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train · 44.6k rows

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e5ab51c6-504b-4d36-8927-6c261f50c81f	naturalproofs-mathematical-theorem-proving-in	2104.01112	null	https://arxiv.org/abs/2104.01112v2	https://arxiv.org/pdf/2104.01112v2.pdf	NaturalProofs: Mathematical Theorem Proving in Natural Language	Understanding and creating mathematics using natural mathematical language - the mixture of symbolic and natural language used by humans - is a challenging and important problem for driving progress in machine learning. As a step in this direction, we develop NaturalProofs, a multi-domain corpus of mathematical statements and their proofs, written in natural mathematical language. NaturalProofs unifies broad coverage, deep coverage, and low-resource mathematical sources, allowing for evaluating both in-distribution and zero-shot generalization. Using NaturalProofs, we benchmark strong neural methods on mathematical reference retrieval and generation tasks which test a system's ability to determine key results that appear in a proof. Large-scale sequence models show promise compared to classical information retrieval methods, yet their performance and out-of-domain generalization leave substantial room for improvement. NaturalProofs opens many avenues for research on challenging mathematical tasks.	['Kyunghyun Cho', 'Yejin Choi', 'Hannaneh Hajishirzi', 'Ronan Le Bras', 'Jiacheng Liu', 'Sean Welleck']	2021-03-24	null	null	null	null	['automated-theorem-proving', 'automated-theorem-proving']	['miscellaneous', 'reasoning']	[ 3.51381391e-01 -6.25073761e-02 -2.99115062e-01 -2.87598252e-01 -1.13746762e+00 -1.12221766e+00 1.20007312e+00 6.97987974e-01 -1.58466399e-01 8.03939819e-01 1.59213126e-01 -6.26877725e-01 -3.19870502e-01 -1.01810145e+00 -9.94315743e-01 -2.06136122e-01 -3.16600531e-01 3.56164098e-01 1.31792009e-01 -4.36157733e-01 8.50701928e-01 1.67249098e-01 -1.22323370e+00 4.26368356e-01 1.04122496e+00 3.12938899e-01 2.15808824e-01 9.25192773e-01 -5.57204306e-01 1.31913340e+00 -7.87050068e-01 -8.42172980e-01 -1.61615759e-01 -3.33859771e-01 -1.03952551e+00 -9.67083931e-01 1.15644383e+00 -3.95002335e-01 -4.05745476e-01 1.34533489e+00 1.25724003e-01 -2.08753664e-02 9.41938221e-01 -1.31971788e+00 -1.13765514e+00 9.42722142e-01 7.03451186e-02 4.90795821e-01 7.85267413e-01 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2cd22d36-9290-4949-b580-cfc38aa9c606	visualsparta-sparse-transformer-fragment	2101.00265	null	https://arxiv.org/abs/2101.00265v2	https://arxiv.org/pdf/2101.00265v2.pdf	VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words	Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of both accuracy and efficiency. VisualSparta is capable of outperforming previous state-of-the-art scalable methods in MSCOCO and Flickr30K. We also show that it achieves substantial retrieving speed advantages, i.e., for a 1 million image index, VisualSparta using CPU gets ~391X speedup compared to CPU vector search and ~5.4X speedup compared to vector search with GPU acceleration. Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index. To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for large-scale datasets, with significant accuracy improvement compared to previous state-of-the-art methods.	['Kyusong Lee', 'Tiancheng Zhao', 'Xiaopeng Lu']	2021-01-01	null	https://aclanthology.org/2021.acl-long.389	https://aclanthology.org/2021.acl-long.389.pdf	acl-2021-5	['cross-modal-information-retrieval']	['miscellaneous']	[ 0.21146666 -0.91497165 -0.27187505 -0.05693946 -1.5694447 -0.7036804 0.6858588 0.28486887 -0.4650398 0.07881122 0.11864594 -0.31545544 -0.51240087 -0.75606674 -0.531248 -0.62430733 0.30303952 0.8273231 0.6312891 -0.18335879 0.5320459 0.39767852 -2.0542386 0.5414989 0.35485992 1.3177412 0.5110781 0.5691347 -0.03126717 0.58603454 -0.27229926 -0.2551034 0.27642724 0.15578927 -1.038673 -0.30394402 0.8653585 -0.3822315 -0.4927457 0.71437347 0.7329287 0.03535798 0.5776302 -1.1171023 -0.6879439 0.11461785 -0.9148062 0.25218284 0.5492007 -0.27727196 1.2290834 -1.1137437 0.82852536 1.2287023 0.41840568 -0.08307506 -0.8885124 -0.6332302 -0.26859426 0.54687095 -1.8669796 -0.14904602 0.11635192 -0.1641655 1.2329826 0.86634266 0.64907825 0.503853 0.10123315 1.2479889 0.8140302 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59c08668-eac6-4617-8dc8-09a8bd9fe582	estisr-adapting-efficient-scene-text-image	2306.02443	null	https://arxiv.org/abs/2306.02443v1	https://arxiv.org/pdf/2306.02443v1.pdf	ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes	While scene text image super-resolution (STISR) has yielded remarkable improvements in accurately recognizing scene text, prior methodologies have placed excessive emphasis on optimizing performance, rather than paying due attention to efficiency - a crucial factor in ensuring deployment of the STISR-STR pipeline. In this work, we propose a novel Efficient Scene Text Image Super-resolution (ESTISR) Network for resource-limited deployment platform. ESTISR's functionality primarily depends on two critical components: a CNN-based feature extractor and an efficient self-attention mechanism used for decoding low-resolution images. We designed a re-parameterized inverted residual block specifically suited for resource-limited circumstances as the feature extractor. Meanwhile, we proposed a novel self-attention mechanism, softmax shrinking, based on a kernel-based approach. This innovative technique offers linear complexity while also naturally incorporating discriminating low-level features into the self-attention structure. Extensive experiments on TextZoom show that ESTISR retains a high image restoration quality and improved STR accuracy of low-resolution images. Furthermore, ESTISR consistently outperforms current methods in terms of actual running time and peak memory consumption, while achieving a better trade-off between performance and efficiency.	['Jie Shao', 'Yihan Xu', 'Xin Man', 'Minghao Fu']	2023-06-04	null	null	null	null	['image-super-resolution', 'super-resolution', 'image-restoration']	['computer-vision', 'computer-vision', 'computer-vision']	[ 6.12367988e-01 -2.83708096e-01 -4.76467423e-02 -4.16916132e-01 -1.12567616e+00 -1.96909457e-02 5.48776031e-01 -3.04375082e-01 -4.49987531e-01 4.33685482e-01 4.11378533e-01 4.92902435e-02 -1.39512464e-01 -6.70753539e-01 -6.49382710e-01 -6.34900093e-01 3.87866765e-01 -3.29112150e-02 4.01023239e-01 -1.64724156e-01 5.32711864e-01 6.13557458e-01 -1.61877441e+00 5.14852107e-01 9.19955194e-01 8.38383138e-01 7.47123361e-01 8.49397898e-01 -3.79930027e-02 1.14950180e+00 -4.09768999e-01 -1.17173515e-01 1.85714468e-01 -3.66181284e-01 -5.13086855e-01 6.59567043e-02 6.92630827e-01 -6.05908334e-01 -5.06062269e-01 8.18780899e-01 6.27091110e-01 1.61807179e-01 3.12903345e-01 -4.82111186e-01 -9.29380655e-01 7.78559029e-01 -9.34442282e-01 5.55749416e-01 2.49217436e-01 6.30862918e-03 9.88113821e-01 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8a1a451a-4bfe-48b4-a56b-bf544a591d45	guideformer-transformers-for-image-guided	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Rho_GuideFormer_Transformers_for_Image_Guided_Depth_Completion_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Rho_GuideFormer_Transformers_for_Image_Guided_Depth_Completion_CVPR_2022_paper.pdf	GuideFormer: Transformers for Image Guided Depth Completion	Depth completion has been widely studied to predict a dense depth image from its sparse measurement and a single color image. However, most state-of-the-art methods rely on static convolutional neural networks (CNNs) which are not flexible enough for capturing the dynamic nature of input contexts. In this paper, we propose GuideFormer, a fully transformer-based architecture for dense depth completion. We first process sparse depth and color guidance images with separate transformer branches to extract hierarchical and complementary token representations. Each branch consists of a stack of self-attention blocks and has key design features to make our model suitable for the task. We also devise an effective token fusion method based on guided-attention mechanism. It explicitly models information flow between the two branches and captures inter-modal dependencies that cannot be obtained from depth or color image alone. These properties allow GuideFormer to enjoy various visual dependencies and recover precise depth values while preserving fine details. We evaluate GuideFormer on the KITTI dataset containing real-world driving scenes and provide extensive ablation studies. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art methods.	['Youngjung Kim', 'Jinsung Ha', 'Kyeongha Rho']	2022-01-01	null	null	null	cvpr-2022-1	['depth-completion']	['computer-vision']	[ 2.51489013e-01 -3.80151421e-02 -2.11977080e-01 -5.77525198e-01 -7.67215431e-01 -2.57594019e-01 5.91977119e-01 -2.49708056e-01 -1.98042005e-01 4.00218219e-01 4.18113291e-01 -1.69013608e-02 1.59141004e-01 -8.04212451e-01 -6.45698905e-01 -7.30813086e-01 1.23647422e-01 1.81690902e-01 3.23584437e-01 -1.49266332e-01 2.46033639e-01 2.70294368e-01 -1.74252188e+00 7.28821874e-01 9.34602737e-01 1.16812932e+00 5.71978211e-01 5.63498080e-01 -3.21270078e-01 1.26628911e+00 -2.71612257e-01 -6.54313639e-02 3.67197603e-01 -2.75904208e-01 -6.06553793e-01 -2.82202531e-02 7.71306038e-01 -8.15912664e-01 -8.37139010e-01 1.01064360e+00 3.55661184e-01 2.60261539e-02 3.21363211e-01 -9.96358156e-01 -7.93117046e-01 5.13184071e-01 -6.95563674e-01 2.92604506e-01 1.89245984e-01 2.80653864e-01 1.09686697e+00 -9.98028994e-01 4.31049496e-01 1.34300339e+00 2.54595727e-01 7.13545084e-01 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2.83850074e-01 7.75570631e-01 -8.35104883e-01 -3.26831311e-01 8.65944568e-03]	[8.937849044799805, -2.4447083473205566]
25bf4838-dc9a-4cf0-b12f-75ebabf978af	estimating-egocentric-3d-human-pose-in-the	2201.07929	null	https://arxiv.org/abs/2201.07929v1	https://arxiv.org/pdf/2201.07929v1.pdf	Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision	Egocentric 3D human pose estimation with a single fisheye camera has drawn a significant amount of attention recently. However, existing methods struggle with pose estimation from in-the-wild images, because they can only be trained on synthetic data due to the unavailability of large-scale in-the-wild egocentric datasets. Furthermore, these methods easily fail when the body parts are occluded by or interacting with the surrounding scene. To address the shortage of in-the-wild data, we collect a large-scale in-the-wild egocentric dataset called Egocentric Poses in the Wild (EgoPW). This dataset is captured by a head-mounted fisheye camera and an auxiliary external camera, which provides an additional observation of the human body from a third-person perspective during training. We present a new egocentric pose estimation method, which can be trained on the new dataset with weak external supervision. Specifically, we first generate pseudo labels for the EgoPW dataset with a spatio-temporal optimization method by incorporating the external-view supervision. The pseudo labels are then used to train an egocentric pose estimation network. To facilitate the network training, we propose a novel learning strategy to supervise the egocentric features with the high-quality features extracted by a pretrained external-view pose estimation model. The experiments show that our method predicts accurate 3D poses from a single in-the-wild egocentric image and outperforms the state-of-the-art methods both quantitatively and qualitatively.	['Christian Theobalt', 'Diogo Luvizon', 'Kripasindhu Sarkar', 'Weipeng Xu', 'Lingjie Liu', 'Jian Wang']	2022-01-20	null	http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Estimating_Egocentric_3D_Human_Pose_in_the_Wild_With_External_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Estimating_Egocentric_3D_Human_Pose_in_the_Wild_With_External_CVPR_2022_paper.pdf	cvpr-2022-1	['egocentric-pose-estimation']	['computer-vision']	[-1.03399418e-01 1.78849518e-01 1.52540933e-02 -4.59379792e-01 -4.38356310e-01 -2.53029466e-01 3.40056837e-01 -7.05529153e-01 -5.22241950e-01 5.10696590e-01 3.30815583e-01 6.06671333e-01 1.59303695e-01 -5.17445564e-01 -8.41834128e-01 -5.07007480e-01 1.55118182e-01 6.44700468e-01 1.41123071e-01 -2.32862160e-01 -2.57905900e-01 2.62782186e-01 -1.30234694e+00 -1.72237769e-01 4.79940891e-01 7.68514991e-01 2.03066930e-01 5.40434539e-01 6.86083198e-01 3.86809051e-01 -2.61074543e-01 -2.57428765e-01 5.08190095e-01 -3.69816333e-01 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f4eab759-b830-4f88-88e5-9ba44b341f90	a-collaborative-approach-using-neural	2205.10559	null	https://arxiv.org/abs/2205.10559v1	https://arxiv.org/pdf/2205.10559v1.pdf	A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning	In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor environments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Nevertheless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE-RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.	['Elena Simona Lohan', 'Sven Casteleyn', 'Joaquín Torres-Sospedra', 'Pavel Pascacio']	2022-05-21	null	null	null	null	['indoor-localization']	['computer-vision']	[ 1.73644245e-01 -2.89931387e-01 -7.43342787e-02 -3.51080686e-01 -6.83564901e-01 -5.37854135e-01 1.97323129e-01 5.98490238e-01 -5.49664140e-01 1.04115021e+00 1.53347459e-02 -1.69812128e-01 -5.75789392e-01 -8.79443526e-01 -5.91519237e-01 -9.37517166e-01 -1.38794137e-02 1.57439131e-02 1.31621018e-01 1.44530430e-01 8.78152847e-02 4.17562932e-01 -1.50808156e+00 -5.76342940e-01 1.12472081e+00 1.29280949e+00 3.06699425e-01 5.79938531e-01 8.89947787e-02 1.95080012e-01 -9.34269965e-01 -5.71205579e-02 -1.68875679e-01 3.14133205e-02 3.33679944e-01 -7.37894714e-01 -2.20538497e-01 -1.78740546e-01 1.53119162e-01 5.82474589e-01 1.02052414e+00 1.78253040e-01 2.03097954e-01 -9.63625848e-01 -8.24083835e-02 4.94429410e-01 -3.27177197e-01 -7.90901389e-03 8.29579353e-01 -5.66104710e-01 3.39227855e-01 -7.86229968e-01 -6.41847029e-02 3.72558713e-01 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87f4a677-515a-412a-ac56-8cbec26611e3	fpgahart-a-toolflow-for-throughput-oriented	2305.19896	null	https://arxiv.org/abs/2305.19896v1	https://arxiv.org/pdf/2305.19896v1.pdf	fpgaHART: A toolflow for throughput-oriented acceleration of 3D CNNs for HAR onto FPGAs	Surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval are just few of the many applications in which 3D Convolutional Neural Networks are exploited. However, their extensive use is restricted by their high computational and memory requirements, especially when integrated into systems with limited resources. This study proposes a toolflow that optimises the mapping of 3D CNN models for Human Action Recognition onto FPGA devices, taking into account FPGA resources and off-chip memory characteristics. The proposed system employs Synchronous Dataflow (SDF) graphs to model the designs and introduces transformations to expand and explore the design space, resulting in high-throughput designs. A variety of 3D CNN models were evaluated using the proposed toolflow on multiple FPGA devices, demonstrating its potential to deliver competitive performance compared to earlier hand-tuned and model-specific designs.	['Dimitrios Tzovaras', 'Christos-Savvas Bouganis', 'Petros Toupas']	2023-05-31	null	null	null	null	['video-retrieval', 'autonomous-vehicles', 'action-recognition-in-videos', 'action-recognition']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 8.84099826e-02 -7.08683953e-02 -5.62097847e-01 -2.46648863e-01 5.44337273e-01 -4.85195547e-01 5.58725297e-01 -6.67081401e-02 -4.00938541e-01 1.46249369e-01 -1.37876153e-01 -7.08076179e-01 7.16345236e-02 -7.28363812e-01 -3.93434495e-01 -1.68596745e-01 -2.36140966e-01 -5.82459942e-02 4.76313502e-01 1.33333579e-01 1.37105561e-03 1.24411011e+00 -1.65663600e+00 1.28790006e-01 2.42332090e-02 1.27628541e+00 -3.09448153e-01 6.80077493e-01 -1.46113992e-01 8.62698019e-01 -7.21592069e-01 -1.84830993e-01 5.76388240e-01 6.22927882e-02 -2.24431887e-01 1.26006946e-01 3.78867626e-01 -6.64570689e-01 -6.18614912e-01 7.01851964e-01 3.80926937e-01 -4.83613282e-01 2.68194050e-01 -1.35976136e+00 -1.36545554e-01 1.71435043e-01 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048861e6-b361-4fa8-818d-3f9787799825	a-closer-look-at-the-training-dynamics-of	2303.11098	null	https://arxiv.org/abs/2303.11098v1	https://arxiv.org/pdf/2303.11098v1.pdf	A closer look at the training dynamics of knowledge distillation	In this paper we revisit the efficacy of knowledge distillation as a function matching and metric learning problem. In doing so we verify three important design decisions, namely the normalisation, soft maximum function, and projection layers as key ingredients. We theoretically show that the projector implicitly encodes information on past examples, enabling relational gradients for the student. We then show that the normalisation of representations is tightly coupled with the training dynamics of this projector, which can have a large impact on the students performance. Finally, we show that a simple soft maximum function can be used to address any significant capacity gap problems. Experimental results on various benchmark datasets demonstrate that using these insights can lead to superior or comparable performance to state-of-the-art knowledge distillation techniques, despite being much more computationally efficient. In particular, we obtain these results across image classification (CIFAR100 and ImageNet), object detection (COCO2017), and on more difficult distillation objectives, such as training data efficient transformers, whereby we attain a 77.2% top-1 accuracy with DeiT-Ti on ImageNet.	['Krystian Mikolajczyk', 'Roy Miles']	2023-03-20	null	null	null	null	['metric-learning', 'metric-learning']	['computer-vision', 'methodology']	[ 1.74014345e-01 2.03290135e-01 -2.73994356e-01 -2.13178307e-01 -3.53084028e-01 -5.35969973e-01 7.47348607e-01 6.76295236e-02 -7.98027217e-01 7.31571615e-01 1.36078879e-01 -4.77215379e-01 -5.00607848e-01 -8.55308950e-01 -1.13548207e+00 -6.44945264e-01 -1.42729715e-01 3.37026954e-01 1.44370809e-01 -1.38227731e-01 1.89119697e-01 3.81612509e-01 -1.57411516e+00 2.42981806e-01 7.70414650e-01 1.21458840e+00 2.29601234e-01 7.76572466e-01 1.69365630e-01 1.16841841e+00 -3.24145555e-01 -8.12953472e-01 3.29374760e-01 -6.02068640e-02 -1.16351318e+00 -4.50687200e-01 6.75410807e-01 -5.49731314e-01 -6.89376950e-01 1.03938282e+00 4.69183594e-01 3.07664096e-01 6.64822340e-01 -1.09566677e+00 -1.03626013e+00 9.29680824e-01 -1.30346417e-01 4.90647733e-01 -1.53392702e-02 1.57077372e-01 1.36352801e+00 -1.03049755e+00 3.54732245e-01 1.17160439e+00 6.98062420e-01 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-1.21453881e+00 -8.29398334e-01 -3.21197987e-01 3.66311133e-01 3.64863902e-01 -6.37728155e-01 -3.45118999e-01 6.07735552e-02]	[9.370302200317383, 3.296186923980713]
1adad9f7-91a5-4202-9340-73eecdb8319d	chard-clinical-health-aware-reasoning-across	2210.04191	null	https://arxiv.org/abs/2210.04191v2	https://arxiv.org/pdf/2210.04191v2.pdf	CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models	We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions. We collect and present an associated dataset, CHARDat, consisting of explanations about 52 health conditions across three clinical dimensions. We conduct extensive experiments using BART and T5 along with data augmentation, and perform automatic, human, and qualitative analyses. We show that while our models can perform decently, CHARD is very challenging with strong potential for further exploration.	['Eduard Hovy', 'Anatole Gershman', 'Bogdan Sacaleanu', 'Vivek Khetan', 'Steven Y. Feng']	2022-10-09	null	null	null	null	['clinical-knowledge']	['miscellaneous']	[ 4.02502626e-01 1.38600767e+00 -5.92063248e-01 -4.84949440e-01 -8.87177885e-01 -1.90321252e-01 7.25474775e-01 7.63513982e-01 2.86622226e-01 9.67734039e-01 1.40033746e+00 -8.53689611e-01 -3.91108185e-01 -5.97928405e-01 -2.78722435e-01 5.70327649e-03 -2.57298917e-01 9.04852450e-01 -4.20104861e-01 -8.63968432e-02 -1.80372000e-02 1.70239225e-01 -5.89656293e-01 9.87497449e-01 1.10955513e+00 3.03959846e-01 -6.64854586e-01 7.90601492e-01 1.34248346e-01 1.39387667e+00 -6.48969054e-01 -9.21993256e-01 -3.46711844e-01 -7.66388595e-01 -1.34272087e+00 7.97574222e-02 -3.49231772e-02 -4.05610353e-01 -3.05146575e-01 4.96127635e-01 6.20312274e-01 -3.92661154e-01 9.73818123e-01 -1.13302028e+00 -1.36367369e+00 1.13807511e+00 -2.96612028e-02 1.66227534e-01 7.40482688e-01 5.26829064e-01 9.52746212e-01 -6.76610827e-01 1.17832983e+00 1.42721117e+00 8.30855787e-01 1.22731853e+00 -1.23367453e+00 -3.68683130e-01 9.39377919e-02 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5.81375480e-01 -1.05862923e-01 -7.92155042e-02 2.31209353e-01]	[9.127631187438965, 7.801990509033203]
d16514e2-3410-401c-b5fe-cede025f2ec5	proof-supplement-learning-sparse-causal	1411.1557	null	http://arxiv.org/abs/1411.1557v1	http://arxiv.org/pdf/1411.1557v1.pdf	Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)	This article contains detailed proofs and additional examples related to the UAI-2013 submission `Learning Sparse Causal Models is not NP-hard'. It describes the FCI+ algorithm: a method for sound and complete causal model discovery in the presence of latent confounders and/or selection bias, that has worst case polynomial complexity of order $N^{2(k+1)}$ in the number of independence tests, for sparse graphs over $N$ nodes, bounded by node degree $k$. The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes et al., 2000) that is also sound and complete, but has worst case complexity exponential in $N$.	['Tom Heskes', 'Joris M. Mooij', 'Tom Claassen']	2014-11-06	null	null	null	null	['model-discovery']	['miscellaneous']	[ 4.37819511e-01 7.09031343e-01 -6.47313952e-01 -3.08643818e-01 -8.60518873e-01 -5.34090102e-01 1.98174402e-01 2.81367719e-01 -1.46367073e-01 1.39354897e+00 1.04799099e-01 -6.63429916e-01 -9.52885509e-01 -8.85950804e-01 -1.14499605e+00 -7.70600736e-01 -1.19234037e+00 8.22761297e-01 2.19467327e-01 4.06984448e-01 -5.36664911e-02 2.10315697e-02 -1.19706988e+00 -2.04591349e-01 6.90677524e-01 3.01781297e-01 -3.98671836e-01 8.69857669e-01 4.71828341e-01 8.50910723e-01 -1.42979011e-01 -7.59969950e-01 -7.88449943e-02 -5.39614081e-01 -1.01541328e+00 -7.76303172e-01 3.03254008e-01 1.12978272e-01 -5.83623350e-01 1.11781335e+00 4.18571830e-01 -3.31005245e-01 4.77205634e-01 -1.55437076e+00 -3.18715215e-01 1.63147676e+00 -6.46805346e-01 3.48275065e-01 4.08510447e-01 -3.96203458e-01 1.20981073e+00 -3.85004580e-01 6.87769055e-01 1.64352977e+00 7.44833171e-01 4.23444957e-01 -1.69242454e+00 -1.30279398e+00 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-1.01225579e+00 -8.47828388e-01 -2.90390134e-01 1.20281488e-01 -5.43705523e-01 -7.52013028e-01 -1.13407271e-02]	[7.661834716796875, 5.309223651885986]
b2cc982c-4a6a-4482-873f-446620e730b7	data-boost-text-data-augmentation-through-1	2012.02952	null	https://arxiv.org/abs/2012.02952v1	https://arxiv.org/pdf/2012.02952v1.pdf	Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation	Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which augments data through reinforcement learning guided conditional generation. We evaluate Data Boost on three diverse text classification tasks under five different classifier architectures. The result shows that Data Boost can boost the performance of classifiers especially in low-resource data scenarios. For instance, Data Boost improves F1 for the three tasks by 8.7% on average when given only 10% of the whole data for training. We also compare Data Boost with six prior text augmentation methods. Through human evaluations (N=178), we confirm that Data Boost augmentation has comparable quality as the original data with respect to readability and class consistency.	['Soroush Vosoughi', 'Lili Wang', 'Weicheng Ma', 'Chenyan Jia', 'Guangxuan Xu', 'Ruibo Liu']	2020-12-05	data-boost-text-data-augmentation-through	https://aclanthology.org/2020.emnlp-main.726	https://aclanthology.org/2020.emnlp-main.726.pdf	emnlp-2020-11	['text-augmentation']	['natural-language-processing']	[ 3.63675714e-01 3.21550876e-01 -4.83643621e-01 -4.74227428e-01 -6.54690027e-01 -4.08022314e-01 9.72905993e-01 6.65815473e-01 -7.69292891e-01 1.23489547e+00 4.48538810e-01 -3.59446555e-01 2.29442015e-01 -7.26101220e-01 -5.88162124e-01 -3.59731555e-01 4.22246695e-01 6.40425861e-01 -5.00984967e-01 -5.37361920e-01 2.27713928e-01 -2.74449419e-02 -1.58916950e+00 3.85138959e-01 1.32847619e+00 6.16906226e-01 -1.84419677e-02 5.88671982e-01 -3.67966294e-01 6.20305717e-01 -9.72727418e-01 -5.57483017e-01 2.20545474e-02 -1.63393334e-01 -1.09893489e+00 -6.67015463e-02 6.49053395e-01 -4.54720348e-01 -2.88240641e-01 5.95895231e-01 6.18223250e-01 2.20802724e-01 5.40301502e-01 -1.33039176e+00 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7931adde-d062-4eb9-bc0c-24e1746b3eb1	unsupervised-manifold-alignment-with-joint	2207.02968	null	https://arxiv.org/abs/2207.02968v2	https://arxiv.org/pdf/2207.02968v2.pdf	Unsupervised Manifold Alignment with Joint Multidimensional Scaling	We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences between data instances across the datasets, to a common low-dimensional Euclidean space. Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis into a joint optimization problem to simultaneously generate isometric embeddings of data and learn correspondences between instances from two different datasets, while only requiring intra-dataset pairwise dissimilarities as input. This unique characteristic makes our approach applicable to datasets without access to the input features, such as solving the inexact graph matching problem. We propose an alternating optimization scheme to solve the problem that can fully benefit from the optimization techniques for MDS and Wasserstein Procrustes. We demonstrate the effectiveness of our approach in several applications, including joint visualization of two datasets, unsupervised heterogeneous domain adaptation, graph matching, and protein structure alignment. The implementation of our work is available at https://github.com/BorgwardtLab/JointMDS	['Karsten Borgwardt', 'Carlos Oliver', 'Bowen Fan', 'Dexiong Chen']	2022-07-06	null	null	null	null	['graph-matching']	['graphs']	[ 1.07421927e-01 -6.39536008e-02 -8.58059004e-02 -5.00242114e-01 -8.18470061e-01 -7.81329811e-01 3.24817866e-01 4.95853931e-01 -3.22634399e-01 5.20435810e-01 4.99910526e-02 -1.52817324e-01 -4.18417633e-01 -5.23342669e-01 -5.35473347e-01 -6.90784872e-01 -2.94102058e-02 7.94846833e-01 -2.27785856e-03 3.47484760e-02 2.43352190e-01 6.76643491e-01 -1.31143057e+00 -7.33280927e-02 9.52344596e-01 4.51108396e-01 2.33314261e-01 6.90906644e-01 -2.41845891e-01 -6.57668710e-02 -1.65552869e-01 -1.48329467e-01 4.79328543e-01 -3.06370646e-01 -8.46087992e-01 1.31326318e-01 7.18261123e-01 -1.55777927e-03 -3.26228827e-01 1.07165086e+00 6.00723207e-01 1.20925754e-01 7.55128980e-01 -1.68301010e+00 -8.82704675e-01 -6.17791973e-02 -8.28241467e-01 1.00375429e-01 4.99900997e-01 -1.12356104e-01 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31e067f8-662d-4571-852d-af5201726bf0	sign-language-translation-in-a-healthcare	null	null	https://aclanthology.org/2021.triton-1.13	https://aclanthology.org/2021.triton-1.13.pdf	Sign Language Translation in a Healthcare Setting	Communication between healthcare professionals and deaf patients is challenging, and the current COVID-19 pandemic makes this issue even more acute. Sign language interpreters can often not enter hospitals and face masks make lipreading impossible. To address this urgent problem, we developed a system which allows healthcare professionals to translate sentences that are frequently used in the diagnosis and treatment of COVID-19 into Sign Language of the Netherlands (NGT). Translations are displayed by means of videos and avatar animations. The architecture of the system is such that it could be extended to other applications and other sign languages in a relatively straightforward way.	