Split (2)

train · 44.6k rows

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c53f2873-fac7-4fc5-9957-55680c5e2b6a	zero-extremely-efficient-collective	2306.10209	null	https://arxiv.org/abs/2306.10209v1	https://arxiv.org/pdf/2306.10209v1.pdf	ZeRO++: Extremely Efficient Collective Communication for Giant Model Training	Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability. However, when training on low-bandwidth clusters, or at scale which forces batch size per GPU to be small, ZeRO's effective throughput is limited because of high communication volume from gathering weights in forward pass, backward pass, and averaging gradients. This paper introduces three communication volume reduction techniques, which we collectively refer to as ZeRO++, targeting each of the communication collectives in ZeRO. First is block-quantization based all-gather. Second is data remapping that trades-off communication for more memory. Third is a novel all-to-all based quantized gradient averaging paradigm as replacement of reduce-scatter collective, which preserves accuracy despite communicating low precision data. Collectively, ZeRO++ reduces communication volume of ZeRO by 4x, enabling up to 2.16x better throughput at 384 GPU scale.	['Yuxiong He', 'Lei Yang', 'Feng Yan', 'Olatunji Ruwase', 'Samyam Rajbhandari', 'Connor Holmes', 'Sam Ade Jacobs', 'Heyang Qin', 'Guanhua Wang']	2023-06-16	null	null	null	null	['quantization']	['methodology']	[-3.72391790e-01 -6.45110905e-01 -1.44864917e-01 -4.93658096e-01 -9.78165567e-01 -2.13244006e-01 4.29227054e-01 6.16366327e-01 -7.44390368e-01 4.78884488e-01 2.64187723e-01 -4.85964745e-01 2.13912353e-01 -1.00587821e+00 -6.54447854e-01 -6.22689247e-01 -1.49520159e-01 1.63511798e-01 3.57041299e-01 -1.70072496e-01 2.84170508e-01 3.38801801e-01 -1.50810075e+00 5.44289410e-01 6.20716810e-01 9.15112853e-01 1.90883830e-01 9.48770344e-01 -3.39872450e-01 8.71906698e-01 -5.35156906e-01 -5.55817820e-02 1.37009352e-01 -4.39745411e-02 -6.94696128e-01 -4.51589793e-01 3.00104350e-01 -6.96175754e-01 -2.11362690e-02 6.59290433e-01 6.77584291e-01 1.79525778e-01 1.17874052e-02 -1.10613847e+00 -2.29046136e-01 4.28867757e-01 -1.04678226e+00 3.94319981e-01 6.88755661e-02 -1.90800312e-03 9.10123289e-01 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85caffe9-096e-444f-8dfe-ab361cde0945	docbed-a-multi-stage-ocr-solution-for	2202.01414	null	https://arxiv.org/abs/2202.01414v1	https://arxiv.org/pdf/2202.01414v1.pdf	DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts	Digitization of newspapers is of interest for many reasons including preservation of history, accessibility and search ability, etc. While digitization of documents such as scientific articles and magazines is prevalent in literature, one of the main challenges for digitization of newspaper lies in its complex layout (e.g. articles spanning multiple columns, text interrupted by images) analysis, which is necessary to preserve human read-order. This work provides a major breakthrough in the digitization of newspapers on three fronts: first, releasing a dataset of 3000 fully-annotated, real-world newspaper images from 21 different U.S. states representing an extensive variety of complex layouts for document layout analysis; second, proposing layout segmentation as a precursor to existing optical character recognition (OCR) engines, where multiple state-of-the-art image segmentation models and several post-processing methods are explored for document layout segmentation; third, providing a thorough and structured evaluation protocol for isolated layout segmentation and end-to-end OCR.	['Suchitra Sathyanarayana', 'Sujitha Martin', 'Guang Yang', 'Negin Sokhandan', 'Wenzhen Zhu']	2022-02-03	null	null	null	null	['document-layout-analysis']	['computer-vision']	[ 4.15052146e-01 -3.53956044e-01 -1.17052125e-03 -1.23770282e-01 -6.67203724e-01 -1.16565812e+00 5.74976981e-01 4.41771716e-01 -3.21913272e-01 5.15039921e-01 2.10933313e-01 -5.63679397e-01 -2.22110346e-01 -4.58281070e-01 -6.73701406e-01 -3.24375540e-01 1.56152591e-01 5.81881642e-01 2.77550012e-01 -4.06213030e-02 9.97636795e-01 1.02597094e+00 -1.10960519e+00 3.54892910e-01 1.00936544e+00 5.30393541e-01 3.63695115e-01 1.18248260e+00 -6.87014878e-01 5.49189568e-01 -8.71361315e-01 -6.10501885e-01 -8.53661597e-02 -2.43012041e-01 -1.06894779e+00 6.79126978e-01 6.40891254e-01 -1.74446210e-01 -3.80319268e-01 1.12608671e+00 2.17038676e-01 -1.97604209e-01 7.24888921e-01 -6.49718583e-01 -1.22893679e+00 6.98732972e-01 -8.48206758e-01 1.46060914e-01 5.28759837e-01 -2.19619527e-01 8.93707395e-01 -3.67726594e-01 1.07986569e+00 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734a3df1-cc97-4204-add3-9bc7e982a3ec	minimax-robust-landmine-detection-using	2111.08379	null	https://arxiv.org/abs/2111.08379v2	https://arxiv.org/pdf/2111.08379v2.pdf	Minimax Robust Landmine Detection Using Forward-Looking Ground-Penetrating Radar	We propose a robust likelihood-ratio test (LRT) to detect landmines and unexploded ordnance using a forward-looking ground-penetrating radar. Instead of modeling the distributions of the target and clutter returns with parametric families, we construct a band of feasible probability densities under each hypothesis. The LRT is then devised based on the least favorable densities within the bands. This detector is designed to maximize the worst-case performance over all feasible density pairs and, hence, does not require strong assumptions about the clutter and noise distributions. The proposed technique is evaluated using electromagnetic field simulation data of shallow-buried targets. We show that, compared to detectors based on parametric models, robust detectors can lead to significantly reduced false alarm rates, particularly in cases where there is a mismatch between the assumed model and the true distributions.	['Michael Fauß', 'Abdelhak M. Zoubir', 'Fauzia Ahmad', 'Afief Dias Pambudi']	2021-11-16	null	null	null	null	['landmine']	['computer-vision']	[ 4.67230588e-01 -1.55193001e-01 1.23499744e-01 -3.06088597e-01 -9.24837530e-01 -5.22537887e-01 3.18616152e-01 -1.98046669e-01 -4.01322186e-01 9.68083024e-01 -3.55232298e-01 -7.24661469e-01 -5.94449759e-01 -1.02753651e+00 -4.70363766e-01 -8.82572234e-01 -4.28130448e-01 4.42960441e-01 2.55420774e-01 1.11346230e-01 1.35855049e-01 9.30422485e-01 -1.38410068e+00 -1.28983825e-01 8.03439796e-01 1.23070920e+00 1.42983049e-01 6.60327852e-01 2.78239250e-01 4.03943479e-01 -6.91678226e-01 8.11952204e-02 4.29453015e-01 -3.20339143e-01 3.20446827e-02 -6.88414723e-02 9.43247303e-02 -3.81543666e-01 -1.91145912e-01 1.30899632e+00 4.09995019e-01 -1.60031140e-01 1.17215240e+00 -9.40859616e-01 -5.13616987e-02 6.62129670e-02 -9.18645918e-01 4.29580599e-01 7.65510723e-02 -2.22688243e-01 5.60199618e-01 -9.26506042e-01 -1.05944276e-01 1.08151472e+00 7.34624028e-01 1.23755179e-01 -1.14347982e+00 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1f103232-99a0-40f7-85a4-eea68dff4921	dual-branched-spatio-temporal-fusion-network	2202.13336	null	https://arxiv.org/abs/2202.13336v1	https://arxiv.org/pdf/2202.13336v1.pdf	Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast	Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data. Effective mining of these data is the key to accurate TCs track forecasting. However, existing methods face the problem that the model complexity is too high or it is difficult to efficiently extract features from multi-modal data. In this paper, we propose the Dual-Branched spatio-temporal Fusion Network (DBF-Net) -- a novel multi-horizon tropical cyclone track forecasting model which fuses the multi-modal features efficiently. DBF-Net contains a TC features branch that extracts temporal features from 1D inherent features of TCs and a pressure field branch that extracts spatio-temporal features from reanalysis 2D pressure field. Through the encoder-decoder-based architecture and efficient feature fusion, DBF-Net can fully mine the information of the two types of data, and achieve good TCs track prediction results. Extensive experiments on historical TCs track data in the Northwest Pacific show that our DBF-Net achieves significant improvement compared with existing statistical and deep learning TCs track forecast methods.	['Zhenwei Shi', 'Xiaoyi Geng', 'Kun Hao', 'Zili Liu']	2022-02-27	null	null	null	null	['tropical-cyclone-track-forecasting']	['time-series']	[-4.00580525e-01 -8.15535843e-01 -3.00101668e-01 -6.42851889e-01 -9.56217647e-01 -4.15046245e-01 6.24868512e-01 -2.49404669e-01 -2.99433805e-03 6.65996492e-01 4.52779204e-01 -7.49880433e-01 -2.73885250e-01 -9.81209874e-01 -4.27805245e-01 -8.84141862e-01 -6.93710506e-01 1.83991060e-01 3.33921820e-01 -7.62804747e-01 2.92401403e-01 4.43269193e-01 -1.60764074e+00 5.63795209e-01 8.71217906e-01 1.43082058e+00 4.37011681e-02 5.72783768e-01 -2.23890319e-01 9.10098612e-01 -1.72596164e-02 3.71599972e-01 4.59632576e-01 -4.38742526e-02 -2.84213513e-01 -5.07038713e-01 2.71338731e-01 -4.21982974e-01 -7.10176349e-01 7.06786454e-01 3.70737195e-01 1.93996444e-01 4.52926189e-01 -1.12250090e+00 2.07323343e-01 3.03465605e-01 -6.84719145e-01 9.11996126e-01 -3.06780308e-01 -2.90549472e-02 7.63639212e-01 -9.66317534e-01 2.56034374e-01 1.15433979e+00 1.06842017e+00 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-2.84569979e-01 -9.57713187e-01 -8.79679084e-01 -3.62575084e-01 1.07167673e+00 -7.78097749e-01 -2.65933841e-01 -8.44263881e-02]	[6.6157941818237305, 2.817659854888916]
2a8bd753-1db3-4162-9852-02dda2d4f6ce	neural-topic-models-with-survival-supervision	2007.07796	null	https://arxiv.org/abs/2007.07796v1	https://arxiv.org/pdf/2007.07796v1.pdf	Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate	In time-to-event prediction problems, a standard approach to estimating an interpretable model is to use Cox proportional hazards, where features are selected based on lasso regularization or stepwise regression. However, these Cox-based models do not learn how different features relate. As an alternative, we present an interpretable neural network approach to jointly learn a survival model to predict time-to-event outcomes while simultaneously learning how features relate in terms of a topic model. In particular, we model each subject as a distribution over "topics", which are learned from clinical features as to help predict a time-to-event outcome. From a technical standpoint, we extend existing neural topic modeling approaches to also minimize a survival analysis loss function. We study the effectiveness of this approach on seven healthcare datasets on predicting time until death as well as hospital ICU length of stay, where we find that neural survival-supervised topic models achieves competitive accuracy with existing approaches while yielding interpretable clinical "topics" that explain feature relationships.	['Jeremy C. Weiss', 'Ren Zuo', 'Linhong Li', 'George H. Chen', 'Amanda Coston']	2020-07-15	null	null	null	null	['time-to-event-prediction']	['time-series']	[ 2.82834917e-01 4.99121577e-01 -7.10472167e-01 -9.82512355e-01 -1.17692912e+00 -1.22041982e-02 1.31654546e-01 7.71796227e-01 -3.68630178e-02 8.16665173e-01 6.91208482e-01 -5.03369272e-01 -6.07885242e-01 -7.47343898e-01 -6.17197633e-01 -5.50852239e-01 -6.26666963e-01 8.69448662e-01 -6.72040880e-01 1.37796134e-01 -6.37049743e-05 2.20345985e-02 -8.50891113e-01 2.19295427e-01 9.15418446e-01 8.05306017e-01 -4.75303173e-01 4.59986597e-01 8.42868015e-02 1.01041055e+00 -3.20713937e-01 -8.89531597e-02 -2.02911109e-01 -4.47051555e-01 -6.78483367e-01 -2.47342065e-01 1.78607047e-01 -2.86396295e-01 -3.28112394e-01 4.09706801e-01 2.11126804e-01 6.43366128e-02 1.32450449e+00 -1.43025219e+00 -6.92790806e-01 7.74227321e-01 -1.80088162e-01 -1.15453400e-01 -9.13925394e-02 -2.02525958e-01 1.01302767e+00 -8.25384378e-01 3.65657091e-01 1.11873162e+00 1.12864244e+00 5.60560524e-01 -1.67569876e+00 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7.81253040e-01 -3.84098083e-01 -5.65573990e-01 4.76230770e-01]	[7.898685932159424, 5.703148365020752]
44bcc7cb-3e1d-419b-8055-8ac16eb9ebe7	a-new-backbone-for-hyperspectral-image	2108.07739	null	https://arxiv.org/abs/2108.07739v3	https://arxiv.org/pdf/2108.07739v3.pdf	A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging	The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way. It is generally implemented by two components: an optical encoder that compresses HD signals into a 2D measurement and an algorithm decoder that retrieves the HD data upon the hardware-encoded measurement. Over a broad range of SCI applications, hyperspectral imaging (HSI) and video compressive sensing have received significant research attention in recent years. Among existing SCI reconstruction algorithms, deep learning-based methods stand out as their promising performance and efficient inference. However, the deep reconstruction network may suffer from overlarge model size and highly-specialized network design, which inevitably lead to costly training time, high memory usage, and limited flexibility, thus discouraging the deployments of SCI systems in practical scenarios. In this paper, we tackle the above challenges by proposing a simple yet highly efficient reconstruction method, namely stacked residual network (SRN), by revisiting the residual learning strategy with nested structures and spatial-invariant property. The proposed SRN empowers high-fidelity data retrieval with fewer computation operations and negligible model size compared with existing networks, and also serves as a versatile backbone applicable for both hyperspectral and video data. Based on the proposed backbone, we first develop the channel attention enhanced SRN (CAE-SRN) to explore the spectral inter-dependencies for fine-grained spatial estimation in HSI. We then employ SRN as a deep denoiser and incorporate it into a generalized alternating projection (GAP) framework -- resulting in GAP-SRN -- to handle the video compressive sensing task. Experimental results demonstrate the state-of-the-art performance, high computational efficiency of the proposed SRN on two SCI applications.	['Zhiqiang Tao', 'Yun Fu', 'Xin Yuan', 'Yulun Zhang', 'Jiamian Wang']	2021-08-17	null	null	null	null	['video-compressive-sensing']	['computer-vision']	[ 7.93067217e-01 -5.56411028e-01 7.23214000e-02 -2.90842857e-02 -7.52868712e-01 -3.47978100e-02 2.61668831e-01 -7.11911023e-01 -3.21238376e-02 6.59669518e-01 3.23028833e-01 -3.18627685e-01 -5.99368632e-01 -7.13157296e-01 -7.21757531e-01 -1.17985415e+00 1.63597524e-01 -2.14188233e-01 -1.42459393e-01 4.98546883e-02 3.96090411e-02 4.38510239e-01 -1.53613853e+00 7.32171685e-02 1.05842984e+00 1.31602156e+00 7.20908284e-01 2.29757532e-01 2.86451727e-01 9.58006501e-01 7.74688497e-02 1.60820205e-02 4.57468390e-01 -5.78416288e-01 -2.15301782e-01 3.07672858e-01 1.20264858e-01 -7.34827578e-01 -1.02775407e+00 1.11324084e+00 6.87004566e-01 1.71677038e-01 3.02437484e-01 -7.56108940e-01 -8.78645122e-01 4.17928606e-01 -8.68957341e-01 3.15401852e-02 2.52311170e-01 2.06656441e-01 6.98289573e-01 -1.06846297e+00 3.45424980e-01 8.01809132e-01 7.17173576e-01 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ef3c3521-b792-4f9c-b3e4-942d42e7b27d	a-template-based-hybrid-model-for-chinese	null	null	https://aclanthology.org/W12-6323	https://aclanthology.org/W12-6323.pdf	A Template Based Hybrid Model for Chinese Personal Name Disambiguation	null	['Lidia S. Chao', 'Hao Zong', 'Derek F. Wong']	2012-12-01	a-template-based-hybrid-model-for-chinese-1	https://aclanthology.org/W12-6323	https://aclanthology.org/W12-6323.pdf	ws-2012-12	['text-clustering']	['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 1.08379531e+00 -1.42548037e+00 -5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01 1.03678203e+00 -5.39447069e-01 1.19919395e+00 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574f2f40-4fe8-406e-bb86-f96660bdf0e2	learning-to-influence-human-behavior-with	2303.02265	null	https://arxiv.org/abs/2303.02265v3	https://arxiv.org/pdf/2303.02265v3.pdf	Learning to Influence Human Behavior with Offline Reinforcement Learning	When interacting with people, AI agents do not just influence the state of the world -- they also influence the actions people take in response to the agent, and even their underlying intentions and strategies. Accounting for and leveraging this influence has mostly been studied in settings where it is sufficient to assume that human behavior is near-optimal: competitive games, or general-sum settings like autonomous driving alongside human drivers. Instead, we focus on influence in settings where there is a need to capture human suboptimality. For instance, imagine a collaborative task in which, due either to cognitive biases or lack of information, people do not perform very well -- how could an agent influence them towards more optimal behavior? Assuming near-optimal human behavior will not work here, and so the agent needs to learn from real human data. But experimenting online with humans is potentially unsafe, and creating a high-fidelity simulator of the environment is often impractical. Hence, we focus on learning from an offline dataset of human-human interactions. Our observation is that offline reinforcement learning (RL) can learn to effectively influence suboptimal humans by extending and combining elements of observed human-human behavior. We demonstrate that offline RL can solve two challenges with effective influence. First, we show that by learning from a dataset of suboptimal human-human interaction on a variety of tasks -- none of which contains examples of successful influence -- an agent can learn influence strategies to steer humans towards better performance even on new tasks. Second, we show that by also modeling and conditioning on human behavior, offline RL can learn to affect not just the human's actions but also their underlying strategy, and adapt to changes in their strategy.	['Sergey Levine', 'Anca Dragan', 'Joey Hong']	2023-03-03	null	null	null	null	['offline-rl']	['playing-games']	[ 8.19661543e-02 5.06973684e-01 1.92574486e-01 -7.11216554e-02 -9.98485908e-02 -5.43738782e-01 5.53754389e-01 -1.61985159e-01 -8.81326199e-01 9.96168613e-01 1.98538214e-01 -3.17609370e-01 -8.19622502e-02 -7.19998777e-01 -7.65897334e-01 -5.42620659e-01 -1.86835900e-01 9.23993707e-01 1.66431338e-01 -6.99968755e-01 6.95132762e-02 3.74351263e-01 -1.68041921e+00 -2.11200401e-01 7.36514568e-01 3.86836007e-02 2.00249642e-01 1.17691958e+00 5.37051082e-01 1.21776223e+00 -6.78798378e-01 2.81755789e-03 5.62779307e-01 -7.52928495e-01 -4.26629633e-01 2.61877567e-01 -2.45805115e-01 -6.73978567e-01 -5.65045893e-01 7.15377033e-01 4.56325620e-01 3.24281842e-01 5.09623528e-01 -1.62827718e+00 -1.74709544e-01 6.45026863e-01 3.03326361e-02 -3.13454755e-02 5.44242084e-01 1.13159120e+00 6.61025524e-01 3.46404970e-01 5.74675262e-01 1.37036371e+00 5.38917363e-01 7.92215765e-01 -1.33534026e+00 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-6.34115458e-01 6.82846785e-01 4.06838715e-01 -9.23105121e-01 -8.75801086e-01 -4.70122427e-01]	[4.297763824462891, 1.9149256944656372]
47d0ca3a-968f-4b7b-bdde-5d60cae1d52d	from-known-to-the-unknown-transferring	1811.12772	null	http://arxiv.org/abs/1811.12772v1	http://arxiv.org/pdf/1811.12772v1.pdf	From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts	Current Visual Question Answering (VQA) systems can answer intelligent questions about `Known' visual content. However, their performance drops significantly when questions about visually and linguistically `Unknown' concepts are presented during inference (`Open-world' scenario). A practical VQA system should be able to deal with novel concepts in real world settings. To address this problem, we propose an exemplar-based approach that transfers learning (i.e., knowledge) from previously `Known' concepts to answer questions about the `Unknown'. We learn a highly discriminative joint embedding space, where visual and semantic features are fused to give a unified representation. Once novel concepts are presented to the model, it looks for the closest match from an exemplar set in the joint embedding space. This auxiliary information is used alongside the given Image-Question pair to refine visual attention in a hierarchical fashion. Since handling the high dimensional exemplars on large datasets can be a significant challenge, we introduce an efficient matching scheme that uses a compact feature description for search and retrieval. To evaluate our model, we propose a new split for VQA, separating Unknown visual and semantic concepts from the training set. Our approach shows significant improvements over state-of-the-art VQA models on the proposed Open-World VQA dataset and standard VQA datasets.	['Moshiur R. Farazi', 'Salman H. Khan', 'Nick Barnes']	2018-11-30	null	null	null	null	['novel-concepts']	['reasoning']	[-3.84786422e-03 1.68960367e-03 -4.73573431e-02 -4.81509447e-01 -1.26390576e+00 -9.03507054e-01 5.61730564e-01 4.41294014e-01 -4.46947008e-01 5.54375768e-01 2.23521680e-01 -1.21998101e-01 -9.94120240e-02 -8.13619137e-01 -9.07296240e-01 -6.21281862e-01 3.53267729e-01 7.32539117e-01 6.01827145e-01 -4.11617726e-01 3.16684395e-01 4.08292532e-01 -2.02636266e+00 7.96686292e-01 7.52161920e-01 1.00765443e+00 5.50955415e-01 6.88870966e-01 -7.62636364e-01 6.64004385e-01 -7.06387043e-01 -6.25987411e-01 6.02087900e-02 -2.27606863e-01 -1.31771624e+00 5.16313389e-02 1.04384542e+00 -1.56051710e-01 -1.93269029e-01 9.25564051e-01 2.79789686e-01 4.85171765e-01 8.06307554e-01 -1.45934474e+00 -1.21871567e+00 -4.18167450e-02 -3.31354707e-01 4.45373833e-01 5.78599095e-01 2.19082475e-01 1.37903094e+00 -1.19960225e+00 1.00037467e+00 1.41454136e+00 1.61069229e-01 7.77438343e-01 -1.22647011e+00 -3.25830400e-01 3.82948220e-01 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585114d4-d7d8-409f-9b39-7d15dcaa8007	towards-computationally-efficient	2302.12676	null	https://arxiv.org/abs/2302.12676v1	https://arxiv.org/pdf/2302.12676v1.pdf	Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs	Responsibility attribution is a key concept of accountable multi-agent decision making. Given a sequence of actions, responsibility attribution mechanisms quantify the impact of each participating agent to the final outcome. One such popular mechanism is based on actual causality, and it assigns (causal) responsibility based on the actions that were found to be pivotal for the considered outcome. However, the inherent problem of pinpointing actual causes and consequently determining the exact responsibility assignment has shown to be computationally intractable. In this paper, we aim to provide a practical algorithmic solution to the problem of responsibility attribution under a computational budget. We first formalize the problem in the framework of Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) augmented by a specific class of Structural Causal Models (SCMs). Under this framework, we introduce a Monte Carlo Tree Search (MCTS) type of method which efficiently approximates the agents' degrees of responsibility. This method utilizes the structure of a novel search tree and a pruning technique, both tailored to the problem of responsibility attribution. Other novel components of our method are (a) a child selection policy based on linear scalarization and (b) a backpropagation procedure that accounts for a minimality condition that is typically used to define actual causality. We experimentally evaluate the efficacy of our algorithm through a simulation-based test-bed, which includes three team-based card games.	['Goran Radanovic', 'Stelios Triantafyllou']	2023-02-24	null	null	null	null	['card-games']	['playing-games']	[ 3.79711181e-01 6.02569461e-01 -3.39989603e-01 -1.37309924e-01 -4.28890616e-01 -4.28444564e-01 7.38142133e-01 4.97524142e-01 -5.79638064e-01 1.15119362e+00 2.16448501e-01 -2.95049071e-01 -7.74110675e-01 -9.24171865e-01 -4.14568037e-01 -7.26770341e-01 -2.57418692e-01 1.12950492e+00 2.60999501e-01 -9.36925337e-02 1.85822353e-01 2.08660826e-01 -1.25522208e+00 -2.21059546e-01 8.22643340e-01 6.48894429e-01 1.34970739e-01 6.35681510e-01 2.64258444e-01 1.34807754e+00 -6.53121293e-01 -5.43232322e-01 2.36664101e-01 -4.63189840e-01 -1.07942951e+00 1.09968379e-01 -4.81247157e-01 -4.92044032e-01 3.10781121e-01 1.00531733e+00 1.94396690e-01 1.24432787e-01 8.60124111e-01 -1.51876473e+00 8.89242962e-02 1.23765469e+00 -3.67033809e-01 1.66658819e-01 3.89162332e-01 -8.95263702e-02 1.19945133e+00 -2.96088606e-02 6.39483690e-01 1.39807832e+00 2.53261387e-01 4.85063642e-01 -1.36340249e+00 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4e137572-4735-4558-bca3-205ca6c8f2b5	greenhouse-gases-emissions-estimating	2212.10844	null	https://arxiv.org/abs/2212.10844v1	https://arxiv.org/pdf/2212.10844v1.pdf	Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning	As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, specifically designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, by countries or by revenues buckets. We also compare our results to those of other providers and find our estimates to be more accurate. Thanks to the proposed explainability tools using Shapley values, our model is fully interpretable, the user being able to understand which factors split explain the GHG emissions for each particular company.	['François Soupé', 'Laurent Carlier', 'Thibaut Heurtebize', 'Jeremi Assael']	2022-12-21	null	null	null	null	['interpretable-machine-learning']	['methodology']	[-2.80690163e-01 6.14486635e-01 -5.44771910e-01 -7.51941800e-02 -7.43725121e-01 -7.14878619e-01 6.96542442e-01 2.01865777e-01 -1.78690076e-01 1.11567557e+00 8.06509927e-02 -7.27927327e-01 -4.37517941e-01 -1.19593716e+00 -5.74478626e-01 -4.89118934e-01 1.31191298e-01 6.37201846e-01 -2.88431704e-01 1.43981099e-01 1.90480679e-01 4.19790745e-01 -1.09772098e+00 1.54482380e-01 8.98495018e-01 1.06997859e+00 1.89510077e-01 5.06647408e-01 -5.93168139e-02 7.11472571e-01 -4.34371620e-01 -3.89693499e-01 7.09341943e-01 -2.76462108e-01 -7.95935512e-01 -1.76347882e-01 7.07001090e-02 -1.72261849e-01 3.70586425e-01 1.13432920e+00 -6.37554005e-02 -3.79450291e-01 8.20807517e-01 -1.24948072e+00 -4.64344919e-01 1.07484055e+00 -4.72295195e-01 -1.52522013e-01 -7.31185228e-02 1.38558656e-01 1.20447838e+00 -1.98305398e-01 4.07676309e-01 8.37517381e-01 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568c2d19-9381-4d16-8a7d-e5488aa71ca7	poincare-glove-hyperbolic-word-embeddings-1	null	null	https://openreview.net/forum?id=Ske5r3AqK7	https://openreview.net/pdf?id=Ske5r3AqK7	Poincare Glove: Hyperbolic Word Embeddings	Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian product of hyperbolic spaces which we theoretically connect to the Gaussian word embeddings and their Fisher geometry. This connection allows us to introduce a novel principled hypernymy score for word embeddings. Moreover, we adapt the well-known Glove algorithm to learn unsupervised word embeddings in this type of Riemannian manifolds. We further explain how to solve the analogy task using the Riemannian parallel transport that generalizes vector arithmetics to this new type of geometry. Empirically, based on extensive experiments, we prove that our embeddings, trained unsupervised, are the first to simultaneously outperform strong and popular baselines on the tasks of similarity, analogy and hypernymy detection. In particular, for word hypernymy, we obtain new state-of-the-art on fully unsupervised WBLESS classification accuracy.	['Octavian-Eugen Ganea', 'Gary Becigneul', 'Alexandru Tifrea*']	2019-05-01	null	null	null	iclr-2019-5	['learning-word-embeddings']	['methodology']	[-3.36194217e-01 3.34123343e-01 -3.80632132e-02 -2.00147763e-01 -1.20610923e-01 -6.18079364e-01 8.94286036e-01 2.41506726e-01 -5.68007350e-01 -6.49389401e-02 4.55670983e-01 -4.30604279e-01 -3.74765277e-01 -8.70289981e-01 -2.34540924e-01 -7.21382856e-01 -1.46743640e-01 4.21716094e-01 -2.71687329e-01 -5.39127409e-01 3.99223655e-01 5.05509734e-01 -1.38182831e+00 -4.18563008e-01 8.49274635e-01 5.52424550e-01 -1.05945051e-01 5.09925783e-01 8.82032961e-02 4.73567814e-01 -4.45417070e-04 -8.24020743e-01 1.87203929e-01 -2.49457762e-01 -1.15978050e+00 9.44468006e-02 5.81819832e-01 -8.37555453e-02 -5.89987695e-01 1.15486181e+00 1.63025647e-01 3.87539595e-01 1.21222842e+00 -1.22755468e+00 -1.41883075e+00 4.21988606e-01 -1.42692074e-01 -8.05461872e-03 1.82473585e-01 -1.81488007e-01 1.95910323e+00 -1.32793939e+00 6.29524529e-01 1.22473669e+00 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c61c97e6-f620-4c67-b4df-e107dfdc142f	pyrca-a-library-for-metric-based-root-cause	2306.11417	null	https://arxiv.org/abs/2306.11417v1	https://arxiv.org/pdf/2306.11417v1.pdf	PyRCA: A Library for Metric-based Root Cause Analysis	We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It provides a holistic framework to uncover the complicated metric causal dependencies and automatically locate root causes of incidents. It offers a unified interface for multiple commonly used RCA models, encompassing both graph construction and scoring tasks. This library aims to provide IT operations staff, data scientists, and researchers a one-step solution to rapid model development, model evaluation and deployment to online applications. In particular, our library includes various causal discovery methods to support causal graph construction, and multiple types of root cause scoring methods inspired by Bayesian analysis, graph analysis and causal analysis, etc. Our GUI dashboard offers practitioners an intuitive point-and-click interface, empowering them to easily inject expert knowledge through human interaction. With the ability to visualize causal graphs and the root cause of incidents, practitioners can quickly gain insights and improve their workflow efficiency. This technical report introduces PyRCA's architecture and major functionalities, while also presenting benchmark performance numbers in comparison to various baseline models. Additionally, we demonstrate PyRCA's capabilities through several example use cases.	['Steven C. H. Hoi', 'Doyen Sahoo', 'Manpreet Singh', 'Himanshu Mittal', 'Wenzhuo Yang', 'Chenghao Liu']	2023-06-20	null	null	null	null	['graph-construction', 'causal-discovery']	['graphs', 'knowledge-base']	[-1.45018503e-01 1.30937025e-01 -4.78370726e-01 -7.35711232e-02 -2.68006861e-01 -6.21048927e-01 6.11066461e-01 6.51209772e-01 4.47888732e-01 4.80493158e-01 4.74941283e-01 -1.05240405e+00 -9.02392924e-01 -9.94448364e-01 -2.38555431e-01 -4.06633466e-01 -8.25453937e-01 4.13865685e-01 -2.30947621e-02 -4.76190224e-02 4.00111914e-01 6.74762487e-01 -1.05626452e+00 3.97591203e-01 5.68766236e-01 6.12903178e-01 -5.31953946e-02 7.74495900e-01 -3.43567729e-02 1.46320069e+00 -3.81971747e-01 -2.76197702e-01 -1.26758143e-01 -1.84102148e-01 -1.06430328e+00 -5.91535509e-01 -8.35321024e-02 -1.67298362e-01 -4.22098935e-01 3.87452275e-01 2.75412649e-01 -4.54584509e-01 6.17365956e-01 -1.86457193e+00 -4.08372760e-01 1.03012037e+00 -7.10106313e-01 5.70522845e-01 8.26893628e-01 2.76132584e-01 1.27009356e+00 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264c172b-f66f-4f90-9c7e-c0708f0fb296	tsup-speaker-diarization-system-for	2210.14653	null	https://arxiv.org/abs/2210.14653v1	https://arxiv.org/pdf/2210.14653v1.pdf	TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge	This paper describes the TSUP team's submission to the ISCSLP 2022 conversational short-phrase speaker diarization (CSSD) challenge which particularly focuses on short-phrase conversations with a new evaluation metric called conversational diarization error rate (CDER). In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively. Our major findings are summarized as follows. First, the SC approach is more favored over the other two approaches under the new CDER metric. Second, tuning on hyperparameters is essential to CDER for all three types of speaker diarization systems. Specifically, CDER becomes smaller when the length of sub-segments setting longer. Finally, multi-system fusion through DOVER-LAP will worsen the CDER metric on the challenge data. Our submitted SC system eventually ranks the third place in the challenge.	['Lei Xie', 'Qing Wang', 'Li Zhang', 'Yang Sun', 'Xiaoyue Yang', 'Gaosheng Zhang', 'Huan Zhao', 'Bowen Pang']	2022-10-26	null	null	null	null	['activity-detection']	['computer-vision']	[ 6.32869229e-02 2.24358197e-02 1.54505640e-01 -4.93270248e-01 -1.32003331e+00 -5.88895321e-01 7.32007205e-01 -2.85230726e-01 -3.37920874e-01 4.18822438e-01 7.17627823e-01 -3.37158471e-01 1.44332185e-01 1.73130721e-01 8.86953697e-02 -7.65196204e-01 -4.30607945e-02 5.78614712e-01 6.15258329e-02 -2.00743645e-01 -2.69191582e-02 2.95479655e-01 -1.44033289e+00 7.83984065e-02 9.12188411e-01 8.05881500e-01 2.95071397e-02 9.54488933e-01 -7.06038773e-02 2.58279443e-02 -1.03636885e+00 -2.00838163e-01 8.46782401e-02 -5.54070413e-01 -7.14569807e-01 -1.86097845e-01 3.57544363e-01 3.51163768e-03 -2.51545161e-01 8.07404578e-01 1.19480538e+00 4.25811768e-01 6.25097454e-01 -1.51669133e+00 -9.90802050e-02 1.05617726e+00 -4.36017931e-01 5.23742795e-01 4.55268055e-01 4.31981646e-02 1.06875038e+00 -1.04408312e+00 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1.09378171e+00 -9.06773329e-01 -6.46417201e-01 -1.52374959e+00 -7.19430327e-01 7.69725218e-02 5.55518210e-01 -5.31708837e-01 -8.67182493e-01 -1.41936883e-01]	[14.58634090423584, 6.1583123207092285]
