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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730850ed-f196-4b91-94f7-a2f5a2fcde0c | a-joint-training-framework-for-open-world | null | null | https://openreview.net/forum?id=HozL9BGbnKr | https://openreview.net/pdf?id=HozL9BGbnKr | A Joint Training Framework for Open-World Knowledge Graph Embeddings | Knowledge Graphs(KGs) represent factual information as graphs of entities connected by relations. Knowledge graph embeddings have emerged as a popular approach to encode this information for various downstream tasks like natural language inference, question answering and dialogue generation. As knowledge bases expand, ... | ['Balaraman Ravindran', 'Mitesh M Khapra', 'Beethika Tripathi', 'Karthik V'] | 2021-06-22 | null | null | null | akbc-2021-10 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-2.81592906e-01 6.65491283e-01 -3.70746017e-01 -1.23953290e-01
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-2.93032348e-01 7.30165064e-01 2.73946494e-01 -4.94657099... | [9.101128578186035, 8.08977222442627] |
d934773b-a41d-4e02-bf3f-379f8e6f1f7c | deep-quality-a-deep-no-reference-quality | 1609.07170 | null | http://arxiv.org/abs/1609.07170v1 | http://arxiv.org/pdf/1609.07170v1.pdf | Deep Quality: A Deep No-reference Quality Assessment System | Image quality assessment (IQA) continues to garner great interest in the
research community, particularly given the tremendous rise in consumer video
capture and streaming. Despite significant research effort in IQA in the past
few decades, the area of no-reference image quality assessment remains a great
challenge and... | ['Alexander Wong', 'Prajna Paramita Dash', 'Akshaya Mishra'] | 2016-09-22 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.14563870e-01 -5.54515660e-01 7.56502599e-02 -3.88110071e-01
-1.08495784e+00 -2.15531588e-01 4.47918087e-01 2.42844131e-02
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-3.36217463e-01 -2.95087278e-01 9.45833791e-03 -3.09656739... | [11.806092262268066, -1.8198254108428955] |
0709f91f-d435-4fbe-ad30-b60656221aa4 | cot-mote-exploring-contextual-masked-auto | 2304.10195 | null | https://arxiv.org/abs/2304.10195v1 | https://arxiv.org/pdf/2304.10195v1.pdf | CoT-MoTE: Exploring ConTextual Masked Auto-Encoder Pre-training with Mixture-of-Textual-Experts for Passage Retrieval | Passage retrieval aims to retrieve relevant passages from large collections of the open-domain corpus. Contextual Masked Auto-Encoding has been proven effective in representation bottleneck pre-training of a monolithic dual-encoder for passage retrieval. Siamese or fully separated dual-encoders are often adopted as bas... | ['Songlin Hu', 'Peng Wang', 'Xing Wu', 'Guangyuan Ma'] | 2023-04-20 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-1.81668013e-01 -4.58231419e-01 -7.70688653e-02 -1.34154350e-01
-1.74624753e+00 -7.18122900e-01 7.74688125e-01 3.82973313e-01
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7.33388215e-02 6.36088729e-01 7.14137927e-02 -4.46002811... | [11.460837364196777, 7.698537349700928] |
d06d07d4-8549-421b-8db2-48a3f162a9cb | cheating-off-your-neighbors-improving | 2306.06078 | null | https://arxiv.org/abs/2306.06078v1 | https://arxiv.org/pdf/2306.06078v1.pdf | Cheating off your neighbors: Improving activity recognition through corroboration | Understanding the complexity of human activities solely through an individual's data can be challenging. However, in many situations, surrounding individuals are likely performing similar activities, while existing human activity recognition approaches focus almost exclusively on individual measurements and largely ign... | ['Christine Julien', 'Edison Thomaz', 'Evan King', 'Jingyi An', 'Haoxiang Yu'] | 2023-05-27 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 5.76330245e-01 -1.11257628e-01 -7.82384425e-02 -3.01628262e-01
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-1.82310387e-01 1.78302571e-01 1.70586929e-01 1.27498075... | [7.9137282371521, 0.5964123606681824] |
2c85cdf0-94f3-4048-a52f-46af5677ba08 | srcd-semantic-reasoning-with-compound-domains | 2307.01750 | null | https://arxiv.org/abs/2307.01750v2 | https://arxiv.org/pdf/2307.01750v2.pdf | SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection | This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structures of self-augmented compound cross-domain samples to enhance the model's generalization ability. Different from DGOD trained on multiple sou... | ['Song Guo', 'Xinghao Ding', 'Yue Huang', 'Luyao Tang', 'Jingcai Guo', 'Zhijie Rao'] | 2023-07-04 | null | null | null | null | ['object-detection'] | ['computer-vision'] | [ 3.32631111e-01 -5.69384806e-02 -3.69531244e-01 -3.87806654e-01
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3.05590034e-01 1.97614849e-01 5.38073421e-01 -2.54064649... | [10.075669288635254, 2.4394288063049316] |
386d0396-36df-4d1c-9c94-5cdcc60d0a9c | megacrn-meta-graph-convolutional-recurrent | 2212.05989 | null | https://arxiv.org/abs/2212.05989v2 | https://arxiv.org/pdf/2212.05989v2.pdf | MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling | Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity and non-stationarity implied in the graph streams, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure ... | ['Shintaro Fukushima', 'Toyotaro Suzumura', 'Xuan Song', 'Yasumasa Kobayashi', 'Quanjun Chen', 'Puneet Jeph', 'Jiawei Yong', 'Zhaonan Wang', 'Renhe Jiang'] | 2022-12-12 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-1.68560833e-01 -1.87939167e-01 -3.73647839e-01 -1.65493235e-01
-5.79481542e-01 -4.43307817e-01 7.89424181e-01 2.57208496e-01
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-7.89804995e-01 2.09706485e-01 1.25999749e-01 -4.63839710... | [6.737337112426758, 2.699059247970581] |
3bd00cef-9210-4d32-8f80-245db6e93daf | learning-3d-human-pose-estimation-from-dozens | 2212.14474 | null | https://arxiv.org/abs/2212.14474v1 | https://arxiv.org/pdf/2212.14474v1.pdf | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats | Deep learning-based 3D human pose estimation performs best when trained on large amounts of labeled data, making combined learning from many datasets an important research direction. One obstacle to this endeavor are the different skeleton formats provided by different datasets, i.e., they do not label the same set of ... | ['Bastian Leibe', 'Alexander Hermans', 'István Sárándi'] | 2022-12-29 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 7.85622671e-02 1.67147309e-01 -4.41757143e-01 -4.42283422e-01
-9.05811191e-01 -4.75938946e-01 3.86333764e-01 -1.37573332e-01
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-1.16296090e-01 -3.97235006e-01 -7.65856266e-01 -6.31620467e-01
1.35483503e-01 9.25402164e-01 4.42101844e-02 1.51848635... | [6.926694393157959, -0.9942705631256104] |
5d475081-4ffc-442d-82ad-a733d10474c4 | optimality-of-thompson-sampling-with | 2302.01544 | null | https://arxiv.org/abs/2302.01544v1 | https://arxiv.org/pdf/2302.01544v1.pdf | Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits | In the stochastic multi-armed bandit problem, a randomized probability matching policy called Thompson sampling (TS) has shown excellent performance in various reward models. In addition to the empirical performance, TS has been shown to achieve asymptotic problem-dependent lower bounds in several models. However, its ... | ['Masashi Sugiyama', 'Chao-Kai Chiang', 'Junya Honda', 'Jongyeong Lee'] | 2023-02-03 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-7.62051195e-02 -1.96786504e-02 -7.55467594e-01 -1.06536247e-01
-1.10799479e+00 -6.25509202e-01 3.01000625e-01 -3.12456023e-02
-4.81049091e-01 1.12476993e+00 9.57120061e-02 -7.28335559e-01
-7.05281734e-01 -6.64070070e-01 -8.11754823e-01 -8.95049930e-01
1.43644303e-01 5.91588378e-01 6.63620383e-02 1.08690098... | [4.529558181762695, 3.2811362743377686] |
86a283d1-ebb8-41ef-919e-51b81a0eb083 | heterogeneous-temporal-graph-transformer-an | null | null | https://dl.acm.org/doi/abs/10.1145/3447548.3467168 | https://dl.acm.org/doi/pdf/10.1145/3447548.3467168 | heterogeneous temporal graph transformer: an intelligent system for evolving android malware detection | The explosive growth and increasing sophistication of Android malware call for new defensive techniques to protect mobile users against novel threats. To address this challenge, in this paper, we propose and develop an intelligent system named Dr.Droid to jointly model malware propagation and evolution for their detect... | ['Qi Xiong', 'Yinming Mei', 'Kui Wang', 'Wenqiang Wan', 'Yanfang Ye', 'Shifu Hou', 'Mingxuan Ju', 'Yujie Fan'] | 2021-08-14 | null | null | null | kdd-2021-8 | ['android-malware-detection', 'mobile-security'] | ['miscellaneous', 'miscellaneous'] | [-5.97785041e-02 -3.82407218e-01 -5.59829175e-01 1.42074540e-01
-6.46818206e-02 -7.26953566e-01 7.46926308e-01 -7.09817559e-02
1.31159440e-01 2.64577180e-01 1.02140814e-01 -5.84904075e-01
-2.46510729e-01 -9.23160672e-01 -6.20423734e-01 -3.37816328e-01
-3.78381997e-01 5.34095243e-02 5.62617421e-01 -2.90041089... | [14.384057998657227, 9.661163330078125] |
82e24f91-70b3-4581-a10b-4b5aa2cd9fa7 | efficient-inference-in-phylogenetic-indel | null | null | http://papers.nips.cc/paper/3406-efficient-inference-in-phylogenetic-indel-trees | http://papers.nips.cc/paper/3406-efficient-inference-in-phylogenetic-indel-trees.pdf | Efficient Inference in Phylogenetic InDel Trees | Accurate and efficient inference in evolutionary trees is a central problem in computational biology. Realistic models require tracking insertions and deletions along the phylogenetic tree, making inference challenging. We propose new sampling techniques that speed up inference and improve the quality of the samples. W... | ['Michael. I. Jordan', 'Alexandre Bouchard-Côté', 'Dan Klein'] | 2008-12-01 | null | null | null | neurips-2008-12 | ['multiple-sequence-alignment'] | ['medical'] | [ 6.91442072e-01 -4.81092066e-01 -3.12936902e-01 -4.69002932e-01
-5.11883795e-01 -9.26598549e-01 7.92515054e-02 5.43663144e-01
-7.25306571e-01 1.24084353e+00 -1.31374430e-02 -5.00011563e-01
-2.14665115e-01 -5.81202388e-01 -7.38568544e-01 -8.09333563e-01
-1.13493674e-01 1.00033987e+00 5.31813204e-01 1.07723765... | [4.868743419647217, 5.155880451202393] |
cbbb5cbc-1053-4f62-9155-e17caa503d79 | fmg-net-and-w-net-multigrid-inspired-deep | 2304.02725 | null | https://arxiv.org/abs/2304.02725v1 | https://arxiv.org/pdf/2304.02725v1.pdf | FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation | Accurate medical imaging segmentation is critical for precise and effective medical interventions. However, despite the success of convolutional neural networks (CNNs) in medical image segmentation, they still face challenges in handling fine-scale features and variations in image scales. These challenges are particula... | ['David Fuentes', 'Beatrice Riviere', 'Adrian Celaya'] | 2023-04-05 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 1.86166137e-01 1.04659162e-01 -2.35791802e-01 -3.26651514e-01
-1.02924740e+00 -4.14642543e-01 1.23447157e-01 2.68706977e-01
-5.87905705e-01 6.38564050e-01 -1.81845829e-01 -4.92382824e-01
-8.88394341e-02 -7.52075076e-01 -4.07459855e-01 -7.59226620e-01
-8.57864693e-02 6.57683372e-01 3.34575117e-01 -1.60892397... | [14.499258995056152, -2.530097246170044] |
b99b97c5-434a-4d7f-b857-94c06dbdac06 | improving-the-neural-network-based-machine | null | null | https://aclanthology.org/Y18-1038 | https://aclanthology.org/Y18-1038.pdf | Improving the neural network-based machine transliteration for low-resourced language pair | null | ['Fatiha Sadat', 'Ngoc Tan Le'] | null | null | null | null | paclic-2018-12 | ['transliteration'] | ['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... | [-7.351063251495361, 3.6975409984588623] |
5f56a25c-ab80-4b25-9bef-5929b35df4fe | entity-relation-and-event-extraction-with | 1909.03546 | null | https://arxiv.org/abs/1909.03546v2 | https://arxiv.org/pdf/1909.03546v2.pdf | Entity, Relation, and Event Extraction with Contextualized Span Representations | We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by enumerating, refining, and scoring text spans designed to capture local (within-sentence) a... | ['Hannaneh Hajishirzi', 'Yi Luan', 'Ulme Wennberg', 'David Wadden'] | 2019-09-08 | entity-relation-and-event-extraction-with-1 | https://aclanthology.org/D19-1585 | https://aclanthology.org/D19-1585.pdf | ijcnlp-2019-11 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.70485526e-01 1.68898657e-01 -3.85228723e-01 -4.13079888e-01
-1.10974383e+00 -7.55477846e-01 5.92236757e-01 9.22072649e-01
-4.45638150e-01 7.35987961e-01 8.72642338e-01 -1.93472207e-01
-1.94119856e-01 -7.56078959e-01 -4.31422621e-01 6.87445849e-02
-3.96930546e-01 4.92227763e-01 2.87244260e-01 -1.44544750... | [9.314842224121094, 9.08706283569336] |
83d7db60-32c0-43ca-a36d-dc1845b112a7 | 3d-pose-estimation-and-future-motion | 2111.13285 | null | https://arxiv.org/abs/2111.13285v1 | https://arxiv.org/pdf/2111.13285v1.pdf | 3D Pose Estimation and Future Motion Prediction from 2D Images | This paper considers to jointly tackle the highly correlated tasks of estimating 3D human body poses and predicting future 3D motions from RGB image sequences. Based on Lie algebra pose representation, a novel self-projection mechanism is proposed that naturally preserves human motion kinematics. This is further facili... | ['Li Cheng', 'Minglun Gong', 'Sen Wang', 'Xinxin Zuo', 'Youdong Ma', 'Ji Yang'] | 2021-11-26 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 4.50609140e-02 9.23634171e-02 -8.12739059e-02 -1.56695545e-01