['Anika Smeijers', 'Shani Mende-Gillings', 'Lyke Esselink', 'Floris Roelofsen']	null	null	null	null	triton-2021-7	['lipreading', 'sign-language-translation']	['computer-vision', 'computer-vision']	[ 2.49416325e-02 1.70855612e-01 3.93585814e-03 -1.88096836e-01 -2.56295174e-01 -6.72508121e-01 5.32722354e-01 -1.35036513e-01 -8.67815256e-01 9.16597605e-01 5.08738220e-01 -6.87322855e-01 1.95388392e-01 -2.04600528e-01 -7.59689063e-02 -4.79185611e-01 2.74084777e-01 7.32337534e-01 1.37237519e-01 -1.78466797e-01 5.16069233e-02 7.06899166e-01 -1.60378122e+00 1.40628055e-01 6.97473109e-01 1.88226923e-01 3.37046891e-01 7.46985316e-01 -3.82827669e-01 4.45209920e-01 -7.96977997e-01 -1.48035333e-01 -4.98221780e-04 -5.83479822e-01 -7.12053597e-01 -2.70619482e-01 1.10289462e-01 -9.67004359e-01 1.13150723e-01 7.59028018e-01 9.69175220e-01 -4.12514865e-01 6.30472362e-01 -1.18592536e+00 -1.88177481e-01 1.85431138e-01 -1.52611092e-01 -2.52550095e-01 9.02399242e-01 2.50100285e-01 9.12006479e-03 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6a3b393e-2fb6-4ff3-b1fe-2d042050d9f3	skill-based-differences-in-spatio-temporal	1603.07738	null	http://arxiv.org/abs/1603.07738v1	http://arxiv.org/pdf/1603.07738v1.pdf	Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2	Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focused on tactical combat. Mastering MOBAs requires extensive practice, as is exemplified in the popular MOBA Defence of the Ancients 2 (DotA 2). In this paper, we present three data-driven measures of spatio-temporal behavior in DotA 2: 1) Zone changes; 2) Distribution of team members and: 3) Time series clustering via a fuzzy approach. We present a method for obtaining accurate positional data from DotA 2. We investigate how behavior varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results indicate that spatio-temporal behavior of MOBA teams is related to team skill, with professional teams having smaller within-team distances and conducting more zone changes than amateur teams. The temporal distribution of the within-team distances of professional and high-skilled teams also generally follows patterns distinct from lower skill ranks.	['Matthias Schubert', 'John Maguire', 'Derrek Chu', 'Iris Yuhui Wang', 'Diego Klabjan', 'Anders Drachen', 'Tobias Mahlmann', 'Matthew Yancey']	2016-03-24	null	null	null	null	['dota-2', 'time-series-clustering']	['playing-games', 'time-series']	[-5.64711154e-01 -4.92663920e-01 4.78743643e-01 1.54536650e-01 -3.58575225e-01 -9.93627191e-01 2.78507143e-01 3.55881691e-01 -7.81720757e-01 6.30129099e-01 3.08096129e-02 -2.72969276e-01 -1.22485757e+00 -9.87568140e-01 -1.37950212e-01 -6.15438998e-01 -4.82801676e-01 7.89286792e-01 6.76552534e-01 -1.07083797e+00 6.46384597e-01 4.85617280e-01 -1.55032110e+00 4.82746273e-01 5.46651244e-01 8.09330523e-01 2.14465708e-01 6.73826098e-01 7.24959373e-02 1.18293321e+00 -1.16980910e+00 -4.31529969e-01 6.50432646e-01 -5.75650632e-01 -6.26895308e-01 -1.88164562e-01 -3.03104818e-01 3.77295196e-01 -1.14819929e-02 7.70009458e-01 2.68921226e-01 5.84201336e-01 5.35031497e-01 -1.09141552e+00 2.28234574e-01 7.78548479e-01 -7.64132738e-01 7.64919221e-01 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a40f87c0-8e40-4463-be12-ecdbe73bd112	a-probabilistic-autoencoder-for-causal	2212.04235	null	https://arxiv.org/abs/2212.04235v1	https://arxiv.org/pdf/2212.04235v1.pdf	A probabilistic autoencoder for causal discovery	The paper addresses the problem of finding the causal direction between two associated variables. The proposed solution is to build an autoencoder of their joint distribution and to maximize its estimation capacity relative to both the marginal distributions. It is shown that the resulting two capacities cannot, in general, be equal. This leads to a new criterion for causal discovery: the higher capacity is consistent with the unconstrained choice of a distribution representing the cause while the lower capacity reflects the constraints imposed by the mechanism on the distribution of the effect. Estimation capacity is defined as the ability of the auto-encoder to represent arbitrary datasets. A regularization term forces it to decide which one of the variables to model in a more generic way i.e., while maintaining higher model capacity. The causal direction is revealed by the constraints encountered while encoding the data instead of being measured as a property of the data itself. The idea is implemented and tested using a restricted Boltzmann machine.	['Matthias Feiler']	2022-12-08	null	null	null	null	['causal-discovery']	['knowledge-base']	[ 2.29933634e-01 5.15112340e-01 -2.27001414e-01 -3.32080692e-01 -1.29641712e-01 -3.02247882e-01 9.08108473e-01 -1.58255380e-02 -6.10555410e-01 9.57632780e-01 3.79358411e-01 -1.74806833e-01 -5.11657119e-01 -9.23239291e-01 -7.18799770e-01 -1.19862425e+00 -7.80513510e-02 7.41171360e-01 -1.70225948e-01 3.05404335e-01 4.32802856e-01 3.95843595e-01 -1.36632812e+00 -1.05455011e-01 3.58087629e-01 6.78280056e-01 5.38863063e-01 7.31873214e-01 2.83363159e-03 9.11552608e-01 -5.07218897e-01 -1.01871997e-01 -7.51050003e-03 -7.06874609e-01 -9.28038716e-01 -2.72345953e-02 2.13558972e-02 -3.35400254e-01 -4.52392012e-01 1.20306170e+00 1.49413958e-01 2.86030825e-02 8.48584175e-01 -1.02067327e+00 -6.71054304e-01 7.02434003e-01 6.19942062e-02 2.98717231e-01 1.50479838e-01 -1.87395751e-01 1.32583439e+00 -3.86022896e-01 5.45478582e-01 1.02258646e+00 -4.18270603e-02 4.62075800e-01 -1.52087927e+00 -2.86404192e-01 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be72f08c-2d6d-4f84-b1fa-c030c3ee0756	conditional-goal-oriented-trajectory	2210.15449	null	https://arxiv.org/abs/2210.15449v1	https://arxiv.org/pdf/2210.15449v1.pdf	Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation	This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed to extract the interactive features between agent-to-agent and agent-to-goals using a graph-based vectorized representation. Further, the Conditional Goal Prediction Network (CGPNet) focuses on goal interactive prediction via a combined form of marginal and conditional goal predictors. Finally, the Goaloriented Trajectory Forecasting Network (GTFNet) is proposed to implement trajectory interactive prediction via the conditional goal-oriented predictors, with the predicted future states of the other interacting agent taken as inputs. In addition, a new goal interactive loss is developed to better learn the joint probability distribution over goal candidates between two interacting agents. In the end, the proposed method is conducted on Argoverse motion forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset. The comparative results demonstrate the superior performance of our proposed CGTP model than the mainstream prediction methods.	['Dongbin Zhao', 'Yifeng Pan', 'Shuai Lu', 'Qichao Zhang', 'Ding Li']	2022-10-19	null	null	null	null	['trajectory-forecasting']	['computer-vision']	[-4.56898883e-02 9.50589702e-02 -3.06467742e-01 -5.20078063e-01 -7.11335778e-01 1.41183182e-03 9.54749227e-01 -1.39936507e-01 -1.66347876e-01 7.45138347e-01 6.16888106e-01 -1.82368994e-01 -3.47798586e-01 -8.72403502e-01 -4.98085946e-01 -6.03999615e-01 -6.43024206e-01 5.61060190e-01 5.28370500e-01 -1.86337337e-01 2.59740531e-01 1.82672232e-01 -1.42767882e+00 8.17215145e-02 9.40924406e-01 7.50223994e-01 4.44568515e-01 9.23691213e-01 1.07694082e-01 1.51748013e+00 4.79251705e-03 -2.50152260e-01 1.44266859e-01 -5.31110227e-01 -7.05971539e-01 5.33335209e-02 -1.22554071e-01 -5.93475521e-01 -6.40850842e-01 6.77928865e-01 8.36970210e-02 1.01510251e+00 8.19154501e-01 -1.96009803e+00 -2.62632608e-01 5.21661520e-01 -1.05571337e-01 3.58064845e-02 5.50864875e-01 9.25668061e-01 9.05876637e-01 -4.79553103e-01 7.49966383e-01 1.51011109e+00 2.58395910e-01 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bdc0c73c-7bc3-408c-a8a3-12e1cb398169	leveraging-monolingual-data-with-self	2005.04816	null	https://arxiv.org/abs/2005.04816v1	https://arxiv.org/pdf/2005.04816v1.pdf	Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation	Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The second direction employs monolingual data with self-supervision to pre-train translation models, followed by fine-tuning on small amounts of supervised data. In this work, we join these two lines of research and demonstrate the efficacy of monolingual data with self-supervision in multilingual NMT. We offer three major results: (i) Using monolingual data significantly boosts the translation quality of low-resource languages in multilingual models. (ii) Self-supervision improves zero-shot translation quality in multilingual models. (iii) Leveraging monolingual data with self-supervision provides a viable path towards adding new languages to multilingual models, getting up to 33 BLEU on ro-en translation without any parallel data or back-translation.	['Yonghui Wu', 'Mia Chen', 'Ankur Bapna', 'Aditya Siddhant', 'Sneha Kudugunta', 'Orhan Firat', 'Naveen Arivazhagan', 'Yuan Cao']	2020-05-11	leveraging-monolingual-data-with-self-1	https://aclanthology.org/2020.acl-main.252	https://aclanthology.org/2020.acl-main.252.pdf	acl-2020-6	['low-resource-neural-machine-translation']	['natural-language-processing']	[ 7.17895338e-03 -1.77528828e-01 -7.01171696e-01 -3.31878275e-01 -1.25097930e+00 -8.73075843e-01 8.60207200e-01 -2.90696084e-01 -7.62627065e-01 1.07188332e+00 4.09716189e-01 -8.32300544e-01 4.83197957e-01 -4.40829515e-01 -1.05659425e+00 -6.67689368e-02 4.12995100e-01 8.52860808e-01 -4.26316082e-01 -8.34887624e-01 -1.52342483e-01 1.34483159e-01 -7.44316280e-01 4.40674603e-01 1.22578704e+00 2.16380209e-01 4.39017743e-01 3.09747666e-01 -1.67072073e-01 2.77151376e-01 -2.99482107e-01 -7.07963347e-01 5.11829793e-01 -5.90364397e-01 -9.16255951e-01 -3.98630559e-01 5.09633720e-01 -5.00278324e-02 6.10070536e-03 8.46849322e-01 6.59865499e-01 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5269b841-2697-470f-bf3f-37fcf48a7e5e	parts-unsupervised-segmentation-with-slots	null	null	http://openaccess.thecvf.com//content/ICCV2021/html/Zoran_PARTS_Unsupervised_Segmentation_With_Slots_Attention_and_Independence_Maximization_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Zoran_PARTS_Unsupervised_Segmentation_With_Slots_Attention_and_Independence_Maximization_ICCV_2021_paper.pdf	PARTS: Unsupervised Segmentation With Slots, Attention and Independence Maximization	From an early age, humans perceive the visual world as composed of coherent objects with distinctive properties such as shape, size, and color. There is great interest in building models that are able to learn similar structure, ideally in an unsupervised manner. Learning such structure from complex 3D scenes that include clutter, occlusions, interactions, and camera motion is still an open challenge. We present a model that is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner. Our model (named PARTS) builds on recent approaches that utilize iterative amortized inference and transition dynamics for deep generative models. We achieve dramatic improvements in performance by introducing several novel contributions. We introduce a recurrent slot-attention like encoder which allows for top-down influence during inference. Unlike prior work, we eschew using an auto-regressive prior when modeling image sequences, and demonstrate that a fixed frame-independent prior is superior for the purpose of scene segmentation and representation learning. We demonstrate our model's success on three different video datasets (the popular benchmark CLEVRER; a simulated 3D Playroom environment; and a real-world Robotics Arm dataset). Finally, we analyze the contributions of the various model components and the representations learned by the model.	['Danilo J. Rezende', 'Alexander Lerchner', 'Rishabh Kabra', 'Daniel Zoran']	2021-01-01	null	null	null	iccv-2021-1	['scene-segmentation']	['computer-vision']	[ 2.93819904e-01 3.20601724e-02 -1.63238216e-02 -5.77370465e-01 -4.29681897e-01 -6.14546657e-01 8.90457988e-01 -1.72626451e-01 -1.21662892e-01 2.69712538e-01 2.89093971e-01 -6.53360263e-02 1.22123189e-01 -4.39318299e-01 -1.02177238e+00 -4.85771328e-01 -8.82339254e-02 6.04197621e-01 4.20705676e-01 -3.23568583e-02 8.61089677e-02 5.63933551e-01 -1.68365943e+00 3.28701556e-01 4.11397427e-01 6.87068522e-01 5.21704435e-01 8.82703066e-01 1.59408003e-01 1.17346275e+00 -3.87810171e-01 -1.10107474e-01 3.20273221e-01 -4.71493244e-01 -8.06488633e-01 9.20777142e-01 5.08105516e-01 -4.87975806e-01 -5.47317207e-01 6.84309721e-01 1.15679823e-01 3.33062470e-01 7.61508524e-01 -1.06960559e+00 -4.25403833e-01 2.91330665e-01 -5.61597228e-01 1.67252675e-01 2.66529709e-01 5.00310063e-01 9.59266663e-01 -6.57431662e-01 8.73172820e-01 1.47064352e+00 3.90250623e-01 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5899d7a7-b108-4e6b-9300-30f9201bd6aa	computer-aided-diagnosis-of-lung-nodule-using	1708.05897	null	http://arxiv.org/abs/1708.05897v2	http://arxiv.org/pdf/1708.05897v2.pdf	Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization	We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule classification focusing on (i) usefulness of gradient tree boosting (XGBoost) and (ii) effectiveness of parameter optimization using Bayesian optimization (Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of local binary pattern was used for calculating feature vectors. Support vector machine (SVM) or XGBoost was trained using the feature vectors and their labels. TPE or random search was used for parameter optimization of SVM and XGBoost. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. In conclusion, XGBoost was better than SVM for classifying lung nodules. TPE was more efficient than random search for parameter optimization.	['Osamu Sugiyama', 'Mizuho Nishio', 'Tomohiro Kuroda', 'Mitsuo Nishizawa', 'Ryosuke Kojima', 'Masahiro Yakami', 'Kaori Togashi']	2017-08-19	null	null	null	null	['lung-nodule-classification']	['medical']	[-2.36647248e-01 -1.87030673e-01 -6.43307388e-01 -4.12420154e-01 -7.84630775e-01 -1.92872345e-01 5.40351391e-01 4.13395762e-01 -3.16496968e-01 8.62282574e-01 4.73680235e-02 -9.99769270e-01 -5.99888980e-01 -7.84542620e-01 -1.16025582e-01 -8.37195218e-01 -1.96029350e-01 6.81377769e-01 7.07211018e-01 2.77032912e-01 1.73109144e-01 5.75877488e-01 -1.33393061e+00 3.91102284e-01 7.56421387e-01 1.28623104e+00 3.62854838e-01 1.11435568e+00 1.81514712e-03 6.26481354e-01 -3.21804643e-01 -1.47582069e-01 3.80999029e-01 -6.08396411e-01 -7.88195193e-01 -1.92017198e-01 1.37234181e-01 -1.10191911e-01 8.04649219e-02 4.35772747e-01 5.51759839e-01 -2.75363743e-01 1.01808739e+00 -1.06591094e+00 -1.32243037e-01 1.34323508e-01 -2.18739733e-02 5.11741221e-01 -1.51849598e-01 3.15331906e-01 7.38115251e-01 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bde499b4-6c11-41e3-9ea5-7e554b64babe	cldice-a-novel-topology-preserving-loss	null	null	http://openaccess.thecvf.com//content/CVPR2021/html/Shit_clDice_-_A_Novel_Topology-Preserving_Loss_Function_for_Tubular_Structure_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Shit_clDice_-_A_Novel_Topology-Preserving_Loss_Function_for_Tubular_Structure_CVPR_2021_paper.pdf	clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation	Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerlineDice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.	['Bjoern H. Menze', 'Ulrich Bauer', 'Josien P. W. Pluim', 'Andrey Zhylka', 'Alexander Unger', 'Ivan Ezhov', 'Anjany Sekuboyina', 'Johannes C. Paetzold', 'Suprosanna Shit']	2021-06-19	null	null	null	cvpr-2021-1	['graph-similarity']	['graphs']	[-2.43475810e-02 4.17650938e-01 -1.06088318e-01 -3.49847645e-01 1.57407179e-01 -7.84170270e-01 3.96951348e-01 6.44738972e-01 -3.25515300e-01 5.28199673e-01 -1.25708461e-01 -4.92049336e-01 -1.56339526e-01 -1.08746350e+00 -7.20328212e-01 -5.26272297e-01 -3.75013828e-01 4.08456534e-01 7.49957383e-01 -5.15879616e-02 7.49778524e-02 9.58803058e-01 -9.47113514e-01 -4.86338258e-01 1.15634239e+00 1.01512277e+00 -1.45492479e-01 4.15315062e-01 -4.48933840e-01 3.28675061e-01 -5.53778559e-03 -8.56476724e-01 4.92662400e-01 -2.28740320e-01 -9.67901468e-01 -1.58450574e-01 6.17010772e-01 6.94433674e-02 -2.91384071e-01 1.27942228e+00 3.18706900e-01 3.64275300e-03 9.70543087e-01 -1.35520530e+00 -4.01047736e-01 6.29248321e-01 -6.48968995e-01 2.45073244e-01 -3.05866480e-01 1.15083657e-01 8.32604408e-01 -5.69150269e-01 8.62437963e-01 1.25750399e+00 1.09439337e+00 2.48940334e-01 -1.62300158e+00 -2.91857123e-01 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5.87646961e-01 2.32944995e-01 -7.40955412e-01 -3.15111428e-01 -3.18932831e-02]	[14.29952335357666, -2.6468253135681152]
7fee4d6b-b83e-4f28-b196-5bdd0ba1558c	selection-strategies-for-commonsense	2202.09163	null	https://arxiv.org/abs/2202.09163v2	https://arxiv.org/pdf/2202.09163v2.pdf	Selection Strategies for Commonsense Knowledge	Selection strategies are broadly used in first-order logic theorem proving to select those parts of a large knowledge base that are necessary to proof a theorem at hand. Usually, these selection strategies do not take the meaning of symbol names into account. In knowledge bases with commonsense knowledge, symbol names are usually chosen to have a meaning and this meaning provides valuable information for selection strategies. We introduce the vector-based selection strategy, a purely statistical selection technique for commonsense knowledge based on word embeddings. We compare different commonsense knowledge selection techniques for the purpose of theorem proving and demonstrate the usefulness of vector-based selection with a case study.	['Claudia Schon']	2022-02-18	null	null	null	null	['automated-theorem-proving', 'automated-theorem-proving']	['miscellaneous', 'reasoning']	[ 1.78568631e-01 -1.90421734e-02 -5.13218701e-01 -2.64702857e-01 -5.78498095e-03 -6.53495312e-01 7.19916821e-01 5.18406212e-01 -4.68425602e-01 8.26002300e-01 2.80029923e-01 -7.22181201e-01 -5.64561367e-01 -1.35014725e+00 -5.26202559e-01 -4.15011942e-01 1.13358855e-01 2.90170074e-01 2.84095436e-01 -5.97673953e-01 5.44974744e-01 4.46010321e-01 -1.64360392e+00 1.99171409e-01 7.74746656e-01 5.77103198e-01 4.84937690e-02 4.03710902e-01 -5.96064031e-01 1.24364030e+00 -6.62601769e-01 -4.80013043e-01 -1.57698952e-02 -5.53270698e-01 -1.22488105e+00 -6.15403175e-01 4.25536454e-01 -2.51904894e-02 -3.18184555e-01 1.63814533e+00 1.12629823e-01 5.11535048e-01 7.98891366e-01 -1.35139501e+00 -7.45754719e-01 1.27580082e+00 3.88642311e-01 4.47221160e-01 6.83668196e-01 -8.34207088e-02 1.77123690e+00 -4.73540485e-01 8.68091762e-01 1.31894040e+00 3.21202129e-01 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c634aab2-9c8e-4673-b5dd-6640c3abc52c	symmetry-based-text-line-detection-in-natural	null	null	http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Symmetry-Based_Text_Line_2015_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Symmetry-Based_Text_Line_2015_CVPR_paper.pdf	Symmetry-Based Text Line Detection in Natural Scenes	Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes. Therefore, scene text detection and recognition have become active research topics in computer vision. In this work, we investigate the problem of scene text detection from an alternative perspective and propose a novel algorithm for it. Different from traditional methods, which mainly make use of the properties of single characters or strokes, the proposed algorithm exploits the symmetry property of character groups and allows for direct extraction of text lines from natural images. The experiments on the latest ICDAR benchmarks demonstrate that the proposed algorithm achieves state-of-the-art performance. Moreover, compared to conventional approaches, the proposed algorithm shows stronger adaptability to texts in challenging scenarios.	['Wei Shen', 'Cong Yao', 'Zheng Zhang', 'Xiang Bai']	2015-06-01	null	null	null	cvpr-2015-6	['line-detection']	['computer-vision']	[ 5.73408306e-01 -6.53220117e-01 -1.23542473e-01 -1.16652735e-01 -2.47856215e-01 -4.56225932e-01 9.89112020e-01 3.28921616e-01 -5.61521590e-01 3.36575538e-01 9.82012972e-02 -2.75247216e-01 2.34260768e-01 -7.34405398e-01 -2.31589422e-01 -7.61720836e-01 4.10208851e-01 1.35041758e-01 6.26599729e-01 -1.67192705e-02 6.69584095e-01 5.88714778e-01 -1.37417150e+00 3.29609156e-01 8.97297621e-01 7.95399308e-01 2.30581179e-01 5.73081136e-01 -5.52760541e-01 8.72086585e-01 -6.44983768e-01 -3.77308190e-01 1.93753630e-01 -5.38604200e-01 -4.76216555e-01 6.26732826e-01 4.98337507e-01 -3.83381724e-01 -5.32659709e-01 1.11961031e+00 4.01764870e-01 -1.79419927e-02 7.75390506e-01 -8.52293968e-01 -5.09269118e-01 6.12012267e-01 -1.10645926e+00 2.56694943e-01 3.61418962e-01 -4.00887400e-01 8.57055783e-01 -1.03726804e+00 5.75538993e-01 9.64819610e-01 4.73246485e-01 1.34720206e-01 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d4fd58c1-6951-4162-b6d4-e78df300b0f8	recognizing-textures-with-mobile-cameras-for	1711.00558	null	http://arxiv.org/abs/1711.00558v1	http://arxiv.org/pdf/1711.00558v1.pdf	Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications	As smartphone rooted distractions become commonplace, the lack of compelling safety measures has led to a rise in the number of injuries to distracted walkers. Various solutions address this problem by sensing a pedestrian's walking environment. Existing camera-based approaches have been largely limited to obstacle detection and other forms of object detection. Instead, we present TerraFirma, an approach that performs material recognition on the pedestrian's walking surface. We explore, first, how well commercial off-the-shelf smartphone cameras can learn texture to distinguish among paving materials in uncontrolled outdoor urban settings. Second, we aim at identifying when a distracted user is about to enter the street, which can be used to support safety functions such as warning the user to be cautious. To this end, we gather a unique dataset of street/sidewalk imagery from a pedestrian's perspective, that spans major cities like New York, Paris, and London. We demonstrate that modern phone cameras can be enabled to distinguish materials of walking surfaces in urban areas with more than 90% accuracy, and accurately identify when pedestrians transition from sidewalk to street.	['Marco Gruteser', 'Shubham Jain']	2017-11-01	null	null	null	null	['material-recognition']	['computer-vision']	[ 2.04020366e-01 -4.15913522e-01 -7.02460557e-02 8.11462253e-02 -9.89020288e-01 -4.57836121e-01 4.91626054e-01 2.10991040e-01 -4.11053807e-01 5.49006343e-01 2.44515091e-01 -5.82653880e-01 4.12512302e-01 -1.05345368e+00 -4.25092876e-01 -5.78051567e-01 3.68312478e-01 -1.58547610e-01 5.81301928e-01 -3.48780632e-01 3.85678977e-01 4.27498370e-01 -1.88689017e+00 -1.04797655e-03 7.96494126e-01 8.85546088e-01 1.37639880e-01 6.95720911e-01 2.54058719e-01 3.26388538e-01 -3.09807450e-01 -2.79314190e-01 1.58951133e-01 1.95998326e-01 -2.51094997e-01 2.33841538e-01 6.84984505e-01 -5.85693598e-01 -4.94013727e-01 8.16212416e-01 5.75986087e-01 1.54964343e-01 5.39248526e-01 -1.38483059e+00 -2.10470632e-02 -4.56924915e-01 -5.40419519e-01 6.22002423e-01 7.68907428e-01 7.99445212e-01 5.08246839e-01 -7.38266826e-01 -8.93243477e-02 1.00005877e+00 7.33326256e-01 2.06808805e-01 -9.90874588e-01 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4.00824755e-01 6.77927017e-01 -1.07926285e+00 -2.88065970e-01 9.91714522e-02]	[7.859802722930908, -0.984255313873291]
572c3dbd-d97c-4fb1-a5de-12b00f586c27	inf-net-automatic-covid-19-lung-infection	2004.14133	null	https://arxiv.org/abs/2004.14133v4	https://arxiv.org/pdf/2004.14133v4.pdf	Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images	Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.	['Ge-Peng Ji', 'Deng-Ping Fan', 'Huazhu Fu', 'Jianbing Shen', 'Geng Chen', 'Tao Zhou', 'Yi Zhou', 'Ling Shao']	2020-04-22	null	null	null	null	['camouflage-segmentation']	['computer-vision']	[ 3.98660660e-01 -6.22516684e-02 -3.39653134e-01 -2.76606828e-01 -7.86087394e-01 -2.80786008e-01 1.83314174e-01 -1.15012832e-01 -4.81400818e-01 5.84962308e-01 -1.80688454e-03 -3.75750810e-01 1.99106783e-01 -6.42896950e-01 -5.29001296e-01 -7.45755792e-01 1.22500844e-01 8.02579105e-01 4.64940518e-01 4.16588068e-01 -3.53600800e-01 3.74721855e-01 -7.13376820e-01 2.01897413e-01 1.01901293e+00 1.03377867e+00 7.76908517e-01 5.59528172e-01 -2.71406561e-01 6.34537756e-01 -6.07319586e-02 7.46570081e-02 3.61230485e-02 -4.84646857e-01 -7.97312796e-01 1.03633225e-01 7.77514204e-02 -7.01770663e-01 -1.77631199e-01 9.13858056e-01 4.95926708e-01 -3.21166545e-01 8.78946245e-01 -9.95247304e-01 -3.48181754e-01 1.66484416e-01 -6.83985293e-01 5.58145761e-01 -2.53682703e-01 2.43398562e-01 4.85785455e-01 -8.18864465e-01 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ac95a276-8419-469c-af02-ba372d85a34f	conservative-distributional-reinforcement	2201.07286	null	https://arxiv.org/abs/2201.07286v2	https://arxiv.org/pdf/2201.07286v2.pdf	Conservative Distributional Reinforcement Learning with Safety Constraints	Safety exploration can be regarded as a constrained Markov decision problem where the expected long-term cost is constrained. Previous off-policy algorithms convert the constrained optimization problem into the corresponding unconstrained dual problem by introducing the Lagrangian relaxation technique. However, the cost function of the above algorithms provides inaccurate estimations and causes the instability of the Lagrange multiplier learning. In this paper, we present a novel off-policy reinforcement learning algorithm called Conservative Distributional Maximum a Posteriori Policy Optimization (CDMPO). At first, to accurately judge whether the current situation satisfies the constraints, CDMPO adapts distributional reinforcement learning method to estimate the Q-function and C-function. Then, CDMPO uses a conservative value function loss to reduce the number of violations of constraints during the exploration process. In addition, we utilize Weighted Average Proportional Integral Derivative (WAPID) to update the Lagrange multiplier stably. Empirical results show that the proposed method has fewer violations of constraints in the early exploration process. The final test results also illustrate that our method has better risk control.	['Kai Lv', 'Shuo Wang', 'Sheng Han', 'Youfang Lin', 'Hengrui Zhang']	2022-01-18	null	null	null	null	['distributional-reinforcement-learning']	['methodology']	[-1.50360495e-01 2.16193900e-01 -9.20728028e-01 -7.71493092e-02 -8.67434502e-01 -2.95471162e-01 2.64300585e-01 5.01966439e-02 -7.96045542e-01 1.39809573e+00 3.91646959e-02 -5.68904281e-01 -3.02818894e-01 -7.13614285e-01 -4.37979072e-01 -1.03826988e+00 -5.30173108e-02 2.14483857e-01 1.13933414e-01 -5.16473651e-02 4.78780508e-01 1.14134870e-01 -7.99762726e-01 -3.02979290e-01 1.15076137e+00 1.09128189e+00 1.60335302e-01 6.53857514e-02 1.74085498e-01 6.16033494e-01 -4.83190119e-01 1.88223813e-02 3.61871690e-01 -2.90949553e-01 -5.09347618e-01 -1.56679764e-01 -4.84900028e-01 -7.51991093e-01 8.10706839e-02 1.43914473e+00 5.08952737e-01 5.58704197e-01 5.07323682e-01 -1.47237790e+00 -8.61479193e-02 4.65858817e-01 -1.04600847e+00 2.42217034e-01 2.00440258e-01 3.21993023e-01 6.70021236e-01 -4.37004536e-01 2.48162165e-01 1.48755276e+00 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d9ea23fb-05c0-4bce-bd91-183db94a8263	highly-efficient-binary-neural-networks-for	2202.12375	null	https://arxiv.org/abs/2202.12375v1	https://arxiv.org/pdf/2202.12375v1.pdf	Highly-Efficient Binary Neural Networks for Visual Place Recognition	VPR is a fundamental task for autonomous navigation as it enables a robot to localize itself in the workspace when a known location is detected. Although accuracy is an essential requirement for a VPR technique, computational and energy efficiency are not less important for real-world applications. CNN-based techniques archive state-of-the-art VPR performance but are computationally intensive and energy demanding. Binary neural networks (BNN) have been recently proposed to address VPR efficiently. Although a typical BNN is an order of magnitude more efficient than a CNN, its processing time and energy usage can be further improved. In a typical BNN, the first convolution is not completely binarized for the sake of accuracy. Consequently, the first layer is the slowest network stage, requiring a large share of the entire computational effort. This paper presents a class of BNNs for VPR that combines depthwise separable factorization and binarization to replace the first convolutional layer to improve computational and energy efficiency. Our best model achieves state-of-the-art VPR performance while spending considerably less time and energy to process an image than a BNN using a non-binary convolution as a first stage.	['Shoaib Ehsan', 'Klaus D. McDonald-Maier', 'Michael Milford', 'Bruno Ferrarini']	2022-02-24	null	null	null	null	['visual-place-recognition']	['computer-vision']	[ 2.11172611e-01 -6.98800012e-02 -2.23391354e-01 -4.53358255e-02 -4.68775630e-02 -4.36480343e-01 1.93730757e-01 2.48134509e-01 -9.65122879e-01 4.62161154e-01 -6.14196360e-01 -9.58435178e-01 -4.91886400e-02 -1.18308020e+00 -7.70197093e-01 -4.38844174e-01 2.33221263e-01 1.52951315e-01 3.03415835e-01 -5.27573884e-01 2.58016407e-01 1.04387462e+00 -1.49986875e+00 -5.95472977e-02 8.33654284e-01 1.36081147e+00 8.39011848e-01 6.05946958e-01 -1.78243279e-01 5.31156123e-01 -5.63809037e-01 -2.20867217e-01 5.20389855e-01 -1.94039848e-02 -6.39056742e-01 -4.71991420e-01 2.97151208e-02 -2.47191176e-01 -6.99417174e-01 1.03518319e+00 3.79160404e-01 5.22751093e-01 4.99870211e-01 -1.14470077e+00 -3.26854676e-01 2.70397604e-01 -5.94986916e-01 2.11355671e-01 -2.12938577e-01 -6.55126898e-03 7.36891210e-01 -6.90136254e-01 2.17378110e-01 8.03326070e-01 5.20968318e-01 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-7.46053398e-01 -3.52106899e-01 -5.36950827e-01 2.78751165e-01 -8.84831488e-01 -1.46278873e-01 -3.56014550e-01]	[8.098162651062012, -1.8869426250457764]