71361450-d7e9-4fae-b366-83b0e3df2fd3	towards-self-supervised-gaze-estimation	2203.10974	null	https://arxiv.org/abs/2203.10974v2	https://arxiv.org/pdf/2203.10974v2.pdf	Towards Self-Supervised Gaze Estimation	Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification. These self-supervised methods aim at maximizing agreement between features extracted from two differently transformed views of the same image, which results in learning an invariant representation with respect to appearance and geometric image transformations. However, the effectiveness of these approaches remains unclear in the context of gaze estimation, a structured regression task that requires equivariance under geometric transformations (e.g., rotations, horizontal flip). In this work, we propose SwAT, an equivariant version of the online clustering-based self-supervised approach SwAV, to learn more informative representations for gaze estimation. We demonstrate that SwAT, with ResNet-50 and supported with uncurated unlabeled face images, outperforms state-of-the-art gaze estimation methods and supervised baselines in various experiments. In particular, we achieve up to 57% and 25% improvements in cross-dataset and within-dataset evaluation tasks on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze).	['Sergio Escalera', 'Simone Scardapane', 'Cristina Palmero', 'Arya Farkhondeh']	2022-03-21	null	null	null	null	['gaze-estimation', 'online-clustering']	['computer-vision', 'computer-vision']	[ 3.05184931e-01 1.40931770e-01 -2.44527012e-01 -8.72126460e-01 -4.66944635e-01 -3.21971804e-01 4.47862357e-01 -3.31203133e-01 -4.29393113e-01 4.08915341e-01 1.73443928e-01 2.93718725e-01 -1.02925718e-01 -6.53800964e-02 -8.84954572e-01 -7.76803911e-01 3.48329619e-02 2.06478074e-01 -1.24481320e-01 3.50241400e-02 3.50628585e-01 7.58350864e-02 -2.14577556e+00 -1.43089026e-01 6.70598269e-01 9.58623588e-01 -2.92820424e-01 4.66509134e-01 2.84669608e-01 6.67943656e-01 -1.90385014e-01 -5.64072490e-01 5.11056781e-02 -5.20872116e-01 -9.65077341e-01 1.43245101e-01 1.41383743e+00 -2.64535427e-01 -1.22887515e-01 1.10587394e+00 4.24922168e-01 6.20608181e-02 9.29925919e-01 -1.63760006e+00 -9.84618723e-01 2.34233335e-01 -1.09391606e+00 1.60163403e-01 3.20043266e-01 7.91324377e-02 1.08561730e+00 -1.00841188e+00 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ea17ea68-401d-4e93-a369-a9fcf1103d42	predicting-retrosynthetic-pathways-using-a	1910.08036	null	https://arxiv.org/abs/1910.08036v1	https://arxiv.org/pdf/1910.08036v1.pdf	Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy	We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce new metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks has a very good performance with few weaknesses due to the bias induced during the training process. The use of the newly introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks through focusing on the performance of the single-step model only.	['Valerio Zullo', 'Riccardo Petraglia', 'Anna Iuliano', 'Rico Andreas Haeuselmann', 'Costas Bekas', 'Teodoro Laino', 'Riccardo Pisoni', 'Philippe Schwaller', 'Vishnu H Nair']	2019-10-17	null	null	null	null	['retrosynthesis']	['medical']	[ 4.44270045e-01 5.40679157e-01 -3.88419896e-01 9.50664580e-02 -3.95422518e-01 -1.13506317e+00 7.87402391e-01 7.31548429e-01 -4.12691653e-01 1.06144679e+00 -6.42126128e-02 -5.88801920e-01 -3.88623506e-01 -7.52157092e-01 -4.59444404e-01 -8.52976620e-01 -5.76556660e-02 7.54200161e-01 3.19488525e-01 -4.42422688e-01 6.23080075e-01 6.98194623e-01 -1.73374569e+00 1.40115142e-01 1.08213031e+00 8.54848027e-01 3.77446741e-01 5.98446012e-01 -6.61131293e-02 4.41783547e-01 -3.67678791e-01 -6.92516565e-01 1.75797582e-01 -1.21648826e-01 -7.23725498e-01 -6.00544155e-01 -8.47076699e-02 3.27410161e-01 2.01566055e-01 6.65777802e-01 6.72326863e-01 3.05549830e-01 8.97521853e-01 -6.85530722e-01 2.13828892e-01 1.06601477e+00 4.97936875e-01 -1.21619157e-01 5.89600921e-01 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a1abebc6-fd53-4aaa-9efa-b1a8751fa421	a-dataset-for-audio-visual-sound-event	2302.07315	null	https://arxiv.org/abs/2302.07315v1	https://arxiv.org/pdf/2302.07315v1.pdf	A dataset for Audio-Visual Sound Event Detection in Movies	Audio event detection is a widely studied audio processing task, with applications ranging from self-driving cars to healthcare. In-the-wild datasets such as Audioset have propelled research in this field. However, many efforts typically involve manual annotation and verification, which is expensive to perform at scale. Movies depict various real-life and fictional scenarios which makes them a rich resource for mining a wide-range of audio events. In this work, we present a dataset of audio events called Subtitle-Aligned Movie Sounds (SAM-S). We use publicly-available closed-caption transcripts to automatically mine over 110K audio events from 430 movies. We identify three dimensions to categorize audio events: sound, source, quality, and present the steps involved to produce a final taxonomy of 245 sounds. We discuss the choices involved in generating the taxonomy, and also highlight the human-centered nature of sounds in our dataset. We establish a baseline performance for audio-only sound classification of 34.76% mean average precision and show that incorporating visual information can further improve the performance by about 5%. Data and code are made available for research at https://github.com/usc-sail/mica-subtitle-aligned-movie-sounds	['Shrikanth Narayanan', 'Veena Vijai', 'Krishna Somandepalli', 'Digbalay Bose', 'Rajat Hebbar']	2023-02-14	null	null	null	null	['sound-event-detection', 'sound-classification']	['audio', 'audio']	[ 2.35406578e-01 -5.89522719e-01 7.17325211e-02 -4.33100045e-01 -1.44206667e+00 -8.66821766e-01 2.25359261e-01 4.71604317e-01 -2.11894929e-01 2.87410200e-01 5.17976463e-01 4.81326412e-03 7.02238753e-02 -4.36426431e-01 -5.06855667e-01 -4.35476094e-01 -1.87574089e-01 -1.75072595e-01 3.51236999e-01 1.26781678e-02 2.67541081e-01 1.29100919e-01 -2.00962257e+00 7.80177534e-01 2.59944737e-01 1.24038446e+00 1.74304515e-01 1.11977065e+00 1.65546164e-02 6.69511616e-01 -7.50192225e-01 -4.09650773e-01 1.21552579e-03 -5.60936034e-01 -6.25470102e-01 -1.02979936e-01 4.33263391e-01 -6.05839398e-03 -1.24658845e-01 8.99956644e-01 9.20793414e-01 1.50956556e-01 4.06565726e-01 -1.52424717e+00 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f621b4da-1a21-4eb6-980b-f77d65a7a0d5	imitation-learning-as-state-matching-via	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Imitation_Learning_As_State_Matching_via_Differentiable_Physics_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Imitation_Learning_As_State_Matching_via_Differentiable_Physics_CVPR_2023_paper.pdf	Imitation Learning As State Matching via Differentiable Physics	Existing imitation learning (IL) methods such as inverse reinforcement learning (IRL) usually have a double-loop training process, alternating between learning a reward function and a policy and tend to suffer long training time and high variance. In this work, we identify the benefits of differentiable physics simulators and propose a new IL method, i.e., Imitation Learning via Differentiable Physics (ILD), which gets rid of the double-loop design and achieves significant improvements in final performance, convergence speed, and stability. The proposed ILD incorporates the differentiable physics simulator as a physics prior into its computational graph for policy learning. It unrolls the dynamics by sampling actions from a parameterized policy, simply minimizing the distance between the expert trajectory and the agent trajectory, and back-propagating the gradient into the policy via temporal physics operators. With the physics prior, ILD policies can not only be transferable to unseen environment specifications but also yield higher final performance on a variety of tasks. In addition, ILD naturally forms a single-loop structure, which significantly improves the stability and training speed. To simplify the complex optimization landscape induced by temporal physics operations, ILD dynamically selects the learning objectives for each state during optimization. In our experiments, we show that ILD outperforms state-of-the-art methods in a variety of continuous control tasks with Brax, requiring only one expert demonstration. In addition, ILD can be applied to challenging deformable object manipulation tasks and can be generalized to unseen configurations.	['Zhongwen Xu', 'Xiao Ma', 'Siwei Chen']	2023-01-01	null	null	null	cvpr-2023-1	['continuous-control', 'deformable-object-manipulation']	['playing-games', 'robots']	[-1.03131412e-02 -8.95083044e-03 -2.47122526e-01 1.69661418e-01 -3.29171240e-01 -6.29280686e-01 5.05339682e-01 -5.76280579e-02 -5.93717754e-01 8.40260088e-01 -4.17857438e-01 -3.59391868e-01 -4.37235177e-01 -6.13103330e-01 -1.14929605e+00 -7.83715069e-01 -2.28794098e-01 6.05865359e-01 4.46626097e-01 -3.41417938e-01 9.26250815e-02 6.21851504e-01 -1.10741377e+00 -5.62586904e-01 1.29485488e+00 7.42605448e-01 6.24442101e-01 4.37474132e-01 1.29233584e-01 6.57386124e-01 -4.10157084e-01 1.19495124e-01 1.65117294e-01 -2.52599061e-01 -3.77803802e-01 -1.50862513e-02 1.19463287e-01 -4.79325175e-01 -6.22757852e-01 8.92782390e-01 5.66428185e-01 6.33503675e-01 5.08481085e-01 -1.22147751e+00 -6.39886439e-01 2.84419149e-01 -4.59860891e-01 -2.18945295e-01 1.62991777e-01 7.53976524e-01 6.18980527e-01 -6.59669995e-01 4.66663867e-01 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d52d00ce-cc87-4c4b-baba-47bf1b8beb0d	unsupervised-jpeg-domain-adaptation-for	null	null	https://hal.archives-ouvertes.fr/hal-03374780/	https://hal.archives-ouvertes.fr/hal-03374780v1/document	Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics	Domain adaptation is a major issue for doing practical forensics. Since examined images are likely to come from a different development pipeline compared to the ones used for training our models, that may disturb them by a lot, degrading their performances. In this paper, we present a method enabling to make a forgery detector more robust to distributions different but related to its training one, inspired by [1]. The strategy exhibited in this paper foster a detector to find a feature invariant space where source and target distributions are close. Our study deals more precisely with discrepancies observed due to JPEG compressions and our experiments reveal that the proposed adaptation scheme can reasonably reduce the mismatch, even with a rather small target set with no labels when the source domain is properly selected. On top of that, when a small portion of labelled target images is available this method reduces the gap with mix training while being unsupervised.	['Patrick Bas', 'Jérémie Boulanger', 'Vincent Itier', 'Rony Abecidan']	2021-12-09	null	null	null	wifs-ieee-international-workshop-on	['jpeg-forgery-localization', 'image-forensics']	['computer-vision', 'computer-vision']	[ 1.96307063e-01 -3.28216814e-02 -2.80115753e-02 -1.75014123e-01 -7.07349896e-01 -6.98603928e-01 8.12491357e-01 1.13698497e-01 -5.47555447e-01 8.11655521e-01 -2.82791518e-02 -6.83977380e-02 -1.49157792e-01 -6.44376516e-01 -7.09231079e-01 -7.84514546e-01 2.78846532e-01 7.42670238e-01 6.52590334e-01 -1.79069504e-01 5.87549269e-01 7.33742476e-01 -1.63347971e+00 4.33731169e-01 6.71830714e-01 4.36991036e-01 3.64201784e-01 5.92377603e-01 -6.04898483e-02 7.52057791e-01 -1.08990514e+00 -7.72761703e-01 5.80222070e-01 -4.60804492e-01 -8.75164390e-01 3.94711167e-01 3.32921654e-01 -1.84698239e-01 -6.66424036e-02 1.36599851e+00 5.33736825e-01 -4.49474789e-02 1.03123176e+00 -1.12627971e+00 -8.98576379e-02 5.15480161e-01 -4.38002020e-01 3.57663393e-01 2.34592542e-01 2.80116856e-01 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5588b6a7-3c47-4640-a088-9ca8060e5e66	improving-generalizability-in-implicitly-1	2204.02261	null	https://arxiv.org/abs/2204.02261v1	https://arxiv.org/pdf/2204.02261v1.pdf	Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors	Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human well-being such as content moderation. New kinds of abusive language continually emerge in online discussions in response to current events (e.g., COVID-19), and the deployed abuse detection systems should be updated regularly to remain accurate. In this paper, we show that general abusive language classifiers tend to be fairly reliable in detecting out-of-domain explicitly abusive utterances but fail to detect new types of more subtle, implicit abuse. Next, we propose an interpretability technique, based on the Testing Concept Activation Vector (TCAV) method from computer vision, to quantify the sensitivity of a trained model to the human-defined concepts of explicit and implicit abusive language, and use that to explain the generalizability of the model on new data, in this case, COVID-related anti-Asian hate speech. Extending this technique, we introduce a novel metric, Degree of Explicitness, for a single instance and show that the new metric is beneficial in suggesting out-of-domain unlabeled examples to effectively enrich the training data with informative, implicitly abusive texts.	['Svetlana Kiritchenko', 'Kathleen C. Fraser', 'Isar Nejadgholi']	2022-04-05	null	https://aclanthology.org/2022.acl-long.378	https://aclanthology.org/2022.acl-long.378.pdf	acl-2022-5	['abusive-language', 'abuse-detection']	['natural-language-processing', 'natural-language-processing']	[ 4.14016545e-02 2.48382818e-02 -4.22458351e-01 -4.73256648e-01 -4.36682254e-01 -7.49853015e-01 5.37315309e-01 4.39218074e-01 -4.10234839e-01 9.70649779e-01 3.32223296e-01 -4.51348513e-01 8.10517184e-03 -3.62596810e-01 -3.84075791e-01 -3.62331063e-01 -4.49509323e-02 4.16867733e-01 -8.44686478e-02 -4.51781660e-01 2.16405973e-01 6.13539517e-01 -8.96780372e-01 2.99617320e-01 1.06560123e+00 3.58949393e-01 -5.44281423e-01 5.24312615e-01 -1.47055313e-01 9.00123000e-01 -1.11087501e+00 -8.78570080e-01 -2.45143339e-01 -5.41941106e-01 -1.05590451e+00 1.48936629e-01 2.39101127e-01 -6.37633026e-01 -9.07711461e-02 1.20450890e+00 1.21918462e-01 3.97501923e-02 6.71451807e-01 -1.33127284e+00 -7.46166050e-01 9.16057706e-01 -7.17784166e-01 8.62900436e-01 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b604042e-1eaa-482c-8ddf-462231cd84bb	does-knowledge-transfer-always-help-to-learn	1912.02986	null	https://arxiv.org/abs/1912.02986v2	https://arxiv.org/pdf/1912.02986v2.pdf	How Does an Approximate Model Help in Reinforcement Learning?	One of the key approaches to save samples in reinforcement learning (RL) is to use knowledge from an approximate model such as its simulator. However, how much does an approximate model help to learn a near-optimal policy of the true unknown model? Despite numerous empirical studies of transfer reinforcement learning, an answer to this question is still elusive. In this paper, we study the sample complexity of RL while an approximate model of the environment is provided. For an unknown Markov decision process (MDP), we show that the approximate model can effectively reduce the complexity by eliminating sub-optimal actions from the policy searching space. In particular, we provide an algorithm that uses $\widetilde{O}(N/(1-\gamma)^3/\varepsilon^2)$ samples in a generative model to learn an $\varepsilon$-optimal policy, where $\gamma$ is the discount factor and $N$ is the number of near-optimal actions in the approximate model. This can be much smaller than the learning-from-scratch complexity $\widetilde{\Theta}(SA/(1-\gamma)^3/\varepsilon^2)$, where $S$ and $A$ are the sizes of state and action spaces respectively. We also provide a lower bound showing that the above upper bound is nearly-tight if the value gap between near-optimal actions and sub-optimal actions in the approximate model is sufficiently large. Our results provide a very precise characterization of how an approximate model helps reinforcement learning when no additional assumption on the model is posed.	['Lin F. Yang', 'Fei Feng', 'Wotao Yin']	2019-12-06	null	null	null	null	['transfer-reinforcement-learning']	['methodology']	[-1.43738091e-02 2.87290543e-01 -4.18204367e-01 -1.47538912e-02 -9.30006266e-01 -7.20409393e-01 2.22979292e-01 5.59520610e-02 -9.02953744e-01 1.20412910e+00 -3.76596481e-01 -6.55784726e-01 -2.69555092e-01 -8.39204252e-01 -1.06185746e+00 -8.20654333e-01 -4.35071826e-01 6.42423272e-01 3.65172207e-01 -1.93072066e-01 2.07152680e-01 2.74944723e-01 -1.44011796e+00 -2.74397194e-01 8.58270705e-01 9.17328715e-01 2.58102864e-01 9.54991221e-01 -6.77156448e-02 5.92428505e-01 -7.17675626e-01 1.21962398e-01 5.08355141e-01 -9.99466062e-01 -7.23374844e-01 -3.11677605e-02 -9.27450359e-02 -5.64313471e-01 -2.02679053e-01 1.33401060e+00 3.63535732e-01 3.51859003e-01 4.06959146e-01 -1.17820692e+00 1.58914834e-01 8.03883791e-01 -3.77751678e-01 2.22564235e-01 1.40265808e-01 4.92931575e-01 6.52904689e-01 1.62670702e-01 4.20907915e-01 1.24664199e+00 2.47560292e-01 5.32739103e-01 -1.28419197e+00 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-8.76629233e-01 -3.44072849e-01 2.37739787e-01 -7.86354423e-01 -5.02661884e-01 -3.75180721e-01]	[4.330489635467529, 2.7785613536834717]
38937659-5d95-44fb-a821-dff969052093	predictive-and-diagnosis-models-of-stroke	2306.05289	null	https://arxiv.org/abs/2306.05289v1	https://arxiv.org/pdf/2306.05289v1.pdf	Predictive and diagnosis models of stroke from hemodynamic signal monitoring	This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 minutes of monitoring, to predict the exitus during the first 3 hours of monitoring, and to predict the stroke recurrence in just 15 minutes of monitoring. Patients with difficult access to a \acrshort{CT} scan, and all patients that arrive at the stroke unit of a specialized hospital will benefit from these positive results. The results obtained from the real-time developed models are the following: stroke diagnosis around $98\%$ precision ($97.8\%$ Sensitivity, $99.5\%$ Specificity), exitus prediction with $99.8\%$ precision ($99.8\%$ Sens., $99.9\%$ Spec.) and $98\%$ precision predicting stroke recurrence ($98\%$ Sens., $99\%$ Spec.).	['José L. Ayala', 'Gemma Reig Roselló', 'José L. Risco-Martín', 'Luis García-Terriza']	2023-05-30	null	null	null	null	['specificity']	['natural-language-processing']	[-2.50746667e-01 -1.32068098e-01 -2.10551426e-01 -3.65761489e-01 -7.52700329e-01 -3.17763716e-01 -5.79480529e-02 4.06271517e-01 -6.82762504e-01 1.08040261e+00 8.90845060e-02 -1.00004363e+00 -6.25410616e-01 -9.37893331e-01 -1.83184862e-01 -3.10228467e-01 -8.66159797e-01 8.69240701e-01 2.13880673e-01 2.42642149e-01 2.09032014e-01 9.48807955e-01 -1.19320178e+00 3.84550184e-01 9.44487453e-01 1.09221351e+00 -6.62558451e-02 6.53441072e-01 -1.32455630e-02 7.57699192e-01 -4.98255402e-01 1.10102154e-01 3.85014027e-01 -4.83307272e-01 -4.33517992e-01 -7.09066808e-01 -2.83384115e-01 -7.03257680e-01 -3.16084415e-01 2.76190162e-01 7.92136014e-01 2.98909456e-01 1.05226660e+00 -9.29181814e-01 7.09132493e-01 2.95234829e-01 -2.51015157e-01 6.26854718e-01 7.85135403e-02 3.02843899e-01 2.19589651e-01 -6.33737922e-01 4.32216346e-01 2.44880810e-01 5.06738544e-01 4.03739333e-01 -8.67709517e-01 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-6.67184651e-01 1.48079142e-01 1.11478809e-02 -7.77463198e-01 -3.50283772e-01 1.34597898e-01]	[14.152853012084961, 3.0088353157043457]
cf81fab3-1648-4f77-91e7-176990b14391	on-the-origins-of-bias-in-nlp-through-the	2305.09281	null	https://arxiv.org/abs/2305.09281v1	https://arxiv.org/pdf/2305.09281v1.pdf	On the Origins of Bias in NLP through the Lens of the Jim Code	In this paper, we trace the biases in current natural language processing (NLP) models back to their origins in racism, sexism, and homophobia over the last 500 years. We review literature from critical race theory, gender studies, data ethics, and digital humanities studies, and summarize the origins of bias in NLP models from these social science perspective. We show how the causes of the biases in the NLP pipeline are rooted in social issues. Finally, we argue that the only way to fix the bias and unfairness in NLP is by addressing the social problems that caused them in the first place and by incorporating social sciences and social scientists in efforts to mitigate bias in NLP models. We provide actionable recommendations for the NLP research community to do so.	['Gavin Abercrombie', 'Fatma Elsafoury']	2023-05-16	null	null	null	null	['ethics']	['miscellaneous']	[ 2.07260042e-01 7.72275269e-01 -8.19376290e-01 -6.56405151e-01 3.04745380e-02 -6.89616203e-01 6.91432834e-01 8.55188012e-01 -9.41271305e-01 4.51214820e-01 1.29231465e+00 -8.00793946e-01 -1.52965412e-01 -4.14645791e-01 -4.19408679e-01 2.94633824e-02 7.00970769e-01 1.48369774e-01 -5.39998710e-01 -2.17974842e-01 1.04769599e+00 3.04841429e-01 -9.79932249e-01 6.54184446e-02 1.24746966e+00 2.17972789e-02 -7.68744826e-01 1.55423179e-01 -5.52280128e-01 8.95297229e-01 -3.55902672e-01 -8.56333017e-01 3.99140865e-01 -3.86930823e-01 -8.52090180e-01 -7.65204668e-01 7.30952382e-01 -2.38381386e-01 -8.71083513e-02 1.28093481e+00 6.82077706e-01 -1.97792612e-02 6.37853563e-01 -1.35634124e+00 -1.17596447e+00 1.23994136e+00 -7.74108350e-01 3.80048424e-01 3.86761695e-01 4.19830263e-01 8.07363391e-01 -7.65673876e-01 1.20669436e+00 2.10271764e+00 6.48337722e-01 6.10681474e-01 -1.11101043e+00 -1.22880065e+00 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61315081-7f2d-4e93-8c59-79757e50914a	proposal-tracking-and-segmentation-pts-a	1907.01203	null	https://arxiv.org/abs/1907.01203v2	https://arxiv.org/pdf/1907.01203v2.pdf	Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation	Video object segmentation (VOS) aims at pixel-level object tracking given only the annotations in the first frame. Due to the large visual variations of objects in video and the lack of training samples, it remains a difficult task despite the upsurging development of deep learning. Toward solving the VOS problem, we bring in several new insights by the proposed unified framework consisting of object proposal, tracking and segmentation components. The object proposal network transfers objectness information as generic knowledge into VOS; the tracking network identifies the target object from the proposals; and the segmentation network is performed based on the tracking results with a novel dynamic-reference based model adaptation scheme. Extensive experiments have been conducted on the DAVIS'17 dataset and the YouTube-VOS dataset, our method achieves the state-of-the-art performance on several video object segmentation benchmarks. We make the code publicly available at https://github.com/sydney0zq/PTSNet.	['Chang Huang', 'Yongchao Gong', 'Han Shen', 'Wenyu Liu', 'Qiang Zhou', 'Xinggang Wang', 'Lichao Huang', 'Zilong Huang']	2019-07-02	null	null	null	null	['one-shot-visual-object-segmentation']	['computer-vision']	[-3.52127217e-02 -1.18161537e-01 -4.96753097e-01 -2.60660082e-01 -5.70769131e-01 -4.53473389e-01 2.16255978e-01 -2.72728205e-01 -3.61255020e-01 4.00190562e-01 -2.34311685e-01 3.58459800e-02 3.07793319e-01 -3.75331283e-01 -9.29159701e-01 -5.88025451e-01 6.51617125e-02 3.61974210e-01 1.21405351e+00 1.14123136e-01 7.70267919e-02 3.47819149e-01 -1.35541117e+00 9.00968164e-02 6.57482326e-01 1.47643661e+00 4.20466572e-01 5.68718433e-01 -2.71591216e-01 7.87955046e-01 -3.48306179e-01 -1.31307125e-01 5.86105883e-01 -1.49591058e-01 -8.95853758e-01 3.53376359e-01 6.84441745e-01 -5.41927814e-01 -4.42876607e-01 1.27657390e+00 2.67802447e-01 4.54926193e-02 2.08564445e-01 -1.61839926e+00 -4.77155179e-01 4.35346812e-01 -6.74911261e-01 5.26385486e-01 -3.44782062e-02 4.84412253e-01 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6ee73d9a-0fce-426d-846e-8576c2165f7a	rcdt-relational-remote-sensing-change	2212.04869	null	https://arxiv.org/abs/2212.04869v1	https://arxiv.org/pdf/2212.04869v1.pdf	RCDT: Relational Remote Sensing Change Detection with Transformer	Deep learning based change detection methods have received wide attentoion, thanks to their strong capability in obtaining rich features from images. However, existing AI-based CD methods largely rely on three functionality-enhancing modules, i.e., semantic enhancement, attention mechanisms, and correspondence enhancement. The stacking of these modules leads to great model complexity. To unify these three modules into a simple pipeline, we introduce Relational Change Detection Transformer (RCDT), a novel and simple framework for remote sensing change detection tasks. The proposed RCDT consists of three major components, a weight-sharing Siamese Backbone to obtain bi-temporal features, a Relational Cross Attention Module (RCAM) that implements offset cross attention to obtain bi-temporal relation-aware features, and a Features Constrain Module (FCM) to achieve the final refined predictions with high-resolution constraints. Extensive experiments on four different publically available datasets suggest that our proposed RCDT exhibits superior change detection performance compared with other competing methods. The therotical, methodogical, and experimental knowledge of this study is expected to benefit future change detection efforts that involve the cross attention mechanism.	