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8dbfbba1-6155-4ec3-93c0-9db805674773 | a-new-class-of-efficient-adaptive-filters-for | 2104.09641 | null | https://arxiv.org/abs/2104.09641v2 | https://arxiv.org/pdf/2104.09641v2.pdf | A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling | Nonlinear models are known to provide excellent performance in real-world applications that often operate in non-ideal conditions. However, such applications often require online processing to be performed with limited computational resources. To address this problem, we propose a new class of efficient nonlinear model... | ['Aurelio Uncini', 'Amir Hussain', 'Michele Scarpiniti', 'Simone Scardapane', 'Alireza Nezamdoust', 'Danilo Comminiello'] | 2021-04-19 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.05474272e-01 -3.68905634e-01 3.28902513e-01 -2.74199724e-01
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-3.51336867e-01 8.39648247e-02 1.45619243e-01 -2.21431717... | [15.070866584777832, 5.77302885055542] |
bb6b05b1-1ecb-4680-8293-77b41af11e3e | structural-explanations-for-graph-neural | 2302.02139 | null | https://arxiv.org/abs/2302.02139v1 | https://arxiv.org/pdf/2302.02139v1.pdf | Structural Explanations for Graph Neural Networks using HSIC | Graph neural networks (GNNs) are a type of neural model that tackle graphical tasks in an end-to-end manner. Recently, GNNs have been receiving increased attention in machine learning and data mining communities because of the higher performance they achieve in various tasks, including graph classification, link predic... | ['Makoto Yamada', 'Ayato Toyokuni'] | 2023-02-04 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 3.49821985e-01 5.68835557e-01 -4.81594384e-01 -4.43474442e-01
1.50664821e-01 -1.98793098e-01 3.10440809e-01 3.19808513e-01
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-3.04669976e-01 3.89829189e-01 -2.43485197e-01 -7.89631754... | [7.380868434906006, 6.256572723388672] |
7cb85883-603d-4ed8-a810-faeb333ffac6 | word-reordering-for-zero-shot-cross-lingual | null | null | https://aclanthology.org/2021.emnlp-main.338 | https://aclanthology.org/2021.emnlp-main.338.pdf | Word Reordering for Zero-shot Cross-lingual Structured Prediction | Adapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. Even with uniform word form representations (MUSE, mBERT), word order discrepancies may hurt the adaptat... | ['Xiaoling Wang', 'Yuanbin Wu', 'Fei Huang', 'Zhongqiang Huang', 'Tao Wang', 'Yong Jiang', 'Tao Ji'] | null | null | null | null | emnlp-2021-11 | ['morphological-tagging'] | ['natural-language-processing'] | [ 4.87127192e-02 1.63076781e-02 -6.84508324e-01 -7.52131879e-01
-7.67568588e-01 -6.28554046e-01 4.46728356e-02 4.01088566e-01
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1.27903745e-02 5.49347520e-01 3.66196901e-01 -4.69444454... | [10.511908531188965, 9.765788078308105] |
0a9e1d5c-5ff8-4a99-9edd-833347621d04 | gesture2path-imitation-learning-for-gesture | 2209.09375 | null | https://arxiv.org/abs/2209.09375v1 | https://arxiv.org/pdf/2209.09375v1.pdf | Gesture2Path: Imitation Learning for Gesture-aware Navigation | As robots increasingly enter human-centered environments, they must not only be able to navigate safely around humans, but also adhere to complex social norms. Humans often rely on non-verbal communication through gestures and facial expressions when navigating around other people, especially in densely occupied spaces... | ['Sören Pirk', 'Alexander Toshev', 'Tingnan Zhang', 'Leila Takayama', 'Anthony Francis', 'Emre Fisher', 'Edward Lee', 'Catie Cuan'] | 2022-09-19 | null | null | null | null | ['social-navigation'] | ['robots'] | [ 1.11574784e-01 3.48329186e-01 1.94824219e-01 -4.80994403e-01
-7.55496919e-02 -4.32743073e-01 8.95721018e-01 -4.44653332e-01
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-3.60132188e-01 8.20241451e-01 5.70638888e-02 -5.52806616... | [5.061191558837891, 0.5625148415565491] |
4fdcf9a3-bdaa-4d66-813d-1a34e359b252 | large-displacement-3d-object-tracking-with | 2207.12620 | null | https://arxiv.org/abs/2207.12620v1 | https://arxiv.org/pdf/2207.12620v1.pdf | Large-displacement 3D Object Tracking with Hybrid Non-local Optimization | Optimization-based 3D object tracking is known to be precise and fast, but sensitive to large inter-frame displacements. In this paper we propose a fast and effective non-local 3D tracking method. Based on the observation that erroneous local minimum are mostly due to the out-of-plane rotation, we propose a hybrid appr... | ['Xueying Qin', 'Fan Zhong', 'Xinran Lin', 'Xuhui Tian'] | 2022-07-26 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-2.53640950e-01 -4.61738527e-01 6.92837453e-03 3.36232521e-02
-9.40038562e-01 -5.55368066e-01 1.32061347e-01 2.34715551e-01
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4.82585207e-02 -6.37215555e-01 -5.85724115e-01 -6.93677366e-01
3.70162725e-02 5.24317563e-01 8.21015120e-01 1.09057650... | [6.955056667327881, -2.2484281063079834] |
9386a8a7-1c6b-40ed-9d0a-74c1d2253239 | blenderproc | 1911.01911 | null | https://arxiv.org/abs/1911.01911v1 | https://arxiv.org/pdf/1911.01911v1.pdf | BlenderProc | BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks. These can be used in a variety of use cases including segmentation, depth, normal and pose estimation and many others. A key feature of our extension of blender is the simple to... | ['Mohamad Elbadrawy', 'Youssef Zidan', 'Maximilian Denninger', 'Martin Sundermeyer', 'Dmitry Olefir', 'Ahsan Lodhi', 'Harinandan Katam', 'Dominik Winkelbauer'] | 2019-10-25 | null | null | null | null | ['depth-image-estimation', '3d-object-recognition', 'surface-normals-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-7.90089667e-02 1.64837301e-01 1.11449204e-01 -4.17436361e-01
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-1.10355206e-02 3.43400568e-01 -2.02711985e-01 -3.69116396e-01
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-1.82659671e-01 5.16808569e-01 9.10719693e-01 -4.84091610... | [8.737567901611328, -2.8661885261535645] |
399ec814-a1a9-427e-9448-2b1051188906 | speaker-recognition-with-two-step-multi-modal | 2210.15903 | null | https://arxiv.org/abs/2210.15903v1 | https://arxiv.org/pdf/2210.15903v1.pdf | Speaker recognition with two-step multi-modal deep cleansing | Neural network-based speaker recognition has achieved significant improvement in recent years. A robust speaker representation learns meaningful knowledge from both hard and easy samples in the training set to achieve good performance. However, noisy samples (i.e., with wrong labels) in the training set induce confusio... | ['Haizhou Li', 'Zhan Shi', 'Kong Aik Lee', 'Ruijie Tao'] | 2022-10-28 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 1.30992070e-01 -6.79761842e-02 3.24238271e-01 -5.45345187e-01
-1.24227941e+00 -2.98375696e-01 2.84738749e-01 -9.76353064e-02
-1.81352809e-01 6.13254011e-01 2.87067354e-01 -4.97952104e-02
8.15578476e-02 -4.56031770e-01 -6.07356310e-01 -9.70264256e-01
2.46531263e-01 4.42512855e-02 -1.51946247e-01 -4.35588621... | [14.582782745361328, 5.928656101226807] |
f3b723fb-2477-4f19-afd4-3e9c2bfe3976 | encoder-decoder-based-convolutional-neural | 2003.05586 | null | https://arxiv.org/abs/2003.05586v5 | https://arxiv.org/pdf/2003.05586v5.pdf | Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting | In this paper, we propose two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN). The en... | ['Ken-ichi Fukui', 'Pongpisit Thanasutives', 'Boonserm Kijsirikul', 'Masayuki Numao'] | 2020-03-12 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 5.33438027e-02 -1.03402592e-01 2.54686594e-01 -2.79362172e-01
-6.06855452e-01 -1.88564971e-01 6.71928227e-01 -6.58131689e-02
-8.55547130e-01 6.66625738e-01 2.57920235e-01 -1.29104570e-01
5.55692613e-01 -1.15030897e+00 -8.54247689e-01 -6.02757394e-01
-9.28361043e-02 2.58120805e-01 8.39363873e-01 -2.43770123... | [8.413675308227539, -0.3091357350349426] |
563947ec-f9d5-4faa-9c14-a5ece1a67cae | universal-perturbation-attack-on | 2211.00366 | null | https://arxiv.org/abs/2211.00366v1 | https://arxiv.org/pdf/2211.00366v1.pdf | Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metrics | Universal adversarial perturbation attacks are widely used to analyze image classifiers that employ convolutional neural networks. Nowadays, some attacks can deceive image- and video-quality metrics. So sustainability analysis of these metrics is important. Indeed, if an attack can confuse the metric, an attacker can e... | ['Dmitriy Vatolin', 'Anastasia Antsiferova', 'Ekaterina Shumitskaya'] | 2022-11-01 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.29591912e-01 -3.07222277e-01 2.24826932e-01 -1.48777202e-01
-7.28010058e-01 -8.36095393e-01 4.83423024e-01 -6.44884678e-03
-4.60000753e-01 6.29268348e-01 -3.86254668e-01 -4.88975823e-01
-1.88267633e-01 -8.43695402e-01 -7.86278367e-01 -8.29379201e-01
-3.30654591e-01 -5.01928210e-01 3.15753013e-01 -4.19632405... | [5.508032321929932, 7.900035858154297] |
a85d73e8-a1c4-416a-b1f2-0d7da4ffbd17 | real-time-physics-based-object-pose-tracking | 2211.13572 | null | https://arxiv.org/abs/2211.13572v2 | https://arxiv.org/pdf/2211.13572v2.pdf | Real-Time Physics-Based Object Pose Tracking during Non-Prehensile Manipulation | We propose a method to track the 6D pose of an object over time, while the object is under non-prehensile manipulation by a robot. At any given time during the manipulation of the object, we assume access to the robot joint controls and an image from a camera. We use the robot joint controls to perform a physics-based ... | ['Mehmet Dogar', 'Rafael Papallas', 'Zisong Xu'] | 2022-11-24 | null | null | null | null | ['pose-tracking'] | ['computer-vision'] | [ 1.21570505e-01 5.60530610e-02 7.48405382e-02 1.98350519e-01
-1.17329553e-01 -5.51533341e-01 4.18292880e-01 2.26287737e-01
-7.50141621e-01 4.54705268e-01 -3.82683843e-01 5.78711592e-02
-6.16716556e-02 -6.30946219e-01 -9.99454796e-01 -5.94426394e-01
-2.08296720e-03 1.04811406e+00 7.05023944e-01 1.17728494... | [5.972313404083252, -0.8771315813064575] |
00490bd7-60b7-482f-aa91-7cee16f6d420 | enhancing-speech-articulation-analysis-using | 2305.10775 | null | https://arxiv.org/abs/2305.10775v1 | https://arxiv.org/pdf/2305.10775v1.pdf | Enhancing Speech Articulation Analysis using a Geometric Transformation of the X-ray Microbeam Dataset | Accurate analysis of speech articulation is crucial for speech analysis. However, X-Y coordinates of articulators strongly depend on the anatomy of the speakers and the variability of pellet placements, and existing methods for mapping anatomical landmarks in the X-ray Microbeam Dataset (XRMB) fail to capture the entir... | ['Carol Y. Espy-Wilson', 'Mark Tiede', 'Ahmed Adel Attia'] | 2023-05-18 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-5.36205590e-01 1.22122020e-01 -4.48596239e-01 3.90521390e-03
-9.83533859e-01 -8.32114577e-01 3.46903920e-01 1.18564017e-01
-2.35047787e-01 2.98080832e-01 7.48997092e-01 -3.32660884e-01
-1.74659476e-01 -1.81321621e-01 -3.64355326e-01 -6.33977354e-01
2.67698139e-01 5.03947377e-01 1.63568944e-01 2.27275431... | [14.29703140258789, 4.960597515106201] |
27a45146-ba0a-4eb0-b65f-6b0bb8c29cb4 | robustness-of-demonstration-based-learning | 2210.10693 | null | https://arxiv.org/abs/2210.10693v1 | https://arxiv.org/pdf/2210.10693v1.pdf | Robustness of Demonstration-based Learning Under Limited Data Scenario | Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario. Simply augmenting the input with some demonstrations can significantly improve performance on few-shot NER. However, why such demonstrations are beneficial for the learning process rema... | ['Diyi Yang', 'Ruiyi Zhang', 'Yanzhe Zhang', 'Hongxin Zhang'] | 2022-10-19 | null | null | null | null | ['few-shot-ner'] | ['natural-language-processing'] | [-1.35075748e-01 1.81155726e-01 -3.00928783e-02 -3.85941178e-01
-4.97135550e-01 -6.95156157e-01 5.93625128e-01 1.38401426e-02
-6.55721962e-01 7.82832503e-01 3.54446620e-01 -3.96856368e-01
1.39778703e-01 -4.32378173e-01 -9.00276959e-01 -5.33905983e-01
-5.31148501e-02 2.55035073e-01 4.43533570e-01 -4.48974401... | [10.590140342712402, 8.513237953186035] |
4d2abcf7-0d8d-4f4b-86f2-9db715beae2b | classification-of-long-sequential-data-using | 2201.02143 | null | https://arxiv.org/abs/2201.02143v2 | https://arxiv.org/pdf/2201.02143v2.pdf | Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks | Classification of long sequential data is an important Machine Learning task and appears in many application scenarios. Recurrent Neural Networks, Transformers, and Convolutional Neural Networks are three major techniques for learning from sequential data. Among these methods, Temporal Convolutional Networks (TCNs) whi... | ['Zhirong Yang', 'Tong Yu', 'Ruslan Khalitov', 'Lei Cheng'] | 2022-01-06 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 1.50303140e-01 -5.58063805e-01 -3.06860059e-01 -2.67738372e-01
-1.27253577e-01 -4.13612843e-01 4.19477999e-01 -6.76086321e-02
-5.57576120e-01 7.70468950e-01 -1.10890511e-02 -6.14668906e-01
1.16139568e-01 -6.51163638e-01 -5.55859625e-01 -8.92618597e-01
-2.20270932e-01 2.66873538e-01 5.63841760e-01 -3.84232402... | [10.817400932312012, 6.287044525146484] |
957c1301-0052-4f28-af73-8d2005f3767d | multilingual-and-cross-lingual-document | 2101.11302 | null | https://arxiv.org/abs/2101.11302v2 | https://arxiv.org/pdf/2101.11302v2.pdf | Multilingual and cross-lingual document classification: A meta-learning approach | The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in limited-resource setting and demonstrate its effectiveness in two different settings: few-shot, cross-lingu... | ['Ekaterina Shutova', 'Pushkar Mishra', 'Helen Yannakoudakis', 'Niels van der Heijden'] | 2021-01-27 | null | https://aclanthology.org/2021.eacl-main.168 | https://aclanthology.org/2021.eacl-main.168.pdf | eacl-2021-2 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-1.68601036e-01 -4.82867420e-01 -6.33290410e-01 -2.35544771e-01
-1.41641915e+00 -5.90162516e-01 1.03096390e+00 1.09713569e-01
-9.51331437e-01 8.80142987e-01 1.34476036e-01 -2.76580602e-01
3.65078487e-02 -3.59218389e-01 -6.00907207e-01 -3.25606227e-01
1.17727995e-01 8.28476667e-01 7.45271221e-02 -3.20496261... | [10.936445236206055, 9.672897338867188] |
e4bb81a1-8479-4e77-89f9-4c84d92e23c7 | zero-shot-anomaly-detection-with-pre-trained | 2306.09269 | null | https://arxiv.org/abs/2306.09269v1 | https://arxiv.org/pdf/2306.09269v1.pdf | Zero-Shot Anomaly Detection with Pre-trained Segmentation Models | This technical report outlines our submission to the zero-shot track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. Building on the performance of the WINCLIP framework, we aim to enhance the system's localization capabilities by integrating zero-shot segmentation models. In addition, we perform for... | ['Bernhard Kainz', 'Johanna P. Müller', 'James Batten', 'Matthew Baugh'] | 2023-06-15 | null | null | null | null | ['zero-shot-segmentation', 'anomaly-detection'] | ['computer-vision', 'methodology'] | [ 7.20108375e-02 -4.04294617e-02 1.26708955e-01 -5.08860312e-02
-6.11318052e-01 -5.70719838e-01 6.67243302e-01 4.08634037e-01
-5.42434990e-01 7.20684379e-02 -2.07238510e-01 -1.92575261e-01
1.58513084e-01 -2.80111343e-01 -6.08725727e-01 -2.34567046e-01
-1.16537780e-01 6.58716932e-02 9.41873610e-01 7.24489316... | [8.634286880493164, 0.15152281522750854] |
2ab996e2-d6ce-4370-ba6b-be76e6d4502e | neural-relation-extraction-with-selective | null | null | https://aclanthology.org/p16-1200 | https://aclanthology.org/p16-1200.pdf | Neural Relation Extraction with Selective Attention over Instances | null | ['Huanbo Luan', 'Yankai Lin', 'Maosong Sun', 'Zhiyuan Liu', 'Shiqi Shen'] | 2016-08-01 | neural-relation-extraction-with-selective-1 | https://aclanthology.org/P16-1200 | https://aclanthology.org/P16-1200.pdf | acl-2016-8 | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392036437988281, 15.869193077087402] |
73fdbca0-1497-4f0a-9c2e-07f1cbba48f6 | a-fully-end-to-end-deep-learning-approach-for | 1703.04699 | null | http://arxiv.org/abs/1703.04699v1 | http://arxiv.org/pdf/1703.04699v1.pdf | A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition | This paper addresses the problem of simultaneous 3D reconstruction and
material recognition and segmentation. Enabling robots to recognise different
materials (concrete, metal etc.) in a scene is important for many tasks, e.g.