4c7c984d-4186-4c41-87e1-1de300c5f8c3	feature-transformation-ensemble-model-with	2005.08463	null	https://arxiv.org/abs/2005.08463v3	https://arxiv.org/pdf/2005.08463v3.pdf	Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification	In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge. Specifically, we proposes to construct an ensemble prediction model by performing diverse feature transformations after a feature extraction network. On each branch prediction network of the model we use a batch spectral regularization term to suppress the singular values of the feature matrix during pre-training to improve the generalization ability of the model. The proposed model can then be fine tuned in the target domain to address few-shot classification. We also further apply label propagation, entropy minimization and data augmentation to mitigate the shortage of labeled data in target domains. Experiments are conducted on a number of CD-FSL benchmark tasks with four target domains and the results demonstrate the superiority of our proposed model.	['Zhenpeng Li', 'Jieping Ye', 'Zhen Zhao', 'Yuhong Guo', 'Jianan Jiang', 'Bingyu Liu']	2020-05-18	null	null	null	null	['cross-domain-few-shot', 'cross-domain-few-shot-learning']	['computer-vision', 'computer-vision']	[ 6.38686121e-01 -6.74272180e-02 -1.87997103e-01 -6.37788296e-01 -6.22134387e-01 -3.24445754e-01 4.59449261e-01 -1.97252199e-01 -1.75127357e-01 8.16987216e-01 7.31933638e-02 7.21562132e-02 -2.77451456e-01 -7.12484121e-01 -3.84789467e-01 -5.39862514e-01 2.42066860e-01 1.88980162e-01 7.22684935e-02 -4.26596880e-01 5.49131557e-02 2.33581737e-01 -1.37964845e+00 2.87782729e-01 1.14802134e+00 1.09295762e+00 -1.47956371e-01 1.95532054e-01 5.04322387e-02 8.01245868e-01 -1.95326060e-01 -2.39641964e-01 4.78938073e-01 -4.57493335e-01 -6.79852962e-01 3.96440774e-01 1.45528659e-01 4.00411449e-02 -1.51191443e-01 9.13029611e-01 4.18669939e-01 7.60088742e-01 8.54782581e-01 -1.33402634e+00 -6.07340038e-01 3.48239750e-01 -5.04476309e-01 8.91286135e-02 5.50029315e-02 1.81981906e-01 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6514b025-8447-42e2-aeb5-932cf2195ef9	neural-distance-embeddings-for-biological	2109.09740	null	https://arxiv.org/abs/2109.09740v2	https://arxiv.org/pdf/2109.09740v2.pdf	Neural Distance Embeddings for Biological Sequences	The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research. However, popular machine learning approaches, based on continuous Euclidean spaces, have struggled with the discrete combinatorial formulation of the edit distance that models evolution and the hierarchical relationship that characterises real-world datasets. We present Neural Distance Embeddings (NeuroSEED), a general framework to embed sequences in geometric vector spaces, and illustrate the effectiveness of the hyperbolic space that captures the hierarchical structure and provides an average 22% reduction in embedding RMSE against the best competing geometry. The capacity of the framework and the significance of these improvements are then demonstrated devising supervised and unsupervised NeuroSEED approaches to multiple core tasks in bioinformatics. Benchmarked with common baselines, the proposed approaches display significant accuracy and/or runtime improvements on real-world datasets. As an example for hierarchical clustering, the proposed pretrained and from-scratch methods match the quality of competing baselines with 30x and 15x runtime reduction, respectively.	['Pietro Liò', 'Jure Leskovec', 'Petar Veličković', 'Michal Pándy', 'Rex Ying', 'Gabriele Corso']	2021-09-20	null	http://proceedings.neurips.cc/paper/2021/hash/9a1de01f893e0d2551ecbb7ce4dc963e-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/9a1de01f893e0d2551ecbb7ce4dc963e-Paper.pdf	neurips-2021-12	['multiple-sequence-alignment']	['medical']	[ 3.86848956e-01 -2.19391491e-02 3.41150939e-01 -3.96237791e-01 -5.87288618e-01 -5.18291175e-01 5.91703176e-01 8.42598379e-01 -6.91957474e-01 6.06959105e-01 1.02717742e-01 -6.05336623e-04 -5.28642237e-01 -5.23868740e-01 -5.30191362e-01 -1.07768917e+00 -4.07211453e-01 5.37502766e-01 1.26493469e-01 -2.56656379e-01 6.68356538e-01 6.88085258e-01 -1.57307506e+00 -3.21495272e-02 7.11449623e-01 7.83165336e-01 2.36345410e-01 9.61471915e-01 -6.39632270e-02 -5.67849912e-02 -5.20268559e-01 -4.20379639e-01 2.75172353e-01 -3.12418461e-01 -6.28779709e-01 -3.81193250e-01 4.71304238e-01 3.09534699e-01 -4.26309615e-01 8.87291074e-01 8.32748771e-01 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5509f9ed-4a2d-45bc-8605-f4f90f594948	memoreader-large-scale-reading-comprehension	null	null	https://aclanthology.org/D18-1237	https://aclanthology.org/D18-1237.pdf	MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller	Machine reading comprehension helps machines learn to utilize most of the human knowledge written in the form of text. Existing approaches made a significant progress comparable to human-level performance, but they are still limited in understanding, up to a few paragraphs, failing to properly comprehend lengthy document. In this paper, we propose a novel deep neural network architecture to handle a long-range dependency in RC tasks. In detail, our method has two novel aspects: (1) an advanced memory-augmented architecture and (2) an expanded gated recurrent unit with dense connections that mitigate potential information distortion occurring in the memory. Our proposed architecture is widely applicable to other models. We have performed extensive experiments with well-known benchmark datasets such as TriviaQA, QUASAR-T, and SQuAD. The experimental results demonstrate that the proposed method outperforms existing methods, especially for lengthy documents.	['Sathish Reddy Indurthi', 'Seohyun Back', 'Jihie Kim', 'Jaegul Choo', 'Seunghak Yu']	2018-10-01	null	null	null	emnlp-2018-10	['triviaqa']	['miscellaneous']	[ 2.04301447e-01 -2.17777919e-02 2.20914662e-01 -3.91593248e-01 -7.43450701e-01 -2.42010728e-01 4.73288536e-01 2.04674244e-01 -4.87882406e-01 7.18049824e-01 4.43460375e-01 -5.11235833e-01 -1.56207262e-02 -7.86852598e-01 -8.78904521e-01 -5.09212315e-01 2.10304722e-01 4.75736111e-01 2.73341537e-01 -4.74958628e-01 6.91903830e-01 1.06155179e-01 -1.27078545e+00 3.43773544e-01 1.20321822e+00 6.56584918e-01 5.57827353e-01 7.55850017e-01 -1.62164405e-01 1.17847681e+00 -7.42638528e-01 -2.99238712e-01 -2.93035775e-01 -5.10182619e-01 -1.12138367e+00 -4.12144661e-01 3.33509088e-01 -5.84330022e-01 -3.71267021e-01 6.72022760e-01 5.41176617e-01 5.77398717e-01 5.63076019e-01 -3.82014275e-01 -1.22149575e+00 8.21382344e-01 -6.38813436e-01 6.82118237e-01 3.01064640e-01 -1.19900875e-01 8.61113966e-01 -1.04110146e+00 1.20741852e-01 1.44125307e+00 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0f8dc25d-6f0d-4e77-917f-d599dd485163	interactive-segmentation-as-gaussian-process	2302.14578	null	https://arxiv.org/abs/2302.14578v1	https://arxiv.org/pdf/2302.14578v1.pdf	Interactive Segmentation as Gaussian Process Classification	Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. Albeit achieving promising performance, they do not fully and explicitly utilize and propagate the click information, inevitably leading to unsatisfactory segmentation results, even at clicked points. Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image. To solve this model, we utilize amortized variational inference to approximate the intractable GP posterior in a data-driven manner and then decouple the approximated GP posterior into double space forms for efficient sampling with linear complexity. Then, we correspondingly construct a GP classification framework, named GPCIS, which is integrated with the deep kernel learning mechanism for more flexibility. The main specificities of the proposed GPCIS lie in: 1) Under the explicit guidance of the derived GP posterior, the information contained in clicks can be finely propagated to the entire image and then boost the segmentation; 2) The accuracy of predictions at clicks has good theoretical support. These merits of GPCIS as well as its good generality and high efficiency are substantiated by comprehensive experiments on several benchmarks, as compared with representative methods both quantitatively and qualitatively.	['Yefeng Zheng', 'Deyu Meng', 'Yawen Huang', 'Yuexiang Li', 'Qian Zhao', 'Hong Wang', 'Minghao Zhou']	2023-02-28	null	null	null	null	['interactive-segmentation']	['computer-vision']	[ 1.73959032e-01 9.98584032e-02 -3.14590782e-01 -2.99072504e-01 -1.01385033e+00 -3.69694471e-01 4.58177269e-01 -1.64378315e-01 -3.68724048e-01 6.43378198e-01 -3.53514940e-01 -1.94624513e-01 -8.83724019e-02 -6.89620256e-01 -8.01410198e-01 -9.60227787e-01 5.51587164e-01 3.43163520e-01 6.02081895e-01 4.23084825e-01 2.74857342e-01 2.54136920e-01 -1.41255152e+00 -1.35223091e-01 1.40717423e+00 1.17615700e+00 2.65194058e-01 4.59304005e-01 -2.87131429e-01 3.02022517e-01 -2.05160961e-01 -7.62943447e-01 -2.43253317e-02 -9.91502106e-02 -5.87312818e-01 3.88899535e-01 1.61468431e-01 -3.58000547e-01 -1.83510438e-01 1.34631896e+00 2.63949722e-01 9.97367576e-02 8.18147480e-01 -1.19123793e+00 -5.50917387e-01 2.50821173e-01 -9.56747472e-01 -2.13477090e-01 -6.86459094e-02 2.61888742e-01 1.17048681e+00 -9.99192059e-01 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aa277e7b-dd76-4f87-925e-d9d01554f8c9	few-shot-class-incremental-learning-by	2203.17030	null	https://arxiv.org/abs/2203.17030v2	https://arxiv.org/pdf/2203.17030v2.pdf	Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks	New classes arise frequently in our ever-changing world, e.g., emerging topics in social media and new types of products in e-commerce. A model should recognize new classes and meanwhile maintain discriminability over old classes. Under severe circumstances, only limited novel instances are available to incrementally update the model. The task of recognizing few-shot new classes without forgetting old classes is called few-shot class-incremental learning (FSCIL). In this work, we propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT), which synthesizes fake FSCIL tasks from the base dataset. The data format of fake tasks is consistent with the `real' incremental tasks, and we can build a generalizable feature space for the unseen tasks through meta-learning. Besides, LIMIT also constructs a calibration module based on transformer, which calibrates the old class classifiers and new class prototypes into the same scale and fills in the semantic gap. The calibration module also adaptively contextualizes the instance-specific embedding with a set-to-set function. LIMIT efficiently adapts to new classes and meanwhile resists forgetting over old classes. Experiments on three benchmark datasets (CIFAR100, miniImageNet, and CUB200) and large-scale dataset, i.e., ImageNet ILSVRC2012 validate that LIMIT achieves state-of-the-art performance.	['De-Chuan Zhan', 'ShiLiang Pu', 'Di Xie', 'Liang Ma', 'Han-Jia Ye', 'Da-Wei Zhou']	2022-03-31	null	null	null	null	['few-shot-class-incremental-learning']	['methodology']	[ 2.22497612e-01 -5.40576987e-02 -3.98561209e-01 -5.70527911e-01 -4.08069104e-01 -4.81112629e-01 5.28288603e-01 -8.74702036e-02 -3.68985802e-01 7.11511314e-01 -2.43726641e-01 1.13076732e-01 6.24932051e-02 -1.00202858e+00 -9.03415263e-01 -5.04280686e-01 1.22709863e-01 3.71032000e-01 5.81205130e-01 -3.43809545e-01 8.15225765e-02 -1.16656034e-03 -1.62782764e+00 8.61001611e-01 8.68132412e-01 1.15757346e+00 1.25230670e-01 1.00697570e-01 -3.10942113e-01 5.67334116e-01 -6.48379564e-01 -7.86996484e-01 3.16148698e-01 -1.92684814e-01 -6.79949522e-01 2.61001080e-01 4.43231612e-01 -2.77567804e-01 -5.17065942e-01 1.12702858e+00 1.55918270e-01 1.24780364e-01 3.23501408e-01 -1.64278936e+00 -1.29321992e+00 7.45615840e-01 -3.61521006e-01 3.37832838e-01 -4.61736321e-02 2.57571787e-01 6.14803195e-01 -1.39940536e+00 8.31073165e-01 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5bfa02b6-46d6-4125-b019-80a391899d5e	detecting-post-stroke-aphasia-using-eeg-based	2303.07739	null	https://arxiv.org/abs/2303.07739v1	https://arxiv.org/pdf/2303.07739v1.pdf	Detecting post-stroke aphasia using EEG-based neural envelope tracking of natural speech	[Objective]. After a stroke, one-third of patients suffer from aphasia, a language disorder that impairs communication ability. The standard behavioral tests used to diagnose aphasia are time-consuming and have low ecological validity. Neural tracking of the speech envelope is a promising tool for investigating brain responses to natural speech. The speech envelope is crucial for speech understanding, encompassing cues for processing linguistic units. In this study, we aimed to test the potential of the neural envelope tracking technique for detecting language impairments in individuals with aphasia (IWA). [Approach]. We recorded EEG from 27 IWA in the chronic phase after stroke and 22 controls while they listened to a story. We quantified neural envelope tracking in a broadband frequency range as well as in the delta, theta, alpha, beta, and gamma frequency bands using mutual information analysis. Besides group differences in neural tracking measures, we also tested its suitability for detecting aphasia using a Support Vector Machine (SVM) classifier. We further investigated the required recording length for the SVM to detect aphasia and to obtain reliable outcomes. [Results]. IWA displayed decreased neural envelope tracking compared to controls in the broad, delta, theta, and gamma band. Neural tracking in these frequency bands effectively captured aphasia at the individual level (SVM accuracy 84%, AUC 88%). High-accuracy and reliable detection could be obtained with 5-7 minutes of recording time. [Significance]. Our study shows that neural tracking of speech is an effective biomarker for aphasia. We demonstrated its potential as a diagnostic tool with high reliability, individual-level detection of aphasia, and time-efficient assessment. This work represents a significant step towards more automatic, objective, and ecologically valid assessments of language impairments in aphasia.	['Maaike Vandermosten', 'Tom Francart', 'Jonas Vanthornhout', 'Ramtin Mehraram', 'Jill Kries', 'Pieter De Clercq']	2023-03-14	null	null	null	null	['eeg', 'eeg']	['methodology', 'time-series']	[ 1.09115012e-01 -4.13322270e-01 -9.57019851e-02 1.32097438e-01 -6.61559999e-01 -5.68981886e-01 3.54354084e-01 3.78618568e-01 -7.29626298e-01 7.60214567e-01 6.35057867e-01 -3.61225545e-01 -4.37434018e-01 -6.28591001e-01 -6.10796809e-02 -5.31006396e-01 -5.43086886e-01 5.13230935e-02 1.95254236e-01 -9.32092965e-02 2.64288008e-01 6.03948832e-01 -1.26393187e+00 5.88817954e-01 1.04552042e+00 6.96738660e-01 5.78003585e-01 5.57520092e-01 2.96423882e-02 3.87564659e-01 -1.05239570e+00 4.99908589e-02 -4.16677445e-01 -7.92066813e-01 -7.78739512e-01 -1.93251431e-01 -2.13776752e-01 -5.31065881e-01 -5.48615694e-01 9.19838428e-01 9.49331582e-01 1.32425025e-01 7.50491619e-01 -7.99826384e-01 -1.71124265e-01 5.44071972e-01 -6.87000481e-03 8.89177322e-01 6.22607410e-01 3.54900837e-01 4.97147560e-01 -5.69397271e-01 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7.00676441e-01 3.74587923e-02 -5.59110999e-01 -3.40419680e-01 -1.55786574e+00 -7.13283360e-01 -1.23505719e-01 3.60399723e-01 -7.85365283e-01 -9.20822978e-01 -4.23829108e-01]	[13.19955825805664, 3.370156764984131]
ea472f9f-4228-49c7-9e0e-a213402eb416	deep-learning-for-asynchronous-massive-access	2305.07278	null	https://arxiv.org/abs/2305.07278v1	https://arxiv.org/pdf/2305.07278v1.pdf	Deep Learning for Asynchronous Massive Access with Data Frame Length Diversity	Grant-free non-orthogonal multiple access has been regarded as a viable approach to accommodate access for a massive number of machine-type devices with small data packets. The sporadic activation of the devices creates a multiuser setup where it is suitable to use compressed sensing in order to detect the active devices and decode their data. We consider asynchronous access of machine-type devices that send data packets of different frame sizes, leading to data length diversity. We address the composite problem of activity detection, channel estimation, and data recovery by posing it as a structured sparse recovery, having three-level sparsity caused by sporadic activity, symbol delay, and data length diversity. We approach the problem through approximate message passing with a backward propagation algorithm (AMP-BP), tailored to exploit the sparsity, and in particular the data length diversity. Moreover, we unfold the proposed AMP-BP into a model-driven network, termed learned AMP-BP (LAMP-BP), which enhances detection performance. The results show that the proposed LAMP-BP outperforms existing methods in activity detection and data recovery accuracy.	['Petar Popovski', 'Bo Ai', 'Wei Chen', 'Yanna Bai']	2023-05-12	null	null	null	null	['activity-detection']	['computer-vision']	[ 8.76097798e-01 1.29900843e-01 -7.70858467e-01 1.87967256e-01 -6.50998354e-01 -1.66094095e-01 3.22761953e-01 -7.57314079e-03 -2.71827966e-01 7.93946981e-01 3.03387612e-01 -4.65885878e-01 -2.02292521e-02 -4.20627892e-01 -3.56785089e-01 -8.17617536e-01 -5.71969330e-01 1.48096889e-01 1.64006057e-03 2.60165155e-01 1.10960171e-01 4.02793735e-01 -1.01436365e+00 -1.62502266e-02 4.58600998e-01 1.41491199e+00 2.44977400e-01 6.86841488e-01 1.42646343e-01 7.99483359e-01 -5.39035022e-01 1.68618128e-01 3.29463542e-01 -4.19029474e-01 -3.29116762e-01 3.60267043e-01 -5.41062832e-01 -5.11104465e-01 -6.21643841e-01 4.75934923e-01 7.10776031e-01 -1.59720987e-01 4.16936010e-01 -1.11297345e+00 3.28688532e-01 6.01170719e-01 -9.56500649e-01 4.12969738e-01 7.27418125e-01 -2.48834044e-01 4.75450724e-01 -6.95598841e-01 3.42770964e-01 8.91112745e-01 5.80718219e-01 1.75584748e-01 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28c9bfee-6356-457f-bcb3-632521fbd111	passive-indoor-localization-with-wifi	2111.14281	null	https://arxiv.org/abs/2111.14281v1	https://arxiv.org/pdf/2111.14281v1.pdf	Passive Indoor Localization with WiFi Fingerprints	This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow control signals such as request to send (RTS) and clear to send (CTS) are utilized. In our model, received signal strength indicator (RSSI) and channel state information (CSI) are used as fingerprints for several algorithms, including deterministic, probabilistic and neural networks localization algorithms. We further investigated localization algorithms performance through extensive on-site experiments with various models of phones at hundreds of testing locations. We demonstrate that our passive scheme achieves an average localization error of 0.8 m when the phone is actively transmitting data frames and 1.5 m when it is not transmitting data frames.	['Kishore Reddy Tarimala', 'Robert Westendorp', 'Tao Lu', 'Xiaodai Dong', 'Ahmed Elmoogy', 'Kai Ren', 'Brosnan Yuen', 'Minh Tu Hoang']	2021-11-29	null	null	null	null	['indoor-localization']	['computer-vision']	[ 3.44321102e-01 -2.21018478e-01 -5.75448096e-01 -3.31140995e-01 -8.35273266e-01 -8.10795128e-01 6.76656365e-02 -1.10376015e-01 -3.79232526e-01 1.07596195e+00 -2.91727215e-01 -6.60331130e-01 -1.74014315e-01 -9.50259209e-01 -7.25728154e-01 -6.51490867e-01 -3.84340554e-01 -4.98742498e-02 2.27966249e-01 6.20076239e-01 2.71819144e-01 5.17254114e-01 -1.07972240e+00 -1.55709684e-01 6.79880440e-01 1.37514389e+00 3.01657856e-01 7.90790558e-01 -1.64476454e-01 4.37933177e-01 -1.17629492e+00 2.56480515e-01 -1.76359758e-01 -3.49223837e-02 -2.07916215e-01 -5.70088446e-01 -1.47940721e-02 -5.06509483e-01 -3.33667636e-01 6.26652300e-01 5.54018438e-01 -3.79358679e-02 1.62297323e-01 -1.33408821e+00 -1.95696075e-02 7.46274471e-01 -5.80497503e-01 1.20931916e-01 1.06679392e+00 -4.62475419e-01 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33bd8e91-d771-4516-af85-08dc2bd19d32	attribute-based-representations-for-accurate	2212.00789	null	https://arxiv.org/abs/2212.00789v1	https://arxiv.org/pdf/2212.00789v1.pdf	Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection	Video anomaly detection (VAD) is a challenging computer vision task with many practical applications. As anomalies are inherently ambiguous, it is essential for users to understand the reasoning behind a system's decision in order to determine if the rationale is sound. In this paper, we propose a simple but highly effective method that pushes the boundaries of VAD accuracy and interpretability using attribute-based representations. Our method represents every object by its velocity and pose. The anomaly scores are computed using a density-based approach. Surprisingly, we find that this simple representation is sufficient to achieve state-of-the-art performance in ShanghaiTech, the largest and most complex VAD dataset. Combining our interpretable attribute-based representations with implicit, deep representation yields state-of-the-art performance with a $99.1\%, 93.3\%$, and $85.9\%$ AUROC on Ped2, Avenue, and ShanghaiTech, respectively. Our method is accurate, interpretable, and easy to implement.	['Yedid Hoshen', 'Tal Reiss']	2022-12-01	null	null	null	null	['video-anomaly-detection', 'abnormal-event-detection-in-video', 'abnormal-event-detection-in-video']	['computer-vision', 'computer-vision', 'methodology']	[-9.39983949e-02 -1.84884429e-01 1.54985234e-01 -4.79360610e-01 -5.90224385e-01 -4.02269840e-01 5.13345182e-01 3.27268630e-01 -1.22684017e-01 3.80899191e-01 -1.30292073e-01 -6.45773768e-01 1.05322160e-01 -5.62016666e-01 -6.38074577e-01 -3.62118274e-01 -2.41965160e-01 3.15543622e-01 2.59123921e-01 -2.08368897e-01 1.89621985e-01 5.77547491e-01 -1.81504047e+00 1.52455345e-01 1.03149772e+00 1.57840776e+00 -3.56207252e-01 6.77886188e-01 -1.97983310e-02 9.55965221e-01 -6.65575147e-01 -4.94967371e-01 2.49465272e-01 -3.54330152e-01 -4.27291155e-01 2.91915499e-02 8.51395547e-01 -6.74946070e-01 -5.43017745e-01 1.06028330e+00 7.43244514e-02 -3.49498056e-02 1.17782557e+00 -1.58010805e+00 -5.42452812e-01 -3.03031085e-03 -7.14954615e-01 4.62386638e-01 1.10710077e-01 3.90243858e-01 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cba88ff1-92c4-4fb2-93e4-d617cbe5efac	mpg-a-multi-ingredient-pizza-image-generator	2012.02821	null	https://arxiv.org/abs/2012.02821v2	https://arxiv.org/pdf/2012.02821v2.pdf	MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs	Multilabel conditional image generation is a challenging problem in computer vision. In this work we propose Multi-ingredient Pizza Generator (MPG), a conditional Generative Neural Network (GAN) framework for synthesizing multilabel images. We design MPG based on a state-of-the-art GAN structure called StyleGAN2, in which we develop a new conditioning technique by enforcing intermediate feature maps to learn scalewise label information. Because of the complex nature of the multilabel image generation problem, we also regularize synthetic image by predicting the corresponding ingredients as well as encourage the discriminator to distinguish between matched image and mismatched image. To verify the efficacy of MPG, we test it on Pizza10, which is a carefully annotated multi-ingredient pizza image dataset. MPG can successfully generate photo-realist pizza images with desired ingredients. The framework can be easily extend to other multilabel image generation scenarios.	['Vladimir Pavlovic', 'Ricardo Guerrero', 'Guoyao Hao', 'Fangda Han']	2020-12-04	null	null	null	null	['conditional-image-generation']	['computer-vision']	[ 6.06050134e-01 4.35087174e-01 -4.04790640e-02 -3.03693831e-01 -1.01683319e+00 -8.50139856e-01 9.38542008e-01 -5.60574651e-01 1.55029103e-01 7.97375917e-01 1.59139946e-01 -1.09825015e-01 2.46187523e-01 -9.27545726e-01 -1.13987672e+00 -9.18464780e-01 4.30661142e-01 5.42821884e-01 -4.06932294e-01 -1.50159001e-01 -1.89462200e-01 1.60111666e-01 -1.33726513e+00 4.70100999e-01 9.62387502e-01 6.25253320e-01 1.08187512e-01 6.57466531e-01 2.36524716e-01 9.13441420e-01 -6.44662261e-01 -5.15879750e-01 6.05862796e-01 -1.08479691e+00 -6.25195742e-01 3.69637907e-01 8.30316961e-01 -7.97000229e-02 7.08269775e-02 1.04049134e+00 5.55412233e-01 -1.57420546e-01 9.32770193e-01 -1.50616705e+00 -1.12142110e+00 7.48926163e-01 -5.44676542e-01 -4.98605132e-01 1.77096471e-01 3.83208364e-01 1.01252043e+00 -5.86058915e-01 7.11769402e-01 1.32806993e+00 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482450d4-00b9-400d-b07f-e9c2ca7b8771	condition-invariant-semantic-segmentation	2305.17349	null	https://arxiv.org/abs/2305.17349v1	https://arxiv.org/pdf/2305.17349v1.pdf	Condition-Invariant Semantic Segmentation	Adaptation of semantic segmentation networks to different visual conditions from those for which ground-truth annotations are available at training is vital for robust perception in autonomous cars and robots. However, previous work has shown that most feature-level adaptation methods, which employ adversarial training and are validated on synthetic-to-real adaptation, provide marginal gains in normal-to-adverse condition-level adaptation, being outperformed by simple pixel-level adaptation via stylization. Motivated by these findings, we propose to leverage stylization in performing feature-level adaptation by aligning the deep features extracted by the encoder of the network from the original and the stylized view of each input image with a novel feature invariance loss. In this way, we encourage the encoder to extract features that are invariant to the style of the input, allowing the decoder to focus on parsing these features and not on further abstracting from the specific style of the input. We implement our method, named Condition-Invariant Semantic Segmentation (CISS), on the top-performing domain adaptation architecture and demonstrate a significant improvement over previous state-of-the-art methods both on Cityscapes$\to$ACDC and Cityscapes$\to$Dark Zurich adaptation. In particular, CISS is ranked first among all published unsupervised domain adaptation methods on the public ACDC leaderboard. Our method is also shown to generalize well to domains unseen during training, outperforming competing domain adaptation approaches on BDD100K-night and Nighttime Driving. Code is publicly available at https://github.com/SysCV/CISS .	['Luc van Gool', 'Fisher Yu', 'David Bruggemann', 'Christos Sakaridis']	2023-05-27	null	null	null	null	['unsupervised-domain-adaptation']	['methodology']	[ 0.31835076 0.23397242 0.03405418 -0.6897573 -0.78668517 -0.9214415 0.6456972 -0.40937957 -0.62783813 0.68172514 -0.0939568 -0.33089235 0.49431476 -0.625046 -1.2575952 -0.5738063 0.46761551 0.47144982 0.3430166 -0.446908 -0.19166185 0.50987196 -1.4461296 0.0628941 0.8987546 0.9216936 0.09576723 0.63545036 0.17159049 0.25478083 -0.65803486 -0.5179907 0.6472962 -0.48492506 -0.7038241 0.11631109 0.7005395 -0.3007607 -0.3585568 1.0459429 0.4528913 0.14394964 0.63633114 -1.3135693 -0.74245703 -0.03416599 -0.33433735 0.03047783 -0.04392627 0.6062739 0.6885944 -0.71636385 0.8147859 1.0480591 0.518157 0.9564689 -1.3532873 -0.97652197 0.3542805 0.10165399 -1.1886027 -0.55081224 0.6898222 -0.540576 1.0100144 -0.28018695 0.35710388 1.5069429 -0.01636939 0.68396467 1.1090231 -0.22178844 0.34461012 0.27109748 -0.20697981 0.51724595 0.29700774 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35695ec7-f000-4342-81ee-f95286d76231	collabkg-a-learnable-human-machine	2307.00769	null	https://arxiv.org/abs/2307.00769v1	https://arxiv.org/pdf/2307.00769v1.pdf	CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction	In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential. However, existing IE toolkits have several non-trivial problems, such as not supporting multi-tasks, not supporting automatic updates. In this work, we present CollabKG, a learnable human-machine-cooperative IE toolkit for KG and EKG construction. Specifically, for the multi-task issue, CollabKG unifies different IE subtasks, including named entity recognition (NER), entity-relation triple extraction (RE), and event extraction (EE), and supports both KG and EKG. Then, combining advanced prompting-based IE technology, the human-machine-cooperation mechanism with LLMs as the assistant machine is presented which can provide a lower cost as well as a higher performance. Lastly, owing to the two-way interaction between the human and machine, CollabKG with learning ability allows self-renewal. Besides, CollabKG has several appealing features (e.g., customization, training-free, propagation, etc.) that make the system powerful, easy-to-use, and high-productivity. We holistically compare our toolkit with other existing tools on these features. Human evaluation quantitatively illustrates that CollabKG significantly improves annotation quality, efficiency, and stability simultaneously.	