['Xiao Huang', 'Kaixuan Lu']	2022-12-09	null	null	null	null	['change-detection']	['computer-vision']	[ 4.57202673e-01 -3.37930679e-01 1.75289631e-01 -4.94691014e-01 -6.37608111e-01 -2.10838690e-01 9.16598678e-01 -4.13857587e-02 -3.76921177e-01 4.86598998e-01 2.96151966e-01 -2.06190962e-02 -2.65356958e-01 -1.01595128e+00 -5.73283792e-01 -8.65830183e-01 -9.81560722e-02 6.46705702e-02 4.87315893e-01 -4.17164028e-01 8.35311562e-02 8.66190076e-01 -1.67741001e+00 9.45595875e-02 1.29803336e+00 1.14629948e+00 2.73667425e-01 3.51484627e-01 -2.58373041e-02 5.50978541e-01 1.05492637e-01 -1.89091802e-01 3.18143755e-01 -3.21251482e-01 -6.25797212e-01 -9.17041581e-03 3.20452720e-01 -4.54283893e-01 -3.71513754e-01 1.06747675e+00 6.91277266e-01 3.38983923e-01 3.80064309e-01 -9.13391471e-01 -7.56224275e-01 5.23697793e-01 -7.36868799e-01 4.68923151e-01 3.48700918e-02 1.92986131e-01 1.21733618e+00 -1.06674159e+00 5.60831130e-01 1.22168505e+00 9.15905058e-01 -4.81274985e-02 -1.00713837e+00 -6.49003744e-01 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f8a122d2-8f74-4d63-b069-4f66f6fd3a5e	multi-objective-conflict-based-search-for	2101.03805	null	https://arxiv.org/abs/2101.03805v5	https://arxiv.org/pdf/2101.03805v5.pdf	A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding	Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. The goal of the problem is thus to find a Pareto-optimal set of solutions instead of a single optimal solution. Naively applying existing multi-objective search algorithms, such as multi-objective A* (MOA), to multi-agent path finding may prove to be inefficient as the dimensionality of the search space grows exponentially with the number of agents. This article presents an approach named Multi-Objective Conflict-Based Search (MO-CBS) that attempts to address this so-called curse of dimensionality by leveraging prior Conflict-Based Search (CBS), a well-known algorithm for single-objective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We also develop several variants of MO-CBS to improve its performance. We prove that MO-CBS and its variants can compute the entire Pareto-optimal set. Numerical results show that MO-CBS outperforms MOM, a recently developed state-of-the-art multi-objective multi-agent planner.	['Howie Choset', 'Sivakumar Rathinam', 'Zhongqiang Ren']	2021-01-11	null	null	null	null	['multi-agent-path-finding']	['playing-games']	[-3.22083151e-03 -4.31604944e-02 -5.16706347e-01 2.21650541e-01 -8.42536747e-01 -8.17734122e-01 1.49354994e-01 5.79110086e-01 -4.86219823e-01 1.26619565e+00 -5.58969416e-02 -2.81968504e-01 -9.88786757e-01 -8.81130159e-01 -3.30472916e-01 -6.07048690e-01 -6.44593954e-01 1.11265099e+00 2.54354268e-01 -5.15997171e-01 5.22399187e-01 4.34701920e-01 -1.14483595e+00 -2.40381047e-01 1.22960150e+00 6.05403543e-01 3.07555914e-01 8.30141127e-01 6.81352094e-02 1.57571495e-01 -7.97448337e-01 1.77139342e-02 3.58810842e-01 -3.03968042e-01 -1.17335212e+00 -4.21771780e-02 -5.70309877e-01 -2.27997661e-01 2.21874952e-01 9.13479924e-01 4.08250391e-01 6.21793568e-01 5.06554425e-01 -2.13659668e+00 -1.30362749e-01 5.89298487e-01 -7.15478063e-01 6.89149722e-02 6.40890241e-01 1.15913138e-01 1.09548199e+00 -8.48561376e-02 5.53877532e-01 1.34789443e+00 2.70035148e-01 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e892718b-077e-4b5a-9edc-dc1be51dfc7a	old-is-gold-linguistic-driven-approach-for	null	null	https://aclanthology.org/N19-1243	https://aclanthology.org/N19-1243.pdf	Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text	Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively maps entities and relations within a short text to its mentions of a background knowledge graph. Falcon overcomes the challenges of short text using a light-weight linguistic approach relying on a background knowledge graph. Falcon performs joint entity and relation linking of a short text by leveraging several fundamental principles of English morphology (e.g. compounding, headword identification) and utilizes an extended knowledge graph created by merging entities and relations from various knowledge sources. It uses the context of entities for finding relations and does not require training data. Our empirical study using several standard benchmarks and datasets show that Falcon significantly outperforms state-of-the-art entity and relation linking for short text query inventories.	['S{\\"o}ren Auer', 'Maria Esther Vidal', "Isaiah o Mulang{'}", 'on', 'Saeedeh Shekarpour', 'Ahmad Sakor', 'Kuldeep Singh', 'Jens Lehmann']	2019-06-01	null	null	null	naacl-2019-6	['implicit-relations']	['natural-language-processing']	[-1.88320279e-01 3.79468739e-01 -5.21645963e-01 -1.05402626e-01 -5.84096193e-01 -1.03176451e+00 7.80638099e-01 1.00130296e+00 -9.12626922e-01 1.03218174e+00 2.12536231e-01 -4.64143276e-01 -3.24449509e-01 -1.13499177e+00 -7.54920900e-01 9.65573341e-02 1.19100012e-01 9.15663898e-01 6.13586187e-01 -5.24452090e-01 -2.29281653e-03 2.92886704e-01 -1.00507629e+00 1.35241717e-01 1.14968669e+00 6.53829396e-01 2.92422939e-02 2.83398390e-01 -8.53195190e-01 7.94613659e-01 -6.66127086e-01 -1.20706367e+00 -1.76452354e-01 1.09202363e-01 -1.23309720e+00 -6.05928361e-01 4.88705873e-01 3.70130718e-01 -1.19560316e-01 1.00538540e+00 4.99949574e-01 1.93337664e-01 6.76753819e-01 -9.78484929e-01 -7.96523571e-01 1.27628779e+00 -4.35445964e-01 4.38826531e-01 6.36598766e-01 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2448d569-6969-46a6-bdf0-2f5da182d921	apt-36k-a-large-scale-benchmark-for-animal	2206.05683	null	https://arxiv.org/abs/2206.05683v2	https://arxiv.org/pdf/2206.05683v2.pdf	APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking	Animal pose estimation and tracking (APT) is a fundamental task for detecting and tracking animal keypoints from a sequence of video frames. Previous animal-related datasets focus either on animal tracking or single-frame animal pose estimation, and never on both aspects. The lack of APT datasets hinders the development and evaluation of video-based animal pose estimation and tracking methods, limiting real-world applications, e.g., understanding animal behavior in wildlife conservation. To fill this gap, we make the first step and propose APT-36K, i.e., the first large-scale benchmark for animal pose estimation and tracking. Specifically, APT-36K consists of 2,400 video clips collected and filtered from 30 animal species with 15 frames for each video, resulting in 36,000 frames in total. After manual annotation and careful double-check, high-quality keypoint and tracking annotations are provided for all the animal instances. Based on APT-36K, we benchmark several representative models on the following three tracks: (1) supervised animal pose estimation on a single frame under intra- and inter-domain transfer learning settings, (2) inter-species domain generalization test for unseen animals, and (3) animal pose estimation with animal tracking. Based on the experimental results, we gain some empirical insights and show that APT-36K provides a valuable animal pose estimation and tracking benchmark, offering new challenges and opportunities for future research. The code and dataset will be made publicly available at https://github.com/pandorgan/APT-36K.	['DaCheng Tao', 'Long Lan', 'Jing Zhang', 'Yufei Xu', 'Junjie Yang', 'Yuxiang Yang']	2022-06-12	null	null	null	null	['animal-pose-estimation']	['computer-vision']	[-1.62785634e-01 -5.60460865e-01 -3.46226543e-01 -2.67331958e-01 -4.00731534e-01 -7.46138215e-01 1.20279416e-01 2.26359770e-01 -7.98248649e-01 5.14847398e-01 -1.37532294e-01 2.54982680e-01 3.05275619e-02 -3.79776120e-01 -1.06151998e+00 -4.76377338e-01 -7.19013691e-01 2.13898569e-01 5.60735762e-01 6.58976510e-02 -8.59544352e-02 7.51569569e-01 -1.46144474e+00 -1.29495010e-01 1.42415598e-01 1.01842189e+00 2.23445743e-01 6.52526498e-01 4.82883871e-01 5.02878308e-01 -3.53779197e-01 -5.13652086e-01 3.46815467e-01 -2.88628429e-01 -5.55989802e-01 -1.91157520e-01 6.25156224e-01 -8.04010808e-01 -4.84845132e-01 9.56262290e-01 2.66550899e-01 8.73987302e-02 3.03506345e-01 -1.73918033e+00 -2.62810707e-01 3.80153328e-01 -7.39271462e-01 6.45224035e-01 3.00783932e-01 5.45121014e-01 7.15821922e-01 -4.63593096e-01 5.69855332e-01 1.15527904e+00 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c20e55fb-eda9-4119-a365-5c0b2b4e4892	vision-models-are-more-robust-and-fair-when	2202.08360	null	https://arxiv.org/abs/2202.08360v2	https://arxiv.org/pdf/2202.08360v2.pdf	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision	Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images. Applied to ImageNet, this leads to object centric features that perform on par with supervised features on most object-centric downstream tasks. In this work, we question if using this ability, we can learn any salient and more representative information present in diverse unbounded set of images from across the globe. To do so, we train models on billions of random images without any data pre-processing or prior assumptions about what we want the model to learn. We scale our model size to dense 10 billion parameters to avoid underfitting on a large data size. We extensively study and validate our model performance on over 50 benchmarks including fairness, robustness to distribution shift, geographical diversity, fine grained recognition, image copy detection and many image classification datasets. The resulting model, not only captures well semantic information, it also captures information about artistic style and learns salient information such as geolocations and multilingual word embeddings based on visual content only. More importantly, we discover that such model is more robust, more fair, less harmful and less biased than supervised models or models trained on object centric datasets such as ImageNet.	['Piotr Bojanowski', 'Armand Joulin', 'Levent Sagun', 'Ishan Misra', 'Mathilde Caron', 'Isaac Seessel', 'Quentin Duval', 'Priya Goyal']	2022-02-16	vision-models-are-more-robust-and-fair-when-1	https://arxiv.org/abs/2202.08360	https://arxiv.org/pdf/2202.08360.pdf	null	['traffic-sign-recognition', 'self-supervised-image-classification', 'semi-supervised-image-classification', 'fine-grained-image-classification', 'multilingual-word-embeddings', 'meme-classification']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'natural-language-processing']	[-2.03603003e-02 -7.36477822e-02 -5.37161112e-01 -4.86459106e-01 -6.04209423e-01 -6.95578158e-01 9.85595286e-01 3.75273198e-01 -6.21620655e-01 6.07911825e-01 5.92090666e-01 4.67915684e-02 -1.42378524e-01 -8.00137401e-01 -1.04608762e+00 -5.24869323e-01 -2.22241282e-01 3.36428076e-01 5.27493022e-02 -1.91650420e-01 5.51811635e-01 5.04440546e-01 -1.79288578e+00 1.40884161e-01 7.38707066e-01 1.25360763e+00 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bb894aa8-92b4-417d-a802-523a92f872e7	provably-convergent-policy-optimization-via	2306.14133	null	https://arxiv.org/abs/2306.14133v1	https://arxiv.org/pdf/2306.14133v1.pdf	Provably Convergent Policy Optimization via Metric-aware Trust Region Methods	Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning. In this paper, we exploit more flexible metrics and examine two natural extensions of policy optimization with Wasserstein and Sinkhorn trust regions, namely Wasserstein policy optimization (WPO) and Sinkhorn policy optimization (SPO). Instead of restricting the policy to a parametric distribution class, we directly optimize the policy distribution and derive their closed-form policy updates based on the Lagrangian duality. Theoretically, we show that WPO guarantees a monotonic performance improvement, and SPO provably converges to WPO as the entropic regularizer diminishes. Moreover, we prove that with a decaying Lagrangian multiplier to the trust region constraint, both methods converge to global optimality. Experiments across tabular domains, robotic locomotion, and continuous control tasks further demonstrate the performance improvement of both approaches, more robustness of WPO to sample insufficiency, and faster convergence of SPO, over state-of-art policy gradient methods.	['Chaoyue Zhao', 'Lijun Ding', 'Niao He', 'Jun Song']	2023-06-25	null	null	null	null	['policy-gradient-methods', 'continuous-control']	['methodology', 'playing-games']	[-2.50778317e-01 4.15935628e-02 -6.99109137e-01 -3.04532405e-02 -6.87648892e-01 -7.21902549e-01 4.52084184e-01 1.63933560e-01 -8.56473565e-01 1.45410168e+00 1.86182767e-01 -3.36068362e-01 -4.42023784e-01 -3.87181878e-01 -9.09845889e-01 -8.66312504e-01 -3.52947146e-01 2.12354705e-01 7.64110982e-02 -3.06442022e-01 3.43350887e-01 2.70300895e-01 -1.00980127e+00 -5.18844783e-01 1.07390130e+00 1.05135119e+00 4.61691208e-02 5.13445497e-01 4.76246089e-01 7.24206269e-01 -2.20946863e-01 8.77419859e-02 5.05527437e-01 -2.95765847e-01 -7.34935641e-01 -1.61835253e-01 7.34806433e-02 -4.82050508e-01 -2.61911124e-01 1.35724437e+00 4.32369530e-01 6.62093282e-01 5.91613173e-01 -1.22844791e+00 -5.99183083e-01 4.93340075e-01 -6.43668354e-01 3.39910761e-02 1.72582358e-01 2.37239078e-01 1.16617835e+00 -4.39775527e-01 6.72544539e-01 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d63483b8-d1ab-4ad9-944b-c822c6d1a036	diacritics-restoration-using-neural-networks	null	null	https://aclanthology.org/L18-1247	https://aclanthology.org/L18-1247.pdf	Diacritics Restoration Using Neural Networks	null	['Jan Haji{\\v{c}}', "Pavel Stra{\\v{n}}{\\'a}k", "Jakub N{\\'a}plava", 'Milan Straka']	2018-05-01	diacritics-restoration-using-neural-networks-1	https://aclanthology.org/L18-1247	https://aclanthology.org/L18-1247.pdf	lrec-2018-5	['irish-text-diacritization', 'croatian-text-diacritization', 'french-text-diacritization', 'romanian-text-diacritization', 'turkish-text-diacritization', 'hungarian-text-diacritization', 'slovak-text-diacritization', 'spanish-text-diacritization', 'latvian-text-diacritization', 'czech-text-diacritization', 'vietnamese-text-diacritization']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 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3df37b65-0298-4d46-af20-c5e82b268c80	universal-image-manipulation-detection-using	1808.06323	null	http://arxiv.org/abs/1808.06323v2	http://arxiv.org/pdf/1808.06323v2.pdf	Universal Image Manipulation Detection using Deep Siamese Convolutional Neural Network	Detection of different types of image editing operations carried out on an image is an important problem in image forensics. It gives the information about the processing history of an image, and also can expose forgeries present in an image. There have been few methods proposed to detect different types of image editing operations in a single framework. However, all the operations have to be known a priori in the training phase. But, in real-forensics scenarios it may not be possible to know about the editing operations carried out on an image. To solve this problem, we propose a novel deep learning-based method which can differentiate between different types of image editing operations. The proposed method classifies image patches in a pair-wise fashion as either similarly or differently processed using a deep siamese neural network. Once the network learns feature that can discriminate between different image editing operations, it can differentiate between different image editing operations not present in the training stage. The experimental results show the efficacy of the proposed method in detecting/discriminating different image editing operations.	['Yosha Singh Tomar', 'Jaya Singh', 'Aniruddha Mazumdar', 'Prabin Kumar Bora']	2018-08-20	null	null	null	null	['image-manipulation-detection', 'image-forensics']	['computer-vision', 'computer-vision']	[ 5.54055393e-01 -6.96770608e-01 2.55216926e-01 -2.92403340e-01 -2.96750456e-01 -6.23662174e-01 6.69508636e-01 4.65562224e-01 -5.48004150e-01 3.68940681e-02 -3.31811935e-01 -2.15215474e-01 1.57268178e-02 -9.67019796e-01 -7.26307571e-01 -9.09798801e-01 -1.54208289e-02 2.61781216e-01 2.90628463e-01 -1.35352373e-01 9.58862305e-01 1.13023531e+00 -1.55058599e+00 6.29764676e-01 4.38941628e-01 9.44691062e-01 3.98342907e-01 6.71997786e-01 -1.37221470e-01 9.20274377e-01 -1.04270113e+00 -3.93733591e-01 4.96091843e-01 -3.12287301e-01 -5.76695204e-01 3.44113231e-01 5.13142943e-01 -5.40381491e-01 -5.67880273e-01 1.44653726e+00 3.06269526e-01 1.19341657e-01 7.51112938e-01 -1.14796948e+00 -5.81587672e-01 1.45248786e-01 -6.84938192e-01 1.09541941e+00 6.61653653e-02 1.45559251e-01 2.91271240e-01 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5331a5c4-c532-4c71-8b7d-c9340f4af07c	learning-audio-text-agreement-for-open	2206.15400	null	https://arxiv.org/abs/2206.15400v2	https://arxiv.org/pdf/2206.15400v2.pdf	Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting	In this paper, we propose a novel end-to-end user-defined keyword spotting method that utilizes linguistically corresponding patterns between speech and text sequences. Unlike previous approaches requiring speech keyword enrollment, our method compares input queries with an enrolled text keyword sequence. To place the audio and text representations within a common latent space, we adopt an attention-based cross-modal matching approach that is trained in an end-to-end manner with monotonic matching loss and keyword classification loss. We also utilize a de-noising loss for the acoustic embedding network to improve robustness in noisy environments. Additionally, we introduce the LibriPhrase dataset, a new short-phrase dataset based on LibriSpeech for efficiently training keyword spotting models. Our proposed method achieves competitive results on various evaluation sets compared to other single-modal and cross-modal baselines.	['Hong-Goo Kang', 'Soo-Whan Chung', 'Doyeon Kim', 'Hyewon Han', 'Hyeon-Kyeong Shin']	2022-06-30	null	null	null	null	['keyword-spotting']	['speech']	[ 3.22677255e-01 -2.35570788e-01 -3.76367778e-01 -4.73403543e-01 -1.97463334e+00 -7.57925570e-01 6.28947020e-01 9.52106342e-02 -7.90603042e-01 9.64878052e-02 5.89505255e-01 -2.49365613e-01 -1.01908907e-01 -2.68037707e-01 -7.20134795e-01 -3.91455114e-01 3.58840227e-01 2.99496204e-01 9.07299593e-02 1.03864916e-01 1.47849977e-01 -1.60754044e-02 -1.25148702e+00 4.92863387e-01 5.68211854e-01 9.63797748e-01 3.46114486e-01 9.69118178e-01 2.25119106e-02 2.57258654e-01 -3.99456710e-01 -3.23193133e-01 1.54651940e-01 -1.10315621e-01 -6.85685635e-01 -1.00749813e-01 9.48840559e-01 -1.85629383e-01 -7.22460508e-01 7.22482562e-01 9.88723338e-01 5.66040814e-01 4.90018815e-01 -1.02652860e+00 -6.29554331e-01 7.98189700e-01 -7.92489871e-02 1.27471834e-01 8.54780793e-01 1.37465015e-01 1.54608774e+00 -1.45697844e+00 1.04108050e-01 1.28929353e+00 7.30052590e-01 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19f60af7-bd47-4289-aa93-4e5065851ff5	unsupervised-deep-learning-for-bayesian-brain	1904.11319	null	https://arxiv.org/abs/1904.11319v2	https://arxiv.org/pdf/1904.11319v2.pdf	Unsupervised Deep Learning for Bayesian Brain MRI Segmentation	Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning to implement segmentation tools that are computationally efficient at test time. However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset. To develop a deep learning-based segmentation model for a new image dataset (e.g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches. In this paper, we propose an alternative strategy that combines a conventional probabilistic atlas-based segmentation with deep learning, enabling one to train a segmentation model for new MRI scans without the need for any manually segmented images. Our experiments include thousands of brain MRI scans and demonstrate that the proposed method achieves good accuracy for a brain MRI segmentation task for different MRI contrasts, requiring only approximately 15 seconds at test time on a GPU. The code is freely available at http://voxelmorph.mit.edu.	['Mert R. Sabuncu', 'Evan Yu', 'Polina Golland', 'Juan Eugenio Iglesias', 'Adrian V. Dalca', 'Bruce Fischl']	2019-04-25	null	null	null	null	['zero-shot-segmentation', 'brain-image-segmentation']	['computer-vision', 'medical']	[ 3.26841474e-01 -1.31607637e-01 1.62322715e-01 -6.32305562e-01 -1.08329213e+00 -4.76547509e-01 3.14333379e-01 3.08188647e-01 -8.65717053e-01 5.60463011e-01 -3.06358159e-01 -3.11776131e-01 1.48496956e-01 -7.35479355e-01 -6.30313098e-01 -8.51561725e-01 3.44666280e-02 8.55839491e-01 5.84943712e-01 2.45367989e-01 2.76329249e-01 5.47429562e-01 -1.14168334e+00 -2.17833165e-02 8.70089769e-01 8.81881893e-01 4.73809570e-01 5.16638219e-01 -1.94364876e-01 1.70549184e-01 -4.38069344e-01 -2.09359258e-01 3.23434681e-01 -3.35584730e-01 -7.97620177e-01 1.97505862e-01 1.88296810e-02 -3.43187898e-01 -6.30062595e-02 1.30018914e+00 6.27456188e-01 3.71573456e-02 6.24021709e-01 -8.28172207e-01 -6.28773496e-02 5.68162620e-01 -7.65317738e-01 5.59117317e-01 -2.46315479e-01 3.08164030e-01 4.49191183e-01 -7.11834133e-01 3.53152156e-01 6.12776339e-01 6.78935051e-01 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-2.11854339e-01 -8.97990227e-01 -2.96984464e-01 3.06027591e-01 -7.07959980e-02 -9.87785399e-01 8.79994929e-02 1.02971435e-01]	[14.385137557983398, -2.3020551204681396]
3a3bc3b4-0db5-423c-bd24-e2869434bd50	nuaa-qmul-aiit-at-memotion-3-multi-modal	2302.08326	null	https://arxiv.org/abs/2302.08326v1	https://arxiv.org/pdf/2302.08326v1.pdf	NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis	This paper describes the participation of our NUAA-QMUL-AIIT team in the Memotion 3 shared task on meme emotion analysis. We propose a novel multi-modal fusion method, Squeeze-and-Excitation Fusion (SEFusion), and embed it into our system for emotion classification in memes. SEFusion is a simple fusion method that employs fully connected layers, reshaping, and matrix multiplication. SEFusion learns a weight for each modality and then applies it to its own modality feature. We evaluate the performance of our system on the three Memotion 3 sub-tasks. Among all participating systems in this Memotion 3 shared task, our system ranked first on task A, fifth on task B, and second on task C. Our proposed SEFusion provides the flexibility to fuse any features from different modalities. The source code for our method is published on https://github.com/xxxxxxxxy/memotion3-SEFusion.	['Arkaitz Zubiaga', 'Jing Ma', 'XIAOYU GUO']	2023-02-16	null	null	null	null	['emotion-classification', 'emotion-classification']	['computer-vision', 'natural-language-processing']	[-3.78018498e-01 -3.78997326e-01 1.80367395e-01 -4.40289706e-01 -8.20470095e-01 -3.69165599e-01 6.49324954e-01 1.24090053e-01 -8.50126088e-01 7.02554107e-01 4.87245530e-01 2.73047835e-01 3.09257925e-01 -3.31789970e-01 -4.28912818e-01 -5.34552515e-01 1.24519676e-01 1.46045819e-01 -4.61414814e-01 -4.85003084e-01 1.18687391e-01 2.76048705e-02 -1.30055356e+00 1.02510440e+00 6.73809648e-01 1.32744920e+00 -2.39681099e-02 9.44547117e-01 -1.57374665e-01 9.60193694e-01 -3.84163737e-01 -6.00834608e-01 -1.84814353e-02 -3.12911034e-01 -8.71649206e-01 -3.45292419e-01 2.32525453e-01 1.42354652e-01 -6.19065404e-01 7.24526167e-01 6.77881896e-01 7.99196124e-01 7.61017323e-01 -1.64085793e+00 -5.99064231e-01 8.24781656e-01 -4.59903717e-01 4.51544672e-01 3.47209364e-01 -1.55665874e-01 9.88513827e-01 -1.52434611e+00 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b22b34c0-75fb-4e81-aff1-9344ba094731	relation-matters-foreground-aware-graph-based	2206.02355	null	https://arxiv.org/abs/2206.02355v1	https://arxiv.org/pdf/2206.02355v1.pdf	Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection	Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emphasis of the adaptation process from global to local in virtue of fine-grained feature alignment methods. However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected. In this case, only seeking one-vs-one alignment does not necessarily ensure the precise knowledge transfer. Moreover, conventional alignment-based approaches may be vulnerable to catastrophic overfitting regarding those less transferable regions (e.g. backgrounds) due to the accumulation of inaccurate localization results in the target domain. To remedy these issues, we first formulate DAOD as an open-set domain adaptation problem, in which the foregrounds and backgrounds are seen as the ``known classes'' and ``unknown class'' respectively. Accordingly, we propose a new and general framework for DAOD, named Foreground-aware Graph-based Relational Reasoning (FGRR), which incorporates graph structures into the detection pipeline to explicitly model the intra- and inter-domain foreground object relations on both pixel and semantic spaces, thereby endowing the DAOD model with the capability of relational reasoning beyond the popular alignment-based paradigm. The inter-domain visual and semantic correlations are hierarchically modeled via bipartite graph structures, and the intra-domain relations are encoded via graph attention mechanisms. Empirical results demonstrate that the proposed FGRR exceeds the state-of-the-art performance on four DAOD benchmarks.	['Yizhou Yu', 'Xinghao Ding', 'Yue Huang', 'Xiaoguang Han', 'Hong-Yu Zhou', 'Jiongcheng Li', 'Chaoqi Chen']	2022-06-06	null	null	null	null	['relational-reasoning']	['natural-language-processing']	[ 2.36077905e-01 -2.76920311e-02 -1.41073808e-01 -4.00453091e-01 -2.27343023e-01 -4.04371172e-01 6.27807558e-01 1.38495356e-01 -3.34767140e-02 3.95529926e-01 -1.06568895e-01 -2.97449436e-02 -3.12389970e-01 -1.14831090e+00 -7.57649601e-01 -7.09525049e-01 2.64016658e-01 4.64270383e-01 9.79074240e-01 -1.63882956e-01 -6.98698834e-02 5.87292731e-01 -1.45006657e+00 4.39457208e-01 9.40044165e-01 1.13071215e+00 1.85863703e-01 2.18719393e-01 -5.22619903e-01 8.51996779e-01 -5.30163646e-01 -6.15254462e-01 2.06680745e-01 -4.54638511e-01 -6.73097789e-01 2.65091598e-01 7.14085758e-01 -3.47635776e-01 -5.34455836e-01 1.42549241e+00 1.87097192e-01 6.03855662e-02 5.44457436e-01 -1.53684354e+00 -1.17976844e+00 4.35177147e-01 -8.39668632e-01 4.87598836e-01 -1.34930117e-02 1.81067541e-01 1.13827133e+00 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a42b7630-295a-44ac-98eb-acf99c0a61d7	long-short-range-context-neural-networks-for	1708.06555	null	http://arxiv.org/abs/1708.06555v1	http://arxiv.org/pdf/1708.06555v1.pdf	Long-Short Range Context Neural Networks for Language Modeling	The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learning of short range dependencies, which generally model the syntactic properties of a language and/or long range dependencies, which are semantic in nature. We propose in this paper a new multi-span architecture, which separately models the short and long context information while it dynamically merges them to perform the language modeling task. This is done through a novel recurrent Long-Short Range Context (LSRC) network, which explicitly models the local (short) and global (long) context using two separate hidden states that evolve in time. This new architecture is an adaptation of the Long-Short Term Memory network (LSTM) to take into account the linguistic properties. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art language modeling techniques.	['Mittul Singh', 'Clayton Greenberg', 'Dietrich Klakow', 'Youssef Oualil']	2017-08-22	long-short-range-context-neural-networks-for-1	https://aclanthology.org/D16-1154	https://aclanthology.org/D16-1154.pdf	emnlp-2016-11	['text-compression']	['natural-language-processing']	[ 6.09574020e-02 -9.59881619e-02 -3.88156176e-01 -7.51464367e-01 -4.50228304e-01 -1.40228346e-01 8.53487492e-01 4.13418740e-01 -8.56759846e-01 7.71009564e-01 4.30722743e-01 -6.87044024e-01 1.38700530e-01 -7.82132983e-01 -6.32113159e-01 -5.89838743e-01 -2.61285841e-01 6.02227211e-01 3.01710457e-01 -2.55227268e-01 1.48040742e-01 4.46710885e-01 -1.35873687e+00 5.02469659e-01 6.72016740e-01 6.99295700e-01 6.88714147e-01 9.04266357e-01 -9.64281738e-01 1.13261867e+00 -4.47607875e-01 -3.67060862e-02 -3.85394663e-01 -1.86123237e-01 -1.03147137e+00 -2.55835265e-01 -1.31729431e-02 1.66705504e-01 -1.52683273e-01 8.28625321e-01 1.58732608e-01 4.35676485e-01 2.43070737e-01 -4.80066776e-01 -2.63111830e-01 1.12456167e+00 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d04594f9-1c58-457e-847f-99501fa5e530	multi-modal-visual-place-recognition-in	2105.07800	null	https://arxiv.org/abs/2105.07800v2	https://arxiv.org/pdf/2105.07800v2.pdf	Multi-modal Visual Place Recognition in Dynamics-Invariant Perception Space	Visual place recognition is one of the essential and challenging problems in the fields of robotics. In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments. We achieve this by first designing a novel deep learning architecture to generate the static semantic segmentation and recover the static image directly from the corresponding dynamic image. We then innovatively leverage the spatial-pyramid-matching model to encode the static semantic segmentation into feature vectors. In parallel, the static image is encoded using the popular Bag-of-words model. On the basis of the above multi-modal features, we finally measure the similarity between the query image and target landmark by the joint similarity of their semantic and visual codes. Extensive experiments demonstrate the effectiveness and robustness of the proposed approach for place recognition in dynamic environments.	