robotic interventions in nuclear decommissioning. Previous work on 3D semantic
reconstruction... | ['Cheng Zhao', 'Rustam Stolkin', 'Li Sun'] | 2017-03-14 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 4.74794000e-01 4.62097228e-02 2.53968269e-01 -4.00875241e-01
-7.45622694e-01 -5.23694456e-01 6.63667858e-01 1.80092677e-01
-4.09565210e-01 1.53985888e-01 -3.84737372e-01 -4.30036098e-01
1.81362540e-01 -7.89297581e-01 -9.71397519e-01 -4.62118655e-01
2.97489107e-01 9.74765897e-01 5.05615354e-01 1.74358904... | [8.276694297790527, -2.702944755554199] |
7af74764-25cd-4382-ac79-066d94b65bd5 | a-time-vertex-signal-processing-framework | 1705.02307 | null | http://arxiv.org/abs/1705.02307v1 | http://arxiv.org/pdf/1705.02307v1.pdf | A Time-Vertex Signal Processing Framework | An emerging way to deal with high-dimensional non-euclidean data is to assume
that the underlying structure can be captured by a graph. Recently, ideas have
begun to emerge related to the analysis of time-varying graph signals. This
work aims to elevate the notion of joint harmonic analysis to a full-fledged
framework ... | ['Nathanaël Perraudin', 'Andreas Loukas', 'Benjamin Ricaud', 'Francesco Grassi'] | 2017-05-05 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 3.29907835e-01 7.53323957e-02 5.07466257e-01 3.37086990e-03
-8.34405243e-01 -3.79627854e-01 4.32120115e-01 1.91994369e-01
6.88977027e-03 4.00166005e-01 1.85846865e-01 4.19613495e-02
-6.77206874e-01 -7.83569276e-01 -6.31187439e-01 -9.39267755e-01
-7.00106382e-01 -2.75025889e-02 1.09929167e-01 -4.90378171... | [15.397059440612793, 5.605896949768066] |
5e06b808-c0d5-4fbe-8e3e-6e5a863fb694 | a-generic-diffusion-based-approach-for-3d | 2210.05669 | null | https://arxiv.org/abs/2210.05669v2 | https://arxiv.org/pdf/2210.05669v2.pdf | A generic diffusion-based approach for 3D human pose prediction in the wild | Predicting 3D human poses in real-world scenarios, also known as human pose forecasting, is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and occlusions. To address these challenges, we propose a diffusion-based approach that can predict given noisy observations. We frame the prediction... | ['Alexandre Alahi', 'Taylor Mordan', 'Sara Rajabzadeh', 'Yasamin Medghalchi', 'Mohammadreza Mofayezi', 'Ali Rasekh', 'Saeed Saadatnejad'] | 2022-10-11 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [ 1.09907992e-01 2.03172311e-01 3.41595143e-01 -4.46701556e-01
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-3.17792371e-02 7.39365935e-01 3.23923260e-01 -3.22518289... | [7.1702165603637695, -0.5548414587974548] |
0b2d0687-74b8-49ad-a905-c18f76ef2434 | insurance-contract-for-high-renewable-energy | 2209.10363 | null | https://arxiv.org/abs/2209.10363v1 | https://arxiv.org/pdf/2209.10363v1.pdf | Insurance Contract for High Renewable Energy Integration | The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium ... | ['Xiaojun Lin', 'Jianwei Huang', 'Hao Wang', 'Dongwei Zhao'] | 2022-09-21 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-3.09114724e-01 4.42095876e-01 -4.98737007e-01 1.26798809e-01
-7.96043217e-01 -7.32051194e-01 -8.50122944e-02 -2.30281129e-02
8.20175856e-02 1.28693998e+00 2.43080959e-01 -3.56517971e-01
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-2.14463659e-03 1.44158930e-01 -6.06309712e-01 -2.65067548... | [5.490260124206543, 2.6501708030700684] |
c446dac0-691a-40a1-9d05-a30efdf4db23 | remote-photoplethysmography-correspondence | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Siqi_Liu_Remote_Photoplethysmography_Correspondence_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Siqi_Liu_Remote_Photoplethysmography_Correspondence_ECCV_2018_paper.pdf | Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection | 3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention. Recently, remote Photoplethysmography (rPPG) is employed as an intrinsic liveness cue which is independent of the mask appearance. Although existing rPPG-based methods achieve promising results on both in... | ['Si-Qi Liu', 'Xiangyuan Lan', 'Pong C. Yuen'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.41124356e-01 -4.34803754e-01 2.16104146e-02 -2.26807147e-01
-6.33614838e-01 -4.65366453e-01 4.63479966e-01 -5.08476794e-01
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2.61602476e-02 -2.72288859e-01 -2.99078703e-01 -1.02707744e+00
8.74078348e-02 -4.07294959e-01 3.57381180e-02 1.70271188... | [13.732780456542969, 2.421980857849121] |
73e73c2f-73a8-4a46-bb55-6a74255034a0 | multimodal-performers-for-genomic-selection | null | null | https://doi.org/10.1016/j.atech.2021.100017 | https://doi.org/10.1016/j.atech.2021.100017 | Multimodal Performers for Genomic Selection and Crop Yield Prediction | Working towards optimal crop yields is a crucial step towards securing a stable food supply for the world. To this end, approaches to model and predict crop yields can help speed up research and reduce costs. However, crop yield prediction is very challenging due to the dependencies on factors such as genotype and envi... | ['Keith L. Downing', 'Muath Alsheikh', 'Stein Bergersen', 'Susanne Windju', 'Håkon Måløy'] | 2021-10-20 | null | null | null | smart-agricultural-technology-2021-10 | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [-1.46993071e-01 -6.75465018e-02 -3.26176196e-01 -5.37150085e-01
-2.03155205e-01 -5.41820467e-01 3.36685367e-02 5.75034976e-01
-7.66119640e-03 6.98769808e-01 7.85294697e-02 -4.99079943e-01
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-4.11475152e-01 1.35479137e-01 -3.06467891e-01 -5.13951182... | [9.347122192382812, -1.6211140155792236] |
6e0d02d4-1411-4470-8f8c-fcf596248751 | improving-self-organizing-maps-with | 2009.02174 | null | https://arxiv.org/abs/2009.02174v1 | https://arxiv.org/pdf/2009.02174v1.pdf | Improving Self-Organizing Maps with Unsupervised Feature Extraction | The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn efficient prototypes when dealing with complex datasets. We propose in this work to improve the SOM performance by using extracted features... | ['Benoit Miramond', 'Laurent Rodriguez', 'Lyes Khacef'] | 2020-09-04 | null | null | null | null | ['unsupervised-image-classification', 'unsupervised-mnist'] | ['computer-vision', 'methodology'] | [ 3.26039493e-01 -1.17140241e-01 9.14728194e-02 -4.52323169e-01
-1.24714095e-02 -2.84083128e-01 7.25781024e-01 3.00229698e-01
-9.17891860e-01 8.35653245e-01 6.18799180e-02 2.34657481e-01
-2.77847677e-01 -9.00451958e-01 -7.27454364e-01 -1.09410417e+00
-4.91020717e-02 4.60101992e-01 7.35641003e-01 9.70185548... | [8.210295677185059, 2.7004010677337646] |
db162006-5ec8-4539-8cf0-7cdf186c339a | distributed-learning-via-filtered | 2007.09392 | null | https://arxiv.org/abs/2007.09392v1 | https://arxiv.org/pdf/2007.09392v1.pdf | Distributed Learning via Filtered Hyperinterpolation on Manifolds | Learning mappings of data on manifolds is an important topic in contemporary machine learning, with applications in astrophysics, geophysics, statistical physics, medical diagnosis, biochemistry, 3D object analysis. This paper studies the problem of learning real-valued functions on manifolds through filtered hyperinte... | ['Guido Montúfar', 'Yu Guang Wang'] | 2020-07-18 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-3.99761200e-01 -7.35596791e-02 1.02571048e-01 -1.27288878e-01
-8.51986349e-01 -4.26605374e-01 3.47773492e-01 3.95654291e-01
-4.28365648e-01 8.74772668e-01 -2.79587567e-01 -1.72630996e-01
-3.71743053e-01 -8.63647640e-01 -1.07069635e+00 -8.74019027e-01
-3.35898757e-01 7.93602884e-01 1.51543040e-02 2.78762549... | [6.858456611633301, 4.316257953643799] |
cbc55542-e214-431d-a3c7-3ca0f3acf9b9 | swapped-face-detection-using-deep-learning | 1909.04217 | null | https://arxiv.org/abs/1909.04217v1 | https://arxiv.org/pdf/1909.04217v1.pdf | Swapped Face Detection using Deep Learning and Subjective Assessment | The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. Transferring one person's face from a source image to a target image of... | ['Zohreh Raziei', 'Michael Hahsler', 'Paul Krueger', 'Eric C. Larson', 'Eli V. Olinick', 'Xinyi Ding'] | 2019-09-10 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 9.60420966e-02 9.00320858e-02 2.52936333e-01 -5.30101657e-01
-8.50365698e-01 -7.66491234e-01 4.63564813e-01 -3.33971232e-01
-3.19304675e-01 5.97343385e-01 -9.67871174e-02 -1.89131573e-01
2.27856308e-01 -5.98321676e-01 -7.86318421e-01 -5.38545489e-01
-5.71733415e-02 3.82626355e-01 -4.84604724e-02 7.25221708... | [12.740562438964844, 1.0409080982208252] |
562fda9f-ee37-4a74-a3e2-476316341ffd | instructions-as-backdoors-backdoor | 2305.14710 | null | https://arxiv.org/abs/2305.14710v1 | https://arxiv.org/pdf/2305.14710v1.pdf | Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models | Instruction-tuned models are trained on crowdsourcing datasets with task instructions to achieve superior performance. However, in this work we raise security concerns about this training paradigm. Our studies demonstrate that an attacker can inject backdoors by issuing very few malicious instructions among thousands o... | ['Muhao Chen', 'Chaowei Xiao', 'Fei Wang', 'Mingyu Derek Ma', 'Jiashu Xu'] | 2023-05-24 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 2.73047872e-02 -8.94258618e-02 -4.99171674e-01 3.04116547e-04
-9.45981324e-01 -1.40637362e+00 5.51354468e-01 1.80312917e-01
-6.88893199e-01 8.21821809e-01 1.35155367e-02 -7.43145227e-01
5.03467679e-01 -6.91650569e-01 -1.18560278e+00 -6.51869059e-01
4.74881351e-01 2.71317542e-01 4.80389357e-01 -4.06345487... | [5.976677894592285, 7.808117866516113] |
7b59f1df-80e6-4fb0-a553-66dc1adc58a1 | consistency-based-semi-supervised-learning | null | null | http://papers.nips.cc/paper/9259-consistency-based-semi-supervised-learning-for-object-detection | http://papers.nips.cc/paper/9259-consistency-based-semi-supervised-learning-for-object-detection.pdf | Consistency-based Semi-supervised Learning for Object detection | Making a precise annotation in a large dataset is crucial to the performance of object detection. While the object detection task requires a huge number of annotated samples to guarantee its performance, placing bounding boxes for every object in each sample is time-consuming and costs a lot. To alleviate this problem,... | ['Jeesoo Kim', 'Nojun Kwak', 'Seungeui Lee', 'Jisoo Jeong'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.28676474e-01 -2.44659469e-01 1.42895579e-01 -4.27117437e-01
-5.17850459e-01 -3.67329299e-01 3.46529365e-01 3.13142657e-01
-6.24751329e-01 4.85982984e-01 -3.77180248e-01 -2.01213226e-01
2.97712088e-01 -7.88252771e-01 -5.50376773e-01 -8.45599174e-01
3.65032315e-01 1.79226086e-01 1.03552353e+00 2.84093142... | [9.1648588180542, 1.1649394035339355] |
bac627e2-eb02-4969-93a0-fc5cc5673336 | assessing-the-use-of-prosody-in-constituency | 2106.07794 | null | https://arxiv.org/abs/2106.07794v1 | https://arxiv.org/pdf/2106.07794v1.pdf | Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts | This work explores constituency parsing on automatically recognized transcripts of conversational speech. The neural parser is based on a sentence encoder that leverages word vectors contextualized with prosodic features, jointly learning prosodic feature extraction with parsing. We assess the utility of the prosody in... | ['Mari Ostendorf', 'Trang Tran'] | 2021-06-14 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 4.99822170e-01 9.46202755e-01 -2.03145549e-01 -6.86646461e-01
-1.40060115e+00 -8.13168705e-01 7.51649961e-02 8.12638253e-02
-3.67862225e-01 5.71875393e-01 1.21274281e+00 -5.42470753e-01
2.80635655e-01 -4.43606138e-01 -4.80613947e-01 -2.81674892e-01
-7.98381567e-02 2.43247181e-01 -1.39669269e-01 -3.72049093... | [10.48840618133545, 9.508644104003906] |
b2aecdee-60b2-485e-a942-5f8c4e4cd40b | massively-multi-lingual-event-understanding | 2305.10561 | null | https://arxiv.org/abs/2305.10561v1 | https://arxiv.org/pdf/2305.10561v1.pdf | Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search | In this paper, we present ISI-Clear, a state-of-the-art, cross-lingual, zero-shot event extraction system and accompanying user interface for event visualization & search. Using only English training data, ISI-Clear makes global events available on-demand, processing user-supplied text in 100 languages ranging from Afr... | ['Elizabeth Boschee', 'Steven Fincke', 'Joel Barry', 'Shantanu Agarwal', 'Chris Jenkins'] | 2023-05-17 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [-3.27955902e-01 -2.71145314e-01 -1.15540020e-01 -1.09139338e-01