['Wenjuan Han', 'Jinan Xu', 'Xingyu Cui', 'Ning Cheng', 'Yufeng Chen', 'Xiang Wei']	2023-07-03	null	null	null	null	['graph-construction', 'knowledge-graphs', 'event-extraction', 'named-entity-recognition-ner', 'cg']	['graphs', 'knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-6.77199423e-01 4.57449675e-01 -4.02517110e-01 -2.18998894e-01 -5.80132067e-01 -3.90674978e-01 3.92699659e-01 3.95044327e-01 -4.10294741e-01 9.91230667e-01 1.18347138e-01 -2.65812486e-01 -3.79647434e-01 -8.67129028e-01 -5.24049878e-01 -4.58738565e-01 -1.32450283e-01 5.92191637e-01 5.18172979e-01 -3.37310694e-02 -5.15272200e-01 3.92068475e-01 -1.25714004e+00 2.38211975e-01 9.77592409e-01 7.44417131e-01 2.00280279e-01 3.77971858e-01 -1.64093405e-01 9.89426434e-01 -3.16943467e-01 -6.40937626e-01 -2.90606081e-01 7.22483695e-02 -1.29784358e+00 -4.53679711e-01 -3.50823879e-01 -2.01002825e-02 -2.77562678e-01 4.91001070e-01 6.59284174e-01 -3.82419191e-02 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b6d40f5a-81ea-43fb-a96f-2f23a9ccb1d0	how-robust-are-character-based-word	1704.04441	null	http://arxiv.org/abs/1704.04441v1	http://arxiv.org/pdf/1704.04441v1.pdf	How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?	This paper investigates the robustness of NLP against perturbed word forms. While neural approaches can achieve (almost) human-like accuracy for certain tasks and conditions, they often are sensitive to small changes in the input such as non-canonical input (e.g., typos). Yet both stability and robustness are desired properties in applications involving user-generated content, and the more as humans easily cope with such noisy or adversary conditions. In this paper, we study the impact of noisy input. We consider different noise distributions (one type of noise, combination of noise types) and mismatched noise distributions for training and testing. Moreover, we empirically evaluate the robustness of different models (convolutional neural networks, recurrent neural networks, non-neural models), different basic units (characters, byte pair encoding units), and different NLP tasks (morphological tagging, machine translation).	['Günter Neumann', 'Josef van Genabith', 'Georg Heigold']	2017-04-14	how-robust-are-character-based-word-1	https://aclanthology.org/W18-1807	https://aclanthology.org/W18-1807.pdf	ws-2018-3	['morphological-tagging']	['natural-language-processing']	[ 4.31189597e-01 -1.70188114e-01 4.77772439e-03 5.21485284e-02 -6.64501667e-01 -1.17143059e+00 5.80295980e-01 3.76106352e-01 -8.06636810e-01 9.79596138e-01 1.05846748e-01 -7.28800535e-01 2.06553832e-01 -7.98043370e-01 -8.99404228e-01 -6.97683275e-01 2.06378940e-02 3.82895768e-01 3.28096628e-01 -2.53575951e-01 2.49017537e-01 7.40205050e-01 -1.34176159e+00 2.51655728e-01 8.17774534e-01 4.82490510e-01 -1.85244679e-01 9.11925554e-01 -6.73603788e-02 2.74369717e-01 -9.62719738e-01 -8.14950883e-01 3.99062365e-01 -9.45954546e-02 -6.10895693e-01 -3.90656650e-01 1.63338348e-01 8.51419270e-02 -2.35598683e-01 1.56390834e+00 8.15234244e-01 2.14172319e-01 6.75016403e-01 -8.78131270e-01 -9.43051338e-01 8.87107193e-01 -3.78853716e-02 2.83854693e-01 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bfa0a607-4781-461b-8a7d-d47cef740478	delete-retrieve-generate-a-simple-approach-to	1804.06437	null	http://arxiv.org/abs/1804.06437v1	http://arxiv.org/pdf/1804.06437v1.pdf	Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer	We consider the task of text attribute transfer: transforming a sentence to alter a specific attribute (e.g., sentiment) while preserving its attribute-independent content (e.g., changing "screen is just the right size" to "screen is too small"). Our training data includes only sentences labeled with their attribute (e.g., positive or negative), but not pairs of sentences that differ only in their attributes, so we must learn to disentangle attributes from attribute-independent content in an unsupervised way. Previous work using adversarial methods has struggled to produce high-quality outputs. In this paper, we propose simpler methods motivated by the observation that text attributes are often marked by distinctive phrases (e.g., "too small"). Our strongest method extracts content words by deleting phrases associated with the sentence's original attribute value, retrieves new phrases associated with the target attribute, and uses a neural model to fluently combine these into a final output. On human evaluation, our best method generates grammatical and appropriate responses on 22% more inputs than the best previous system, averaged over three attribute transfer datasets: altering sentiment of reviews on Yelp, altering sentiment of reviews on Amazon, and altering image captions to be more romantic or humorous.	['Percy Liang', 'Robin Jia', 'Juncen Li', 'He He']	2018-04-17	delete-retrieve-generate-a-simple-approach-to-1	https://aclanthology.org/N18-1169	https://aclanthology.org/N18-1169.pdf	naacl-2018-6	['text-attribute-transfer']	['natural-language-processing']	[ 7.94094265e-01 2.98929870e-01 1.73353270e-01 -9.84964848e-01 -9.64604855e-01 -1.30490184e+00 5.28650165e-01 3.02282363e-01 -5.11724353e-01 1.02250636e+00 2.92254567e-01 -2.85003096e-01 6.20283842e-01 -1.01340795e+00 -9.44027007e-01 -5.41012943e-01 6.57579124e-01 6.13572419e-01 -3.71327341e-01 -5.11223197e-01 4.22827899e-01 2.83474803e-01 -1.28887403e+00 7.53915071e-01 6.45520568e-01 6.95831120e-01 -3.11608642e-01 6.62602842e-01 -3.35203916e-01 4.58469808e-01 -1.11453998e+00 -9.54271317e-01 2.25491390e-01 -6.59927368e-01 -7.88587868e-01 -9.64155793e-02 8.57814074e-01 -1.67270288e-01 6.76088780e-02 1.25774002e+00 6.01011753e-01 3.29091437e-02 9.84478295e-01 -1.19963408e+00 -1.31711161e+00 7.15569675e-01 -3.98262024e-01 -5.56543171e-02 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98335196-92bf-4b5a-ac42-5ef2cc6a5728	unmasking-the-abnormal-events-in-video	1705.08182	null	http://arxiv.org/abs/1705.08182v3	http://arxiv.org/pdf/1705.08182v3.pdf	Unmasking the abnormal events in video	We propose a novel framework for abnormal event detection in video that requires no training sequences. Our framework is based on unmasking, a technique previously used for authorship verification in text documents, which we adapt to our task. We iteratively train a binary classifier to distinguish between two consecutive video sequences while removing at each step the most discriminant features. Higher training accuracy rates of the intermediately obtained classifiers represent abnormal events. To the best of our knowledge, this is the first work to apply unmasking for a computer vision task. We compare our method with several state-of-the-art supervised and unsupervised methods on four benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve state-of-the-art results, while running in real-time at 20 frames per second.	['Marius Popescu', 'Radu Tudor Ionescu', 'Sorina Smeureanu', 'Bogdan Alexe']	2017-05-23	unmasking-the-abnormal-events-in-video-1	http://openaccess.thecvf.com/content_iccv_2017/html/Ionescu_Unmasking_the_Abnormal_ICCV_2017_paper.html	http://openaccess.thecvf.com/content_ICCV_2017/papers/Ionescu_Unmasking_the_Abnormal_ICCV_2017_paper.pdf	iccv-2017-10	['abnormal-event-detection-in-video', 'abnormal-event-detection-in-video', 'authorship-verification']	['computer-vision', 'methodology', 'natural-language-processing']	[ 5.35827875e-01 -1.65997326e-01 -1.52669877e-01 -1.51257530e-01 -2.40573511e-01 -4.49285001e-01 8.63741934e-01 4.41810876e-01 -6.13227248e-01 4.21121240e-01 -2.53359139e-01 -2.23620281e-01 2.70126402e-01 -4.10321176e-01 -4.11485553e-01 -4.77900147e-01 -3.52375418e-01 2.26754382e-01 4.90820438e-01 1.52322456e-01 5.57655334e-01 6.28844678e-01 -1.73306251e+00 6.78281784e-01 5.12800515e-01 1.04640555e+00 -4.72083569e-01 1.11785233e+00 1.05422035e-01 1.00996161e+00 -7.83266425e-01 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0102f958-906e-458f-b43b-11c11e3e81b6	a-review-of-the-trends-and-challenges-in	2301.08826	null	https://arxiv.org/abs/2301.08826v1	https://arxiv.org/pdf/2301.08826v1.pdf	A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis	Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.	['Linda Galligan', 'Petrea Redmond', 'Jacquie Mcdonald', 'Christopher Dann', 'Yan Li', 'Xiaohui Tao', 'Thanveer Shaik']	2023-01-20	null	null	null	null	['aspect-based-sentiment-analysis']	['natural-language-processing']	[ 2.04444110e-01 3.83718222e-01 -2.78704375e-01 -4.10713434e-01 -1.02166243e-01 -6.40756249e-01 4.64446545e-01 1.02095056e+00 -1.91272289e-01 6.75711572e-01 6.05071783e-01 -3.99458736e-01 -4.11402792e-01 -7.10003674e-01 -1.83480233e-01 -4.12779778e-01 5.03727198e-01 4.44970936e-01 -1.46480769e-01 -8.90853167e-01 9.54456389e-01 5.68207026e-01 -1.95641422e+00 8.05823267e-01 1.14055896e+00 5.46952188e-01 -6.18920512e-02 1.04133546e+00 -1.20803738e+00 1.33950436e+00 -1.44881713e+00 -5.59539676e-01 -4.69882816e-01 -2.78995156e-01 -1.18069661e+00 1.70731425e-01 5.28358698e-01 3.45739037e-01 3.97356540e-01 8.71266305e-01 8.29880357e-01 2.75219530e-01 4.56438184e-01 -1.30466640e+00 -7.36185849e-01 6.35029018e-01 -4.44026142e-01 3.49891394e-01 9.64537144e-01 -5.83652675e-01 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5241fdda-2be9-488c-96bf-3c2a7b2f6d00	multi3nlu-a-multilingual-multi-intent-multi	2212.10455	null	https://arxiv.org/abs/2212.10455v2	https://arxiv.org/pdf/2212.10455v2.pdf	MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue	Task-oriented dialogue (TOD) systems have been widely deployed in many industries as they deliver more efficient customer support. These systems are typically constructed for a single domain or language and do not generalise well beyond this. To support work on Natural Language Understanding (NLU) in TOD across multiple languages and domains simultaneously, we constructed MULTI3NLU++, a multilingual, multi-intent, multi-domain dataset. MULTI3NLU++ extends the English only NLU++ dataset to include manual translations into a range of high, medium, and low resource languages (Spanish, Marathi, Turkish and Amharic), in two domains (BANKING and HOTELS). Because of its multi-intent property, MULTI3NLU++ represents complex and natural user goals, and therefore allows us to measure the realistic performance of TOD systems in a varied set of the world's languages. We use MULTI3NLU++ to benchmark state-of-the-art multilingual models for the NLU tasks of intent detection and slot labelling for TOD systems in the multilingual setting. The results demonstrate the challenging nature of the dataset, particularly in the low-resource language setting, offering ample room for future experimentation in multi-domain multilingual TOD setups.	['Alexandra Birch', 'Anna Korhonen', 'Ivan Vulić', 'Liane Guillou', 'Evgeniia Razumovskaia', 'Nikita Moghe']	2022-12-20	null	null	null	null	['intent-detection']	['natural-language-processing']	[-3.63877207e-01 2.02599347e-01 -3.54778528e-01 -4.15380895e-01 -1.02588463e+00 -9.91322935e-01 9.14625525e-01 1.03281282e-01 -5.55718362e-01 9.99008000e-01 6.46416068e-01 -4.95468199e-01 2.71691620e-01 -3.84918749e-01 -2.11801216e-01 4.71053421e-02 1.45843521e-01 1.41271758e+00 1.27039820e-01 -1.02043855e+00 -2.79592603e-01 -1.09698594e-01 -8.05289626e-01 5.14969885e-01 7.15092242e-01 5.21000862e-01 2.97458410e-01 5.69865346e-01 -2.89138168e-01 9.81403470e-01 -5.72645724e-01 -6.06901109e-01 1.55931413e-01 -2.27457091e-01 -1.33500612e+00 1.03790618e-01 9.99449268e-02 -4.26545471e-01 5.15125878e-02 4.01453674e-01 6.85661554e-01 3.10098357e-03 5.82756877e-01 -1.13100743e+00 -5.43961227e-01 5.77465177e-01 -3.45763564e-01 5.16007952e-02 9.96403694e-01 1.34771347e-01 1.24818134e+00 -7.96694040e-01 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a99a8ace-9310-46de-b019-ad3779fa9731	dekgci-a-double-sided-recommendation-model	2306.13837	null	https://arxiv.org/abs/2306.13837v1	https://arxiv.org/pdf/2306.13837v1.pdf	DEKGCI: A double-sided recommendation model for integrating knowledge graph and user-item interaction graph	Both knowledge graphs and user-item interaction graphs are frequently used in recommender systems due to their ability to provide rich information for modeling users and items. However, existing studies often focused on one of these sources (either the knowledge graph or the user-item interaction graph), resulting in underutilization of the benefits that can be obtained by integrating both sources of information. In this paper, we propose DEKGCI, a novel double-sided recommendation model. In DEKGCI, we use the high-order collaborative signals from the user-item interaction graph to enrich the user representations on the user side. Additionally, we utilize the high-order structural and semantic information from the knowledge graph to enrich the item representations on the item side. DEKGCI simultaneously learns the user and item representations to effectively capture the joint interactions between users and items. Three real-world datasets are adopted in the experiments to evaluate DEKGCI's performance, and experimental results demonstrate its high effectiveness compared to seven state-of-the-art baselines in terms of AUC and ACC.	['Ruirui Shang', 'Mao Chen', 'Zeyu Zeng', 'Yajing Yang']	2023-06-24	null	null	null	null	['knowledge-graphs']	['knowledge-base']	[-2.01468393e-01 -1.19756117e-01 -6.11913383e-01 -3.29400867e-01 -5.63243404e-02 -4.15633440e-01 3.13205510e-01 1.57202575e-02 -7.93312863e-03 3.89968604e-01 6.34265482e-01 -6.02644160e-02 -4.93558854e-01 -8.07263136e-01 -3.87727141e-01 -3.95259291e-01 -1.22707024e-01 1.41594484e-01 1.09219618e-01 -2.70095646e-01 1.00482963e-02 -1.47401690e-01 -1.27777719e+00 3.84941459e-01 1.18869245e+00 1.08032978e+00 1.51371881e-01 1.27825871e-01 -2.63150573e-01 5.90809941e-01 -2.09010422e-01 -5.67177057e-01 1.91492900e-01 -4.17961329e-01 -3.36842686e-01 3.72967334e-03 1.77952901e-01 -2.85314918e-01 -5.38025916e-01 1.01602530e+00 3.02842766e-01 4.64648753e-01 6.38164222e-01 -1.11870062e+00 -1.28401315e+00 1.02340996e+00 -5.49284458e-01 1.43237129e-01 6.11325979e-01 -4.76879954e-01 1.63509762e+00 -1.07804823e+00 2.80864269e-01 1.24737978e+00 5.71234822e-01 1.92374051e-01 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-1.02688003e+00 6.79780066e-01 -1.80129930e-01 -1.13184142e+00 -7.31341308e-03 -4.58431751e-01]	[10.209549903869629, 5.622807025909424]
74c29d9b-0a10-4fe3-842a-dafe799d0cec	gaussian-process-probes-gpp-for-uncertainty	2305.18213	null	https://arxiv.org/abs/2305.18213v1	https://arxiv.org/pdf/2305.18213v1.pdf	Gaussian Process Probes (GPP) for Uncertainty-Aware Probing	Understanding which concepts models can and cannot represent has been fundamental to many tasks: from effective and responsible use of models to detecting out of distribution data. We introduce Gaussian process probes (GPP), a unified and simple framework for probing and measuring uncertainty about concepts represented by models. As a Bayesian extension of linear probing methods, GPP asks what kind of distribution over classifiers (of concepts) is induced by the model. This distribution can be used to measure both what the model represents and how confident the probe is about what the model represents. GPP can be applied to any pre-trained model with vector representations of inputs (e.g., activations). It does not require access to training data, gradients, or the architecture. We validate GPP on datasets containing both synthetic and real images. Our experiments show it can (1) probe a model's representations of concepts even with a very small number of examples, (2) accurately measure both epistemic uncertainty (how confident the probe is) and aleatory uncertainty (how fuzzy the concepts are to the model), and (3) detect out of distribution data using those uncertainty measures as well as classic methods do. By using Gaussian processes to expand what probing can offer, GPP provides a data-efficient, versatile and uncertainty-aware tool for understanding and evaluating the capabilities of machine learning models.	['Been Kim', 'Thomas L. Griffiths', 'Jason Baldridge', 'Alexander Ku', 'Zi Wang']	2023-05-29	null	null	null	null	['gaussian-processes']	['methodology']	[ 3.66595574e-02 3.66684914e-01 -1.00481123e-01 -3.60214919e-01 -9.51249242e-01 -9.73239243e-01 9.66550052e-01 3.40885967e-01 -3.05760533e-01 5.93746603e-01 -8.78228247e-02 -3.64983261e-01 -1.37504935e-01 -9.43893254e-01 -8.66312861e-01 -8.24894309e-01 -9.52904206e-03 1.04401064e+00 5.91962457e-01 3.20225060e-01 5.16839981e-01 4.89467174e-01 -1.45261467e+00 3.38632703e-01 6.17903173e-01 1.21672833e+00 -7.09477291e-02 4.08843160e-01 -3.01966578e-01 5.44679761e-01 -6.26502156e-01 -2.38986984e-01 -2.36060396e-01 5.04108742e-02 -6.06688857e-01 -3.46328050e-01 -5.20043038e-02 5.54286055e-02 1.70928478e-01 1.48601282e+00 2.40964461e-02 6.27625883e-02 1.16519356e+00 -1.23550427e+00 -6.56333506e-01 7.27434516e-01 -3.90181303e-01 5.82926035e-01 3.18176955e-01 4.38606590e-02 7.39500582e-01 -8.31372380e-01 2.44874775e-01 1.70437539e+00 4.64880407e-01 5.54660678e-01 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ca0122fe-6413-49bc-85d5-b1ee3cf4ba39	detect-camouflaged-spam-content-via	1908.11561	null	https://arxiv.org/abs/1908.11561v1	https://arxiv.org/pdf/1908.11561v1.pdf	Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation	The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters. This paper proposes a novel framework to jointly model Chinese variational, semantic, and contextualized representations for Chinese text spam detection task. In particular, a Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on a Chinese character variation graph. The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global). Furthermore, an enhanced bidirectional language model, with a combination gate function and an aggregation learning function, is proposed to integrate the graph and text information while capturing the sequential information. Extensive experiments have been conducted on both SMS and review datasets, to show the proposed method outperforms a series of state-of-the-art models for Chinese spam detection.	['Guoxiu He', 'Zhuoren Jiang', 'Zhe Gao', 'Yangyang Kang', 'Xiaozhong Liu', 'Qiong Zhang', 'Luo Si', 'Changlong Sun']	2019-08-30	detect-camouflaged-spam-content-via-1	https://aclanthology.org/D19-1640	https://aclanthology.org/D19-1640.pdf	ijcnlp-2019-11	['spam-detection']	['natural-language-processing']	[-1.24157168e-01 -6.52753770e-01 -1.62634730e-01 -2.89068580e-01 -4.16070998e-01 -2.87464380e-01 8.77619326e-01 -8.91439393e-02 -2.69487858e-01 1.77672148e-01 5.05396426e-01 -5.72884917e-01 2.71855921e-01 -4.77252305e-01 -6.21010289e-02 -7.96139181e-01 2.17322826e-01 1.36901259e-01 5.66363692e-01 -3.67653638e-01 2.71849960e-01 1.77112252e-01 -9.89051580e-01 2.51769334e-01 1.58799875e+00 5.89129210e-01 4.24798489e-01 5.79506338e-01 -7.45087564e-01 6.65894806e-01 -5.71894825e-01 -6.16281450e-01 -1.23034678e-01 -5.56499302e-01 -2.42688030e-01 3.71732831e-01 1.08932093e-01 -9.53315571e-02 -7.76082098e-01 1.43840933e+00 4.33773100e-01 2.20150113e-01 6.71616137e-01 -9.52854097e-01 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e8900320-e8a6-484a-bf12-98df187b957b	a-human-machine-collaborative-framework-for	null	null	https://aclanthology.org/2021.acl-long.436	https://aclanthology.org/2021.acl-long.436.pdf	A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues	Conversational dialogue systems (CDSs) are hard to evaluate due to the complexity of natural language. Automatic evaluation of dialogues often shows insufficient correlation with human judgements. Human evaluation is reliable but labor-intensive. We introduce a human-machine collaborative framework, HMCEval, that can guarantee reliability of the evaluation outcomes with reduced human effort. HMCEval casts dialogue evaluation as a sample assignment problem, where we need to decide to assign a sample to a human or a machine for evaluation. HMCEval includes a model confidence estimation module to estimate the confidence of the predicted sample assignment, and a human effort estimation module to estimate the human effort should the sample be assigned to human evaluation, as well as a sample assignment execution module that finds the optimum assignment solution based on the estimated confidence and effort. We assess the performance of HMCEval on the task of evaluating malevolence in dialogues. The experimental results show that HMCEval achieves around 99{\%} evaluation accuracy with half of the human effort spared, showing that HMCEval provides reliable evaluation outcomes while reducing human effort by a large amount.	['Maarten de Rijke', 'Pengjie Ren', 'Yangjun Zhang']	2021-08-01	null	null	null	acl-2021-5	['dialogue-evaluation']	['natural-language-processing']	[-1.62143677e-01 8.31633031e-01 2.54846483e-01 -8.22957158e-01 -8.24968338e-01 -6.01127326e-01 7.46398211e-01 3.63012433e-01 -6.48688078e-01 9.53800678e-01 1.45548331e-02 -2.54683226e-01 1.38229430e-01 -5.60216725e-01 1.64917693e-01 -2.63671398e-01 2.86044508e-01 1.24313426e+00 1.95677146e-01 -2.57230610e-01 4.53815311e-01 -1.25994653e-01 -1.21250534e+00 3.75637412e-01 1.04665768e+00 8.88722539e-01 1.14521898e-01 1.26671815e+00 -5.77754378e-02 1.20261228e+00 -1.39651477e+00 -8.29477251e-01 -1.76438794e-01 -6.54002070e-01 -1.47363114e+00 1.41877323e-01 -3.00666183e-01 -5.52736223e-01 4.04621542e-01 7.14970231e-01 6.61665261e-01 3.01380903e-01 8.51344585e-01 -1.53604972e+00 2.98439234e-01 6.49937987e-01 -3.91206555e-02 -2.26239011e-01 8.66484761e-01 2.77592659e-01 9.52338874e-01 -5.62991083e-01 5.17765760e-01 1.51777148e+00 3.26032221e-01 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4233c31f-9caf-4589-9d8b-ef488c85ee98	cs60075-team2-at-semeval-2021-task-1-lexical	2106.02340	null	https://arxiv.org/abs/2106.02340v1	https://arxiv.org/pdf/2106.02340v1.pdf	cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora	This paper describes the performance of the team cs60075_team2 at SemEval 2021 Task 1 - Lexical Complexity Prediction. The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of $0.784$ in sub-task 1 (single word) and $0.836$ in sub-task 2 (multiple word expressions).	['Sai Mahesh Pokala', 'Tanurima Halder', 'Sayantan Adak', 'Abhilash Nandy']	2021-06-04	null	null	null	null	['lexical-complexity-prediction', 'lexical-analysis']	['natural-language-processing', 'natural-language-processing']	[-5.11990726e-01 1.27697095e-01 6.02844357e-02 -1.79222897e-01 -1.00724721e+00 -7.34092474e-01 6.82658672e-01 4.01186913e-01 -9.01309192e-01 6.95752025e-01 3.01774025e-01 -4.85267580e-01 -3.32935527e-02 -6.04277670e-01 -4.83266145e-01 7.63665959e-02 -1.22643918e-01 6.52941704e-01 2.96507895e-01 -4.66666788e-01 4.91208375e-01 4.92101461e-02 -1.20109320e+00 4.60606366e-01 9.57865655e-01 9.12204802e-01 3.54806334e-01 5.45087039e-01 -4.82963651e-01 8.41823995e-01 -6.15368664e-01 -9.86655354e-01 1.71443790e-01 -5.37128225e-02 -8.68307292e-01 -4.34055597e-01 3.05284858e-01 1.81557909e-01 -8.65745768e-02 9.61274147e-01 4.59539086e-01 -2.54420936e-02 7.52409041e-01 -7.41037667e-01 -2.88565844e-01 1.32146764e+00 -1.65782839e-01 5.22334933e-01 3.74599457e-01 2.06874773e-01 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2e1d9926-0a66-4531-8d69-e55db9346dd2	sdf-stylegan-implicit-sdf-based-stylegan-for	2206.12055	null	https://arxiv.org/abs/2206.12055v1	https://arxiv.org/pdf/2206.12055v1.pdf	SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation	We present a StyleGAN2-based deep learning approach for 3D shape generation, called SDF-StyleGAN, with the aim of reducing visual and geometric dissimilarity between generated shapes and a shape collection. We extend StyleGAN2 to 3D generation and utilize the implicit signed distance function (SDF) as the 3D shape representation, and introduce two novel global and local shape discriminators that distinguish real and fake SDF values and gradients to significantly improve shape geometry and visual quality. We further complement the evaluation metrics of 3D generative models with the shading-image-based Fr\'echet inception distance (FID) scores to better assess visual quality and shape distribution of the generated shapes. Experiments on shape generation demonstrate the superior performance of SDF-StyleGAN over the state-of-the-art. We further demonstrate the efficacy of SDF-StyleGAN in various tasks based on GAN inversion, including shape reconstruction, shape completion from partial point clouds, single-view image-based shape generation, and shape style editing. Extensive ablation studies justify the efficacy of our framework design. Our code and trained models are available at https://github.com/Zhengxinyang/SDF-StyleGAN.	['Xin Tong', 'Peng-Shuai Wang', 'Yang Liu', 'Xin-Yang Zheng']	2022-06-24	null	null	null	null	['3d-shape-generation', '3d-shape-representation']	['computer-vision', 'computer-vision']	[ 1.33305058e-01 5.94452135e-02 3.80394578e-01 -3.64538819e-01 -7.16745734e-01 -1.00542092e+00 7.33417988e-01 -5.87654352e-01 3.24830472e-01 5.05000055e-01 2.31648758e-01 -2.48502851e-01 3.07879210e-01 -1.25154841e+00 -7.23569274e-01 -5.30138671e-01 2.50453770e-01 4.32346940e-01 -3.81438464e-01 -2.32553467e-01 9.19877142e-02 9.59891796e-01 -1.03351259e+00 2.71293789e-01 8.86464357e-01 9.36766744e-01 -8.85267034e-02 7.76050329e-01 -2.29482189e-01 1.64002240e-01 -5.61772466e-01 -4.10562009e-01 7.10290551e-01 -6.19418144e-01 -3.45650524e-01 1.97523683e-01 4.32992101e-01 -6.35525286e-01 -2.35294804e-01 7.78742075e-01 9.54166710e-01 -2.23700151e-01 8.55253816e-01 -1.15405202e+00 -1.21255934e+00 -8.89607593e-02 -6.32551432e-01 -3.72377366e-01 3.62780064e-01 5.51702619e-01 6.27019584e-01 -1.36043477e+00 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c0b98e6a-fbb9-416f-b61a-a8fe2c37f1bb	e-fcnn-for-tiny-facial-expression-recognition	null	null	https://doi.org/10.1007/s10489-020-01855-5	https://sci-hub.st//https://link.springer.com/article/10.1007/s10489-020-01855-5	E-FCNN for tiny facial expression recognition	As a hot issue in recent years, facial expression recognition(FER) has been widely applied in many fields, but it still faces great challenges in tiny facial expression recognition. Currently, most of the FER networks only consider images of ideal sizes. Their recognition accuracy would significantly decrease as the image resolution decreases. However, images captured by surveillance cameras often contain tiny faces with low-resolution. This paper proposes an edge-aware feedback convolutional neural network(E-FCNN) for tiny FER, which associates image super-resolution and facial expression recognition together. To effectively leverage the texture information of faces, we design a novel three-stream super-resolution network, which is embedded with an edge-enhancement block as one branch. The other two branches are the up-sampling branch and SR(Super-Resolution) primary branch. Specifically, visual features are extracted from tiny images based on a hierarchical strategy, and then put into a feedback block with fused results of the three branches. Experiments are performed on down-sampled images in four facial expression datasets: CK+, FER2013, BU-3DFE, RAF-DB. The results demonstrate the favorable performance of our network.	['Qiyu Cheng', 'Jie Shao']	2020-08-20	null	null	null	applied-intelligence-2020-8	['facial-expression-recognition']	['computer-vision']	[ 4.41723675e-01 -4.25107539e-01 -8.27072114e-02 -5.66677868e-01 -4.11785722e-01 2.77695358e-01 1.58332825e-01 -8.85535777e-01 -1.29962415e-01 7.54180133e-01 2.03049302e-01 3.34930867e-01 2.13368684e-01 -8.63749146e-01 -5.84585845e-01 -9.31014836e-01 1.63435817e-01 -5.17055750e-01 9.90464911e-02 -5.52105904e-01 -2.18620703e-01 6.24744236e-01 -1.78598535e+00 6.26002967e-01 6.25117421e-01 1.82125986e+00 -1.84595361e-01 2.52204746e-01 -2.70096183e-01 1.21740532e+00 -2.98346102e-01 -5.03419101e-01 1.06540263e-01 -3.98304254e-01 -3.13015461e-01 3.28365237e-01 3.46887976e-01 -7.18200207e-01 -4.17108774e-01 1.28535068e+00 6.38928890e-01 -2.71243602e-01 2.27621198e-01 -1.19886339e+00 -8.36707771e-01 1.53906003e-01 -1.14920163e+00 3.59045774e-01 1.70788363e-01 -5.24727367e-02 2.57011861e-01 -1.10059142e+00 5.44709682e-01 1.47460294e+00 6.75560117e-01 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b1ce8202-afc3-442b-91a8-b4e6ccf15d5c	token-level-sequence-labeling-for-spoken	2210.15734	null	https://arxiv.org/abs/2210.15734v1	https://arxiv.org/pdf/2210.15734v1.pdf	Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models	End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation. However, these systems model sequence labeling as a sequence prediction task causing a divergence from its well-established token-level tagging formulation. We build compositional end-to-end SLU systems that explicitly separate the added complexity of recognizing spoken mentions in SLU from the NLU task of sequence labeling. By relying on intermediate decoders trained for ASR, our end-to-end systems transform the input modality from speech to token-level representations that can be used in the traditional sequence labeling framework. This composition of ASR and NLU formulations in our end-to-end SLU system offers direct compatibility with pre-trained ASR and NLU systems, allows performance monitoring of individual components and enables the use of globally normalized losses like CRF, making them attractive in practical scenarios. Our models outperform both cascaded and direct end-to-end models on a labeling task of named entity recognition across SLU benchmarks.	