-01
-5.57156503e-01 -6.66335940e-01 3.98976803e-01 5.31145692e-01
-5.62180996e-01 8.28111172e-01 -2.40719274e-01 -2.11950839e-01
-6.31627619e-01 -8.21730077e-01 -3.23571652e-01 -1.16159725e+00
5.20987622e-02 -2.87847906e-01 2.33410165e-01 -4.47130948... | [9.962021827697754, -1.8925178050994873] |
0d4485bc-2668-4b71-889c-ada7fe50a9c2 | an-empirical-study-of-topic-transition-in | 2111.14188 | null | https://arxiv.org/abs/2111.14188v3 | https://arxiv.org/pdf/2111.14188v3.pdf | An Empirical Study of Topic Transition in Dialogue | Transitioning between topics is a natural component of human-human dialog. Although topic transition has been studied in dialogue for decades, only a handful of corpora based studies have been performed to investigate the subtleties of topic transitions. Thus, this study annotates 215 conversations from the switchboard... | ['Vincent Wade', 'Benjamin R. Cowan', 'Christian Saam', 'Emer Gilmartin', 'Brendan Spillane', 'Mayank Soni'] | 2021-11-28 | null | https://aclanthology.org/2022.codi-1.12 | https://aclanthology.org/2022.codi-1.12.pdf | coling-codi-crac-2022-10 | ['open-domain-dialog'] | ['natural-language-processing'] | [-7.76322857e-02 8.47717464e-01 -1.63922027e-01 -6.09834909e-01
-4.82332498e-01 -9.21527624e-01 1.26155961e+00 3.94284278e-01
-8.01358670e-02 1.01904547e+00 5.53729475e-01 -4.62976635e-01
7.33143687e-02 -6.22824550e-01 9.71719399e-02 -1.35041818e-01
-2.47127280e-01 1.32034719e+00 6.72801077e-01 -4.94173408... | [12.918129920959473, 8.029666900634766] |
342939f0-f74d-47d3-a40e-44c95d53d309 | griprank-bridging-the-gap-between-retrieval | 2305.18144 | null | https://arxiv.org/abs/2305.18144v1 | https://arxiv.org/pdf/2305.18144v1.pdf | GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking | Retrieval-enhanced text generation, which aims to leverage passages retrieved from a large passage corpus for delivering a proper answer given the input query, has shown remarkable progress on knowledge-intensive language tasks such as open-domain question answering and knowledge-enhanced dialogue generation. However, ... | ['Zhoujun Li', 'Zhao Yan', 'Xinnian Liang', 'Jian Yang', 'Jiaheng Liu', 'Hongcheng Guo', 'Jiaqi Bai'] | 2023-05-29 | null | null | null | null | ['dialogue-generation', 'passage-ranking', 'open-domain-question-answering', 'answer-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.68448627e-01 3.72523218e-01 1.17937941e-02 -2.80327126e-02
-1.60423136e+00 -7.90785551e-01 8.55853617e-01 2.48554006e-01
-3.77668589e-01 1.11952412e+00 5.89615226e-01 -3.45289558e-01
-2.20180556e-01 -1.22026467e+00 -8.32449436e-01 -3.36328894e-01
8.35288912e-02 1.07747018e+00 3.91497612e-01 -6.22925997... | [11.457934379577637, 8.069330215454102] |
caf9e2f8-42a8-4c98-a856-852f76e699da | deepfakes-a-new-threat-to-face-recognition | 1812.08685 | null | http://arxiv.org/abs/1812.08685v1 | http://arxiv.org/pdf/1812.08685v1.pdf | DeepFakes: a New Threat to Face Recognition? Assessment and Detection | It is becoming increasingly easy to automatically replace a face of one
person in a video with the face of another person by using a pre-trained
generative adversarial network (GAN). Recent public scandals, e.g., the faces
of celebrities being swapped onto pornographic videos, call for automated ways
to detect these De... | ['Pavel Korshunov', 'Sebastien Marcel'] | 2018-12-20 | null | null | null | null | ['lip-sync-1'] | ['computer-vision'] | [ 4.96580526e-02 4.63906042e-02 1.87825620e-01 -1.39361560e-01
-5.87808490e-01 -8.75295401e-01 5.79552412e-01 -7.05828786e-01
-1.28925040e-01 8.60209465e-01 1.43071413e-01 -2.70253327e-02
3.52993935e-01 -7.37094164e-01 -7.98278093e-01 -8.21001768e-01
-5.64008169e-02 -4.44121249e-02 2.16971301e-02 2.67277323... | [12.692086219787598, 1.0992428064346313] |
af2a176b-2d58-40e2-ba52-d502bf372d35 | optiforest-optimal-isolation-forest-for | 2306.12703 | null | https://arxiv.org/abs/2306.12703v2 | https://arxiv.org/pdf/2306.12703v2.pdf | OptIForest: Optimal Isolation Forest for Anomaly Detection | Anomaly detection plays an increasingly important role in various fields for critical tasks such as intrusion detection in cybersecurity, financial risk detection, and human health monitoring. A variety of anomaly detection methods have been proposed, and a category based on the isolation forest mechanism stands out du... | ['Xiaolong Xu', 'Amin Beheshti', 'Mark Dras', 'Wanchun Dou', 'Lianyong Qi', 'Hongsheng Hu', 'Xuyun Zhang', 'Haolong Xiang'] | 2023-06-22 | null | null | null | null | ['anomaly-detection', 'intrusion-detection', 'benchmarking', 'benchmarking'] | ['methodology', 'miscellaneous', 'miscellaneous', 'robots'] | [ 4.93808985e-02 -2.75039345e-01 -4.29878503e-01 -1.07600123e-01
-1.87768310e-01 -3.63745153e-01 5.00856280e-01 2.75807530e-01
-3.66686016e-01 2.16549367e-01 -2.99270004e-01 -7.66044974e-01
-3.57300431e-01 -8.18494439e-01 -2.86197782e-01 -8.97523701e-01
-4.73307848e-01 2.13124275e-01 4.27787662e-01 -3.56148295... | [7.558152675628662, 2.627147674560547] |
cf58f7d2-c04f-4c01-8075-1211d76052b3 | unsupervised-video-summarization-with-a | 2105.11131 | null | https://arxiv.org/abs/2105.11131v1 | https://arxiv.org/pdf/2105.11131v1.pdf | Unsupervised Video Summarization with a Convolutional Attentive Adversarial Network | With the explosive growth of video data, video summarization, which attempts to seek the minimum subset of frames while still conveying the main story, has become one of the hottest topics. Nowadays, substantial achievements have been made by supervised learning techniques, especially after the emergence of deep learni... | ['Yanning Zhang', 'Shizhou Zhang', 'Shucheng Li', 'Yanbing Lv', 'Guoqiang Liang'] | 2021-05-24 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 3.61724764e-01 -1.77945912e-01 -1.13100477e-01 -2.49063194e-01
-9.60814536e-01 -1.35434479e-01 6.17166221e-01 -1.94218953e-03
-3.59017164e-01 7.73841977e-01 5.85946500e-01 2.10919932e-01
2.43516028e-01 -7.70955503e-01 -7.58855999e-01 -8.10611248e-01
1.57125086e-01 8.76021236e-02 2.81542957e-01 -8.64829868... | [10.39399528503418, 0.44246768951416016] |
598b7d7d-cd0f-4b1f-9524-e62a5ec1d68e | a-wireless-vision-dataset-for-privacy | 2205.11962 | null | https://arxiv.org/abs/2205.11962v1 | https://arxiv.org/pdf/2205.11962v1.pdf | A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition | Human Activity Recognition (HAR) has recently received remarkable attention in numerous applications such as assisted living and remote monitoring. Existing solutions based on sensors and vision technologies have obtained achievements but still suffering from considerable limitations in the environmental requirement. W... | ['Yuanwei Liu', 'Zhiyuan Shi', 'Yanling Hao'] | 2022-05-24 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 6.42713249e-01 -3.97252381e-01 -3.22864294e-01 -1.96718901e-01
-4.05854076e-01 -1.75518375e-02 2.62506187e-01 -4.12203759e-01
-5.41764677e-01 8.70871842e-01 4.19363454e-02 3.50625068e-02
-3.83223057e-01 -6.96123064e-01 -4.34381425e-01 -9.78028297e-01
8.45151767e-02 -1.89214036e-01 4.32879627e-01 1.02778770... | [7.181371688842773, 0.6580095887184143] |
a31357c8-130d-4099-aafb-28efed428f10 | particle-filtering-for-plca-model-with | 1703.09772 | null | http://arxiv.org/abs/1703.09772v1 | http://arxiv.org/pdf/1703.09772v1.pdf | Particle Filtering for PLCA model with Application to Music Transcription | Automatic Music Transcription (AMT) consists in automatically estimating the