-1.27915597e+00 -8.97395849e-01 8.93505573e-01 1.24427474e+00
-6.46387458e-01 7.21867800e-01 7.55629897e-01 -5.78719914e-01
-4.39225227e-01 -9.14902866e-01 -1.05247460e-01 -3.96425366e-01
-5.68051100e-01 7.30994046e-01 1.47790164e-01 -2.35063732... | [8.893353462219238, 9.164680480957031] |
59226799-fe22-49ec-baf3-b7dcb0f269d4 | deep-semantic-multimodal-hashing-network-for | 1901.02662 | null | https://arxiv.org/abs/1901.02662v3 | https://arxiv.org/pdf/1901.02662v3.pdf | Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals | Hashing has been widely applied to multimodal retrieval on large-scale multimedia data due to its efficiency in computation and storage. In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable image-text and video-text retrieval. The proposed deep hashing framework leverages 2-... | ['Zechao Li', 'Lu Jin', 'Jinhui Tang'] | 2019-01-09 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-3.66488606e-01 -5.40086925e-01 -5.84869862e-01 -3.16275418e-01
-1.41382158e+00 -3.15623134e-01 5.15317440e-01 1.11167721e-01
-4.27356571e-01 2.01311082e-01 2.05512673e-01 2.98084050e-01
-2.31086701e-01 -6.30348980e-01 -7.46510863e-01 -1.01413023e+00
-2.53165543e-01 3.88087839e-01 1.28465056e-01 -2.66712494... | [11.449974060058594, 0.8966483473777771] |
f54dde82-0d13-4f4a-8577-b3fc9b0e4a5e | an-lmi-framework-for-contraction-based | 2301.08398 | null | https://arxiv.org/abs/2301.08398v1 | https://arxiv.org/pdf/2301.08398v1.pdf | An LMI Framework for Contraction-based Nonlinear Control Design by Derivatives of Gaussian Process Regression | Contraction theory formulates the analysis of nonlinear systems in terms of Jacobian matrices. Although this provides the potential to develop a linear matrix inequality (LMI) framework for nonlinear control design, conditions are imposed not on controllers but on their partial derivatives, which makes control design c... | ['Kenji Kashima', 'Yu Kawano'] | 2023-01-20 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.09338918e-01 1.28605485e-01 4.90230098e-02 5.17877042e-01
-5.20254195e-01 -6.22783482e-01 2.90486157e-01 -2.90515304e-01
-3.52586061e-01 1.04957175e+00 -7.13223696e-01 -4.21568125e-01
-6.09315872e-01 -3.38162959e-01 -7.86501467e-01 -1.21629858e+00
1.89811364e-01 3.45541447e-01 -1.82010829e-01 -9.58187580... | [5.286634922027588, 2.602344274520874] |
09f5cda3-e88c-4d97-880f-df23b1a2f354 | biologically-inspired-sleep-algorithm-for-1 | null | null | https://openreview.net/forum?id=r1xGnA4Kvr | https://openreview.net/pdf?id=r1xGnA4Kvr | Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks | Current artificial neural networks (ANNs) can perform and excel at a variety of tasks ranging from image classification to spam detection through training on large datasets of labeled data. While the trained network may perform well on similar testing data, inputs that differ even slightly from the training data may tr... | ['Ramyaa Ramyaa', 'Giri Krishnan', 'Timothy Tadros', 'Maxim Bazhenov'] | 2020-05-01 | null | null | null | iclr-2020-1 | ['spam-detection'] | ['natural-language-processing'] | [ 5.50668955e-01 -7.38743320e-02 5.46736300e-01 -3.14108849e-01
2.24221200e-01 -1.03314543e+00 4.58127677e-01 2.47510592e-03
-6.14175439e-01 8.21732223e-01 -1.56553879e-01 -3.15561861e-01
-8.22368264e-02 -7.54909754e-01 -8.04690421e-01 -9.34253037e-01
-1.75818786e-01 1.26834482e-01 2.83285588e-01 -5.24992526... | [5.635304927825928, 7.82358980178833] |
62da7eae-290d-4ec8-a49f-a6f172a6211c | hybrik-transformer | 2302.04774 | null | https://arxiv.org/abs/2302.04774v4 | https://arxiv.org/pdf/2302.04774v4.pdf | 3D Human Pose and Shape Estimation via HybrIK-Transformer | HybrIK relies on a combination of analytical inverse kinematics and deep learning to produce more accurate 3D pose estimation from 2D monocular images. HybrIK has three major components: (1) pretrained convolution backbone, (2) deconvolution to lift 3D pose from 2D convolution features, (3) analytical inverse kinematic... | ['Boris N. Oreshkin'] | 2023-02-09 | null | null | null | null | ['3d-pose-estimation', '3d-human-pose-estimation', '3d-human-pose-and-shape-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-4.95406210e-01 -1.35973776e-02 1.56911779e-02 -2.77105629e-01
-5.69344461e-01 -5.77987134e-01 5.54828286e-01 -5.52683592e-01
-2.71328181e-01 7.84751236e-01 4.68398869e-01 -3.78604084e-01
-1.65182903e-01 -5.44613421e-01 -1.16629231e+00 -3.94118726e-01
-1.95034161e-01 6.65817559e-01 -3.44512425e-02 -4.76871252... | [7.05758810043335, -1.0615205764770508] |
424a0f54-e964-4391-ad71-c42cf3e7ec70 | an-empirical-study-for-vietnamese | 2010.09623 | null | https://arxiv.org/abs/2010.09623v2 | https://arxiv.org/pdf/2010.09623v2.pdf | An Empirical Study for Vietnamese Constituency Parsing with Pre-training | In this work, we use a span-based approach for Vietnamese constituency parsing. Our method follows the self-attention encoder architecture and a chart decoder using a CKY-style inference algorithm. We present analyses of the experiment results of the comparison of our empirical method using pre-training models XLM-Robe... | ['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Duc-Vu Nguyen', 'Xuan-Thien Pham', 'Tuan-Vi Tran'] | 2020-10-19 | null | null | null | null | ['constituency-parsing', 'vietnamese-parsing', 'vietnamese-datasets'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.22259209e-01 7.47898161e-01 -6.41252577e-01 -7.49791324e-01
-1.16487610e+00 -8.62621725e-01 4.51352328e-01 8.91852379e-03
-6.25944316e-01 1.32716525e+00 7.42515385e-01 -9.87263918e-01
4.97202754e-01 -8.90915155e-01 -8.73135388e-01 -1.30952671e-01
2.02334478e-01 6.09083891e-01 4.34611104e-02 -5.76685250... | [10.365315437316895, 9.714954376220703] |
3a28f9af-e5bc-47dc-bfb0-f35f45628568 | mask-is-all-you-need-rethinking-mask-r-cnn | 2109.03426 | null | https://arxiv.org/abs/2109.03426v1 | https://arxiv.org/pdf/2109.03426v1.pdf | Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection | Due to the large success in object detection and instance segmentation, Mask R-CNN attracts great attention and is widely adopted as a strong baseline for arbitrary-shaped scene text detection and spotting. However, two issues remain to be settled. The first is dense text case, which is easy to be neglected but quite p... | ['Weiping Wang', 'Hongbin Wang', 'Ning Jiang', 'Zhihong Tian', 'Dayan Wu', 'Youhui Guo', 'Yu Zhou', 'Xugong Qin'] | 2021-09-08 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.45470130e-01 -1.10098319e-02 -1.24465756e-01 -2.34505758e-01
-6.11786783e-01 -4.19473559e-01 2.91770428e-01 -8.69456008e-02
-3.38934928e-01 4.12628084e-01 -2.41605893e-01 -2.87419349e-01
1.53490096e-01 -6.40487373e-01 -6.46247864e-01 -8.53476524e-01
5.95796943e-01 5.85796714e-01 6.02719069e-01 1.12822898... | [11.986595153808594, 2.2287213802337646] |
5a1ad1cd-72a4-44d8-b247-9ecc093ef9f4 | regulation-of-mouse-learning-and-mood-by-the | 2306.14556 | null | https://arxiv.org/abs/2306.14556v1 | https://arxiv.org/pdf/2306.14556v1.pdf | Regulation of Mouse Learning and Mood by the Anti-Inflammatory Cytokine Interleukin-10 | Major depressive disorder is a widespread mood disorder. One of the most debilitating symptoms patients often experience is cognitive impairment. Recent findings suggest that inflammation is associated with depression and impaired cognition. Pro-inflammatory cytokines are elevated in the blood of depressed patients and... | ['Ryan Joseph Worthen'] | 2023-06-26 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-1.50313556e-01 -9.87913609e-01 -1.12711981e-01 1.16485812e-01
-2.78651923e-01 -1.88132301e-01 1.38520315e-01 1.06830454e+00
-1.07811964e+00 7.88451433e-01 2.77966391e-02 2.50557177e-02
-4.84955013e-02 -9.26106036e-01 -5.32202721e-01 -9.93739605e-01
1.09167434e-01 3.74576539e-01 -1.51438221e-01 -1.93646371... | [14.324746131896973, -2.743135690689087] |
9de14740-3938-4692-a79b-76ffc4d3e580 | bilingual-correspondence-recursive | null | null | https://aclanthology.org/D15-1146 | https://aclanthology.org/D15-1146.pdf | Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation | null | ['Deyi Xiong', 'Biao Zhang', 'Yang Liu', 'Min Zhang', 'Junfeng Yao', 'Jinsong Su'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['learning-semantic-representations'] | ['methodology'] | [-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... | [-7.361844539642334, 3.6810719966888428] |
f08fa905-4e3d-4e50-8d8b-6e794a8b7da0 | many-languages-one-parser | 1602.01595 | null | http://arxiv.org/abs/1602.01595v4 | http://arxiv.org/pdf/1602.01595v4.pdf | Many Languages, One Parser | We train one multilingual model for dependency parsing and use it to parse
sentences in several languages. The parsing model uses (i) multilingual word
clusters and embeddings; (ii) token-level language information; and (iii)
language-specific features (fine-grained POS tags). This input representation
enables the pars... | ['Chris Dyer', 'Miguel Ballesteros', 'Waleed Ammar', 'Noah A. Smith', 'George Mulcaire'] | 2016-02-04 | many-languages-one-parser-1 | https://aclanthology.org/Q16-1031 | https://aclanthology.org/Q16-1031.pdf | tacl-2016-1 | ['cross-lingual-zero-shot-dependency-parsing'] | ['natural-language-processing'] | [-4.05858159e-01 2.27594785e-02 -5.26651919e-01 -5.78456342e-01
-1.18577147e+00 -1.07516742e+00 3.85012627e-01 5.70756435e-01
-8.68068278e-01 8.07673216e-01 6.81157291e-01 -9.21938479e-01
4.72183347e-01 -7.33018339e-01 -4.14694250e-01 -2.22633764e-01
-2.25068033e-01 5.01699686e-01 1.77729875e-01 -3.47654969... | [10.49532699584961, 9.883546829223633] |
045b07b0-66ac-45df-b0f9-8261469b7d7c | early-icu-mortality-prediction-and-survival | 2109.03048 | null | https://arxiv.org/abs/2109.03048v1 | https://arxiv.org/pdf/2109.03048v1.pdf | Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure | Respiratory failure is the one of major causes of death in critical care unit. During the outbreak of COVID-19, critical care units experienced an extreme shortage of mechanical ventilation because of respiratory failure related syndromes. To help this, the early mortality risk prediction in patients who suffer respira... | ['Chun-An Chou', 'Yilin Yin'] | 2021-09-06 | null | null | null | null | ['icu-mortality', 'respiratory-failure'] | ['medical', 'medical'] | [ 4.46145721e-02 -5.93847454e-01 -9.12195072e-03 5.39103448e-02
-9.37339440e-02 -3.40577692e-01 -2.82755464e-01 4.36533540e-01
-5.77084541e-01 8.96432638e-01 -3.94396074e-02 -7.11261034e-01
-8.04784656e-01 -4.11182225e-01 4.37339395e-02 -7.55783260e-01
-3.78505588e-01 9.62169051e-01 1.45423546e-01 2.35668957... | [8.012141227722168, 6.118525981903076] |
0c5ecb62-e3e4-4f6a-bd0d-195d8d5e24b9 | augmenting-dl-with-adversarial-training-for | null | null | https://dl.acm.org/doi/abs/10.1145/3386580 | https://dl.acm.org/doi/abs/10.1145/3386580 | Augmenting DL with Adversarial Training for Robust Prediction of Epilepsy Seizures | Epilepsy is a chronic medical condition that involves abnormal brain activity causing patients to lose control of awareness or motor activity. As a result, detection of pre-ictal states, before the onset of a seizure, can be lifesaving. The problem is challenging because it is difficult to discern between electroenceph... | ['Hazem', 'Mohhamad; Hajj', 'Reem; Dhaybi', 'Marc; Mahmoud', 'Amir; Djandji', 'Hussein'] | 2020-06-01 | null | null | null | null | ['epilepsy-prediction'] | ['medical'] | [ 5.13494253e-01 -6.49822503e-03 2.35007018e-01 -7.55110383e-02
-8.53148937e-01 -3.10019076e-01 2.39343569e-01 2.11541712e-01
-3.06877285e-01 9.91021633e-01 1.27163097e-01 -3.94965649e-01
-1.82612389e-01 -2.29331061e-01 -5.05119264e-01 -8.02588761e-01
-5.26680887e-01 1.80968717e-01 -3.29306524e-04 -7.72978738... | [13.243879318237305, 3.526808261871338] |
1a95340a-4928-4059-b122-c9636384262d | unified-chinese-license-plate-detection-and | 2205.03582 | null | https://arxiv.org/abs/2205.03582v1 | https://arxiv.org/pdf/2205.03582v1.pdf | Unified Chinese License Plate Detection and Recognition with High Efficiency | Recently, deep learning-based methods have reached an excellent performance on License Plate (LP) detection and recognition tasks. However, it is still challenging to build a robust model for Chinese LPs since there are not enough large and representative datasets. In this work, we propose a new dataset named Chinese R... | ['Mei Xie', 'Zheng Ma', 'Zhiwei Xie', 'Peicheng Wu', 'Xinchen Lu', 'Shuai Tao', 'Linjie Deng', 'Yanxiang Gong'] | 2022-05-07 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [-5.68660498e-01 -5.95152497e-01 -2.43945807e-01 -1.78049371e-01