['Shinji Watanabe', 'Alan W Black', 'Florian Metze', 'Brian Yan', 'Siddharth Dalmia', 'Siddhant Arora']	2022-10-27	null	null	null	null	['spoken-language-understanding', 'spoken-language-understanding']	['natural-language-processing', 'speech']	[ 4.45572257e-01 4.14988220e-01 -1.93792969e-01 -8.18058252e-01 -1.10656869e+00 -7.91924119e-01 5.43297350e-01 1.44410580e-01 -7.10570872e-01 6.09038591e-01 7.09536731e-01 -5.86176157e-01 7.99175799e-01 -4.99273688e-01 -8.27473700e-01 -1.06249593e-01 2.87846811e-02 6.34670973e-01 2.45585535e-02 -1.47271097e-01 -2.15580702e-01 -1.10424004e-01 -1.10053337e+00 3.65522891e-01 7.45797992e-01 8.19495797e-01 3.24063987e-01 9.57505941e-01 -5.24730861e-01 1.10903430e+00 -2.85601258e-01 -1.14540145e-01 -1.03727669e-01 -7.11984217e-01 -9.72110868e-01 5.77689223e-02 4.52935845e-01 -3.10220510e-01 -2.77263731e-01 6.19002938e-01 5.28518260e-01 2.82794982e-01 5.52666008e-01 -7.86876678e-01 -8.15804183e-01 8.92872632e-01 -1.94205299e-01 -6.09723888e-02 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4f114dff-146d-4441-8b81-065c89d4fb93	addressing-limitations-of-encoder-decoder	null	null	https://aclanthology.org/2022.coling-1.137	https://aclanthology.org/2022.coling-1.137.pdf	Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL	Most attempts on Text-to-SQL task using encoder-decoder approach show a big problem of dramatic decline in performance for new databases. For the popular Spider dataset, despite models achieving 70% accuracy on its development or test sets, the same models show a huge decline below 20% accuracy for unseen databases. The root causes for this problem are complex and they cannot be easily fixed by adding more manually created training. In this paper we address the problem and propose a solution that is a hybrid system using automated training-data augmentation technique. Our system consists of a rule-based and a deep learning components that interact to understand crucial information in a given query and produce correct SQL as a result. It achieves double-digit percentage improvement for databases that are not part of the Spider corpus.	['Vadim Sheinin', 'Elahe Khorashani', 'Hangu Yeo', 'Ngoc Phuoc An Vo', 'Irene Manotas', 'Octavian Popescu']	null	null	null	null	coling-2022-10	['text-to-sql']	['computer-code']	[ 1.35370083e-02 3.63856703e-01 -1.09292246e-01 -8.77980173e-01 -8.48158360e-01 -4.09327000e-01 6.51594698e-01 4.19740468e-01 -2.36428931e-01 8.30113292e-01 1.08756535e-01 -5.74064493e-01 1.44104570e-01 -1.06423509e+00 -1.28139508e+00 2.30659336e-01 2.00571403e-01 1.06959426e+00 5.65633774e-01 -6.84784591e-01 4.38179016e-01 2.83090472e-01 -1.66320455e+00 8.17326605e-01 5.77176213e-01 9.00541365e-01 -1.36880353e-01 9.13878500e-01 -5.12563646e-01 1.03625405e+00 -8.05388629e-01 -7.17810392e-01 3.18976223e-01 5.37919812e-02 -9.30317938e-01 -3.25358778e-01 6.02983713e-01 -4.48079079e-01 -1.49790347e-01 6.18400693e-01 6.24265015e-01 -6.48407042e-01 2.63284624e-01 -1.01700830e+00 -6.70619845e-01 7.79115677e-01 -3.39120537e-01 1.75295815e-01 4.98837531e-01 1.67371258e-01 8.24048400e-01 -8.97103429e-01 6.57081187e-01 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0490d6d7-4bd4-46f6-b7ba-5e4df7255017	learned-cone-beam-ct-reconstruction-using	2201.07562	null	https://arxiv.org/abs/2201.07562v1	https://arxiv.org/pdf/2201.07562v1.pdf	Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations	Learned iterative reconstruction algorithms for inverse problems offer the flexibility to combine analytical knowledge about the problem with modules learned from data. This way, they achieve high reconstruction performance while ensuring consistency with the measured data. In computed tomography, extending such approaches from 2D fan-beam to 3D cone-beam data is challenging due to the prohibitively high GPU memory that would be needed to train such models. This paper proposes to use neural ordinary differential equations to solve the reconstruction problem in a residual formulation via numerical integration. For training, there is no need to backpropagate through several unrolled network blocks nor through the internals of the solver. Instead, the gradients are obtained very memory-efficiently in the neural ODE setting allowing for training on a single consumer graphics card. The method is able to reduce the root mean squared error by over 30% compared to the best performing classical iterative reconstruction algorithm and produces high quality cone-beam reconstructions even in a sparse view scenario.	['Andreas Maier', 'Lina Felsner', 'Lukas Folle', 'Mingxuan Gu', 'Fabian Wagner', 'Mareike Thies']	2022-01-19	null	null	null	null	['numerical-integration']	['miscellaneous']	[ 2.71426290e-01 1.65600330e-01 4.41871047e-01 -3.04997265e-01 -7.17080712e-01 -2.07207754e-01 2.02278852e-01 1.08192731e-02 -8.18829000e-01 7.49819160e-01 -2.14267120e-01 -6.16627812e-01 -4.31222916e-01 -8.01296413e-01 -5.55767536e-01 -7.52739131e-01 9.21919346e-02 8.09815884e-01 -3.25639546e-02 7.62002766e-02 1.25515833e-01 9.78197813e-01 -1.16009140e+00 1.26500994e-01 3.74067724e-01 1.12209165e+00 2.35570088e-01 7.82392144e-01 7.78532252e-02 9.37638044e-01 2.07083628e-01 1.75666302e-01 2.16455415e-01 -4.80580002e-01 -6.40366137e-01 6.58927858e-02 2.73879617e-01 -8.43447208e-01 -2.00200498e-01 6.64349973e-01 7.42497563e-01 2.18161151e-01 4.96580482e-01 -5.78932881e-01 1.47289291e-01 1.44395351e-01 -3.39491487e-01 6.44552112e-02 1.26148522e-01 6.43119738e-02 4.56493288e-01 -1.04304910e+00 8.03570271e-01 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-1.32153094e+00 -5.89559853e-01 -6.23467147e-01 -5.87228417e-01 -8.47772229e-03 3.63887966e-01 -4.22964662e-01 -3.62583548e-01 -3.61941367e-01]	[13.358211517333984, -2.5856244564056396]
a85de8c3-74a1-4aaf-938c-3653d18f997d	towards-robust-named-entity-recognition-for	1906.07592	null	https://arxiv.org/abs/1906.07592v1	https://arxiv.org/pdf/1906.07592v1.pdf	Towards Robust Named Entity Recognition for Historic German	Recent advances in language modeling using deep neural networks have shown that these models learn representations, that vary with the network depth from morphology to semantic relationships like co-reference. We apply pre-trained language models to low-resource named entity recognition for Historic German. We show on a series of experiments that character-based pre-trained language models do not run into trouble when faced with low-resource datasets. Our pre-trained character-based language models improve upon classical CRF-based methods and previous work on Bi-LSTMs by boosting F1 score performance by up to 6%. Our pre-trained language and NER models are publicly available under https://github.com/stefan-it/historic-ner .	['Johannes Baiter', 'Stefan Schweter']	2019-06-18	towards-robust-named-entity-recognition-for-1	https://aclanthology.org/W19-4312	https://aclanthology.org/W19-4312.pdf	ws-2019-8	['low-resource-named-entity-recognition']	['natural-language-processing']	[-1.53428152e-01 3.99865545e-02 -2.40631923e-01 -5.26439667e-01 -1.03755581e+00 -6.86802208e-01 6.79317832e-01 3.45919371e-01 -1.03953075e+00 9.52765942e-01 4.91962105e-01 -6.20380461e-01 4.09347534e-01 -8.58488560e-01 -8.16359162e-01 8.00758079e-02 -1.17026888e-01 6.74631059e-01 -9.03532803e-02 -1.63172796e-01 3.17003727e-02 5.84457934e-01 -6.07559025e-01 4.05412972e-01 6.55558646e-01 4.45310414e-01 5.16527407e-02 7.99489141e-01 -5.91705263e-01 9.65903461e-01 -6.10682130e-01 -7.10931599e-01 -2.53822744e-01 -2.47391947e-02 -1.17407322e+00 -6.34409308e-01 3.01468343e-01 -1.44188151e-01 -4.87317026e-01 7.75852621e-01 3.82535845e-01 2.26140574e-01 4.93730366e-01 -6.36855602e-01 -1.17513466e+00 1.15396297e+00 -1.97639205e-02 2.80999571e-01 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1df7efe3-7a21-4c3d-9a12-fe30630e60b5	pretrained-ensemble-learning-for-fine-grained	null	null	https://aclanthology.org/D19-5020	https://aclanthology.org/D19-5020.pdf	Pretrained Ensemble Learning for Fine-Grained Propaganda Detection	In this paper, we describe our team{'}s effort on the fine-grained propaganda detection on sentence level classification (SLC) task of NLP4IF 2019 workshop co-located with the EMNLP-IJCNLP 2019 conference. Our top performing system results come from applying ensemble average on three pretrained models to make their predictions. The first two models use the uncased and cased versions of Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) while the third model uses Universal Sentence Encoder (USE) (Cer et al. 2018). Out of 26 participating teams, our system is ranked in the first place with 68.8312 F1-score on the development dataset and in the sixth place with 61.3870 F1-score on the testing dataset.	['Mahmoud Al-Ayyoub', 'Ibraheem Tuffaha', 'Ali Fadel']	2019-11-01	null	null	null	ws-2019-11	['propaganda-detection']	['natural-language-processing']	[ 1.59645677e-01 3.01110029e-01 -1.25237688e-01 -8.48186463e-02 -1.02827299e+00 -6.09054565e-01 1.09602427e+00 2.44622186e-01 -5.08003533e-01 9.32545602e-01 7.98581123e-01 -5.05793810e-01 8.72404501e-03 -5.43942094e-01 -8.57217014e-01 -1.39791206e-01 1.54352754e-01 2.00942546e-01 5.23337582e-03 -4.87332523e-01 7.52577960e-01 5.34039326e-02 -7.27715135e-01 9.39039826e-01 1.00434363e+00 7.50668645e-01 -3.00468713e-01 1.01158512e+00 1.14482835e-01 1.67771924e+00 -1.14433026e+00 -9.12780344e-01 -1.21976353e-01 -2.69568384e-01 -1.14307117e+00 -7.72200108e-01 7.65906096e-01 -1.05569981e-01 -3.14713061e-01 1.10034072e+00 4.13375556e-01 -3.17045480e-01 8.10407698e-01 -6.75960183e-01 -9.64635015e-01 1.24319577e+00 -4.18394178e-01 5.48882127e-01 4.64193344e-01 -7.23665282e-02 9.32461739e-01 -9.53927100e-01 9.02681589e-01 1.37885904e+00 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0c2315bf-e275-431a-adcd-3da5c6320eb6	high-similarity-pass-attention-for-single	2305.15768	null	https://arxiv.org/abs/2305.15768v1	https://arxiv.org/pdf/2305.15768v1.pdf	High-Similarity-Pass Attention for Single Image Super-Resolution	Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually used the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to the NLA with randomly selected regions stimulated our interest to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations.	['C. L. Philip Chen', 'Wenzhong Guo', 'Guang-Yong Chen', 'Min Gan', 'Jian-Nan Su']	2023-05-25	null	null	null	null	['image-super-resolution']	['computer-vision']	[ 2.38832533e-01 -9.55321640e-02 3.23440693e-02 -4.03273374e-01 -8.90532017e-01 -7.11769015e-02 4.81431723e-01 -3.04200083e-01 -1.12144351e-01 6.28956079e-01 5.27022660e-01 1.52441874e-01 -3.92060041e-01 -7.52986133e-01 -9.31672215e-01 -8.63013089e-01 1.92298144e-02 2.03674033e-01 3.59406441e-01 -3.93325269e-01 -1.70127191e-02 6.73810542e-01 -1.67688990e+00 4.65293646e-01 1.00385821e+00 9.41549599e-01 7.35921562e-01 1.88875109e-01 1.10123567e-01 6.66128933e-01 -5.98757923e-01 -2.79112428e-01 1.94034234e-01 -5.77946603e-01 -7.61554122e-01 -9.47472975e-02 3.94179732e-01 -2.74221003e-01 -5.88353157e-01 1.27693820e+00 6.75493717e-01 5.15976787e-01 5.29906571e-01 -7.12875247e-01 -1.20260203e+00 6.76083267e-01 -7.33442187e-01 5.94962716e-01 2.69448012e-01 2.66361833e-02 1.20484900e+00 -1.09714162e+00 6.29904509e-01 1.43779337e+00 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91966bb7-62d9-4dac-bb45-18b67b08755f	therbligs-in-action-video-understanding	2304.03631	null	https://arxiv.org/abs/2304.03631v1	https://arxiv.org/pdf/2304.03631v1.pdf	Therbligs in Action: Video Understanding through Motion Primitives	In this paper we introduce a rule-based, compositional, and hierarchical modeling of action using Therbligs as our atoms. Introducing these atoms provides us with a consistent, expressive, contact-centered representation of action. Over the atoms we introduce a differentiable method of rule-based reasoning to regularize for logical consistency. Our approach is complementary to other approaches in that the Therblig-based representations produced by our architecture augment rather than replace existing architectures' representations. We release the first Therblig-centered annotations over two popular video datasets - EPIC Kitchens 100 and 50-Salads. We also broadly demonstrate benefits to adopting Therblig representations through evaluation on the following tasks: action segmentation, action anticipation, and action recognition - observing an average 10.5\%/7.53\%/6.5\% relative improvement, respectively, over EPIC Kitchens and an average 8.9\%/6.63\%/4.8\% relative improvement, respectively, over 50 Salads. Code and data will be made publicly available.	['Yiannis Aloimonos', 'Cornelia Fermuller', 'Michael Maynord', 'Eadom Dessalene']	2023-04-06	null	http://openaccess.thecvf.com//content/CVPR2023/html/Dessalene_Therbligs_in_Action_Video_Understanding_Through_Motion_Primitives_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Dessalene_Therbligs_in_Action_Video_Understanding_Through_Motion_Primitives_CVPR_2023_paper.pdf	cvpr-2023-1	['action-anticipation', 'action-recognition-in-videos', 'video-understanding', 'action-segmentation']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 2.44883925e-01 7.19960511e-01 -6.20997906e-01 -4.99602079e-01 -5.86566687e-01 -2.64372885e-01 8.04583371e-01 1.96942929e-02 -7.48021081e-02 6.30218267e-01 6.71274424e-01 1.99317992e-01 -1.33877471e-01 -6.20379150e-01 -8.55450690e-01 -3.53994220e-01 -8.90492946e-02 2.15127245e-01 3.02486092e-01 -2.97942996e-01 7.98741952e-02 1.69825941e-01 -1.60680699e+00 9.31718767e-01 6.23601973e-01 1.15730417e+00 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b9799594-9d57-43bd-9671-15a1724604f9	leveraging-frequency-analysis-for-deep-fake	2003.08685	null	https://arxiv.org/abs/2003.08685v3	https://arxiv.org/pdf/2003.08685v3.pdf	Leveraging Frequency Analysis for Deep Fake Image Recognition	Deep neural networks can generate images that are astonishingly realistic, so much so that it is often hard for humans to distinguish them from actual photos. These achievements have been largely made possible by Generative Adversarial Networks (GANs). While deep fake images have been thoroughly investigated in the image domain - a classical approach from the area of image forensics - an analysis in the frequency domain has been missing so far. In this paper, we address this shortcoming and our results reveal that in frequency space, GAN-generated images exhibit severe artifacts that can be easily identified. We perform a comprehensive analysis, showing that these artifacts are consistent across different neural network architectures, data sets, and resolutions. In a further investigation, we demonstrate that these artifacts are caused by upsampling operations found in all current GAN architectures, indicating a structural and fundamental problem in the way images are generated via GANs. Based on this analysis, we demonstrate how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.	['Lea Schönherr', 'Joel Frank', 'Dorothea Kolossa', 'Thorsten Holz', 'Asja Fischer', 'Thorsten Eisenhofer']	2020-03-19	null	https://proceedings.icml.cc/static/paper_files/icml/2020/1539-Paper.pdf	https://proceedings.icml.cc/static/paper_files/icml/2020/1539-Paper.pdf	icml-2020-1	['image-forensics']	['computer-vision']	[ 4.94448811e-01 2.42560118e-01 3.75404239e-01 3.07065509e-02 -5.04822493e-01 -6.86053276e-01 6.89075708e-01 -1.74964011e-01 -1.00764848e-01 8.07967901e-01 7.93730766e-02 -1.52589470e-01 1.09171465e-01 -9.53308821e-01 -8.91426146e-01 -7.64072895e-01 1.10171489e-01 1.30797327e-01 -4.50097118e-03 -4.47072476e-01 2.00887322e-01 4.27815974e-01 -1.61946118e+00 4.19321805e-01 6.00502670e-01 7.63495803e-01 -3.65601659e-01 4.45002943e-01 -2.62707621e-02 8.79127741e-01 -1.22543633e+00 -7.18440771e-01 4.72341746e-01 -7.94112384e-01 -6.29008234e-01 -3.32931392e-02 5.40883899e-01 -6.60099745e-01 -4.31664973e-01 1.45194077e+00 3.58502746e-01 -4.73139733e-01 6.85190916e-01 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474052a4-9894-466e-9978-f1c746fb3228	graph-convolutional-network-for-swahili-news	2103.09325	null	https://arxiv.org/abs/2103.09325v1	https://arxiv.org/pdf/2103.09325v1.pdf	Graph Convolutional Network for Swahili News Classification	This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification. In particular, we focus our experimentation on the sparsely-labelled semi-supervised context which is representative of the practical constraints facing low-resourced African languages. We follow up on this result by introducing a variant of the Text GCN model which utilises a bag of words embedding rather than a naive one-hot encoding to reduce the memory footprint of Text GCN whilst demonstrating similar predictive performance.	['Tyler Martin', 'Alexandros Kastanos']	2021-03-16	null	null	null	null	['news-classification']	['natural-language-processing']	[ 4.29025114e-01 2.98113078e-01 -3.62321645e-01 -6.55189991e-01 -3.47181141e-01 -3.90336782e-01 1.13011205e+00 6.49453580e-01 -8.09260845e-01 4.65817094e-01 9.88662660e-01 -9.50155795e-01 -1.36757120e-01 -1.00911355e+00 -3.45395267e-01 -3.52041811e-01 -5.11345804e-01 7.42749929e-01 -6.54841959e-02 -4.49040711e-01 3.54772657e-01 2.51075268e-01 -9.85996366e-01 4.40901309e-01 2.47377545e-01 5.13033211e-01 -3.25920373e-01 8.37561309e-01 -2.08567888e-01 1.05400324e+00 -4.26945508e-01 -4.24682260e-01 1.82277262e-01 -2.51088172e-01 -1.21628332e+00 -1.44557565e-01 6.17282331e-01 -1.64189905e-01 -6.01283908e-01 6.51992977e-01 5.39687335e-01 4.40074503e-01 5.91153502e-01 -6.58727765e-01 -9.16105747e-01 9.50354815e-01 -4.73779142e-01 7.79730380e-01 4.44250315e-01 -9.25439298e-02 1.39548826e+00 -6.01113975e-01 8.95167530e-01 1.35285592e+00 9.66190577e-01 7.45753273e-02 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4a34c29c-a466-4333-b343-8be2251d712f	doubly-aligned-incomplete-multi-view	1903.02785	null	http://arxiv.org/abs/1903.02785v1	http://arxiv.org/pdf/1903.02785v1.pdf	Doubly Aligned Incomplete Multi-view Clustering	Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of $L_{2,1}$-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages.	['Songcan Chen', 'Menglei Hu']	2019-03-07	null	null	null	null	['incomplete-multi-view-clustering']	['computer-vision']	[ 7.64843524e-02 -2.04654098e-01 -1.97248191e-01 -1.97175920e-01 -4.22563344e-01 -5.22098243e-01 2.81994462e-01 -5.46371564e-02 -1.23018980e-01 5.08862674e-01 1.26799300e-01 3.79364428e-05 -4.99850929e-01 -5.12620211e-01 -3.40294302e-01 -9.48110878e-01 3.91671330e-01 5.55652857e-01 -2.06387788e-01 3.53256315e-02 1.17709264e-01 5.85332327e-02 -1.41082954e+00 1.78286597e-01 1.08130443e+00 5.74542701e-01 3.07871789e-01 -5.82554415e-02 -5.27027957e-02 4.57304120e-01 -2.80396730e-01 -3.14526886e-01 4.26130384e-01 -2.69765139e-01 -5.28657734e-01 7.90457904e-01 1.20331384e-01 6.82861730e-02 9.03246477e-02 1.05271387e+00 3.01501185e-01 6.65734857e-02 4.22564358e-01 -1.48695242e+00 -4.15267140e-01 3.46809477e-01 -1.18647134e+00 -2.02588037e-01 2.27712005e-01 -2.80720562e-01 1.05495465e+00 -9.08072770e-01 6.96007371e-01 9.04913008e-01 2.27138162e-01 2.45313480e-01 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-1.00991929e+00 6.78470433e-01 -5.46810664e-02 -7.98548579e-01 -3.86952341e-01 5.97069897e-02]	[8.220624923706055, 4.645137310028076]
37e24c35-0c83-413a-a72c-52b2d7997abc	semiblind-hyperspectral-unmixing-in-the	1507.01661	null	http://arxiv.org/abs/1507.01661v1	http://arxiv.org/pdf/1507.01661v1.pdf	Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches	The dictionary-aided sparse regression (SR) approach has recently emerged as a promising alternative to hyperspectral unmixing (HU) in remote sensing. By using an available spectral library as a dictionary, the SR approach identifies the underlying materials in a given hyperspectral image by selecting a small subset of spectral samples in the dictionary to represent the whole image. A drawback with the current SR developments is that an actual spectral signature in the scene is often assumed to have zero mismatch with its corresponding dictionary sample, and such an assumption is considered too ideal in practice. In this paper, we tackle the spectral signature mismatch problem by proposing a dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER) framework. The main idea is to incorporate dictionary correcting variables in an SR formulation. A simple and low per-iteration complexity algorithm is tailor-designed for practical realization of DANSER. Using the same dictionary correcting idea, we also propose a robust subspace solution for dictionary pruning. Extensive simulations and real-data experiments show that the proposed method is effective in mitigating the undesirable spectral signature mismatch effects.	['Tsung-Han Chan', 'José Bioucas-Dias', 'Wing-Kin Ma', 'Xiao Fu']	2015-07-07	null	null	null	null	['hyperspectral-unmixing']	['computer-vision']	[ 7.91629970e-01 -4.35238272e-01 -9.10524130e-02 -8.62119123e-02 -7.28501439e-01 -3.44222069e-01 1.43466681e-01 -2.50459492e-01 -5.25907911e-02 6.48020446e-01 1.74307302e-01 8.45913193e-04 -4.23060149e-01 -8.03432941e-01 -3.66374046e-01 -1.30469692e+00 3.80701363e-01 2.47148022e-01 -4.15311486e-01 -2.53028601e-01 2.97612939e-02 4.60078955e-01 -1.50896001e+00 -1.06222264e-01 1.42602062e+00 9.13021207e-01 7.24867404e-01 1.72905371e-01 1.44368336e-01 4.12613183e-01 -2.24072233e-01 1.76826313e-01 8.24831605e-01 -6.61144972e-01 -8.36437494e-02 8.38414907e-01 4.09203142e-01 -1.26297146e-01 -3.83713245e-01 1.55574179e+00 5.63153982e-01 2.71031410e-01 3.82318437e-01 -6.11897349e-01 -1.97658956e-01 5.89636981e-01 -1.01268339e+00 -2.59152986e-02 -5.63751012e-02 -3.07310164e-01 8.89774024e-01 -1.20852423e+00 3.66334140e-01 7.65003562e-01 6.73466980e-01 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33856af2-5505-4029-91ce-b9b924247b82	explainable-deep-few-shot-anomaly-detection	2108.00462	null	https://arxiv.org/abs/2108.00462v1	https://arxiv.org/pdf/2108.00462v1.pdf	Explainable Deep Few-shot Anomaly Detection with Deviation Networks	Existing anomaly detection paradigms overwhelmingly focus on training detection models using exclusively normal data or unlabeled data (mostly normal samples). One notorious issue with these approaches is that they are weak in discriminating anomalies from normal samples due to the lack of the knowledge about the anomalies. Here, we study the problem of few-shot anomaly detection, in which we aim at using a few labeled anomaly examples to train sample-efficient discriminative detection models. To address this problem, we introduce a novel weakly-supervised anomaly detection framework to train detection models without assuming the examples illustrating all possible classes of anomaly. Specifically, the proposed approach learns discriminative normality (regularity) by leveraging the labeled anomalies and a prior probability to enforce expressive representations of normality and unbounded deviated representations of abnormality. This is achieved by an end-to-end optimization of anomaly scores with a neural deviation learning, in which the anomaly scores of normal samples are imposed to approximate scalar scores drawn from the prior while that of anomaly examples is enforced to have statistically significant deviations from these sampled scores in the upper tail. Furthermore, our model is optimized to learn fine-grained normality and abnormality by top-K multiple-instance-learning-based feature subspace deviation learning, allowing more generalized representations. Comprehensive experiments on nine real-world image anomaly detection benchmarks show that our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings. Our model can also offer explanation capability as a result of its prior-driven anomaly score learning. Code and datasets are available at: https://git.io/DevNet.	['Anton Van Den Hengel', 'Chunhua Shen', 'Choubo Ding', 'Guansong Pang']	2021-08-01	null	null	null	null	['supervised-anomaly-detection']	['computer-vision']	[ 4.00616199e-01 -3.95641923e-02 -5.88887520e-02 -5.21100342e-01 -9.11345065e-01 -2.02805877e-01 6.18559718e-01 2.61252254e-01 -5.29581718e-02 1.08136043e-01 -6.20888397e-02 -4.87229750e-02 -7.91634247e-02 -5.72800636e-01 -6.19984329e-01 -8.13775539e-01 -2.85220414e-01 6.38681948e-01 2.97182295e-02 -1.15049124e-01 3.30353111e-01 4.82002169e-01 -1.67534566e+00 1.43163770e-01 1.09216332e+00 1.19833863e+00 -5.59045136e-01 5.42478383e-01 -1.92661732e-01 4.75349009e-01 -5.57460606e-01 -6.04111366e-02 5.30464768e-01 -5.37095129e-01 -2.84824222e-01 3.21281850e-01 7.44291365e-01 -5.73314488e-01 -1.79357708e-01 1.26443160e+00 3.26613575e-01 2.24368006e-01 8.40969205e-01 -1.50743175e+00 -6.82696998e-01 1.23644195e-01 -7.78957903e-01 5.55546880e-01 3.00799191e-01 3.96220386e-01 1.26282907e+00 -1.04913926e+00 2.01470122e-01 1.02001548e+00 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-8.02217960e-01 -8.96187007e-01 1.01271689e+00 6.51647627e-01 -6.17595732e-01 -9.20871198e-01 -3.66463959e-01]	[7.63838529586792, 2.331756591796875]
0e101dfe-0a42-4b0b-a434-8359680c35ea	acoustic-echo-cancellation-with-the-dual	2010.14337	null	https://arxiv.org/abs/2010.14337v1	https://arxiv.org/pdf/2010.14337v1.pdf	Acoustic echo cancellation with the dual-signal transformation LSTM network	This paper applies the dual-signal transformation LSTM network (DTLN) to the task of real-time acoustic echo cancellation (AEC). The DTLN combines a short-time Fourier transformation and a learned feature representation in a stacked network approach, which enables robust information processing in the time-frequency and in the time domain, which also includes phase information. The model is only trained on 60~h of real and synthetic echo scenarios. The training setup includes multi-lingual speech, data augmentation, additional noise and reverberation to create a model that should generalize well to a large variety of real-world conditions. The DTLN approach produces state-of-the-art performance on clean and noisy echo conditions reducing acoustic echo and additional noise robustly. The method outperforms the AEC-Challenge baseline by 0.30 in terms of Mean Opinion Score (MOS).	['Bernd T. Meyer', 'Nils L. Westhausen']	2020-10-27	null	null	null	null	['acoustic-echo-cancellation', 'acoustic-echo-cancellation']	['medical', 'speech']	[ 4.25390244e-01 -3.16713721e-01 8.72363150e-01 -3.25131327e-01 -1.52634764e+00 -4.93565977e-01 3.72508854e-01 -2.83669084e-01 -5.53673744e-01 3.18067104e-01 5.53559005e-01 -3.18093866e-01 2.42486224e-03 -2.45415762e-01 -6.65151894e-01 -7.65385628e-01 -5.51711023e-01 -2.83827871e-01 -7.91294128e-02 -4.62005019e-01 -2.43380710e-01 3.91462445e-01 -1.43819439e+00 3.95911276e-01 7.00538993e-01 1.05050766e+00 5.06928042e-02 1.12218523e+00 2.37910926e-01 2.43366048e-01 -9.98797774e-01 -1.88522056e-01 2.81244516e-01 -4.25547928e-01 -1.74049735e-01 -6.19336367e-01 5.67837536e-01 5.07378429e-02 -2.92597711e-01 9.99421537e-01 1.31395495e+00 3.49122465e-01 3.30903202e-01 -7.10493863e-01 -1.75776035e-01 5.65116167e-01 -1.44955739e-01 3.13343614e-01 3.62918377e-01 -2.72236671e-02 5.55754483e-01 -1.31870496e+00 1.48588762e-01 1.19802392e+00 1.16688383e+00 4.32125241e-01 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-5.56585491e-01 1.56076357e-01 9.88114834e-01 -1.41986497e-02 -7.15752304e-01 -5.63578196e-02]	[15.00440788269043, 5.947412967681885]