notes in an audio recording, through three attributes: onset time, duration and
pitch. Probabilistic Latent Component Analysis (PLCA) has become very popular
for this task. PLCA is a spectrogram factorization method, able to model a
magnitude ... | ['D. Cazau', 'W. Yuancheng', 'O. Adam', 'G. Revillon'] | 2017-03-28 | null | null | null | null | ['music-transcription'] | ['music'] | [ 2.02784374e-01 -2.87525117e-01 2.71022201e-01 1.12700164e-01
-9.79412615e-01 -7.46580005e-01 5.53459585e-01 1.30448356e-01
-4.61721957e-01 6.65029943e-01 2.88869619e-01 7.24224374e-02
-6.18767440e-01 -4.57735270e-01 -1.90447971e-01 -9.54888284e-01
-8.03195536e-02 5.49378872e-01 1.25714503e-02 -7.50233904... | [15.722349166870117, 5.452995300292969] |
54ce921e-2c56-47e1-bcbd-83b92058467b | decomposed-human-motion-prior-for-video-pose | 2305.18743 | null | https://arxiv.org/abs/2305.18743v2 | https://arxiv.org/pdf/2305.18743v2.pdf | Decomposed Human Motion Prior for Video Pose Estimation via Adversarial Training | Estimating human pose from video is a task that receives considerable attention due to its applicability in numerous 3D fields. The complexity of prior knowledge of human body movements poses a challenge to neural network models in the task of regressing keypoints. In this paper, we address this problem by incorporatin... | ['Kai Zhang', 'Weixi Gu', 'Zhaoyu Zheng', 'Zhengdi Yu', 'Xiang Zhou', 'Wenshuo Chen'] | 2023-05-30 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [ 1.05544589e-01 1.35086421e-02 -2.70464242e-01 -1.25978798e-01
-5.20695269e-01 -2.14848325e-01 4.56701785e-01 -4.45909798e-01
-9.38568115e-01 6.48706853e-01 3.11304480e-01 1.40043646e-01
2.51309633e-01 -2.84835726e-01 -1.03667307e+00 -4.60814923e-01
-3.30050960e-02 3.67232151e-02 2.93753982e-01 -2.95389682... | [7.1696391105651855, -0.7052644491195679] |
ba021f9e-ff6c-4a55-a393-5db6210fee07 | recommendation-system-based-upper-confidence | 1909.04190 | null | https://arxiv.org/abs/1909.04190v1 | https://arxiv.org/pdf/1909.04190v1.pdf | Recommendation System-based Upper Confidence Bound for Online Advertising | In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the ... | ['Elena Simona Lohan', 'Flavian vasile', 'Nhan Nguyen-Thanh', 'Kinda Khawam', 'Dana Marinca', 'Steven Martin', 'Dominique Quadri', 'David Rohde'] | 2019-09-09 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-3.30611646e-01 2.35478237e-01 -9.84373689e-01 -8.17114636e-02
-9.84237552e-01 -3.64921749e-01 2.66236663e-01 -2.76534140e-01
-4.56825048e-01 1.32584107e+00 3.55327711e-03 -1.02924299e+00
-1.01305747e+00 -6.22236729e-01 -7.31467903e-01 -7.31895447e-01
-2.90562898e-01 6.27853513e-01 -1.40009085e-02 -3.86081785... | [4.527740955352783, 3.2103469371795654] |
eb9fc1e6-de2e-4b28-a915-afd2630a7840 | empirical-risk-minimization-and-stochastic | 1806.10701 | null | http://arxiv.org/abs/1806.10701v2 | http://arxiv.org/pdf/1806.10701v2.pdf | Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data | Empirical risk minimization is the main tool for prediction problems, but its
extension to relational data remains unsolved. We solve this problem using
recent ideas from graph sampling theory to (i) define an empirical risk for
relational data and (ii) obtain stochastic gradients for this empirical risk
that are autom... | ['Peter Orbanz', 'Wenda Zhou', 'Morgane Austern', 'Victor Veitch', 'David M. Blei'] | 2018-06-27 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 1.00585982e-01 5.69005847e-01 -5.20259023e-01 -4.08153027e-01
-9.44566309e-01 -2.68334746e-01 4.50653583e-01 1.18828893e-01
-1.10146195e-01 6.73255503e-01 -2.07070276e-01 -5.57463944e-01
-3.87538105e-01 -9.13472831e-01 -6.45406604e-01 -6.39551163e-01
-2.45551452e-01 7.51058936e-01 -1.54957697e-01 1.10439815... | [7.570588111877441, 4.508666038513184] |
687a01a4-5349-408f-858f-0ee4f10fd48b | pct-point-cloud-transformer | 2012.09688 | null | https://arxiv.org/abs/2012.09688v4 | https://arxiv.org/pdf/2012.09688v4.pdf | PCT: Point cloud transformer | The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays... | ['Shi-Min Hu', 'Ralph R. Martin', 'Tai-Jiang Mu', 'Zheng-Ning Liu', 'Jun-Xiong Cai', 'Meng-Hao Guo'] | 2020-12-17 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [ 6.56065345e-03 -3.96076739e-01 -1.83039501e-01 -3.46483916e-01
-5.71179688e-01 -6.46972775e-01 5.93388021e-01 2.56183773e-01
-1.91390306e-01 -7.14161340e-03 -3.28148991e-01 -5.22646010e-01
-2.07550317e-01 -1.11768901e+00 -1.13530290e+00 -5.17323494e-01
-3.08541596e-01 7.06003964e-01 1.56666517e-01 -1.50162159... | [7.941089153289795, -3.619476079940796] |
48eebc34-eaff-44ac-b4d7-b4f26857d11d | learning-off-road-terrain-traversability-with | 2305.18896 | null | https://arxiv.org/abs/2305.18896v1 | https://arxiv.org/pdf/2305.18896v1.pdf | Learning Off-Road Terrain Traversability with Self-Supervisions Only | Estimating the traversability of terrain should be reliable and accurate in diverse conditions for autonomous driving in off-road environments. However, learning-based approaches often yield unreliable results when confronted with unfamiliar contexts, and it is challenging to obtain manual annotations frequently for ne... | ['Inwook Shim', 'Sungdae Sim', 'Junwon Seo'] | 2023-05-30 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 2.25446001e-01 2.11743005e-02 -4.66184139e-01 -9.95865583e-01
-7.03008294e-01 -6.27010405e-01 4.72881675e-01 2.29175851e-01
-4.11286175e-01 8.37084711e-01 -1.47098631e-01 -5.42768776e-01
-1.05564278e-02 -1.05372036e+00 -8.48356366e-01 -4.11889404e-01
-3.85918498e-01 3.36303413e-01 5.76859772e-01 -3.22171092... | [8.170353889465332, -1.775512933731079] |
c97df95f-3785-4161-8094-f5083d98e03e | smart-a-situation-model-for-algebra-story | 2012.14011 | null | https://arxiv.org/abs/2012.14011v1 | https://arxiv.org/pdf/2012.14011v1.pdf | SMART: A Situation Model for Algebra Story Problems via Attributed Grammar | Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interp... | ['Song-Chun Zhu', 'Siyuan Huang', 'Daniel Ciao', 'Ran Gong', 'Qing Li', 'Yining Hong'] | 2020-12-27 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 2.21741334e-01 2.33376533e-01 9.56188887e-02 -5.17459452e-01
-2.47474387e-01 -4.97524261e-01 3.27117980e-01 1.40326470e-01
-2.93799281e-01 4.59411830e-01 7.16852471e-02 -4.97405797e-01
-4.90003645e-01 -1.30837452e+00 -6.58898711e-01 -2.28505805e-01
1.91917002e-01 6.70379698e-01 -5.91481216e-02 -3.91844571... | [9.649306297302246, 7.485450267791748] |
706897f0-d759-4c93-bfe7-c1cfd4f1bb2b | solving-a-new-3d-bin-packing-problem-with | 1708.05930 | null | http://arxiv.org/abs/1708.05930v1 | http://arxiv.org/pdf/1708.05930v1.pdf | Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method | In this paper, a new type of 3D bin packing problem (BPP) is proposed, in
which a number of cuboid-shaped items must be put into a bin one by one
orthogonally. The objective is to find a way to place these items that can
minimize the surface area of the bin. This problem is based on the fact that
there is no fixed-size... | ['Xiaodong Zhang', 'Yinghui Xu', 'Haoyuan Hu', 'Xiaowei Yan', 'Longfei Wang'] | 2017-08-20 | null | null | null | null | ['3d-bin-packing'] | ['miscellaneous'] | [-4.74089056e-01 -2.53157198e-01 -4.80360001e-01 -5.21834679e-02