-7.22144127e-01 -1.18318334e-01 5.82970798e-01 -5.56107879e-01
-6.57747626e-01 4.79631484e-01 5.46207666e-01 3.32998931e-01
3.23218286e-01 -1.31945550e-01 -7.80543804e-01 -4.51010704e-01
-1.73627347e-01 3.32361400e-01 1.99849233e-01 -3.33854914... | [7.1775641441345215, -0.6116892099380493] |
1ae6c26c-da89-4594-b43a-5737c126ad2f | domain-generalization-for-mammographic-image | 2304.10226 | null | https://arxiv.org/abs/2304.10226v4 | https://arxiv.org/pdf/2304.10226v4.pdf | Domain Generalization for Mammographic Image Analysis with Contrastive Learning | The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires large data with diverse styles and qualities. The diversity of data often comes from the use of various ... | ['Jie-Zhi Cheng', 'Dinggang Shen', 'Chunling Liu', 'Zaiyi Liu', 'Yajia Gu', 'Xiangyu Zhao', 'Dongdong Chen', 'Xi Ouyang', 'Chenjin Lei', 'Sheng Wang', 'Lichi Zhang', 'Zhiming Cui', 'Zheren Li'] | 2023-04-20 | null | null | null | null | ['style-generalization', 'breast-density-classification', 'self-learning'] | ['computer-vision', 'medical', 'natural-language-processing'] | [ 3.10728788e-01 6.25399798e-02 -1.69073865e-01 -8.76994431e-01
-7.92537868e-01 -8.41748416e-02 2.96686769e-01 5.27530946e-02
-2.93044090e-01 4.60087925e-01 6.72653392e-02 -3.83959591e-01
-2.51898497e-01 -6.68989062e-01 -5.50062835e-01 -9.90456343e-01
1.24855831e-01 4.67203945e-01 8.23672563e-02 -2.47036219... | [14.813976287841797, -2.0026071071624756] |
fcd53fa7-83e4-4529-af82-e239c01aafee | advances-in-hyperspectral-image | 1310.5107 | null | http://arxiv.org/abs/1310.5107v1 | http://arxiv.org/pdf/1310.5107v1.pdf | Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods | Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These pecul... | ['Jón Atli Benediktsson', 'Gustavo Camps-Valls', 'Devis Tuia', 'Lorenzo Bruzzone'] | 2013-10-18 | null | null | null | null | ['classification-of-hyperspectral-images', 'remote-sensing-image-classification'] | ['computer-vision', 'miscellaneous'] | [ 6.96173310e-01 -9.66399908e-02 -1.41945094e-01 -4.71897542e-01
-4.05720919e-01 -6.40059769e-01 5.61039090e-01 2.44340394e-02
-2.13935032e-01 7.94873059e-01 -3.28968853e-01 1.38757601e-02
-7.46924996e-01 -6.95236742e-01 -3.12676467e-02 -1.20579410e+00
-3.66810769e-01 2.21775874e-01 -2.41422012e-01 -3.67141142... | [9.993253707885742, -1.9149678945541382] |
ac81425e-bc7d-4d31-9481-c3c38b87a7a2 | multi-microphone-speaker-separation-by | 2303.07143 | null | https://arxiv.org/abs/2303.07143v1 | https://arxiv.org/pdf/2303.07143v1.pdf | Multi-Microphone Speaker Separation by Spatial Regions | We consider the task of region-based source separation of reverberant multi-microphone recordings. We assume pre-defined spatial regions with a single active source per region. The objective is to estimate the signals from the individual spatial regions as captured by a reference microphone while retaining a correspond... | ['Emanuël A. P. Habets', 'Wolfgang Mack', 'Srikanth Raj Chetupalli', 'Julian Wechsler'] | 2023-03-13 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 5.01856387e-01 -2.41272956e-01 2.67113388e-01 -3.79076183e-01
-1.43541539e+00 -1.00343752e+00 4.14370596e-01 -1.26233637e-01
-4.23844665e-01 4.22326118e-01 5.33590853e-01 -9.28461626e-02
-3.18496585e-01 -2.43055880e-01 -9.74368572e-01 -7.79744148e-01
-3.16978693e-01 -2.95017153e-01 1.69890746e-01 1.78414851... | [15.111133575439453, 5.769516468048096] |
1b1d3600-b2d1-45a0-a1fa-e1ca8fc112a1 | reasoning-over-different-types-of-knowledge | 2212.05767 | null | https://arxiv.org/abs/2212.05767v6 | https://arxiv.org/pdf/2212.05767v6.pdf | A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal | Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering and recommendation ... | ['Fuchun Sun', 'Xinwang Liu', 'Sihang Zhou', 'Siwei Wang', 'Wenxuan Tu', 'Yue Liu', 'Meng Liu', 'Lingyuan Meng', 'Ke Liang'] | 2022-12-12 | null | null | null | null | ['knowledge-graph-embedding', 'general-knowledge'] | ['graphs', 'miscellaneous'] | [-3.69607836e-01 6.46863461e-01 -7.78911233e-01 -1.33508340e-01
-1.44428894e-01 -5.60185075e-01 4.71010774e-01 2.37522185e-01
1.54762715e-01 8.13408554e-01 1.44963652e-01 -6.40915632e-01
-6.78851008e-01 -1.35127866e+00 -5.81269443e-01 -3.24824870e-01
1.16783539e-02 5.05815029e-01 5.75208485e-01 -4.22182530... | [8.863030433654785, 7.893924236297607] |
d05483ec-faaf-4939-90e3-2f662ae45133 | camera-based-image-forgery-localization-using | 1808.09714 | null | http://arxiv.org/abs/1808.09714v1 | http://arxiv.org/pdf/1808.09714v1.pdf | Camera-based Image Forgery Localization using Convolutional Neural Networks | Camera fingerprints are precious tools for a number of image forensics tasks.
A well-known example is the photo response non-uniformity (PRNU) noise pattern,
a powerful device fingerprint. Here, to address the image forgery localization
problem, we rely on noiseprint, a recently proposed CNN-based camera model
fingerpr... | ['Luisa Verdoliva', 'Davide Cozzolino'] | 2018-08-29 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 2.44771436e-01 -5.52878976e-01 9.28922147e-02 1.14726778e-02
-7.83878446e-01 -6.72773957e-01 3.11975896e-01 -2.00587347e-01
4.50964607e-02 3.57395172e-01 -5.01052327e-02 -1.79533467e-01
8.05070996e-02 -6.11698925e-01 -1.07372308e+00 -8.02161336e-01
4.18718159e-01 -4.22972977e-01 1.81013852e-01 3.18461031... | [12.3809814453125, 0.9952765703201294] |
152ea822-e34f-4e83-885a-521c07c7fae8 | a-new-baseline-for-greenai-finding-the | 2302.10798 | null | https://arxiv.org/abs/2302.10798v2 | https://arxiv.org/pdf/2302.10798v2.pdf | Lightweight Parameter Pruning for Energy-Efficient Deep Learning: A Binarized Gating Module Approach | The subject of green AI has been gaining attention within the deep learning community given the recent trend of ever larger and more complex neural network models. Existing solutions for reducing the computational load of training at inference time usually involve pruning the network parameters. Pruning schemes often c... | ['Sean Moran', 'Ruibo Shi', 'Fran Silavong', 'Pheobe Sun', 'Varun Babbar', 'Xiaoying Zhi'] | 2023-02-17 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 4.71283734e-01 3.61462116e-01 -8.28359574e-02 -5.91207027e-01
-1.95792884e-01 -2.38888800e-01 -1.07678249e-01 8.46172050e-02
-9.13670540e-01 7.24904656e-01 -5.65008163e-01 -6.57156229e-01
-3.82736802e-01 -1.05656660e+00 -7.30986834e-01 -6.22183800e-01
-1.45039037e-01 2.41742343e-01 4.02362198e-01 1.79227620... | [8.55786418914795, 3.1786952018737793] |
3b3ff4f5-ba39-4f7e-9dbb-374c2d40af8b | higher-order-graph-attention-network-for | 2306.15526 | null | https://arxiv.org/abs/2306.15526v1 | https://arxiv.org/pdf/2306.15526v1.pdf | Higher-order Graph Attention Network for Stock Selection with Joint Analysis | Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation an... | ['Yan Ge', 'Zheng Li', 'Xiang Li', 'Yiping Xia', 'Yang Qiao'] | 2023-06-27 | null | null | null | null | ['graph-attention', 'stock-prediction'] | ['graphs', 'time-series'] | [-8.34471643e-01 3.72776687e-02 -5.11198163e-01 -6.95097670e-02
2.32080162e-01 -6.20350301e-01 6.02863133e-01 4.20496054e-02
3.17046903e-02 3.28202665e-01 5.31875908e-01 -8.42294633e-01
-4.88554716e-01 -1.31156027e+00 -6.44752145e-01 -3.07502747e-01
-4.99695927e-01 4.13686544e-01 7.83374235e-02 -4.35179770... | [4.3458662033081055, 4.324032306671143] |
4128e61e-75cf-4fe6-b140-d09529cbb740 | a-data-dependent-multiscale-model-for | 1808.01047 | null | https://arxiv.org/abs/1808.01047v4 | https://arxiv.org/pdf/1808.01047v4.pdf | A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability | Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes. Its occurrence may lead to the propagation of significant estimation errors in the unmixing process. To address this issue, extended linear mixing models have been proposed which... | ['José Carlos Moreira Bermudez', 'Tales Imbiriba', 'Ricardo Augusto Borsoi'] | 2018-08-02 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.21179724e-01 -8.03539634e-01 5.52804708e-01 -1.12237133e-01
-4.43357527e-01 -5.45468032e-01 4.24212813e-01 4.32420410e-02
-3.94494563e-01 9.15914893e-01 -5.85175604e-02 1.49693445e-03
-4.71867323e-01 -7.42094576e-01 -3.89469326e-01 -1.11535418e+00
2.46265605e-01 3.70131999e-01 1.89416949e-02 -2.52756663... | [10.078707695007324, -2.089751720428467] |
ad17a78c-9733-452f-bf25-3eb249a0b868 | silveralign-mt-based-silver-data-algorithm | 2210.06207 | null | https://arxiv.org/abs/2210.06207v2 | https://arxiv.org/pdf/2210.06207v2.pdf | SilverAlign: MT-Based Silver Data Algorithm For Evaluating Word Alignment | Word alignments are essential for a variety of NLP tasks. Therefore, choosing the best approaches for their creation is crucial. However, the scarce availability of gold evaluation data makes the choice difficult. We propose SilverAlign, a new method to automatically create silver data for the evaluation of word aligne... | ['Hinrich Schütze', 'Silvia Severini', 'Abdullatif Köksal'] | 2022-10-12 | null | null | null | null | ['word-alignment'] | ['natural-language-processing'] | [ 2.71911155e-02 -2.69070119e-01 -3.79194528e-01 -2.89133161e-01
-1.31335044e+00 -9.88614440e-01 6.46177709e-01 2.18806982e-01
-9.41263974e-01 1.04150724e+00 2.09995866e-01 -5.81056893e-01
3.45827699e-01 -5.31737149e-01 -3.86241049e-01 -4.35574710e-01
4.33867961e-01 1.15421975e+00 2.01785803e-01 -6.46137536... | [11.235960960388184, 10.187694549560547] |
0febbf12-be27-44f7-aae0-559af9b3f324 | bcot-a-markerless-high-precision-3d-object | 2203.13437 | null | https://arxiv.org/abs/2203.13437v1 | https://arxiv.org/pdf/2203.13437v1.pdf | BCOT: A Markerless High-Precision 3D Object Tracking Benchmark | Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects without using markers. In this paper, we present a multi-view approach to estimate the accurate 3D poses of real moving objects, and then use bin... | ['Xueying Qin', 'Jason Gu', 'Te Li', 'Wenxuan Chen', 'Fan Zhong', 'Xin Cao', 'Shiqiang Zhu', 'Bin Wang', 'Jiachen Li'] | 2022-03-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_BCOT_A_Markerless_High-Precision_3D_Object_Tracking_Benchmark_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_BCOT_A_Markerless_High-Precision_3D_Object_Tracking_Benchmark_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-object-tracking'] | ['computer-vision'] | [-2.72459567e-01 -5.20972729e-01 -1.46388197e-02 5.99207468e-02
-5.82611799e-01 -9.29511011e-01 4.44204271e-01 -5.39920151e-01
-3.75398666e-01 2.93666780e-01 -4.26622748e-01 -9.90485549e-02
2.35217661e-01 -2.08762422e-01 -8.77218008e-01 -7.26698518e-01
1.96576431e-01 7.23619223e-01 1.00874352e+00 1.92424461... | [6.869147300720215, -2.154954671859741] |
63377b83-9894-4598-aa4e-b062fa1de5bc | compressive-self-localization-using-relative | 2208.08863 | null | https://arxiv.org/abs/2208.08863v1 | https://arxiv.org/pdf/2208.08863v1.pdf | Compressive Self-localization Using Relative Attribute Embedding | The use of relative attribute (e.g., beautiful, safe, convenient) -based image embeddings in visual place recognition, as a domain-adaptive compact image descriptor that is orthogonal to the typical approach of absolute attribute (e.g., color, shape, texture) -based image embeddings, is explored in this paper. | ['Kanji Tanaka', 'Ryogo Yamamoto'] | 2022-08-03 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-1.63294598e-01 -8.47755596e-02 -3.86493146e-01 -4.42396134e-01
-6.58270717e-02 -5.84707201e-01 1.09841990e+00 6.39907956e-01
-8.17794919e-01 5.85594594e-01 3.52334946e-01 5.82745522e-02
-3.55168253e-01 -6.33715987e-01 -2.96823353e-01 -6.85436428e-01
-7.67008141e-02 -5.78547455e-02 -2.24357411e-01 -2.23734409... | [7.720747947692871, -1.5958683490753174] |
8d71ef41-7c7f-4cb0-8222-a7d29993cc41 | real-time-hand-gesture-identification-in | 2303.02321 | null | https://arxiv.org/abs/2303.02321v1 | https://arxiv.org/pdf/2303.02321v1.pdf | Real-Time Hand Gesture Identification in Thermal Images | Hand gesture-based human-computer interaction is an important problem that is well explored using color camera data. In this work we proposed a hand gesture detection system using thermal images. Our system is capable of handling multiple hand regions in a frame and process it fast for real-time applications. Our syste... | ['Soumyabrata Dey', 'James Ballow'] | 2023-03-04 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition', 'hand-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.14802432e-01 -8.06895375e-01 -1.66605264e-02 -2.13257074e-01
-2.89786696e-01 -6.74547672e-01 1.97959796e-01 -5.67366540e-01
-1.05483437e+00 3.19356263e-01 -1.55402005e-01 -4.61349696e-01
4.06919003e-01 -4.17067528e-01 -5.98103367e-03 -9.53349769e-01
4.25938983e-03 5.39483845e-01 6.81720197e-01 2.14863166... | [6.497348308563232, -0.35542652010917664] |
7152836e-a683-4373-a572-3ce2da5d5a4b | don-t-freeze-finetune-encoders-for-better | 2307.01168 | null | https://arxiv.org/abs/2307.01168v1 | https://arxiv.org/pdf/2307.01168v1.pdf | Don't freeze: Finetune encoders for better Self-Supervised HAR | Recently self-supervised learning has been proposed in the field of human activity recognition as a solution to the labelled data availability problem. The idea being that by using pretext tasks such as reconstruction or contrastive predictive coding, useful representations can be learned that then can be used for clas... | ['Paul Lukowicz', 'Dominique Nshimyimana', 'Vitor Fortes Rey'] | 2023-07-03 | null | null | null | null | ['self-supervised-learning', 'activity-recognition', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'time-series'] | [ 7.16237605e-01 2.72091389e-01 -3.52277756e-01 -3.55552673e-01