-1.26615608e+00 -4.57426995e-01 2.00334042e-01 -6.63274705e-01
-1.98984072e-01 4.94959205e-01 -2.58638233e-01 -2.89574713e-01
5.07976711e-01 -7.12327540e-01 -9.69659925e-01 -5.79013467e-01
4.41518366e-01 4.69998151e-01 7.03738093e-01 1.34653971... | [9.842683792114258, -4.902424335479736] |
e277abb7-b05a-42d8-addd-cbd97e78d307 | monograspnet-6-dof-grasping-with-a-single-rgb | 2209.13036 | null | https://arxiv.org/abs/2209.13036v2 | https://arxiv.org/pdf/2209.13036v2.pdf | MonoGraspNet: 6-DoF Grasping with a Single RGB Image | 6-DoF robotic grasping is a long-lasting but unsolved problem. Recent methods utilize strong 3D networks to extract geometric grasping representations from depth sensors, demonstrating superior accuracy on common objects but perform unsatisfactorily on photometrically challenging objects, e.g., objects in transparent o... | ['Benjamin Busam', 'Nassir Navab', 'Federico Tombari', 'Fabian Manhardt', 'Yan Di', 'HyunJun Jung', 'Shun-Cheng Wu', 'Dianye Huang', 'Guangyao Zhai'] | 2022-09-26 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-7.40901008e-02 -6.34297878e-02 1.25173077e-01 -3.30250531e-01
-5.99270880e-01 -8.85840595e-01 1.73809230e-01 -1.48701981e-01
9.49097648e-02 6.47215992e-02 -4.09517884e-02 2.12053210e-01
-3.44960541e-01 -6.71769202e-01 -1.03666759e+00 -7.16409385e-01
-1.70171589e-01 7.02503264e-01 2.65547186e-01 -2.16987863... | [5.862917423248291, -0.951188325881958] |
f1d0c130-ad2d-45b6-8f72-00a58cf18846 | synthetic-traffic-generation-with-wasserstein | null | null | https://ieeexplore.ieee.org/document/10001157 | https://ieeexplore.ieee.org/document/10001157 | Synthetic Traffic Generation with Wasserstein Generative Adversarial Networks | Network traffic data are critical for network research. With the help of synthetic traffic, researchers can readily generate data for network simulation and performance evaluation. However, the state-of-the-art traffic generators are either too simple to generate realistic traffic or require the implementation of origi... | ['Chih–Yu Wang', 'Po–Yu Chou', 'Yu–Ying Chen', 'Chao–Lun Wu'] | 2022-12-05 | null | null | null | ieee-global-communications-conference-2022-12 | ['synthetic-data-generation', 'synthetic-data-generation', 'intelligent-communication'] | ['medical', 'miscellaneous', 'time-series'] | [ 1.95926011e-01 -1.27640992e-01 7.79486671e-02 -1.63831905e-01
-1.51694998e-01 -6.48763835e-01 6.46473467e-01 -8.69004071e-01
-2.98684705e-02 1.16226161e+00 -5.31661630e-01 -9.33561146e-01
2.93600261e-01 -1.37161517e+00 -5.47652006e-01 -5.82557738e-01
1.07910074e-01 7.77747333e-01 7.85790443e-01 -2.51595676... | [5.382530212402344, 7.479783058166504] |
f5164fe8-1939-4ca1-af8c-66021265c343 | agmn-association-graph-based-graph-matching | 2301.04733 | null | https://arxiv.org/abs/2301.04733v1 | https://arxiv.org/pdf/2301.04733v1.pdf | AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms | Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in the computer-aided diagnosis of coronary artery disease (CAD). Inspired by the training procedure of interventional cardiologists for interpre... | ['Weihua Zhou', 'Guang-Uei Hung', 'Drew Pienta', 'Michele Esposito', 'Jingfeng Jiang', 'Zhihui Xu', 'Chen Zhao'] | 2023-01-11 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 1.97482944e-01 5.20551264e-01 -3.57557386e-01 -5.01747310e-01
-6.19696975e-01 -7.06066728e-01 -8.73706788e-02 3.18369180e-01
4.17294241e-02 4.18231696e-01 -6.39022663e-02 -9.00510192e-01
-1.72886327e-01 -1.05281866e+00 -2.37932801e-01 -3.91907483e-01
-2.13331982e-01 6.47141576e-01 1.75938725e-01 3.50541443... | [14.556967735290527, -2.4355316162109375] |
fba39afd-1c12-41f3-9dc7-da6fc0217920 | vision-transformers-and-yolov5-based-driver | 2209.01401 | null | https://arxiv.org/abs/2209.01401v1 | https://arxiv.org/pdf/2209.01401v1.pdf | Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework | Human drivers have distinct driving techniques, knowledge, and sentiments due to unique driving traits. Driver drowsiness has been a serious issue endangering road safety; therefore, it is essential to design an effective drowsiness detection algorithm to bypass road accidents. Miscellaneous research efforts have been ... | ['Mallikharjuna Rao K', 'Jai Vardhan', 'Kundrapu Supriya', 'Ghanta Sai Krishna'] | 2022-09-03 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 2.03913003e-02 5.03907204e-02 2.21113046e-03 -4.00276899e-01
-1.97318524e-01 -3.33891600e-01 4.40627843e-01 -2.81758100e-01
-2.52806932e-01 4.18224275e-01 -1.35764465e-01 -4.15126354e-01
-2.09453419e-01 -5.79138696e-01 -2.40688264e-01 -7.48287737e-01
3.81246984e-01 -2.73601443e-01 1.68588758e-01 -4.24595118... | [7.932761192321777, -0.6844950914382935] |
8fb8c129-9d21-4812-8464-f3d35c1eda6c | dag-matters-gflownets-enhanced-explainer-for | 2303.02448 | null | https://arxiv.org/abs/2303.02448v1 | https://arxiv.org/pdf/2303.02448v1.pdf | DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks | Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years. Existing literature mainly focus on selecting a subgraph, through combinatorial optimization, to provide faithful explanations. However, the exponential size of candidate subgraphs limits the appli... | ['Yan Pang', 'Jianye Hao', 'Zhigang Li', 'Yinchuan Li', 'Wenqian Li'] | 2023-03-04 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 2.90884495e-01 8.59647512e-01 -3.76316667e-01 -3.62002462e-01
-3.27017993e-01 -4.11925256e-01 6.46309853e-01 -5.18974587e-02
1.94289200e-02 8.91622365e-01 1.31018624e-01 -6.90723360e-01
-1.88413620e-01 -1.08306170e+00 -1.18176162e+00 -5.93111694e-01
-4.27880958e-02 4.86397624e-01 1.99543372e-01 -5.61031746... | [7.436333179473877, 6.238124847412109] |
312aea8f-aa35-4068-994f-672039100f8c | defending-water-treatment-networks-exploiting | 2008.12618 | null | https://arxiv.org/abs/2008.12618v1 | https://arxiv.org/pdf/2008.12618v1.pdf | Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection | While Water Treatment Networks (WTNs) are critical infrastructures for local communities and public health, WTNs are vulnerable to cyber attacks. Effective detection of attacks can defend WTNs against discharging contaminated water, denying access, destroying equipment, and causing public fear. While there are extensiv... | ['Leilei Sun', 'Jingbo Zhou', 'Pengyang Wang', 'Bowen Du', 'Yanjie Fu', 'Dongjie Wang'] | 2020-08-26 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 2.40810484e-01 -1.56634152e-01 4.82461005e-02 2.00591311e-01
-2.22266600e-01 -6.79908872e-01 6.36050045e-01 6.48072124e-01
-2.19449610e-01 1.37869362e-02 1.22625388e-01 -5.23931205e-01
-3.08429062e-01 -1.27982807e+00 -4.75179374e-01 -1.13388765e+00
-5.95531166e-01 -4.66654301e-01 3.96857440e-01 -1.86070070... | [7.195394992828369, 2.704976797103882] |
dee0ba74-d430-4a53-b22a-87a7a4f89306 | graph-neural-networks-for-improved-el-nino | 2012.01598 | null | https://arxiv.org/abs/2012.01598v3 | https://arxiv.org/pdf/2012.01598v3.pdf | Graph Neural Networks for Improved El Niño Forecasting | Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale atmospheri... | ['Björn Lütjens', 'Salomey Osei', 'Willa Potosnak', 'Ernest Pokropek', 'Arthur Fender C. Bucker', 'Emma Erickson', 'Salva Rühling Cachay'] | 2020-12-02 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-5.66077292e-01 1.07695274e-01 -2.07666948e-01 -3.50882977e-01
6.89952374e-01 -5.22301853e-01 1.11191523e+00 1.96924210e-01
2.10387528e-01 8.99110496e-01 4.06619042e-01 -1.33671248e+00
-3.61542255e-01 -1.40513730e+00 -4.09559995e-01 -3.42852235e-01
-9.46890950e-01 5.87237835e-01 -4.60805707e-02 -9.20120656... | [6.593557357788086, 2.8413736820220947] |
5e70f77b-018d-4c51-abe7-2989f20fae70 | a-novel-speech-feature-fusion-algorithm-for | 2212.00329 | null | https://arxiv.org/abs/2212.00329v1 | https://arxiv.org/pdf/2212.00329v1.pdf | A Novel Speech Feature Fusion Algorithm for Text-Independent Speaker Recognition | A novel speech feature fusion algorithm with independent vector analysis (IVA) and parallel convolutional neural network (PCNN) is proposed for text-independent speaker recognition. Firstly, some different feature types, such as the time domain (TD) features and the frequency domain (FD) features, can be extracted from... | ['Ye Zhang', 'Chengben Xu', 'Biao Ma'] | 2022-12-01 | null | null | null | null | ['speaker-recognition', 'text-independent-speaker-recognition'] | ['speech', 'speech'] | [-1.09303348e-01 -6.43639982e-01 2.31212720e-01 -5.77543795e-01
-5.01170933e-01 -2.83244699e-01 4.34875727e-01 -3.67838591e-01
-2.59042114e-01 1.01468183e-01 3.99156034e-01 -1.19544432e-01
-2.42969275e-01 -4.72469151e-01 -2.24217877e-01 -1.26087296e+00
3.23199318e-03 -4.24702205e-02 -2.94795215e-01 -2.49547556... | [14.457198143005371, 6.015100002288818] |
613070e2-1f67-4870-98e5-e68d45d60777 | acsc-automatic-calibration-for-non-repetitive | 2011.08516 | null | https://arxiv.org/abs/2011.08516v1 | https://arxiv.org/pdf/2011.08516v1.pdf | ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems | Recently, the rapid development of Solid-State LiDAR (SSL) enables low-cost and efficient obtainment of 3D point clouds from the environment, which has inspired a large quantity of studies and applications. However, the non-uniformity of its scanning pattern, and the inconsistency of the ranging error distribution brin... | ['Dian Liu', 'Yunxiang He', 'Zhenchao Ouyang', 'Jianwei Niu', 'Jiahe Cui'] | 2020-11-17 | null | null | null | null | ['camera-auto-calibration', '3d-geometry-perception'] | ['computer-vision', 'computer-vision'] | [ 1.62116051e-01 -6.40360117e-01 1.60797179e-01 -5.10141790e-01
-5.96055508e-01 -5.07107198e-01 3.44847053e-01 -2.04744875e-01
-1.36900619e-01 3.86063993e-01 -4.46263731e-01 -1.22497544e-01
-2.12376580e-01 -7.82559097e-01 -5.62103808e-01 -5.30459106e-01
3.43335420e-01 5.58536887e-01 5.12484848e-01 1.11179732... | [8.078310012817383, -2.601487398147583] |
e4f6de7a-e953-40e8-a015-16ab02987bc8 | structured-aspect-extraction | null | null | https://aclanthology.org/C16-1219 | https://aclanthology.org/C16-1219.pdf | Structured Aspect Extraction | Aspect extraction identifies relevant features from a textual description of an entity, e.g., a phone, and is typically targeted to product descriptions, reviews, and other short texts as an enabling task for, e.g., opinion mining and information retrieval. Current aspect extraction methods mostly focus on aspect terms... | ['Omer Gunes', 'Giorgio Orsi', 'Tim Furche'] | 2016-12-01 | structured-aspect-extraction-1 | https://aclanthology.org/C16-1219 | https://aclanthology.org/C16-1219.pdf | coling-2016-12 | ['aspect-extraction'] | ['natural-language-processing'] | [ 3.07085097e-01 1.71467051e-01 -5.05887926e-01 -4.25755054e-01
-7.30680406e-01 -9.13061559e-01 6.39193714e-01 7.71830976e-01
-3.29967231e-01 5.44156015e-01 3.31199467e-01 -5.78044116e-01
9.76778939e-02 -8.99185240e-01 -2.49018237e-01 -5.81234097e-01
2.88293958e-01 6.26697183e-01 3.53775546e-02 9.51717049... | [11.29774284362793, 6.736935138702393] |
1c2b55b0-2748-4c64-878d-67c9c2eb05b1 | egmm-an-evidential-version-of-the-gaussian | 2010.01333 | null | https://arxiv.org/abs/2010.01333v3 | https://arxiv.org/pdf/2010.01333v3.pdf | EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering | The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new model-based clustering algorithm, called EGMM (evidential GMM), in the theoretical framework of belief functions to better characterize cluster-... | ['Quan Pan', 'Zhun-Ga Liu', 'Thierry Denoeux', 'Lianmeng Jiao'] | 2020-10-03 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [-2.29019284e-01 2.10502326e-01 1.16453506e-01 -2.68704355e-01
-5.79721808e-01 -1.62956312e-01 7.09418774e-01 1.85236156e-01
-4.37982827e-01 4.34723586e-01 -2.83754408e-01 5.11699654e-02
-6.82050943e-01 -4.58515465e-01 -2.70680487e-01 -1.34166420e+00
-7.35405907e-02 9.15330827e-01 -1.02023268e-02 4.14772838... | [7.3129377365112305, 4.335756778717041] |