['Changyin Sun', 'Teng Wang', 'Lin Wu']	2021-05-17	null	null	null	null	['visual-place-recognition']	['computer-vision']	[ 2.39935607e-01 -2.71983802e-01 4.14030254e-02 -3.78925264e-01 -7.47731686e-01 -6.53102398e-01 8.12255323e-01 -1.02962004e-02 -4.97204363e-01 1.35194927e-01 1.39757439e-01 1.14323296e-01 -3.51827264e-01 -7.64537513e-01 -8.05543482e-01 -8.55483472e-01 2.60540664e-01 5.06132655e-03 2.13765562e-01 -1.08802140e-01 4.24206704e-01 5.30076206e-01 -1.64854741e+00 -2.15108618e-01 7.44313121e-01 1.02855408e+00 5.06746411e-01 2.27348626e-01 5.45801334e-02 4.33699071e-01 -3.64304781e-02 1.60495773e-01 4.13568825e-01 -2.66145408e-01 -6.09034836e-01 4.93003011e-01 2.86122650e-01 -1.63054422e-01 -7.45699048e-01 1.22536922e+00 3.56604964e-01 5.97876251e-01 4.32216674e-01 -1.12307656e+00 -7.45256722e-01 6.27661273e-02 -4.62531030e-01 6.97596669e-02 4.52494323e-01 1.19444273e-01 9.22241747e-01 -8.48407924e-01 6.12706900e-01 1.20392907e+00 1.19684331e-01 1.36732921e-01 -9.81708288e-01 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752f3077-e8aa-4443-975a-9af1d79dd315	scaling-novel-object-detection-with-weakly	2207.05205	null	https://arxiv.org/abs/2207.05205v3	https://arxiv.org/pdf/2207.05205v3.pdf	Scaling Novel Object Detection with Weakly Supervised Detection Transformers	A critical object detection task is finetuning an existing model to detect novel objects, but the standard workflow requires bounding box annotations which are time-consuming and expensive to collect. Weakly supervised object detection (WSOD) offers an appealing alternative, where object detectors can be trained using image-level labels. However, the practical application of current WSOD models is limited, as they only operate at small data scales and require multiple rounds of training and refinement. To address this, we propose the Weakly Supervised Detection Transformer, which enables efficient knowledge transfer from a large-scale pretraining dataset to WSOD finetuning on hundreds of novel objects. Additionally, we leverage pretrained knowledge to improve the multiple instance learning (MIL) framework often used in WSOD methods. Our experiments show that our approach outperforms previous state-of-the-art models on large-scale novel object detection datasets, and our scaling study reveals that class quantity is more important than image quantity for WSOD pretraining. The code is available at https://github.com/tmlabonte/weakly-supervised-DETR.	['Neel Joshi', 'Vibhav Vineet', 'Xin Wang', 'Yale Song', 'Tyler LaBonte']	2022-07-11	null	null	null	null	['weakly-supervised-object-detection']	['computer-vision']	[ 2.57337898e-01 1.29634393e-02 -3.29795539e-01 -2.32493252e-01 -8.56529117e-01 -6.90346718e-01 5.67646265e-01 3.99699539e-01 -6.42523110e-01 4.27199990e-01 -1.65971741e-01 1.49066616e-02 1.25347152e-01 -6.30208015e-01 -1.02131975e+00 -5.52987933e-01 6.32710233e-02 5.33883274e-01 8.71184349e-01 7.78499246e-02 1.62597150e-01 5.91641545e-01 -1.72308254e+00 2.99210012e-01 4.93817717e-01 9.63862479e-01 4.41918314e-01 8.03019941e-01 -1.01728640e-01 5.48891425e-01 -3.20100576e-01 -3.60427916e-01 4.76915300e-01 -8.54043476e-03 -8.16740990e-01 -7.57769123e-02 9.96041358e-01 -5.18440664e-01 -1.67098969e-01 1.07525814e+00 4.96418536e-01 -1.05176726e-02 5.16260743e-01 -1.24347413e+00 -6.88582778e-01 6.20957792e-01 -5.92375278e-01 7.02077568e-01 -2.33538553e-01 3.89290273e-01 1.08051062e+00 -1.28593135e+00 6.28719032e-01 1.15448022e+00 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df1739c4-f534-4a18-a3da-2a5c05b7af0c	bayesian-risk-averse-q-learning-with	2305.11300	null	https://arxiv.org/abs/2305.11300v1	https://arxiv.org/pdf/2305.11300v1.pdf	Bayesian Risk-Averse Q-Learning with Streaming Observations	We consider a robust reinforcement learning problem, where a learning agent learns from a simulated training environment. To account for the model mis-specification between this training environment and the real environment due to lack of data, we adopt a formulation of Bayesian risk MDP (BRMDP) with infinite horizon, which uses Bayesian posterior to estimate the transition model and impose a risk functional to account for the model uncertainty. Observations from the real environment that is out of the agent's control arrive periodically and are utilized by the agent to update the Bayesian posterior to reduce model uncertainty. We theoretically demonstrate that BRMDP balances the trade-off between robustness and conservativeness, and we further develop a multi-stage Bayesian risk-averse Q-learning algorithm to solve BRMDP with streaming observations from real environment. The proposed algorithm learns a risk-averse yet optimal policy that depends on the availability of real-world observations. We provide a theoretical guarantee of strong convergence for the proposed algorithm.	['Enlu Zhou', 'Yuhao Wang']	2023-05-18	null	null	null	null	['q-learning']	['methodology']	[ 3.15565802e-02 5.87324202e-01 -2.19750419e-01 -1.53589249e-01 -1.10265231e+00 -2.64221579e-01 3.01868051e-01 1.78768083e-01 -8.06502461e-01 9.55003977e-01 -1.30605519e-01 -3.05777580e-01 -4.96462017e-01 -8.34092796e-01 -9.23016071e-01 -8.28409672e-01 -4.44432139e-01 5.14572740e-01 1.55433387e-01 2.15401158e-01 2.16021508e-01 -9.37373564e-02 -9.78630781e-01 -5.05533457e-01 8.98487985e-01 1.10706663e+00 4.08096313e-01 7.66080141e-01 6.37182891e-01 8.15766931e-01 -4.76407558e-01 6.88557839e-03 6.06487393e-01 -1.98651895e-01 -1.87766135e-01 3.87161644e-03 -4.77093846e-01 -8.97345603e-01 -2.52043277e-01 1.48683453e+00 4.00313169e-01 4.61136907e-01 7.31238663e-01 -1.21214414e+00 1.99447289e-01 5.33794940e-01 -6.45650744e-01 9.16701108e-02 6.51250407e-03 5.05536556e-01 6.70778275e-01 -4.67621163e-02 2.11509705e-01 1.38836944e+00 2.12751180e-01 5.89069605e-01 -9.62407351e-01 -5.87572336e-01 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0948e59f-a307-45e8-a53e-964af4097a44	differential-machine-learning	2005.02347	null	https://arxiv.org/abs/2005.02347v4	https://arxiv.org/pdf/2005.02347v4.pdf	Differential Machine Learning	Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our machinery is applicable to arbitrary Derivatives instruments or trading books, under arbitrary stochastic models of the underlying market variables. It effectively resolves computational bottlenecks of Derivatives risk reports and capital calculations. Differential ML is a general extension of supervised learning, where ML models are trained on examples of not only inputs and labels but also differentials of labels wrt inputs. It is also applicable in many situations outside finance, where high quality first-order derivatives wrt training inputs are available. Applications in Physics, for example, may leverage differentials known from first principles to learn function approximations more effectively. In finance, AAD computes pathwise differentials with remarkable efficacy so differential ML algorithms provide extremely effective pricing and risk approximations. We can produce fast analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or regulations like XVA, CCR, FRTB or SIMM-MVA. TensorFlow implementation is available on https://github.com/differential-machine-learning	['Brian Huge', 'Antoine Savine']	2020-05-05	differential-machine-learning-1	null	null	arxiv-2020-5	['mathematical-proofs']	['miscellaneous']	[-7.77741134e-01 -6.20477349e-02 2.86647381e-04 -1.54328033e-01 -9.56985772e-01 -8.62158656e-01 6.24326408e-01 6.82676882e-02 -1.93825826e-01 7.59657145e-01 -9.61641222e-02 -1.09883356e+00 -3.81359279e-01 -8.31578672e-01 -4.92816240e-01 -4.71427888e-01 -5.05694270e-01 6.85634315e-01 -4.35042739e-01 -3.35515201e-01 4.66029137e-01 9.08858359e-01 -7.96024501e-01 8.64672735e-02 8.54177535e-01 1.56026745e+00 -7.49930799e-01 7.75776267e-01 -2.70728916e-01 9.86886084e-01 -2.24183723e-01 -9.73934650e-01 7.94008613e-01 -1.55491054e-01 -5.86890578e-01 -4.42303449e-01 3.35056260e-02 -5.19703925e-01 -1.38472363e-01 8.98283303e-01 3.22682083e-01 -2.58937031e-02 1.11008048e+00 -1.12275159e+00 -6.24122083e-01 5.55742443e-01 -6.25804007e-01 4.82306659e-01 -2.57719029e-02 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b81034a4-4dda-416e-bd36-dabb1e697809	the-lottery-ticket-hypothesis-for-vision	2211.01484	null	https://arxiv.org/abs/2211.01484v3	https://arxiv.org/pdf/2211.01484v3.pdf	Data Level Lottery Ticket Hypothesis for Vision Transformers	The conventional lottery ticket hypothesis (LTH) claims that there exists a sparse subnetwork within a dense neural network and a proper random initialization method called the winning ticket, such that it can be trained from scratch to almost as good as the dense counterpart. Meanwhile, the research of LTH in vision transformers (ViTs) is scarcely evaluated. In this paper, we first show that the conventional winning ticket is hard to find at the weight level of ViTs by existing methods. Then, we generalize the LTH for ViTs to input data consisting of image patches inspired by the input dependence of ViTs. That is, there exists a subset of input image patches such that a ViT can be trained from scratch by using only this subset of patches and achieve similar accuracy to the ViTs trained by using all image patches. We call this subset of input patches the em winning tickets, which represent a significant amount of information in the input data. We use a ticket selector to generate the winning tickets based on the informativeness of patches for various types of ViT, including DeiT, LV-ViT, and Swin Transformers. The experiments show that there is a clear difference between the performance of models trained with winning tickets and randomly selected subsets, which verifies our proposed theory. We elaborate on the analogical similarity between our proposed Data-LTH-ViTs and the conventional LTH to further verify the integrity of our theory. The Source codes are available at https://github.com/shawnricecake/vit-lottery-ticket-input.	['Yanzhi Wang', 'Xiaolong Ma', 'Hao Tang', 'Xin Meng', 'Geng Yuan', 'Peiyan Dong', 'Minghai Qin', 'Zhenglun Kong', 'Xuan Shen']	2022-11-02	null	null	null	null	['analogical-similarity']	['reasoning']	[ 1.10436983e-01 2.84067899e-01 -1.93209857e-01 -2.74422050e-01 -3.16728145e-01 -1.96857363e-01 4.48625118e-01 -5.26353300e-01 -1.82347130e-02 5.77732623e-01 2.94261463e-02 -1.06288850e-01 -1.14304706e-01 -1.11767316e+00 -1.04398453e+00 -9.66248870e-01 4.18371886e-01 4.20073509e-01 4.11152303e-01 -2.31841519e-01 6.28879219e-02 1.98882297e-01 -1.68528295e+00 5.21936595e-01 8.77896249e-01 1.16429102e+00 7.22661257e-01 -2.84309797e-02 -1.34365141e-01 8.31116676e-01 -5.22440076e-01 -2.72898316e-01 5.77831209e-01 -6.04293883e-01 -7.28026032e-01 5.52145876e-02 3.93609256e-01 -1.94834888e-01 -5.40996015e-01 1.06635487e+00 4.52710927e-01 -8.23237225e-02 7.29103088e-01 -1.25900996e+00 -6.19087398e-01 8.04007649e-01 -5.59291363e-01 3.29462849e-02 -1.68138430e-01 2.33471259e-01 9.67614472e-01 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824037a2-0789-4be8-803d-b1a43a5946b3	distilling-multi-step-reasoning-capabilities	2212.00193	null	https://arxiv.org/abs/2212.00193v2	https://arxiv.org/pdf/2212.00193v2.pdf	Distilling Reasoning Capabilities into Smaller Language Models	Step-by-step reasoning approaches like chain of thought (CoT) have proved to be very effective in inducing reasoning capabilities in large language models. However, the success of the CoT approach is fundamentally tied to the model size, and billion parameter-scale models are often needed to get CoT to work. In this paper, we propose a knowledge distillation approach that leverages the step-by-step CoT reasoning capabilities of larger models and distills these abilities into smaller models. In this work, we propose an alternative reasoning scheme, Socratic CoT, that learns a decomposition of the original problem into a sequence of subproblems and uses it to guide the intermediate reasoning steps. We use Socratic CoT to train a combination of two small distilled models: a problem decomposer and a subproblem solver. In practice, given a new problem, the two distilled models work in sync to decompose and solve complex problems. On multiple reasoning datasets (GSM8K, StrategyQA, and SVAMP), our proposed distillation strategies boosts the performance of smaller models over 70% compared to the baselines. Finally, we investigate when Socratic CoT is an effective alternative to CoT, demonstrating cases where a much smaller model (GPT-2 large) can outperform a 10X larger model (GPT-3 6B). Our code is available here: https://github.com/kumar-shridhar/Distiiling-LM	['Mrinmaya Sachan', 'Alessandro Stolfo', 'Kumar Shridhar']	2022-12-01	null	null	null	null	['problem-decomposition', 'gsm8k', 'strategyqa']	['miscellaneous', 'natural-language-processing', 'reasoning']	[-2.70161241e-01 5.06140828e-01 -1.35748647e-02 -2.57422149e-01 -1.01671863e+00 -9.19076860e-01 4.23764467e-01 6.41443431e-02 -2.54204363e-01 6.45421922e-01 2.51426607e-01 -9.59518015e-01 -7.00455233e-02 -7.73597836e-01 -8.31282973e-01 -3.83772373e-01 3.20022523e-01 1.06341040e+00 1.08607627e-01 -5.49253285e-01 6.52655885e-02 2.85681456e-01 -9.55788255e-01 6.08706474e-01 1.05743575e+00 5.48654318e-01 7.88560063e-02 9.17731702e-01 -3.21875870e-01 1.48437667e+00 -3.21655005e-01 -8.09054673e-01 4.22182530e-01 -3.30809623e-01 -1.35203457e+00 -3.96639377e-01 1.98043570e-01 -3.62164468e-01 -9.98485982e-02 7.98191905e-01 2.60422468e-01 9.33426544e-02 3.92603189e-01 -1.12257850e+00 -5.03563106e-01 1.31499958e+00 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be23ed2e-43c1-4358-88c1-caaf4c0f6d9f	planning-for-goal-oriented-dialogue-systems	1910.08137	null	https://arxiv.org/abs/1910.08137v1	https://arxiv.org/pdf/1910.08137v1.pdf	Planning for Goal-Oriented Dialogue Systems	Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft. This is in large part due to the rapidly growing market demand for dialogue agents capable of goal-oriented behaviour. Due to the business process nature of these conversations, end-to-end machine learning systems are generally not a viable option, as the generated dialogue agents must be deployable and verifiable on behalf of the businesses authoring them. In this work, we propose a paradigm shift in the creation of goal-oriented complex dialogue systems that dramatically eliminates the need for a designer to manually specify a dialogue tree, which nearly all current systems have to resort to when the interaction pattern falls outside standard patterns such as slot filling. We propose a declarative representation of the dialogue agent to be processed by state-of-the-art planning technology. Our proposed approach covers all aspects of the process; from model solicitation to the execution of the generated plans/dialogue agents. Along the way, we introduce novel planning encodings for declarative dialogue synthesis, a variety of interfaces for working with the specification as a dialogue architect, and a robust executor for generalized contingent plans. We have created prototype implementations of all components, and in this paper, we further demonstrate the resulting system empirically.	['Luis A. Lastras-Montano', 'Shubham Agarwal', 'Tathagata Chakraborti', 'Miroslav Vodolan', 'Arunima Chaudhary', 'Ondrej Bajgar', 'Charlie Wiecha', 'Josef Ondrej', 'Christian Muise']	2019-10-17	null	null	null	null	['goal-oriented-dialogue-systems']	['natural-language-processing']	[ 1.08686179e-01 8.56070101e-01 4.29734662e-02 -4.52857882e-01 -5.53249955e-01 -7.67237782e-01 9.77578104e-01 5.23161590e-02 -2.07037553e-01 9.12040353e-01 4.13108617e-01 -5.68153858e-01 -1.57707166e-02 -1.03953528e+00 7.87159242e-03 -1.55629575e-01 6.86503202e-02 1.13011611e+00 3.95325571e-01 -9.06944156e-01 3.46759170e-01 3.14112186e-01 -1.43165362e+00 3.31729472e-01 7.44064748e-01 7.95783818e-01 8.98758098e-02 8.82624567e-01 -6.73170805e-01 1.03679490e+00 -7.35498846e-01 -3.22405219e-01 1.78447664e-01 -8.26219678e-01 -1.14420879e+00 3.99618804e-01 -5.57332218e-01 -4.76166219e-01 2.54506975e-01 6.85636520e-01 2.20740795e-01 9.84862745e-02 4.92541343e-01 -1.49205327e+00 2.64086008e-01 9.77474034e-01 9.04803500e-02 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416b7c83-2bd0-428b-b30e-a60ceea90a73	minimizing-energy-consumption-in-mu-mimo-via	2306.05162	null	https://arxiv.org/abs/2306.05162v1	https://arxiv.org/pdf/2306.05162v1.pdf	Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss	Transmit antenna muting (TAM) in multiple-user multiple-input multiple-output (MU-MIMO) networks allows reducing the power consumption of the base station (BS) by properly utilizing only a subset of antennas in the BS. In this paper, we consider the downlink transmission of an MU-MIMO network where TAM is formulated to minimize the number of active antennas in the BS while guaranteeing the per-user throughput requirements. To address the computational complexity of the combinatorial optimization problem, we propose an algorithm called neural antenna muting (NAM) with an asymmetric custom loss function. NAM is a classification neural network trained in a supervised manner. The classification error in this scheme leads to either sub-optimal energy consumption or lower quality of service (QoS) for the communication link. We control the classification error probability distribution by designing an asymmetric loss function such that the erroneous classification outputs are more likely to result in fulfilling the QoS requirements. Furthermore, we present three heuristic algorithms and compare them with the NAM. Using a 3GPP compliant system-level simulator, we show that NAM achieves $\sim73\%$ energy saving compared to the full antenna configuration in the BS with $\sim95\%$ reliability in achieving the user throughput requirements while being around $1000\times$ and $24\times$ less computationally intensive than the greedy heuristic algorithm and the fixed column antenna muting algorithm, respectively.	['Nandana Rajatheva', 'Thorsten Wild', 'Stefan Wesemann', 'Jafar Mohammadi', 'Nuwanthika Rajapaksha']	2023-06-08	null	null	null	null	['combinatorial-optimization']	['methodology']	[ 5.69789410e-01 5.47175288e-01 -1.69163764e-01 -9.43158641e-02 -5.34689844e-01 -2.91859150e-01 -4.84976679e-01 -2.78923400e-02 -4.45741683e-01 1.10957599e+00 -6.03402913e-01 -1.01567030e+00 -4.75826085e-01 -1.18319070e+00 -9.29693997e-01 -1.12838352e+00 -4.33890313e-01 -1.86200708e-01 -3.00612003e-01 -1.02212997e-02 -2.05012217e-01 4.51934457e-01 -1.12313604e+00 -9.77147669e-02 6.12800777e-01 1.67474520e+00 2.73860604e-01 5.61193705e-01 5.05494893e-01 3.94804657e-01 -7.05853224e-01 -2.52268970e-01 3.98322940e-01 -8.12849164e-01 -3.09306890e-01 4.83152531e-02 -1.35201231e-01 -6.81254715e-02 -4.16519880e-01 9.57021952e-01 9.42326963e-01 1.46838993e-01 5.97024262e-01 -1.46725798e+00 -5.31874523e-02 7.81362474e-01 -4.56422776e-01 8.02395269e-02 -3.50063771e-01 -5.89727104e-01 8.44880581e-01 -4.50180441e-01 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bed2b0e3-b9d5-488a-8a84-3fe032ccb857	learning-diverse-policies-in-moba-games-via	2110.14221	null	https://arxiv.org/abs/2110.14221v1	https://arxiv.org/pdf/2110.14221v1.pdf	Learning Diverse Policies in MOBA Games via Macro-Goals	Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings. Even though these AI systems have achieved or even exceeded human-level performance, they still suffer from the lack of policy diversity. In this paper, we propose a novel Macro-Goals Guided framework, called MGG, to learn diverse policies in MOBA games. MGG abstracts strategies as macro-goals from human demonstrations and trains a Meta-Controller to predict these macro-goals. To enhance policy diversity, MGG samples macro-goals from the Meta-Controller prediction and guides the training process towards these goals. Experimental results on the typical MOBA game Honor of Kings demonstrate that MGG can execute diverse policies in different matches and lineups, and also outperform the state-of-the-art methods over 102 heroes.	['Lanxiao Huang', 'Wei Yang', 'Qiang Fu', 'Deheng Ye', 'Weixuan Wang', 'Guoan Han', 'Fuhao Qiu', 'Zhenjie Lian', 'Guangwei Chen', 'Liang Wang', 'Xueying Du', 'Bei Shi', 'Yiming Gao']	2021-10-27	null	http://proceedings.neurips.cc/paper/2021/hash/86dba86754c0ad93997a11fa947d97b2-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/86dba86754c0ad93997a11fa947d97b2-Paper.pdf	neurips-2021-12	['dota-2']	['playing-games']	[-6.25677109e-01 -1.80689365e-01 -2.30598703e-01 -1.14791520e-01 -5.18317461e-01 -3.68901908e-01 5.26656449e-01 -4.20236826e-01 -6.14856005e-01 1.17791760e+00 -8.03675205e-02 -2.02874422e-01 -2.10848182e-01 -7.18343973e-01 -5.57092667e-01 -4.89937007e-01 -3.89025658e-01 9.05222356e-01 5.33507824e-01 -9.18057501e-01 4.58969206e-01 7.87165985e-02 -1.88214183e+00 1.29731551e-01 9.66348767e-01 7.84763277e-01 3.75004143e-01 9.69548941e-01 2.23004103e-01 1.48857939e+00 -9.60057080e-01 -2.90764961e-02 6.05079710e-01 -6.14043057e-01 -6.59055293e-01 -4.42295849e-01 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e397e030-49a3-471c-85d3-6624318b97ea	toward-a-logical-theory-of-fairness-and-bias	2306.13659	null	https://arxiv.org/abs/2306.13659v1	https://arxiv.org/pdf/2306.13659v1.pdf	Toward A Logical Theory Of Fairness and Bias	Fairness in machine learning is of considerable interest in recent years owing to the propensity of algorithms trained on historical data to amplify and perpetuate historical biases. In this paper, we argue for a formal reconstruction of fairness definitions, not so much to replace existing definitions but to ground their application in an epistemic setting and allow for rich environmental modelling. Consequently we look into three notions: fairness through unawareness, demographic parity and counterfactual fairness, and formalise these in the epistemic situation calculus.	['Vaishak Belle']	2023-06-08	null	null	null	null	['fairness', 'fairness']	['computer-vision', 'miscellaneous']	[ 1.89082533e-01 6.76172137e-01 -3.10604692e-01 -3.93740118e-01 -4.91593219e-02 -4.65521455e-01 1.26826549e+00 4.65055496e-01 -9.44957972e-01 1.23801780e+00 6.68741584e-01 -6.47161603e-01 -4.81548220e-01 -8.69299412e-01 -5.25745571e-01 -5.27041316e-01 -1.77869171e-01 1.37959406e-01 -3.29000264e-01 -1.71956554e-01 2.73595572e-01 4.03794050e-01 -1.42683363e+00 -1.88103750e-01 9.60915029e-01 5.45896947e-01 -4.88090307e-01 2.80936092e-01 9.82600451e-02 1.16079986e+00 -3.99873972e-01 -1.19627452e+00 1.07422300e-01 -4.98001367e-01 -1.00341332e+00 -5.85703254e-01 -9.83995944e-03 3.52136716e-02 -9.47318822e-02 1.22228205e+00 4.46134031e-01 1.23323441e-01 6.51157081e-01 -1.39152670e+00 -6.82217479e-01 1.26133120e+00 -1.36617005e-01 2.85977200e-02 1.49396047e-01 7.40702897e-02 1.02319777e+00 -1.71132341e-01 6.58424377e-01 1.34028637e+00 8.78111959e-01 9.15100157e-01 -1.22867322e+00 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2.52917141e-01 3.18137974e-01 -4.65333730e-01 -7.23231256e-01 -9.97892320e-02]	[8.641676902770996, 5.544895648956299]
098311aa-8a2a-4345-b8b8-5f045f7eccbb	genomic-interpreter-a-hierarchical-genomic	2306.05143	null	https://arxiv.org/abs/2306.05143v2	https://arxiv.org/pdf/2306.05143v2.pdf	Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer	Given the increasing volume and quality of genomics data, extracting new insights requires interpretable machine-learning models. This work presents Genomic Interpreter: a novel architecture for genomic assay prediction. This model outperforms the state-of-the-art models for genomic assay prediction tasks. Our model can identify hierarchical dependencies in genomic sites. This is achieved through the integration of 1D-Swin, a novel Transformer-based block designed by us for modelling long-range hierarchical data. Evaluated on a dataset containing 38,171 DNA segments of 17K base pairs, Genomic Interpreter demonstrates superior performance in chromatin accessibility and gene expression prediction and unmasks the underlying `syntax' of gene regulation.	['Guy-Bart Stan', 'Yiren Zhao', 'William A V Beardall', 'Akashaditya Das', 'Zehui Li']	2023-06-08	null	null	null	null	['interpretable-machine-learning']	['methodology']	[ 4.40596402e-01 4.43508863e-01 -5.78486443e-01 -5.11318088e-01 -9.84954178e-01 -1.03159189e+00 2.22544685e-01 5.88572204e-01 1.18459783e-01 1.06950676e+00 4.34232891e-01 -7.69455552e-01 -5.26833057e-01 -4.98831928e-01 -1.00187576e+00 -1.00608635e+00 -2.97511727e-01 8.73193741e-01 4.01423872e-01 4.99185100e-02 3.25794458e-01 3.34750503e-01 -1.29230070e+00 7.86045432e-01 8.37755203e-01 6.02008402e-01 -1.83175132e-01 1.11745012e+00 8.64443704e-02 4.16677237e-01 -3.72164875e-01 -2.99116522e-01 -2.75697082e-01 -6.40641332e-01 -5.50986886e-01 -7.33016431e-01 3.32748115e-01 1.58833817e-01 -8.30903277e-02 2.57126629e-01 1.00486803e+00 -5.96497059e-01 7.62028456e-01 -8.48382413e-01 -3.37244302e-01 5.83026230e-01 -3.10886115e-01 4.73559648e-01 4.65757132e-01 1.76876932e-01 1.20252275e+00 -6.06316984e-01 1.40494299e+00 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b01e2112-5db9-4e7d-a59e-63aab2b85a60	decoding-strategies-for-neural-referring	null	null	https://aclanthology.org/W18-6563	https://aclanthology.org/W18-6563.pdf	Decoding Strategies for Neural Referring Expression Generation	RNN-based sequence generation is now widely used in NLP and NLG (natural language generation). Most work focusses on how to train RNNs, even though also decoding is not necessarily straightforward: previous work on neural MT found seq2seq models to radically prefer short candidates, and has proposed a number of beam search heuristics to deal with this. In this work, we assess decoding strategies for referring expression generation with neural models. Here, expression length is crucial: output should neither contain too much or too little information, in order to be pragmatically adequate. We find that most beam search heuristics developed for MT do not generalize well to referring expression generation (REG), and do not generally outperform greedy decoding. We observe that beam search heuristics for termination seem to override the model{'}s knowledge of what a good stopping point is. Therefore, we also explore a recent approach called trainable decoding, which uses a small network to modify the RNN{'}s hidden state for better decoding results. We find this approach to consistently outperform greedy decoding for REG.	['Sina Zarrie{\\ss}', 'David Schlangen']	2018-11-01	null	null	null	ws-2018-11	['referring-expression-generation']	['computer-vision']	[ 5.92875421e-01 5.48238277e-01 -1.45969152e-01 -3.82579654e-01 -9.82697666e-01 -7.40514278e-01 5.95472634e-01 -2.78043896e-01 -4.65723008e-01 1.26229632e+00 5.89171112e-01 -7.52266467e-01 1.27317908e-03 -8.08339536e-01 -5.56175590e-01 -6.60788536e-01 3.66628200e-01 6.07170522e-01 -5.37238829e-02 -5.04196823e-01 3.71083647e-01 2.64027834e-01 -1.20408463e+00 3.47585440e-01 7.21886635e-01 3.20767134e-01 2.99441516e-01 7.95178890e-01 -4.41117823e-01 9.73274350e-01 -9.82826352e-01 -5.56838691e-01 -6.27289712e-02 -1.07915986e+00 -1.26204836e+00 -3.55268866e-01 8.54455866e-03 2.86109392e-02 1.92566618e-01 9.64897096e-01 8.76492441e-01 2.44788885e-01 5.49051106e-01 -8.26337636e-01 -4.90113407e-01 1.19491911e+00 2.07350813e-02 2.87036806e-01 5.11469126e-01 2.72426218e-01 1.04180169e+00 -3.01668853e-01 9.20568287e-01 1.18663752e+00 4.98063058e-01 1.09079933e+00 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1bba59d2-4816-49d4-8b1c-e6ed48ff7cd4	order-aware-generative-modeling-using-the-3d	null	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Order-Aware_Generative_Modeling_Using_the_3D-Craft_Dataset_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Order-Aware_Generative_Modeling_Using_the_3D-Craft_Dataset_ICCV_2019_paper.pdf	Order-Aware Generative Modeling Using the 3D-Craft Dataset	In this paper, we study the problem of sequentially building houses in the game of Minecraft, and demonstrate that learning the ordering can make for more effective autoregressive models. Given a partially built house made by a human player, our system tries to place additional blocks in a human-like manner to complete the house. We introduce a new dataset, HouseCraft, for this new task. HouseCraft contains the sequential order in which 2,500 Minecraft houses were built from scratch by humans. The human action sequences enable us to learn an order-aware generative model called Voxel-CNN. In contrast to many generative models where the sequential generation ordering either does not matter (e.g. holistic generation with GANs), or is manually/arbitrarily set by simple rules (e.g. raster-scan order), our focus is on an ordered generation that imitates humans. To evaluate if a generative model can accurately predict human-like actions, we propose several novel quantitative metrics. We demonstrate that our Voxel-CNN model is simple and effective at this creative task, and can serve as a strong baseline for future research in this direction. The HouseCraft dataset and code with baseline models will be made publicly available.	