3.16926360e-01 -4.79768902e-01 -4.34146374e-01 2.90172964e-01
-3.05661559e-01 1.01631832e+00 -1.09147735e-01 -5.32780051e-01
-5.77093899e-01 -1.32334149e+00 -8.00115526e-01 -7.17150569e-01
-4.42755789e-01 9.96022880e-01 2.11410195e-01 -6.05407476... | [5.020750522613525, 2.7419192790985107] |
7fa247af-12bf-4d05-acaa-2bfa772e73d9 | leverage-interactive-affinity-for-affordance | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Luo_Leverage_Interactive_Affinity_for_Affordance_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Luo_Leverage_Interactive_Affinity_for_Affordance_Learning_CVPR_2023_paper.pdf | Leverage Interactive Affinity for Affordance Learning | Perceiving potential "action possibilities" (i.e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions. Prevailing affordance learning algorithms often adopt the label assignment paradigm a... | ['DaCheng Tao', 'Yang Cao', 'Jing Zhang', 'Wei Zhai', 'Hongchen Luo'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [-4.12958376e-02 -7.24583631e-03 -2.83626139e-01 -3.87120157e-01
-2.43182674e-01 -4.86653298e-01 2.83979326e-01 -1.45189971e-01
-2.05027401e-01 4.50679719e-01 2.74374217e-01 1.81633905e-01
-2.85816789e-01 -2.38627642e-01 -8.71429443e-01 -4.99104023e-01
-1.97221801e-01 4.67531770e-01 4.91123497e-01 -2.91671872... | [5.166680812835693, -0.0973045825958252] |
85498c03-0366-4482-b28b-b5d898b9c314 | architect-regularize-and-replay-arr-a | 2301.02464 | null | https://arxiv.org/abs/2301.02464v1 | https://arxiv.org/pdf/2301.02464v1.pdf | Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning | In recent years we have witnessed a renewed interest in machine learning methodologies, especially for deep representation learning, that could overcome basic i.i.d. assumptions and tackle non-stationary environments subject to various distributional shifts or sample selection biases. Within this context, several compu... | ['Davide Maltoni', 'Gabriele Graffieti', 'Lorenzo Pellegrini', 'Vincenzo Lomonaco'] | 2023-01-06 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 1.75945401e-01 -3.75383973e-01 -1.01948194e-01 -5.78658104e-01
-6.52706683e-01 -5.44020951e-01 9.87645566e-01 2.29347140e-01
-6.84868634e-01 7.71715343e-01 3.88507321e-02 -3.39583158e-01
-5.07653534e-01 -7.09958017e-01 -5.17544091e-01 -8.55974376e-01
-2.46712476e-01 7.19094217e-01 1.56182364e-01 -2.41747558... | [9.407938003540039, 3.159766435623169] |
32401cdd-fa4a-4aa3-ac48-d60fff7817b2 | hdformer-high-order-directed-transformer-for | 2302.01825 | null | https://arxiv.org/abs/2302.01825v2 | https://arxiv.org/pdf/2302.01825v2.pdf | HDFormer: High-order Directed Transformer for 3D Human Pose Estimation | Human pose estimation is a challenging task due to its structured data sequence nature. Existing methods primarily focus on pair-wise interaction of body joints, which is insufficient for scenarios involving overlapping joints and rapidly changing poses. To overcome these issues, we introduce a novel approach, the High... | ['Xuansong Xie', 'Yifeng Geng', 'Bin Luo', 'Hanbing Liu', 'Zhi-Qi Cheng', 'Wei Liu', 'Wangmeng Xiang', 'Jun-Yan He', 'Hanyuan Chen'] | 2023-02-03 | null | null | null | null | ['3d-pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-5.07151783e-01 4.16492932e-02 -1.37930572e-01 -1.69845298e-01
-7.75534868e-01 -5.73282801e-02 1.41290084e-01 -2.67958373e-01
-4.77149546e-01 5.41438460e-01 3.88935328e-01 1.01709992e-01
-3.62041332e-02 -6.27801538e-01 -8.51893306e-01 -4.27145392e-01
-1.92085519e-01 8.66680741e-01 2.51611561e-01 -4.34610784... | [7.137869358062744, -0.6967793107032776] |
1ef79cee-fbe8-4005-bcbd-c8ebf4d1bbb4 | referring-transformer-a-one-step-approach-to | 2106.03089 | null | https://arxiv.org/abs/2106.03089v2 | https://arxiv.org/pdf/2106.03089v2.pdf | Referring Transformer: A One-step Approach to Multi-task Visual Grounding | As an important step towards visual reasoning, visual grounding (e.g., phrase localization, referring expression comprehension/segmentation) has been widely explored Previous approaches to referring expression comprehension (REC) or segmentation (RES) either suffer from limited performance, due to a two-stage setup, or... | ['Leonid Sigal', 'Muchen Li'] | 2021-06-06 | null | http://proceedings.neurips.cc/paper/2021/hash/a376802c0811f1b9088828288eb0d3f0-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a376802c0811f1b9088828288eb0d3f0-Paper.pdf | neurips-2021-12 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 4.44219381e-01 3.32718730e-01 -2.54959404e-01 -4.84522909e-01
-1.53690052e+00 -7.72095263e-01 7.78961599e-01 4.96941507e-02
-3.81127328e-01 4.14481878e-01 4.07224268e-01 -4.73325878e-01
6.11188829e-01 -3.86762768e-01 -1.06596851e+00 -4.48169827e-01
5.10994375e-01 4.63525593e-01 1.56144276e-01 -1.49649113... | [10.660099983215332, 1.5343003273010254] |
2f4fbb3e-f829-4ec8-b7b1-8d8a12a8eb3e | nips-conversational-intelligence-challenge | null | null | https://aclanthology.org/C18-1312 | https://aclanthology.org/C18-1312.pdf | NIPS Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager | We present bot{\#}1337: a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI). The aim of the competition was to implement a bot capable of conversing with humans based on a given passage of text. To enable conversation, we implemented a set of skills for our bot, including chit... | ['Yurii Kuratov', 'Idris Yusupov'] | 2018-08-01 | nips-conversational-intelligence-challenge-1 | https://aclanthology.org/C18-1312 | https://aclanthology.org/C18-1312.pdf | coling-2018-8 | ['goal-oriented-dialog', 'short-text-conversation', 'goal-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.64362371e-01 8.44066322e-01 5.77183425e-01 -3.83364022e-01
-7.12674677e-01 -6.50964200e-01 7.88466990e-01 2.29074107e-03
-4.99617785e-01 8.07392061e-01 4.44702774e-01 -1.43628493e-01
1.99307457e-01 -3.51740807e-01 -7.77567551e-02 -3.20678502e-01
2.65499085e-01 1.12948143e+00 4.04028654e-01 -6.46494985... | [12.843008995056152, 8.025388717651367] |
6082d7fa-91bb-4c94-917a-349fcae8e0d5 | an-overview-of-open-ended-evolution-editorial | 1909.04430 | null | https://arxiv.org/abs/1909.04430v1 | https://arxiv.org/pdf/1909.04430v1.pdf | An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue | Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known ... | ['Takashi Ikegami', 'Steen Rasmussen', 'Norman Packard', 'Alastair Channon', 'Tim Taylor', 'Mark A. Bedau', 'Kenneth O. Stanley'] | 2019-09-10 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.02886453e-01 2.38765806e-01 3.37740660e-01 -6.66249618e-02
6.64199591e-01 -7.71295190e-01 8.16523373e-01 3.24408114e-02
-3.32580179e-01 1.05404103e+00 3.01783681e-01 -9.31730717e-02
-1.63057923e-01 -6.04906499e-01 -5.55207074e-01 -6.19271040e-01
-2.75986016e-01 2.59973288e-01 4.61265706e-02 -8.70281279... | [5.570057392120361, 4.201204299926758] |
8bf9cb97-1113-4ee4-8c95-cd137a313b3e | data-mining-textual-responses-to-uncover | 1703.08544 | null | http://arxiv.org/abs/1703.08544v2 | http://arxiv.org/pdf/1703.08544v2.pdf | Data-Mining Textual Responses to Uncover Misconception Patterns | An important, yet largely unstudied, problem in student data analysis is to
detect misconceptions from students' responses to open-response questions.