-5.10713458e-01 -3.98936749e-01 1.04797626e+00 3.56121719e-01
-6.77445233e-01 9.71557021e-01 2.54596770e-01 1.09848902e-01
-4.74781901e-01 -5.17141521e-01 -6.07750237e-01 -5.90620637e-01
-1.02301925e-01 6.17284894e-01 4.48878437e-01 -1.08885430... | [9.880827903747559, 3.045367956161499] |
3fccc9d4-5a4a-4165-a0a2-1c2bc96ae32a | learning-from-what-is-already-out-there-few | 2301.03769 | null | https://arxiv.org/abs/2301.03769v1 | https://arxiv.org/pdf/2301.03769v1.pdf | Learning from What is Already Out There: Few-shot Sign Language Recognition with Online Dictionaries | Today's sign language recognition models require large training corpora of laboratory-like videos, whose collection involves an extensive workforce and financial resources. As a result, only a handful of such systems are publicly available, not to mention their limited localization capabilities for less-populated sign ... | ['Marek Hrúz', 'Matyáš Boháček'] | 2023-01-10 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 1.23301029e-01 -4.17379498e-01 -5.37288129e-01 -4.35862154e-01
-1.07548523e+00 -3.94622117e-01 4.78713393e-01 -7.24825740e-01
-7.42908478e-01 3.75202209e-01 3.41333330e-01 8.24132338e-02
6.43406287e-02 -4.30507272e-01 -6.19354129e-01 -6.00226879e-01
-7.69133791e-02 5.78146696e-01 5.29166222e-01 -3.23090643... | [9.146193504333496, -6.467928409576416] |
bf1acdf1-6633-45e7-b47e-3988176ea50a | prix-lm-pretraining-for-multilingual | 2110.08443 | null | https://arxiv.org/abs/2110.08443v2 | https://arxiv.org/pdf/2110.08443v2.pdf | Prix-LM: Pretraining for Multilingual Knowledge Base Construction | Knowledge bases (KBs) contain plenty of structured world and commonsense knowledge. As such, they often complement distributional text-based information and facilitate various downstream tasks. Since their manual construction is resource- and time-intensive, recent efforts have tried leveraging large pretrained languag... | ['Muhao Chen', 'Nigel Collier', 'Ivan Vulić', 'Fangyu Liu', 'Wenxuan Zhou'] | 2021-10-16 | null | https://aclanthology.org/2022.acl-long.371 | https://aclanthology.org/2022.acl-long.371.pdf | acl-2022-5 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-6.20127320e-01 2.08124325e-01 -9.40706074e-01 -2.10796788e-01
-1.05943680e+00 -7.17959523e-01 6.07758045e-01 3.92465174e-01
-6.20166421e-01 1.51780760e+00 6.56196654e-01 -4.57236916e-01
1.39848337e-01 -9.05861437e-01 -1.14106572e+00 -2.02583708e-02
2.19449192e-01 6.51587844e-01 1.06323116e-01 -6.46346092... | [9.541332244873047, 8.808162689208984] |
141a7dda-ff71-42ab-9f01-eedba263bec4 | multi-label-meta-weighting-for-long-tailed | 2306.10122 | null | https://arxiv.org/abs/2306.10122v1 | https://arxiv.org/pdf/2306.10122v1.pdf | Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation | This paper investigates the problem of scene graph generation in videos with the aim of capturing semantic relations between subjects and objects in the form of $\langle$subject, predicate, object$\rangle$ triplets. Recognizing the predicate between subject and object pairs is imbalanced and multi-label in nature, rang... | ['Cees G. M. Snoek', 'Pascal Mettes', 'Yingjun Du', 'Shuo Chen'] | 2023-06-16 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation', 'meta-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 4.62854683e-01 8.24774243e-03 -4.09177810e-01 -4.48494375e-01
-8.69191468e-01 -5.70624173e-01 5.05696177e-01 5.77605255e-02
-2.44765967e-01 6.72145188e-01 3.30609888e-01 -5.90898842e-02
-2.32349232e-01 -7.91157544e-01 -1.18492353e+00 -7.12014019e-01
-1.83902025e-01 5.91764987e-01 2.23854110e-01 -9.97711495... | [10.313591957092285, 1.7061268091201782] |
71baf2d4-0bd2-43d3-a608-995fcf65743d | visual-prompt-based-personalized-federated | 2303.08678 | null | https://arxiv.org/abs/2303.08678v1 | https://arxiv.org/pdf/2303.08678v1.pdf | Visual Prompt Based Personalized Federated Learning | As a popular paradigm of distributed learning, personalized federated learning (PFL) allows personalized models to improve generalization ability and robustness by utilizing knowledge from all distributed clients. Most existing PFL algorithms tackle personalization in a model-centric way, such as personalized layer par... | ['DaCheng Tao', 'Baoyuan Wu', 'Li Shen', 'Yan Sun', 'Wansen Wu', 'Guanghao Li'] | 2023-03-15 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-4.07151163e-01 -1.77113220e-01 -5.74443281e-01 -6.87287569e-01
-7.57261515e-01 -4.21628654e-01 2.41968155e-01 -7.53115043e-02
-1.14074498e-01 6.36530995e-01 6.90276846e-02 -1.45349562e-01
-1.93905622e-01 -6.07957423e-01 -8.19857299e-01 -9.97499466e-01
1.57076329e-01 7.86529958e-01 1.64651811e-01 3.49090695... | [5.8199262619018555, 6.2872796058654785] |
6ea15585-5125-4f0b-9a11-a7635f1d0221 | joint-biomedical-entity-and-relation | 2105.13456 | null | https://arxiv.org/abs/2105.13456v2 | https://arxiv.org/pdf/2105.13456v2.pdf | Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference | Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any external knowledge during inference. Due to the exponential growth of biomedical publications, models that do not go beyond their fixed set o... | ['Quan Hung Tran', 'ChengXiang Zhai', 'Heng Ji', 'Tuan Lai'] | 2021-05-27 | null | https://aclanthology.org/2021.acl-long.488 | https://aclanthology.org/2021.acl-long.488.pdf | acl-2021-5 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 6.52044639e-02 7.34552801e-01 -4.51176524e-01 -2.32131481e-01
-6.47096097e-01 -3.47904533e-01 3.62223089e-01 9.13764715e-01
-3.88165414e-01 9.89138901e-01 3.09101820e-01 -3.56870711e-01
-2.89115071e-01 -1.14809322e+00 -9.81873274e-01 -4.07490134e-01
-9.24462220e-04 6.68069124e-01 2.53666252e-01 -6.79501593... | [8.636580467224121, 8.70535945892334] |
cdf18f97-e3cf-4206-b587-1c82beef07eb | starss22-a-dataset-of-spatial-recordings-of | 2206.01948 | null | https://arxiv.org/abs/2206.01948v2 | https://arxiv.org/pdf/2206.01948v2.pdf | STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events | This report presents the Sony-TAu Realistic Spatial Soundscapes 2022 (STARS22) dataset for sound event localization and detection, comprised of spatial recordings of real scenes collected in various interiors of two different sites. The dataset is captured with a high resolution spherical microphone array and delivered... | ['Tuomas Virtanen', 'Yuki Mitsufuji', 'Shusuke Takahashi', 'Naoya Takahashi', 'Yuichiro Koyama', 'Daniel Krause', 'Sharath Adavanne', 'Parthasaarathy Sudarsanam', 'Kazuki Shimada', 'Archontis Politis'] | 2022-06-04 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 2.33147621e-01 -5.73920012e-01 8.65104139e-01 -8.35925341e-02
-1.61569989e+00 -9.48229551e-01 6.11475289e-01 7.43535981e-02
-3.45237225e-01 3.18891048e-01 5.43541968e-01 1.20015934e-01
-8.15876648e-02 -2.75855631e-01 -4.70377505e-01 -6.25667810e-01
-3.51106197e-01 -3.32131013e-02 5.98722100e-01 2.08348408... | [15.125121116638184, 5.2570719718933105] |
83de367b-b4e7-4dd5-8063-7b0b1a1726a8 | trust-your-nabla-gradient-based-intervention | 2211.13715 | null | https://arxiv.org/abs/2211.13715v2 | https://arxiv.org/pdf/2211.13715v2.pdf | Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery | Inferring causal structure from data is a challenging task of fundamental importance in science. Observational data are often insufficient to identify a system's causal structure uniquely. While conducting interventions (i.e., experiments) can improve the identifiability, such samples are usually challenging and expens... | ['Piotr Miłoś', 'Łukasz Kuciński', 'Stefan Bauer', 'Yashas Annadani', 'Nino Scherrer', 'Aleksandra Nowak', 'Michał Zając', 'Mateusz Olko'] | 2022-11-24 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.57367986e-01 5.99281639e-02 -8.81526411e-01 -1.72957599e-01
-6.14535511e-01 -5.33176899e-01 6.72761738e-01 2.73998290e-01
-1.89696506e-01 1.00524807e+00 3.31156939e-01 -9.25364375e-01
-6.52011395e-01 -5.48841298e-01 -1.11256969e+00 -5.49795151e-01
-4.41044927e-01 4.27617580e-01 -9.84899998e-02 3.77242833... | [7.86415958404541, 5.243180274963379] |
c2ec22ae-a2c1-4c16-8536-78e8d1df2eab | matrix-completion-with-heterogonous-cost | 2203.12120 | null | https://arxiv.org/abs/2203.12120v3 | https://arxiv.org/pdf/2203.12120v3.pdf | Matrix Completion with Heterogonous Cost | The matrix completion problem has been studied broadly under many underlying conditions. The problem has been explored under adaptive or non-adaptive, exact or estimation, single-phase or multi-phase, and many other categories. In most of these cases, the observation cost of each entry is uniform and has the same cost ... | ['Ilqar Ramazanli'] | 2022-03-23 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 4.19345796e-01 5.42996824e-02 -3.09779018e-01 1.17183469e-01
-5.60460091e-01 -9.56966579e-01 1.67015091e-01 4.70083177e-01
-2.44449854e-01 8.13863158e-01 8.38375092e-02 -2.45733514e-01
-4.89703447e-01 -6.39606595e-01 -7.81064868e-01 -9.67002571e-01
-4.65513259e-01 7.25560069e-01 1.57973275e-01 -3.85881141... | [6.936349391937256, 4.711359977722168] |
f840cb58-1287-44a5-8f11-8d3837d16737 | spanproto-a-two-stage-span-based-prototypical | 2210.09049 | null | https://arxiv.org/abs/2210.09049v2 | https://arxiv.org/pdf/2210.09049v2.pdf | SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition | Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. Previous methods solve this problem based on token-wise classification, which ignores the information of entity boundaries, and inevitably the performance is affected by the massive non-entity tokens. To this end, w... | ['Chengyu Wang', 'Chengcheng Han', 'Ming Gao', 'Jun Huang', 'Songfang Huang', 'Minghui Qiu', 'Chuanqi Tan', 'Jianing Wang'] | 2022-10-17 | null | null | null | null | ['few-shot-ner'] | ['natural-language-processing'] | [-2.27989912e-01 1.58130437e-01 -4.48465109e-01 -3.88426363e-01
-1.13086677e+00 -6.85641170e-01 3.40529174e-01 5.14004648e-01
-6.51432574e-01 7.13163912e-01 4.04390037e-01 3.99003848e-02
3.18549186e-01 -8.27347517e-01 -5.21430433e-01 -5.13648510e-01
-9.55278948e-02 4.35929596e-01 5.44075370e-01 1.19744621... | [9.566177368164062, 9.400917053222656] |
4cea5fe2-4407-4f14-821a-7ec622abee62 | dcl-net-deep-correspondence-learning-network | 2210.05232 | null | https://arxiv.org/abs/2210.05232v1 | https://arxiv.org/pdf/2210.05232v1.pdf | DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation | Establishment of point correspondence between camera and object coordinate systems is a promising way to solve 6D object poses. However, surrogate objectives of correspondence learning in 3D space are a step away from the true ones of object pose estimation, making the learning suboptimal for the end task. In this pape... | ['Kui Jia', 'Jiehong Lin', 'Hongyang Li'] | 2022-10-11 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.85123199e-01 -1.02172710e-01 -3.21541697e-01 -4.17649746e-01
-9.82564270e-01 -4.39196140e-01 5.02673924e-01 -1.35286823e-01
-1.87718272e-01 3.82301390e-01 -5.40481769e-02 3.05628508e-01
-4.24797982e-01 -5.00666261e-01 -9.80967879e-01 -4.93251979e-01
1.54612675e-01 7.42268562e-01 5.11017442e-02 1.43640980... | [7.500960350036621, -2.651764392852783] |
1ef2348f-15c3-4c74-8eed-39dbd1821cf6 | a-fast-successive-qp-algorithm-for-general | 2212.06983 | null | https://arxiv.org/abs/2212.06983v1 | https://arxiv.org/pdf/2212.06983v1.pdf | A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization | The mean and variance of portfolio returns are the standard quantities to measure the expected return and risk of a portfolio. Efficient portfolios that provide optimal trade-offs between mean and variance warrant consideration. To express a preference among these efficient portfolios, investors have put forward many m... | ['Daniel P. Palomar', 'Xiwen Wang', 'Shengjie Xiu'] | 2022-12-14 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-1.65421307e-01 -3.65384072e-01 -2.32214943e-01 -2.05372080e-01
-6.77932799e-01 -7.24537730e-01 1.50327399e-01 -1.24442548e-01
-1.76715910e-01 8.87012303e-01 -3.80192280e-01 -4.85694379e-01
-8.64303112e-01 -9.28144217e-01 -3.29184920e-01 -8.03430974e-01
5.97652718e-02 3.42822999e-01 8.56200010e-02 -1.37064219... | [5.036386489868164, 3.95029354095459] |
bac47a84-096f-4f34-be69-5768f8fab8e5 | bsp-net-generating-compact-meshes-via-binary | 1911.06971 | null | https://arxiv.org/abs/1911.06971v6 | https://arxiv.org/pdf/1911.06971v6.pdf | BSP-Net: Generating Compact Meshes via Binary Space Partitioning | Polygonal meshes are ubiquitous in the digital 3D domain, yet they have only played a minor role in the deep learning revolution. Leading methods for learning generative models of shapes rely on implicit functions, and generate meshes only after expensive iso-surfacing routines. To overcome these challenges, we are ins... | ['Hao Zhang', 'Andrea Tagliasacchi', 'Zhiqin Chen'] | 2019-11-16 | bsp-net-generating-compact-meshes-via-binary-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_BSP-Net_Generating_Compact_Meshes_via_Binary_Space_Partitioning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_BSP-Net_Generating_Compact_Meshes_via_Binary_Space_Partitioning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-shape-representation'] | ['computer-vision'] | [-1.31994560e-01 5.12033284e-01 7.39458278e-02 -9.13469940e-02