616340a6-70c0-4c4c-abff-71886d8afed9 | exploiting-personalized-invariance-for-better | 2211.11243 | null | https://arxiv.org/abs/2211.11243v1 | https://arxiv.org/pdf/2211.11243v1.pdf | Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning | Recently, data heterogeneity among the training datasets on the local clients (a.k.a., Non-IID data) has attracted intense interest in Federated Learning (FL), and many personalized federated learning methods have been proposed to handle it. However, the distribution shift between the training dataset and testing datas... | ['Jie Zhang', 'Song Guo', 'Xueyang Tang'] | 2022-11-21 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-3.37563396e-01 -4.56153542e-01 -5.36406398e-01 -4.67414469e-01
-8.83648694e-01 -7.25663424e-01 4.25957114e-01 -3.62067401e-01
8.49262029e-02 7.06449628e-01 1.83407947e-01 -1.86276630e-01
-7.81974792e-01 -5.68342209e-01 -7.48499990e-01 -1.20191872e+00
-2.07034275e-01 4.67706263e-01 -8.51489455e-02 4.03465889... | [5.827668190002441, 6.314483642578125] |
c158a2b9-e869-430b-b028-e99197a8f36c | icsvr-investigating-compositional-and | 2306.16533 | null | https://arxiv.org/abs/2306.16533v1 | https://arxiv.org/pdf/2306.16533v1.pdf | ICSVR: Investigating Compositional and Semantic Understanding in Video Retrieval Models | Video retrieval (VR) involves retrieving the ground truth video from the video database given a text caption or vice-versa. The two important components of compositionality: objects \& attributes and actions are joined using correct semantics to form a proper text query. These components (objects \& attributes, actions... | ['Vasudev Lal', 'Avinash Madasu'] | 2023-06-28 | null | null | null | null | ['video-retrieval', 'video-understanding', 'retrieval'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 2.18694672e-01 -5.27654409e-01 -4.35676962e-01 -2.04282179e-01
-6.90953374e-01 -7.81682730e-01 8.16862822e-01 -1.13976829e-01
-3.17131996e-01 3.75957936e-01 2.98055440e-01 1.97358839e-02
-3.08244795e-01 -5.09888172e-01 -9.33991015e-01 -4.53304410e-01
-2.83018090e-02 4.44351792e-01 4.66085494e-01 -1.55174345... | [10.273093223571777, 0.8577874302864075] |
28b221c0-1aa2-412f-91b3-955830e263f8 | two-dimensional-deep-regression-for-early | 2111.08069 | null | https://arxiv.org/abs/2111.08069v1 | https://arxiv.org/pdf/2111.08069v1.pdf | Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat | Crop yield prediction is one of the tasks of Precision Agriculture that can be automated based on multi-source periodic observations of the fields. We tackle the yield prediction problem using a Convolutional Neural Network (CNN) trained on data that combines radar satellite imagery and on-ground information. We presen... | ['John W. Sheppard', 'Giorgio Morales'] | 2021-11-15 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 2.90693223e-01 -1.53683409e-01 -1.35966703e-01 -4.80233699e-01
1.05343349e-01 -5.36086380e-01 2.58280426e-01 4.65835989e-01
-2.41590098e-01 8.28302264e-01 3.66936699e-02 -7.83381999e-01
-3.16545933e-01 -1.70816410e+00 -7.69951701e-01 -7.83144832e-01
-6.71011508e-01 -5.84084056e-02 -7.09893554e-02 -8.28137994... | [9.374858856201172, -1.6208165884017944] |
4e52bbec-c887-4c5a-a5a4-fcf78a3701de | gans-in-computer-vision-ebook | null | null | https://github.com/The-AI-Summer/GANs-in-Computer-Vision | https://theaisummer.com/gans-computer-vision-ebook/ | GANs in computer vision ebook | In this article-series we are reviewing the most fundamental works of Generative Adversarial Networks in Computer Vision. We start from the very beginning from concepts such as generative learning, adversarial learning. We provide some code and illustrations for educational purposes. The goal is to focus on the intuiti... | ['Nikolas Adaloglou', 'Sergios Karagianakos'] | 2020-06-10 | null | null | null | ebook-2020-6 | ['video-to-video-synthesis'] | ['computer-vision'] | [ 3.30719441e-01 7.07845688e-01 4.09259871e-02 -1.90143138e-01
-5.87271750e-01 -6.87720537e-01 5.77389300e-01 -8.65124762e-01
-6.04841299e-03 9.58141744e-01 -8.05311427e-02 -4.02639747e-01
-4.79490235e-02 -7.64624596e-01 -5.90713382e-01 -9.79692936e-01
1.77216493e-02 1.65349960e-01 -2.93983221e-01 -4.98114794... | [11.732053756713867, -0.1784614622592926] |
30d81e0e-f2f2-452e-8a53-c7ea4116e27e | adversarial-evasion-attacks-practicality-in | 2306.05494 | null | https://arxiv.org/abs/2306.05494v1 | https://arxiv.org/pdf/2306.05494v1.pdf | Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning | Machine Learning (ML) has become ubiquitous, and its deployment in Network Intrusion Detection Systems (NIDS) is inevitable due to its automated nature and high accuracy in processing and classifying large volumes of data. However, ML has been found to have several flaws, on top of them are adversarial attacks, which a... | ['Ashraf Matrawy', 'Mohamed el Shehaby'] | 2023-06-08 | null | null | null | null | ['adversarial-attack', 'network-intrusion-detection'] | ['adversarial', 'miscellaneous'] | [ 1.52751803e-01 1.49321124e-01 -4.46675569e-02 -3.58179748e-01
-2.11574882e-01 -9.33944046e-01 7.08947659e-01 -3.44279036e-02
-3.80149901e-01 5.85933089e-01 -5.51653266e-01 -8.97215724e-01
-7.54077965e-03 -9.41339195e-01 -6.42755210e-01 -3.01748604e-01
-4.17122006e-01 4.30430323e-01 2.52634853e-01 -1.76485285... | [5.532015800476074, 7.538974761962891] |
6d9913a8-8164-43d6-9253-6faa68540b89 | improving-ctc-based-speech-recognition-via | 2203.03582 | null | https://arxiv.org/abs/2203.03582v1 | https://arxiv.org/pdf/2203.03582v1.pdf | Improving CTC-based speech recognition via knowledge transferring from pre-trained language models | Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2.0 models. Due to the conditional independence assumption, CTC-based models are always weaker than attention-based encoder-decoder model... | ['Pengyuan Zhang', 'Ji Xu', 'Gaofeng Cheng', 'Long Ma', 'Yike Zhang', 'Songjun Cao', 'Keqi Deng'] | 2022-02-22 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 2.19363526e-01 2.35245749e-01 -1.41064599e-01 -1.77656457e-01
-9.95123208e-01 -2.17067048e-01 5.61785102e-01 -1.68127760e-01
-7.12338448e-01 6.74950182e-01 3.76722246e-01 -6.33322060e-01
4.13908541e-01 -4.28515464e-01 -6.16323352e-01 -6.17347062e-01
4.49068785e-01 4.79078263e-01 4.62100744e-01 -1.97164431... | [14.434467315673828, 6.847388744354248] |
23023824-304d-4e65-88f6-44ccb8f0e3f7 | few-shot-fine-tuning-vs-in-context-learning-a | 2305.16938 | null | https://arxiv.org/abs/2305.16938v2 | https://arxiv.org/pdf/2305.16938v2.pdf | Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation | Few-shot fine-tuning and in-context learning are two alternative strategies for task adaptation of pre-trained language models. Recently, in-context learning has gained popularity over fine-tuning due to its simplicity and improved out-of-domain generalization, and because extensive evidence shows that fine-tuned model... | ['Yanai Elazar', 'Dietrich Klakow', 'Shauli Ravfogel', 'Tiago Pimentel', 'Marius Mosbach'] | 2023-05-26 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [-1.19887851e-01 -3.80633324e-01 -3.25336546e-01 -5.08939445e-01
-8.30195069e-01 -7.09440947e-01 9.08615232e-01 1.90092012e-01
-8.21734428e-01 8.08804750e-01 3.24841648e-01 -1.67002961e-01
-2.70345181e-01 -5.68879366e-01 -6.08002543e-01 -4.32962328e-01
1.86281782e-02 5.86354911e-01 6.29300356e-01 -4.31083083... | [10.69269847869873, 8.314579963684082] |
eb564e12-0bb8-458b-8e51-004a32832e15 | bright-graph-neural-networks-in-real-time | 2205.13084 | null | https://arxiv.org/abs/2205.13084v2 | https://arxiv.org/pdf/2205.13084v2.pdf | BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection | Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk pr... | ['Jiawei Jiang', 'Ce Zhang', 'Ramesh Raghunathan', 'Yinan Shan', 'Yang Zhao', 'Zitao Zhang', 'Susie Xi Rao', 'Zhichao Han', 'Mingxuan Lu'] | 2022-05-25 | null | null | null | null | ['entity-embeddings'] | ['methodology'] | [-2.81569749e-01 1.89016517e-02 -4.78596270e-01 -2.55392283e-01
-1.97875306e-01 -2.89425045e-01 2.61373907e-01 5.88506162e-01
-4.65876520e-01 1.31738365e-01 -3.89722496e-01 -1.04181635e+00
4.95207570e-02 -1.49698770e+00 -9.46981728e-01 6.96625561e-02
-7.62628376e-01 6.36971295e-01 4.12833631e-01 -3.40148032... | [7.0106353759765625, 6.023726463317871] |
0a6ff4f6-d756-4346-8724-2c6eb1d82593 | towards-unbiased-multi-label-zero-shot | 2203.03483 | null | https://arxiv.org/abs/2203.03483v1 | https://arxiv.org/pdf/2203.03483v1.pdf | Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention | Multi-label zero-shot learning extends conventional single-label zero-shot learning to a more realistic scenario that aims at recognizing multiple unseen labels of classes for each input sample. Existing works usually exploit attention mechanism to generate the correlation among different labels. However, most of them ... | ['Fushuo Huo', 'Yuanyuan Xu', 'Jingcai Guo', 'Song Guo', 'Ziming Liu'] | 2022-03-07 | null | null | null | null | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [ 2.94681519e-01 -4.84021753e-02 -3.30621868e-01 -5.13392508e-01
-7.01700151e-01 -1.65087327e-01 5.61852872e-01 2.10543916e-01
-2.05218256e-01 5.29219329e-01 3.06881636e-01 3.59079957e-01
-1.47254556e-01 -9.10212636e-01 -4.89399940e-01 -1.05678725e+00
7.72817850e-01 1.62995875e-01 3.95083755e-01 -1.51374564... | [10.036310195922852, 2.6022746562957764] |
ce757fca-30b7-4950-b710-a4e26fe94324 | sequential-person-recognition-in-photo-albums | 1611.09967 | null | http://arxiv.org/abs/1611.09967v1 | http://arxiv.org/pdf/1611.09967v1.pdf | Sequential Person Recognition in Photo Albums with a Recurrent Network | Recognizing the identities of people in everyday photos is still a very
challenging problem for machine vision, due to non-frontal faces, changes in
clothing, location, lighting and similar. Recent studies have shown that rich
relational information between people in the same photo can help in recognizing
their identit... | ['Anton Van Den Hengel', 'Bohan Zhuang', 'Chunhua Shen', 'Yao Li', 'Lingqiao Liu', 'Guosheng Lin'] | 2016-11-30 | sequential-person-recognition-in-photo-albums-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Sequential_Person_Recognition_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Sequential_Person_Recognition_CVPR_2017_paper.pdf | cvpr-2017-7 | ['person-recognition'] | ['computer-vision'] | [ 4.20928448e-01 -4.70281899e-01 1.25318691e-01 -8.30570519e-01
-2.76680112e-01 -5.24568796e-01 7.93597221e-01 -2.32482895e-01
-2.55490661e-01 4.73140359e-01 3.60266954e-01 3.78720462e-01
2.97209680e-01 -4.25596744e-01 -8.61170650e-01 -4.58478481e-01
1.75856426e-01 2.84253001e-01 1.24923820e-02 -1.68331131... | [14.52519702911377, 0.9661141037940979] |
8e4978a6-2674-46bb-865b-c5e67792c5ea | barcode-annotations-for-medical-image | 1505.05212 | null | http://arxiv.org/abs/1505.05212v1 | http://arxiv.org/pdf/1505.05212v1.pdf | Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation | This paper proposes to generate and to use barcodes to annotate medical
images and/or their regions of interest such as organs, tumors and tissue
types. A multitude of efficient feature-based image retrieval methods already
exist that can assign a query image to a certain image class. Visual
annotations may help to inc... | ['Hamid. R. Tizhoosh'] | 2015-05-19 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 3.19391072e-01 -1.07418612e-01 -5.71628988e-01 -4.99420613e-01
-1.15208030e+00 -4.12721157e-01 7.29564309e-01 7.70047843e-01
-4.08166796e-01 7.05298960e-01 8.18362087e-02 -4.15118873e-01
-4.76634949e-01 -9.42012012e-01 -1.73764035e-01 -8.24949503e-01
4.83694375e-02 2.81592399e-01 2.13639215e-01 2.85530508... | [14.292566299438477, -1.4684250354766846] |
e56279bd-1067-4f38-a98c-9c38ef01c9a6 | ldfa-latent-diffusion-face-anonymization-for | 2302.08931 | null | https://arxiv.org/abs/2302.08931v1 | https://arxiv.org/pdf/2302.08931v1.pdf | LDFA: Latent Diffusion Face Anonymization for Self-driving Applications | In order to protect vulnerable road users (VRUs), such as pedestrians or cyclists, it is essential that intelligent transportation systems (ITS) accurately identify them. Therefore, datasets used to train perception models of ITS must contain a significant number of vulnerable road users. However, data protection regul... | ['Martin Lauer', 'Jannik Quehl', 'Royden Wagner', 'Kevin Rösch', 'Marvin Klemp'] | 2023-02-17 | null | null | null | null | ['face-detection', 'face-anonymization'] | ['computer-vision', 'computer-vision'] | [ 2.41733700e-01 4.35036302e-01 2.46141702e-01 -5.61371744e-01