Misconception detection enables instructors to deliver more targeted feedback
on the misconceptions exhibited by many students in their class, thus improving
the quality... | ['Andrew S. Lan', 'Joshua J. Michalenko', 'Richard G. Baraniuk'] | 2017-03-24 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 7.95198604e-02 -8.83188546e-02 -2.53141411e-02 -4.83538508e-01
-6.73220754e-01 -8.27294230e-01 1.39828131e-01 1.02613580e+00
6.73396736e-02 4.58962947e-01 6.91404119e-02 -1.06294513e+00
-4.04215485e-01 -8.12591970e-01 -6.96803987e-01 -1.32390574e-01
6.55519009e-01 -1.41741931e-01 5.02253115e-01 -3.20546180... | [10.161417007446289, 7.432184219360352] |
0b3b6241-ddfd-4ffa-8531-30d50bf24f47 | cross-lingual-transfer-can-worsen-bias-in | 2305.12709 | null | https://arxiv.org/abs/2305.12709v1 | https://arxiv.org/pdf/2305.12709v1.pdf | Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis | Sentiment analysis (SA) systems are widely deployed in many of the world's languages, and there is well-documented evidence of demographic bias in these systems. In languages beyond English, scarcer training data is often supplemented with transfer learning using pre-trained models, including multilingual models traine... | ['Adam Lopez', 'Björn Ross', 'Seraphina Goldfarb-Tarrant'] | 2023-05-22 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-3.38240862e-01 1.48708057e-02 -7.04327881e-01 -8.51501465e-01
-8.28190506e-01 -7.48995364e-01 8.78791988e-01 3.13902497e-01
-7.52306521e-01 1.15414548e+00 6.12484217e-01 -6.91571832e-01
5.81146955e-01 -6.89658582e-01 -7.30511308e-01 -2.35660255e-01
2.94980347e-01 5.33768296e-01 -3.99315864e-01 -6.76136196... | [9.864645957946777, 10.163237571716309] |
2f995554-e308-452e-a1d0-a998d08e7af9 | saint-improved-neural-networks-for-tabular | 2106.01342 | null | https://arxiv.org/abs/2106.01342v1 | https://arxiv.org/pdf/2106.01342v1.pdf | SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training | Tabular data underpins numerous high-impact applications of machine learning from fraud detection to genomics and healthcare. Classical approaches to solving tabular problems, such as gradient boosting and random forests, are widely used by practitioners. However, recent deep learning methods have achieved a degree of ... | ['Tom Goldstein', 'C. Bayan Bruss', 'Avi Schwarzschild', 'Micah Goldblum', 'Gowthami Somepalli'] | 2021-06-02 | saint-improved-neural-networks-for-tabular-1 | https://openreview.net/forum?id=nL2lDlsrZU | https://openreview.net/pdf?id=nL2lDlsrZU | null | ['insurance-prediction'] | ['miscellaneous'] | [-2.03025728e-01 -6.46900535e-02 -8.52999210e-01 -5.08437276e-01
-8.89134586e-01 -2.19107121e-01 6.24120414e-01 4.54724401e-01
-2.09384918e-01 1.20258832e+00 3.23434651e-01 -5.70100486e-01
-2.11340904e-01 -1.15756249e+00 -7.53191948e-01 -7.48242497e-01
7.85626546e-02 9.11589503e-01 -4.66939062e-01 -3.57227951... | [8.593734741210938, 4.160357475280762] |
0ff94cce-af1d-42d1-863f-7866ca891ba7 | seq2path-generating-sentiment-tuples-as-paths | null | null | https://aclanthology.org/2022.findings-acl.174 | https://aclanthology.org/2022.findings-acl.174.pdf | Seq2Path: Generating Sentiment Tuples as Paths of a Tree | Aspect-based sentiment analysis (ABSA) tasks aim to extract sentiment tuples from a sentence. Recent generative methods such as Seq2Seq models have achieved good performance by formulating the output as a sequence of sentiment tuples. However, the orders between the sentiment tuples do not naturally exist and the gener... | ['Longjun Cai', 'Xiaoying Zhu', 'Jingchao Yang', 'Yi Shen', 'Yue Mao'] | null | null | null | null | findings-acl-2022-5 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 3.02897483e-01 1.53356031e-01 -3.05665415e-02 -8.26744914e-01
-8.37325633e-01 -6.59359217e-01 4.46539134e-01 9.16581899e-02
-9.48776379e-02 9.39681232e-01 3.87586355e-01 -4.01127517e-01
2.77245790e-01 -1.08186865e+00 -9.39842045e-01 -6.74603820e-01
2.21487314e-01 6.77215695e-01 5.62976189e-02 -4.27101254... | [11.546422004699707, 6.730895519256592] |
778868bf-56b9-406c-ad3b-ec4b1976a018 | rb-ccr-radial-based-combined-cleaning-and | 2105.04009 | null | https://arxiv.org/abs/2105.04009v1 | https://arxiv.org/pdf/2105.04009v1.pdf | RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification | Real-world classification domains, such as medicine, health and safety, and finance, often exhibit imbalanced class priors and have asynchronous misclassification costs. In such cases, the classification model must achieve a high recall without significantly impacting precision. Resampling the training data is the stan... | ['Michał Woźniak', 'Colin Bellinger', 'Michał Koziarski'] | 2021-05-09 | null | null | null | null | ['classification'] | ['methodology'] | [ 1.80880576e-01 -1.49386570e-01 -6.09786451e-01 -5.71842492e-01
-9.85668898e-01 -3.37184876e-01 3.20947707e-01 6.68169439e-01
-2.88781703e-01 1.14274764e+00 -1.71584114e-01 -4.55712348e-01
-2.80916393e-01 -1.05542660e+00 -7.06254184e-01 -7.10096061e-01
2.14397073e-01 6.23879552e-01 1.38674006e-01 1.39471799... | [8.76247501373291, 4.2169508934021] |
03dc263f-de01-4f24-8819-957db3b8d620 | a-sliced-wasserstein-distance-based-approach | 2302.01459 | null | https://arxiv.org/abs/2302.01459v1 | https://arxiv.org/pdf/2302.01459v1.pdf | A sliced-Wasserstein distance-based approach for out-of-class-distribution detection | There exist growing interests in intelligent systems for numerous medical imaging, image processing, and computer vision applications, such as face recognition, medical diagnosis, character recognition, and self-driving cars, among others. These applications usually require solving complex classification problems invol... | ['Gustavo K Rohde', 'Yan Zhuang', 'Abu Hasnat Mohammad Rubaiyat', 'Mohammad Shifat E Rabbi'] | 2023-02-02 | null | null | null | null | ['medical-diagnosis', 'feature-engineering'] | ['medical', 'methodology'] | [ 3.87982011e-01 -2.20518529e-01 4.29538451e-02 -2.85589010e-01
-6.37827814e-01 -3.56766939e-01 3.91868979e-01 4.05065566e-02
-2.26794958e-01 7.22571850e-01 -5.98930359e-01 -3.96103233e-01
-3.15615624e-01 -8.69643569e-01 -5.06654859e-01 -1.10207129e+00
1.74710333e-01 5.27665317e-01 2.26395577e-01 9.67952162... | [7.585346698760986, 1.9625024795532227] |
d86128cb-77ba-4bff-8245-afd7b289fb4d | thompson-sampling-on-symmetric-stable-bandits | 1907.03821 | null | https://arxiv.org/abs/1907.03821v2 | https://arxiv.org/pdf/1907.03821v2.pdf | Thompson Sampling on Symmetric $α$-Stable Bandits | Thompson Sampling provides an efficient technique to introduce prior knowledge in the multi-armed bandit problem, along with providing remarkable empirical performance. In this paper, we revisit the Thompson Sampling algorithm under rewards drawn from symmetric $\alpha$-stable distributions, which are a class of heavy-... | ['Alex Pentland', 'Abhimanyu Dubey'] | 2019-07-08 | null | null | null | null | ['sequential-bayesian-inference'] | ['time-series'] | [-8.67899358e-02 -2.01367915e-01 -7.62727022e-01 -4.32620078e-01
-1.02777565e+00 -5.10371149e-01 2.82722771e-01 -2.05973148e-01
-3.38946611e-01 1.27241480e+00 7.12929741e-02 -6.39369428e-01