-7.18750894e-01 -7.27733314e-01 6.27758086e-01 -9.67678055e-02
2.27240831e-01 4.57810014e-01 -6.55309930e-02 -2.81278491e-01
2.52461713e-02 -1.48766160e+00 -1.24346387e+00 -5.95294297e-01
2.32633986e-02 9.90473211e-01 2.26563364e-01 -2.04329550... | [8.671662330627441, -3.6393113136291504] |
fd4b5232-eac9-4386-8146-4303dfa194c7 | extended-fastslam-using-cellular-multipath | 2301.07560 | null | https://arxiv.org/abs/2301.07560v2 | https://arxiv.org/pdf/2301.07560v2.pdf | Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information | Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath c... | ['Fredrik Tufvesson', 'Russ Whiton', 'Junshi Chen'] | 2023-01-18 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-3.69392663e-01 1.28177935e-02 1.40369073e-01 -1.61519591e-02
-8.16708624e-01 -7.67433345e-01 3.52728009e-01 -4.46974665e-01
-5.93124628e-01 1.20296681e+00 -5.17871827e-02 -6.95109248e-01
-8.51999447e-02 -5.05472600e-01 -7.15645373e-01 -9.05275881e-01
-3.57149452e-01 2.82366931e-01 -2.26116423e-02 -2.10936308... | [6.266557216644287, 1.0977281332015991] |
04780bec-f26f-40a8-bfa5-ca4a9e4ac5ec | open-source-hamnosys-parser-for-multilingual | 2204.06924 | null | https://arxiv.org/abs/2204.06924v3 | https://arxiv.org/pdf/2204.06924v3.pdf | Handling sign language transcription system with the computer-friendly numerical multilabels | This paper presents our recent developments in the automatic processing of sign language corpora using the Hamburg Sign Language Annotation System (HamNoSys). We designed an automated tool to convert HamNoSys annotations into numerical labels for defined initial features of body and hand positions. Our proposed numeric... | ['Milena Olech', 'Marta Plantykow', 'Sylwia Majchrowska'] | 2022-04-14 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-2.06530094e-01 9.01124850e-02 -7.23618045e-02 -6.86264336e-01
-8.38034570e-01 -6.78541899e-01 4.28079337e-01 -1.66104227e-01
-6.50466442e-01 8.79805624e-01 4.65013236e-01 5.57884062e-03
9.06978250e-02 -2.85619289e-01 -1.51309490e-01 -6.15060508e-01
3.30528587e-01 6.32900059e-01 3.98249596e-01 -1.51584983... | [9.129755973815918, -6.428381443023682] |
dc48ac40-ca61-46f6-9c72-82a6b045c6f5 | reconstruction-guided-attention-improves-the | 2209.13620 | null | https://arxiv.org/abs/2209.13620v2 | https://arxiv.org/pdf/2209.13620v2.pdf | Reconstruction-guided attention improves the robustness and shape processing of neural networks | Many visual phenomena suggest that humans use top-down generative or reconstructive processes to create visual percepts (e.g., imagery, object completion, pareidolia), but little is known about the role reconstruction plays in robust object recognition. We built an iterative encoder-decoder network that generates an ob... | ['Gregory J. Zelinsky', 'Hossein Adeli', 'Seoyoung Ahn'] | 2022-09-27 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 6.13933682e-01 1.27711589e-03 3.28095108e-01 -1.66951627e-01
-4.62807506e-01 -5.85404217e-01 8.36329103e-01 -1.42932862e-01
-5.13904691e-01 4.68276113e-01 3.49525928e-01 -3.97081494e-01
-5.85263371e-02 -6.56195462e-01 -1.08867073e+00 -8.15061927e-01
1.19159743e-01 3.51393409e-02 5.88616021e-02 -9.87179279... | [10.124659538269043, 2.3653786182403564] |
5293e48f-7790-423d-878c-cc5e89358761 | educational-multi-question-generation-for | null | null | https://aclanthology.org/2022.bea-1.26 | https://aclanthology.org/2022.bea-1.26.pdf | Educational Multi-Question Generation for Reading Comprehension | Automated question generation has made great advances with the help of large NLP generation models. However, typically only one question is generated for each intended answer. We propose a new task, Multi-Question Generation, aimed at generating multiple semantically similar but lexically diverse questions assessing th... | ['Katherine Stasaski', 'Tony Tu', 'Manav Rathod'] | null | null | null | null | naacl-bea-2022-7 | ['question-generation'] | ['natural-language-processing'] | [ 2.94442862e-01 7.54607022e-01 4.49259311e-01 -2.19180137e-01
-1.53530908e+00 -9.92326736e-01 6.62635088e-01 5.43625712e-01
-2.06124142e-01 1.01667142e+00 6.17712796e-01 -5.86149216e-01
-2.88291067e-01 -9.44596887e-01 -3.48070830e-01 1.37935475e-01
5.45337677e-01 6.88369215e-01 4.68453020e-01 -6.35636568... | [11.520038604736328, 8.101999282836914] |
69cb75ca-bc59-4707-b74c-c28c841319d1 | fine-grained-action-detection-with-rgb-and | 2302.02755 | null | https://arxiv.org/abs/2302.02755v1 | https://arxiv.org/pdf/2302.02755v1.pdf | Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks | As participants of the MediaEval 2022 Sport Task, we propose a two-stream network approach for the classification and detection of table tennis strokes. Each stream is a succession of 3D Convolutional Neural Network (CNN) blocks using attention mechanisms. Each stream processes different 4D inputs. Our method utilizes ... | ['Pierre-Etienne Martin', 'Finn Bartels', 'Leonard Hacker'] | 2023-02-06 | null | null | null | null | ['fine-grained-action-detection', 'action-classification', 'stroke-classification'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 2.32320458e-01 -1.90999657e-01 -1.04649328e-01 -8.31913948e-03
-6.04274869e-01 -6.16117597e-01 8.04157674e-01 -1.35164589e-01
-1.05957818e+00 3.37957710e-01 1.54084608e-01 -5.50823398e-02
3.42036545e-01 -7.69480109e-01 -8.92347217e-01 -5.24241447e-01
1.08885460e-01 4.14840192e-01 6.70780778e-01 -1.61465377... | [7.867064476013184, 0.0860719308257103] |
a69dce0e-73e8-46fb-ba80-ddedea1a70d9 | session-based-sequential-skip-prediction-via | 1902.04743 | null | http://arxiv.org/abs/1902.04743v1 | http://arxiv.org/pdf/1902.04743v1.pdf | Session-based Sequential Skip Prediction via Recurrent Neural Networks | The focus of WSDM cup 2019 is session-based sequential skip prediction, i.e.
predicting whether users will skip tracks, given their immediately preceding
interactions in their listening session. This paper provides the solution of
our team \textbf{ekffar} to this challenge. We focus on
recurrent-neural-network-based de... | ['Lin Zhu', 'Yihong Chen'] | 2019-02-13 | null | null | null | null | ['sequential-skip-prediction'] | ['time-series'] | [ 1.67713821e-01 -7.57936090e-02 -3.17224503e-01 -4.37985659e-01
-8.75165701e-01 -3.83231431e-01 3.12717408e-01 -2.08521113e-01
-3.75657588e-01 5.66815615e-01 6.28403842e-01 -3.38278115e-01
-2.97326863e-01 -3.54378313e-01 -6.91122711e-01 -2.91368425e-01
-1.76450029e-01 4.50767308e-01 1.42024130e-01 -3.16488385... | [15.61312198638916, 5.190537452697754] |
6ff8cf64-f2d2-40bb-8509-e86eadf7b831 | self-supervised-contrastive-attributed-graph | 2110.08264 | null | https://arxiv.org/abs/2110.08264v1 | https://arxiv.org/pdf/2110.08264v1.pdf | Self-supervised Contrastive Attributed Graph Clustering | Attributed graph clustering, which learns node representation from node attribute and topological graph for clustering, is a fundamental but challenging task for graph analysis. Recently, methods based on graph contrastive learning (GCL) have obtained impressive clustering performance on this task. Yet, we observe that... | ['Xinbo Gao', 'Ming Yang', 'Quanxue Gao', 'Wei Xia'] | 2021-10-15 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 2.80186273e-02 1.25773609e-01 -2.21635446e-01 -5.11178792e-01
-6.17703557e-01 -3.75894487e-01 4.89729375e-01 6.15624905e-01
5.78309521e-02 2.31291935e-01 -2.20079720e-01 -8.73026848e-02
-3.66805971e-01 -7.79263854e-01 -4.39855874e-01 -9.53542829e-01
-4.82450068e-01 5.01448154e-01 1.92185566e-01 2.61935562... | [7.324424743652344, 5.968616485595703] |
a5ccfd77-6fae-4ab3-aba3-52b5f967d451 | reflection-removal-using-a-dual-pixel-sensor | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Punnappurath_Reflection_Removal_Using_a_Dual-Pixel_Sensor_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Punnappurath_Reflection_Removal_Using_a_Dual-Pixel_Sensor_CVPR_2019_paper.pdf | Reflection Removal Using a Dual-Pixel Sensor | Reflection removal is the challenging problem of removing unwanted reflections that occur when imaging a scene that is behind a pane of glass. In this paper, we show that most cameras have an overlooked mechanism that can greatly simplify this task. Specifically, modern DLSR and smartphone cameras use dual pixel (DP) ... | [' Michael S. Brown', 'Abhijith Punnappurath'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['reflection-removal'] | ['computer-vision'] | [ 1.15631068e+00 -1.40059248e-01 6.13523483e-01 -1.40819564e-01
-6.44178867e-01 -4.47631180e-01 3.24934393e-01 -4.45442349e-01
-3.40876132e-01 5.41755974e-01 1.51947320e-01 -1.29219085e-01
2.72019684e-01 -6.31592035e-01 -5.67969739e-01 -1.19423544e+00
6.58736944e-01 -2.48109981e-01 4.82008010e-01 1.52948678... | [10.117599487304688, -2.8109686374664307] |
483de1cd-8084-48e2-9d77-fe68d292dd5b | unified-gradient-reweighting-for-model | 2010.13228 | null | https://arxiv.org/abs/2010.13228v1 | https://arxiv.org/pdf/2010.13228v1.pdf | Unified Gradient Reweighting for Model Biasing with Applications to Source Separation | Recent deep learning approaches have shown great improvement in audio source separation tasks. However, the vast majority of such work is focused on improving average separation performance, often neglecting to examine or control the distribution of the results. In this paper, we propose a simple, unified gradient rewe... | ['Paris Smaragdis', 'Dimitrios Bralios', 'Efthymios Tzinis'] | 2020-10-25 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.11502746e-01 -2.29674980e-01 7.14680180e-02 -3.80998284e-01
-7.26417065e-01 -5.22328615e-01 4.47396278e-01 1.06709458e-01
-4.84554976e-01 4.67648536e-01 1.41233400e-01 -2.37538040e-01
-3.51260960e-01 -3.38640630e-01 -4.01806861e-01 -9.52752769e-01
-1.41057283e-01 2.56235480e-01 3.70578259e-01 -1.12982243... | [15.408809661865234, 5.601019859313965] |
0d0f0627-c7e3-4939-a09a-3490d0080665 | updet-universal-multi-agent-reinforcement | 2101.08001 | null | https://arxiv.org/abs/2101.08001v3 | https://arxiv.org/pdf/2101.08001v3.pdf | UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers | Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model architecture related to fixed input and output dimensions. This hinders the experience accumulation and transfer of the learned agent over ... | ['Xiaodan Liang', 'Xiaojun Chang', 'Fengda Zhu', 'Siyi Hu'] | 2021-01-20 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-1.34242907e-01 1.62419468e-01 -1.54671654e-01 1.44339323e-01
-3.63652349e-01 -4.54277217e-01 6.58258677e-01 6.70209620e-03
-8.85684133e-01 8.56861353e-01 -3.26317586e-02 -2.58946776e-01
-3.48509192e-01 -6.39822066e-01 -6.90965354e-01 -7.65508413e-01
5.74603900e-02 7.88868368e-01 4.61977243e-01 -6.10648036... | [3.860304594039917, 1.7740404605865479] |
a5893ef2-58f6-47c7-bc5d-0e6a5d276386 | comprehensive-privacy-analysis-on-federated | 2205.11857 | null | https://arxiv.org/abs/2205.11857v2 | https://arxiv.org/pdf/2205.11857v2.pdf | Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attacks | In recent years, recommender systems are crucially important for the delivery of personalized services that satisfy users' preferences. With personalized recommendation services, users can enjoy a variety of recommendations such as movies, books, ads, restaurants, and more. Despite the great benefits, personalized reco... | ['Hongzhi Yin', 'Wei Yuan', 'Shijie Zhang'] | 2022-05-24 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [-1.20397275e-02 -1.06793620e-01 -4.61780876e-01 -5.80002785e-01
-3.02894831e-01 -1.22751772e+00 3.13163429e-01 -5.10941111e-02
1.37693286e-01 3.94809127e-01 1.79776445e-01 -4.69902426e-01
-3.39861959e-01 -1.01051307e+00 -5.17891586e-01 -7.49464035e-01
-1.12938480e-02 -3.29504609e-02 -8.81275088e-02 -2.40149468... | [5.902075290679932, 6.760597229003906] |
532c1619-0dc9-4076-9063-68e092bec0fa | 3d-pictorial-structures-revisited-multiple | null | null | https://ieeexplore.ieee.org/document/7360209/authors#authors | http://campar.in.tum.de/pub/belagiannis2016pami/belagiannis2016pami.pdf | 3D Pictorial Structures Revisited: Multiple Human Pose Estimation | We address the problem of 3D pose estimation of multiple humans from multiple views. The transition from single to multiple human pose estimation and from the 2D to 3D space is challenging due to a much larger state space, occlusions and across-view ambiguities when not knowing the identity of the humans in advance. To... | ['Slobodan Ilic', 'Mykhaylo Andriluka', 'Vasileios Belagiannis', 'Nassir Navab', 'Sikandar Amin', 'Bernt Schiele'] | 2016-10-01 | null | null | null | null | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [ 2.60486484e-01 1.81198403e-01 1.81155831e-01 -2.22110674e-01
-4.81679499e-01 -5.74453235e-01 6.30333483e-01 -9.14750472e-02
-6.49638295e-01 7.43050218e-01 3.84177850e-03 4.49437708e-01
9.19912979e-02 -1.67515725e-01 -8.26988816e-01 -4.42628741e-01
-3.56283225e-02 1.07973468e+00 6.16499126e-01 -7.78918192... | [7.045414924621582, -0.9893955588340759] |