-7.40446746e-01 -8.28350663e-01 7.19017446e-01 -4.74665821e-01
-4.80528742e-01 7.33101904e-01 1.28801197e-01 -3.32542866e-01
3.83099884e-01 -1.22672081e+00 -8.73920977e-01 -3.52741390e-01
2.75042236e-01 6.35971010e-01 -6.78361133e-02 -6.41215146... | [12.781546592712402, 0.7018473744392395] |
0a1664d3-24ce-4541-a8dc-c3410ef67035 | self-supervised-and-weakly-supervised | 2212.03125 | null | https://arxiv.org/abs/2212.03125v4 | https://arxiv.org/pdf/2212.03125v4.pdf | Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations | Previous work on action representation learning focused on global representations for short video clips. In contrast, many practical applications, such as video alignment, strongly demand learning the intensive representation of long videos. In this paper, we introduce a new framework of contrastive action representati... | ['Deng Cai', 'Boxi Wu', 'Yuqi Lin', 'Chenxi Huang', 'Renbo Tu', 'Minghao Chen'] | 2022-12-06 | null | null | null | null | ['action-classification', 'video-alignment'] | ['computer-vision', 'computer-vision'] | [ 4.40395623e-01 -4.23721462e-01 -6.17062449e-01 -4.49756861e-01
-9.87451911e-01 -4.14833188e-01 8.10158610e-01 -6.47277981e-02
-4.31861997e-01 6.90833747e-01 7.22700298e-01 1.97797984e-01
-1.03012137e-01 -4.66069996e-01 -9.43629086e-01 -8.44328821e-01
-3.61894518e-01 1.63151413e-01 3.55196953e-01 -3.50387767... | [8.641005516052246, 0.7576315999031067] |
f7e058ea-6237-40ab-8b37-0f295551d6b0 | parameter-selection-why-we-should-pay-more | 2107.05393 | null | https://arxiv.org/abs/2107.05393v1 | https://arxiv.org/pdf/2107.05393v1.pdf | Parameter Selection: Why We Should Pay More Attention to It | The importance of parameter selection in supervised learning is well known. However, due to the many parameter combinations, an incomplete or an insufficient procedure is often applied. This situation may cause misleading or confusing conclusions. In this opinion paper, through an intriguing example we point out that t... | ['Chih-Jen Lin', 'Si-An Chen', 'Tsung-Han Yang', 'Jie-Jyun Liu'] | 2021-07-08 | null | https://aclanthology.org/2021.acl-short.104 | https://aclanthology.org/2021.acl-short.104.pdf | acl-2021-5 | ['medical-code-prediction'] | ['medical'] | [ 3.40226829e-01 8.12015980e-02 -3.54912996e-01 -4.32569951e-01
-5.76047003e-01 -4.86184299e-01 3.55063200e-01 6.48034573e-01
-5.35216749e-01 8.70742440e-01 3.06023676e-02 -4.25642401e-01
-5.46070278e-01 -4.71364528e-01 -3.96785498e-01 -1.01626456e+00
1.43903896e-01 5.02610445e-01 2.44187132e-01 -3.29626016... | [8.721521377563477, 4.649308681488037] |
deed8e78-baa2-43cf-9957-929b8bfaf68a | transition-based-dependency-parsing-with-3 | null | null | https://aclanthology.org/D16-1254 | https://aclanthology.org/D16-1254.pdf | Transition-Based Dependency Parsing with Heuristic Backtracking | null | ['Miguel Ballesteros', 'Chris Dyer', 'Jacob Buckman'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.224766731262207, 3.7800936698913574] |
64283fb1-6c28-4c3a-87a6-e1cfe9275b06 | cross-modal-attention-congruence | 2212.10549 | null | https://arxiv.org/abs/2212.10549v2 | https://arxiv.org/pdf/2212.10549v2.pdf | Cross-modal Attention Congruence Regularization for Vision-Language Relation Alignment | Despite recent progress towards scaling up multimodal vision-language models, these models are still known to struggle on compositional generalization benchmarks such as Winoground. We find that a critical component lacking from current vision-language models is relation-level alignment: the ability to match directiona... | ['Louis-Philippe Morency', 'Ruslan Salakhutdinov', 'Paul Pu Liang', 'Rulin Shao', 'Rohan Pandey'] | 2022-12-20 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 3.01649958e-01 6.56735152e-02 -1.48113519e-01 -2.40040585e-01
-4.26933169e-01 -6.80594146e-01 9.60448742e-01 1.47312716e-01
-4.27383661e-01 5.65136336e-02 4.69908178e-01 -2.78117776e-01
-7.76864141e-02 -5.75215816e-01 -1.10055709e+00 -5.03726423e-01
2.94419557e-01 4.72168028e-01 1.58568099e-01 -5.50714135... | [10.593247413635254, 1.6743592023849487] |
75e312ff-0f8a-4d23-b3ba-6f35777142ed | inductive-relation-prediction-from-relational | 2304.00215 | null | https://arxiv.org/abs/2304.00215v3 | https://arxiv.org/pdf/2304.00215v3.pdf | Inductive Relation Prediction from Relational Paths and Context with Hierarchical Transformers | Relation prediction on knowledge graphs (KGs) is a key research topic. Dominant embedding-based methods mainly focus on the transductive setting and lack the inductive ability to generalize to new entities for inference. Existing methods for inductive reasoning mostly mine the connections between entities, i.e., relati... | ['Zhendong Mao', 'Quan Wang', 'Jiaang Li'] | 2023-04-01 | null | null | null | null | ['inductive-relation-prediction'] | ['graphs'] | [-8.78400952e-02 8.57980192e-01 -8.07507515e-01 -4.30307478e-01
-8.14540163e-02 -6.62557423e-01 7.29220569e-01 6.24291897e-01
1.47019401e-02 8.10377479e-01 6.24632955e-01 -4.65364426e-01
-4.46864247e-01 -1.52659464e+00 -9.01602030e-01 -2.07832336e-01
-3.20772529e-01 8.00679445e-01 3.59730601e-01 -2.96310216... | [8.956839561462402, 7.9471659660339355] |
613b151b-4fcd-4e3e-82e3-b8e2c445850f | policy-based-self-competition-for-planning | 2306.04403 | null | https://arxiv.org/abs/2306.04403v1 | https://arxiv.org/pdf/2306.04403v1.pdf | Policy-Based Self-Competition for Planning Problems | AlphaZero-type algorithms may stop improving on single-player tasks in case the value network guiding the tree search is unable to approximate the outcome of an episode sufficiently well. One technique to address this problem is transforming the single-player task through self-competition. The main idea is to compute a... | ['Dominik Gerhard Grimm', 'Jakob Burger', 'Quirin Göttl', 'Jonathan Pirnay'] | 2023-06-07 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 9.69118178e-02 5.24286211e-01 -3.62569213e-01 -2.80586239e-02
-8.08431804e-01 -6.15090251e-01 5.37855685e-01 2.97095329e-01
-8.27324450e-01 1.11808014e+00 -1.01387925e-01 -3.88202459e-01
-5.67714751e-01 -7.45387197e-01 -7.05229402e-01 -8.56367946e-01
-3.41925621e-01 1.10751796e+00 5.26666045e-01 -3.86357635... | [4.174142360687256, 2.129335641860962] |
20ba1f95-bbaf-4979-9411-f66729a0e31e | koala-an-index-for-quantifying-overlaps-with | 2303.14770 | null | https://arxiv.org/abs/2303.14770v1 | https://arxiv.org/pdf/2303.14770v1.pdf | Koala: An Index for Quantifying Overlaps with Pre-training Corpora | In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour. Despite the importance, there is no public tool that supports such analysis of pre-training corpora at large scale. To help research in this space, we launch Koala, a search... | ['Ehsan Shareghi', 'Gholamreza Haffari', 'Xuanli He', 'Thuy-Trang Vu'] | 2023-03-26 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-7.46012926e-02 -1.16014160e-01 -2.04572871e-01 -1.55146653e-02
-1.21915042e+00 -8.42463255e-01 4.60286230e-01 4.71450895e-01
-7.30593860e-01 6.04479671e-01 3.44426960e-01 -1.02504230e+00
-1.35698706e-01 -5.90524912e-01 -7.87930965e-01 -4.87323284e-01
-4.86579956e-03 5.20271897e-01 1.06763005e-01 2.14205552... | [10.623682975769043, 9.771267890930176] |
09c88e1b-41c3-4f67-869f-78c2faa4fe9f | video-instance-segmentation-by-instance-flow | 2110.10599 | null | https://arxiv.org/abs/2110.10599v1 | https://arxiv.org/pdf/2110.10599v1.pdf | Video Instance Segmentation by Instance Flow Assembly | Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes. While two-stage box-based methods achieve top performances in the image domain, they cannot easily extend their superiority into the video domain. This is because they usually deal with features or... | ['Yan Lu', 'Xiao Li', 'Jinglu Wang', 'Xiang Li'] | 2021-10-20 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 4.10627037e-01 -2.44675040e-01 -4.17342871e-01 -3.73504192e-01
-8.31823051e-01 -6.69102132e-01 4.82526630e-01 2.17518240e-01
-4.34347123e-01 3.80781025e-01 -2.26521462e-01 1.27331108e-01
-7.51372278e-02 -5.86368620e-01 -9.49550688e-01 -6.22643173e-01
-1.62761316e-01 2.31979370e-01 8.60897005e-01 -2.21546106... | [9.131818771362305, -0.08862917870283127] |
bb0b9647-8988-4f15-a663-a1b8982a9cae | 3d-dense-face-alignment-with-fused-features | 2203.04643 | null | https://arxiv.org/abs/2203.04643v1 | https://arxiv.org/pdf/2203.04643v1.pdf | 3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs | In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner. This is achieved by seamlessly combining standard convolutional neural networks (CNNs) with Graph Convolution Networks (GCNs). By iteratively and hier... | ['Yalin Zheng', 'Xiaowei Huang', 'Yihong Qiao', 'Xiaoyun Yang', 'Yitian Zhao', 'Dongxu Gao', 'Xu Chen', 'Yanda Meng'] | 2022-03-09 | null | null | null | null | ['3d-face-reconstruction', 'face-alignment', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.23681992e-01 2.97602236e-01 8.20718110e-02 -7.31147647e-01
-2.32105657e-01 -2.15996087e-01 5.31548262e-01 -1.96062565e-01
-5.76369949e-02 -6.18345402e-02 -4.60375508e-04 -1.02002509e-02
1.35267526e-01 -8.12677503e-01 -8.72045994e-01 -1.80463418e-01
-8.27947408e-02 6.16235495e-01 -2.74258912e-01 -7.94899985... | [13.235624313354492, 0.11884382367134094] |
1cfc19b6-3822-4014-99da-c8c7c4037684 | sat-size-aware-transformer-for-3d-point-cloud | 2301.06869 | null | https://arxiv.org/abs/2301.06869v1 | https://arxiv.org/pdf/2301.06869v1.pdf | SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation | Transformer models have achieved promising performances in point cloud segmentation. However, most existing attention schemes provide the same feature learning paradigm for all points equally and overlook the enormous difference in size among scene objects. In this paper, we propose the Size-Aware Transformer (SAT) tha... | ['Xiangyang Gong', 'Fangyu Liu', 'Chinwai Chiu', 'Yongping Xiong', 'Junjie Zhou'] | 2023-01-17 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-2.66701542e-02 -1.83765128e-01 -6.69736192e-02 -3.55372548e-01
-8.46449971e-01 -2.87489653e-01 2.52958447e-01 7.83213675e-02
-3.64590168e-01 3.12529981e-01 -3.60381813e-03 -5.74421026e-02
-3.33272636e-01 -9.31391478e-01 -8.81984055e-01 -8.53499830e-01
-1.84033047e-02 6.67694449e-01 9.09849644e-01 -1.49218351... | [7.924476146697998, -3.3699522018432617] |
d48338a5-0a2b-4e66-9adc-ddca46335359 | attention-u-net-learning-where-to-look-for | 1804.03999 | null | http://arxiv.org/abs/1804.03999v3 | http://arxiv.org/pdf/1804.03999v3.pdf | Attention U-Net: Learning Where to Look for the Pancreas | We propose a novel attention gate (AG) model for medical imaging that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us ... | ['Nils Y. Hammerla', 'Kensaku Mori', 'Loic Le Folgoc', 'Steven McDonagh', 'Mattias Heinrich', 'Jo Schlemper', 'Daniel Rueckert', 'Ozan Oktay', 'Matthew Lee', 'Kazunari Misawa', 'Bernhard Kainz', 'Ben Glocker'] | 2018-04-11 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 5.08709669e-01 5.90903819e-01 -2.45846063e-01 -3.27940464e-01
-7.01913893e-01 -1.81749880e-01 1.47038713e-01 4.76829678e-01
-5.33071816e-01 4.26709324e-01 7.47003853e-02 -3.66156667e-01
7.89828449e-02 -6.86759830e-01 -8.06214094e-01 -8.16549063e-01
-1.49426207e-01 1.19020171e-01 4.34692472e-01 2.24136170... | [14.679428100585938, -2.5836570262908936] |
3e3e3e76-2aa7-40c3-85ff-ce3d549dc83c | extended-pipeline-for-content-based-feature | 1805.05324 | null | http://arxiv.org/abs/1805.05324v1 | http://arxiv.org/pdf/1805.05324v1.pdf | Extended pipeline for content-based feature engineering in music genre recognition | We present a feature engineering pipeline for the construction of musical
signal characteristics, to be used for the design of a supervised model for
musical genre identification. The key idea is to extend the traditional
two-step process of extraction and classification with additive stand-alone
phases which are no lo... | ['Tina Raissi', 'Alessandro Tibo', 'Paolo Bientinesi'] | 2018-05-12 | null | null | null | null | ['music-genre-recognition'] | ['music'] | [ 3.12550992e-01 -8.57297704e-02 4.44909632e-01 -1.82585746e-01
-3.32651615e-01 -5.93341589e-01 5.37680149e-01 4.64668244e-01
-4.18894380e-01 1.27914160e-01 1.53867468e-01 1.32743735e-02
-8.05891037e-01 -5.00544667e-01 2.91174725e-02 -6.95088983e-01
-3.85617971e-01 3.35508078e-01 1.69807911e-01 -4.70543683... | [15.817154884338379, 5.334970474243164] |