[' C. Lawrence Zitnick', ' Arthur Szlam', ' Shubham Tulsiani', ' Charles R. Qi', ' Jerry Ma', ' Haoqi Fan', ' Kavya Srinet', ' Jonathan Gray', ' Haonan Yu', ' Xinlei Chen', ' Saining Xie', ' Tong Xiao', ' Demi Guo', 'Zhuoyuan Chen']	2019-10-01	null	null	null	iccv-2019-10	['house-generation']	['computer-vision']	[ 1.67062894e-01 3.10151845e-01 1.50593445e-01 -1.88192949e-01 -4.28288639e-01 -7.43360937e-01 9.74874258e-01 -5.48817813e-01 -7.91329294e-02 6.76292777e-01 6.22406363e-01 -1.27313644e-01 1.03545532e-01 -1.23791742e+00 -8.73541653e-01 -5.30015469e-01 7.51657337e-02 8.45982909e-01 3.90950628e-02 -4.16740566e-01 7.63272792e-02 3.46335828e-01 -1.47745335e+00 3.70050430e-01 6.80670798e-01 4.64361310e-01 3.40648472e-01 9.53798056e-01 5.23992062e-01 1.23621154e+00 -6.19808912e-01 -4.01563734e-01 6.20266199e-01 -1.00749111e+00 -9.47940767e-01 5.31528234e-01 1.71355575e-01 -6.96563423e-01 -4.02407676e-01 5.16553760e-01 5.14254630e-01 3.89389485e-01 8.80192757e-01 -1.25901282e+00 -8.23628545e-01 9.07920599e-01 -2.74462402e-01 -2.50411242e-01 3.80740285e-01 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b7be33d1-3ee2-4d15-b122-8d52ae67d57c	investigating-the-nature-of-3d-generalization	2304.09358	null	https://arxiv.org/abs/2304.09358v1	https://arxiv.org/pdf/2304.09358v1.pdf	Investigating the Nature of 3D Generalization in Deep Neural Networks	Visual object recognition systems need to generalize from a set of 2D training views to novel views. The question of how the human visual system can generalize to novel views has been studied and modeled in psychology, computer vision, and neuroscience. Modern deep learning architectures for object recognition generalize well to novel views, but the mechanisms are not well understood. In this paper, we characterize the ability of common deep learning architectures to generalize to novel views. We formulate this as a supervised classification task where labels correspond to unique 3D objects and examples correspond to 2D views of the objects at different 3D orientations. We consider three common models of generalization to novel views: (i) full 3D generalization, (ii) pure 2D matching, and (iii) matching based on a linear combination of views. We find that deep models generalize well to novel views, but they do so in a way that differs from all these existing models. Extrapolation to views beyond the range covered by views in the training set is limited, and extrapolation to novel rotation axes is even more limited, implying that the networks do not infer full 3D structure, nor use linear interpolation. Yet, generalization is far superior to pure 2D matching. These findings help with designing datasets with 2D views required to achieve 3D generalization. Code to reproduce our experiments is publicly available: https://github.com/shoaibahmed/investigating_3d_generalization.git	['Thomas Breuel', 'David Krueger', 'Shoaib Ahmed Siddiqui']	2023-04-19	null	null	null	null	['object-recognition']	['computer-vision']	[-1.17880926e-01 -1.13783605e-01 -1.13512479e-01 -6.84519768e-01 -1.25929695e-02 -1.03452218e+00 6.62064970e-01 -5.03764749e-01 -6.56143948e-02 4.84728515e-01 7.14428648e-02 -4.33292300e-01 -3.56792621e-02 -5.65835595e-01 -9.03043389e-01 -4.95180815e-01 8.24656487e-02 3.64088416e-01 1.71437576e-01 -8.41741189e-02 2.84989029e-01 1.20657384e+00 -1.66708386e+00 4.81150329e-01 3.27773452e-01 7.91606128e-01 1.59821570e-01 7.32456326e-01 1.21222354e-01 -6.66150525e-02 -3.57370317e-01 -9.78439897e-02 8.02696764e-01 -4.21166956e-01 -7.64513373e-01 3.53997618e-01 1.03529024e+00 -5.85300982e-01 -4.42225128e-01 8.31925452e-01 4.56530482e-01 1.76123142e-01 8.37851226e-01 -1.21938217e+00 -1.38704789e+00 -3.05910200e-01 -5.44209242e-01 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c5091ed4-5557-4020-88ea-2ac1350dbee9	learning-probabilistic-temporal-safety	2211.03461	null	https://arxiv.org/abs/2211.03461v1	https://arxiv.org/pdf/2211.03461v1.pdf	Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains	We propose a framework for learning a fragment of probabilistic computation tree logic (pCTL) formulae from a set of states that are labeled as safe or unsafe. We work in a relational setting and combine ideas from relational Markov Decision Processes with pCTL model-checking. More specifically, we assume that there is an unknown relational pCTL target formula that is satisfied by only safe states, and has a horizon of maximum $k$ steps and a threshold probability $\alpha$. The task then consists of learning this unknown formula from states that are labeled as safe or unsafe by a domain expert. We apply principles of relational learning to induce a pCTL formula that is satisfied by all safe states and none of the unsafe ones. This formula can then be used as a safety specification for this domain, so that the system can avoid getting into dangerous situations in future. Following relational learning principles, we introduce a candidate formula generation process, as well as a method for deciding which candidate formula is a satisfactory specification for the given labeled states. The cases where the expert knows and does not know the system policy are treated, however, much of the learning process is the same for both cases. We evaluate our approach on a synthetic relational domain.	['Luc De Raedt', 'Jean-François Raskin', 'Wen-Chi Yang', 'Gavin Rens']	2022-11-07	null	null	null	null	['relational-reasoning']	['natural-language-processing']	[ 2.33614430e-01 8.11582744e-01 -3.54774266e-01 -3.98623884e-01 -1.03836894e+00 -6.58053935e-01 7.13390529e-01 2.12691769e-01 -1.08750373e-01 8.52972865e-01 -8.37396309e-02 -9.06341553e-01 -1.16695188e-01 -1.38859499e+00 -8.81726921e-01 -5.91013849e-01 -3.97772640e-01 8.42203677e-01 7.35681891e-01 9.62750465e-02 -1.50029674e-01 4.47412431e-01 -1.39251745e+00 5.00605166e-01 1.75233766e-01 9.17889416e-01 -5.35242796e-01 6.97247684e-01 3.45694572e-02 1.56566155e+00 -1.97839931e-01 -1.11559771e-01 1.67292356e-01 -3.71241570e-01 -1.20101964e+00 -2.24913031e-01 -9.03375894e-02 -1.61686450e-01 -1.61467746e-01 1.24823773e+00 -2.86381871e-01 -1.35553136e-01 5.27525842e-01 -1.53554893e+00 2.31285259e-01 1.32720661e+00 7.16646537e-02 -2.95753390e-01 3.88876736e-01 5.41613936e-01 1.08008564e+00 2.01226041e-01 5.27009428e-01 1.59407496e+00 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853e7748-42fa-4452-8267-5fce18834be6	supervised-topological-data-analysis-for	2302.13948	null	https://arxiv.org/abs/2302.13948v1	https://arxiv.org/pdf/2302.13948v1.pdf	Supervised topological data analysis for MALDI imaging applications	We propose a new algebraic topological framework, which obtains intrinsic information from the MALDI data and transforms it to reflect topological persistence in the data. Our framework has two main advantages. First, the topological persistence helps us to distinguish the signal from noise. Second, it compresses the MALDI data, which results in saving storage space, and also optimizes the computational time for further classification tasks. We introduce an algorithm that performs our topological framework and depends on a single tuning parameter. Furthermore, we show that it is computationally efficient. Following the persistence extraction, logistic regression and random forest classifiers are executed based on the resulting persistence transformation diagrams to classify the observational units into binary class labels, describing the lung cancer subtypes. Further, we utilized the proposed framework in a real-world MALDI data set, and the competitiveness of the methods is illustrated via cross-validation.	['Anastasios Stefanou', 'Vladimir Vutov', 'Gideon Klaila']	2023-02-27	null	null	null	null	['topological-data-analysis']	['graphs']	[ 4.29935813e-01 -2.20163435e-01 -4.49860871e-01 -2.10786387e-01 -6.32310688e-01 -5.33898950e-01 5.39189339e-01 5.16726375e-01 -3.84391457e-01 6.65811360e-01 -1.42994717e-01 -5.43090105e-01 -6.60740554e-01 -1.00181222e+00 -5.61420023e-01 -9.83325362e-01 -5.89049935e-01 5.66371500e-01 4.43435311e-01 1.96700022e-01 3.07123661e-01 6.72552526e-01 -1.25011468e+00 9.16162357e-02 1.15364647e+00 1.08374071e+00 7.29045644e-02 4.89805639e-01 8.91140029e-02 2.63261110e-01 -1.08454950e-01 -3.42832834e-01 4.89552207e-02 4.43552993e-02 -5.62039196e-01 -4.12727624e-01 1.31131411e-01 2.16441900e-01 -4.18658525e-01 9.10395563e-01 2.81947315e-01 -2.86514968e-01 1.00912464e+00 -9.81159151e-01 -1.05802625e-01 3.52437586e-01 -3.27704966e-01 1.10001460e-01 -1.27864584e-01 -1.01836555e-01 8.86735022e-01 -7.99341738e-01 8.08519244e-01 1.00085473e+00 8.98301542e-01 -4.80394028e-02 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2fed1ae9-f7dd-4d01-aa96-5186540d1c04	local-activity-tuned-image-filtering-for	1707.02637	null	http://arxiv.org/abs/1707.02637v4	http://arxiv.org/pdf/1707.02637v4.pdf	Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing	In this paper, two local activity-tuned filtering frameworks are proposed for noise removal and image smoothing, where the local activity measurement is given by the clipped and normalized local variance or standard deviation. The first framework is a modified anisotropic diffusion for noise removal of piece-wise smooth image. The second framework is a local activity-tuned Relative Total Variation (LAT-RTV) method for image smoothing. Both frameworks employ the division of gradient and the local activity measurement to achieve noise removal. In addition, to better capture local information, the proposed LAT-RTV uses the product of gradient and local activity measurement to boost the performance of image smoothing. Experimental results are presented to demonstrate the efficiency of the proposed methods on various applications, including depth image filtering, clip-art compression artifact removal, image smoothing, and image denoising.	['Huihui Bai', 'Lili Meng', 'Yao Zhao', 'Lijun Zhao', 'Jie Liang', 'Anhong Wang']	2017-07-09	null	null	null	null	['image-smoothing']	['computer-vision']	[ 5.16085625e-01 -4.80602264e-01 1.48575559e-01 -1.78243175e-01 -5.32008886e-01 -8.63471925e-02 4.74305242e-01 -1.71243697e-01 -4.20173824e-01 5.13421834e-01 6.34785771e-01 1.68335795e-01 -2.47982755e-01 -6.52365506e-01 6.12432649e-03 -1.31390929e+00 2.52570361e-01 -7.91591942e-01 6.42586768e-01 -4.97457422e-02 5.29667795e-01 4.74006504e-01 -1.17323160e+00 1.51669353e-01 1.02394879e+00 1.29413950e+00 3.02126974e-01 5.67499936e-01 -2.53352284e-01 7.05278993e-01 -4.49431449e-01 1.98038861e-01 4.26514328e-01 -5.21149039e-01 -2.03427255e-01 2.27969646e-01 4.13277268e-01 -2.78673917e-01 -1.80215150e-01 1.41752863e+00 8.98446321e-01 4.59618062e-01 5.58497429e-01 -4.52371478e-01 -5.00350296e-01 1.59929395e-01 -1.03665578e+00 6.44086659e-01 6.95652002e-03 1.84438169e-01 -1.25605846e-02 -7.59932339e-01 5.14030457e-01 1.45455468e+00 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b225abdc-f1f7-4171-b9a1-f107aec8b0e4	data-augmentation-and-multimodal-learning-for	2204.11678	null	https://arxiv.org/abs/2204.11678v1	https://arxiv.org/pdf/2204.11678v1.pdf	Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images	Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually better mimics drug response of patients with cancer compared to CCLs. We investigate multimodal neural network (MM-Net) and data augmentation for drug response prediction in PDXs. The MM-Net learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs) where the multi-modality refers to the tumor features. We explore whether the integration of WSIs with GE improves predictions as compared with models that use GE alone. We use two methods to address the limited number of response values: 1) homogenize drug representations which allows to combine single-drug and drug-pairs treatments into a single dataset, 2) augment drug-pair samples by switching the order of drug features which doubles the sample size of all drug-pair samples. These methods enable us to combine single-drug and drug-pair treatments, allowing us to train multimodal and unimodal neural networks (NNs) without changing architectures or the dataset. Prediction performance of three unimodal NNs which use GE are compared to assess the contribution of data augmentation methods. NN that uses the full dataset which includes the original and the augmented drug-pair treatments as well as single-drug treatments significantly outperforms NNs that ignore either the augmented drug-pairs or the single-drug treatments. In assessing the contribution of multimodal learning based on the MCC metric, MM-Net statistically significantly outperforms all the baselines. Our results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs.	['Rick L. Stevens', 'James H. Doroshow', 'Yvonne A. Evrard', 'Maulik Shukla', 'Alexander T. Pearson', 'Sara Kochanny', 'James M. Dolezal', 'Yitan Zhu', 'Thomas Brettin', 'Alexander Partin']	2022-04-25	null	null	null	null	['drug-response-prediction']	['medical']	[ 3.36327016e-01 -3.54418635e-01 -6.62006736e-01 -1.24403805e-01 -8.81060719e-01 -4.76744652e-01 8.07470620e-01 5.78216195e-01 -5.13045430e-01 9.76008236e-01 3.75939399e-01 -5.39231241e-01 -2.75968611e-01 -7.65485227e-01 -5.57312727e-01 -1.14013672e+00 -8.31777521e-04 6.00474060e-01 -1.97919250e-01 -3.29431534e-01 -2.19502375e-01 8.12049806e-01 -1.03914082e+00 4.13538426e-01 6.05032682e-01 1.01252019e+00 -2.00386718e-03 5.44042051e-01 9.80664566e-02 5.02565086e-01 -1.67143360e-01 1.24996625e-01 4.05625790e-01 -2.70567209e-01 -3.82174075e-01 -2.73295164e-01 6.25991702e-01 -3.76299828e-01 -6.59732461e-01 6.68850720e-01 8.17689598e-01 -2.28784144e-01 7.44526029e-01 -1.31219518e+00 -4.03430551e-01 2.20360205e-01 -5.82733274e-01 -1.04415506e-01 7.78331906e-02 5.03483176e-01 7.70676851e-01 -6.73325598e-01 8.42889726e-01 8.08210373e-01 8.52351069e-01 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236cc115-5b79-4758-8ee4-5bfb001d1e7a	tokenized-graph-transformer-with-neighborhood	2305.12677	null	https://arxiv.org/abs/2305.12677v1	https://arxiv.org/pdf/2305.12677v1.pdf	Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs	Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs. To this end, we propose a Neighborhood Aggregation Graph Transformer (NAGphormer) that treats each node as a sequence containing a series of tokens constructed by our proposed Hop2Token module. For each node, Hop2Token aggregates the neighborhood features from different hops into different representations, producing a sequence of token vectors as one input. In this way, NAGphormer could be trained in a mini-batch manner and thus could scale to large graphs. Moreover, we mathematically show that compared to a category of advanced Graph Neural Networks (GNNs), called decoupled Graph Convolutional Networks, NAGphormer could learn more informative node representations from multi-hop neighborhoods. In addition, we propose a new data augmentation method called Neighborhood Augmentation (NrAug) based on the output of Hop2Token that augments simultaneously the features of neighborhoods from global as well as local views to strengthen the training effect of NAGphormer. Extensive experiments on benchmark datasets from small to large demonstrate the superiority of NAGphormer against existing graph Transformers and mainstream GNNs, and the effectiveness of NrAug for further boosting NAGphormer.	['Kun He', 'Gaichao Li', 'Kaiyuan Gao', 'Chang Liu', 'Jinsong Chen']	2023-05-22	null	null	null	null	['graph-representation-learning']	['methodology']	[-1.46097645e-01 3.93886179e-01 -2.25316897e-01 -1.58899471e-01 -2.46668026e-01 -4.09545660e-01 5.95835447e-01 5.86940527e-01 -4.75437678e-02 5.06344557e-01 2.64036179e-01 -4.46803719e-01 -1.35151492e-02 -1.43869174e+00 -7.60407388e-01 -7.29681194e-01 -2.34935477e-01 2.46788323e-01 2.31372133e-01 -3.95714760e-01 -2.00696930e-01 5.00226617e-01 -1.05830014e+00 -5.65311871e-02 9.30048943e-01 1.02684772e+00 8.23795646e-02 4.50516939e-01 -1.86921552e-01 8.60820234e-01 -3.62044811e-01 -3.37549418e-01 1.81476012e-01 -1.45976856e-01 -6.19800389e-01 -8.67465585e-02 4.25134778e-01 -2.21375287e-01 -8.55110705e-01 9.97576773e-01 3.56188118e-01 4.32479531e-01 2.71641850e-01 -1.33679748e+00 -1.21787059e+00 1.16520035e+00 -5.17133415e-01 1.81303382e-01 1.38736278e-01 1.53618097e-01 1.35648715e+00 -9.42251623e-01 4.31947052e-01 1.18544090e+00 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7dccc7f8-cf05-4dba-b14c-60c5b9f57879	scalable-causal-structure-learning-new	2110.07785	null	https://arxiv.org/abs/2110.07785v2	https://arxiv.org/pdf/2110.07785v2.pdf	Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine	Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care. This paper provides a practical review and tutorial on scalable causal structure learning models with examples of real-world data to help health care audiences understand and apply them. We reviewed traditional (combinatorial and score-based methods) for causal structure discovery and machine learning-based schemes. We also highlighted recent developments in biomedicine where causal structure learning can be applied to discover structures such as gene networks, brain connectivity networks, and those in cancer epidemiology. We also compared the performance of traditional and machine learning-based algorithms for causal discovery over some benchmark data sets. Machine learning-based approaches, including deep learning, have many advantages over traditional approaches, such as scalability, including a greater number of variables, and potentially being applied in a wide range of biomedical applications, such as genetics, if sufficient data are available. Furthermore, these models are more flexible than traditional models and are poised to positively affect many applications in the future.	['Yejin Kim', 'Xiaoqian Jiang', 'Can Li', 'Kai Zhang', 'Pulakesh Upadhyaya']	2021-10-15	null	null	null	null	['epidemiology']	['medical']	[ 5.61564982e-01 2.96685874e-01 -6.73247993e-01 -4.24506783e-01 -5.27186930e-01 -2.28123575e-01 4.97390509e-01 7.67174184e-01 -7.10115805e-02 1.14721906e+00 7.77496219e-01 -7.34791577e-01 -1.08562326e+00 -7.75223494e-01 -7.46708989e-01 -8.30188096e-01 -9.06386673e-01 6.22604251e-01 2.04906538e-02 -3.59272622e-02 1.91152111e-01 2.64213651e-01 -9.20479655e-01 3.88537794e-01 8.09104800e-01 2.83579260e-01 -3.82049456e-02 4.23266709e-01 7.39086568e-02 8.22284818e-01 -3.37660402e-01 -1.96147844e-01 -5.14862061e-01 -5.55077136e-01 -1.01803601e+00 -7.65512347e-01 -7.10058361e-02 1.90345615e-01 -2.67205864e-01 6.28380537e-01 6.25780165e-01 -3.29116255e-01 6.28266990e-01 -1.02203536e+00 -5.12986660e-01 8.01717103e-01 -6.10828400e-01 4.33534056e-01 4.75658983e-01 -1.13788471e-01 1.16149163e+00 -3.94098520e-01 6.76749706e-01 1.59983766e+00 6.88140094e-01 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8a9dd8d1-786d-4ab2-a02b-e52f8504010d	interweaved-graph-and-attention-network-for	2304.14045	null	https://arxiv.org/abs/2304.14045v1	https://arxiv.org/pdf/2304.14045v1.pdf	Interweaved Graph and Attention Network for 3D Human Pose Estimation	Despite substantial progress in 3D human pose estimation from a single-view image, prior works rarely explore global and local correlations, leading to insufficient learning of human skeleton representations. To address this issue, we propose a novel Interweaved Graph and Attention Network (IGANet) that allows bidirectional communications between graph convolutional networks (GCNs) and attentions. Specifically, we introduce an IGA module, where attentions are provided with local information from GCNs and GCNs are injected with global information from attentions. Additionally, we design a simple yet effective U-shaped multi-layer perceptron (uMLP), which can capture multi-granularity information for body joints. Extensive experiments on two popular benchmark datasets (i.e. Human3.6M and MPI-INF-3DHP) are conducted to evaluate our proposed method.The results show that IGANet achieves state-of-the-art performance on both datasets. Code is available at https://github.com/xiu-cs/IGANet.	['Xia Li', 'Yingxuan You', 'Wenhao Li', 'Runwei Ding', 'Hong Liu', 'Ti Wang']	2023-04-27	null	null	null	null	['3d-human-pose-estimation']	['computer-vision']	[-2.55955100e-01 2.91032910e-01 -2.36526087e-01 -2.38485023e-01 -3.70169640e-01 -7.36567541e-04 4.15227294e-01 -2.20897540e-01 -3.41588914e-01 4.51026976e-01 4.27389830e-01 1.44316927e-02 2.11652860e-01 -7.23722935e-01 -1.01141644e+00 -2.68639207e-01 -2.34878689e-01 5.69477201e-01 3.18209916e-01 -1.50454164e-01 -2.60640323e-01 2.68316627e-01 -1.06333256e+00 1.47970468e-01 5.43773949e-01 8.35507989e-01 1.14282556e-01 7.00852752e-01 3.12267274e-01 8.73269081e-01 -2.11054787e-01 -4.57028717e-01 1.87730312e-01 -2.90145427e-01 -1.01063681e+00 7.42365047e-02 4.05004263e-01 -5.20474553e-01 -7.55803585e-01 7.40182936e-01 6.90183640e-01 6.87767416e-02 3.71222794e-01 -1.33601975e+00 -8.07564318e-01 5.29321849e-01 -9.57803369e-01 2.32775554e-01 3.71278614e-01 3.18912238e-01 1.10794377e+00 -7.70851910e-01 5.60307860e-01 1.52812684e+00 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4fb97b81-87d3-4ea3-95da-6d7ef3a90f41	en-compactness-self-distillation-embedding	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Kong_En-Compactness_Self-Distillation_Embedding__Contrastive_Generation_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Kong_En-Compactness_Self-Distillation_Embedding__Contrastive_Generation_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.pdf	En-Compactness: Self-Distillation Embedding & Contrastive Generation for Generalized Zero-Shot Learning	Generalized zero-shot learning (GZSL) requires a classifier trained on seen classes that can recognize objects from both seen and unseen classes. Due to the absence of unseen training samples, the classifier tends to bias towards seen classes. To mitigate this problem, feature generation based models are proposed to synthesize visual features for unseen classes. However, these features are generated in the visual feature space which lacks of discriminative ability. Therefore, some methods turn to find a better embedding space for the classifier training. They emphasize the inter-class relationships of seen classes, leading the embedding space overfitted to seen classes and unfriendly to unseen classes. Instead, in this paper, we propose an Intra-Class Compactness Enhancement method (ICCE) for GZSL. Our ICCE promotes intra-class compactness with inter-class separability on both seen and unseen classes in the embedding space and visual feature space. By promoting the intra-class relationships but the inter-class structures, we can distinguish different classes with better generalization. Specifically, we propose a Self-Distillation Embedding (SDE) module and a Semantic-Visual Contrastive Generation (SVCG) module. The former promotes intra-class compactness in the embedding space, while the latter accomplishes it in the visual feature space. The experiments demonstrate that our ICCE outperforms the state-of-the-art methods on four datasets and achieves competitive results on the remaining dataset.	['Yanyun Qu', 'Yuan Xie', 'Chengjie Wang', 'Jun Liu', 'Ming Hong', 'Xiaofan Li', 'Zuodong Gao', 'Xia Kong']	2022-01-01	null	null	null	cvpr-2022-1	['generalized-zero-shot-learning', 'generalized-zero-shot-learning']	['computer-vision', 'methodology']	[ 1.17770463e-01 6.65758029e-02 -9.50008556e-02 -3.40723872e-01 -2.29941875e-01 -3.65520656e-01 7.35318959e-01 1.08874924e-01 -2.21640170e-01 3.33535165e-01 9.31141824e-02 1.36484191e-01 -2.40616798e-01 -1.20324624e+00 -2.81359434e-01 -9.63198185e-01 3.02516669e-01 5.33103235e-02 4.99312222e-01 -1.64113000e-01 1.80650130e-01 5.98636210e-01 -2.07874012e+00 5.76872051e-01 8.92214715e-01 1.01770937e+00 2.47250974e-01 2.75522918e-01 -3.81286353e-01 4.48145241e-01 -5.33495247e-01 -1.61809340e-01 2.93992907e-01 -4.88163173e-01 -4.86161828e-01 3.64010870e-01 4.97649252e-01 -9.80070047e-03 -2.17242077e-01 1.13398838e+00 3.97854179e-01 3.25121135e-01 9.25430179e-01 -1.64278686e+00 -1.07313359e+00 1.06149651e-01 -4.17544067e-01 1.37124844e-02 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3.01964432e-01 6.21961951e-01 3.46658379e-01 5.30461192e-01 1.05981922e+00 -6.66450858e-01 -6.14905417e-01 -7.94554710e-01 -8.07692707e-01 8.30891132e-01 2.61961430e-01 -1.06309938e+00 -7.95059085e-01 -9.12008509e-02]	[9.896726608276367, 2.4027037620544434]
057eb181-45ba-4973-87eb-a8d845867138	size-generalizability-of-graph-neural	2305.15611	null	https://arxiv.org/abs/2305.15611v1	https://arxiv.org/pdf/2305.15611v1.pdf	Size Generalizability of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective	We investigate the question of whether the knowledge learned by graph neural networks (GNNs) from small graphs is generalizable to large graphs in the same domain. Prior works suggest that the distribution shift, particularly in the degree distribution, between graphs of different sizes can lead to performance degradation in the graph classification task. However, this may not be the case for biological datasets where the degrees are bounded and the distribution shift of degrees is small. Even with little degree distribution shift, our observations show that GNNs' performance on larger graphs from the same datasets still degrades, suggesting other causes. In fact, there has been a lack of exploration in real datasets to understand the types and properties of distribution shifts caused by various graph sizes. Furthermore, previous analyses of size generalizability mostly focus on the spatial domain. To fill these gaps, we take the spectral perspective and study the size generalizability of GNNs on biological data. We identify a distribution shift between small and large graphs in the eigenvalues of the normalized Laplacian/adjacency matrix, indicating a difference in the global node connectivity, which is found to be correlated with the node closeness centrality. We further find that despite of the variations in global connectivity, graphs of different sizes share similar local connectivity, which can be utilized to improve the size generalizability of GNNs. Based on our spectral insights and empirical observations, we propose a model-agnostic strategy, SIA, which uses size-irrelevant local structural features, i.e., the local closeness centrality of a node, to guide the learning process. Our empirical results demonstrate that our strategy improves the graph classification performance of various GNNs on small and large graphs when training with only small graphs.	['Danai Koutra', 'Gaotang Li', 'Yujun Yan']	2023-05-24	null	null	null	null	['graph-classification']	['graphs']	[ 1.07383154e-01 1.51757509e-01 -2.67280996e-01 6.51234835e-02 2.73245037e-01 -8.58664215e-01 3.35998774e-01 3.74490440e-01 2.28230399e-03 6.57888651e-01 -2.86950404e-03 -4.85776544e-01 -6.00220263e-01 -1.09327459e+00 -8.15903544e-01 -9.26883042e-01 -6.16854191e-01 3.52025211e-01 2.87273914e-01 -3.40469927e-01 1.15067810e-01 5.91425955e-01 -8.72871459e-01 -8.38173702e-02 7.42495656e-01 5.84051490e-01 1.48298284e-02 4.41785216e-01 6.46559745e-02 1.81810647e-01 -6.47891521e-01 -1.36702091e-01 5.50516427e-01 -4.43454504e-01 -7.10651040e-01 -7.89600760e-02 5.40188313e-01 2.92737097e-01 -8.50738823e-01 1.25157082e+00 3.87214154e-01 -8.95698965e-02 7.16981530e-01 -1.39034641e+00 -7.40630269e-01 1.07128847e+00 -7.41958916e-01 6.70266032e-01 2.05655750e-02 1.59681424e-01 1.26488698e+00 -1.10624649e-01 7.57412970e-01 1.31399083e+00 1.08627152e+00 2.57524580e-01 -1.59866393e+00 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6d93973a-ae19-4cfa-a62c-99091872a235	non-lexical-neural-architecture-for-fine	null	null	https://aclanthology.org/D15-1025	https://aclanthology.org/D15-1025.pdf	Non-lexical neural architecture for fine-grained POS Tagging	null	['re', 'Kevin L{\\"o}ser', 'Alex Allauzen', 'Matthieu Labeau']	2015-09-01	null	null	null	emnlp-2015-9	['morphological-tagging']	['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 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ae838745-f0b7-44ae-8665-c75dd270db6d	lddmm-face-large-deformation-diffeomorphic-1	null	null	https://openreview.net/forum?id=iy2b91gvZpf	https://openreview.net/pdf?id=iy2b91gvZpf	LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Cross-annotation Face Alignment	We innovatively propose a flexible and consistent cross-annotation face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way. Instead of predicting facial landmarks via heatmap or coordinate regression, we formulate the face alignment task in a diffeomorphic registration manner and predict momenta that uniquely parameterize the deformation between initial boundary and true boundary. We then perform large deformation diffeomorphic metric mapping (LDDMM) simultaneously for curve and landmark to localize the facial landmarks. Due to the novel embedding of LDDMM into a deep network, LDDMM-Face can consistently annotate facial landmarks without ambiguity and flexibly handle various annotation schemes, and can even predict dense annotations from sparse ones. Our method can be easily integrated into various face alignment networks. We extensively evaluate LDDMM-Face on four benchmark datasets: 300W, WFLW, HELEN and COFW-68. LDDMM-Face distinguishes itself with outstanding performance when dealing with within-dataset cross-annotation learning (sparse-to-dense) and cross-dataset learning (different training and testing datasets). In addition, LDDMM-Face shows promising results on the most challenging task of cross-dataset cross-annotation learning (different training and testing datasets with different annotations).	