6cc269d3-295b-445d-9cd3-37b9551f36be | does-constituency-analysis-enhance-domain | 2112.02955 | null | https://arxiv.org/abs/2112.02955v1 | https://arxiv.org/pdf/2112.02955v1.pdf | Does constituency analysis enhance domain-specific pre-trained BERT models for relation extraction? | Recently many studies have been conducted on the topic of relation extraction. The DrugProt track at BioCreative VII provides a manually-annotated corpus for the purpose of the development and evaluation of relation extraction systems, in which interactions between chemicals and genes are studied. We describe the ensem... | ['Claire Nédellec', 'Pierre Zweigenbaum', 'Robert Bossy', 'Louise Deléger', 'Anfu Tang'] | 2021-11-25 | null | null | null | null | ['drugprot'] | ['natural-language-processing'] | [ 2.12139487e-01 5.96881151e-01 -4.30905163e-01 -4.20082927e-01
-5.62233210e-01 -7.37164974e-01 6.38376772e-01 8.33365560e-01
-9.41566974e-02 1.38094473e+00 3.59877646e-01 -6.58598721e-01
-2.53360808e-01 -7.25292027e-01 -6.69934332e-01 -5.63811123e-01
-1.32790981e-02 7.28612721e-01 2.00737447e-01 -2.74083406... | [8.469704627990723, 8.7819242477417] |
e6c60932-d0bc-4ddf-8642-7188eb1e7c0b | noise-estimation-for-generative-diffusion | 2104.02600 | null | https://arxiv.org/abs/2104.02600v2 | https://arxiv.org/pdf/2104.02600v2.pdf | Noise Estimation for Generative Diffusion Models | Generative diffusion models have emerged as leading models in speech and image generation. However, in order to perform well with a small number of denoising steps, a costly tuning of the set of noise parameters is needed. In this work, we present a simple and versatile learning scheme that can step-by-step adjust thos... | ['Lior Wolf', 'Eliya Nachmani', 'Robin San-Roman'] | 2021-04-06 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 9.16314274e-02 -2.05145534e-02 4.06266868e-01 -7.96986520e-02
-7.37536907e-01 -4.76564080e-01 7.14829266e-01 2.59091765e-01
-4.68044788e-01 6.48921609e-01 -2.19157830e-01 -2.33929724e-01
-9.69296917e-02 -8.89423370e-01 -3.76978964e-01 -8.42285872e-01
1.96191698e-01 3.88605058e-01 4.98860955e-01 -4.17650402... | [15.123665809631348, 5.917774677276611] |
cf8c677f-4ffc-4e7a-8f5a-072f0a53c7a5 | cell-detection-on-image-based-immunoassays | 1810.09707 | null | http://arxiv.org/abs/1810.09707v1 | http://arxiv.org/pdf/1810.09707v1.pdf | Cell detection on image-based immunoassays | Cell detection and counting in the image-based ELISPOT and Fluorospot
immunoassays is considered a bottleneck. The task has remained hard to
automatize, and biomedical researchers often have to rely on results that are
not accurate. Previously proposed solutions are heuristic, and data-based
solutions are subject to a ... | [] | 2018-10-23 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-7.32088313e-02 -6.59468174e-01 1.98763207e-01 -3.29466201e-02
-4.80600148e-01 -8.51163805e-01 3.40468943e-01 5.46880126e-01
-6.28580749e-01 1.22292471e+00 -6.48449719e-01 -2.68597245e-01
4.79948632e-02 -6.70081556e-01 -3.72691602e-01 -1.02997792e+00
1.01644590e-01 1.04967260e+00 2.10313782e-01 2.08479583... | [14.169873237609863, -3.164111852645874] |
386bef42-f868-40f5-bb11-ade76b249b66 | when-fair-classification-meets-noisy | 2307.03306 | null | https://arxiv.org/abs/2307.03306v2 | https://arxiv.org/pdf/2307.03306v2.pdf | When Fair Classification Meets Noisy Protected Attributes | The operationalization of algorithmic fairness comes with several practical challenges, not the least of which is the availability or reliability of protected attributes in datasets. In real-world contexts, practical and legal impediments may prevent the collection and use of demographic data, making it difficult to en... | ['Christo Wilson', 'Pablo Kvitca', 'Avijit Ghosh'] | 2023-07-06 | null | null | null | null | ['fairness', 'classification-1', 'fairness'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 2.58964241e-01 -4.85834517e-02 -3.26413482e-01 -8.69674504e-01
-8.02378237e-01 -7.35052884e-01 4.40298527e-01 5.05880058e-01
-7.88448513e-01 1.18927228e+00 1.98512927e-01 -5.95096231e-01
-4.21900392e-01 -7.68060327e-01 -2.77565330e-01 -6.12815499e-01
-2.01915260e-02 4.28246051e-01 -5.27137876e-01 -9.50751174... | [8.844497680664062, 5.304582118988037] |
9870d5f0-4653-4d05-a11a-f565f79eb877 | mattnet-modular-attention-network-for | 1801.08186 | null | http://arxiv.org/abs/1801.08186v3 | http://arxiv.org/pdf/1801.08186v3.pdf | MAttNet: Modular Attention Network for Referring Expression Comprehension | In this paper, we address referring expression comprehension: localizing an
image region described by a natural language expression. While most recent work
treats expressions as a single unit, we propose to decompose them into three
modular components related to subject appearance, location, and relationship to
other o... | ['Jimei Yang', 'Xiaohui Shen', 'Xin Lu', 'Tamara L. Berg', 'Licheng Yu', 'Zhe Lin', 'Mohit Bansal'] | 2018-01-24 | mattnet-modular-attention-network-for-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_MAttNet_Modular_Attention_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_MAttNet_Modular_Attention_CVPR_2018_paper.pdf | cvpr-2018-6 | ['generalized-referring-expression-segmentation', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.90803134e-01 2.58866429e-01 -8.52545723e-02 -6.17287040e-01
-5.05229950e-01 -3.88622642e-01 4.58878636e-01 2.45347455e-01
-4.80130672e-01 2.63749808e-01 2.93104887e-01 -5.84178865e-02
4.09594268e-01 -7.19377041e-01 -7.62070537e-01 -4.14510131e-01
1.92678437e-01 1.52134657e-01 5.53938806e-01 -1.49498805... | [10.458418846130371, 1.392624020576477] |
71eda82f-9fc0-401b-98d6-1ac6e9772a5a | idea-interpretable-dynamic-ensemble | 2201.05336 | null | https://arxiv.org/abs/2201.05336v1 | https://arxiv.org/pdf/2201.05336v1.pdf | IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction | We enhance the accuracy and generalization of univariate time series point prediction by an explainable ensemble on the fly. We propose an Interpretable Dynamic Ensemble Architecture (IDEA), in which interpretable base learners give predictions independently with sparse communication as a group. The model is composed o... | ['Tong Zhang', 'Kani Chen', 'Mengyue Zha'] | 2022-01-14 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 3.75419781e-02 4.55029607e-01 8.03880841e-02 -7.54159451e-01
-6.40975714e-01 -5.90945423e-01 7.16602564e-01 -2.69170552e-01
1.67844981e-01 9.06885028e-01 5.20548224e-01 -5.01197934e-01
-4.02224302e-01 -3.53265136e-01 -9.76299882e-01 -6.68773949e-01
-8.33117664e-01 7.66924977e-01 -5.12215436e-01 -6.84203744... | [6.9954986572265625, 3.0992963314056396] |
1b73c128-a9d0-4841-a6e2-52ba4cce3c4f | captioning-near-future-activity-sequences | 1908.00943 | null | https://arxiv.org/abs/1908.00943v5 | https://arxiv.org/pdf/1908.00943v5.pdf | Prediction and Description of Near-Future Activities in Video | Most of the existing works on human activity analysis focus on recognition or early recognition of the activity labels from complete or partial observations. Similarly, almost all of the existing video captioning approaches focus on the observed events in videos. Predicting the labels and the captions of future activit... | ['Amit K. Roy-Chowdhury', 'Mahmudul Hasan', 'Tahmida Mahmud', 'Mohammad Billah'] | 2019-08-02 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 9.24061358e-01 -3.69403698e-02 -6.34909749e-01 -6.24481916e-01
-4.93797988e-01 -4.19216543e-01 8.22432816e-01 7.29614720e-02
-1.72964856e-01 8.07834685e-01 6.67401910e-01 1.63372755e-01
2.39594921e-01 -1.38318256e-01 -8.33383441e-01 -4.58590060e-01
-3.24938655e-01 2.59233057e-01 6.70341969e-01 3.21004719... | [8.500168800354004, 0.6015111804008484] |
06775c4f-f626-4f2c-be73-62882290f698 | carbon-aware-ev-charging | 2209.12373 | null | https://arxiv.org/abs/2209.12373v1 | https://arxiv.org/pdf/2209.12373v1.pdf | Carbon-Aware EV Charging | This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration. The objective of the proposed charging scheme is to minimize the carbon emissions contributed by EV charging events, while simultaneously satisfying cons... | ['Yize Chen', 'Yuanyuan Shi', 'Yuexin Bian', 'Kai-Wen Cheng'] | 2022-09-26 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-6.96912035e-02 -1.06412144e-02 -2.44399667e-01 -3.60181630e-01
-5.32804787e-01 -9.27505612e-01 5.94228446e-01 2.83475071e-01
-3.19506377e-01 9.08020198e-01 -1.87977239e-01 -5.94353795e-01
-3.67044091e-01 -1.32808232e+00 -5.49161434e-01 -8.37940991e-01
1.58329695e-01 4.68827844e-01 -3.82573634e-01 3.95852700... | [5.6335673332214355, 2.359947443008423] |
1a97aa92-d067-4e52-8bdc-04ca3409d322 | structuring-representation-geometry-with | 2306.13924 | null | https://arxiv.org/abs/2306.13924v1 | https://arxiv.org/pdf/2306.13924v1.pdf | Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning | Self-supervised learning converts raw perceptual data such as images to a compact space where simple Euclidean distances measure meaningful variations in data. In this paper, we extend this formulation by adding additional geometric structure to the embedding space by enforcing transformations of input space to corresp... | ['Stefanie Jegelka', 'Soledad Villar', 'Derek Lim', 'Joshua Robinson', 'Sharut Gupta'] | 2023-06-24 | null | null | null | null | ['contrastive-learning', 'self-supervised-learning', 'contrastive-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 2.08129361e-01 2.18410626e-01 -4.38799337e-02 -4.30962026e-01
-5.89599609e-01 -1.01533473e+00 8.28652143e-01 8.00887868e-02
-5.13577938e-01 2.60058939e-01 4.97605324e-01 4.45033535e-02
9.14630014e-03 -6.21057153e-01 -8.83159220e-01 -5.45875847e-01
8.79541859e-02 -6.13734052e-02 -9.99359414e-02 -1.66093260... | [9.043534278869629, 2.8060855865478516] |
a481a658-92fa-471c-b06b-5b3456cb6ab2 | task-driven-and-experience-based-question | null | null | https://aclanthology.org/2022.lrec-1.670 | https://aclanthology.org/2022.lrec-1.670.pdf | Task-Driven and Experience-Based Question Answering Corpus for In-Home Robot Application in the House3D Virtual Environment | At present, more and more work has begun to pay attention to the long-term housekeeping robot scene. Naturally, we wonder whether the robot can answer the questions raised by the owner according to the actual situation at home. These questions usually do not have a clear text context, are directly related to the actual... | ['Yang Liu', 'Liubo Ouyang', 'Zhuoqun Xu'] | null | null | null | null | lrec-2022-6 | ['general-knowledge'] | ['miscellaneous'] | [-4.09983128e-01 3.59068990e-01 5.01886010e-01 -4.89959598e-01
-6.39026225e-01 -6.23814285e-01 5.30229867e-01 2.66463578e-01
-5.90247631e-01 7.93842912e-01 4.73278224e-01 -3.22238058e-01
-1.36731520e-01 -7.20594406e-01 -5.06215215e-01 -4.53026593e-02
1.14097670e-01 9.29904878e-01 3.32160354e-01 -9.01382089... | [4.446498394012451, 0.6592406630516052] |
fea6a145-393d-4c1d-8c68-14be81a99b11 | logo-net-large-scale-deep-logo-detection-and | 1511.02462 | null | http://arxiv.org/abs/1511.02462v2 | http://arxiv.org/pdf/1511.02462v2.pdf | LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks | Logo detection from images has many applications, particularly for brand
recognition and intellectual property protection. Most existing studies for
logo recognition and detection are based on small-scale datasets which are not
comprehensive enough when exploring emerging deep learning techniques. In this
paper, we int... | ['Hui Xue', 'Steven C. H. Hoi', 'Qiang Wu', 'Hantang Liu', 'Yue Wu', 'Xiongwei Wu', 'Huiqiong Wang'] | 2015-11-08 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [ 1.57291852e-02 -4.52348113e-01 -6.75234914e-01 -2.60803074e-01
-4.07086492e-01 -5.48667490e-01 3.14676821e-01 2.28420809e-01
3.08065444e-01 -1.53309733e-01 -3.09478551e-01 -2.66973078e-01
2.54042417e-01 -1.15639699e+00 -8.93856049e-01 -3.97963166e-01
-2.04761103e-01 4.90782708e-01 -5.99796064e-02 -1.99006364... | [9.32280158996582, 1.3059922456741333] |
f77ec3f8-03e2-4b12-86fb-5c926e95889c | visual-knowledge-tracing | 2207.10157 | null | https://arxiv.org/abs/2207.10157v2 | https://arxiv.org/pdf/2207.10157v2.pdf | Visual Knowledge Tracing | Each year, thousands of people learn new visual categorization tasks -- radiologists learn to recognize tumors, birdwatchers learn to distinguish similar species, and crowd workers learn how to annotate valuable data for applications like autonomous driving. As humans learn, their brain updates the visual features it e... | ['Oisin Mac Aodha', 'Pietro Perona', 'Neehar Kondapaneni'] | 2022-07-20 | null | null | null | null | ['classification'] | ['methodology'] | [-4.65078512e-03 -1.62349001e-01 -1.83471918e-01 -3.54431719e-01
-7.03893676e-02 -6.58005536e-01 6.53367937e-01 4.36519057e-01
-9.25823569e-01 6.16761029e-01 -1.17976978e-01 -1.92772001e-01
2.21566990e-01 -3.72070760e-01 -4.60066110e-01 -5.10221541e-01
-1.23499550e-01 5.21715224e-01 3.19736242e-01 1.98565740... | [9.77791690826416, 2.083139419555664] |
8d82b9cd-cff6-4c26-9f88-1bf30e14e2b0 | zoho-at-semeval-2019-task-9-semi-supervised | 1902.10623 | null | http://arxiv.org/abs/1902.10623v2 | http://arxiv.org/pdf/1902.10623v2.pdf | Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion Mining | This paper describes our submission for the SemEval-2019 Suggestion Mining
task. A simple Convolutional Neural Network (CNN) classifier with contextual
word representations from a pre-trained language model was used for sentence
classification. The model is trained using tri-training, a semi-supervised