fbc0554d-c8ae-4641-9623-12200c2054db | wider-face-and-pedestrian-challenge-2018 | 1902.06854 | null | http://arxiv.org/abs/1902.06854v1 | http://arxiv.org/pdf/1902.06854v1.pdf | WIDER Face and Pedestrian Challenge 2018: Methods and Results | This paper presents a review of the 2018 WIDER Challenge on Face and
Pedestrian. The challenge focuses on the problem of precise localization of
human faces and bodies, and accurate association of identities. It comprises of
three tracks: (i) WIDER Face which aims at soliciting new approaches to advance
the state-of-th... | ['Jian-Feng Wang', 'Yiheng Liu', 'Shiying Luo', 'Nikolay Sergievskiy', 'Boyan Zhou', 'Bingpeng Ma', 'Cheng Chi', 'Shifeng Zhang', 'Zuoxin Li', 'Ping Luo', 'Yuanjun Xiong', 'Ye Yuan', 'Xilin Chen', 'Wengang Zhou', 'Shuai Shao', 'Quanquan Li', 'Qiaokang Xie', 'Peng Cheng', 'Mingjun Sun', 'Lin Chen', 'Dongzhan Zhou', 'Don... | 2019-02-19 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.18387774e-01 -2.05834076e-01 1.42957807e-01 -3.91533345e-01
-7.35981584e-01 -5.69642544e-01 6.30864620e-01 -4.66376603e-01
-4.24565315e-01 5.68631709e-01 2.64101118e-01 2.00383380e-01
2.70784676e-01 -3.66255701e-01 -5.10428011e-01 -3.63311261e-01
-1.65929988e-01 4.84489143e-01 -3.25051211e-02 -5.14700934... | [14.013762474060059, 0.8720253109931946] |
b1ae7d17-657c-4c03-9f91-3cf97a1b09b0 | deep-leaning-based-ultra-fast-stair-detection | 2201.05275 | null | https://arxiv.org/abs/2201.05275v2 | https://arxiv.org/pdf/2201.05275v2.pdf | Deep Leaning-Based Ultra-Fast Stair Detection | Staircases are some of the most common building structures in urban environments. Stair detection is an important task for various applications, including the environmental perception of exoskeleton robots, humanoid robots, and rescue robots and the navigation of visually impaired people. Most existing stair detection ... | ['Zhiyong Tang', 'Shuang Qiu', 'Zhongcai Pei', 'Chen Wang'] | 2022-01-14 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 3.12887169e-02 -4.38405573e-01 -7.58853108e-02 -1.21657401e-01
-2.11600915e-01 -4.68565166e-01 1.22525118e-01 -4.93379235e-02
-5.48370421e-01 5.64545453e-01 -2.73667760e-02 -2.28519976e-01
-2.72979643e-02 -8.23280632e-01 -6.39711261e-01 -5.69294691e-01
3.20717752e-01 4.35726136e-01 9.71342504e-01 -4.41380709... | [8.144654273986816, -1.9089630842208862] |
e99b2966-fda7-4bf1-b0d7-b4ecb7c2f5d9 | variable-selection-for-nonparametric-learning | 1806.00569 | null | http://arxiv.org/abs/1806.00569v2 | http://arxiv.org/pdf/1806.00569v2.pdf | Variable Selection for Nonparametric Learning with Power Series Kernels | In this paper, we propose a variable selection method for general
nonparametric kernel-based estimation. The proposed method consists of
two-stage estimation: (1) construct a consistent estimator of the target
function, (2) approximate the estimator using a few variables by l1-type
penalized estimation. We see that the... | ['Mitsuaki Nishikimi', 'Kenta Kanamori', 'Wataru Kumagai', 'Kota Matsui', 'Takafumi Kanamori'] | 2018-06-02 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-8.33911523e-02 -3.65551084e-01 -6.21316254e-01 -5.50293088e-01
-7.62086272e-01 -3.90871800e-02 1.92653075e-01 -1.05083488e-01
-2.57305622e-01 1.28642762e+00 -4.05946344e-01 -3.15659881e-01
-2.15205163e-01 -7.84705698e-01 -5.32860696e-01 -8.33044589e-01
-1.85342103e-01 3.03593367e-01 1.71613619e-01 1.96173653... | [7.357145309448242, 4.152252674102783] |
3d68f6d4-d51c-42eb-841f-56673c3f1fc2 | syntax-driven-approach-for-semantic-role | null | null | https://aclanthology.org/2022.lrec-1.772 | https://aclanthology.org/2022.lrec-1.772.pdf | Syntax-driven Approach for Semantic Role Labeling | As an important task to analyze the semantic structure of a sentence, semantic role labeling (SRL) aims to locate the semantic role (e.g., agent) of noun phrases with respect to a given predicate and thus plays an important role in downstream tasks such as dialogue systems. To achieve a better performance in SRL, a mod... | ['Yan Song', 'Fei Xia', 'Han Qin', 'Yuanhe Tian'] | null | null | null | null | lrec-2022-6 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 1.52863964e-01 2.17977434e-01 -4.04459596e-01 -7.31659412e-01
-3.95597428e-01 -6.75967395e-01 5.15146911e-01 4.11801308e-01
-6.28525317e-01 7.06274152e-01 9.42095101e-01 -2.72932529e-01
2.43406519e-01 -8.56785119e-01 -7.07333565e-01 -3.97618860e-01
1.16642773e-01 4.82846498e-01 3.84390265e-01 -7.06816018... | [10.425036430358887, 9.186175346374512] |
ccbedebe-710a-423c-8a7d-87544b49d557 | fedala-adaptive-local-aggregation-for | 2212.01197 | null | https://arxiv.org/abs/2212.01197v2 | https://arxiv.org/pdf/2212.01197v2.pdf | FedALA: Adaptive Local Aggregation for Personalized Federated Learning | A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in p... | ['Haibing Guan', 'Ruhui Ma', 'Zhengui Xue', 'Tao Song', 'Hao Wang', 'Yang Hua', 'Jianqing Zhang'] | 2022-12-02 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-2.91089118e-01 -1.63408667e-01 -4.23754096e-01 -5.80443680e-01
-1.23341513e+00 -4.72024798e-01 6.67117178e-01 -3.33388746e-01
-3.42662334e-02 6.21894658e-01 2.84321487e-01 -1.42183363e-01
4.86102141e-03 -6.47209525e-01 -8.60347807e-01 -8.17839086e-01
-4.33118753e-02 3.76876593e-01 7.05197304e-02 3.43866557... | [5.829578876495361, 6.287938594818115] |
3a5563e4-542b-49c6-89e3-474f5244c583 | 3d-object-classification-via-spherical | 1712.04426 | null | http://arxiv.org/abs/1712.04426v1 | http://arxiv.org/pdf/1712.04426v1.pdf | 3D Object Classification via Spherical Projections | In this paper, we introduce a new method for classifying 3D objects. Our main
idea is to project a 3D object onto a spherical domain centered around its
barycenter and develop neural network to classify the spherical projection. We
introduce two complementary projections. The first captures depth variations of
a 3D obj... | ['Qi-Xing Huang', 'Karthik Ramani', 'Zhangjie Cao'] | 2017-12-12 | null | null | null | null | ['3d-object-classification', '3d-classification'] | ['computer-vision', 'computer-vision'] | [-1.11096904e-01 9.55641195e-02 -1.03965513e-01 -4.64812011e-01
-1.55222282e-01 -6.84495330e-01 6.55940533e-01 -3.60532820e-01
-4.55940589e-02 -2.16780931e-01 2.72984445e-01 -2.37676516e-01
3.06236297e-01 -8.41668487e-01 -8.23518693e-01 -4.04938161e-01
2.59783238e-01 7.38266408e-01 5.99742651e-01 1.24876358... | [8.212218284606934, -3.394007682800293] |
3f9b6815-5501-4c02-ac56-a072028b73fa | when-a-rf-beats-a-cnn-and-gru-together-a | 2206.08004 | null | https://arxiv.org/abs/2206.08004v1 | https://arxiv.org/pdf/2206.08004v1.pdf | When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification | Internet traffic classification is widely used to facilitate network management. It plays a crucial role in Quality of Services (QoS), Quality of Experience (QoE), network visibility, intrusion detection, and traffic trend analyses. While there is no theoretical guarantee that deep learning (DL)-based solutions perform... | ['Chen Hajaj', 'Amit Dvir', 'Ran Dubin', 'Ofek Bader', 'Adi Lichy'] | 2022-06-16 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-1.02422491e-01 -3.91734391e-01 -4.95798737e-01 -1.15088850e-01
-1.79108996e-02 -3.11124802e-01 6.99750364e-01 4.08821464e-01
-4.22801465e-01 8.13019037e-01 -5.08870542e-01 -1.09942830e+00
-5.01330853e-01 -8.89115512e-01 -2.80781746e-01 -7.05513716e-01
-4.72088009e-01 6.67236745e-01 5.13465405e-01 -3.60219270... | [5.118988037109375, 7.242120742797852] |
7a484d84-ae6c-4d21-b747-ee9de5ad167b | gist-aiter-system-for-the-diarization-task-of | 2209.10357 | null | https://arxiv.org/abs/2209.10357v4 | https://arxiv.org/pdf/2209.10357v4.pdf | GIST-AiTeR System for the Diarization Task of the 2022 VoxCeleb Speaker Recognition Challenge | This report describes the submission system of the GIST-AiTeR team at the 2022 VoxCeleb Speaker Recognition Challenge (VoxSRC) Track 4. Our system mainly includes speech enhancement, voice activity detection , multi-scaled speaker embedding, probabilistic linear discriminant analysis-based speaker clustering, and overl... | ['Hong Kook Kim', 'Ji Won Kim', 'Kyeong Wan Park', 'Yechan Yu', 'Dongkeon Park'] | 2022-09-21 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-6.86301738e-02 3.29336822e-01 -4.97555435e-02 -4.37905550e-01
-1.21331620e+00 -4.42233503e-01 6.75155520e-01 -3.04742277e-01
-4.05350983e-01 -6.60288483e-02 5.81239343e-01 -1.53885454e-01
2.63375282e-01 2.27556467e-01 1.16579339e-01 -4.82327849e-01
-3.66380870e-01 3.99998963e-01 1.77210927e-01 -1.50853261... | [14.42809009552002, 6.040250778198242] |
6ad03229-5f1f-4423-bffa-5e33bd9b56db | re-thinking-supertags-in-linear-context-free | null | null | https://openreview.net/forum?id=udaYBjwpU_M | https://openreview.net/pdf?id=udaYBjwpU_M | Re-thinking Supertags in Linear Context-free Rewriting Systems for Constituency Parsing | Recently, a supertagging-based approach for parsing discontinuous constituent trees with linear context-free rewriting systems (LCFRS) was introduced. We reformulate their algorithm for the extraction of supertags from treebanks to be more concise. Moreover, we add some extensions that give us control over the extracti... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.51482671e-01 6.69202268e-01 1.03718936e-02 -5.36327541e-01
-1.24123967e+00 -9.76414979e-01 4.02761936e-01 2.82412589e-01
-2.76136428e-01 8.22592735e-01 3.62172335e-01 -7.05439806e-01
3.30813885e-01 -8.70481312e-01 -3.47564638e-01 -4.27533686e-01
-3.26783538e-01 2.90899009e-01 8.93751681e-01 -7.68029988... | [10.317934036254883, 9.723258018493652] |
33d6a725-e74d-48c1-bd84-bab754d3aadd | flow-based-anomaly-detection | 2010.03002 | null | https://arxiv.org/abs/2010.03002v3 | https://arxiv.org/pdf/2010.03002v3.pdf | OneFlow: One-class flow for anomaly detection based on a minimal volume region | We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region. Contrary to density-based methods, OneFlow is constructed in such a way that its result typically does not depend on the structure of outliers. This is caused by the fact that during train... | ['Przemysław Spurek', 'Jacek Tabor', 'Łukasz Struski', 'Marcin Sendera', 'Marek Śmieja', 'Łukasz Maziarka'] | 2020-10-06 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-7.07659960e-01 -1.60883889e-01 -2.15451717e-01 -3.65637958e-01
-1.98167950e-01 -4.35492814e-01 3.21716040e-01 8.34371567e-01
-2.52923351e-02 5.16065955e-01 -3.36795390e-01 -2.37975597e-01
-1.87508643e-01 -7.70590127e-01 -6.04454458e-01 -6.67070985e-01
-5.42020977e-01 5.03554702e-01 6.07676625e-01 9.70195420... | [7.593977928161621, 2.4613823890686035] |
9086bd35-91fd-44a0-92d0-a4ce7c16cf02 | multi-granulariy-time-based-transformer-for | 2304.05257 | null | https://arxiv.org/abs/2304.05257v1 | https://arxiv.org/pdf/2304.05257v1.pdf | Multi-granulariy Time-based Transformer for Knowledge Tracing | In this paper, we present a transformer architecture for predicting student performance on standardized tests. Specifically, we leverage students historical data, including their past test scores, study habits, and other relevant information, to create a personalized model for each student. We then use these models to ... | ['Tong Zhou'] | 2023-04-11 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [ 1.07154131e-01 -2.12533712e-01 -7.67832279e-01 -6.93440020e-01
-7.43577421e-01 -6.25247359e-01 2.55586773e-01 6.02110147e-01
-2.18886837e-01 6.81094170e-01 3.60187471e-01 -6.91432595e-01
-5.67392826e-01 -9.85468507e-01 -6.46656692e-01 -2.37193517e-03
1.53807804e-01 2.83218533e-01 3.24235797e-01 -1.85161620... | [10.166895866394043, 7.1608991622924805] |
684e562e-8979-4ef0-97f8-ac22d2161684 | deviant-depth-equivariant-network-for | 2207.10758 | null | https://arxiv.org/abs/2207.10758v1 | https://arxiv.org/pdf/2207.10758v1.pdf | DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection | Modern neural networks use building blocks such as convolutions that are equivariant to arbitrary 2D translations. However, these vanilla blocks are not equivariant to arbitrary 3D translations in the projective manifold. Even then, all monocular 3D detectors use vanilla blocks to obtain the 3D coordinates, a task for ... | ['Xiaoming Liu', 'Armin Parchami', 'Enrique Corona', 'Garrick Brazil', 'Abhinav Kumar'] | 2022-07-21 | null | null | null | null | ['3d-object-detection-from-monocular-images'] | ['computer-vision'] | [-4.12544221e-01 -1.32286251e-01 -1.34164527e-01 -1.74513727e-01