-6.04075849e-01 -8.08962405e-01 -8.67166281e-01 -7.58246243e-01
-6.30808175e-02 8.08353364e-01 -2.69261807e-01 2.54588425... | [4.557809352874756, 3.288393974304199] |
3dc753bf-1892-4c0c-a64f-87f8d0b9eab6 | towards-learning-to-detect-and-predict | 1910.03973 | null | https://arxiv.org/abs/1910.03973v1 | https://arxiv.org/pdf/1910.03973v1.pdf | Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors | In essence, successful grasp boils down to correct responses to multiple contact events between fingertips and objects. In most scenarios, tactile sensing is adequate to distinguish contact events. Due to the nature of high dimensionality of tactile information, classifying spatiotemporal tactile signals using conventi... | ['Michael Yu Wang', 'Zicheng Kan', 'Yazhan Zhang', 'Weihao Yuan'] | 2019-10-09 | null | null | null | null | ['contact-detection'] | ['robots'] | [ 3.94170195e-01 -5.40175438e-01 1.54044449e-01 -1.40857831e-01
-3.30878705e-01 -6.67715311e-01 7.09487824e-03 -9.26685613e-03
-3.22198838e-01 4.87788200e-01 -2.29303554e-01 1.09329395e-01
-4.57588732e-01 -8.35754395e-01 -8.70173454e-01 -7.60912180e-01
-3.31061304e-01 6.73183352e-02 2.84343481e-01 -1.12290755... | [5.846062660217285, -0.8230483531951904] |
e26d07eb-faf7-4642-88cc-429ef42cd228 | deep-logismos-deep-learning-graph-based-3d | 1801.08599 | null | http://arxiv.org/abs/1801.08599v1 | http://arxiv.org/pdf/1801.08599v1.pdf | Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans | This paper reports Deep LOGISMOS approach to 3D tumor segmentation by
incorporating boundary information derived from deep contextual learning to
LOGISMOS - layered optimal graph image segmentation of multiple objects and
surfaces. Accurate and reliable tumor segmentation is essential to tumor growth
analysis and treat... | ['Jianhua Yao', 'Zhihui Guo', 'Milan Sonka', 'Le Lu', 'Ronald M. Summers', 'Ling Zhang', 'Mohammadhadi Bagheri'] | 2018-01-25 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 2.71587372e-01 3.49194556e-01 -3.11649799e-01 -6.86763227e-02
-8.10263216e-01 -2.33541161e-01 1.14168987e-01 5.99517405e-01
-3.22375625e-01 5.21588504e-01 -1.64571386e-02 -3.05352867e-01
-6.89475983e-02 -7.35016942e-01 -4.61774647e-01 -1.04074705e+00
-3.40913445e-01 6.72953546e-01 2.71850497e-01 1.16675436... | [14.558305740356445, -2.5583221912384033] |
d852ddab-8456-4467-b824-9eca5a0cb85c | igformer-interaction-graph-transformer-for | 2207.12100 | null | https://arxiv.org/abs/2207.12100v1 | https://arxiv.org/pdf/2207.12100v1.pdf | IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition | Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel Interaction Graph Transformer (IGFormer) network for skeleton-based interaction recognition via modeling the interactive body parts as graph... | ['Jun Liu', 'James Bailey', 'Hossein Rahmani', 'Qiuhong Ke', 'Yunsheng Pang'] | 2022-07-25 | null | null | null | null | ['human-interaction-recognition'] | ['computer-vision'] | [ 1.72333941e-01 2.11884543e-01 -3.77892137e-01 -3.49152952e-01
2.50722587e-01 -1.00278236e-01 3.50218773e-01 -2.00965270e-01
1.08112954e-01 2.28838578e-01 5.58763087e-01 3.42714727e-01
-3.22655857e-01 -7.65493631e-01 -4.28580552e-01 -4.18860555e-01
-2.23678544e-01 6.23804510e-01 5.09303927e-01 -3.03697228... | [8.07697868347168, 0.47686266899108887] |
c2386f3f-46d6-4434-89ea-0fd5073fd854 | realsmilenet-a-deep-end-to-end-network-for | 2010.03203 | null | https://arxiv.org/abs/2010.03203v1 | https://arxiv.org/pdf/2010.03203v1.pdf | RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition | Smiles play a vital role in the understanding of social interactions within different communities, and reveal the physical state of mind of people in both real and deceptive ways. Several methods have been proposed to recognize spontaneous and posed smiles. All follow a feature-engineering based pipeline requiring cost... | ['Shafin Rahman', 'Tom Gedeon', 'Md Zakir Hossain', 'Yan Yang'] | 2020-10-07 | null | null | null | null | ['smile-recognition'] | ['computer-vision'] | [ 1.75358076e-02 1.03283748e-02 4.06581044e-01 -8.53102326e-01
-2.57018626e-01 -2.22052038e-01 8.80623639e-01 -3.33224952e-01
-2.95979291e-01 4.20994252e-01 1.82427224e-02 1.33566573e-01
2.19452053e-01 -4.85378802e-01 -3.29976380e-01 -4.94738489e-01
-1.80945784e-01 4.82867360e-01 -2.36289665e-01 -1.27369221... | [13.423590660095215, 1.6530399322509766] |
eb2c56d9-4148-4acb-b2f0-dd8c0c0b0e53 | key-value-information-extraction-from-full | 2304.13530 | null | https://arxiv.org/abs/2304.13530v1 | https://arxiv.org/pdf/2304.13530v1.pdf | Key-value information extraction from full handwritten pages | We propose a Transformer-based approach for information extraction from digitized handwritten documents. Our approach combines, in a single model, the different steps that were so far performed by separate models: feature extraction, handwriting recognition and named entity recognition. We compare this integrated appro... | ['Christopher Kermorvant', 'Mélodie Boillet', 'Solène Tarride'] | 2023-04-26 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 1.77565739e-01 1.89000756e-01 -3.90016645e-01 -2.18837157e-01
-1.08975554e+00 -1.05697668e+00 9.71055567e-01 3.63920778e-01
-6.26767695e-01 9.00903046e-01 3.32591861e-01 -3.51749480e-01
-2.14077994e-01 -6.06229901e-01 -6.34590566e-01 -2.80210942e-01
4.12544996e-01 1.05568099e+00 6.84191287e-01 -3.60295437... | [11.719536781311035, 2.831719160079956] |
4b52bd75-275f-482e-ad6c-0457399ee99b | optimal-compression-for-minimizing | 2211.02012 | null | https://arxiv.org/abs/2211.02012v1 | https://arxiv.org/pdf/2211.02012v1.pdf | Optimal Compression for Minimizing Classification Error Probability: an Information-Theoretic Approach | We formulate the problem of performing optimal data compression under the constraints that compressed data can be used for accurate classification in machine learning. We show that this translates to a problem of minimizing the mutual information between data and its compressed version under the constraint on error pro... | ['Weiyu Xu', 'Ao Tang', 'Jingchao Gao'] | 2022-11-03 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.39739120e-01 2.75881529e-01 -5.49730837e-01 -5.65759063e-01
-1.11166203e+00 -2.76824057e-01 -8.02639499e-02 6.82719469e-01
-5.85226297e-01 4.66812223e-01 -8.32556859e-02 -2.70926118e-01
-7.02288508e-01 -6.56892121e-01 -5.81038594e-01 -7.01614261e-01
-1.22488052e-01 7.62937605e-01 -4.36182261e-01 4.70133007... | [7.71693229675293, 4.194887638092041] |
972f15f1-1b65-42e4-8f46-c67ff7f9e94f | deep-learning-based-identification-of-sub | 2207.09598 | null | https://arxiv.org/abs/2207.09598v1 | https://arxiv.org/pdf/2207.09598v1.pdf | Deep learning-based identification of sub-nuclear structures in FIB-SEM images | Three-dimensional volumetric imaging of cells allows for in situ visualization, thus preserving contextual insights into cellular processes. Despite recent advances in machine learning methods, morphological analysis of sub-nuclear structures have proven challenging due to both the shallow contrast profile and the tech... | ['Vignesh Kasinath', 'Petrus H. Zwart', 'Danielle Jorgens', 'Abby Dernburg', 'Fan Wu', 'Harald F. Hess', 'C. Shan Xu', 'Song Pang', 'Eric J. Roberts', 'Niraj Gupta'] | 2022-07-19 | null | null | null | null | ['electron-tomography', 'morphological-analysis'] | ['medical', 'natural-language-processing'] | [ 3.07086289e-01 1.03523560e-01 4.15540665e-01 -2.69988596e-01