bootstrapping me... | ['Sri Ananda Seelan', 'Sai Prasanna'] | 2019-02-27 | zoho-at-semeval-2019-task-9-semi-supervised-1 | https://aclanthology.org/S19-2225 | https://aclanthology.org/S19-2225.pdf | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 1.30671173e-01 4.49429035e-01 -2.43727013e-01 -6.77010417e-01
-7.70692110e-01 -4.33302432e-01 5.11211932e-01 5.12441456e-01
-8.99502099e-01 1.14285982e+00 2.31546193e-01 -8.75273347e-01
1.19603530e-01 -4.61091667e-01 -5.24331808e-01 -7.32786283e-02
-8.79398361e-02 6.39340818e-01 2.38769814e-01 -6.80644393... | [10.86599349975586, 7.60838508605957] |
b913126a-bff9-44eb-985c-cda2e6dee8be | lagrange-motion-analysis-and-view-embeddings | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chai_Lagrange_Motion_Analysis_and_View_Embeddings_for_Improved_Gait_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chai_Lagrange_Motion_Analysis_and_View_Embeddings_for_Improved_Gait_Recognition_CVPR_2022_paper.pdf | Lagrange Motion Analysis and View Embeddings for Improved Gait Recognition | Gait is considered the walking pattern of human body, which includes both shape and motion cues. However, the main-stream appearance-based methods for gait recognition rely on the shape of silhouette. It is unclear whether motion can be explicitly represented in the gait sequence modeling. In this paper, we analyze... | ['Yunhong Wang', 'Zilong Li', 'Shaoxiong Zhang', 'Annan Li', 'Tianrui Chai'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['gait-recognition'] | ['computer-vision'] | [-1.73286766e-01 -1.12044781e-01 -1.81243479e-01 -2.87764426e-02
9.78436545e-02 -2.15866357e-01 4.19949323e-01 -2.76411593e-01
-2.93643028e-01 5.55243671e-01 3.82084161e-01 3.89124639e-02
-3.27614322e-02 -7.72935390e-01 -1.62476242e-01 -7.39141524e-01
-4.59858567e-01 5.01293875e-02 4.50006276e-01 -2.25837231... | [14.227476119995117, 1.436474323272705] |
624cceb2-9ad6-4841-83ee-9d4f892d28cb | parametric-reshaping-of-portraits-in-videos | 2205.02538 | null | https://arxiv.org/abs/2205.02538v1 | https://arxiv.org/pdf/2205.02538v1.pdf | Parametric Reshaping of Portraits in Videos | Sharing short personalized videos to various social media networks has become quite popular in recent years. This raises the need for digital retouching of portraits in videos. However, applying portrait image editing directly on portrait video frames cannot generate smooth and stable video sequences. To this end, we p... | ['Xiaogang Jin', 'Yong-Liang Yang', 'Wenxin Sun', 'Xiangjun Tang'] | 2022-05-05 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 3.72135133e-01 -1.87211540e-02 2.99588311e-02 -3.58731002e-01
-2.98078805e-01 -6.91306829e-01 3.46860141e-01 -6.46655679e-01
-9.62656364e-02 5.65801203e-01 3.00814897e-01 4.40590650e-01
9.42119360e-02 -7.02554464e-01 -8.38301361e-01 -7.64033437e-01
3.88311148e-01 -9.38796252e-02 2.03547582e-01 -1.78102627... | [12.781636238098145, -0.2935020327568054] |
3cfb4e7e-c838-47eb-969f-44586af45d23 | integrated-triaging-for-fast-reading | 1909.13128 | null | https://arxiv.org/abs/1909.13128v1 | https://arxiv.org/pdf/1909.13128v1.pdf | Integrated Triaging for Fast Reading Comprehension | Although according to several benchmarks automatic machine reading comprehension (MRC) systems have recently reached super-human performance, less attention has been paid to their computational efficiency. However, efficiency is of crucial importance for training and deployment in real world applications. This paper in... | ['Boyi Li', 'Kilian Q. Weinberger', 'Ni Lao', 'John Blitzer', 'Lequn Wang', 'Felix Wu'] | 2019-09-28 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 4.83955383e-01 3.55868042e-01 1.57027543e-02 -3.87428224e-01
-8.31093490e-01 -6.65291250e-01 4.67475742e-01 4.52607334e-01
-7.93785870e-01 7.20417380e-01 3.00379097e-01 -7.60299325e-01
-3.02283168e-02 -7.06488252e-01 -7.09920883e-01 -3.33211392e-01
1.01024866e-01 5.65097809e-01 4.87030804e-01 -4.11068976... | [11.174077033996582, 8.301637649536133] |
4a5abd84-53c7-499e-98ff-2f816ffb1e76 | learning-to-view-decision-transformers-for | 2301.09544 | null | https://arxiv.org/abs/2301.09544v1 | https://arxiv.org/pdf/2301.09544v1.pdf | Learning to View: Decision Transformers for Active Object Detection | Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environment. In most robotic systems, perception is typically independent of motion planning. For example, traditional object detection is passive: ... | ['Arnie Sen', 'Rajasimman Madhivanan', 'Ding Zhao', 'Xuewei Qi', 'Mohit Deshpande', 'Nathalie Majcherczyk', 'Wenhao Ding'] | 2023-01-23 | null | null | null | null | ['active-object-detection', 'motion-planning'] | ['computer-vision', 'robots'] | [ 3.31689626e-01 5.84724784e-01 -1.57356963e-01 -3.92168254e-01
-6.42538428e-01 -7.14381516e-01 4.04636353e-01 6.60983399e-02
-8.35204601e-01 7.01365292e-01 1.85087062e-02 -6.10529270e-04
-8.46352130e-02 -8.19500089e-01 -1.00914991e+00 -9.48301375e-01
-2.08164424e-01 4.08818096e-01 4.30768639e-01 -1.34234428... | [4.61674690246582, 0.827681303024292] |
f93876a9-2d5e-4a8f-9eec-96ca255ba5ae | ear-u-net-efficientnet-and-attention-based | 2110.01014 | null | https://arxiv.org/abs/2110.01014v1 | https://arxiv.org/pdf/2110.01014v1.pdf | EAR-U-Net: EfficientNet and attention-based residual U-Net for automatic liver segmentation in CT | Purpose: This paper proposes a new network framework called EAR-U-Net, which leverages EfficientNetB4, attention gate, and residual learning techniques to achieve automatic and accurate liver segmentation. Methods: The proposed method is based on the U-Net framework. First, we use EfficientNetB4 as the encoder to extra... | ['Haiying Wang', 'Lubiao Zhou', 'Peiqing Lv', 'Xiangyang Zhang', 'Jinke Wang'] | 2021-10-03 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-1.27585456e-01 3.99668887e-02 -2.98991889e-01 -1.25871375e-01
-6.76683486e-01 -1.89759731e-01 3.16243649e-01 1.09219290e-01
-5.33425868e-01 6.97917163e-01 4.30481851e-01 -3.00110430e-01
2.10090168e-02 -6.23231590e-01 -4.03586894e-01 -7.74803340e-01
-9.53761935e-02 -5.19318655e-02 2.66512662e-01 8.97394046... | [14.558320045471191, -2.6657490730285645] |
59671223-f87c-4150-9997-d15f6b594053 | stay-on-topic-with-classifier-free-guidance | 2306.17806 | null | https://arxiv.org/abs/2306.17806v1 | https://arxiv.org/pdf/2306.17806v1.pdf | Stay on topic with Classifier-Free Guidance | Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Py... | ['Stella Biderman', 'Pawan Sasanka Ammanamanchi', 'Elad Levi', 'Alexander Spangher', 'Honglu Fan', 'Guillaume Sanchez'] | 2023-06-30 | null | null | null | null | ['code-generation', 'image-generation', 'zero-shot-learning', 'text-generation', 'machine-translation', 'lambada', 'common-sense-reasoning'] | ['computer-code', 'computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'reasoning'] | [ 2.84770995e-01 6.48387730e-01 -1.48714662e-01 -3.55741918e-01
-1.11218262e+00 -4.84875113e-01 1.01329923e+00 7.17419386e-02
-1.66442230e-01 8.70971799e-01 5.33163548e-01 -6.62388623e-01
1.46205276e-01 -3.48864377e-01 -8.27909946e-01 -1.02005228e-01
1.72172382e-01 7.41046369e-01 -2.04994544e-01 -3.38614285... | [11.538152694702148, 8.770659446716309] |
fa984957-44f6-40a6-8961-5915c9e5de42 | adversarial-machine-learning-based | 2208.05073 | null | https://arxiv.org/abs/2208.05073v1 | https://arxiv.org/pdf/2208.05073v1.pdf | Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services | In this paper, we study the expanding attack surface of Adversarial Machine Learning (AML) and the potential attacks against Vehicle-to-Microgrid (V2M) services. We present an anticipatory study of a multi-stage gray-box attack that can achieve a comparable result to a white-box attack. Adversaries aim to deceive the t... | ['Burak Kantarci', 'Ahmed Omara'] | 2022-08-09 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [-9.47460830e-02 4.00271088e-01 -5.00484146e-02 5.66336885e-02
-9.06627119e-01 -1.11272991e+00 6.15084827e-01 -7.42299706e-02
1.58669706e-02 7.30491996e-01 -5.92853010e-01 -9.15699959e-01
3.81261349e-01 -1.01844025e+00 -1.02032614e+00 -1.27853286e+00
-6.86750531e-01 2.10225776e-01 -6.00854792e-02 -3.38526890... | [5.560283660888672, 7.491052150726318] |
c96e7945-0585-4a08-b054-00cfa4c71424 | countering-the-influence-of-essay-length-in | null | null | https://aclanthology.org/2021.sustainlp-1.4 | https://aclanthology.org/2021.sustainlp-1.4.pdf | Countering the Influence of Essay Length in Neural Essay Scoring | Previous work has shown that automated essay scoring systems, in particular machine learning-based systems, are not capable of assessing the quality of essays, but are relying on essay length, a factor irrelevant to writing proficiency. In this work, we first show that state-of-the-art systems, recent neural essay scor... | ['Michael Strube', 'Sungho Jeon'] | null | null | null | null | emnlp-sustainlp-2021-11 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-1.43448487e-01 -5.32712676e-02 -1.43455118e-01 -6.43251181e-01
-4.84296113e-01 -5.83310187e-01 4.39500242e-01 4.82361794e-01
-7.88156271e-01 7.18151808e-01 2.36392036e-01 -3.56298864e-01
-4.47327942e-01 -9.56167758e-01 -4.23728563e-02 3.05757709e-02
7.78060257e-01 6.35417879e-01 1.23148337e-01 -4.19444740... | [11.285841941833496, 9.352557182312012] |
2775557a-5dc6-4ac6-9fd1-61235f64d964 | a-dynamic-mode-decomposition-approach-for | 2203.00004 | null | https://arxiv.org/abs/2203.00004v2 | https://arxiv.org/pdf/2203.00004v2.pdf | A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs | We propose a novel robust decentralized graph clustering algorithm that is provably equivalent to the popular spectral clustering approach. Our proposed method uses the existing wave equation clustering algorithm that is based on propagating waves through the graph. However, instead of using a fast Fourier transform (F... | ['Tuhin Sahai', 'Stefan Klus', 'Hongyu Zhu'] | 2022-02-26 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 5.07857017e-02 -4.68549505e-02 2.67260134e-01 2.86515057e-01
-5.76541007e-01 -8.20089042e-01 3.01716477e-01 3.97376567e-01
-2.52185374e-01 2.01578736e-01 -2.65948117e-01 -4.19423252e-01
-5.92047155e-01 -9.64243054e-01 -4.28283036e-01 -1.12718904e+00
-6.36911213e-01 4.69457299e-01 3.00288886e-01 -1.50241882... | [7.076850891113281, 5.071138381958008] |
d9f087a8-295b-42af-af8d-4a85433857bb | r-2-range-regularization-for-model | 2303.08253 | null | https://arxiv.org/abs/2303.08253v1 | https://arxiv.org/pdf/2303.08253v1.pdf | R^2: Range Regularization for Model Compression and Quantization | Model parameter regularization is a widely used technique to improve generalization, but also can be used to shape the weight distributions for various purposes. In this work, we shed light on how weight regularization can assist model quantization and compression techniques, and then propose range regularization (R^2)... | ['Saurabh Adya', 'Minsik Cho', 'Srijan Mishra', 'Chungkuk Yoo', 'Arnav Kundu'] | 2023-03-14 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 2.89800018e-01 -4.36348841e-02 -1.07431984e+00 -5.60535312e-01
-8.46550703e-01 -2.10380912e-01 2.27867514e-01 2.59541154e-01
-5.98090887e-01 4.21407074e-01 3.75954092e-01 -5.80351889e-01
-3.35377790e-02 -6.68116510e-01 -7.92795241e-01 -4.38516945e-01
-9.88555104e-02 1.77216396e-01 1.34030178e-01 -2.32673779... | [8.65261459350586, 3.264244794845581] |
19bff1ff-8598-4adf-8c32-031a3394de67 | attribute-value-generation-from-product-title | null | null | https://aclanthology.org/2021.ecnlp-1.2 | https://aclanthology.org/2021.ecnlp-1.2.pdf | Attribute Value Generation from Product Title using Language Models | Identifying the value of product attribute is essential for many e-commerce functions such as product search and product recommendations. Therefore, identifying attribute values from unstructured product descriptions is a critical undertaking for any e-commerce retailer. What makes this problem challenging is the diver... | ['Manish Pandey', 'Pawan Goyal', 'Kalyani Roy'] | null | null | null | null | acl-ecnlp-2021-8 | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 1.70733705e-01 1.36340320e-01 -5.54213226e-01 -8.51016998e-01
-1.05177510e+00 -9.32244480e-01 5.00673950e-01 2.74321586e-01
-2.97667474e-01 4.96900052e-01 3.40826623e-02 -4.60038185e-01
-1.25599101e-01 -1.14249861e+00 -4.43900645e-01 -4.86864477e-01
4.70103323e-02 1.10757124e+00 -9.55183804e-03 -5.70182383... | [9.975106239318848, 6.28148889541626] |
f4cfee60-f07b-4b29-a0ac-a273b68b40bb | framework-for-2d-ad-placements-in-lineartv | 2212.02450 | null | https://arxiv.org/abs/2212.02450v1 | https://arxiv.org/pdf/2212.02450v1.pdf | Framework for 2D Ad placements in LinearTV | Virtual Product placement(VPP) is the advertising technique of digitally placing a branded object into the scene of a movie or TV show. This type of advertising provides the ability for brands to reach consumers without interrupting the viewing experience with a commercial break, as the products are seen in the backgro... | ['Sia Gholami', 'Karan Sindwani', 'Divya Bhargavi'] | 2022-12-05 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 5.34234047e-01 6.47333115e-02 1.08873390e-01 -2.61855632e-01
-4.34916258e-01 -1.36410046e+00 5.46662450e-01 4.96847212e-01
1.68833837e-01 -8.99584889e-02 -1.20233171e-01 -6.50295794e-01
2.90793777e-01 -7.23468482e-01 -8.74572515e-01 -7.63701871e-02
2.20071405e-01 6.38431251e-01 6.16088629e-01 -1.39379725... | [9.407934188842773, -2.6435210704803467] |