-3.15767884e-01 -8.11518431e-01 6.45349205e-01 -8.12996507e-01
-6.27606034e-01 3.17360163e-01 7.77972117e-02 -3.07939321e-01
5.77178538e-01 -7.40161419e-01 -1.01565146e+00 -5.93534172e-01
1.05051585e-01 2.15197757e-01 4.64151651e-01 -2.27127850... | [7.90233850479126, -2.5248119831085205] |
80f631ce-8e46-4fad-9283-f2b3bcc72579 | generalized-category-discovery | 2201.02609 | null | https://arxiv.org/abs/2201.02609v2 | https://arxiv.org/pdf/2201.02609v2.pdf | Generalized Category Discovery | In this paper, we consider a highly general image recognition setting wherein, given a labelled and unlabelled set of images, the task is to categorize all images in the unlabelled set. Here, the unlabelled images may come from labelled classes or from novel ones. Existing recognition methods are not able to deal with ... | ['Andrew Zisserman', 'Andrea Vedaldi', 'Kai Han', 'Sagar Vaze'] | 2022-01-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.pdf | cvpr-2022-1 | ['fine-grained-visual-recognition', 'open-world-semi-supervised-learning'] | ['computer-vision', 'computer-vision'] | [ 7.28125334e-01 5.79937622e-02 -2.49675751e-01 -6.90714180e-01
-7.44030595e-01 -7.29014218e-01 8.19312453e-01 -1.39220968e-01
-6.67587817e-01 6.06535792e-01 7.12567046e-02 -3.59439105e-02
-1.15927458e-01 -5.62035799e-01 -9.87980604e-01 -9.12140548e-01
1.93325654e-01 8.18502843e-01 2.18338862e-01 -9.57516804... | [9.768752098083496, 2.5030040740966797] |
387c367c-0109-46f1-b6d2-f01b38e19507 | multi-domain-conversation-quality-evaluation | 1911.08567 | null | https://arxiv.org/abs/1911.08567v1 | https://arxiv.org/pdf/1911.08567v1.pdf | Multi-domain Conversation Quality Evaluation via User Satisfaction Estimation | An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and employ annotation schemes with limit... | ['Praveen Kumar Bodigutla', 'Lazaros Polymenakos', 'Spyros Matsoukas'] | 2019-11-18 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-2.12045357e-01 5.82249582e-01 -3.42064142e-01 -1.15150654e+00
-1.23096585e+00 -4.11135226e-01 4.79840577e-01 3.20804268e-01
-5.05984783e-01 1.08047152e+00 7.84453034e-01 -1.75940424e-01
-7.35338731e-03 -3.35928500e-01 4.89103198e-01 -1.60759911e-02
1.97045058e-01 7.31728554e-01 -1.26577109e-01 -9.82317984... | [12.859465599060059, 8.034111976623535] |
e2b9a15a-85e3-441e-9208-624d8cecf37d | an-automatic-cardiac-segmentation-framework | 1909.05488 | null | https://arxiv.org/abs/1909.05488v1 | https://arxiv.org/pdf/1909.05488v1.pdf | An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image | LGE CMR is an efficient technology for detecting infarcted myocardium. An efficient and objective ventricle segmentation method in LGE can benefit the location of the infarcted myocardium. In this paper, we proposed an automatic framework for LGE image segmentation. There are just 5 labeled LGE volumes with about 15 sl... | ['Wei Wang', 'Kuanquan Wang', 'Yashu Liu', 'Gongning Luo', 'Chengqin Ye'] | 2019-09-12 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [-3.95551994e-02 5.10133728e-02 4.58846577e-02 -3.13477367e-01
-7.81225801e-01 -5.59754193e-01 -2.08136857e-01 -2.93114223e-02
-4.97346729e-01 7.66002536e-01 9.27259102e-02 -2.19258815e-01
1.98567554e-01 -5.32812119e-01 -3.16314191e-01 -6.44898176e-01
-4.07342374e-01 4.77901369e-01 4.43983257e-01 3.43768507... | [14.152968406677246, -2.409914970397949] |
e08cfb8c-ba41-4875-99aa-c36076a756cc | auditing-predictive-models-for-intersectional | 2306.13064 | null | https://arxiv.org/abs/2306.13064v1 | https://arxiv.org/pdf/2306.13064v1.pdf | Auditing Predictive Models for Intersectional Biases | Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we propose Conditional Bias Scan (CBS), a flexible au... | ['Daniel B. Neill', 'Edward McFowland III', 'Kate S. Boxer'] | 2023-06-22 | null | null | null | null | ['fairness', 'fairness', 'bias-detection'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 4.10401374e-01 3.35635871e-01 -9.73189890e-01 -7.67382622e-01
-8.00415754e-01 -6.04743838e-01 5.74241757e-01 7.98071742e-01
-6.33434415e-01 9.68643963e-01 5.62499046e-01 -1.10117805e+00
-4.69364464e-01 -8.79202902e-01 -2.36869052e-01 -4.20021743e-01
7.35058039e-02 4.66623753e-01 -2.29720827e-02 3.20232153... | [8.837315559387207, 5.3762054443359375] |
fe728266-c8b1-4af1-bad4-1a34695fb876 | robust-face-recognition-via-multimodal-deep | 1509.00244 | null | http://arxiv.org/abs/1509.00244v1 | http://arxiv.org/pdf/1509.00244v1.pdf | Robust Face Recognition via Multimodal Deep Face Representation | Face images appeared in multimedia applications, e.g., social networks and
digital entertainment, usually exhibit dramatic pose, illumination, and
expression variations, resulting in considerable performance degradation for
traditional face recognition algorithms. This paper proposes a comprehensive
deep learning frame... | ['DaCheng Tao', 'Changxing Ding'] | 2015-09-01 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 2.11868420e-01 -1.71072349e-01 -7.94809163e-02 -9.38695014e-01
-6.01790130e-01 -1.54820323e-01 4.49942350e-01 -5.96994281e-01
-3.42881858e-01 5.43682635e-01 6.03579618e-02 1.55264974e-01
8.73980895e-02 -5.15068948e-01 -6.93645298e-01 -8.83046508e-01
-2.32362047e-01 -1.76350579e-01 -6.21611238e-01 -1.53296858... | [13.306188583374023, 0.940438985824585] |
9c14cb71-e410-4a45-aea2-811d81713a65 | sir-nerd-a-chinese-named-entity-recognition | null | null | https://aclanthology.org/W12-6322 | https://aclanthology.org/W12-6322.pdf | SIR-NERD: A Chinese Named Entity Recognition and Disambiguation System using a Two-Stage Method | null | ['Zehuan Peng', 'Xianpei Han', 'Le Sun'] | 2012-12-01 | sir-nerd-a-chinese-named-entity-recognition-1 | https://aclanthology.org/W12-6322 | https://aclanthology.org/W12-6322.pdf | ws-2012-12 | ['chinese-named-entity-recognition'] | ['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... | [-7.143136024475098, 3.8487985134124756] |
bb7470ee-1239-4902-994e-e8605842fad3 | dual-information-enhanced-multi-view | 2211.14987 | null | https://arxiv.org/abs/2211.14987v1 | https://arxiv.org/pdf/2211.14987v1.pdf | Dual Information Enhanced Multi-view Attributed Graph Clustering | Multi-view attributed graph clustering is an important approach to partition multi-view data based on the attribute feature and adjacent matrices from different views. Some attempts have been made in utilizing Graph Neural Network (GNN), which have achieved promising clustering performance. Despite this, few of them pa... | ['Haizhang Zhang', 'Chang-Dong Wang', 'Xi-Ran Zhu', 'Man-Sheng Chen', 'Jia-Qi Lin'] | 2022-11-28 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-6.89233616e-02 1.56543236e-02 -3.34332436e-01 -7.85255730e-02
-5.41549385e-01 -2.65253156e-01 4.29482311e-01 1.12503260e-01
2.30707020e-01 1.00611664e-01 4.16510701e-01 1.79337114e-01
-5.53637564e-01 -6.83679044e-01 -1.26647964e-01 -1.20080090e+00
-1.52508169e-01 4.17686105e-01 -1.09549858e-01 1.30333513... | [8.207466125488281, 4.75744104385376] |
2138a529-57da-4fd2-83b1-4a418951ad74 | clip2point-transfer-clip-to-point-cloud | 2210.01055 | null | https://arxiv.org/abs/2210.01055v2 | https://arxiv.org/pdf/2210.01055v2.pdf | CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training | Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language pre-training models to 3D vision. PointCLIP converts point cloud data to multi-view depth maps, adopting CLIP for shape classification. However, its performance is rest... | ['WangMeng Zuo', 'Wanli Ouyang', 'Rynson W. H. Lau', 'Xiaoshui Huang', 'Yunhan Yang', 'Bowen Dong', 'Tianyu Huang'] | 2022-10-03 | null | null | null | null | ['training-free-3d-point-cloud-classification', '3d-point-cloud-classification', 'zero-shot-transfer-3d-point-cloud', 'point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.14666019e-02 -2.73541093e-01 -2.40949765e-01 -2.74305731e-01
-8.58004808e-01 -5.14562666e-01 8.01682353e-01 -2.63091296e-01
-1.65203944e-01 -1.62762105e-02 1.38408870e-01 -1.12893954e-01
2.78821081e-01 -9.95316625e-01 -8.89089048e-01 -4.90848839e-01
3.80882412e-01 3.71783406e-01 6.35966182e-01 -1.67723745... | [8.131536483764648, -3.289747714996338] |
ce5c8afd-6249-480d-be93-666d4ccdaa1f | deep-bayesian-inference-for-seismic-imaging | 2110.04825 | null | https://arxiv.org/abs/2110.04825v3 | https://arxiv.org/pdf/2110.04825v3.pdf | Deep Bayesian inference for seismic imaging with tasks | We propose to use techniques from Bayesian inference and deep neural networks to translate uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as horizon tracking. Seismic imaging is an ill-posed inverse problem because of bandwidth and aperture limitations, which is hampered by the pres... | ['Felix J. Herrmann', 'Gabrio Rizzuti', 'Ali Siahkoohi'] | 2021-10-10 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 2.81425327e-01 2.33995110e-01 3.91473711e-01 -1.58432797e-01
-1.03755510e+00 -3.83759707e-01 5.05918324e-01 -2.90532380e-01
-5.79654992e-01 8.24503243e-01 3.62124175e-01 -1.34684592e-01
-6.39850438e-01 -8.05121720e-01 -8.62754464e-01 -1.03545547e+00
-1.01739734e-01 4.92369711e-01 2.65511692e-01 1.49287790... | [6.842742919921875, 3.4971163272857666] |
3e90a1ce-778e-48b6-881b-b65bd4724758 | lr-csnet-low-rank-deep-unfolding-network-for | 2212.09088 | null | https://arxiv.org/abs/2212.09088v1 | https://arxiv.org/pdf/2212.09088v1.pdf | LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing | Deep unfolding networks (DUNs) have proven to be a viable approach to compressive sensing (CS). In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS. Real-world image patches are often well-represented by low-rank approximations. LR-CSNet exploits this property by adding a low-rank ... | ['Zhenming Peng', 'Fabian Gieseke', 'Stefan Oehmcke', 'Christian Igel', 'Lei LI', 'Tianfang Zhang'] | 2022-12-18 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 4.68685120e-01 -2.91357003e-02 9.42499191e-02 -1.74838871e-01
-8.34698200e-01 -8.74116644e-02 5.03071547e-01 -3.85134220e-01
-1.93194866e-01 5.60115457e-01 3.78656149e-01 4.02751528e-02
-6.82186931e-02 -6.53197587e-01 -9.47359741e-01 -8.10494363e-01
-1.70879588e-01 -8.88352022e-02 -1.09218545e-01 -2.69081116... | [11.258906364440918, -2.073730230331421] |
c2304116-f92c-4b93-ba50-209d4aa750f4 | dereverberation-using-joint-estimation-of-dry | 2007.12581 | null | https://arxiv.org/abs/2007.12581v1 | https://arxiv.org/pdf/2007.12581v1.pdf | Dereverberation using joint estimation of dry speech signal and acoustic system | The purpose of speech dereverberation is to remove quality-degrading effects of a time-invariant impulse response filter from the signal. In this report, we describe an approach to speech dereverberation that involves joint estimation of the dry speech signal and of the room impulse response. We explore deep learning m... | ['Keunwoo Choi', 'Simon Durand', 'Sanna Wager'] | 2020-07-24 | null | null | null | null | ['room-impulse-response', 'speech-dereverberation'] | ['audio', 'speech'] | [ 1.32576466e-01 -2.67325729e-01 7.21275747e-01 -3.62516373e-01
-1.29721928e+00 -5.37376881e-01 5.80730960e-02 -5.99430978e-01
-1.40260577e-01 5.63207209e-01 7.47303545e-01 -4.69876289e-01
-5.99635132e-02 -1.28994684e-03 -5.86813152e-01 -9.28336799e-01
-2.73506786e-03 -4.06984836e-01 -2.88807720e-01 -1.84987590... | [15.099871635437012, 5.946348190307617] |
0a810469-aa60-4530-b75d-a32387186eb5 | learning-to-identify-physical-parameters-from | 2009.08292 | null | https://arxiv.org/abs/2009.08292v1 | https://arxiv.org/pdf/2009.08292v1.pdf | Learning to Identify Physical Parameters from Video Using Differentiable Physics | Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. Video prediction models often learn a latent representation of video which is encoded from input frames and decoded back into images. Even wh... | ['Jörg Stückler', 'Michael Möller', 'Rama Krishna Kandukuri', 'Jan Achterhold'] | 2020-09-17 | null | null | null | null | ['predict-future-video-frames'] | ['computer-vision'] | [ 4.38683659e-01 7.02489465e-02 -3.48467350e-01 -2.62057960e-01
-2.00232342e-01 -3.38588089e-01 9.88803685e-01 -3.65323365e-01
-8.36884305e-02 6.32761300e-01 2.80299067e-01 -1.17705077e-01
-1.95957616e-01 -6.24951541e-01 -1.24674475e+00 -9.26642001e-01
-3.86897564e-01 1.38558194e-01 1.86044484e-01 3.19913983... | [8.48789119720459, 0.2133188247680664] |
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