['Xiaoying Tang', 'Roger Tam', 'Pujin Cheng', 'Junyan Lyu', 'Huilin Yang']	2021-09-29	null	null	null	null	['face-alignment']	['computer-vision']	[-4.75260556e-01 2.63645321e-01 -1.28788009e-01 -7.01336384e-01 -8.44738126e-01 -5.49872577e-01 5.56489408e-01 -6.00180805e-01 -7.94929937e-02 2.67375886e-01 2.34859675e-01 4.54452902e-01 -2.18218621e-02 -4.65499878e-01 -5.36331117e-01 -5.43450773e-01 6.00509234e-02 8.79659235e-01 -2.38889217e-01 -2.01044500e-01 -1.25944003e-01 7.73886323e-01 -1.14698970e+00 -1.10367075e-01 4.95960325e-01 8.51943493e-01 -1.37917280e-01 -1.48733752e-03 4.47567292e-02 3.03934719e-02 6.76041469e-02 -7.40278542e-01 6.16355896e-01 -3.66927952e-01 -9.72155273e-01 5.02616987e-02 1.11185920e+00 -9.45348889e-02 -1.75455481e-01 9.77935016e-01 7.38046050e-01 -2.25960955e-01 6.66156769e-01 -1.40779519e+00 -7.77224422e-01 7.48134628e-02 -1.03997052e+00 -1.71139002e-01 3.87190878e-01 5.44906892e-02 7.55977571e-01 -1.39947355e+00 8.56856883e-01 1.47985721e+00 1.22365534e+00 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42d820ae-6a88-4c5c-a3fd-3baf9dd6f19a	adapting-discriminative-reranking-to-grounded	null	null	https://aclanthology.org/P13-1022	https://aclanthology.org/P13-1022.pdf	Adapting Discriminative Reranking to Grounded Language Learning	null	['Raymond Mooney', 'Joohyun Kim']	2013-08-01	null	null	null	acl-2013-8	['grounded-language-learning']	['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 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61774cc2-83fb-40db-9294-4285a5137e9e	kuaisar-a-unified-search-and-recommendation	2306.07705	null	https://arxiv.org/abs/2306.07705v3	https://arxiv.org/pdf/2306.07705v3.pdf	KuaiSAR: A Unified Search And Recommendation Dataset	The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within academia, primarily due to the absence of publicly available datasets. Consequently, a substantial gap has emerged between academia and industry regarding research endeavors in joint optimization using user behavior data from both S&R services. To bridge this gap, we introduce the first large-scale, real-world dataset KuaiSAR of integrated Search And Recommendation behaviors collected from Kuaishou, a leading short-video app in China with over 350 million daily active users. Previous research in this field has predominantly employed publicly available semi-synthetic datasets and simulated, with artificially fabricated search behaviors. Distinct from previous datasets, KuaiSAR contains genuine user behaviors, including the occurrence of each interaction within either search or recommendation service, and the users' transitions between the two services. This work aids in joint modeling of S&R, and utilizing search data for recommender systems (and recommendation data for search engines). Furthermore, due to the various feedback labels associated with user-video interactions, KuaiSAR also supports a broad range of tasks, including intent recommendation, multi-task learning, and modeling of long sequential multi-behavioral patterns. We believe this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the S&R services in practical, real-world applications.	['Jun Xu', 'Xiao Zhang', 'Yang song', 'Yanan Niu', 'Dewei Leng', 'Xiaoxue Zang', 'Zihua Si', 'Zhongxiang Sun']	2023-06-13	null	null	null	null	['multi-task-learning']	['methodology']	[-3.33634764e-02 -7.30439425e-01 -1.06407082e+00 -2.39065647e-01 -8.86503100e-01 -5.31615257e-01 4.41466600e-01 -4.02318507e-01 -2.44427416e-02 -6.87492117e-02 4.84982103e-01 -6.91712856e-01 -4.38930631e-01 -2.65524119e-01 -5.20791471e-01 -2.18913198e-01 2.06585843e-02 3.29585522e-01 -2.54768312e-01 -3.37922961e-01 3.74335676e-01 1.88042387e-01 -1.57831407e+00 4.73227084e-01 6.04367971e-01 1.11541355e+00 3.11270237e-01 4.66688126e-01 2.61492521e-01 7.53450036e-01 -3.86313051e-02 -4.81012642e-01 4.57304806e-01 -5.90882935e-02 -4.15158272e-01 3.18218648e-01 2.21539482e-01 -5.47518253e-01 -9.75496590e-01 4.48329479e-01 4.21960354e-01 5.59168160e-01 4.07139122e-01 -1.33911061e+00 -1.02445841e+00 3.99674326e-01 -5.96998036e-01 3.14728349e-01 5.76120794e-01 2.08949208e-01 1.52572143e+00 -7.75552332e-01 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d168d054-5cb3-43f0-9313-031d3e6accc5	path-aware-siamese-graph-neural-network-for	2208.05781	null	https://arxiv.org/abs/2208.05781v2	https://arxiv.org/pdf/2208.05781v2.pdf	Path-aware Siamese Graph Neural Network for Link Prediction	In this paper, we propose a Path-aware Siamese Graph neural network(PSG) for link prediction tasks. First, PSG captures both nodes and edge features for given two nodes, namely the structure information of k-neighborhoods and relay paths information of the nodes. Furthermore, a novel multi-task GNN framework with self-supervised contrastive learning is proposed for differentiation of positive links and negative links while content and behavior of nodes can be captured simultaneously. We evaluate the proposed algorithm PSG on two link property prediction datasets, ogbl-ddi and ogbl-collab. PSG achieves top 1 performance on ogbl-ddi until submission and top 3 performance on ogbl-collab. The experimental results verify the superiority of our proposed PSG	['Chunqi Wu', 'Yao Qi', 'Hongyang Chen', 'Zhao Li', 'Jingsong Lv']	2022-08-10	null	null	null	null	['link-property-prediction']	['graphs']	[-6.11426115e-01 -7.56860971e-02 -9.99392688e-01 -1.98359981e-01 -1.59679905e-01 -3.69554222e-01 3.19355220e-01 2.29534373e-01 4.13891859e-03 1.20690107e+00 -1.22761456e-02 -4.63254690e-01 -9.13628519e-01 -1.02719057e+00 -6.71034038e-01 -2.62664497e-01 -9.75230813e-01 7.09258914e-01 5.20198762e-01 -1.23030677e-01 -3.18541616e-01 7.48036742e-01 -6.11736536e-01 -1.85624614e-01 1.00903773e+00 1.06209791e+00 -1.48287974e-02 5.85602224e-01 -2.98961345e-02 7.15876639e-01 5.18555380e-02 -4.44853753e-01 3.69939148e-01 -4.31568129e-03 -9.39942777e-01 -3.45607996e-01 6.42768741e-01 8.04341063e-02 -1.16954243e+00 9.29427981e-01 4.11658019e-01 4.62853424e-02 6.53504014e-01 -2.08440924e+00 -5.60316265e-01 1.02662134e+00 -7.47753799e-01 7.07646728e-01 3.13279986e-01 7.49931810e-03 1.62203002e+00 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bc5a128a-609c-4d04-afe7-e58f93f5c473	prd-peer-rank-and-discussion-improve-large	2307.02762	null	https://arxiv.org/abs/2307.02762v1	https://arxiv.org/pdf/2307.02762v1.pdf	PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations	Nowadays, the quality of responses generated by different modern large language models (LLMs) are hard to evaluate and compare automatically. Recent studies suggest and predominantly use LLMs as a reference-free metric for open-ended question answering. More specifically, they use the recognized "strongest" LLM as the evaluator, which conducts pairwise comparisons of candidate models' answers and provides a ranking score. However, this intuitive method has multiple problems, such as bringing in self-enhancement (favoring its own answers) and positional bias. We draw insights and lessons from the educational domain (Cho and MacArthur, 2011; Walsh, 2014) to improve LLM-based evaluations. Specifically, we propose the (1) peer rank (PR) algorithm that takes into account each peer LLM's pairwise preferences of all answer pairs, and outputs a final ranking of models; and (2) peer discussion (PD), where we prompt two LLMs to discuss and try to reach a mutual agreement on preferences of two answers. We conduct experiments on two benchmark datasets. We find that our approaches achieve higher accuracy and align better with human judgments, respectively. Interestingly, PR can induce a relatively accurate self-ranking of models under the anonymous setting, where each model's name is unrevealed. Our work provides space to explore evaluating models that are hard to compare for humans.	['Xinya Du', 'Teerth Patel', 'Ruosen Li']	2023-07-06	null	null	null	null	['question-answering', 'open-question']	['natural-language-processing', 'natural-language-processing']	[-1.72194362e-01 2.21773222e-01 -2.80134112e-01 -6.74654305e-01 -1.18870413e+00 -7.93904662e-01 5.52762032e-01 6.83612406e-01 -4.94282901e-01 5.66958129e-01 4.65362340e-01 -3.34033400e-01 -2.50990629e-01 -6.12851560e-01 -6.88427806e-01 -1.83036715e-01 3.59594405e-01 4.97720599e-01 3.02123040e-01 -2.30466709e-01 5.29206991e-01 -2.54018396e-01 -1.53623557e+00 6.66098058e-01 1.43834198e+00 7.28053331e-01 -4.10318598e-02 6.01766586e-01 -2.90004909e-01 1.11550379e+00 -6.92372322e-01 -1.28910792e+00 9.78372712e-03 -3.78373474e-01 -1.29787004e+00 -3.49799573e-01 8.41024041e-01 -2.40446210e-01 2.38685861e-01 9.96382296e-01 6.11827075e-01 2.39188731e-01 5.92385352e-01 -1.20512724e+00 -9.61294234e-01 1.06806850e+00 -3.58306170e-01 -2.37029642e-01 9.68708575e-01 4.79404964e-02 1.36070836e+00 -8.54999185e-01 5.76805055e-01 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f59affca-850c-4259-8d35-decb1e898cc3	learning-algebraic-representation-for-2	2111.12990	null	https://arxiv.org/abs/2111.12990v2	https://arxiv.org/pdf/2111.12990v2.pdf	Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning	Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate between connectionist and classicist, wherein the latter continually advocates an algebraic treatment in cognitive architectures. In this work, we follow the classicist's call and propose a hybrid approach to improve systematic generalization in reasoning. Specifically, we showcase a prototype with algebraic representation for the abstract spatial-temporal reasoning task of Raven's Progressive Matrices (RPM) and present the ALgebra-Aware Neuro-Semi-Symbolic (ALANS) learner. The ALANS learner is motivated by abstract algebra and the representation theory. It consists of a neural visual perception frontend and an algebraic abstract reasoning backend: the frontend summarizes the visual information from object-based representation, while the backend transforms it into an algebraic structure and induces the hidden operator on the fly. The induced operator is later executed to predict the answer's representation, and the choice most similar to the prediction is selected as the solution. Extensive experiments show that by incorporating an algebraic treatment, the ALANS learner outperforms various pure connectionist models in domains requiring systematic generalization. We further show the generative nature of the learned algebraic representation; it can be decoded by isomorphism to generate an answer.	['Yixin Zhu', 'Song-Chun Zhu', 'Ying Nian Wu', 'Baoxiong Jia', 'Sirui Xie', 'Chi Zhang']	2021-11-25	learning-algebraic-representation-for-1	https://openreview.net/forum?id=gehXu3kDU1P	https://openreview.net/pdf?id=gehXu3kDU1P	null	['abstract-algebra', 'systematic-generalization']	['reasoning', 'reasoning']	[ 0.19066758 0.47343105 0.05542615 -0.21675238 0.23598644 -0.5454501 0.84867585 0.29771328 -0.1938598 0.1857712 0.21377936 -0.6292369 -0.7731846 -0.8714844 -0.5022221 -0.5511246 0.09037403 0.86430365 0.18093969 -0.60789603 0.6715214 0.76339996 -1.5559889 0.6104165 1.0568206 1.0004901 0.48608142 0.54939157 -0.43945584 1.4705247 -0.352497 -0.51377565 0.32260704 -0.82405937 -1.1256921 -0.22538435 0.17966181 0.01079852 -0.2332183 0.8962208 -0.07548217 0.23582935 0.8379566 -1.2769253 -1.1831117 0.8824158 -0.25471509 0.61068344 0.49519727 0.22884658 1.0426545 -0.94677025 0.6724725 1.5289489 0.5405256 0.5509121 -1.471082 -0.22804072 0.35778707 0.7787086 -1.297216 -0.02353235 0.7483608 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52815484-74ff-4556-aa29-6f6175d278d7	constrained-environment-optimization-for	2305.11260	null	https://arxiv.org/abs/2305.11260v1	https://arxiv.org/pdf/2305.11260v1.pdf	Constrained Environment Optimization for Prioritized Multi-Agent Navigation	Traditional approaches to the design of multi-agent navigation algorithms consider the environment as a fixed constraint, despite the influence of spatial constraints on agents' performance. Yet hand-designing conducive environment layouts is inefficient and potentially expensive. The goal of this paper is to consider the environment as a decision variable in a system-level optimization problem, where both agent performance and environment cost are incorporated. Towards this end, we propose novel problems of unprioritized and prioritized environment optimization, where the former considers agents unbiasedly and the latter accounts for agent priorities. We show, through formal proofs, under which conditions the environment can change while guaranteeing completeness (i.e., all agents reach goals), and analyze the role of agent priorities in the environment optimization. We proceed to impose real-world constraints on the environment optimization and formulate it mathematically as a constrained stochastic optimization problem. Since the relation between agents, environment and performance is challenging to model, we leverage reinforcement learning to develop a model-free solution and a primal-dual mechanism to handle constraints. Distinct information processing architectures are integrated for various implementation scenarios, including online/offline optimization and discrete/continuous environment. Numerical results corroborate the theory and demonstrate the validity and adaptability of our approach.	['Amanda Prorok', 'Zhan Gao']	2023-05-18	null	null	null	null	['stochastic-optimization']	['methodology']	[ 8.06452259e-02 -7.01333210e-02 -2.48016477e-01 1.75617300e-02 -1.95887521e-01 -8.39875579e-01 2.87889630e-01 1.58908039e-01 -8.49751234e-01 9.90140975e-01 -4.00541686e-02 -4.57361579e-01 -8.22317839e-01 -8.79590750e-01 -6.56507254e-01 -8.19580674e-01 -3.39397997e-01 5.00109255e-01 -1.01047471e-01 -4.82141137e-01 4.16867882e-01 4.07747418e-01 -1.40736353e+00 -5.96084177e-01 9.28830922e-01 7.57989049e-01 5.80983341e-01 9.63508844e-01 3.64962518e-01 7.35502303e-01 -5.68596482e-01 1.25389710e-01 6.77483261e-01 -1.53799742e-01 -5.52726567e-01 2.82830179e-01 -3.35970879e-01 -4.25450921e-01 -6.14017658e-02 9.62551236e-01 5.48628092e-01 3.64100665e-01 3.81405711e-01 -1.79899824e+00 -3.48639756e-01 4.08554912e-01 -4.57111865e-01 1.02681153e-01 3.04641664e-01 3.02241653e-01 1.01409221e+00 -2.02760547e-01 5.28256357e-01 1.04250062e+00 8.18817914e-02 3.17574739e-01 -1.18060184e+00 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87890072-4568-4493-a014-8868ecbc0153	automated-diabetic-retinopathy-grading-using	2004.06334	null	https://arxiv.org/abs/2004.06334v1	https://arxiv.org/pdf/2004.06334v1.pdf	Automated Diabetic Retinopathy Grading using Deep Convolutional Neural Network	Diabetic Retinopathy is a global health problem, influences 100 million individuals worldwide, and in the next few decades, these incidences are expected to reach epidemic proportions. Diabetic Retinopathy is a subtle eye disease that can cause sudden, irreversible vision loss. The early-stage Diabetic Retinopathy diagnosis can be challenging for human experts, considering the visual complexity of fundus photography retinal images. However, Early Stage detection of Diabetic Retinopathy can significantly alter the severe vision loss problem. The competence of computer-aided detection systems to accurately detect the Diabetic Retinopathy had popularized them among researchers. In this study, we have utilized a pre-trained DenseNet121 network with several modifications and trained on APTOS 2019 dataset. The proposed method outperformed other state-of-the-art networks in early-stage detection and achieved 96.51% accuracy in severity grading of Diabetic Retinopathy for multi-label classification and achieved 94.44% accuracy for single-class classification method. Moreover, the precision, recall, f1-score, and quadratic weighted kappa for our network was reported as 86%, 87%, 86%, and 91.96%, respectively. Our proposed architecture is simultaneously very simple, accurate, and efficient concerning computational time and space.	['Prakash. S. Prasad', 'Vaishali Ninawe', 'Saket S. Chaturvedi', 'Kajol Gupta']	2020-04-14	null	null	null	null	['diabetic-retinopathy-grading']	['medical']	[ 7.39419609e-02 -1.75352588e-01 -1.14163563e-01 -4.03753966e-01 -3.62160087e-01 -1.77726015e-01 1.38899311e-01 8.89637992e-02 -6.20658755e-01 8.84525836e-01 3.73554714e-02 -2.70704567e-01 -2.09480748e-01 -6.54540837e-01 -1.25002906e-01 -7.72984385e-01 1.26684353e-01 1.78072602e-01 2.13301927e-01 2.27528006e-01 3.91950786e-01 6.08631313e-01 -1.79325175e+00 3.25305134e-01 1.50555217e+00 1.08422935e+00 2.56707370e-02 7.71907568e-01 9.43484753e-02 9.83761966e-01 -4.93915617e-01 -3.90297741e-01 1.54849619e-01 -3.19697052e-01 -3.77709568e-01 1.80561662e-01 8.72602105e-01 -5.45612097e-01 -1.57891095e-01 1.15118206e+00 8.18138838e-01 -2.38965273e-01 7.65795350e-01 -5.11239111e-01 -9.04502988e-01 -8.10212567e-02 -9.77785528e-01 4.77418125e-01 -1.25541598e-01 3.53004575e-01 2.90585220e-01 -4.75654125e-01 2.54051596e-01 9.56426203e-01 5.70632756e-01 6.39887154e-01 -8.30663621e-01 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2c407067-2291-4456-8e9e-b0d7582f9add	ubiwear-an-end-to-end-data-driven-framework	2212.14731	null	https://arxiv.org/abs/2212.14731v2	https://arxiv.org/pdf/2212.14731v2.pdf	UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions	It is indisputable that physical activity is vital for an individual's health and wellness. However, a global prevalence of physical inactivity has induced significant personal and socioeconomic implications. In recent years, a significant amount of work has showcased the capabilities of self-tracking technology to create positive health behavior change. This work is motivated by the potential of personalized and adaptive goal-setting techniques in encouraging physical activity via self-tracking. To this end, we propose UBIWEAR, an end-to-end framework for intelligent physical activity prediction, with the ultimate goal to empower data-driven goal-setting interventions. To achieve this, we experiment with numerous machine learning and deep learning paradigms as a robust benchmark for physical activity prediction tasks. To train our models, we utilize, "MyHeart Counts", an open, large-scale dataset collected in-the-wild from thousands of users. We also propose a prescriptive framework for self-tracking aggregated data preprocessing, to facilitate data wrangling of real-world, noisy data. Our best model achieves a MAE of 1087 steps, 65% lower than the state of the art in terms of absolute error, proving the feasibility of the physical activity prediction task, and paving the way for future research.	['Athena Vakali', 'Sofia Yfantidou', 'Asterios Bampakis']	2022-12-30	null	null	null	null	['activity-prediction', 'activity-prediction']	['computer-vision', 'time-series']	[ 2.91085780e-01 2.19778508e-01 -9.58073199e-01 -2.37751901e-01 -4.74532396e-01 -1.38948869e-03 5.85156441e-01 3.93556237e-01 -2.98234463e-01 8.57291758e-01 9.44426537e-01 -2.35927388e-01 -3.17156821e-01 -1.11445296e+00 -6.29960775e-01 -2.99544871e-01 -1.52629107e-01 2.75658220e-02 -1.21099986e-01 -1.72666639e-01 -1.95256993e-01 -8.46011639e-02 -1.48656845e+00 3.94567102e-02 1.11436367e+00 7.54330099e-01 -3.40196878e-01 5.26632905e-01 2.10929468e-01 9.43764627e-01 -1.34348184e-01 -2.34250128e-01 3.60329002e-02 -8.02625418e-01 -6.11849189e-01 -1.21948034e-01 7.74298131e-01 -2.59855628e-01 -4.06265527e-01 4.97189075e-01 7.08096027e-01 4.34090614e-01 -4.46335087e-03 -1.07464910e+00 -8.61253500e-01 5.49137890e-01 2.23354995e-02 3.17285120e-01 4.78660345e-01 7.82300711e-01 7.60482132e-01 -5.36663644e-02 2.63167709e-01 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-4.62837040e-01 -6.48007512e-01 -1.02881348e+00 -2.44266197e-01 8.23995292e-01 1.16420820e-01 -4.58318740e-01 2.08590314e-01 5.27854823e-02]	[13.59363842010498, 3.362592935562134]
62e9007a-a3bd-4227-a746-dcede72dbf8d	cogintac-modeling-the-relationships-between	2205.03540	null	https://arxiv.org/abs/2205.03540v2	https://arxiv.org/pdf/2205.03540v2.pdf	CogIntAc: Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspective	Intention, emotion and action are important psychological factors in human activities, which play an important role in the interaction between individuals. How to model the interaction process between individuals by analyzing the relationship of their intentions, emotions, and actions at the cognitive level is challenging. In this paper, we propose a novel cognitive framework of individual interaction. The core of the framework is that individuals achieve interaction through external action driven by their inner intention. Based on this idea, the interactions between individuals can be constructed by establishing relationships between the intention, emotion and action. Furthermore, we conduct analysis on the interaction between individuals and give a reasonable explanation for the predicting results. To verify the effectiveness of the framework, we reconstruct a dataset and propose three tasks as well as the corresponding baseline models, including action abduction, emotion prediction and action generation. The novel framework shows an interesting perspective on mimicking the mental state of human beings in cognitive science.	['Yajing Sun', 'Luxi Xing', 'Yuqiang Xie', 'Yue Hu', 'Wei Peng']	2022-05-07	null	null	null	null	['action-generation']	['computer-vision']	[ 3.75334658e-02 4.78772342e-01 -1.78948298e-01 -5.71677029e-01 5.18007219e-01 4.39012721e-02 8.50428224e-01 -2.10787565e-01 -1.81830265e-02 4.21060801e-01 7.60881901e-01 2.74676114e-01 -8.06025341e-02 -1.01044810e+00 -2.87207097e-01 -3.95315915e-01 2.61178881e-01 1.02280267e-01 -2.95983434e-01 -4.16090876e-01 3.16496789e-01 -1.62605017e-01 -1.64187908e+00 4.15325999e-01 1.15454769e+00 8.20192575e-01 -2.62664240e-02 3.40797037e-01 -4.08836342e-02 1.07686996e+00 -3.05374056e-01 -6.59349680e-01 -2.17317730e-01 -1.03367770e+00 -1.01424968e+00 6.05761230e-01 -5.19525588e-01 -2.59328425e-01 -3.90138999e-02 8.37825954e-01 2.21781731e-01 3.10599983e-01 6.94153547e-01 -1.62893522e+00 -9.45160568e-01 7.98120439e-01 1.08937398e-01 -4.82220322e-01 1.08650219e+00 2.04811186e-01 8.47307801e-01 -4.43505734e-01 2.62468994e-01 1.50642490e+00 5.16852021e-01 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fb119a22-ea80-4dc1-91fc-13363fd6c598	ordered-and-binary-speaker-embedding	2305.16043	null	https://arxiv.org/abs/2305.16043v1	https://arxiv.org/pdf/2305.16043v1.pdf	Ordered and Binary Speaker Embedding	Modern speaker recognition systems represent utterances by embedding vectors. Conventional embedding vectors are dense and non-structural. In this paper, we propose an ordered binary embedding approach that sorts the dimensions of the embedding vector via a nested dropout and converts the sorted vectors to binary codes via Bernoulli sampling. The resultant ordered binary codes offer some important merits such as hierarchical clustering, reduced memory usage, and fast retrieval. These merits were empirically verified by comprehensive experiments on a speaker identification task with the VoxCeleb and CN-Celeb datasets.	['Dong Wang', 'Lantian Li', 'Namin Wang', 'Xianglong Wang', 'Jiaying Wang']	2023-05-25	null	null	null	null	['speaker-recognition', 'speaker-identification']	['speech', 'speech']	[-1.84949309e-01 -6.43613636e-02 -2.90115744e-01 -7.28626370e-01 -6.07054949e-01 -4.13061172e-01 5.78053772e-01 1.30269557e-01 -3.74878079e-01 4.41634178e-01 3.79301488e-01 -3.93960476e-01 -2.60355920e-01 -4.29340988e-01 -4.62425016e-02 -6.56460404e-01 -2.53491968e-01 5.49966335e-01 -1.18286438e-01 1.25631243e-01 2.59529501e-01 3.97241235e-01 -1.64481533e+00 7.11421892e-02 5.81111014e-01 9.48358178e-01 -2.04490632e-01 5.98713994e-01 -3.61337066e-01 4.72103506e-01 -6.47197008e-01 -5.39453566e-01 -5.64913116e-02 -2.95564115e-01 -4.69262928e-01 6.30591959e-02 4.74428721e-02 -4.10681725e-01 -7.88662553e-01 1.07803571e+00 5.33877850e-01 1.37110397e-01 6.98145032e-01 -1.44638550e+00 -9.47585762e-01 8.00138950e-01 -2.22375482e-01 2.95197904e-01 5.17411053e-01 -3.27628553e-01 1.33652520e+00 -1.10467482e+00 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d3b4fd05-f516-4b56-8521-73ac532da8f2	roma-run-time-object-detection-to-maximize	2210.16083	null	https://arxiv.org/abs/2210.16083v1	https://arxiv.org/pdf/2210.16083v1.pdf	ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy	This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is designed to select an optimal detector out of a set of detectors in real time without label information to maximize real-time object detection accuracy. ROMA utilizing four YOLOv4 detectors on an NVIDIA Jetson Nano shows real-time accuracy improvements by 4 to 37% for a scenario of dynamically varying video contents and detection latency consisting of MOT17Det and MOT20Det datasets, compared to individual YOLOv4 detectors and two state-of-the-art runtime techniques.	['Hans Vandierendonck', 'Blesson Varghese', 'JunKyu Lee']	2022-10-28	null	null	null	null	['real-time-object-detection']	['computer-vision']	[-3.44797015e-01 -9.14685905e-01 -1.51771605e-01 -1.65091917e-01 -4.63655800e-01 -6.45177424e-01 2.60574669e-01 2.49231681e-01 -7.26578057e-01 4.59507629e-02 -5.74966311e-01 -3.41358602e-01 1.62165061e-01 -5.06991863e-01 -5.92348874e-01 -2.84899116e-01 -2.46040881e-01 4.70041484e-01 1.14273310e+00 2.02404544e-01 3.09926033e-01 7.88713336e-01 -1.96552444e+00 3.41153234e-01 3.10142040e-01 1.23510504e+00 8.08517784e-02 1.08767939e+00 3.41385514e-01 7.83799231e-01 -8.80205154e-01 5.63230328e-02 6.51519060e-01 3.39815989e-02 -4.66199294e-02 -1.59987032e-01 8.74027789e-01 -6.75005496e-01 -4.05472696e-01 8.49242210e-01 5.85941792e-01 -6.76927641e-02 3.83149534e-01 -1.50621247e+00 2.11831093e-01 3.17550093e-01 -7.86756396e-01 1.04827929e+00 1.77817255e-01 5.67470431e-01 4.31727141e-01 -5.57468355e-01 5.33090353e-01 1.08011901e+00 6.16126120e-01 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96287837-a82d-4391-a9ec-c49679279065	adapting-to-label-shift-with-bias-corrected	null	null	https://openreview.net/forum?id=rkx-wA4YPS	https://openreview.net/pdf?id=rkx-wA4YPS	Adapting to Label Shift with Bias-Corrected Calibration	Label shift refers to the phenomenon where the marginal probability p(y) of observing a particular class changes between the training and test distributions, while the conditional probability p(x\|y) stays fixed. This is relevant in settings such as medical diagnosis, where a classifier trained to predict disease based on observed symptoms may need to be adapted to a different distribution where the baseline frequency of the disease is higher. Given estimates of p(y\|x) from a predictive model, one can apply domain adaptation procedures including Expectation Maximization (EM) and Black-Box Shift Estimation (BBSE) to efficiently correct for the difference in class proportions between the training and test distributions. Unfortunately, modern neural networks typically fail to produce well-calibrated estimates of p(y\|x), reducing the effectiveness of these approaches. In recent years, Temperature Scaling has emerged as an efficient approach to combat miscalibration. However, the effectiveness of Temperature Scaling in the context of adaptation to label shift has not been explored. In this work, we study the impact of various calibration approaches on shift estimates produced by EM or BBSE. In experiments with image classification and diabetic retinopathy detection, we find that calibration consistently tends to improve shift estimation. In particular, calibration approaches that include class-specific bias parameters are significantly better than approaches that lack class-specific bias parameters, suggesting that reducing systematic bias in the calibrated probabilities is especially important for domain adaptation.	['Anshul Kundaje', 'Amr M. Alexandari', 'Avanti Shrikumar']	2019-09-25	null	null	null	null	['diabetic-retinopathy-detection']	['medical']	[ 6.40078485e-01 -1.70269795e-02 -4.06680346e-01 -7.32410192e-01 -6.55070484e-01 -3.50041240e-01 4.06489223e-01 4.08492088e-01 -6.12378776e-01 9.23216999e-01 -2.79525723e-02 -4.96604621e-01 -1.83502764e-01 -6.68473721e-01 -8.14540505e-01 -1.00985336e+00 3.63527745e-01 5.08434832e-01 1.60941437e-01 3.77205253e-01 2.58429289e-01 3.22169662e-01 -1.43774688e+00 2.30849646e-02 1.06169128e+00 6.52739227e-01 4.19044271e-02 8.16122591e-01 5.93629815e-02 3.04944158e-01 -8.59810591e-01 -2.57683426e-01 1.07579291e-01 -6.50321960e-01 -4.93316323e-01 1.29224256e-01 7.01447487e-01 -2.11522087e-01 4.31374051e-02 1.16933084e+00 3.93111259e-01 3.28891911e-03 1.00366497e+00 -1.09655499e+00 -3.28730792e-01 3.09565872e-01 -7.65416384e-01 2.28641689e-01 -8.52303207e-02 1.05374724e-01 4.96064931e-01 -1.90610975e-01 4.84069675e-01 1.06916738e+00 8.02914798e-01 4.55360383e-01 -1.68107224e+00 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3.36338043e-01 4.46876138e-01 -8.29692662e-01 -3.84659290e-01 1.99171469e-01]	[8.631362915039062, 4.461163520812988]