1989d763-434b-4e2c-9469-67adabc94182 | amrnet-chips-augmentation-in-areial-images | 2009.07168 | null | https://arxiv.org/abs/2009.07168v2 | https://arxiv.org/pdf/2009.07168v2.pdf | AMRNet: Chips Augmentation in Aerial Images Object Detection | Object detection in aerial images is a challenging task due to the following reasons: (1) objects are small and dense relative to images; (2) the object scale varies in a wide range; (3) the number of object in different classes is imbalanced. Many current methods adopt cropping idea: splitting high resolution images i... | ['Hongpeng Wang', 'Ye Tian', 'Xinghao Song', 'Zhiwei Wei', 'Chenzhen Duan'] | 2020-09-15 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 3.54897052e-01 -3.27562392e-01 -9.36678201e-02 -2.00158492e-01
-2.75194466e-01 -7.05453753e-01 -5.14709577e-02 -3.48065794e-01
-3.42137545e-01 4.85209823e-01 -4.53961700e-01 9.33795720e-02
-2.38449369e-02 -9.78500605e-01 -8.90443981e-01 -6.12759233e-01
1.60644636e-01 2.82960415e-01 9.21663463e-01 -3.95025350... | [8.741085052490234, -0.7671900987625122] |
9867e3b5-a5d3-4e28-892f-8e123a827ff7 | tbi-gan-an-adversarial-learning-approach-for | 2208.06099 | null | https://arxiv.org/abs/2208.06099v1 | https://arxiv.org/pdf/2208.06099v1.pdf | TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation | Brain network analysis for traumatic brain injury (TBI) patients is critical for its consciousness level assessment and prognosis evaluation, which requires the segmentation of certain consciousness-related brain regions. However, it is difficult to construct a TBI segmentation model as manually annotated MR scans of T... | ['Lichi Zhang', 'Zengxin Qi', 'Qian Wang', 'Zheren Li', 'Xuehai Wu', 'Ruizhe Zheng', 'Zhe Wang', 'Zeyu Wei', 'Kai Xuan', 'Zhenrong Shen', 'Sheng Wang', 'Di Zang', 'Xiangyu Zhao'] | 2022-08-12 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [ 4.63037461e-01 4.84038237e-03 2.03181863e-01 -3.32999796e-01
-5.84888220e-01 -1.83730230e-01 1.30396619e-01 -1.82798430e-01
-3.55909169e-01 7.11967289e-01 2.82985926e-01 5.73361106e-02
7.18827099e-02 -8.62512827e-01 -3.62585574e-01 -8.11267376e-01
4.02446568e-01 5.25420606e-01 2.41916433e-01 -7.37527609... | [14.104812622070312, -2.200671911239624] |
ef8abe12-5f90-4c5d-88fb-ee32575293aa | automatic-stroke-classification-of-tabla | 2104.09064 | null | https://arxiv.org/abs/2104.09064v1 | https://arxiv.org/pdf/2104.09064v1.pdf | Automatic Stroke Classification of Tabla Accompaniment in Hindustani Vocal Concert Audio | The tabla is a unique percussion instrument due to the combined harmonic and percussive nature of its timbre, and the contrasting harmonic frequency ranges of its two drums. This allows a tabla player to uniquely emphasize parts of the rhythmic cycle (theka) in order to mark the salient positions. An analysis of the lo... | ['Preeti Rao', 'Rohit M. A.'] | 2021-04-19 | null | null | null | null | ['stroke-classification'] | ['methodology'] | [ 4.80028391e-01 -1.83816671e-01 -1.71961024e-01 -2.17580348e-02
-7.09876001e-01 -9.17341828e-01 4.04415369e-01 -1.02137052e-01
-1.82204589e-01 4.68278140e-01 4.23340350e-01 -1.44610777e-01
-5.23111522e-01 -2.11149812e-01 -1.88138373e-02 -5.96195340e-01
1.37730213e-02 5.65140426e-01 2.65412241e-01 -4.68332350... | [15.854453086853027, 5.321338176727295] |
8bdc2fe2-b5f6-4cdd-a757-0068726d9bc9 | physics-informed-neural-networks-for-1 | null | null | http://phmpapers.org/index.php/phmconf/article/view/814 | http://phmpapers.org/index.php/phmconf/article/download/814/phmc_19_814 | Physics-informed neural networks for corrosion-fatigue prognosis | In this paper, we present a novel physics-informed neural network modeling approach for corrosion-fatigue. The hybrid approach is designed to merge physics- informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used to model the relati... | ['Arinan Dourado', 'Felipe A. C. Viana'] | 2019-09-22 | null | null | null | annual-conference-of-the-phm-society-2019-9 | ['physics-informed-machine-learning', 'graph-regression', 'graph-to-sequence'] | ['graphs', 'graphs', 'natural-language-processing'] | [-2.72163842e-02 -5.87972626e-02 6.19869947e-01 -2.10676283e-01
-3.50947589e-01 -1.05476499e-01 6.63291663e-02 1.94844127e-01
-1.20891720e-01 5.52482426e-01 -5.29415607e-02 -1.80872887e-01
-8.64234686e-01 -9.82919037e-01 -8.51643145e-01 -1.03428924e+00
-3.38729203e-01 8.06781948e-01 2.97166884e-01 -7.75641024... | [6.725043773651123, 2.4964754581451416] |
3dc55d07-ccc9-418a-af64-f9d95e980a18 | total-energy-shaping-with-neural | 2112.12999 | null | https://arxiv.org/abs/2112.12999v2 | https://arxiv.org/pdf/2112.12999v2.pdf | Total Energy Shaping with Neural Interconnection and Damping Assignment -- Passivity Based Control | In this work we exploit the universal approximation property of Neural Networks (NNs) to design interconnection and damping assignment (IDA) passivity-based control (PBC) schemes for fully-actuated mechanical systems in the port-Hamiltonian (pH) framework. To that end, we transform the IDA-PBC method into a supervised ... | ['Bayu Jayawardhana', 'Rodolfo Reyes-Baez', 'Santiago Sanchez-Escalonilla'] | 2021-12-24 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-7.52690760e-03 8.76249552e-01 -4.39124584e-01 2.95447737e-01
2.74281621e-01 -4.81109560e-01 4.25878674e-01 -1.47834435e-01
-3.12663913e-01 1.07631767e+00 -3.33669245e-01 -4.22035545e-01
-1.03023124e+00 -5.66544056e-01 -5.14501452e-01 -1.03990710e+00
-2.57570416e-01 1.18380018e-01 -1.49184644e-01 -7.06686974... | [5.477433204650879, 2.654442071914673] |
74f35c44-bb84-4be3-81c2-7ccdcfab0b9a | bundlerecon-ray-bundle-based-3d-neural | 2305.07342 | null | https://arxiv.org/abs/2305.07342v1 | https://arxiv.org/pdf/2305.07342v1.pdf | BundleRecon: Ray Bundle-Based 3D Neural Reconstruction | With the growing popularity of neural rendering, there has been an increasing number of neural implicit multi-view reconstruction methods. While many models have been enhanced in terms of positional encoding, sampling, rendering, and other aspects to improve the reconstruction quality, current methods do not fully leve... | ['Jianke Zhu', 'Weikun Zhang'] | 2023-05-12 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 2.08649486e-02 -1.55599192e-01 -3.98096442e-02 -3.30919802e-01
-3.71604770e-01 -1.14188731e-01 5.46215177e-01 -1.89852089e-01
-2.04515785e-01 8.24114382e-01 3.78431559e-01 1.48898035e-01
2.12251201e-01 -1.40955520e+00 -7.63104796e-01 -6.54403389e-01
5.00929356e-01 3.34641822e-02 5.44285893e-01 -7.94788003... | [9.249850273132324, -3.227365016937256] |
141bb994-eda6-47d4-8da6-cb6e5600176c | realized-recurrent-conditional | 2302.08002 | null | https://arxiv.org/abs/2302.08002v1 | https://arxiv.org/pdf/2302.08002v1.pdf | Realized recurrent conditional heteroskedasticity model for volatility modelling | We propose a new approach to volatility modelling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and distills modeling advances from financial econometrics, high frequency trading data and deep learning. Bayesian inference via the Sequential ... | ['Robert Kohn', 'Minh-Ngoc Tran', 'Chao Wang', 'Chen Liu'] | 2023-02-16 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-8.60560894e-01 -2.80978769e-01 -4.35030609e-02 -4.33064848e-01
-7.77576745e-01 -3.92140657e-01 1.27161944e+00 -8.00581351e-02
-4.37191635e-01 8.67732584e-01 2.41470858e-01 -6.48146510e-01
-2.66985595e-01 -1.25795841e+00 -2.31011927e-01 -6.22391760e-01
-5.46401620e-01 6.50620162e-01 -3.41848850e-01 -2.80858856... | [4.636246204376221, 4.135196208953857] |
a7318c4f-6189-4e9e-8320-0b1cd9f0eada | sports-camera-calibration-via-synthetic-data | 1810.10658 | null | http://arxiv.org/abs/1810.10658v1 | http://arxiv.org/pdf/1810.10658v1.pdf | Sports Camera Calibration via Synthetic Data | Calibrating sports cameras is important for autonomous broadcasting and
sports analysis. Here we propose a highly automatic method for calibrating
sports cameras from a single image using synthetic data. First, we develop a
novel camera pose engine. The camera pose engine has only three significant
free parameters so t... | ['James J. Little', 'Jianhui Chen'] | 2018-10-25 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 1.93591058e-01 -3.06981951e-01 4.65038419e-02 -3.85478348e-01
-1.41526425e+00 -9.18200910e-01 3.12596709e-01 -4.07578558e-01
-7.54174411e-01 4.43466961e-01 -1.19773418e-01 4.36000377e-01
5.25227129e-01 -8.08569729e-01 -1.44423759e+00 -6.43486738e-01
4.02018249e-01 4.65304106e-01 4.73939985e-01 -3.46279263... | [7.509117603302002, -1.4040178060531616] |
3065f3e9-7996-48dd-9fda-22593145b8d6 | system-identification-with-copula-entropy | 2304.12922 | null | https://arxiv.org/abs/2304.12922v1 | https://arxiv.org/pdf/2304.12922v1.pdf | System Identification with Copula Entropy | Identifying differential equation governing dynamical system is an important problem with wide applications. Copula Entropy (CE) is a mathematical concept for measuring statistical independence in information theory. In this paper we propose a method for identifying differential equation of dynamical systems with CE. T... | ['Jian Ma'] | 2023-04-23 | null | null | null | null | ['variable-selection'] | ['methodology'] | [-1.25446573e-01 -4.23491925e-01 2.28488401e-01 3.24576534e-02
-2.21101403e-01 -5.70578218e-01 5.26306033e-01 -8.37736428e-02
-5.57246745e-01 1.16036379e+00 -6.12287462e-01 -3.05018097e-01
-6.84400201e-01 -4.17741984e-01 5.82653992e-02 -1.07329106e+00
-2.58031338e-01 4.55760807e-01 4.94415127e-02 -1.30663425... | [7.113379955291748, 4.015048027038574] |
fd4321d8-f504-4c48-85ef-e8a11d216e07 | an-approach-to-intelligent-pneumonia | 2012.03487 | null | https://arxiv.org/abs/2012.03487v1 | https://arxiv.org/pdf/2012.03487v1.pdf | An Approach to Intelligent Pneumonia Detection and Integration | Each year, over 2.5 million people, most of them in developed countries, die from pneumonia [1]. Since many studies have proved pneumonia is successfully treatable when timely and correctly diagnosed, many of diagnosis aids have been developed, with AI-based methods achieving high accuracies [2]. However, currently, th... | ['Vamsi S. Pidikiti', 'Sayali R. Rajhans', 'Alena Iureva', 'Bonaventure F. P. Dossou'] | 2020-12-07 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 1.72526136e-01 -1.63033605e-01 -2.87475586e-01 4.49070595e-02
-5.33724725e-01 -4.55131263e-01 2.65096933e-01 2.64186323e-01
-4.37874675e-01 1.05027342e+00 2.35446319e-01 -3.46402079e-01
-1.60636708e-01 -6.15592122e-01 -2.89187543e-02 -6.15367293e-01
1.38520911e-01 9.78078425e-01 2.16187879e-01 3.89012843... | [15.578423500061035, -1.6638038158416748] |
91f4c1b6-fba0-4534-a793-1de8cca06d6e | dichromatic-model-based-temporal-color | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yoo_Dichromatic_Model_Based_Temporal_Color_Constancy_for_AC_Light_Sources_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yoo_Dichromatic_Model_Based_Temporal_Color_Constancy_for_AC_Light_Sources_CVPR_2019_paper.pdf | Dichromatic Model Based Temporal Color Constancy for AC Light Sources | Existing dichromatic color constancy approach commonly requires a number of spatial pixels which have high specularity. In this paper, we propose a novel approach to estimate the illuminant chromaticity of AC light source using high-speed camera. We found that the temporal observations of an image pixel at a fixed loca... | [' Jong-Ok Kim', 'Jun-Sang Yoo'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['color-constancy'] | ['computer-vision'] | [ 4.39567149e-01 -8.69299769e-01 2.61919707e-01 -1.78164646e-01
-2.40058899e-01 -8.62705588e-01 3.58072639e-01 -7.55949438e-01
-3.34446967e-01 8.15898657e-01 -1.26841232e-01 3.30173492e-01
2.82909065e-01 -5.73939383e-01 -5.62974930e-01 -1.08118260e+00
3.67451102e-01 -6.41237423e-02 3.01610351e-01 2.34325215... | [10.367412567138672, -2.667145252227783] |
03550177-c6d9-4edc-9f59-8ab0f2d3b31e | use-of-extended-kalman-filtering-in-detecting | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0017931006006533#preview-section-abstract | https://www.sciencedirect.com/science/article/abs/pii/S0017931006006533 | Use of extended Kalman filtering in detecting fouling in heat exchangers | This paper is concerned with how non-linear physical state space models can be applied to on-line detection of fouling in heat exchangers. The model parameters are estimated by using an extended Kalman filter and measurements of inlet and outlet temperatures and mass flow rates. In contrast to most conventional methods... | ['Bernard Desmet', 'Olafur P. Palsson', 'Sylvain Lalot', 'Gudmundur R. Jonsson'] | 2007-01-17 | null | null | null | journal-2007-1 | ['line-detection'] | ['computer-vision'] | [-1.73271447e-02 -4.89178121e-01 9.95018110e-02 1.98558077e-01
2.66498297e-01 -6.20017171e-01 2.67902404e-01 4.05230910e-01
-4.67415825e-02 1.08525884e+00 -4.82325673e-01 -5.34301937e-01
-2.28237018e-01 -6.36011899e-01 -3.24258417e-01 -7.07037747e-01
-5.39729238e-01 1.65774018e-01 -8.82436931e-02 9.66762453... | [6.041321754455566, 2.6544368267059326] |