-4.17724639e-01 -7.77152240e-01 2.07266167e-01 5.87614298e-01
-8.28908145e-01 6.23626769e-01 -4.18635428e-01 -2.97423601e-01
-1.67306006e-01 -5.35178721e-01 -5.15827298e-01 -9.38046575e-01
-3.87908459e-01 9.46716785e-01 5.66708706e-02 1.91484496... | [14.32059383392334, -3.13067364692688] |
dfd4e7da-d833-4067-a119-2e77bf036320 | recent-advances-of-local-mechanisms-in | 2306.01929 | null | https://arxiv.org/abs/2306.01929v1 | https://arxiv.org/pdf/2306.01929v1.pdf | Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work | Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the development of computer vision. They can not only focus on target parts to learn discriminative local representations, but also process inform... | ['Yilong Yin', 'Qiangchang Wang'] | 2023-06-02 | null | null | null | null | ['person-re-identification', 'fine-grained-visual-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.83836415e-01 -2.76518524e-01 -5.43176353e-01 -4.69777256e-01
-9.18369219e-02 -2.23672643e-01 6.85516357e-01 3.12462598e-02
-5.33678114e-01 4.58153754e-01 3.16589862e-01 4.90583271e-01
-9.58026052e-02 -6.11745417e-01 -9.92932245e-02 -9.58978295e-01
1.62537873e-01 -1.50093604e-02 4.77086365e-01 -7.24716112... | [9.840896606445312, 1.9012864828109741] |
d96eaa64-ce93-4ef7-b5b7-d69e75c2bc15 | a-novel-algorithm-for-exact-concave-hull | 2206.11481 | null | https://arxiv.org/abs/2206.11481v1 | https://arxiv.org/pdf/2206.11481v1.pdf | A Novel Algorithm for Exact Concave Hull Extraction | Region extraction is necessary in a wide range of applications, from object detection in autonomous driving to analysis of subcellular morphology in cell biology. There exist two main approaches: convex hull extraction, for which exact and efficient algorithms exist and concave hulls, which are better at capturing real... | ['Murat Can Çobanoğlu', 'Kevin Christopher VanHorn'] | 2022-06-23 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 5.29124141e-01 -2.08732024e-01 6.43405947e-04 -6.55767918e-02
-8.33327830e-01 -8.17101777e-01 1.68940336e-01 5.33893287e-01
-3.02786499e-01 6.59888208e-01 -9.69786420e-02 -4.28813338e-01
-1.79645568e-01 -7.46994734e-01 -6.06981993e-01 -6.88237548e-01
-2.90971518e-01 2.42375970e-01 3.80437672e-01 -8.50860029... | [14.488420486450195, -3.1596810817718506] |
59f00095-9049-42e5-b1c0-3c959b682e78 | qbye-mlpmixer-query-by-example-open | 2206.13231 | null | https://arxiv.org/abs/2206.13231v1 | https://arxiv.org/pdf/2206.13231v1.pdf | QbyE-MLPMixer: Query-by-Example Open-Vocabulary Keyword Spotting using MLPMixer | Current keyword spotting systems are typically trained with a large amount of pre-defined keywords. Recognizing keywords in an open-vocabulary setting is essential for personalizing smart device interaction. Towards this goal, we propose a pure MLP-based neural network that is based on MLPMixer - an MLP model architect... | ['Chul Lee', 'Han Suk Shim', 'Qianhui Wan', 'Waseem Gharbieh', 'Jinmiao Huang'] | 2022-06-23 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.15938470e-01 -1.07859552e-01 -3.71528864e-01 -4.46069568e-01
-8.02595496e-01 -4.65162188e-01 6.03028297e-01 -3.59979272e-01
-7.86049128e-01 2.41839394e-01 3.56125832e-01 -4.99543101e-01
2.78877951e-02 -1.24442898e-01 -9.41120207e-01 -2.85060376e-01
4.33141172e-01 6.04328573e-01 -1.01233356e-01 -5.25484495... | [14.247093200683594, 6.429457187652588] |
40521dac-790c-48d2-9d80-04cdcfa9c396 | seqsleepnet-end-to-end-hierarchical-recurrent | 1809.10932 | null | http://arxiv.org/abs/1809.10932v3 | http://arxiv.org/pdf/1809.10932v3.pdf | SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging | Automatic sleep staging has been often treated as a simple classification
problem that aims at determining the label of individual target polysomnography
(PSG) epochs one at a time. In this work, we tackle the task as a
sequence-to-sequence classification problem that receives a sequence of
multiple epochs as input and... | ['Oliver Y. Chén', 'Navin Cooray', 'Fernando Andreotti', 'Huy Phan', 'Maarten De Vos'] | 2018-09-28 | null | null | null | null | ['sleep-stage-detection', 'sleep-staging'] | ['medical', 'medical'] | [ 6.17480695e-01 6.54364675e-02 -1.66949436e-01 -6.02804542e-01
-7.42151678e-01 -2.39593014e-01 1.02873996e-01 1.43537462e-01
-7.11950302e-01 6.10745013e-01 1.50991336e-01 -3.06002349e-01
6.74457997e-02 -1.18918456e-01 -2.73231566e-01 -8.30911994e-01
-1.29676433e-02 1.35955602e-01 1.04778253e-01 1.41955256... | [13.507197380065918, 3.5224976539611816] |
12966173-430a-4da6-a3a5-c6e4134e0456 | continual-learning-through-human-robot | 2305.16332 | null | https://arxiv.org/abs/2305.16332v1 | https://arxiv.org/pdf/2305.16332v1.pdf | Continual Learning through Human-Robot Interaction -- Human Perceptions of a Continual Learning Robot in Repeated Interactions | For long-term deployment in dynamic real-world environments, assistive robots must continue to learn and adapt to their environments. Researchers have developed various computational models for continual learning (CL) that can allow robots to continually learn from limited training data, and avoid forgetting previous k... | ['Kerstin Dautenhahn', 'Chrystopher L. Nehaniv', 'Patrick Holthaus', 'Zachary De Francesco', 'Ali Ayub'] | 2023-05-22 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-4.19002503e-01 7.31855869e-01 1.33841947e-01 -3.96773219e-01
5.38482368e-02 -4.48158920e-01 -1.77789144e-02 1.20473891e-01
-7.02260554e-01 9.01912570e-01 -4.61454511e-01 -3.92235160e-01
-3.75705719e-01 -1.94918692e-01 -8.84925842e-01 -1.99742407e-01
-4.46972191e-01 6.78795159e-01 2.21114922e-02 -2.24039048... | [4.639204978942871, 0.9508499503135681] |
9f2c1bbd-cbd0-47ab-9e10-a4d4d978701a | recurrent-homography-estimation-using | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cao_Recurrent_Homography_Estimation_Using_Homography-Guided_Image_Warping_and_Focus_Transformer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cao_Recurrent_Homography_Estimation_Using_Homography-Guided_Image_Warping_and_Focus_Transformer_CVPR_2023_paper.pdf | Recurrent Homography Estimation Using Homography-Guided Image Warping and Focus Transformer | We propose the Recurrent homography estimation framework using Homography-guided image Warping and Focus transformer (FocusFormer), named RHWF. Both being appropriately absorbed into the recurrent framework, the homography-guided image warping progressively enhances the feature consistency and the attention-focusin... | ['Hui-Liang Shen', 'Junwei Li', 'Zehua Sheng', 'Beinan Yu', 'Lun Luo', 'Runmin Zhang', 'Si-Yuan Cao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['homography-estimation'] | ['computer-vision'] | [ 5.46332262e-02 1.58332177e-02 -3.64532620e-02 6.31073341e-02
-1.06719089e+00 -2.97069460e-01 5.13012469e-01 -3.68529320e-01
-5.54666854e-02 4.47935939e-01 5.58831990e-01 1.60499483e-01