05652613-15a7-45a9-815d-6534d2d7dbfd	attention-u-net-based-adversarial	2003.10304	null	https://arxiv.org/abs/2003.10304v1	https://arxiv.org/pdf/2003.10304v1.pdf	Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation	Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks in conjunction with an adversarial critic model. It generalized well to CXR images of unseen datasets with different patient profiles, achieving a final DSC of 97.5% on the JSRT dataset.	['András Lukács', 'Balázs Maga', 'Gusztáv Gaál']	2020-03-23	null	null	null	null	['covid-19-image-segmentation']	['computer-vision']	[ 4.02162299e-02 -2.21998561e-02 -1.52339667e-01 -5.55413477e-02 -9.36263323e-01 -5.88132024e-01 1.21511936e-01 8.52991939e-02 -7.16083825e-01 6.18573904e-01 -1.90359786e-01 -9.76973772e-01 -2.87310630e-02 -6.34538770e-01 -3.93778741e-01 -7.22536445e-01 2.72252589e-01 1.09545052e+00 4.30333257e-01 3.73692900e-01 -4.16508138e-01 9.47162449e-01 -6.03052080e-01 2.86576927e-01 5.94065487e-01 9.42882895e-01 5.83167039e-02 1.25918615e+00 1.28787100e-01 1.20701587e+00 -5.03027320e-01 -6.08317316e-01 -4.73884046e-02 -4.58177239e-01 -9.47317600e-01 -1.29346386e-01 2.32893318e-01 -6.71581268e-01 -5.82315922e-01 4.93800700e-01 7.90087938e-01 -2.86944926e-01 7.97572613e-01 -5.85788548e-01 -3.86343718e-01 3.66366118e-01 -3.49472582e-01 5.92581451e-01 -4.59453203e-02 3.12899560e-01 5.76534808e-01 -4.87999588e-01 4.64360386e-01 7.58380353e-01 9.56230700e-01 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c77cff73-9aa9-49b2-b5bd-99357963efca	nlu-for-game-based-learning-in-real-initial	2205.13754	null	https://arxiv.org/abs/2205.13754v1	https://arxiv.org/pdf/2205.13754v1.pdf	NLU for Game-based Learning in Real: Initial Evaluations	Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings. Spoken Dialogue Systems (SDS) are critical for these interactive agents to carry out effective goal-oriented communication with users in real-time. For the real-world (i.e., in-the-wild) deployment of such conversational agents, improving the Natural Language Understanding (NLU) module of the goal-oriented SDS pipeline is crucial, especially with limited task-specific datasets. This study explores the potential benefits of a recently proposed transformer-based multi-task NLU architecture, mainly to perform Intent Recognition on small-size domain-specific educational game datasets. The evaluation datasets were collected from children practicing basic math concepts via play-based interactions in game-based learning settings. We investigate the NLU performances on the initial proof-of-concept game datasets versus the real-world deployment datasets and observe anticipated performance drops in-the-wild. We have shown that compared to the more straightforward baseline approaches, Dual Intent and Entity Transformer (DIET) architecture is robust enough to handle real-world data to a large extent for the Intent Recognition task on these domain-specific in-the-wild game datasets.	['Lama Nachman', 'Saurav Sahay', 'Eda Okur']	2022-05-27	null	https://aclanthology.org/2022.games-1.4	https://aclanthology.org/2022.games-1.4.pdf	games-lrec-2022-6	['intent-recognition', 'spoken-dialogue-systems']	['natural-language-processing', 'speech']	[ 1.23987995e-01 4.82514232e-01 3.98868531e-01 -4.51599181e-01 -7.31170654e-01 -7.92460382e-01 6.85279906e-01 1.72692239e-01 -4.52281654e-01 3.49297136e-01 2.86227226e-01 -3.84290934e-01 -2.51435280e-01 -7.96486855e-01 -3.28119576e-01 -1.30553052e-01 -3.68419588e-01 1.00270987e+00 6.67396247e-01 -9.33682203e-01 -5.40284403e-02 -1.29832342e-01 -2.20326614e+00 5.78342080e-01 7.69118011e-01 5.57072103e-01 3.47650617e-01 1.36240602e+00 -1.85069233e-01 1.45022225e+00 -8.10124218e-01 -2.44479731e-01 2.79995762e-02 -2.70577461e-01 -9.94787574e-01 -1.80715322e-01 4.67976362e-01 -7.01136053e-01 -1.46509126e-01 5.24714530e-01 7.60555983e-01 7.02894270e-01 1.77712694e-01 -1.37181973e+00 -2.09355000e-02 6.07340932e-01 3.33507270e-01 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005a57e4-c58a-4453-a511-e9d450e35f26	modelling-multi-relations-for-convolutional	2210.11711	null	https://arxiv.org/abs/2210.11711v1	https://arxiv.org/pdf/2210.11711v1.pdf	Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding	Representation learning of knowledge graphs aims to embed entities and relations into low-dimensional vectors. Most existing works only consider the direct relations or paths between an entity pair. It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair, and we propose a convolutional and multi-relational representation learning model, ConvMR. The proposed ConvMR model addresses the multi-relation issue in two aspects: (1) Encoding the multi-relations between an entity pair into a unified vector that maintains the semantic connection. (2) Since not all relations are necessary while joining multi-relations, we propose an attention-based relation encoder to automatically assign weights to different relations based on semantic hierarchy. Experimental results on two popular datasets, FB15k-237 and WN18RR, achieved consistent improvements on the mean rank. We also found that ConvMR is efficient to deal with less frequent entities.	['Chun Che Fung', 'Dengya Zhu', 'Kok Wai Wong', 'Sirui Li']	2022-10-21	null	null	null	null	['knowledge-graph-embedding']	['graphs']	[-2.72875309e-01 5.59958577e-01 -5.50463021e-01 -4.56452101e-01 -1.39540702e-01 -4.09292966e-01 3.35187078e-01 5.86671174e-01 -3.49641800e-01 6.44107580e-01 6.07513666e-01 -3.28613728e-01 -5.73567986e-01 -1.46858680e+00 -7.41395533e-01 -1.16348580e-01 -3.27883333e-01 7.27967083e-01 1.99613720e-01 -5.94249845e-01 -2.50869513e-01 3.88304979e-01 -1.36529100e+00 4.97643352e-01 7.65788674e-01 9.13688004e-01 -1.70935661e-01 2.27095515e-01 -2.82930076e-01 9.52117264e-01 -4.94255751e-01 -1.03176224e+00 1.75593689e-01 -1.86217904e-01 -1.42371464e+00 -3.41232836e-01 2.19138518e-01 -1.74789771e-01 -8.44059050e-01 9.37626421e-01 1.87538862e-01 2.66108364e-01 6.94617212e-01 -1.23101735e+00 -1.39032066e+00 1.08056962e+00 -5.07488132e-01 3.65868300e-01 3.29832315e-01 -7.76834905e-01 1.70922112e+00 -6.54877365e-01 7.56246209e-01 1.34599686e+00 6.20920956e-01 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3.60140651e-02 7.72374496e-02 -7.57593572e-01 -7.25157082e-01 6.07571423e-01 6.54583648e-02 -9.74130154e-01 -7.28439465e-02 -3.63664299e-01]	[8.86294174194336, 8.02209758758545]
9253d710-615f-4517-887a-a48caa9149a5	deep-sinogram-completion-with-image-prior-for	2009.07469	null	https://arxiv.org/abs/2009.07469v1	https://arxiv.org/pdf/2009.07469v1.pdf	Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images	Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this paper, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.	['Xiaomeng Li', 'Zhicheng Zhang', 'Lequan Yu', 'Lei Xing']	2020-09-16	null	null	null	null	['metal-artifact-reduction']	['medical']	[ 5.40423870e-01 6.98953718e-02 6.60576373e-02 -2.70389646e-01 -7.57087350e-01 1.29264630e-02 1.31976813e-01 -3.62692297e-01 -1.57588556e-01 6.12534344e-01 5.24348080e-01 -3.84496063e-01 -1.89593613e-01 -7.33256757e-01 -5.80895126e-01 -8.96338344e-01 3.27547312e-01 3.30639929e-01 2.36628979e-01 1.31139502e-01 -3.05238236e-02 5.03459871e-01 -1.99087575e-01 3.23472321e-01 1.18881178e+00 8.75779808e-01 5.33091784e-01 2.10089251e-01 2.02815577e-01 1.12553704e+00 -3.12735558e-01 9.92887542e-02 3.73522401e-01 -7.67317653e-01 -6.93042457e-01 2.01065361e-01 -1.06185563e-01 -5.99282503e-01 -8.09205830e-01 9.66219604e-01 7.01922655e-01 -8.90237372e-03 5.11423349e-01 -5.26685774e-01 -7.53749013e-01 5.72320044e-01 -6.81964815e-01 1.27353847e-01 -7.69838393e-02 3.26866895e-01 -1.38011754e-01 -1.00829256e+00 5.20951688e-01 6.61774755e-01 9.41916227e-01 6.62271321e-01 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9768b305-f450-4846-895b-8bd848acb1b3	end-to-end-memristive-htm-system-for-pattern	2006.11958	null	https://arxiv.org/abs/2006.11958v1	https://arxiv.org/pdf/2006.11958v1.pdf	End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction	Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this paper, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algorithmically, the system is based on hierarchical temporal memory that inherently offers online learning, resiliency, and fault tolerance. Architecturally, it is a full custom mixed-signal design with an underlying digital communication scheme and analog computational modules. Therefore, the proposed system features reconfigurability, real-time processing, low power consumption, and low-latency processing. The proposed architecture is benchmarked to predict on real-world streaming data. The network's mean absolute percentage error on the mixed-signal system is 1.129X lower compared to its baseline algorithm model. This reduction can be attributed to device non-idealities and probabilistic formation of synaptic connections. We demonstrate that the combined effect of Hebbian learning and network sparsity also plays a major role in extending the overall network lifespan. We also illustrate that the system offers 3.46X reduction in latency and 77.02X reduction in power consumption when compared to a custom CMOS digital design implemented at the same technology node. By employing specific low power techniques, such as clock gating, we observe 161.37X reduction in power consumption.	['Dhireesha Kudithipudi', 'Kevin Gomez', 'Abdullah M. Zyarah']	2020-06-22	null	null	null	null	['low-latency-processing']	['robots']	[ 3.82386178e-01 -2.47179836e-01 -6.32707775e-02 -1.65007725e-01 -1.89180486e-02 -2.26727262e-01 3.36763829e-01 1.46068811e-01 -5.46999216e-01 7.82716036e-01 -1.30414084e-01 -1.61824718e-01 -2.41674289e-01 -6.73860252e-01 -9.99554217e-01 -6.92625284e-01 -3.84717107e-01 -1.18445814e-01 7.24332154e-01 -4.89600748e-02 1.95416778e-01 3.48918140e-01 -1.68674147e+00 5.82510412e-01 4.36049998e-01 1.65092528e+00 1.45675346e-01 4.57762271e-01 2.20172778e-01 6.62278831e-01 -2.53746837e-01 -7.56131485e-03 2.25327030e-01 -1.54507920e-01 2.94796899e-02 -5.23208797e-01 3.01621825e-01 -1.32525057e-01 -1.02622437e+00 8.14530909e-01 6.00924075e-01 -4.04329091e-01 5.70349038e-01 -1.06398606e+00 -1.89977244e-01 8.45329344e-01 -3.15708041e-01 6.82838500e-01 4.27188426e-02 1.75272554e-01 6.13300562e-01 -7.45840192e-01 4.12420660e-01 4.97784555e-01 9.75139618e-01 5.69304824e-01 -1.43274224e+00 -1.06939566e+00 -1.09948199e-02 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8a15a4f3-b9a1-453c-aa4f-40982e02d0d3	csts-conditional-semantic-textual-similarity	2305.15093	null	https://arxiv.org/abs/2305.15093v1	https://arxiv.org/pdf/2305.15093v1.pdf	CSTS: Conditional Semantic Textual Similarity	Semantic textual similarity (STS) has been a cornerstone task in NLP that measures the degree of similarity between a pair of sentences, with applications in information retrieval, question answering, and embedding methods. However, it is an inherently ambiguous task, with the sentence similarity depending on the specific aspect of interest. We resolve this ambiguity by proposing a novel task called conditional STS (C-STS) which measures similarity conditioned on an aspect elucidated in natural language (hereon, condition). As an example, the similarity between the sentences "The NBA player shoots a three-pointer." and "A man throws a tennis ball into the air to serve." is higher for the condition "The motion of the ball." (both upward) and lower for "The size of the ball." (one large and one small). C-STS's advantages are two-fold: (1) it reduces the subjectivity and ambiguity of STS, and (2) enables fine-grained similarity evaluation using diverse conditions. C-STS contains almost 20,000 instances from diverse domains and we evaluate several state-of-the-art models to demonstrate that even the most performant fine-tuning and in-context learning models (GPT-4, Flan, SimCSE) find it challenging, with Spearman correlation scores of <50. We encourage the community to evaluate their models on C-STS to provide a more holistic view of semantic similarity and natural language understanding.	['Karthik Narasimhan', 'Danqi Chen', 'Ashwin Kalyan', 'Tanmay Rajpurohit', 'Victoria Graf', 'Vishvak Murahari', 'Howard Chen', 'Carlos E. Jimenez', 'Ameet Deshpande']	2023-05-24	null	null	null	null	['semantic-textual-similarity', 'semantic-similarity']	['natural-language-processing', 'natural-language-processing']	[ 1.74982697e-01 -3.43325257e-01 -3.21964659e-02 -3.98404777e-01 -7.98354328e-01 -7.96587169e-01 7.87436664e-01 7.01034606e-01 -6.13348007e-01 4.58354861e-01 5.95850587e-01 -3.95675212e-01 -2.67034382e-01 -5.81782997e-01 -2.70900041e-01 -3.90504628e-01 7.59501457e-02 5.75726688e-01 3.24810266e-01 -7.40194917e-01 6.10934258e-01 1.90260530e-01 -1.51846457e+00 5.65440595e-01 7.35292673e-01 1.06011105e+00 3.38427305e-01 4.28868860e-01 -4.03901488e-01 6.13284469e-01 -6.62186623e-01 -5.52375317e-01 -9.99655202e-02 -2.69655973e-01 -1.35141230e+00 -5.02119958e-01 4.72311020e-01 3.61713707e-01 -8.67709294e-02 8.89236867e-01 5.30965447e-01 4.53602284e-01 8.12692642e-01 -1.29735708e+00 -6.71185136e-01 5.09277046e-01 -2.02283129e-01 5.52710056e-01 1.01546800e+00 -1.32096365e-01 1.70351887e+00 -8.33030403e-01 5.08112431e-01 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6a3bac85-4be5-4876-a3ab-1b53a49d9177	learning-the-finer-things-bayesian-structure	2303.04339	null	https://arxiv.org/abs/2303.04339v1	https://arxiv.org/pdf/2303.04339v1.pdf	Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level	Successful machine learning methods require a trade-off between memorization and generalization. Too much memorization and the model cannot generalize to unobserved examples. Too much over-generalization and we risk under-fitting the data. While we commonly measure their performance through cross validation and accuracy metrics, how should these algorithms cope in domains that are extremely under-determined where accuracy is always unsatisfactory? We present a novel probabilistic graphical model structure learning approach that can learn, generalize and explain in these elusive domains by operating at the random variable instantiation level. Using Minimum Description Length (MDL) analysis, we propose a new decomposition of the learning problem over all training exemplars, fusing together minimal entropy inferences to construct a final knowledge base. By leveraging Bayesian Knowledge Bases (BKBs), a framework that operates at the instantiation level and inherently subsumes Bayesian Networks (BNs), we develop both a theoretical MDL score and associated structure learning algorithm that demonstrates significant improvements over learned BNs on 40 benchmark datasets. Further, our algorithm incorporates recent off-the-shelf DAG learning techniques enabling tractable results even on large problems. We then demonstrate the utility of our approach in a significantly under-determined domain by learning gene regulatory networks on breast cancer gene mutational data available from The Cancer Genome Atlas (TCGA).	['Eugene Santos Jr', 'Chase Yakaboski']	2023-03-08	null	null	null	null	['memorization']	['natural-language-processing']	[ 4.24606562e-01 5.50440371e-01 -5.94659269e-01 -5.95425129e-01 -9.71815944e-01 -4.91267979e-01 4.88953263e-01 4.26937908e-01 -1.66055858e-01 1.45017910e+00 3.86623410e-03 -6.58688605e-01 -8.33922744e-01 -8.26825082e-01 -1.02478099e+00 -7.92818546e-01 -3.51106077e-01 8.47946286e-01 5.21609001e-02 2.10200101e-01 9.20019001e-02 3.76139551e-01 -1.16058147e+00 3.15432370e-01 8.85962963e-01 8.39257121e-01 -1.18690327e-01 4.32254910e-01 2.49629337e-02 6.96689308e-01 -3.54660928e-01 -3.13852251e-01 -1.37765393e-01 -2.12192208e-01 -1.02848649e+00 -2.27397799e-01 3.49662662e-01 5.74242584e-02 -1.56397074e-01 8.03304136e-01 2.68595934e-01 1.74027227e-04 9.04496074e-01 -1.10376024e+00 -4.53486264e-01 1.07528090e+00 -3.44810754e-01 1.57390118e-01 1.71228319e-01 5.28758299e-03 1.19899714e+00 -5.05616188e-01 5.98334849e-01 1.34349811e+00 8.82265747e-01 5.13171017e-01 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eaf04b4d-5e4b-4b66-ba9a-5d6fa96c62e9	a-brief-review-of-contrastive-learning	2306.05528	null	https://arxiv.org/abs/2306.05528v1	https://arxiv.org/pdf/2306.05528v1.pdf	A brief review of contrastive learning applied to astrophysics	Reliable tools to extract patterns from high-dimensionality spaces are becoming more necessary as astronomical datasets increase both in volume and complexity. Contrastive Learning is a self-supervised machine learning algorithm that extracts informative measurements from multi-dimensional datasets, which has become increasingly popular in the computer vision and Machine Learning communities in recent years. To do so, it maximizes the agreement between the information extracted from augmented versions of the same input data, making the final representation invariant to the applied transformations. Contrastive Learning is particularly useful in astronomy for removing known instrumental effects and for performing supervised classifications and regressions with a limited amount of available labels, showing a promising avenue towards \emph{Foundation Models}. This short review paper briefly summarizes the main concepts behind contrastive learning and reviews the first promising applications to astronomy. We include some practical recommendations on which applications are particularly attractive for contrastive learning.	['Johan Knapen', 'Regina Sarmiento', 'Marc Huertas-Company']	2023-06-08	null	null	null	null	['astronomy']	['miscellaneous']	[ 3.41838449e-01 -6.59608766e-02 -5.28791070e-01 -4.24129725e-01 -7.14205384e-01 -8.26994181e-01 1.00735784e+00 -9.09754857e-02 -4.27886784e-01 7.00743318e-01 1.63663000e-01 1.14555415e-02 -7.97462523e-01 -4.30145144e-01 -2.85820276e-01 -1.09631193e+00 4.81125973e-02 5.53643227e-01 -2.38506794e-01 1.59362987e-01 4.71120864e-01 1.06061471e+00 -1.75345516e+00 -2.39498187e-02 6.24954760e-01 9.38800633e-01 1.59522131e-01 5.68740904e-01 -2.03226671e-01 6.36810601e-01 -2.17288956e-01 -4.81830798e-02 3.64598513e-01 -4.91519421e-01 -4.99614358e-01 1.58961907e-01 5.71117818e-01 2.73353696e-01 -4.07801956e-01 9.29910183e-01 5.04127741e-01 5.35185151e-02 1.19852579e+00 -8.33412290e-01 -3.12746942e-01 1.42218739e-01 -4.57689762e-01 4.80329424e-01 -1.55082896e-01 -4.87285331e-02 1.00982881e+00 -1.08173633e+00 3.41792136e-01 9.10218179e-01 4.89422917e-01 1.64316893e-01 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63961805-f458-4105-9e2e-c5ad22551858	function-driven-diffusion-for-personalized	1610.10025	null	http://arxiv.org/abs/1610.10025v5	http://arxiv.org/pdf/1610.10025v5.pdf	Function Driven Diffusion for Personalized Counterfactual Inference	We consider the problem of constructing diffusion operators high dimensional data $X$ to address counterfactual functions $F$, such as individualized treatment effectiveness. We propose and construct a new diffusion metric $K_F$ that captures both the local geometry of $X$ and the directions of variance of $F$. The resulting diffusion metric is then used to define a localized filtration of $F$ and answer counterfactual questions pointwise, particularly in situations such as drug trials where an individual patient's outcomes cannot be studied long term both taking and not taking a medication. We validate the model on synthetic and real world clinical trials, and create individualized notions of benefit from treatment.	['Alexander Cloninger']	2016-10-31	null	null	null	null	['counterfactual-inference']	['miscellaneous']	[-1.44576877e-01 2.31969222e-01 -6.45926714e-01 -1.95958346e-01 -5.10459363e-01 -6.32475913e-01 3.79028559e-01 1.67549223e-01 -5.85172474e-01 1.13245404e+00 5.17795205e-01 -5.72621346e-01 -8.81756306e-01 -9.89543319e-01 -4.16160494e-01 -6.24030054e-01 -7.80627012e-01 4.17538077e-01 -5.77147126e-01 5.05493768e-02 2.48044610e-01 3.85402590e-01 -9.61534917e-01 2.17670519e-02 1.05553186e+00 8.30143809e-01 -2.00812295e-01 5.00611246e-01 8.02706033e-02 3.07268709e-01 -5.84292233e-01 -4.41694677e-01 4.85225797e-01 -6.57367647e-01 -6.32914007e-01 -7.61248842e-02 -3.15435864e-02 -4.63910662e-02 -1.85689896e-01 1.27765465e+00 7.15840518e-01 7.76744112e-02 8.42435241e-01 -7.89933443e-01 -8.94303262e-01 8.91326308e-01 -4.65348959e-01 3.80257219e-01 2.91744232e-01 4.40050930e-01 8.71015251e-01 -4.63587344e-01 1.06202006e+00 1.29578984e+00 6.66688144e-01 6.97221220e-01 -1.61754668e+00 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c09b9eb6-1240-4c12-8611-f4b99815d578	hierarchical-reinforcement-learning-based	2208.11529	null	https://arxiv.org/abs/2208.11529v1	https://arxiv.org/pdf/2208.11529v1.pdf	Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation	The rapid development of intelligent tasks, e.g., segmentation, detection, classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compression cost while maintaining the original semantic information. However, it is impractical to directly integrate the semantic metric into the traditional codecs since they cannot be optimized in an end-to-end manner. To solve this problem, some pioneering works have applied reinforcement learning to implement image-wise semantic compression. Nevertheless, video semantic compression has not been explored since its complex reference architectures and compression modes. In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC. Specifically, to simplify the complex mode decision of video semantic coding, we divided the action space into frame-level and CTU-level spaces in a hierarchical manner, and then explore the best mode selection for them progressively with the cooperation of frame-level and CTU-level agents. Moreover, since the modes of video semantic coding will exponentially increase with the number of frames in a Group of Pictures (GOP), we carefully investigate the effects of different mode selections for video semantic coding and design a simple but effective mode simplification strategy for it. We have validated our HRLVSC on the video segmentation task with HEVC reference software HM16.19. Extensive experimental results demonstrated that our HRLVSC can achieve over 39% BD-rate saving for video semantic coding under the Low Delay P configuration.	['Zhibo Chen', 'Yue Li', 'Kai Zhang', 'Li Zhang', 'Shiqi Lin', 'Xin Li', 'Guangqi Xie']	2022-08-24	null	null	null	null	['hierarchical-reinforcement-learning']	['methodology']	[ 3.67970556e-01 -2.07959771e-01 -2.61036843e-01 -3.50015283e-01 -3.45769674e-01 1.22287031e-02 6.09890074e-02 -1.35557994e-01 -5.04033387e-01 4.32578921e-01 -5.82246892e-02 -2.85901159e-01 -2.10726038e-01 -9.08494592e-01 -4.53446090e-01 -7.74162233e-01 -1.02351494e-02 1.71628550e-01 7.38988459e-01 1.68762729e-02 5.11975825e-01 7.86851421e-02 -1.55091977e+00 1.76206961e-01 1.09319806e+00 1.15604949e+00 9.05426443e-01 5.95521986e-01 -3.61899406e-01 9.35385048e-01 -4.73539770e-01 -2.38837346e-01 1.05346605e-01 -7.50070035e-01 -9.41790164e-01 4.60037380e-01 -3.47662002e-01 -5.13127148e-01 -2.96066672e-01 1.18616748e+00 3.40067685e-01 2.43119225e-01 3.40426266e-01 -1.22227275e+00 -1.85719222e-01 6.03318214e-01 -6.44108415e-01 2.03981996e-01 1.39845088e-01 3.85151282e-02 8.11651647e-01 -2.40222543e-01 5.02947927e-01 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d0444c1a-0698-432f-bb5b-e5e5c5470a4b	seq2rel-a-sequence-to-sequence-based-approach	null	null	https://openreview.net/forum?id=JUrlIlxIEun	https://openreview.net/pdf?id=JUrlIlxIEun	Seq2rel: A sequence-to-sequence-based approach for document-level relation extraction	Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (RE). Document-level RE requires integrating information within and across sentences, capturing complex interactions between mentions of interacting entities. Most document-level RE methods proposed to date are pipeline-based, requiring entities as input. However, previous work has demonstrated that jointly learning to extract entities and relations can improve performance and be more efficient due to shared parameters and training steps. In this paper, we develop a sequence-to-sequence-based approach that can learn the sub-tasks of document-level RE --- entity extraction, coreference resolution and relation extraction --- in an end-to-end fashion. We evaluate our approach on several datasets, in some cases exceeding the performance of existing methods. Finally, we demonstrate that, under our model, the end-to-end approach outperforms a pipeline-based approach. Our code and models will be made publicly available.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['document-level-relation-extraction', 'joint-entity-and-relation-extraction']	['natural-language-processing', 'natural-language-processing']	[ 2.68617034e-01 3.89704287e-01 -1.52156636e-01 -4.62267786e-01 -1.24390304e+00 -9.30313349e-01 7.12404430e-01 5.38506508e-01 -5.41817546e-01 9.10629749e-01 6.48424447e-01 -3.61063123e-01 -2.04293057e-01 -5.03885984e-01 -6.21048808e-01 6.23541363e-02 -3.80424351e-01 7.98609912e-01 4.05000478e-01 -3.44602287e-01 1.64429918e-01 3.99987936e-01 -1.07262743e+00 5.92973828e-01 8.08338404e-01 4.12245423e-01 -6.75720572e-02 9.52269852e-01 -1.74050391e-01 1.01972926e+00 -4.93207812e-01 -7.88697958e-01 -1.45587131e-01 -4.54977214e-01 -1.58638752e+00 -4.09210652e-01 2.37831548e-01 1.16031602e-01 -3.32505256e-01 6.50417209e-01 5.19162595e-01 3.22719008e-01 4.91941541e-01 -8.21626484e-01 -3.34758162e-01 1.01045871e+00 -4.14476693e-01 4.84820604e-01 7.67337203e-01 -4.04790014e-01 1.50321186e+00 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5c522aeb-655d-4764-b5ee-88d1b092fa32	dual-teacher-integrating-intra-domain-and	2007.06279	null	https://arxiv.org/abs/2007.06279v1	https://arxiv.org/pdf/2007.06279v1.pdf	Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation	Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g.,semi-supervised learning further exploring plentiful unlabeled data, domain adaptation including multi-modality learning and unsupervised domain adaptation resorting to the prior knowledge from additional modality. In this paper, we aim to investigate the feasibility of simultaneously leveraging abundant unlabeled data and well-established cross-modality data for annotation-efficient medical image segmentation. To this end, we propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher, where the student model not only learns from labeled target data (e.g., CT), but also explores unlabeled target data and labeled source data (e.g., MR) by two teacher models. Specifically, the student model learns the knowledge of unlabeled target data from intra-domain teacher by encouraging prediction consistency, as well as the shape priors embedded in labeled source data from inter-domain teacher via knowledge distillation. Consequently, the student model can effectively exploit the information from all three data resources and comprehensively integrate them to achieve improved performance. We conduct extensive experiments on MM-WHS 2017 dataset and demonstrate that our approach is able to concurrently utilize unlabeled data and cross-modality data with superior performance, outperforming semi-supervised learning and domain adaptation methods with a large margin.	['Pheng-Ann Heng', 'Shujun Wang', 'Lequan Yu', 'Kang Li']	2020-07-13	null	null	null	null	['cardiac-segmentation']	['medical']	[ 3.34949225e-01 4.73734289e-01 -6.52856588e-01 -4.35867369e-01 -1.26126909e+00 -5.38604081e-01 2.64218271e-01 5.58736399e-02 -5.45815051e-01 9.41676080e-01 1.38319448e-01 -7.36339986e-02 1.08070321e-01 -4.06734079e-01 -5.55252969e-01 -8.64582658e-01 4.27843571e-01 7.20677137e-01 2.64883995e-01 1.65166169e-01 -3.20137888e-01 1.07159495e-01 -1.03146446e+00 -5.34587801e-02 1.29098821e+00 1.02057385e+00 5.65081477e-01 7.40570277e-02 -1.05620250e-01 6.26802862e-01 -1.40091613e-01 -1.90219209e-02 2.98888870e-02 -3.89879197e-01 -9.00128007e-01 6.44772708e-01 8.83819759e-02 -4.33947533e-01 -2.09469259e-01 1.01860726e+00 5.38266540e-01 1.27550259e-01 7.33199179e-01 -8.63176286e-01 -6.44651711e-01 3.79030794e-01 -1.08338046e+00 2.04957590e-01 -1.13532968e-01 3.10009807e-01 6.12108171e-01 -9.63140488e-01 6.72147870e-01 7.13243544e-01 3.31247240e-01 4.77956921e-01 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3d1e73c5-7d82-4843-9079-a0f6bcb14eaa	high-fidelity-image-inpainting-with-gan	2208.11850	null	https://arxiv.org/abs/2208.11850v1	https://arxiv.org/pdf/2208.11850v1.pdf	High-Fidelity Image Inpainting with GAN Inversion	Image inpainting seeks a semantically consistent way to recover the corrupted image in the light of its unmasked content. Previous approaches usually reuse the well-trained GAN as effective prior to generate realistic patches for missing holes with GAN inversion. Nevertheless, the ignorance of a hard constraint in these algorithms may yield the gap between GAN inversion and image inpainting. Addressing this problem, in this paper, we devise a novel GAN inversion model for image inpainting, dubbed InvertFill, mainly consisting of an encoder with a pre-modulation module and a GAN generator with F&W+ latent space. Within the encoder, the pre-modulation network leverages multi-scale structures to encode more discriminative semantics into style vectors. In order to bridge the gap between GAN inversion and image inpainting, F&W+ latent space is proposed to eliminate glaring color discrepancy and semantic inconsistency. To reconstruct faithful and photorealistic images, a simple yet effective Soft-update Mean Latent module is designed to capture more diverse in-domain patterns that synthesize high-fidelity textures for large corruptions. Comprehensive experiments on four challenging datasets, including Places2, CelebA-HQ, MetFaces, and Scenery, demonstrate that our InvertFill outperforms the advanced approaches qualitatively and quantitatively and supports the completion of out-of-domain images well.	['Tiejian Luo', 'Heng Fan', 'Libo Zhang', 'Yongsheng Yu']	2022-08-25	null	null	null	null	['image-inpainting']	['computer-vision']	[ 5.38910389e-01 1.88002199e-01 -6.00745901e-03 -3.09560329e-01 -8.78413916e-01 -4.27030116e-01 5.10204256e-01 -8.17921281e-01 3.05356354e-01 8.07519913e-01 3.61988753e-01 6.63387105e-02 3.04667294e-01 -9.53946054e-01 -1.11073935e+00 -7.25156009e-01 7.84564972e-01 7.44486153e-02 -2.47953698e-01 -3.76976252e-01 2.18616575e-02 1.09040149e-01 -1.32979476e+00 5.17346442e-01 1.20334649e+00 9.34120238e-01 4.00266588e-01 3.11207145e-01 -6.71559945e-02 1.04835331e+00 -4.94691283e-01 -6.22677743e-01 6.61556602e-01 -1.00900948e+00 -3.99905443e-01 5.06359994e-01 6.37717187e-01 -6.56590760e-01 -5.01233220e-01 1.10590553e+00 2.64920920e-01 -1.21951044e-01 5.49778759e-01 -1.17740929e+00 -1.09168732e+00 1.62299067e-01 -9.78415072e-01 -4.16212022e-01 4.25065905e-01 5.29013634e-01 7.73300886e-01 -8.63022804e-01 7.72155106e-01 1.31921875e+00 6.72441542e-01 5.10312378e-01 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-7.30494559e-01 8.50679398e-01 3.16724867e-01 -5.24420738e-01 -4.66143548e-01 2.71394819e-01]	[11.43864631652832, -1.0613371133804321]