2b45a392-9fbb-4876-814b-b9c771d158e3 | simple-and-effective-unsupervised-speech-1 | 2210.10191 | null | https://arxiv.org/abs/2210.10191v1 | https://arxiv.org/pdf/2210.10191v1.pdf | Simple and Effective Unsupervised Speech Translation | The amount of labeled data to train models for speech tasks is limited for most languages, however, the data scarcity is exacerbated for speech translation which requires labeled data covering two different languages. To address this issue, we study a simple and effective approach to build speech translation systems wi... | ['Juan Pino', 'Michael Auli', 'Wei-Ning Hsu', 'Yun Tang', 'Ilia Kulikov', 'Peng-Jen Chen', 'Hirofumi Inaguma', 'Changhan Wang'] | 2022-10-18 | null | null | null | null | ['speech-to-text-translation', 'unsupervised-speech-recognition'] | ['natural-language-processing', 'speech'] | [ 4.72138166e-01 3.41094077e-01 -4.86476481e-01 -6.66075826e-01
-1.68686581e+00 -7.27704048e-01 7.73109496e-01 -4.55884159e-01
-3.47685069e-01 9.11347151e-01 5.22469103e-01 -9.24392581e-01
6.12387836e-01 -1.42633349e-01 -7.63915479e-01 -4.14497793e-01
6.68789864e-01 1.09006143e+00 -1.08721264e-01 -3.38310093... | [14.489651679992676, 7.166929721832275] |
f33c714f-7a27-44a3-8bcc-945445a581ec | counterfactual-explanations-in-sequential | 2107.02776 | null | https://arxiv.org/abs/2107.02776v2 | https://arxiv.org/pdf/2107.02776v2.pdf | Counterfactual Explanations in Sequential Decision Making Under Uncertainty | Methods to find counterfactual explanations have predominantly focused on one step decision making processes. In this work, we initiate the development of methods to find counterfactual explanations for decision making processes in which multiple, dependent actions are taken sequentially over time. We start by formally... | ['Manuel Gomez-Rodriguez', 'Abir De', 'Stratis Tsirtsis'] | 2021-07-06 | null | http://proceedings.neurips.cc/paper/2021/hash/fd0a5a5e367a0955d81278062ef37429-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/fd0a5a5e367a0955d81278062ef37429-Paper.pdf | neurips-2021-12 | ['decision-making-under-uncertainty', 'counterfactual-explanation', 'decision-making-under-uncertainty'] | ['medical', 'miscellaneous', 'reasoning'] | [ 7.44876146e-01 9.20327544e-01 -3.60769242e-01 -1.96264178e-01
-3.54939610e-01 -4.51872647e-01 9.12057936e-01 2.16968566e-01
-3.56578141e-01 1.07430458e+00 6.95048749e-01 -1.03935897e+00
-8.18633199e-01 -6.44401848e-01 -4.06256706e-01 -5.27511954e-01
-5.30326843e-01 8.01512003e-01 -3.76376420e-01 1.45026073... | [8.109895706176758, 5.55803918838501] |
00b93cdb-b8db-4ab9-a5d3-47f79c49aa76 | situatedgen-incorporating-geographical-and | 2306.12552 | null | https://arxiv.org/abs/2306.12552v1 | https://arxiv.org/pdf/2306.12552v1.pdf | SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning | Recently, commonsense reasoning in text generation has attracted much attention. Generative commonsense reasoning is the task that requires machines, given a group of keywords, to compose a single coherent sentence with commonsense plausibility. While existing datasets targeting generative commonsense reasoning focus o... | ['Xiaojun Wan', 'Yunxiang Zhang'] | 2023-06-21 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 5.36261737e-01 5.44698596e-01 2.51598358e-02 -4.31215644e-01
-9.72825050e-01 -8.19374919e-01 1.40860260e+00 9.29875225e-02
1.02528244e-01 1.09015942e+00 7.25190341e-01 -5.89993954e-01
1.24413118e-01 -1.11095643e+00 -6.80080652e-01 -6.42555803e-02
5.70786655e-01 7.87140191e-01 -5.97744621e-02 -7.50986457... | [11.199752807617188, 8.743819236755371] |
444ff8af-fdb4-4356-97cb-701e30ed78b2 | generative-one-class-models-for-text-based | 1611.05915 | null | http://arxiv.org/abs/1611.05915v1 | http://arxiv.org/pdf/1611.05915v1.pdf | Generative One-Class Models for Text-based Person Retrieval in Forensic Applications | Automatic forensic image analysis assists criminal investigation experts in
the search for suspicious persons, abnormal behaviors detection and identity
matching in images. In this paper we propose a person retrieval system that
uses textual queries (e.g., "black trousers and green shirt") as descriptions
and a one-cla... | ['Hedvig Kjellström', 'David Gerónimo'] | 2016-11-17 | null | null | null | null | ['person-retrieval', 'nlp-based-person-retrival'] | ['computer-vision', 'computer-vision'] | [ 1.94718644e-01 -5.99736571e-01 2.94876456e-01 -4.50386226e-01
-6.68236494e-01 -6.32323086e-01 7.58973420e-01 1.51395068e-01
-6.57956839e-01 5.87907672e-01 -3.63629490e-01 -9.75464508e-02
-5.22122025e-01 -5.86033285e-01 -6.44270480e-02 -6.12331033e-01
1.32959455e-01 1.05242908e+00 4.32313472e-01 4.35332535... | [12.395585060119629, 0.9116864204406738] |
fcc4c7ae-a11a-42cf-a9ef-103bc74a9d5a | mac-a-novel-stochastic-optimization-method | 2304.12248 | null | https://arxiv.org/abs/2304.12248v1 | https://arxiv.org/pdf/2304.12248v1.pdf | MAC, a novel stochastic optimization method | A novel stochastic optimization method called MAC was suggested. The method is based on the calculation of the objective function at several random points and then an empirical expected value and an empirical covariance matrix are calculated. The empirical expected value is proven to converge to the optimum value of th... | ['János Tóth', 'Tamás Turányi', 'Goitom Simret Kidane', 'Attila László Nagy'] | 2023-04-14 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 3.59189250e-02 -3.76900196e-01 9.05063227e-02 8.63584206e-02
-3.05205673e-01 -4.75280106e-01 2.06914589e-01 1.50362924e-01
-6.32508039e-01 1.54214776e+00 -6.00755930e-01 -4.99802202e-01
-7.60354400e-01 -7.54509389e-01 -1.18432939e-01 -1.21719813e+00
-3.37119997e-01 5.60395837e-01 1.31926134e-01 -2.04895392... | [5.708166599273682, 3.4743728637695312] |
31148a15-3b4f-45d9-b8cd-56adf48d5b9f | learning-disentangled-label-representations | 2212.01461 | null | https://arxiv.org/abs/2212.01461v1 | https://arxiv.org/pdf/2212.01461v1.pdf | Learning Disentangled Label Representations for Multi-label Classification | Although various methods have been proposed for multi-label classification, most approaches still follow the feature learning mechanism of the single-label (multi-class) classification, namely, learning a shared image feature to classify multiple labels. However, we find this One-shared-Feature-for-Multiple-Labels (OFM... | ['Kaiqi Huang', 'Xiaotang Chen', 'Naiyu Gao', 'Fei He', 'Jian Jia'] | 2022-12-02 | null | null | null | null | ['pedestrian-attribute-recognition', 'multi-label-learning'] | ['computer-vision', 'methodology'] | [ 3.02555174e-01 -1.15722001e-01 -4.66853857e-01 -7.58571267e-01
-9.95967150e-01 -5.72763324e-01 5.13787270e-01 -4.22527045e-02
-2.05441877e-01 6.51050866e-01 -1.65668711e-01 1.27343655e-01
-3.86702865e-01 -6.42623663e-01 -6.22135639e-01 -1.16962445e+00
4.43615645e-01 1.84506088e-01 2.71189376e-03 2.39674762... | [9.440239906311035, 4.061663627624512] |
f0a2477a-d836-44d8-a1c5-ce1d017b7d6a | shell-theory-a-statistical-model-of-reality | null | null | https://ieeexplore.ieee.org/document/9444188 | http://www.kind-of-works.com/papers/shell_theory_preprint.pdf | Shell Theory: A Statistical Model of Reality | The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically. We believe this problem is
rooted in the mismatch between existing statistical techniques ... | ['Yasuyuki Matsushita', 'Hongdong Li', 'Ngai-Man Cheung', 'Changhao Ren', 'Siying Liu', 'Wen-Yan Lin'] | 2021-05-28 | null | null | null | ieee-transactions-on-pattern-analysis-and-15 | ['unsupervised-anomaly-detection-with-specified-5', 'unsupervised-anomaly-detection-with-specified-4', 'unsupervised-anomaly-detection-with-specified-7', 'unsupervised-anomaly-detection-with-specified-6', 'unsupervised-anomaly-detection-with-specified', 'one-class-classifier'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 1.66949302e-01 5.32651007e-01 9.36288163e-02 -3.59593332e-01
-1.56721890e-01 -6.21377051e-01 1.09429049e+00 9.23023596e-02
-1.81267243e-02 4.00747061e-01 1.63510248e-01 -1.30793869e-01
-7.31662571e-01 -9.78154957e-01 -3.91373515e-01 -1.04949439e+00
-1.28540441e-01 1.18129790e+00 1.53287232e-01 -4.72860970... | [7.314537048339844, 4.594597816467285] |
e6da29a0-02dc-45fa-82c8-d0734ec68372 | combined-machine-learning-and-physics-based | 2303.09073 | null | https://arxiv.org/abs/2303.09073v1 | https://arxiv.org/pdf/2303.09073v1.pdf | Combined Machine Learning and Physics-Based Forecaster for Intra-day and 1-Week Ahead Solar Irradiance Forecasting Under Variable Weather Conditions | Power systems engineers are actively developing larger power plants out of photovoltaics imposing some major challenges which include its intermittent power generation and its poor dispatchability. The issue is that PV is a variable generation source unless additional planning and system additions for mitigation of gen... | ['Arif Sarwat', 'Mohd Tariq', 'Shahid Tufail', 'Hugo Riggs'] | 2023-03-16 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [-9.21560526e-02 -3.60071212e-01 1.31299078e-01 -5.04087880e-02
1.10973030e-01 -1.03954756e+00 8.58041823e-01 1.68654352e-01
5.31442821e-01 1.45658112e+00 9.61918756e-02 -5.35716534e-01
-4.71228123e-01 -1.04434943e+00 1.00628853e-01 -1.10265183e+00
2.59584673e-02 -7.98595622e-02 -4.10139740e-01 -4.16128516... | [6.218637943267822, 2.8170254230499268] |
7cf0b05d-ce41-4095-a95e-cc3c6fc73adf | matching-cnn-meets-knn-quasi-parametric-human | 1504.01220 | null | http://arxiv.org/abs/1504.01220v1 | http://arxiv.org/pdf/1504.01220v1.pdf | Matching-CNN Meets KNN: Quasi-Parametric Human Parsing | Both parametric and non-parametric approaches have demonstrated encouraging
performances in the human parsing task, namely segmenting a human image into
several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim
to develop a new solution with the advantages of both methodologies, namely
supervision... | ['Xiaochun Cao', 'Liang Lin', 'Xiaohui Shen', 'Luoqi Liu', 'Xiaodan Liang', 'Jianchao Yang', 'Si Liu', 'Changsheng Xu', 'Shuicheng Yan'] | 2015-04-06 | matching-cnn-meets-knn-quasi-parametric-human-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Liu_Matching-CNN_Meets_KNN_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Liu_Matching-CNN_Meets_KNN_2015_CVPR_paper.pdf | cvpr-2015-6 | ['human-parsing'] | ['computer-vision'] | [ 3.91050041e-01 5.65844178e-01 -1.50130719e-01 -7.18146443e-01
-1.03047967e+00 -3.83199632e-01 2.69207865e-01 -1.84357762e-02
-5.38562477e-01 3.55490595e-01 -1.45216882e-01 2.61173397e-01
-2.44451568e-01 -6.98460698e-01 -9.81143773e-01 -5.60266852e-01
2.26553872e-01 6.02544725e-01 5.54802001e-01 1.19056627... | [8.671414375305176, 0.018070267513394356] |
5388d5c6-1f40-415d-871f-9149bb9943a0 | difer-differentiable-automated-feature | 2010.08784 | null | https://arxiv.org/abs/2010.08784v3 | https://arxiv.org/pdf/2010.08784v3.pdf | DIFER: Differentiable Automated Feature Engineering | Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to Automated Feature Engineering (AutoFE) to replace expensive human labor. However, existing methods are computationally demanding due to trea... | ['Yihua Huang', 'Chunfeng Yuan', 'Xu Guo', 'Zhuoer Xu', 'Guanghui Zhu'] | 2020-10-17 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 2.02437833e-01 -3.20452213e-01 -8.50062668e-02 -6.48386478e-01
-8.84828210e-01 -3.11376989e-01 3.34472358e-01 2.32648291e-02
-3.14053595e-01 5.29462337e-01 1.36107862e-01 1.08067520e-01
-2.10890874e-01 -7.81751752e-01 -6.87557399e-01 -4.61745173e-01
5.25791720e-02 1.31856143e-01 -2.34065294e-01 -1.35269001... | [9.364721298217773, 3.2788546085357666] |
645414a5-0c0b-44b6-918f-5773815300e8 | on-bottleneck-features-for-text-dependent | 2005.07383 | null | https://arxiv.org/abs/2005.07383v2 | https://arxiv.org/pdf/2005.07383v2.pdf | On Bottleneck Features for Text-Dependent Speaker Verification Using X-vectors | Applying x-vectors for speaker verification has recently attracted great interest, with the focus being on text-independent speaker verification. In this paper, we study x-vectors for text-dependent speaker verification (TD-SV), which remains unexplored. We further investigate the impact of the different bottleneck (BN... | ['Zheng-Hua Tan', 'Achintya Kumar Sarkar'] | 2020-05-15 | null | null | null | null | ['text-independent-speaker-verification', 'text-dependent-speaker-verification'] | ['speech', 'speech'] | [-1.10655345e-01 -4.96097147e-01 -1.85755387e-01 -6.77914619e-01
-1.25794518e+00 -5.01646221e-01 9.05305624e-01 1.00459307e-01
-4.23050433e-01 1.86189935e-01 5.40732145e-01 -8.33739340e-01
-5.03421761e-02 9.29668397e-02 -1.95300132e-01 -1.13164330e+00
-7.33862072e-02 2.19215155e-01 6.20605014e-02 -2.54721135... | [14.352425575256348, 6.109110355377197] |
fa449b54-9ec0-43f2-a126-7d9e9e2e653b | toward-a-deep-neural-approach-for-knowledge | 1606.07211 | null | http://arxiv.org/abs/1606.07211v1 | http://arxiv.org/pdf/1606.07211v1.pdf | Toward a Deep Neural Approach for Knowledge-Based IR | This paper tackles the problem of the semantic gap between a document and a
query within an ad-hoc information retrieval task. In this context, knowledge
bases (KBs) have already been acknowledged as valuable means since they allow
the representation of explicit relations between entities. However, they do not
necessar... | ['Nathalie Bricon-Souf', 'Gia-Hung Nguyen', 'Laure Soulier', 'Lynda Tamine'] | 2016-06-23 | null | null | null | null | ['ad-hoc-information-retrieval', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [-5.30551113e-02 5.63173890e-01 -5.01912415e-01 -6.20121419e-01
-8.46329629e-01 -5.77597082e-01 1.12614655e+00 7.31218040e-01
-5.38898110e-01 3.40609282e-01 8.01823854e-01 -2.39300489e-01
-6.28240168e-01 -1.24017310e+00 -4.98631507e-01 -2.92337656e-01
2.07396001e-01 1.00182760e+00 -2.74565686e-02 -5.69873750... | [10.117773056030273, 8.49109172821045] |
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