17745c1a-c3b6-4725-9bb3-e6a42497a1af	an-f-ratio-based-method-for-estimating-the	2306.05892	null	https://arxiv.org/abs/2306.05892v1	https://arxiv.org/pdf/2306.05892v1.pdf	An F-ratio-Based Method for Estimating the Number of Active Sources in MEG	Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy. To address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found. Our results revealed that the selection of thresholds plays a critical role in determining the method`s overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL). Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function.	['Dimitrios Pantazis', 'Amir Adler', 'John C. Mosher', 'Amita Giri']	2023-06-09	null	null	null	null	['anatomy']	['miscellaneous']	[-2.76836362e-02 -4.07106459e-01 3.52772981e-01 -2.81529784e-01 -7.35464036e-01 -4.67600495e-01 3.48960310e-01 3.91464919e-01 -5.20549893e-01 6.79605365e-01 2.19541013e-01 -2.69622952e-01 -3.40168089e-01 -5.72550774e-01 -5.97812057e-01 -7.96098053e-01 -5.61773479e-01 2.42276698e-01 4.07807946e-01 1.89695746e-01 3.72546941e-01 6.15201116e-01 -1.36368132e+00 -2.46574983e-01 1.03464866e+00 1.03894198e+00 4.87726808e-01 1.78873204e-02 1.51691541e-01 4.04524028e-01 -6.84892714e-01 5.21417782e-02 -1.96764264e-02 -6.87977254e-01 -3.29854369e-01 -4.81370598e-01 -1.26255617e-01 -1.76843658e-01 -3.00762709e-02 1.00273132e+00 9.01542068e-01 1.66829214e-01 8.00876677e-01 -8.61055732e-01 -1.94747210e-01 6.45258904e-01 -5.18749475e-01 6.93632782e-01 2.45435864e-01 -3.95634398e-03 3.57300103e-01 -8.43787849e-01 3.01419318e-01 8.49502742e-01 6.12194180e-01 1.80727092e-03 -1.33837295e+00 -9.97393906e-01 -8.18335824e-03 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40634443-ec46-4d9a-8020-4d75e96aed3e	mo-dehb-evolutionary-based-hyperband-for	2305.04502	null	https://arxiv.org/abs/2305.04502v2	https://arxiv.org/pdf/2305.04502v2.pdf	MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization	Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models. However, in many real-world applications, accuracy is only one of multiple performance criteria that must be considered. Optimizing these objectives simultaneously on a complex and diverse search space remains a challenging task. In this paper, we propose MO-DEHB, an effective and flexible multi-objective (MO) optimizer that extends the recent evolutionary Hyperband method DEHB. We validate the performance of MO-DEHB using a comprehensive suite of 15 benchmarks consisting of diverse and challenging MO problems, including HPO, neural architecture search (NAS), and joint NAS and HPO, with objectives including accuracy, latency and algorithmic fairness. A comparative study against state-of-the-art MO optimizers demonstrates that MO-DEHB clearly achieves the best performance across our 15 benchmarks.	['Janek Thomas', 'Philipp Muller', 'Frank Hutter', 'Ayushi Sharma', 'Noor Awad']	2023-05-08	null	null	null	null	['hyperparameter-optimization', 'architecture-search']	['methodology', 'methodology']	[-1.98965415e-01 -7.62068331e-01 -4.81027216e-01 -3.54087979e-01 -8.41905355e-01 -3.69468480e-01 -1.34784907e-01 -1.09815896e-01 -5.17862201e-01 1.08390760e+00 -3.16563964e-01 -1.70547843e-01 -7.08409131e-01 -3.91830623e-01 -5.92975080e-01 -8.83914948e-01 1.24994025e-01 8.89571369e-01 -1.16010241e-01 -3.23533386e-01 4.07860488e-01 4.62526500e-01 -1.64065993e+00 8.08497965e-02 1.06899095e+00 1.27316260e+00 -2.25727171e-01 7.46268034e-01 -3.59724984e-02 2.15843186e-01 -9.33030725e-01 -6.44971550e-01 2.06914604e-01 -1.57798678e-01 -6.59830034e-01 -4.30540293e-01 2.44556189e-01 4.40789282e-01 1.47143781e-01 8.41010869e-01 1.05201411e+00 3.19851607e-01 3.83700192e-01 -1.59556258e+00 -4.98852760e-01 7.50403404e-01 -5.40753424e-01 4.27191108e-01 -2.39183679e-01 5.18365920e-01 1.31528926e+00 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7.97507882e-01 6.58154905e-01 -5.10934114e-01 -8.65063548e-01 -4.73410785e-01 -7.85157382e-02 -2.09261421e-02 3.63677710e-01 -9.16487992e-01 -3.94589752e-01 -5.64515173e-01]	[6.7087297439575195, 3.918152332305908]
c886d75f-5f25-4859-9da9-042565f05360	scalable-decipherment-for-machine-translation	null	null	https://aclanthology.org/P13-1036	https://aclanthology.org/P13-1036.pdf	Scalable Decipherment for Machine Translation via Hash Sampling	null	['Sujith Ravi']	2013-08-01	null	null	null	acl-2013-8	['decipherment']	['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 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 4.49454933e-02 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6790b4ec-a89e-4882-85ba-50e230d46077	leveraging-passage-retrieval-with-generative	2007.01282	null	https://arxiv.org/abs/2007.01282v2	https://arxiv.org/pdf/2007.01282v2.pdf	Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering	Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages, potentially containing evidence. We obtain state-of-the-art results on the Natural Questions and TriviaQA open benchmarks. Interestingly, we observe that the performance of this method significantly improves when increasing the number of retrieved passages. This is evidence that generative models are good at aggregating and combining evidence from multiple passages.	['Edouard Grave', 'Gautier Izacard']	2020-07-02	null	https://aclanthology.org/2021.eacl-main.74	https://aclanthology.org/2021.eacl-main.74.pdf	eacl-2021-2	['triviaqa']	['miscellaneous']	[-2.86462903e-01 1.77654505e-01 1.45593345e-01 -1.03394344e-01 -1.79338503e+00 -8.96923184e-01 7.58402348e-01 2.03022093e-01 -4.16236609e-01 1.09281862e+00 4.27789599e-01 -3.69911730e-01 -1.78516909e-01 -8.80521357e-01 -8.77430916e-01 -3.55259776e-01 9.18296203e-02 1.06892455e+00 5.40186524e-01 -6.42503917e-01 2.14995176e-01 -1.94929853e-01 -1.53443599e+00 5.32758534e-01 1.04153836e+00 7.95301080e-01 -9.91315842e-02 1.20429492e+00 -4.82516408e-01 8.66462648e-01 -8.63648534e-01 -9.35786843e-01 -2.46514659e-02 -4.62937146e-01 -1.27333379e+00 -3.13514769e-01 7.59799659e-01 -4.03951436e-01 -7.67670348e-02 6.38393164e-01 5.79684556e-01 2.74720937e-01 6.10804677e-01 -7.41279602e-01 -8.55139673e-01 5.39659023e-01 8.41669291e-02 6.84222162e-01 7.18534768e-01 9.72728208e-02 1.53683007e+00 -9.31647718e-01 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5b9eaf18-3c51-431c-ba72-c250e22997a5	language-and-dialect-discrimination-using	null	null	https://aclanthology.org/W16-4825	https://aclanthology.org/W16-4825.pdf	Language and Dialect Discrimination Using Compression-Inspired Language Models	The DSL 2016 shared task continued previous evaluations from 2014 and 2015 that facilitated the study of automated language and dialect identification. This paper describes results for this year{'}s shared task and from several related experiments conducted at the Johns Hopkins University Human Language Technology Center of Excellence (JHU HLTCOE). Previously the HLTCOE has explored the use of compression-inspired language modeling for language and dialect identification, using news, Wikipedia, blog post, and Twitter corpora. The technique we have relied upon is based on prediction by partial matching (PPM), a state of the art text compression technique. Due to the close relationship between adaptive compression and language modeling, such compression techniques can also be applied to multi-way text classification problems, and previous studies have examined tasks such as authorship attribution, email spam detection, and topical classification. We applied our approach to the multi-class decision that considered each dialect or language as a possibility for the given shared task input line. Results for test-set A were in accord with our expectations, however results for test-sets B and C appear to be markedly worse. We had not anticipated the inclusion of multiple communications in differing languages in test-set B (social media) input lines, and had not expected the test-set C (dialectal Arabic) data to be represented phonetically instead of in native orthography.	['Paul McNamee']	2016-12-01	null	null	null	ws-2016-12	['spam-detection', 'text-compression']	['natural-language-processing', 'natural-language-processing']	[ 1.79370418e-01 -2.49318048e-01 4.44021486e-02 -3.70627552e-01 -9.35056865e-01 -7.23276734e-01 1.01794279e+00 5.30129254e-01 -7.65325487e-01 5.51776528e-01 4.97103781e-01 -8.07351053e-01 -2.19022349e-01 -5.08964658e-01 -2.59617299e-01 -1.97534293e-01 1.25159472e-02 8.96999776e-01 -2.55324505e-02 -3.14312786e-01 7.54812598e-01 4.26149815e-01 -1.57099283e+00 7.24319756e-01 9.29369807e-01 3.87066007e-01 1.72368452e-01 1.08681726e+00 -4.41403896e-01 7.50237107e-01 -7.44101822e-01 -6.91021740e-01 1.55416220e-01 -3.82085353e-01 -1.29123497e+00 -3.35502326e-02 8.12543094e-01 -9.79089364e-02 -2.46388599e-01 8.20009947e-01 6.37343943e-01 -7.02172332e-03 8.00014675e-01 -1.06972718e+00 -3.35856587e-01 9.70092773e-01 -3.77239317e-01 -3.83978859e-02 6.88031673e-01 -1.92175910e-01 9.73384082e-01 -8.26168299e-01 8.26240838e-01 1.65380490e+00 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33976af8-76ab-4fa9-9972-faa1ad5928ae	nipd-a-federated-learning-person-detection	2306.15932	null	https://arxiv.org/abs/2306.15932v1	https://arxiv.org/pdf/2306.15932v1.pdf	NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data	Federated learning (FL), a privacy-preserving distributed machine learning, has been rapidly applied in wireless communication networks. FL enables Internet of Things (IoT) clients to obtain well-trained models while preventing privacy leakage. Person detection can be deployed on edge devices with limited computing power if combined with FL to process the video data directly at the edge. However, due to the different hardware and deployment scenarios of different cameras, the data collected by the camera present non-independent and identically distributed (non-IID), and the global model derived from FL aggregation is less effective. Meanwhile, existing research lacks public data set for real-world FL object detection, which is not conducive to studying the non-IID problem on IoT cameras. Therefore, we open source a non-IID IoT person detection (NIPD) data set, which is collected from five different cameras. To our knowledge, this is the first true device-based non-IID person detection data set. Based on this data set, we explain how to establish a FL experimental platform and provide a benchmark for non-IID person detection. NIPD is expected to promote the application of FL and the security of smart city.	['Zhiguo Wang', 'Guangqiang Yin', 'Xinhui Ji', 'Jie Fu', 'Dongsheng Chen', 'Zhihua Dong', 'Zhen Ding', 'Kangning Yin']	2023-06-28	null	null	null	null	['person-identification', 'human-detection']	['computer-vision', 'computer-vision']	[-6.33002371e-02 -4.10602748e-01 -1.25334516e-01 -3.00162464e-01 -1.87079564e-01 -5.08458793e-01 1.89990401e-01 -3.67111862e-01 -3.49396735e-01 7.00318873e-01 1.26818076e-01 -2.73627490e-02 1.08584752e-02 -8.03361475e-01 -4.07099485e-01 -9.19197023e-01 7.29590207e-02 2.63641160e-02 5.48847169e-02 2.96822280e-01 -6.75408304e-01 2.60300428e-01 -1.40784717e+00 4.03925925e-01 4.60113227e-01 1.35892296e+00 -2.72065580e-01 3.17773759e-01 3.10231417e-01 5.60996652e-01 -6.11620069e-01 -9.04739022e-01 8.19185615e-01 -2.76932448e-01 -9.36159492e-02 -8.35425593e-03 2.38022506e-01 -7.86883235e-01 -7.83837259e-01 1.36013842e+00 7.80429065e-01 -3.19588721e-01 3.15327495e-01 -1.90674698e+00 -8.23144555e-01 2.98383921e-01 -5.46166062e-01 -4.64330055e-03 3.94905478e-01 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fe659097-2f84-41a5-a026-547be4bfef47	ghostfacenets-lightweight-face-recognition	null	null	https://ieeexplore.ieee.org/document/10098610	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10098610	GhostFaceNets: Lightweight Face Recognition Model From Cheap Operations	The development of deep learning-based biometric models that can be deployed on devices with constrained memory and computational resources has proven to be a significant challenge. Previous approaches to this problem have not prioritized the reduction of feature map redundancy, but the introduction of Ghost modules represents a major innovation in this area. Ghost modules use a series of inexpensive linear transformations to extract additional feature maps from a set of intrinsic features, allowing for a more comprehensive representation of the underlying information. GhostNetV1 and GhostNetV2, both of which are based on Ghost modules, serve as the foundation for a group of lightweight face recognition models called GhostFaceNets. GhostNetV2 expands upon the original GhostNetV1 by adding an attention mechanism to capture long-range dependencies. Evaluation of GhostFaceNets using various benchmarks reveals that these models offer superior performance while requiring a computational complexity of approximately 60–275 MFLOPs. This is significantly lower than that of State-Of-The-Art (SOTA) big convolutional neural network (CNN) models, which can require hundreds of millions of FLOPs. GhostFaceNets trained with the ArcFace loss on the refined MS-Celeb-1M dataset demonstrate SOTA performance on all benchmarks. In comparison to previous SOTA mobile CNNs, GhostFaceNets greatly improve efficiency for face verification tasks. The GhostFaceNets code is available at: https://github.com/HamadYA/GhostFaceNets .	['Naoufel Werghi', 'Yahya Zweiri', 'Abdulhadi Shoufan', 'Sajid Javed', 'Oussama Abdul Hay', 'Mohamad Alansari']	2023-04-10	null	null	null	ieee-access-2023-4	['face-recognition', 'face-identification', 'face-verification']	['computer-vision', 'computer-vision', 'computer-vision']	[-1.09019421e-01 -1.84703141e-01 -3.94097269e-02 -6.70482337e-01 -5.00520170e-01 -1.90667227e-01 5.52644372e-01 -4.68473643e-01 -4.56531733e-01 3.87568831e-01 -1.24327719e-01 -3.91863614e-01 7.17807887e-03 -7.21406460e-01 -6.25409067e-01 -4.75055486e-01 -1.03464290e-01 1.85522661e-01 -1.96956962e-01 -2.88477600e-01 -1.06377073e-01 5.98806083e-01 -1.46813738e+00 3.74742419e-01 2.80510992e-01 1.25397432e+00 -3.19825709e-01 6.14032626e-01 1.93691440e-02 4.19700146e-01 -5.28305471e-01 -7.88120985e-01 3.67105842e-01 8.28035772e-02 -7.79259026e-01 -5.62650442e-01 1.20529294e+00 -5.65343380e-01 -4.22821611e-01 8.08048129e-01 9.17078078e-01 -3.18430245e-01 5.45689724e-02 -1.34005523e+00 -5.20548761e-01 4.58779961e-01 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59ff8b1b-c5ab-482c-9b91-edb47527041f	dipiq-blind-image-quality-assessment-by	1904.06505	null	http://arxiv.org/abs/1904.06505v1	http://arxiv.org/pdf/1904.06505v1.pdf	dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs	Objective assessment of image quality is fundamentally important in many image processing tasks. In this work, we focus on learning blind image quality assessment (BIQA) models which predict the quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training. Such data are typically collected via subjective testing, which is cumbersome, slow, and expensive. Here we first show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIP) can be obtained automatically at low cost by exploiting large-scale databases with diverse image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no subjective opinions are used for training) model using RankNet, a pairwise learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index. Extensive experiments on four benchmark IQA databases demonstrate that dipIQ outperforms state-of-the-art OU-BIQA models. The robustness of dipIQ is also significantly improved as confirmed by the group MAximum Differentiation (gMAD) competition method. Furthermore, we extend the proposed framework by learning models with ListNet (a listwise L2R algorithm) on quality-discriminable image lists (DIL). The resulting DIL Inferred Quality (dilIQ) index achieves an additional performance gain.	['DaCheng Tao', 'Tongliang Liu', 'Kede Ma', 'Zhou Wang', 'Wentao Liu']	2019-04-13	null	null	null	null	['blind-image-quality-assessment']	['computer-vision']	[ 2.28125393e-01 -3.16176832e-01 7.64337368e-03 -4.38675255e-01 -1.57088089e+00 -6.32453144e-01 3.32030237e-01 1.27810568e-01 -2.87520915e-01 8.70806754e-01 1.85162410e-01 -1.02706261e-01 -4.59862381e-01 -5.93787372e-01 -7.99425006e-01 -7.71308959e-01 -1.07035398e-01 5.16759694e-01 -7.34485984e-02 -1.76978588e-01 3.17918599e-01 3.37658346e-01 -1.66292942e+00 4.49711382e-01 1.28844082e+00 1.69251156e+00 -1.30229637e-01 8.14897597e-01 2.87978739e-01 9.91729081e-01 -7.24375784e-01 -8.81883979e-01 6.35359168e-01 -4.08875585e-01 -7.90597677e-01 1.35633469e-01 1.11561120e+00 -6.63356423e-01 -1.93479702e-01 1.54022276e+00 7.99948931e-01 1.36291571e-02 7.39823878e-01 -1.45502949e+00 -9.85565066e-01 2.05817670e-01 -4.06807631e-01 8.04400817e-02 3.10278893e-01 4.43945706e-01 1.41055560e+00 -9.84279752e-01 3.95956904e-01 1.31647956e+00 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677bb60c-fb04-44c0-b85f-c95cfca95770	similarity-based-unsupervised-spelling	null	null	https://medinform.jmir.org/2021/2/e25530/	https://medinform.jmir.org/2021/2/e25530/PDF	Similarity-Based Unsupervised Spelling Correction Using BioWordVec: Development and Usability Study of Bacterial Culture and Antimicrobial Susceptibility Reports	Background: Existing bacterial culture test results for infectious diseases are written in unrefined text, resulting in many problems, including typographical errors and stop words. Effective spelling correction processes are needed to ensure the accuracy and reliability of data for the study of infectious diseases, including medical terminology extraction. If a dictionary is established, spelling algorithms using edit distance are efficient. However, in the absence of a dictionary, traditional spelling correction algorithms that utilize only edit distances have limitations. Objective: In this research, we proposed a similarity-based spelling correction algorithm using pretrained word embedding with the BioWordVec technique. This method uses a character-level N-grams–based distributed representation through unsupervised learning rather than the existing rule-based method. In other words, we propose a framework that detects and corrects typographical errors when a dictionary is not in place. Methods: For detected typographical errors not mapped to Systematized Nomenclature of Medicine (SNOMED) clinical terms, a correction candidate group with high similarity considering the edit distance was generated using pretrained word embedding from the clinical database. From the embedding matrix in which the vocabulary is arranged in descending order according to frequency, a grid search was used to search for candidate groups of similar words. Thereafter, the correction candidate words were ranked in consideration of the frequency of the words, and the typographical errors were finally corrected according to the ranking. Results: Bacterial identification words were extracted from 27,544 bacterial culture and antimicrobial susceptibility reports, and 16 types of spelling errors and 914 misspelled words were found. The similarity-based spelling correction algorithm using BioWordVec proposed in this research corrected 12 types of typographical errors and showed very high performance in correcting 97.48% (based on F1 score) of all spelling errors. Conclusions: This tool corrected spelling errors effectively in the absence of a dictionary based on bacterial identification words in bacterial culture and antimicrobial susceptibility reports. This method will help build a high-quality refined database of vast text data for electronic health records.	['Jang Wook Sohn', 'Hyung Joon Joo', 'Jong-Ho Kim', 'Se Ha Lee', 'Minji Kang', 'Sung Won Han', 'Taehyeong Kim']	2021-02-22	null	null	null	jmir-medical-informatics-2021-2	['spelling-correction']	['natural-language-processing']	[ 4.13559943e-01 -5.42145848e-01 -1.04798853e-01 1.51658515e-02 -3.23149621e-01 -3.93819064e-01 -3.23166922e-02 1.18270993e+00 -9.83416617e-01 7.11257815e-01 3.88372868e-01 -4.59610641e-01 -4.34335500e-01 -8.47286522e-01 -3.60369742e-01 -6.00550294e-01 1.39782533e-01 3.98205131e-01 -2.46418156e-02 -2.24099532e-01 6.88452780e-01 4.00398403e-01 -1.33043563e+00 4.03549790e-01 1.27600121e+00 3.14156234e-01 5.28782368e-01 6.81150019e-01 -6.31341577e-01 4.92730349e-01 -7.29137480e-01 -1.71593890e-01 -9.90194604e-02 -5.03240049e-01 -5.19775808e-01 -2.38474205e-01 -2.99739271e-01 3.30641381e-02 -2.04559378e-02 1.11843383e+00 6.70086801e-01 -2.37527117e-03 6.55281603e-01 -5.34051895e-01 -8.15281749e-01 2.71934897e-01 1.91370938e-02 1.88466966e-01 4.83422518e-01 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e41c0e3e-e783-43ac-89be-aade04aaf486	foldingnet-point-cloud-auto-encoder-via-deep	1712.07262	null	http://arxiv.org/abs/1712.07262v2	http://arxiv.org/pdf/1712.07262v2.pdf	FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation	Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenges on point clouds. On the encoder side, a graph-based enhancement is enforced to promote local structures on top of PointNet. Then, a novel folding-based decoder deforms a canonical 2D grid onto the underlying 3D object surface of a point cloud, achieving low reconstruction errors even for objects with delicate structures. The proposed decoder only uses about 7% parameters of a decoder with fully-connected neural networks, yet leads to a more discriminative representation that achieves higher linear SVM classification accuracy than the benchmark. In addition, the proposed decoder structure is shown, in theory, to be a generic architecture that is able to reconstruct an arbitrary point cloud from a 2D grid. Our code is available at http://www.merl.com/research/license#FoldingNet	['Yaoqing Yang', 'Chen Feng', 'Dong Tian', 'Yiru Shen']	2017-12-19	foldingnet-point-cloud-auto-encoder-via-deep-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_FoldingNet_Point_Cloud_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_FoldingNet_Point_Cloud_CVPR_2018_paper.pdf	cvpr-2018-6	['3d-point-cloud-linear-classification', 'unsupervised-3d-point-cloud-linear-evaluation']	['computer-vision', 'computer-vision']	[ 2.77392380e-02 5.10871708e-01 6.26580864e-02 -4.45564777e-01 -7.15372205e-01 -3.34294945e-01 3.02820176e-01 1.21945418e-01 -2.20792085e-01 2.15711728e-01 -4.70537841e-01 -2.71005720e-01 3.01137328e-01 -9.10912573e-01 -1.43537199e+00 -3.52305919e-01 1.61992967e-01 8.58265758e-01 2.91290492e-01 -1.04933940e-01 1.01454571e-01 9.90043581e-01 -1.49709189e+00 7.33837113e-02 9.30924237e-01 1.08158243e+00 3.74097049e-01 2.77571857e-01 -2.17365563e-01 2.28984252e-01 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b23b8be1-a071-4713-872f-de2a9891fe8c	conceptbeam-concept-driven-target-speech	2207.11964	null	https://arxiv.org/abs/2207.11964v1	https://arxiv.org/pdf/2207.11964v1.pdf	ConceptBeam: Concept Driven Target Speech Extraction	We propose a novel framework for target speech extraction based on semantic information, called ConceptBeam. Target speech extraction means extracting the speech of a target speaker in a mixture. Typical approaches have been exploiting properties of audio signals, such as harmonic structure and direction of arrival. In contrast, ConceptBeam tackles the problem with semantic clues. Specifically, we extract the speech of speakers speaking about a concept, i.e., a topic of interest, using a concept specifier such as an image or speech. Solving this novel problem would open the door to innovative applications such as listening systems that focus on a particular topic discussed in a conversation. Unlike keywords, concepts are abstract notions, making it challenging to directly represent a target concept. In our scheme, a concept is encoded as a semantic embedding by mapping the concept specifier to a shared embedding space. This modality-independent space can be built by means of deep metric learning using paired data consisting of images and their spoken captions. We use it to bridge modality-dependent information, i.e., the speech segments in the mixture, and the specified, modality-independent concept. As a proof of our scheme, we performed experiments using a set of images associated with spoken captions. That is, we generated speech mixtures from these spoken captions and used the images or speech signals as the concept specifiers. We then extracted the target speech using the acoustic characteristics of the identified segments. We compare ConceptBeam with two methods: one based on keywords obtained from recognition systems and another based on sound source separation. We show that ConceptBeam clearly outperforms the baseline methods and effectively extracts speech based on the semantic representation.	['Kunio Kashino', 'Noboru Harada', 'Akisato Kimura', 'Daisuke Niizumi', 'Daiki Takeuchi', 'Shoko Araki', 'Tsubasa Ochiai', 'Marc Delcroix', 'Yasunori Ohishi']	2022-07-25	null	null	null	null	['speech-extraction']	['speech']	[ 4.82394129e-01 1.64906010e-01 1.11498661e-01 -4.87220496e-01 -1.27254641e+00 -8.52386653e-01 9.48076010e-01 1.83589205e-01 -3.52309376e-01 3.94207239e-01 5.42330742e-01 4.11480665e-02 -1.22035727e-01 -5.55313468e-01 -6.87708616e-01 -9.79562223e-01 2.50662237e-01 4.85068917e-01 -7.80427456e-02 -7.33127221e-02 2.83689976e-01 2.18208477e-01 -1.93729579e+00 4.94315535e-01 3.57710987e-01 1.15810180e+00 5.40469229e-01 6.27649248e-01 -7.90117204e-01 3.53112072e-01 -1.02657461e+00 -6.45512268e-02 -1.32618565e-02 -7.10977495e-01 -7.48042166e-01 3.29794496e-01 2.10619897e-01 2.86412448e-01 3.04190516e-02 9.41096663e-01 3.93151075e-01 1.68293983e-01 7.87914038e-01 -1.35547805e+00 -2.18968868e-01 8.31884980e-01 -5.17829470e-02 -1.20503299e-01 6.04790866e-01 -2.73062110e-01 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2fbf1721-090b-40de-af62-58d9afce6125	expediting-large-scale-vision-transformer-for	2210.01035	null	https://arxiv.org/abs/2210.01035v1	https://arxiv.org/pdf/2210.01035v1.pdf	Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning	Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens. Many advanced approaches have been developed to reduce the total number of tokens in large-scale vision transformers, especially for image classification tasks. Typically, they select a small group of essential tokens according to their relevance with the class token, then fine-tune the weights of the vision transformer. Such fine-tuning is less practical for dense prediction due to the much heavier computation and GPU memory cost than image classification. In this paper, we focus on a more challenging problem, i.e., accelerating large-scale vision transformers for dense prediction without any additional re-training or fine-tuning. In response to the fact that high-resolution representations are necessary for dense prediction, we present two non-parametric operators, a token clustering layer to decrease the number of tokens and a token reconstruction layer to increase the number of tokens. The following steps are performed to achieve this: (i) we use the token clustering layer to cluster the neighboring tokens together, resulting in low-resolution representations that maintain the spatial structures; (ii) we apply the following transformer layers only to these low-resolution representations or clustered tokens; and (iii) we use the token reconstruction layer to re-create the high-resolution representations from the refined low-resolution representations. The results obtained by our method are promising on five dense prediction tasks, including object detection, semantic segmentation, panoptic segmentation, instance segmentation, and depth estimation.	['Han Hu', 'Chao Zhang', 'Zheng Zhang', 'Ding Jia', 'WeiHong Lin', 'Xiao Luo', 'Henghui Ding', 'Yuhui Yuan', 'Weicong Liang']	2022-10-03	null	null	null	null	['panoptic-segmentation']	['computer-vision']	[ 3.68893802e-01 -9.28008705e-02 1.59892011e-02 -2.67819881e-01 -5.84026396e-01 -3.70138772e-02 5.12522995e-01 2.98273385e-01 -5.43263912e-01 2.09433645e-01 -6.70386851e-02 1.25152767e-02 3.89448330e-02 -1.04577684e+00 -6.84001088e-01 -8.83262038e-01 3.24858934e-01 6.34166300e-01 9.23078418e-01 2.82189280e-01 5.09983897e-01 5.45417964e-01 -2.03978848e+00 5.61004758e-01 1.06282222e+00 1.30549955e+00 8.45703900e-01 3.94642174e-01 -4.62089926e-01 7.27170885e-01 -3.21898729e-01 5.75549901e-03 1.13649473e-01 -1.39305249e-01 -8.87417495e-01 3.26504350e-01 1.69014812e-01 -3.31595466e-02 2.76204735e-01 1.07903087e+00 2.85863698e-01 1.49325028e-01 6.51427507e-01 -7.21713662e-01 -2.10324466e-01 5.21029174e-01 -9.73099709e-01 -1.04426518e-01 -1.60223424e-01 -6.64791763e-02 8.62870038e-01 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2c85eda5-9df9-426e-91f0-7b85117150a2	on-the-power-of-refined-skat-selection	2104.02997	null	https://arxiv.org/abs/2104.02997v1	https://arxiv.org/pdf/2104.02997v1.pdf	On the Power of Refined Skat Selection	Skat is a fascinating combinatorial card game, show-casing many of the intrinsic challenges for modern AI systems such as cooperative and adversarial behaviors (among the players), randomness (in the deal), and partial knowledge (due to hidden cards). Given the larger number of tricks and higher degree of uncertainty, reinforcement learning is less effective compared to classical board games like Chess and Go. As within the game of Bridge, in Skat we have a bidding and trick-taking stage. Prior to the trick-taking and as part of the bidding process, one phase in the game is to select two skat cards, whose quality may influence subsequent playing performance drastically. This paper looks into different skat selection strategies. Besides predicting the probability of winning and other hand strength functions we propose hard expert-rules and a scoring functions based on refined skat evaluation features. Experiments emphasize the impact of the refined skat putting algorithm on the playing performance of the bots, especially for AI bidding and AI game selection.	