-2.79185951e-01 -7.90875554e-01 -6.49172187e-01 -8.52592409e-01
2.44437814e-01 -6.06389642e-02 3.52400839e-01 -2.56528705... | [10.58215618133545, -2.03245210647583] |
6ef8acbb-b2cd-4145-94cc-f46ab8793b69 | a-survey-on-medical-document-summarization | 2212.01669 | null | https://arxiv.org/abs/2212.01669v1 | https://arxiv.org/pdf/2212.01669v1.pdf | A Survey on Medical Document Summarization | The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for medical studies. As there is so much data accessible to doctors and pa... | ['Adam Jatowt', 'Sriparna Saha', 'Anubhav Jangra', 'Raghav Jain'] | 2022-12-03 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [-1.36443466e-01 -2.33663302e-02 -5.70125103e-01 -1.51697546e-01
-6.34785354e-01 -2.69736767e-01 3.27832460e-01 1.12726760e+00
-5.23116112e-01 8.50899696e-01 8.52702200e-01 -2.12570414e-01
-2.47460544e-01 -9.55921650e-01 -7.29684457e-02 -5.84215283e-01
-2.00884610e-01 6.80285156e-01 -1.14431672e-01 8.65383297... | [8.032444953918457, 7.175463676452637] |
5d085f47-1a06-47f0-b305-a5f3167d7af6 | a-fully-unsupervised-instance-segmentation | 2306.14875 | null | https://arxiv.org/abs/2306.14875v1 | https://arxiv.org/pdf/2306.14875v1.pdf | A Fully Unsupervised Instance Segmentation Technique for White Blood Cell Images | White blood cells, also known as leukocytes are group of heterogeneously nucleated cells which act as salient immune system cells. These are originated in the bone marrow and are found in blood, plasma, and lymph tissues. Leukocytes kill the bacteria, virus and other kind of pathogens which invade human body through ph... | ['Amartya Bhattacharya', 'Shrijeet Biswas'] | 2023-06-26 | null | null | null | null | ['blood-cell-count', 'instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.08495036e-01 -3.32922071e-01 -9.71547812e-02 2.42177412e-01
2.33107343e-01 -6.16598904e-01 2.40465060e-01 5.63970625e-01
-6.10091388e-01 9.18146729e-01 9.87638086e-02 -1.02177776e-01
4.65253621e-01 -9.44592834e-01 4.32023197e-01 -8.85400474e-01
5.72856545e-01 9.20222104e-01 4.12462533e-01 5.22115648... | [14.835330963134766, -3.132556200027466] |
f53dbe51-8ffc-4150-8818-d5e044be48a5 | aclm-a-selective-denoising-based-generative | 2306.00928 | null | https://arxiv.org/abs/2306.00928v1 | https://arxiv.org/pdf/2306.00928v1.pdf | ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER | Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM Attention-map aware keyword selection for Conditional Language Model fine-tuning), a novel data augmentation approach based on conditional generation to address the d... | ['Dinesh Manocha', 'S Ramaneswaran', 'Sonal Kumar', 'Manan Suri', 'Utkarsh Tyagi', 'Sreyan Ghosh'] | 2023-06-01 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [ 9.59074497e-02 2.22263932e-01 4.44618352e-02 -3.47121060e-01
-1.39057481e+00 -6.68195724e-01 5.00970602e-01 3.15468341e-01
-1.06563330e+00 9.76745725e-01 9.61145818e-01 -3.00761014e-01
3.80680859e-01 -4.59471703e-01 -7.76285529e-01 -2.28347585e-01
2.95672297e-01 4.27820355e-01 -3.00530583e-01 -3.85694653... | [9.776643753051758, 9.523374557495117] |
2e6e3bcd-362a-46cf-849a-9f94545d4a46 | optical-flow-based-online-moving-foreground | 1811.07256 | null | http://arxiv.org/abs/1811.07256v1 | http://arxiv.org/pdf/1811.07256v1.pdf | Optical Flow Based Online Moving Foreground Analysis | Obtained by moving object detection, the foreground mask result is unshaped
and can not be directly used in most subsequent processes. In this paper, we
focus on this problem and address it by constructing an optical flow based
moving foreground analysis framework. During the processing procedure, the
foreground masks ... | ['Jiagang Zhu', 'Wei Zou', 'Junjie Huang', 'Zheng Zhu'] | 2018-11-18 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 4.50973541e-01 -4.50221956e-01 -7.02649206e-02 9.08926129e-03
6.67302758e-02 -6.22273743e-01 6.19016767e-01 -1.92459468e-02
-5.11724353e-01 6.69251680e-01 -4.87704873e-01 -3.74659568e-01
-1.55648887e-02 -8.17532539e-01 -8.57127756e-02 -9.08235013e-01
-6.87343031e-02 3.89331847e-01 1.11680508e+00 3.99435699... | [8.942069053649902, -0.7875672578811646] |
4f049b61-cd6f-4a22-a827-82cbcb56b2c8 | cyclegan-with-a-blur-kernel-for-deconvolution | 1908.09414 | null | https://arxiv.org/abs/1908.09414v3 | https://arxiv.org/pdf/1908.09414v3.pdf | CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry | Deconvolution microscopy has been extensively used to improve the resolution of the wide-field fluorescent microscopy, but the performance of classical approaches critically depends on the accuracy of a model and optimization algorithms. Recently, the convolutional neural network (CNN) approaches have been studied as a... | ['Sang-Eun Lee', 'Sungjun Lim', 'Jong Chul Ye', 'Hyoungjun Park', 'Sunghoe Chang'] | 2019-08-26 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 3.27239424e-01 -2.94340223e-01 5.67553759e-01 -2.08824083e-01
-5.92172444e-01 -5.01632750e-01 4.71196324e-01 -4.41553921e-01
-6.50703669e-01 1.18395519e+00 -3.54568541e-01 -4.38467152e-02
-1.46417826e-01 -3.67579401e-01 -9.53128278e-01 -1.26805246e+00
4.94634539e-01 5.55102378e-02 2.05022603e-01 1.48415208... | [12.308128356933594, -2.6420042514801025] |
526c6a13-6859-4931-b708-7fe2abec9c6f | weakly-supervised-multi-task-learning-for | 1910.12326 | null | https://arxiv.org/abs/1910.12326v1 | https://arxiv.org/pdf/1910.12326v1.pdf | Weakly Supervised Multi-Task Learning for Cell Detection and Segmentation | Cell detection and segmentation is fundamental for all downstream analysis of digital pathology images. However, obtaining the pixel-level ground truth for single cell segmentation is extremely labor intensive. To overcome this challenge, we developed an end-to-end deep learning algorithm to perform both single cell de... | ['Yao Nie', 'Alireza Chamanzar'] | 2019-10-27 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 5.40359616e-01 2.05778956e-01 -2.32911915e-01 -1.58123016e-01
-1.40857339e+00 -6.90924287e-01 1.90504566e-01 8.89413655e-01
-7.99422204e-01 9.18237329e-01 -4.67450023e-01 -6.23680174e-01
2.04557508e-01 -5.56975782e-01 -3.75505030e-01 -1.08284593e+00
1.94737688e-01 7.14568019e-01 3.65370184e-01 1.05910726... | [14.987380981445312, -3.0876870155334473] |
e30acfbe-4524-44f0-ae60-68d0f178d1d9 | decompose-and-realign-tackling-condition | 2306.14408 | null | https://arxiv.org/abs/2306.14408v1 | https://arxiv.org/pdf/2306.14408v1.pdf | Decompose and Realign: Tackling Condition Misalignment in Text-to-Image Diffusion Models | Text-to-image diffusion models have advanced towards more controllable generation via supporting various image conditions (e.g., depth map) beyond text. However, these models are learned based on the premise of perfect alignment between the text and image conditions. If this alignment is not satisfied, the final output... | ['Ying-Cong Chen', 'Yijun Li', 'Guibao Shen', 'Luozhou Wang'] | 2023-06-26 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 6.64963543e-01 4.05001253e-01 -3.18130143e-02 -3.28931361e-01
-6.46516502e-01 -8.42358708e-01 1.02338350e+00 9.77852568e-02