['Stefan Edelkamp']	2021-04-07	null	null	null	null	['board-games']	['playing-games']	[-2.86568135e-01 1.97969690e-01 6.54442832e-02 1.09649785e-01 -5.47484338e-01 -8.93002510e-01 2.75224268e-01 -2.24692762e-01 -5.98900259e-01 9.28910255e-01 -2.68665731e-01 -2.23966137e-01 -6.42898560e-01 -1.03623474e+00 -5.12261450e-01 -8.57967496e-01 -1.13718629e-01 1.04054618e+00 8.64536822e-01 -9.20671046e-01 4.11908776e-01 1.58547074e-01 -1.17843139e+00 1.74897820e-01 5.96999109e-01 1.05259430e+00 1.86843455e-01 8.63190413e-01 1.28087506e-01 1.42666066e+00 -9.46866393e-01 -9.94245946e-01 8.50180864e-01 -5.11436343e-01 -8.27271104e-01 -4.46676850e-01 -4.43066388e-01 -3.73858154e-01 -3.41802269e-01 9.88534391e-01 4.54850852e-01 9.63742733e-02 5.70322454e-01 -1.45292532e+00 2.58448035e-01 1.36574602e+00 -2.47797757e-01 5.06004095e-02 1.55974925e-01 6.23051286e-01 1.14015007e+00 1.69977039e-01 4.52737361e-01 6.15968466e-01 5.42937040e-01 4.22816277e-01 -6.83967829e-01 -8.14962924e-01 -2.85040826e-01 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5af341a7-4ed2-427a-b05e-d93ba51556e4	color-image-denoising-by-chromatic-edges	1304.5587	null	http://arxiv.org/abs/1304.5587v2	http://arxiv.org/pdf/1304.5587v2.pdf	Color image denoising by chromatic edges based vector valued diffusion	In this letter we propose to denoise digital color images via an improved geometric diffusion scheme. By introducing edges detected from all three color channels into the diffusion the proposed scheme avoids color smearing artifacts. Vector valued diffusion is used to control the smoothing and the geometry of color images are taken into consideration. Color edge strength function computed from different planes is introduced and it stops the diffusion spread across chromatic edges. Experimental results indicate that the scheme achieves good denoising with edge preservation when compared to other related schemes.	['Juan C. Moreno', 'V. B. Surya Prasath', 'K. Palaniappan']	2013-04-20	null	null	null	null	['color-image-denoising']	['computer-vision']	[ 5.84909543e-02 -5.84499180e-01 4.81827855e-01 -1.11875460e-02 -2.06732631e-01 -4.78705376e-01 4.08782989e-01 8.29698518e-03 -7.85156727e-01 6.04169250e-01 5.75709641e-02 4.88142446e-02 -7.85678104e-02 -8.49134445e-01 -4.17804951e-03 -1.02944446e+00 -1.78963274e-01 -3.38686079e-01 6.97138190e-01 -2.27219671e-01 7.07224429e-01 6.77063346e-01 -8.47112834e-01 -2.44846530e-02 1.12242734e+00 7.83615291e-01 -7.90832117e-02 1.05967748e+00 -9.97479539e-03 5.95695496e-01 -5.82246542e-01 -2.02204406e-01 6.26014233e-01 -4.91604209e-01 -4.15601641e-01 6.43715680e-01 2.62258295e-03 -4.56093907e-01 -1.90247878e-01 1.74036014e+00 5.01902103e-01 4.18440819e-01 7.20462024e-01 -7.76423812e-01 -1.17308795e+00 2.82639600e-02 -1.34967053e+00 4.55600351e-01 8.61399472e-02 -1.15871765e-02 1.84162825e-01 -7.57351220e-01 7.93653667e-01 1.22189796e+00 7.23319471e-01 3.15591812e-01 -1.30640936e+00 -1.05888747e-01 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516af153-1946-4294-a180-aa4674a26650	a-generic-framework-for-privacy-preserving	1811.04017	null	http://arxiv.org/abs/1811.04017v2	http://arxiv.org/pdf/1811.04017v2.pdf	A generic framework for privacy preserving deep learning	We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user. We report early results on the Boston Housing and Pima Indian Diabetes datasets. While the privacy features apart from Differential Privacy do not impact the prediction accuracy, the current implementation of the framework introduces a significant overhead in performance, which will be addressed at a later stage of the development. We believe this work is an important milestone introducing the first reliable, general framework for privacy preserving deep learning.	['Jonathan Passerat-Palmbach', 'Bobby Wagner', 'Theo Ryffel', 'Jason Mancuso', 'Morten Dahl', 'Daniel Rueckert', 'Andrew Trask']	2018-11-09	null	null	null	null	['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning']	['methodology', 'natural-language-processing']	[-7.19287619e-02 2.48457417e-01 6.42144540e-03 -1.08652771e+00 -9.80119944e-01 -9.05333877e-01 5.42769611e-01 4.55241054e-01 -6.87533915e-01 6.95939004e-01 3.58851850e-01 -5.90122581e-01 -2.80444026e-02 -8.54435802e-01 -5.87133884e-01 -7.93208241e-01 -6.42275751e-01 6.17296733e-02 1.00289658e-02 -6.58376217e-02 -1.65103838e-01 8.41757655e-01 -1.24993062e+00 7.91653037e-01 1.35911035e-03 9.25853074e-01 -9.80083585e-01 5.68849802e-01 1.49202541e-01 8.22850466e-01 -2.43489802e-01 -1.18274713e+00 9.68968451e-01 1.05263941e-01 -1.09205759e+00 -5.89602232e-01 5.34101069e-01 -8.49036694e-01 -4.42231804e-01 9.00859416e-01 5.17377794e-01 -6.27584904e-02 1.87499169e-02 -1.61804116e+00 -6.35492131e-02 6.75714612e-01 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3.44980329e-01 7.61069506e-02 8.07121754e-01 2.17494681e-01 8.65371704e-01 -3.84844273e-01 6.61916077e-01 -2.71243364e-01 -1.02911127e+00 8.53576064e-02 -1.04478705e+00 3.91345620e-01 6.93599761e-01 4.65475142e-01 -1.99664816e-01 -1.48889095e-01]	[5.8924760818481445, 6.7900848388671875]
caf94cc0-eece-4c63-bb2f-f44f51935b95	local-primordial-non-gaussianity-from-the	2307.01753	null	https://arxiv.org/abs/2307.01753v1	https://arxiv.org/pdf/2307.01753v1.pdf	Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies	We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter fNL. Our sample comprises over 12 million targets, covering 14,000 square degrees of the sky, with redshifts in the range 0.2< z < 1.35. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against log-normal simulations with and without fNL and systematics, showing superior performance of the neural network treatment in reducing remaining systematics. Assuming the universality relation, we find fNL $= 47^{+14(+29)}_{-11(-22)}$ at 68\%(95\%) confidence. With a more aggressive treatment, including regression against the full set of imaging maps, our maximum likelihood value shifts slightly to fNL$ \sim 50$ and the uncertainty on fNL increases due to the removal of large-scale clustering information. We apply a series of robustness tests (e.g., cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. Despite extensive efforts to mitigate systematics, our measurements indicate fNL > 0 with a 99.9 percent confidence level. This outcome raises concerns as it could be attributed to unforeseen systematics, including calibration errors or uncertainties associated with low-\ell systematics in the extinction template. Alternatively, it could suggest a scale-dependent fNL model--causing significant non-Gaussianity around large-scale structure while leaving cosmic microwave background scales unaffected. Our results encourage further studies of fNL with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics.	['Hu Zou', 'Zhimin Zhou', 'Christophe Yèche', 'Benjamin Alan Weaver', 'Gregory Tarlé', 'Michael Schubnell', 'Eusebio Sanchez', 'Graziano Rossi', 'Claire Poppett', 'Will Percival', 'Nathalie Palanque-Delabrouille', 'Jundan Nie', 'Jeffrey A. Newman', 'Adam Myers', 'Eva-Maria Mueller', 'Ramon Miquel', 'Aaron Meisner', 'Marc Manera', 'Michael Levi', 'Martin Landriau', 'Theodore Kisner', 'Klaus Honscheid', 'Julien Guy', 'Satya Gontcho A Gontcho', 'Jaime E. Forero-Romero', 'Andreu Font-Ribera', 'Peter Doel', 'Axel de la Macorra', 'Kyle Dawson', 'Shaun Cole', 'Todd Claybaugh', 'David Brooks', 'Jose Bermejo-Climent', 'Benedict Bahr-Kalus', 'Santiago Avila', 'Shadab Alam', 'Steven Ahlen', 'Jessica Nicole Aguilar', 'Florian Beutler', 'Arnaud de Mattia', 'Alex Krolewski', 'Edmond Chaussidon', 'Lado Samushia', 'Anna Porredon', 'Hui Kong', 'Hee-Jong Seo', 'Ashley J. Ross', 'Mehdi Rezaie']	2023-07-04	null	null	null	null	['clustering']	['methodology']	[ 2.44531352e-02 -4.96990122e-02 3.00504804e-01 -3.60582590e-01 -4.23564374e-01 -6.21134162e-01 6.95244849e-01 -5.52203357e-01 -6.24314249e-01 7.47662485e-01 -4.64999378e-02 -7.28083551e-01 -3.65351081e-01 -7.98369706e-01 -4.61748987e-01 -1.31032920e+00 2.59807575e-02 5.69258034e-01 4.53236431e-01 3.65498662e-01 4.39436615e-01 5.81010222e-01 -1.08880389e+00 -5.06998539e-01 8.24752331e-01 7.68263996e-01 2.25756347e-01 6.27495289e-01 3.53779852e-01 7.06254423e-01 -6.29759133e-01 -2.10460126e-01 5.60121000e-01 -5.73910415e-01 -4.92130548e-01 2.16396764e-01 4.56134111e-01 -1.25944912e-01 -4.00103718e-01 1.35849869e+00 4.63332206e-01 2.76648492e-01 9.36195672e-01 -5.87201357e-01 -7.05789208e-01 -1.96179971e-02 -9.36136961e-01 3.27460408e-01 -6.65337920e-01 5.19525528e-01 7.63921559e-01 -7.18256116e-01 5.57981849e-01 1.00548935e+00 6.58025503e-01 -6.09402694e-02 -1.51879001e+00 -6.17591560e-01 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6.65987507e-02 -1.57229364e-01 -6.52000487e-01 -9.32080269e-01 -1.82217538e-01]	[7.255756378173828, 3.3255221843719482]
310add11-a82d-4136-bbbc-541f2220421d	190411126	1904.11126	null	http://arxiv.org/abs/1904.11126v1	http://arxiv.org/pdf/1904.11126v1.pdf	Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks	In the last few years, Deep Learning (DL) has been showing superior performance in different modalities of biomedical image analysis. Several DL architectures have been proposed for classification, segmentation, and detection tasks in medical imaging and computational pathology. In this paper, we propose a new DL architecture, the NABLA-N network, with better feature fusion techniques in decoding units for dermoscopic image segmentation tasks. The NABLA-N network has several advances for segmentation tasks. First, this model ensures better feature representation for semantic segmentation with a combination of low to high-level feature maps. Second, this network shows better quantitative and qualitative results with the same or fewer network parameters compared to other methods. In addition, the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model is used for skin cancer classification. The proposed NABLA-N network and IRRCNN models are evaluated for skin cancer segmentation and classification on the benchmark datasets from the International Skin Imaging Collaboration 2018 (ISIC-2018). The experimental results show superior performance on segmentation tasks compared to the Recurrent Residual U-Net (R2U-Net). The classification model shows around 87% testing accuracy for dermoscopic skin cancer classification on ISIC2018 dataset.	['Theus Aspiras', 'Tarek M. Taha', 'Md Zahangir Alom', 'Vijayan K. Asari']	2019-04-25	null	null	null	null	['skin-cancer-segmentation', 'skin-cancer-classification']	['medical', 'medical']	[ 6.20124221e-01 2.42476583e-01 -3.90903533e-01 -1.94696978e-01 -8.30714822e-01 -1.56944007e-01 2.87990570e-01 1.04582377e-01 -4.78723288e-01 5.33507943e-01 -8.59337226e-02 -2.48214155e-01 -4.90342407e-03 -6.42907679e-01 -2.58913219e-01 -9.41610038e-01 1.75047383e-01 -6.79792762e-02 4.24444109e-01 6.06231615e-02 -4.31303345e-02 4.16185558e-01 -1.03460181e+00 7.17276216e-01 1.06373227e+00 1.38506782e+00 2.72619188e-01 8.34087610e-01 -1.99419811e-01 6.96368158e-01 -1.18527524e-01 -3.29462469e-01 9.23246369e-02 -4.53180552e-01 -1.15498793e+00 -1.71716616e-01 4.39149439e-01 -3.55099477e-02 -1.46180823e-01 1.20533156e+00 6.87450290e-01 -4.03963894e-01 8.70903015e-01 -5.80768943e-01 -4.72949296e-01 3.49404395e-01 -7.81132281e-01 6.80266023e-02 2.05587298e-01 -1.40776753e-01 5.03480315e-01 -2.21218929e-01 7.91267037e-01 9.57473636e-01 1.12580860e+00 8.62529278e-01 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82409106-0ab0-4bcf-a560-e7f639c79ac1	variation-based-cause-effect-identification	2211.12016	null	https://arxiv.org/abs/2211.12016v1	https://arxiv.org/pdf/2211.12016v1.pdf	Variation-based Cause Effect Identification	Mining genuine mechanisms underlying the complex data generation process in real-world systems is a fundamental step in promoting interpretability of, and thus trust in, data-driven models. Therefore, we propose a variation-based cause effect identification (VCEI) framework for causal discovery in bivariate systems from a single observational setting. Our framework relies on the principle of independence of cause and mechanism (ICM) under the assumption of an existing acyclic causal link, and offers a practical realization of this principle. Principally, we artificially construct two settings in which the marginal distributions of one covariate, claimed to be the cause, are guaranteed to have non-negligible variations. This is achieved by re-weighting samples of the marginal so that the resultant distribution is notably distinct from this marginal according to some discrepancy measure. In the causal direction, such variations are expected to have no impact on the effect generation mechanism. Therefore, quantifying the impact of these variations on the conditionals reveals the genuine causal direction. Moreover, we formulate our approach in the kernel-based maximum mean discrepancy, lifting all constraints on the data types of cause-and-effect covariates, and rendering such artificial interventions a convex optimization problem. We provide a series of experiments on real and synthetic data showing that VCEI is, in principle, competitive to other cause effect identification frameworks.	['Bin Yang', 'Karim Said Barsim', 'Mohamed Amine ben Salem']	2022-11-22	null	null	null	null	['causal-discovery']	['knowledge-base']	[ 3.68384957e-01 3.74816239e-01 -4.43732381e-01 -2.44729966e-02 -3.10456961e-01 -5.80548108e-01 9.37280834e-01 3.79339546e-01 1.38727492e-02 9.83212769e-01 3.79484445e-01 -5.40294468e-01 -8.79183292e-01 -7.84833193e-01 -1.14317489e+00 -9.87497330e-01 -3.87202054e-01 2.12451786e-01 -1.16080165e-01 -3.64574157e-02 1.89345971e-01 2.95777053e-01 -1.28547978e+00 -1.94893375e-01 9.15575981e-01 4.17627424e-01 -9.67290103e-02 3.87853086e-01 2.74778575e-01 5.94064176e-01 -5.07949293e-01 -4.06865895e-01 1.39943689e-01 -4.31762278e-01 -6.46098197e-01 1.90178812e-01 -9.99966264e-02 -4.16618288e-02 -6.89527690e-02 8.90129983e-01 2.27687359e-01 -2.35435277e-01 9.41625476e-01 -1.38511097e+00 -4.30399269e-01 6.87742054e-01 -6.29218698e-01 -2.31097043e-02 2.65165418e-01 8.33955333e-02 1.17544615e+00 -6.74644053e-01 5.48922241e-01 1.24421167e+00 4.16590124e-01 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b4f6841e-88a4-4449-a3be-eaf383c5ad86	deep-learning-of-semi-competing-risk-data-via	2212.12028	null	https://arxiv.org/abs/2212.12028v1	https://arxiv.org/pdf/2212.12028v1.pdf	Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm	Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient's entire life. One challenge is that an individual's disease course involves non-terminal (e.g., disease progression) and terminal (e.g., death) events, which form semi-competing relationships. Our motivation comes from the Boston Lung Cancer Study, a large lung cancer survival cohort, which investigates how risk factors influence a patient's disease trajectory. Following developments in the prediction of time-to-event outcomes with neural networks, deep learning has become a focal area for the development of risk prediction methods in survival analysis. However, limited work has been done to predict multi-state or semi-competing risk outcomes, where a patient may experience adverse events such as disease progression prior to death. We propose a novel neural expectation-maximization algorithm to bridge the gap between classical statistical approaches and machine learning. Our algorithm enables estimation of the non-parametric baseline hazards of each state transition, risk functions of predictors, and the degree of dependence among different transitions, via a multi-task deep neural network with transition-specific sub-architectures. We apply our method to the Boston Lung Cancer Study and investigate the impact of clinical and genetic predictors on disease progression and mortality.	['Yi Li', 'Stephen Salerno']	2022-12-22	null	null	null	null	['survival-analysis']	['miscellaneous']	[ 2.77219534e-01 -3.16919506e-01 -6.42864048e-01 -3.11497450e-01 -1.38378692e+00 -1.17227854e-02 5.81670105e-01 6.88334286e-01 -5.62177181e-01 7.76766479e-01 5.60360491e-01 -8.02117944e-01 -3.94066244e-01 -8.46281707e-01 -3.87225270e-01 -7.49732196e-01 -5.27782142e-01 8.74737144e-01 -1.26194283e-01 2.61324376e-01 -4.34182048e-01 4.16786194e-01 -5.56100368e-01 2.35022586e-02 5.83307922e-01 6.94540858e-01 -1.43740505e-01 9.25270557e-01 8.36900845e-02 6.92678034e-01 -1.07027590e-01 -4.42835391e-02 -4.68178749e-01 -4.93897378e-01 -8.66980672e-01 -5.77844739e-01 3.68219707e-03 -2.95415968e-01 -4.39433753e-01 3.93205613e-01 6.23934507e-01 -2.28100643e-01 1.14408588e+00 -1.29297984e+00 -6.42280728e-02 3.10111374e-01 -1.38635665e-01 2.43984312e-01 -3.26701164e-01 -2.54619364e-02 9.58295584e-01 -6.95361197e-01 1.78577229e-01 9.11233425e-01 1.11048388e+00 5.70106506e-01 -1.48298776e+00 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670cd8f3-5453-4c33-9615-3d5d75a50c6c	on-time-series-representations-for-multi	null	null	https://link.springer.com/article/10.1007/s00521-020-04916-5	https://rdcu.be/b3Vh2	On time series representations for multi-label NILM	Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating. With the rise of Internet of things, running energy disaggregation models on the edge is more and more essential for privacy concerns and economic reasons. However, current NILM solutions use data-hungry deep learning models that can recognize only one device and are impossible to run on a device with limited resources. This research investigates in-depth multi-label NILM systems and suggests a novel framework which enables a cost-effective solution. It can be deployed on an embedded device, and thus, privacy can be preserved. The proposed system leverages dimensionality reduction using Signal2Vec, is evaluated on two popular public datasets and outperforms another state-of-the-art multi-label NILM system.	['Christoforos Nalmpantis', 'Dimitris Vrakas']	2020-05-02	null	null	null	neural-computing-and-applications-2020-5	['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring']	['knowledge-base', 'miscellaneous', 'time-series']	[-2.08865166e-01 -1.67652562e-01 -4.18753177e-01 -6.14065051e-01 -5.58435142e-01 -7.25948811e-01 5.13579011e-01 1.91428140e-01 -1.86769322e-01 4.37433153e-01 1.61104634e-01 -3.21595609e-01 2.85860132e-02 -7.92525709e-01 -2.70914614e-01 -7.47498989e-01 3.56280327e-01 3.57429266e-01 -5.96943736e-01 3.63182127e-01 -3.55911195e-01 3.48656714e-01 -1.34397507e+00 6.03541136e-02 4.46079522e-01 1.58415532e+00 -3.61104548e-01 2.85478413e-01 1.14162400e-01 7.78306484e-01 -4.24059629e-01 -6.05235279e-01 4.75400835e-01 5.18717431e-02 -7.15908945e-01 4.14063111e-02 2.50051916e-01 -8.89389396e-01 -4.24108505e-01 1.24329937e+00 6.24971688e-01 -1.20229311e-01 4.84995514e-01 -1.66344452e+00 -7.97978699e-01 6.08190894e-01 -4.97017086e-01 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21e32289-51b8-44ac-b0cc-349c18a7b8b8	an-integrated-transfer-learning-and-multitask	1812.09073	null	http://arxiv.org/abs/1812.09073v1	http://arxiv.org/pdf/1812.09073v1.pdf	An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction	Background: Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, excretion prediction models still have limited accuracy. Aim: This study aims to construct an integrated transfer learning and multitask learning approach for developing quantitative structure-activity relationship models to predict four human pharmacokinetic parameters. Methods: A pharmacokinetic dataset included 1104 U.S. FDA approved small molecule drugs. The dataset included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state and elimination half-life). The pre-trained model was trained on over 30 million bioactivity data. An integrated transfer learning and multitask learning approach was established to enhance the model generalization. Results: The pharmacokinetic dataset was split into three parts (60:20:20) for training, validation and test by the improved Maximum Dissimilarity algorithm with the representative initial set selection algorithm and the weighted distance function. The multitask learning techniques enhanced the model predictive ability. The integrated transfer learning and multitask learning model demonstrated the best accuracies, because deep neural networks have the general feature extraction ability, transfer learning and multitask learning improved the model generalization. Conclusions: The integrated transfer learning and multitask learning approach with the improved dataset splitting algorithm was firstly introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.	['Zhuyifan Ye', 'Dongsheng Cao', 'Yilong Yang', 'Defang Ouyang', 'Xiaoshan Li']	2018-12-21	null	null	null	null	['parameter-prediction']	['miscellaneous']	[-1.73235282e-01 -5.99185705e-01 -3.27642351e-01 -3.11619252e-01 -4.92808610e-01 -3.82579565e-01 7.01761767e-02 6.03210926e-01 -5.76351225e-01 1.09952402e+00 -4.57366228e-01 -4.19196725e-01 -6.38739347e-01 -5.07526398e-01 -3.35271508e-01 -8.47051799e-01 -5.12251556e-01 6.77402139e-01 -1.05552420e-01 1.73667297e-02 1.61709607e-01 4.96177375e-01 -1.03070569e+00 4.14251328e-01 1.50468671e+00 9.54168499e-01 4.58922654e-01 2.42259771e-01 -2.16246784e-01 4.27613050e-01 -4.27771837e-01 4.45432395e-01 1.16445787e-01 -3.74860227e-01 -5.18278837e-01 -4.30635989e-01 -8.61334577e-02 -2.17502549e-01 4.51785833e-01 7.06130505e-01 1.08587933e+00 3.92070115e-01 1.09205127e+00 -1.06370401e+00 -7.85753369e-01 2.47000605e-02 -5.97009838e-01 3.59396875e-01 3.38831574e-01 -1.68465644e-01 3.02694976e-01 -1.14542150e+00 -4.44678143e-02 9.01452184e-01 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e86addb7-b326-4731-9cdf-efe91ac51240	deepmapping2-self-supervised-large-scale	2212.06331	null	https://arxiv.org/abs/2212.06331v2	https://arxiv.org/pdf/2212.06331v2.pdf	DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization	LiDAR mapping is important yet challenging in self-driving and mobile robotics. To tackle such a global point cloud registration problem, DeepMapping converts the complex map estimation into a self-supervised training of simple deep networks. Despite its broad convergence range on small datasets, DeepMapping still cannot produce satisfactory results on large-scale datasets with thousands of frames. This is due to the lack of loop closures and exact cross-frame point correspondences, and the slow convergence of its global localization network. We propose DeepMapping2 by adding two novel techniques to address these issues: (1) organization of training batch based on map topology from loop closing, and (2) self-supervised local-to-global point consistency loss leveraging pairwise registration. Our experiments and ablation studies on public datasets (KITTI, NCLT, and Nebula) demonstrate the effectiveness of our method.	['Chen Feng', 'Li Ding', 'Yiming Li', 'Xinhao Liu', 'Chao Chen']	2022-12-13	null	http://openaccess.thecvf.com//content/CVPR2023/html/Chen_DeepMapping2_Self-Supervised_Large-Scale_LiDAR_Map_Optimization_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_DeepMapping2_Self-Supervised_Large-Scale_LiDAR_Map_Optimization_CVPR_2023_paper.pdf	cvpr-2023-1	['point-cloud-registration']	['computer-vision']	[-2.81690687e-01 -5.89873530e-02 -1.29783183e-01 -6.60833061e-01 -8.81121039e-01 -6.39507055e-01 7.59891987e-01 -1.26822427e-01 -5.81494868e-01 7.88077593e-01 -1.27500117e-01 -1.07719742e-01 -7.85212368e-02 -7.98023880e-01 -1.20165515e+00 -1.52516022e-01 -2.34039262e-01 1.00163007e+00 5.55135667e-01 -2.73486465e-01 1.60834774e-01 6.73021257e-01 -1.46232271e+00 -3.28842372e-01 1.13320911e+00 8.31389666e-01 2.39322186e-01 3.57907355e-01 -1.97551489e-01 3.39773953e-01 -7.71512911e-02 -8.16788450e-02 6.75472260e-01 1.65821929e-02 -6.83229268e-01 -3.00014764e-01 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a2bb0ef4-9f46-43fe-87f1-b587fcc6b758	disco-distilling-phrasal-counterfactuals-with	2212.10534	null	https://arxiv.org/abs/2212.10534v3	https://arxiv.org/pdf/2212.10534v3.pdf	DISCO: Distilling Counterfactuals with Large Language Models	Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization. However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale. When crowdsourced, such data is typically limited in scale and diversity; when generated using supervised methods, it is computationally expensive to extend to new counterfactual dimensions. In this work, we introduce DISCO (DIStilled COunterfactual Data), a new method for automatically generating high quality counterfactual data at scale. DISCO engineers prompts to generate phrasal perturbations with a large general language model. Then, a task-specific teacher model filters these generations to distill high-quality counterfactual data. While task-agnostic, we apply our pipeline to the task of natural language inference (NLI) and find that on challenging evaluations such as the NLI stress test, comparatively smaller student models trained with DISCO generated counterfactuals are more robust (6% absolute) and generalize better across distributions (2%) compared to models trained without data augmentation. Furthermore, DISCO augmented models are 10% more consistent between counterfactual pairs on three evaluation sets, demonstrating that DISCO augmentation enables models to more reliably learn causal representations. Our repository is available at: https://github.com/eric11eca/disco	['Kyle Richardson', 'Ashish Sabharwal', 'Antoine Bosselut', 'Qiyue Gao', 'Zeming Chen']	2022-12-20	null	null	null	null	['sentence-classification']	['natural-language-processing']	[ 2.35834286e-01 5.88743329e-01 -4.02465075e-01 -3.93814266e-01 -1.10700011e+00 -8.18145156e-01 1.23555648e+00 1.30502939e-01 -3.47469240e-01 1.51956344e+00 1.05122745e+00 -6.14057899e-01 1.04647540e-01 -7.65307605e-01 -1.12774169e+00 -9.47970971e-02 -5.52754514e-02 5.60006380e-01 -4.74317431e-01 -2.47772440e-01 1.83229372e-01 1.30728632e-01 -1.41505873e+00 5.97299993e-01 1.11438203e+00 2.38329887e-01 -2.20506191e-01 5.89714527e-01 1.59733787e-01 9.37894642e-01 -1.11977720e+00 -7.26303458e-01 3.16031009e-01 -4.15448129e-01 -8.84800732e-01 -4.68719482e-01 6.22304857e-01 -4.02209163e-01 -2.46166632e-01 5.93494415e-01 8.74765992e-01 2.41848037e-01 9.47699606e-01 -1.41789973e+00 -1.27600813e+00 1.34395850e+00 -3.05132717e-01 4.76548553e-01 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01606442-89c0-4dea-ad14-aff5e58ef4a8	a-human-visual-system-inspired-no-reference	null	null	https://www.mdpi.com/1424-8220/22/18/6775	https://www.mdpi.com/1424-8220/22/18/6775	A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors	Objective quality assessment of natural images plays a key role in many fields related to imaging and sensor technology. Thus, this paper intends to introduce an innovative quality-aware feature extraction method for no-reference image quality assessment (NR-IQA). To be more specific, a various sequence of HVS inspired filters were applied to the color channels of an input image to enhance those statistical regularities in the image to which the human visual system is sensitive. From the obtained feature maps, the statistics of a wide range of local feature descriptors were extracted to compile quality-aware features since they treat images from the human visual system’s point of view. To prove the efficiency of the proposed method, it was compared to 16 state-of-the-art NR-IQA techniques on five large benchmark databases, i.e., CLIVE, KonIQ-10k, SPAQ, TID2013, and KADID-10k. It was demonstrated that the proposed method is superior to the state-of-the-art in terms of three different performance indices.	['Domonkos Varga']	2022-09-07	null	null	null	sensors-2022-9	['blind-image-quality-assessment', 'no-reference-image-quality-assessment']	['computer-vision', 'computer-vision']	[ 1.42110556e-01 -6.49014831e-01 1.06819451e-01 -2.42232114e-01 -6.92407429e-01 -1.53711095e-01 4.49475795e-01 2.53067940e-01 -4.51333374e-01 5.80766261e-01 1.31629586e-01 1.67055070e-01 -4.90078151e-01 -7.35274017e-01 -2.11902350e-01 -7.87647486e-01 -2.00941227e-02 -4.43353713e-01 4.08552825e-01 -2.29274929e-01 6.03436172e-01 5.88565111e-01 -1.93503284e+00 1.80144489e-01 8.76208425e-01 1.35903144e+00 9.39325243e-02 4.26017791e-01 2.27745235e-01 6.13144040e-01 -6.86908960e-01 -2.75245339e-01 2.78228819e-01 -4.59683239e-01 -6.74920499e-01 2.43473157e-01 3.71394932e-01 -2.13425502e-01 -3.24195057e-01 1.31391180e+00 5.54002106e-01 2.66890943e-01 6.89991534e-01 -1.00893748e+00 -6.75633490e-01 -2.57939875e-01 -5.44407129e-01 6.15373552e-01 3.72852087e-01 3.61810923e-01 7.86051810e-01 -9.56799448e-01 6.18475676e-01 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-3.09821844e-01 -8.80156219e-01 -7.53371716e-01 7.34081507e-01 2.66281396e-01 -9.00874197e-01 -2.64505774e-01 1.54331520e-01]	[11.756346702575684, -1.9289743900299072]
551802ab-07ef-4f22-ac70-34f72ac7de6e	end-to-end-facial-deep-learning-feature	2002.03627	null	https://arxiv.org/abs/2002.03627v1	https://arxiv.org/pdf/2002.03627v1.pdf	End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement	In this paper, we propose a novel end-to-end feature compression scheme by leveraging the representation and learning capability of deep neural networks, towards intelligent front-end equipped analysis with promising accuracy and efficiency. In particular, the extracted features are compactly coded in an end-to-end manner by optimizing the rate-distortion cost to achieve feature-in-feature representation. In order to further improve the compression performance, we present a latent code level teacher-student enhancement model, which could efficiently transfer the low bit-rate representation into a high bit rate one. Such a strategy further allows us to adaptively shift the representation cost to decoding computations, leading to more flexible feature compression with enhanced decoding capability. We verify the effectiveness of the proposed model with the facial feature, and experimental results reveal better compression performance in terms of rate-accuracy compared with existing models.	['Shiqi Wang', 'Wenhan Yang', 'Shurun Wang']	2020-02-10	null	null	null	null	['feature-compression']	['computer-vision']	[ 3.59279931e-01 -1.43195257e-01 -2.84379333e-01 -6.20990694e-01 -7.22324193e-01 -3.87734845e-02 2.19346017e-01 6.82039261e-02 -3.00324529e-01 1.98944420e-01 4.33925658e-01 3.97214219e-02 -5.01296639e-01 -8.32790613e-01 -4.79272902e-01 -8.14794421e-01 -7.84483459e-03 -3.72809350e-01 -4.13900703e-01 1.72542557e-01 1.11161858e-01 3.23803127e-01 -1.77086437e+00 1.60550088e-01 1.02908444e+00 1.36488128e+00 3.65540564e-01 3.06996882e-01 3.21309835e-01 5.92631459e-01 -1.33037299e-01 -7.21837938e-01 2.68085182e-01 -3.03463072e-01 -3.63668054e-01 3.14075321e-01 1.45253167e-02 -8.26872528e-01 -6.93634450e-01 1.15697193e+00 6.56435311e-01 1.21803232e-01 4.95500773e-01 -9.06798422e-01 -5.65486789e-01 4.49487090e-01 -5.76906264e-01 -1.31844312e-01 2.05341563e-01 -4.72825542e-02 9.12104785e-01 -8.88276041e-01 2.68229067e-01 1.24807203e+00 4.45480883e-01 3.86607826e-01 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0db36baa-9834-41f8-8b31-9a7b27726932	multi-axis-attentive-prediction-for-sparse	2110.01794	null	https://arxiv.org/abs/2110.01794v1	https://arxiv.org/pdf/2110.01794v1.pdf	Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction	Spatiotemporal prediction of event data is a challenging task with a long history of research. While recent work in spatiotemporal prediction has leveraged deep sequential models that substantially improve over classical approaches, these models are prone to overfitting when the observation is extremely sparse, as in the task of crime event prediction. To overcome these sparsity issues, we present Multi-axis Attentive Prediction for Sparse Event Data (MAPSED). We propose a purely attentional approach to extract both short-term dynamics and long-term semantics of event propagation through two observation angles. Unlike existing temporal prediction models that propagate latent information primarily along the temporal dimension, the MAPSED simultaneously operates over all axes (time, 2D space, event type) of the embedded data tensor. We additionally introduce a novel Frobenius norm-based contrastive learning objective to improve latent representational generalization.Empirically, we validate MAPSED on two publicly accessible urban crime datasets for spatiotemporal sparse event prediction, where MAPSED outperforms both classical and state-of-the-art deep learning models. The proposed contrastive learning objective significantly enhances the MAPSED's ability to capture the semantics and dynamics of the events, resulting in better generalization ability to combat sparse observations.	['Scott Sanner', 'Ga Wu', 'Yi Sui']	2021-10-05	null	null	null	null	['crime-prediction']	['miscellaneous']	[ 2.22800747e-01 -1.15203723e-01 -3.85812521e-01 -4.70517188e-01 -4.95481044e-01 -1.95064902e-01 9.58467662e-01 4.33362544e-01 -3.08336258e-01 4.65331197e-01 1.06346774e+00 3.07771657e-03 -4.30079609e-01 -7.35347390e-01 -6.28478348e-01 -5.06224751e-01 -6.57524705e-01 2.23102421e-01 2.56520182e-01 -1.34037331e-01 1.30937845e-01 4.25349593e-01 -1.22179997e+00 6.65884554e-01 6.27342522e-01 9.26434636e-01 -1.42219067e-01 3.30718815e-01 1.62176237e-01 1.34602225e+00 -1.17043890e-01 -2.23508999e-01 1.33790806e-01 4.31827828e-02 -6.90938234e-01 -3.00388634e-01 3.89712960e-01 -3.54357988e-01 -1.02655101e+00 5.77578604e-01 3.15956324e-01 4.88590032e-01 7.54822433e-01 -1.34982145e+00 -6.26466453e-01 6.56800926e-01 -5.70491910e-01 7.86307693e-01 2.35962614e-01 -3.30533646e-02 1.17426682e+00 -9.61088479e-01 8.03316474e-01 1.05659795e+00 8.25611293e-01 -3.66675295e-02 -1.37082028e+00 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6.78497314e-01 -9.57963049e-01 -9.78125706e-02 2.33380616e-01]	[6.852080345153809, 1.9522889852523804]
1d3d55a2-53f5-48d4-b853-2ff540ebbabf	rf-based-low-snr-classification-of-uavs-using	2009.05519	null	https://arxiv.org/abs/2009.05519v2	https://arxiv.org/pdf/2009.05519v2.pdf	RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks	This paper investigates the problem of classification of unmanned aerial vehicles (UAVs) from radio frequency (RF) fingerprints at the low signal-to-noise ratio (SNR) regime. We use convolutional neural networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the CNN extracts features from the signal transient and envelope. As the SNR decreases, this approach fails dramatically because the information in the transient is lost in the noise, and the envelope is distorted heavily. In contrast to time-series representation of the RF signals, with spectrograms, it is possible to focus only on the desired frequency interval, i.e., 2.4 GHz ISM band, and filter out any other signal component outside of this band. These advantages provide a notable performance improvement over the time-series signals-based methods. To further increase the classification accuracy of the spectrogram-based CNN, we denoise the spectrogram images by truncating them to a limited spectral density interval. Creating a single model using spectrogram images of noisy signals and tuning the CNN model parameters, we achieve a classification accuracy varying from 92% to 100% for an SNR range from -10 dB to 30 dB, which significantly outperforms the existing approaches to our best knowledge.	['Ismail Guvenc', 'Fatih Erden', 'Ender Ozturk']	2020-09-11	null	null	null	null	['drone-controller']	['robots']	[ 4.79366690e-01 -3.12859207e-01 1.17819741e-01 4.65565138e-02 -5.39322913e-01 -7.90827990e-01 3.31293583e-01 -3.74209613e-01 -2.18251094e-01 6.29133701e-01 -1.55350164e-01 -4.92280871e-01 -5.41285932e-01 -1.00842512e+00 -5.83506882e-01 -9.21618283e-01 -6.28692210e-01 -5.39932489e-01 -1.14316605e-01 -2.86292076e-01 -3.51921588e-01 7.56132364e-01 -1.78890705e+00 1.83103502e-01 5.48829377e-01 1.66346407e+00 -1.90545991e-01 8.46839786e-01 4.00131613e-01 5.15893400e-01 -1.33287978e+00 1.94736496e-01 6.21766925e-01 -1.92401603e-01 -4.23594892e-01 -3.36347222e-01 4.04565126e-01 -4.72806782e-01 -7.38145828e-01 1.12614512e+00 4.53286797e-01 1.00594647e-01 2.36617818e-01 -8.82864594e-01 -2.92453051e-01 5.05680919e-01 -3.01573426e-01 2.35074744e-01 2.59000480e-01 -1.24085382e-01 4.32911634e-01 -3.20614308e-01 3.61514837e-01 7.26705611e-01 1.11628151e+00 -3.77341136e-02 -1.18115056e+00 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2c039fa4-7d0c-438b-852a-7030ce4bc686	coder-coupled-diversity-sensitive-momentum	2208.09843	null	https://arxiv.org/abs/2208.09843v1	https://arxiv.org/pdf/2208.09843v1.pdf	CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval	Image-Text Retrieval (ITR) is challenging in bridging visual and lingual modalities. Contrastive learning has been adopted by most prior arts. Except for limited amount of negative image-text pairs, the capability of constrastive learning is restricted by manually weighting negative pairs as well as unawareness of external knowledge. In this paper, we propose our novel Coupled Diversity-Sensitive Momentum Constrastive Learning (CODER) for improving cross-modal representation. Firstly, a novel diversity-sensitive contrastive learning (DCL) architecture is invented. We introduce dynamic dictionaries for both modalities to enlarge the scale of image-text pairs, and diversity-sensitiveness is achieved by adaptive negative pair weighting. Furthermore, two branches are designed in CODER. One learns instance-level embeddings from image/text, and it also generates pseudo online clustering labels for its input image/text based on their embeddings. Meanwhile, the other branch learns to query from commonsense knowledge graph to form concept-level descriptors for both modalities. Afterwards, both branches leverage DCL to align the cross-modal embedding spaces while an extra pseudo clustering label prediction loss is utilized to promote concept-level representation learning for the second branch. Extensive experiments conducted on two popular benchmarks, i.e. MSCOCO and Flicker30K, validate CODER remarkably outperforms the state-of-the-art approaches.	['Jingdong Wang', 'Errui Ding', 'Zhong Ji', 'Yunlong Yu', 'Fu Li', 'Min Yang', 'Boyang xia', 'Wenhao Wu', 'Dongliang He', 'Haoran Wang']	2022-08-21	null	null	null	null	['online-clustering']	['computer-vision']	[ 1.18351623e-01 -4.06185150e-01 -6.03803277e-01 -2.70708621e-01 -9.93315876e-01 -6.16192818e-01 7.76999950e-01 -8.55529383e-02 -4.68805552e-01 2.02800736e-01 1.49855554e-01 7.37774894e-02 -2.14916393e-01 -4.50128496e-01 -5.37644506e-01 -8.65019083e-01 2.67230630e-01 4.09302831e-01 6.93419576e-03 -9.35181379e-02 2.08199129e-01 2.69313268e-02 -1.29817522e+00 5.07810175e-01 9.08664286e-01 1.32390070e+00 3.84072304e-01 2.10435763e-01 -3.21477383e-01 1.07499230e+00 -1.09809607e-01 -5.95087826e-01 3.06496501e-01 -4.83490646e-01 -5.69989741e-01 2.68128365e-01 5.29047132e-01 5.40228188e-02 -5.86010039e-01 1.40312910e+00 6.60344303e-01 1.55261651e-01 9.81395662e-01 -1.57762289e+00 -1.37669694e+00 5.73591650e-01 -1.01029849e+00 1.51761010e-01 1.26595363e-01 3.76308203e-01 1.13402724e+00 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93779c54-0f2f-4974-a6a8-48008b5c6395	self-supervised-regional-and-temporal	2107.14399	null	https://arxiv.org/abs/2107.14399v1	https://arxiv.org/pdf/2107.14399v1.pdf	Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition	Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various methods which leverage numerous unlabeled data. However, many aspects with regard to some unique properties of AUs, such as the regional and relational characteristics, are not sufficiently explored in previous works. Motivated by this, we take the AU properties into consideration and propose two auxiliary AU related tasks to bridge the gap between limited annotations and the model performance in a self-supervised manner via the unlabeled data. Specifically, to enhance the discrimination of regional features with AU relation embedding, we design a task of RoI inpainting to recover the randomly cropped AU patches. Meanwhile, a single image based optical flow estimation task is proposed to leverage the dynamic change of facial muscles and encode the motion information into the global feature representation. Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network with the proposed regional and temporal based auxiliary task learning (RTATL) framework. Extensive experiments on BP4D and DISFA demonstrate the superiority of our method and new state-of-the-art performances are achieved.	['ShiLiang Pu', 'Chunmao Wang', 'Qiang Li', 'Jingjing Wang', 'Jingwei Yan']	2021-07-30	null	null	null	null	['facial-action-unit-detection']	['computer-vision']	[ 1.48682103e-01 8.94178376e-02 -5.24310708e-01 -2.67059028e-01 -4.84595090e-01 -1.01735711e-01 4.09280866e-01 -6.48186564e-01 -1.21294931e-01 7.77502656e-01 4.61987525e-01 4.11501139e-01 -4.28688340e-02 -3.46119583e-01 -4.20282871e-01 -1.12350786e+00 1.87954724e-01 -2.84903467e-01 -6.34897174e-03 -2.54415452e-01 1.21361976e-02 4.20774043e-01 -1.43430591e+00 -3.66981961e-02 8.07984889e-01 1.40081155e+00 -2.33068932e-02 -6.10263459e-02 5.95718175e-02 8.56014669e-01 -2.63663568e-02 -2.18030866e-02 4.51595336e-01 -7.82434165e-01 -5.77582061e-01 4.79412109e-01 2.58800715e-01 -5.63158691e-01 -6.74441576e-01 8.83024037e-01 5.15404284e-01 2.42298916e-01 5.72438300e-01 -1.33698118e+00 -5.23350120e-01 6.88666329e-02 -9.02148187e-01 3.09666932e-01 1.58087835e-01 3.42206925e-01 9.82203722e-01 -1.04088640e+00 6.66283071e-01 1.10017586e+00 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d050db35-f1bd-40bf-9f2b-85db8fb6b1c6	deep-siamese-networks-with-bayesian-non	1811.07386	null	http://arxiv.org/abs/1811.07386v1	http://arxiv.org/pdf/1811.07386v1.pdf	Deep Siamese Networks with Bayesian non-Parametrics for Video Object Tracking	We present a novel algorithm utilizing a deep Siamese neural network as a general object similarity function in combination with a Bayesian optimization (BO) framework to encode spatio-temporal information for efficient object tracking in video. In particular, we treat the video tracking problem as a dynamic (i.e. temporally-evolving) optimization problem. Using Gaussian Process priors, we model a dynamic objective function representing the location of a tracked object in each frame. By exploiting temporal correlations, the proposed method queries the search space in a statistically principled and efficient way, offering several benefits over current state of the art video tracking methods.	['Anthony D. Rhodes', 'Manan Goel']	2018-11-18	null	null	null	null	['video-object-tracking']	['computer-vision']	[-1.65189117e-01 -5.82358241e-01 -1.28710136e-01 2.10424066e-02 -4.20785725e-01 -5.35139680e-01 6.18741989e-01 -1.98817238e-01 -8.25066686e-01 4.22730982e-01 5.14198048e-03 2.75422633e-01 -3.43461514e-01 -4.52949584e-01 -8.69204044e-01 -8.10673177e-01 -1.06064245e-01 5.38537204e-01 5.38453758e-01 3.77707988e-01 1.60135895e-01 7.60614932e-01 -1.23497617e+00 -4.75499988e-01 6.07240021e-01 1.27662861e+00 2.82547921e-01 7.14280725e-01 2.16359034e-01 7.15580642e-01 -4.60958719e-01 -3.16978514e-01 3.70020807e-01 -2.57864743e-01 -4.88853693e-01 1.68139592e-01 7.81665444e-01 -5.54220863e-02 -7.60912240e-01 1.34715414e+00 1.93892017e-01 8.25143456e-01 5.22103906e-01 -1.04396105e+00 -4.46924716e-01 -6.05592541e-02 -3.88492346e-01 4.63380396e-01 1.95268303e-01 3.09504569e-01 8.34397852e-01 -7.42981613e-01 8.02523792e-01 1.14042437e+00 7.05247343e-01 4.41182047e-01 -1.18880785e+00 -3.72528523e-01 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de077cb9-fa5b-489e-94f2-019b958cb899	fauno-the-italian-large-language-model-that	2306.14457	null	https://arxiv.org/abs/2306.14457v1	https://arxiv.org/pdf/2306.14457v1.pdf	Fauno: The Italian Large Language Model that will leave you senza parole!	This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian, demonstrating that obtaining a fine-tuned conversational bot with a single GPU is possible. In addition, we release a collection of datasets for conversational AI in Italian. The datasets on which we fine-tuned Fauno include various topics such as general question answering, computer science, and medical questions. We release our code and datasets on \url{https://github.com/RSTLess-research/Fauno-Italian-LLM}	['Fabrizio Silvestri', 'Emanuele Rodolà', 'Andrea Santilli', 'Giovanni Trappolini', 'Andrea Bacciu']	2023-06-26	null	null	null	null	['question-answering']	['natural-language-processing']	[-5.05731344e-01 2.67492592e-01 -1.45893827e-01 -1.43666655e-01 -8.75789523e-01 -8.56087387e-01 7.88130820e-01 -1.08287007e-01 -3.00632745e-01 8.15196455e-01 4.58173364e-01 -5.70681393e-01 2.34495997e-01 -6.74905360e-01 -2.84307599e-01 -3.22674245e-01 -6.99086785e-02 1.02747750e+00 2.33814850e-01 -5.59148908e-01 1.90953482e-02 2.36421242e-01 -8.63494396e-01 8.43332589e-01 8.10202420e-01 2.57630736e-01 -5.22490814e-02 1.26368928e+00 -2.27089711e-02 1.33119571e+00 -7.30876684e-01 -7.92948484e-01 -2.25052238e-02 -3.98540646e-01 -1.69576085e+00 -3.19263905e-01 2.75198489e-01 -2.47301325e-01 -6.20765805e-01 6.05280936e-01 4.49239284e-01 1.70028627e-01 4.20585901e-01 -1.59202588e+00 -2.98853368e-01 8.90342712e-01 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5a46d8f7-7160-450d-8159-934a8f77c0b6	c2f2neus-cascade-cost-frustum-fusion-for-high	2306.10003	null	https://arxiv.org/abs/2306.10003v1	https://arxiv.org/pdf/2306.10003v1.pdf	C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction	There is an emerging effort to combine the two popular technical paths, i.e., the multi-view stereo (MVS) and neural implicit surface (NIS), in scene reconstruction from sparse views. In this paper, we introduce a novel integration scheme that combines the multi-view stereo with neural signed distance function representations, which potentially overcomes the limitations of both methods. MVS uses per-view depth estimation and cross-view fusion to generate accurate surface, while NIS relies on a common coordinate volume. Based on this, we propose to construct per-view cost frustum for finer geometry estimation, and then fuse cross-view frustums and estimate the implicit signed distance functions to tackle noise and hole issues. We further apply a cascade frustum fusion strategy to effectively captures global-local information and structural consistency. Finally, we apply cascade sampling and a pseudo-geometric loss to foster stronger integration between the two architectures. Extensive experiments demonstrate that our method reconstructs robust surfaces and outperforms existing state-of-the-art methods.	['Wei Yang', 'Junle Wang', 'Zhaojie Zeng', 'Wenkai Liu', 'Yuesong Wang', 'Tao Guan', 'Luoyuan Xu']	2023-06-16	null	null	null	null	['depth-estimation']	['computer-vision']	[ 2.49677330e-01 -1.74316809e-01 3.84226829e-01 -3.24477613e-01 -9.35258329e-01 -5.12210071e-01 4.52884316e-01 -3.19109321e-01 1.72047302e-01 5.29148400e-01 2.11021736e-01 2.49231577e-01 -5.73346317e-02 -1.03258383e+00 -8.09452295e-01 -4.97189224e-01 6.28451824e-01 1.76269501e-01 5.63369453e-01 -2.56029934e-01 3.72385353e-01 5.07294416e-01 -1.78758669e+00 5.54908156e-01 1.15101969e+00 1.21467328e+00 3.16117615e-01 2.99532413e-01 -6.95912018e-02 4.91548002e-01 1.75133601e-01 -4.89734292e-01 4.83041555e-01 -7.52556995e-02 -3.82616907e-01 1.36192590e-01 1.02347231e+00 -6.12466753e-01 -2.47155935e-01 1.07719374e+00 5.86332858e-01 -1.21698961e-01 4.36584532e-01 -6.75215840e-01 -2.30315045e-01 -2.73420513e-01 -9.54585552e-01 -3.06836933e-01 6.75555408e-01 -1.16330925e-02 8.00213337e-01 -1.36971092e+00 7.69771516e-01 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520fc136-144f-4ee2-8200-40340397b90e	an-influence-based-approach-for-root-cause	2105.03092	null	https://arxiv.org/abs/2105.03092v1	https://arxiv.org/pdf/2105.03092v1.pdf	An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks	Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. A popular approach for failure root cause identification is to construct a graph with approximate edges, commonly based on either event co-occurrences or conditional independence tests. However, considerable expert knowledge is typically required for edge pruning. We propose a novel data-driven framework for root cause alarm localization, combining both causal inference and network embedding techniques. In this framework, we design a hybrid causal graph learning method (HPCI), which combines Hawkes Process with Conditional Independence tests, as well as propose a novel Causal Propagation-Based Embedding algorithm (CPBE) to infer edge weights. We subsequently discover root cause alarms in a real-time data stream by applying an influence maximization algorithm on the weighted graph. We evaluate our method on artificial data and real-world telecom data, showing a significant improvement over the best baselines.	['Junjian Ye', 'Xi Zhang', 'Min Zhou', 'Marcus Kalander', 'Keli Zhang']	2021-05-07	null	null	null	null	['fault-localization']	['computer-code']	[ 1.48069933e-01 5.91105483e-02 2.97549665e-02 -1.40706256e-01 -1.72189236e-01 -1.75183564e-01 3.76404375e-01 7.64933944e-01 2.00215369e-01 7.21512616e-01 1.25675872e-01 -5.59989333e-01 -9.75086033e-01 -1.09172368e+00 -2.51454294e-01 -5.57748616e-01 -7.02528834e-01 5.81031382e-01 2.98541874e-01 1.15226969e-01 1.73322171e-01 5.86418271e-01 -1.00478101e+00 -7.92250261e-02 7.00472414e-01 8.79572511e-01 -1.60979956e-01 8.44602883e-01 8.19364712e-02 1.00676143e+00 -7.84542739e-01 -8.45235214e-02 -2.99943924e-01 -5.47257960e-01 -6.11637890e-01 6.72078878e-02 -5.00684559e-01 1.09719872e-01 -5.20445406e-01 7.08127022e-01 5.85439801e-01 -9.64788347e-02 5.58979034e-01 -1.70501351e+00 -7.26326853e-02 8.99202347e-01 -5.64256966e-01 4.52838659e-01 4.01940882e-01 -1.85491204e-01 1.06979907e+00 -5.16784966e-01 9.37070027e-02 1.23669732e+00 6.02749169e-01 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eeb6cebe-16d9-41aa-af35-a7029f95d4e5	channel-attention-is-all-you-need-for-video	null	null	https://ojs.aaai.org/index.php/AAAI/article/download/6693/6547	https://ojs.aaai.org/index.php/AAAI/article/download/6693/6547	Channel Attention Is All You Need for Video Frame Interpolation	Prevailing video frame interpolation techniques rely heavily on optical flow estimation and require additional model complexity and computational cost; it is also susceptible to error propagation in challenging scenarios with large motion and heavy occlusion. To alleviate the limitation, we propose a simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component. Our algorithm employs a special feature reshaping operation, referred to as PixelShuffle, with a channel attention, which replaces the optical flow computation module. The main idea behind the design is to distribute the information in a feature map into multiple channels and extract motion information by attending the channels for pixel-level frame synthesis. The model given by this principle turns out to be effective in the presence of challenging motion and occlusion. We construct a comprehensive evaluation benchmark and demonstrate that the proposed approach achieves outstanding performance compared to the existing models with a component for optical flow computation.	['Kyoung Mu Lee', 'Ning Xu', 'Bohyung Han', 'Heewon Kim', 'Myungsub Choi']	2020-04-03	null	null	null	aaai-conference-on-artificial-intelligence-7	['video-frame-interpolation']	['computer-vision']	[ 1.91689998e-01 -4.37723011e-01 -1.61623687e-01 -1.64689049e-01 -2.38466278e-01 -2.18855470e-01 4.66768771e-01 -4.41872537e-01 -5.17546713e-01 8.62680078e-01 1.64157823e-01 -2.21289828e-01 3.31554174e-01 -7.26265013e-01 -9.01054621e-01 -6.20588303e-01 7.82241449e-02 -2.41687134e-01 3.42718303e-01 -6.93272874e-02 3.57028365e-01 4.03442383e-01 -1.41575384e+00 3.89322191e-02 9.22331452e-01 1.12804377e+00 2.84054637e-01 7.61620939e-01 -1.00982226e-01 1.32357323e+00 -5.22185802e-01 -2.17708632e-01 5.40144563e-01 -5.90175807e-01 -6.84711337e-01 2.76129812e-01 8.09341908e-01 -9.36430097e-01 -5.97642660e-01 8.12838733e-01 6.13389730e-01 2.56874293e-01 1.25293493e-01 -1.10708201e+00 -4.68008101e-01 3.88112292e-02 -5.87535203e-01 4.12303805e-01 2.91346222e-01 4.84380931e-01 6.21412635e-01 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0846b5c3-35cf-4526-b18f-4bf065b65045	learning-policies-with-zero-or-bounded	2106.02684	null	https://arxiv.org/abs/2106.02684v3	https://arxiv.org/pdf/2106.02684v3.pdf	Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs	We address the issue of safety in reinforcement learning. We pose the problem in an episodic framework of a constrained Markov decision process. Existing results have shown that it is possible to achieve a reward regret of $\tilde{\mathcal{O}}(\sqrt{K})$ while allowing an $\tilde{\mathcal{O}}(\sqrt{K})$ constraint violation in $K$ episodes. A critical question that arises is whether it is possible to keep the constraint violation even smaller. We show that when a strictly safe policy is known, then one can confine the system to zero constraint violation with arbitrarily high probability while keeping the reward regret of order $\tilde{\mathcal{O}}(\sqrt{K})$. The algorithm which does so employs the principle of optimistic pessimism in the face of uncertainty to achieve safe exploration. When no strictly safe policy is known, though one is known to exist, then it is possible to restrict the system to bounded constraint violation with arbitrarily high probability. This is shown to be realized by a primal-dual algorithm with an optimistic primal estimate and a pessimistic dual update.	['Chao Tian', 'P. R. Kumar', 'Dileep Kalathil', 'Ruida Zhou', 'Tao Liu']	2021-06-04	null	http://proceedings.neurips.cc/paper/2021/hash/8ec2ba5e96ec1c050bc631abda80f269-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/8ec2ba5e96ec1c050bc631abda80f269-Paper.pdf	neurips-2021-12	['safe-exploration']	['robots']	[ 1.50601044e-01 7.13812888e-01 -2.19116330e-01 -8.62233564e-02 -8.95209670e-01 -8.52482498e-01 1.97581336e-01 1.75618127e-01 -9.49953616e-01 1.38417006e+00 -4.16452259e-01 -6.73024297e-01 -6.71357453e-01 -8.80523980e-01 -7.51684546e-01 -1.00242162e+00 -4.33552444e-01 5.54534256e-01 3.77212167e-02 -1.24541193e-01 3.81708622e-01 3.56593907e-01 -1.45162094e+00 -3.48492444e-01 7.18033910e-01 1.29229391e+00 -4.41222452e-02 6.47163510e-01 3.99011850e-01 6.53659582e-01 -5.47050595e-01 -1.82485297e-01 5.88199258e-01 -5.86302519e-01 -9.20481741e-01 5.39204627e-02 -3.36882502e-01 -3.75247240e-01 -4.31095436e-02 1.55551875e+00 1.05742715e-01 4.41790223e-01 3.57379049e-01 -1.24980330e+00 4.01072651e-02 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d7516c21-656d-437f-a74d-b4ee5a966b26	learning-from-synthetic-humans	1701.01370	null	http://arxiv.org/abs/1701.01370v3	http://arxiv.org/pdf/1701.01370v3.pdf	Learning from Synthetic Humans	Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data. We generate more than 6 million frames together with ground truth pose, depth maps, and segmentation masks. We show that CNNs trained on our synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images. Our results and the new dataset open up new possibilities for advancing person analysis using cheap and large-scale synthetic data.	['Naureen Mahmood', 'Gül Varol', 'Xavier Martin', 'Ivan Laptev', 'Michael J. Black', 'Cordelia Schmid', 'Javier Romero']	2017-01-05	learning-from-synthetic-humans-1	http://openaccess.thecvf.com/content_cvpr_2017/html/Varol_Learning_From_Synthetic_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Varol_Learning_From_Synthetic_CVPR_2017_paper.pdf	cvpr-2017-7	['human-part-segmentation', '2d-human-pose-estimation']	['computer-vision', 'computer-vision']	[ 1.88045219e-01 7.65698031e-02 2.95685321e-01 -4.49842244e-01 -6.35204554e-01 -6.96166635e-01 3.59340936e-01 -2.78748721e-01 -8.28296244e-01 8.70807648e-01 7.15016872e-02 4.47384894e-01 6.35916412e-01 -6.82525218e-01 -7.52258122e-01 -2.84976184e-01 -1.10424147e-03 1.07629788e+00 2.21053898e-01 -1.22630410e-01 -3.30769807e-01 6.14135385e-01 -1.65183306e+00 -5.86343966e-02 2.72033811e-01 9.09467220e-01 -1.30520120e-01 1.04039598e+00 5.09418607e-01 2.27305233e-01 -7.46690273e-01 -5.72546363e-01 7.81591475e-01 -3.84226292e-01 -5.42125523e-01 5.58003187e-01 8.26376915e-01 -8.16330552e-01 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6af98c5a-9348-4662-a24d-89842e0d835a	how-multipurpose-are-language-models	null	null	https://openreview.net/forum?id=d7KBjmI3GmQ	https://openreview.net/pdf?id=d7KBjmI3GmQ	How Multipurpose Are Language Models?	We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.	['Jacob Steinhardt', 'Dawn Song', 'Mantas Mazeika', 'Andy Zou', 'Steven Basart', 'Collin Burns', 'Dan Hendrycks']	2021-01-01	null	null	null	iclr-2021-1	['elementary-mathematics']	['reasoning']	[-2.62778848e-01 3.62349570e-01 -5.96900940e-01 -1.99164540e-01 -1.02410614e+00 -7.42681384e-01 4.23004389e-01 2.98379749e-01 -4.01255786e-01 1.04800975e+00 -4.26276959e-02 -1.03276491e+00 -5.92621624e-01 -6.80464149e-01 -6.38676107e-01 -1.58598423e-01 5.11431754e-01 5.72007835e-01 -5.26159480e-02 -1.72908664e-01 8.37211668e-01 -9.25527960e-02 -1.11737955e+00 1.78962767e-01 1.89943850e+00 4.24050272e-01 -7.91555271e-02 5.56610882e-01 1.20754447e-02 1.11976933e+00 -6.43611610e-01 -1.04315734e+00 -1.44855663e-01 -6.71419352e-02 -1.23004353e+00 -6.53373003e-01 7.81136453e-01 -2.57510096e-01 -5.56247123e-02 1.07487535e+00 3.45873713e-01 1.95452601e-01 1.05032670e+00 -1.20910323e+00 -9.73768473e-01 8.00472617e-01 -6.11699462e-01 3.57349545e-01 7.17192948e-01 2.03597292e-01 1.33310938e+00 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