-3.24539214e-01 7.35417545e-01 3.62314612e-01 -3.62647116e-01
-1.82183638e-01 -5.38645327e-01 -6.08351886e-01 -7.48876333e-01
2.76936710e-01 4.41072822e-01 2.54086763e-01 -1.52685434... | [11.336766242980957, -0.1843075007200241] |
3ed5987e-0574-4b9d-b5a8-60d8fe31c72e | semeval-2015-task-18-broad-coverage-semantic | null | null | https://aclanthology.org/S15-2153 | https://aclanthology.org/S15-2153.pdf | SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing | null | ["Zde{\\v{n}}ka Ure{\\v{s}}ov{\\'a}", 'Jan Haji{\\v{c}}', "Silvie Cinkov{\\'a}", 'Daniel Zeman', 'Stephan Oepen', 'Yusuke Miyao', 'Marco Kuhlmann', 'Dan Flickinger'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.264480113983154, 3.6558444499969482] |
9b0b81e4-a021-47b1-9935-ce800585eca5 | eth2vec-learning-contract-wide-code | 2101.02377 | null | https://arxiv.org/abs/2101.02377v2 | https://arxiv.org/pdf/2101.02377v2.pdf | Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts | Ethereum smart contracts are programs that run on the Ethereum blockchain, and many smart contract vulnerabilities have been discovered in the past decade. Many security analysis tools have been created to detect such vulnerabilities, but their performance decreases drastically when codes to be analyzed are being rewri... | ['Shingo Okamura', 'Jason Paul Cruz', 'Naoto Yanai', 'Nami Ashizawa'] | 2021-01-07 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-2.71821707e-01 5.30236214e-02 -2.53093213e-01 -8.53260010e-02
-7.80882895e-01 -1.18058109e+00 6.87667310e-01 1.40627027e-01
6.31646737e-02 3.05277348e-01 3.38681430e-01 -1.28199589e+00
4.67036098e-01 -9.26653266e-01 -5.09227574e-01 -4.78773206e-01
-2.97561556e-01 1.17727421e-01 2.27853507e-01 -4.29321349... | [6.862297058105469, 7.3234663009643555] |
a6064a9d-73ab-4025-8cbd-2d1b98f47e82 | deep-learning-based-phase-reconstruction-for | 1811.09010 | null | http://arxiv.org/abs/1811.09010v1 | http://arxiv.org/pdf/1811.09010v1.pdf | Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective | This study investigates phase reconstruction for deep learning based monaural
talker-independent speaker separation in the short-time Fourier transform
(STFT) domain. The key observation is that, for a mixture of two sources, with
their magnitudes accurately estimated and under a geometric constraint, the
absolute phas... | ['Zhong-Qiu Wang', 'Ke Tan', 'DeLiang Wang'] | 2018-11-22 | null | null | null | null | ['speaker-separation'] | ['speech'] | [-6.41693324e-02 -3.64675701e-01 -6.08194433e-02 -1.32199094e-01
-1.41684651e+00 -5.87875366e-01 2.08134741e-01 -3.99495754e-03
-2.71624386e-01 5.34109890e-01 2.78755903e-01 -3.56083177e-02
-4.19335127e-01 -4.54358459e-02 -3.32109660e-01 -1.23621750e+00
-3.97882909e-01 3.34579170e-01 1.81453358e-02 -1.31758571... | [15.078801155090332, 5.73229455947876] |
905f688c-2f59-4b74-8825-4d150abc592f | cl-monoise-cross-lingual-lexical | null | null | https://aclanthology.org/2021.wnut-1.56 | https://aclanthology.org/2021.wnut-1.56.pdf | CL-MoNoise: Cross-lingual Lexical Normalization | Social media is notoriously difficult to process for existing natural language processing tools, because of spelling errors, non-standard words, shortenings, non-standard capitalization and punctuation. One method to circumvent these issues is to normalize input data before processing. Most previous work has focused on... | ['Rob van der Goot'] | null | null | null | null | emnlp-wnut-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [ 1.08529590e-01 -1.06505610e-01 -4.52116802e-02 -4.85844076e-01
-7.17672884e-01 -7.08446264e-01 5.81271410e-01 4.17247623e-01
-1.14538872e+00 7.91825056e-01 3.21291775e-01 -4.24757034e-01
5.00567019e-01 -5.23191869e-01 -4.66848731e-01 -2.21551001e-01
5.79866707e-01 4.18166965e-01 3.63946378e-01 -4.00890887... | [10.272357940673828, 10.035526275634766] |
356b9ae0-e427-4a91-920d-e62ec6d30220 | k-nearest-neighbor-optimization-via | 1906.04559 | null | https://arxiv.org/abs/1906.04559v1 | https://arxiv.org/pdf/1906.04559v1.pdf | k-Nearest Neighbor Optimization via Randomized Hyperstructure Convex Hull | In the k-nearest neighbor algorithm (k-NN), the determination of classes for test instances is usually performed via a majority vote system, which may ignore the similarities among data. In this research, the researcher proposes an approach to fine-tune the selection of neighbors to be passed to the majority vote syste... | ['Jasper Kyle Catapang'] | 2019-06-11 | null | null | null | null | ['unsupervised-spatial-clustering'] | ['time-series'] | [-1.00229368e-01 -6.38863891e-02 -5.61421096e-01 -5.14333904e-01
-4.01594907e-01 -5.85546553e-01 4.89411831e-01 4.24805850e-01
-5.90404034e-01 9.90977585e-01 -1.11827426e-01 -5.45013726e-01
-8.59975994e-01 -9.75126088e-01 1.34996116e-01 -8.63654792e-01
3.00425440e-01 6.99406147e-01 1.66222140e-01 -1.98868215... | [8.381767272949219, 4.331751823425293] |
6e9e274d-7cec-48f1-a8b2-4026ff20be51 | investigation-of-english-to-hindi-multimodal | null | null | https://aclanthology.org/2022.wat-1.15 | https://aclanthology.org/2022.wat-1.15.pdf | Investigation of English to Hindi Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation | Machine translation translates one natural language to another, a well-defined natural language processing task. Neural machine translation (NMT) is a widely accepted machine translation approach, but it requires a sufficient amount of training data, which is a challenging issue for low-resource pair translation. Moreo... | ['Sivaji Bandyopadhyay', 'Partha Pakray', 'Riyanka Manna', 'Md Faizal Karim', 'Rahul Singh', 'Sahinur Rahman Laskar'] | null | null | null | null | wat-2022-10 | ['transliteration'] | ['natural-language-processing'] | [ 4.27893639e-01 -1.49769828e-01 -2.48156637e-01 -2.14127913e-01
-1.67339563e+00 -8.82365406e-01 9.18628335e-01 -2.33150303e-01
-5.35934150e-01 1.06564558e+00 2.30594710e-01 -6.30416512e-01
6.97685838e-01 -3.59212130e-01 -9.06415939e-01 -3.74505430e-01
7.11998999e-01 1.08438218e+00 -2.94242859e-01 -5.18154144... | [11.493674278259277, 1.5339449644088745] |
77deba4c-086d-497e-b1cf-0b1bde02f4fa | local-radon-descriptors-for-image-search | 1710.04097 | null | http://arxiv.org/abs/1710.04097v1 | http://arxiv.org/pdf/1710.04097v1.pdf | Local Radon Descriptors for Image Search | Radon transform and its inverse operation are important techniques in medical
imaging tasks. Recently, there has been renewed interest in Radon transform for
applications such as content-based medical image retrieval. However, all
studies so far have used Radon transform as a global or quasi-global image
descriptor by ... | ['Morteza Babaie', 'Amin Khatami', 'M. E. Shiri', 'H. R. Tizhoosh'] | 2017-10-11 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 2.91469336e-01 -4.72724587e-01 -7.16172457e-02 -4.03413355e-01
-1.34830022e+00 -1.16063058e-01 7.90286422e-01 3.33613485e-01
-6.71089530e-01 5.06608725e-01 3.91792119e-01 -3.81215326e-02
-4.21916664e-01 -1.15911305e+00 -3.44932854e-01 -9.77493465e-01
-1.81290165e-01 2.69062072e-01 4.24144506e-01 -7.50772431... | [14.2034273147583, -1.3899301290512085] |
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