38f97498-ec3a-4352-9832-7c6ea0a30861	trex-learning-execution-semantics-from-micro	2012.08680	null	https://arxiv.org/abs/2012.08680v3	https://arxiv.org/pdf/2012.08680v3.pdf	Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity	Detecting semantically similar functions -- a crucial analysis capability with broad real-world security usages including vulnerability detection, malware lineage, and forensics -- requires understanding function behaviors and intentions. This task is challenging as semantically similar functions can be implemented differently, run on different architectures, and compiled with diverse compiler optimizations or obfuscations. Most existing approaches match functions based on syntactic features without understanding the functions' execution semantics. We present Trex, a transfer-learning-based framework, to automate learning execution semantics explicitly from functions' micro-traces and transfer the learned knowledge to match semantically similar functions. Our key insight is that these traces can be used to teach an ML model the execution semantics of different sequences of instructions. We thus train the model to learn execution semantics from the functions' micro-traces, without any manual labeling effort. We then develop a novel neural architecture to learn execution semantics from micro-traces, and we finetune the pretrained model to match semantically similar functions. We evaluate Trex on 1,472,066 function binaries from 13 popular software projects. These functions are from different architectures and compiled with various optimizations and obfuscations. Trex outperforms the state-of-the-art systems by 7.8%, 7.2%, and 14.3% in cross-architecture, optimization, and obfuscation function matching, respectively. Ablation studies show that the pretraining significantly boosts the function matching performance, underscoring the importance of learning execution semantics.	['Baishakhi Ray', 'Suman Jana', 'Junfeng Yang', 'Zhou Xuan', 'Kexin Pei']	2020-12-16	null	null	null	null	['vulnerability-detection']	['miscellaneous']	[-7.62833357e-02 -6.47214532e-01 -6.63759410e-01 -5.54696798e-01 -7.20744133e-01 -1.08274436e+00 2.85698295e-01 2.27344915e-01 -2.05226049e-01 2.08617032e-01 2.35727459e-01 -8.51310432e-01 4.97238457e-01 -8.81033599e-01 -1.12368238e+00 -1.51025519e-01 -3.85571986e-01 6.79637939e-02 3.60429198e-01 -1.95965961e-01 3.92132729e-01 1.25420809e-01 -1.49651742e+00 7.57502496e-01 7.28437006e-01 7.96698332e-01 7.51129761e-02 8.84197176e-01 -2.63571143e-01 9.24990356e-01 -9.85637009e-01 -6.17700398e-01 1.14770658e-01 -1.16078414e-01 -9.18421865e-01 -6.09287560e-01 3.06006551e-01 -5.80146790e-01 -3.24913263e-01 1.27018917e+00 -9.18704122e-02 -1.97545201e-01 3.34679306e-01 -1.25076103e+00 -9.09687340e-01 1.04148710e+00 -3.42064261e-01 2.41030574e-01 2.03150943e-01 4.97331381e-01 1.17309988e+00 -1.42989442e-01 2.46744052e-01 1.37201214e+00 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495ff350-bf9c-41b4-9f8b-b1c3209d8a17	self-supervised-spatiotemporal-representation	2112.05883	null	https://arxiv.org/abs/2112.05883v3	https://arxiv.org/pdf/2112.05883v3.pdf	Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity	Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e.g. speed, temporal order, etc. This work exploits an essential yet under-explored property of videos, the video continuity, to obtain supervision signals for self-supervised representation learning. Specifically, we formulate three novel continuity-related pretext tasks, i.e. continuity justification, discontinuity localization, and missing section approximation, that jointly supervise a shared backbone for video representation learning. This self-supervision approach, termed as Continuity Perception Network (CPNet), solves the three tasks altogether and encourages the backbone network to learn local and long-ranged motion and context representations. It outperforms prior arts on multiple downstream tasks, such as action recognition, video retrieval, and action localization. Additionally, the video continuity can be complementary to other coarse-grained video properties for representation learning, and integrating the proposed pretext task to prior arts can yield much performance gains.	['Yang Wang', 'Juwei Lu', 'Peng Dai', 'Lizhe Chen', 'Zhixiang Chi', 'Niamul Quader', 'Hanwen Liang']	2021-12-11	null	null	null	null	['action-localization']	['computer-vision']	[ 3.97563875e-01 -1.71483517e-01 -9.83216047e-01 -3.30814689e-01 -4.31833595e-01 -2.38138050e-01 7.50902057e-01 -8.23733285e-02 1.14584044e-02 5.45202732e-01 6.88977122e-01 7.43209571e-02 -2.07055017e-01 -3.10159296e-01 -8.66940558e-01 -7.69524992e-01 -1.58036709e-01 -1.44210324e-01 2.30564743e-01 6.77038741e-04 2.43211463e-01 2.64389247e-01 -1.66851342e+00 4.26653028e-01 6.74893498e-01 1.13248348e+00 1.06703304e-01 2.69274473e-01 1.40855432e-01 1.57898533e+00 -4.05428335e-02 3.11480641e-01 5.24084233e-02 -6.49118483e-01 -8.84518266e-01 4.20099616e-01 4.79136109e-01 -4.94406193e-01 -8.98267567e-01 8.91794682e-01 1.56275481e-02 5.40099561e-01 7.97141969e-01 -1.37355828e+00 -7.74501085e-01 3.50392789e-01 -7.39661872e-01 4.11677897e-01 5.57754815e-01 1.85005665e-01 1.19000602e+00 -6.57245755e-01 7.64546871e-01 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17e8a2f4-a879-4179-acdf-05e60f661162	no-regret-learning-in-unknown-games-with	1909.08540	null	https://arxiv.org/abs/1909.08540v2	https://arxiv.org/pdf/1909.08540v2.pdf	No-Regret Learning in Unknown Games with Correlated Payoffs	We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when provided with full information feedback, which unfortunately is unavailable in many real-world scenarios. Bandit feedback alone, i.e., observing outcomes only for the selected action, yields substantially worse performance. In this paper, we consider a natural model where, besides a noisy measurement of the obtained reward, the player can also observe the opponents' actions. This feedback model, together with a regularity assumption on the reward function, allows us to exploit the correlations among different game outcomes by means of Gaussian processes (GPs). We propose a novel confidence-bound based bandit algorithm GP-MW, which utilizes the GP model for the reward function and runs a multiplicative weight (MW) method. We obtain novel kernel-dependent regret bounds that are comparable to the known bounds in the full information setting, while substantially improving upon the existing bandit results. We experimentally demonstrate the effectiveness of GP-MW in random matrix games, as well as real-world problems of traffic routing and movie recommendation. In our experiments, GP-MW consistently outperforms several baselines, while its performance is often comparable to methods that have access to full information feedback.	['Andreas Krause', 'Maryam Kamgarpour', 'Pier Giuseppe Sessa', 'Ilija Bogunovic']	2019-09-18	no-regret-learning-in-unknown-games-with-1	http://papers.nips.cc/paper/9514-no-regret-learning-in-unknown-games-with-correlated-payoffs	http://papers.nips.cc/paper/9514-no-regret-learning-in-unknown-games-with-correlated-payoffs.pdf	neurips-2019-12	['movie-recommendation']	['miscellaneous']	[-8.98908451e-02 -9.39285681e-02 -4.75115120e-01 1.45156682e-01 -1.17313313e+00 -7.36713231e-01 2.31426895e-01 1.93413794e-01 -6.52909279e-01 1.16611385e+00 2.19257064e-02 -4.86607760e-01 -7.60829508e-01 -8.55647326e-01 -9.10864294e-01 -9.15752411e-01 -2.87146926e-01 8.26579213e-01 2.54810959e-01 -2.26619124e-01 1.69242680e-01 1.02039084e-01 -1.00663316e+00 -5.11722453e-02 7.74554670e-01 1.29801416e+00 6.30686581e-02 1.08064663e+00 -5.23141660e-02 1.27785492e+00 -4.38655168e-01 -8.48893881e-01 7.12300181e-01 -3.91495556e-01 -5.71924627e-01 1.53726235e-01 -1.66454047e-01 -7.23647475e-01 -6.00300729e-01 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a8587880-5afa-40d2-b54f-fd1f552ef6b5	cat-localization-and-identification-cascade-1	2301.01970	null	https://arxiv.org/abs/2301.01970v6	https://arxiv.org/pdf/2301.01970v6.pdf	CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection	Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects. The existing works which employ standard detection framework and fixed pseudo-labelling mechanism (PLM) have the following problems: (i) The inclusion of detecting unknown objects substantially reduces the model's ability to detect known ones. (ii) The PLM does not adequately utilize the priori knowledge of inputs. (iii) The fixed selection manner of PLM cannot guarantee that the model is trained in the right direction. We observe that humans subconsciously prefer to focus on all foreground objects and then identify each one in detail, rather than localize and identify a single object simultaneously, for alleviating the confusion. This motivates us to propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer which decouples the detection process via the shared decoder in the cascade decoding way. In the meanwhile, we propose the self-adaptive pseudo-labelling mechanism which combines the model-driven with input-driven PLM and self-adaptively generates robust pseudo-labels for unknown objects, significantly improving the ability of CAT to retrieve unknown objects. Comprehensive experiments on two benchmark datasets, i.e., MS-COCO and PASCAL VOC, show that our model outperforms the state-of-the-art in terms of all metrics in the task of OWOD, incremental object detection (IOD) and open-set detection.	['Fanbing Lv', 'Hongli Liu', 'Thomas H. Li', 'Ying WEI', 'Jiaqi Fan', 'Yuefeng Wang', 'Shuailei Ma']	2023-01-05	cat-localization-and-identification-cascade	http://openaccess.thecvf.com//content/CVPR2023/html/Ma_CAT_LoCalization_and_IdentificAtion_Cascade_Detection_Transformer_for_Open-World_Object_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_CAT_LoCalization_and_IdentificAtion_Cascade_Detection_Transformer_for_Open-World_Object_CVPR_2023_paper.pdf	cvpr-2023-1	['open-world-object-detection']	['computer-vision']	[ 2.01240301e-01 -7.45800510e-02 3.40473801e-02 -2.26917297e-01 -6.43066108e-01 -7.19820261e-01 4.58253771e-01 -6.13052472e-02 -5.04125059e-01 4.76696730e-01 -2.88011581e-01 -4.57650460e-02 3.24217677e-01 -6.28019750e-01 -7.84105122e-01 -7.51873136e-01 3.45745742e-01 5.59644163e-01 8.81177366e-01 4.34377976e-02 2.15081617e-01 3.38082761e-01 -1.87327385e+00 4.04676288e-01 8.29864502e-01 1.14378130e+00 7.36861467e-01 5.88031471e-01 -2.54247218e-01 8.04580927e-01 -5.08854568e-01 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c7c7085a-048f-4f12-99c8-7f47a52e9452	event-detection-in-coarsely-annotated-sports	2004.06172	null	https://arxiv.org/abs/2004.06172v1	https://arxiv.org/pdf/2004.06172v1.pdf	Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions	In problems such as sports video analytics, it is difficult to obtain accurate frame level annotations and exact event duration because of the lengthy videos and sheer volume of video data. This issue is even more pronounced in fast-paced sports such as ice hockey. Obtaining annotations on a coarse scale can be much more practical and time efficient. We propose the task of event detection in coarsely annotated videos. We introduce a multi-tower temporal convolutional network architecture for the proposed task. The network, with the help of multiple receptive fields, processes information at various temporal scales to account for the uncertainty with regard to the exact event location and duration. We demonstrate the effectiveness of the multi-receptive field architecture through appropriate ablation studies. The method is evaluated on two tasks - event detection in coarsely annotated hockey videos in the NHL dataset and event spotting in soccer on the SoccerNet dataset. The two datasets lack frame-level annotations and have very distinct event frequencies. Experimental results demonstrate the effectiveness of the network by obtaining a 55% average F1 score on the NHL dataset and by achieving competitive performance compared to the state of the art on the SoccerNet dataset. We believe our approach will help develop more practical pipelines for event detection in sports video.	['John Zelek', 'Pascale Walters', 'Mehrnaz Fani', 'Kanav Vats', 'David A. Clausi']	2020-04-13	null	null	null	null	['action-spotting']	['computer-vision']	[ 8.80495161e-02 -3.67759764e-01 6.24532104e-02 -2.48550981e-01 -9.75048006e-01 -6.16341531e-01 2.46092379e-01 3.18905801e-01 -9.08360183e-01 5.62429667e-01 2.66521066e-01 3.15163374e-01 6.45220745e-04 -4.96765971e-01 -9.09608364e-01 -4.94852841e-01 -4.73998129e-01 1.84806794e-01 8.69608700e-01 -1.32162377e-01 6.64697215e-02 2.98465490e-01 -1.91506183e+00 8.33193421e-01 1.93804279e-01 1.14885318e+00 2.24055812e-01 9.52081025e-01 4.93930817e-01 9.69256401e-01 -8.01562190e-01 -2.43139312e-01 2.91915566e-01 -3.71337265e-01 -6.13397062e-01 -2.33141147e-02 6.57606721e-01 -3.88093710e-01 -4.86628443e-01 6.26735032e-01 4.62636560e-01 4.08132374e-01 1.42448395e-01 -1.09477830e+00 2.46900156e-01 6.58462882e-01 -4.53747720e-01 9.62521791e-01 3.15225869e-01 3.39431353e-02 8.55390549e-01 -6.87710881e-01 7.60463238e-01 1.00402796e+00 8.35192323e-01 6.98895156e-02 -8.76284719e-01 -5.69513977e-01 1.35140032e-01 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6ab9f551-4f78-450c-a33d-1f60d4475155	diffusion-based-signal-refiner-for-speech	2305.05857	null	https://arxiv.org/abs/2305.05857v2	https://arxiv.org/pdf/2305.05857v2.pdf	Diffusion-based Signal Refiner for Speech Separation	We have developed a diffusion-based speech refiner that improves the reference-free perceptual quality of the audio predicted by preceding single-channel speech separation models. Although modern deep neural network-based speech separation models have show high performance in reference-based metrics, they often produce perceptually unnatural artifacts. The recent advancements made to diffusion models motivated us to tackle this problem by restoring the degraded parts of initial separations with a generative approach. Utilizing the denoising diffusion restoration model (DDRM) as a basis, we propose a shared DDRM-based refiner that generates samples conditioned on the global information of preceding outputs from arbitrary speech separation models. We experimentally show that our refiner can provide a clearer harmonic structure of speech and improves the reference-free metric of perceptual quality for arbitrary preceding model architectures. Furthermore, we tune the variance of the measurement noise based on preceding outputs, which results in higher scores in both reference-free and reference-based metrics. The separation quality can also be further improved by blending the discriminative and generative outputs.	['Yuki Mitsufuji', 'Kazuki Shimada', 'Shusuke Takahashi', 'Yuichiro Koyama', 'Masato Hirano']	2023-05-10	null	null	null	null	['speech-separation', 'speech-enhancement']	['speech', 'speech']	[ 4.01540361e-02 5.76606467e-02 3.77801329e-01 -2.77649760e-01 -1.13793838e+00 -3.40120971e-01 5.69387436e-01 -2.41057217e-01 -1.00272983e-01 3.89337927e-01 7.63205469e-01 6.84465617e-02 -2.27819815e-01 -4.10748214e-01 -3.91762257e-01 -9.30279136e-01 2.64106184e-01 6.60983026e-02 1.98507950e-01 -2.53181428e-01 -1.70547381e-01 5.54042161e-01 -1.56888425e+00 3.08457226e-01 1.12877119e+00 8.19936216e-01 2.87095636e-01 9.53833163e-01 2.20829487e-01 4.46097255e-01 -1.14780498e+00 -2.04972118e-01 2.87762821e-01 -8.90057921e-01 -1.17226839e-01 -7.62890726e-02 3.73143405e-01 -1.99433938e-01 -6.47942662e-01 1.31184900e+00 1.04847956e+00 4.07176346e-01 7.01818347e-01 -9.37981009e-01 -9.96448994e-01 9.02062058e-01 -1.14083022e-01 7.03606978e-02 1.15862288e-01 2.01690570e-01 9.23917055e-01 -9.81345594e-01 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44734bef-75ec-4616-84df-d8907bb96a2c	an-effective-two-branch-model-based-deep	1905.05404	null	https://arxiv.org/abs/1905.05404v2	https://arxiv.org/pdf/1905.05404v2.pdf	An Effective Two-Branch Model-Based Deep Network for Single Image Deraining	Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing. Conventional methods rely on some heuristics to handcraft various priors to remove or separate the rain effects from an image. Recent deep learning models are proposed to learn end-to-end methods to complete this task. However, they often fail to obtain satisfactory results in many realistic scenarios, especially when the observed images suffer from heavy rain. Heavy rain brings not only rain streaks but also haze-like effect caused by the accumulation of tiny raindrops. Different from the existing deep learning deraining methods that mainly focus on handling the rain streaks, we design a deep neural network by incorporating a physical raining image model. Specifically, in the proposed model, two branches are designed to handle both the rain streaks and haze-like effects. An additional submodule is jointly trained to finally refine the results, which give the model flexibility to control the strength of removing the mist. Extensive experiments on several datasets show that our method outperforms the state-of-the-art in both objective assessments and visual quality.	['Anton Van Den Hengel', 'Jie Yang', 'Dehua Xie', 'Dong Gong', 'Yinglong Wang', 'Qinfeng Shi', 'Bing Zeng']	2019-05-14	null	null	null	null	['single-image-deraining']	['computer-vision']	[-1.28935337e-01 -3.82977217e-01 4.76940513e-01 -6.05886221e-01 -2.67997533e-01 -1.43572703e-01 2.49863982e-01 -1.97548479e-01 -3.45109373e-01 8.80519569e-01 -1.57185912e-01 -1.98882282e-01 1.94996536e-01 -7.88073897e-01 -8.10104430e-01 -1.11659539e+00 5.51230684e-02 6.55759498e-02 3.31023484e-01 -4.18221265e-01 7.58142024e-02 3.33972842e-01 -1.60024297e+00 -2.38107994e-01 1.54139316e+00 7.17921674e-01 5.82954824e-01 5.61985433e-01 -6.08415231e-02 9.56657887e-01 -6.58253193e-01 2.27950457e-02 3.71767104e-01 -4.43017989e-01 1.73658237e-01 2.63878405e-01 8.32390964e-01 -6.84582114e-01 -2.68552333e-01 1.17124033e+00 5.74056149e-01 1.65650815e-01 3.59856576e-01 -9.14903641e-01 -4.00501698e-01 -1.02101088e-01 -8.63122463e-01 4.63941127e-01 -1.48376912e-01 4.65904534e-01 4.28366989e-01 -1.04597294e+00 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80a3f901-e5e9-4887-97bd-fa0d50d41c94	dynamic-em-ray-tracing-for-large-urban-scenes	2303.10521	null	https://arxiv.org/abs/2303.10521v2	https://arxiv.org/pdf/2303.10521v2.pdf	Dynamic EM Ray Tracing for Large Urban Scenes with Multiple Receivers	Radio applications are increasingly being used in urban environments for cellular radio systems and safety applications that use vehicle-vehicle, and vehicle-to-infrastructure. We present a novel ray tracing-based radio propagation algorithm that can handle large urban scenes with hundreds or thousands of dynamic objects and receivers. Our approach is based on the use of coherence-based techniques that exploit spatial and temporal coherence for efficient wireless propagation and radio network planning. Our formulation also utilizes channel coherence which is used to determine the effectiveness of the propagation model within a certain time in dynamically generated paths; and spatial consistency which is used to estimate the similarity and accuracy of changes in a dynamic environment with varying propagation models and blocking obstacles. We highlight the performance of our simulator in large urban traffic scenes with an area of 2*2 km^2 and more than 10,000 users and devices. We evaluate the accuracy by comparing the results with discrete model simulations performed using WinProp. In practice, our approach scales linearly with the area of the urban environment and the number of dynamic obstacles or receivers.	['Dinesh Manocha', 'Ruichen Wang']	2023-03-19	null	null	null	null	['blocking']	['natural-language-processing']	[-2.54532695e-01 -2.37836644e-01 3.81957799e-01 9.79268402e-02 -7.23550141e-01 -2.03114778e-01 4.63187099e-01 1.08677156e-01 -5.82711816e-01 1.23536026e+00 -1.88212633e-01 -8.59296024e-01 -1.09355912e-01 -1.27589178e+00 -4.16554660e-01 -6.90535963e-01 -9.56288636e-01 6.81332946e-01 1.00543427e+00 -3.55815113e-01 -1.80644020e-02 6.36496484e-01 -9.38610733e-01 -4.65122730e-01 5.28805256e-01 8.10000241e-01 1.94277018e-01 1.33790898e+00 6.89512119e-02 7.69716382e-01 -5.98790526e-01 3.31186503e-01 -5.78327142e-02 -2.26220295e-01 -3.75606120e-01 -1.27741814e-01 -3.30332816e-01 -2.47100636e-01 -5.92838705e-01 2.67655104e-01 8.18703890e-01 2.03958169e-01 4.36880082e-01 -1.21863627e+00 5.58656454e-01 1.68448493e-01 -5.20526409e-01 7.95030653e-01 4.51340020e-01 1.38740689e-01 1.38191625e-01 -5.68104923e-01 5.36715806e-01 1.01385784e+00 8.01791370e-01 -1.01896279e-01 -8.92494321e-01 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-6.17244005e-01 6.56702816e-01 -5.96392393e-01 -1.68081477e-01 -2.81389564e-01]	[6.238800048828125, 1.1701035499572754]
1ba548da-8547-4715-88c8-0c1f89ceb601	voice-conversion-based-speaker-normalization	2105.01786	null	https://arxiv.org/abs/2105.01786v1	https://arxiv.org/pdf/2105.01786v1.pdf	Voice Conversion Based Speaker Normalization for Acoustic Unit Discovery	Discovering speaker independent acoustic units purely from spoken input is known to be a hard problem. In this work we propose an unsupervised speaker normalization technique prior to unit discovery. It is based on separating speaker related from content induced variations in a speech signal with an adversarial contrastive predictive coding approach. This technique does neither require transcribed speech nor speaker labels, and, furthermore, can be trained in a multilingual fashion, thus achieving speaker normalization even if only few unlabeled data is available from the target language. The speaker normalization is done by mapping all utterances to a medoid style which is representative for the whole database. We demonstrate the effectiveness of the approach by conducting acoustic unit discovery with a hidden Markov model variational autoencoder noting, however, that the proposed speaker normalization can serve as a front end to any unit discovery system. Experiments on English, Yoruba and Mboshi show improvements compared to using non-normalized input.	['Reinhold Häb-Umbach', 'Janek Ebbers', 'Thomas Glarner']	2021-05-04	null	null	null	null	['acoustic-unit-discovery']	['speech']	[ 5.35110950e-01 2.99722552e-01 4.41899821e-02 -5.95810592e-01 -1.12597477e+00 -6.05179429e-01 5.87054908e-01 -1.46489263e-01 -5.31104982e-01 7.08002567e-01 4.27987725e-01 -1.93398193e-01 2.84138024e-01 -3.05668145e-01 -6.92248762e-01 -9.67276454e-01 1.34544045e-01 7.11621106e-01 1.95426345e-02 -3.98465186e-01 -1.35828480e-01 4.99822706e-01 -1.60146892e+00 6.21279702e-03 5.09892106e-01 5.36740482e-01 1.82892889e-01 8.92193079e-01 -2.03301221e-01 6.22919977e-01 -8.37740123e-01 5.50169982e-02 2.00532213e-01 -7.20136702e-01 -7.63174832e-01 2.52346396e-01 2.27941766e-01 -1.54471442e-01 9.10576247e-03 1.07962680e+00 6.24300599e-01 5.40738046e-01 8.32138836e-01 -9.73375142e-01 -3.09869558e-01 1.07743061e+00 -9.08722430e-02 2.79121071e-01 2.26784497e-01 -3.78407776e-01 8.58093619e-01 -8.69136810e-01 3.76635432e-01 1.29904187e+00 1.11250110e-01 8.63420427e-01 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dd686aed-88e7-45ec-a848-dd96bb31da79	a-predicate-function-argument-annotation-of	null	null	https://aclanthology.org/2020.emnlp-main.167	https://aclanthology.org/2020.emnlp-main.167.pdf	A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression	Existing OIE (Open Information Extraction) algorithms are independent of each other such that there exist lots of redundant works; the featured strategies are not reusable and not adaptive to new tasks. This paper proposes a new pipeline to build OIE systems, where an Open-domain Information eXpression (OIX) task is proposed to provide a platform for all OIE strategies. The OIX is an OIE friendly expression of a sentence without information loss. The generation procedure of OIX contains shared works of OIE algorithms so that OIE strategies can be developed on the platform of OIX as inference operations focusing on more critical problems. Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strategies to assemble their algorithm for a specific task so that the adaptability may be significantly increased. This paper focuses on the task of OIX and propose a solution {--} Open Information Annotation (OIA). OIA is a predicate-function-argument annotation for sentences. We label a data set of sentence-OIA pairs and propose a dependency-based rule system to generate OIA annotations from sentences. The evaluation results reveal that learning the OIA from a sentence is a challenge owing to the complexity of natural language sentences, and it is worthy of attracting more attention from the research community.	['Ping Li', 'Kangjie Zheng', 'Xin Wang', 'Zoey Liu', 'Wenyue Hua', 'Mingming Sun']	null	null	null	null	emnlp-2020-11	['open-information-extraction']	['natural-language-processing']	[ 0.07548906 0.51571226 0.0525011 -0.6738369 -0.41833875 -0.513302 0.42613953 0.03851383 -0.10488481 0.7438418 0.21748506 -0.14223172 -0.3892251 -0.9645936 -0.63819605 0.01389639 0.15275995 0.47230887 0.38094798 -0.6204056 0.15781455 -0.02391677 -1.6855751 0.88226074 1.065783 0.9266774 0.45779333 0.54701304 -0.8810136 0.8194722 -0.99059325 -0.6880639 0.21426263 -0.30608365 -1.4318265 -0.3296512 -0.059187 0.12043371 0.06923024 0.97569436 0.32275897 -0.2953543 0.61359286 -1.6362736 -0.5318342 1.1802235 0.10624685 -0.05006865 0.62735176 -0.00984169 1.2428795 -0.7491011 1.0735043 1.074938 0.6031933 0.5922864 -0.6041047 -0.43401015 0.10885726 0.20610648 -1.2467124 -0.10298877 0.54948866 -0.3870283 1.4035821 0.72752786 0.53363645 1.042972 0.2944158 1.0358803 0.87587255 -0.47885132 -0.00637979 0.3387176 0.7021926 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d82fcca7-c8ba-4b3d-998f-bca5d4c7bd32	will-my-robot-achieve-my-goals-predicting-the	2211.16462	null	https://arxiv.org/abs/2211.16462v1	https://arxiv.org/pdf/2211.16462v1.pdf	Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target	As an autonomous system performs a task, it should maintain a calibrated estimate of the probability that it will achieve the user's goal. If that probability falls below some desired level, it should alert the user so that appropriate interventions can be made. This paper considers settings where the user's goal is specified as a target interval for a real-valued performance summary, such as the cumulative reward, measured at a fixed horizon $H$. At each time $t \in \{0, \ldots, H-1\}$, our method produces a calibrated estimate of the probability that the final cumulative reward will fall within a user-specified target interval $[y^-,y^+].$ Using this estimate, the autonomous system can raise an alarm if the probability drops below a specified threshold. We compute the probability estimates by inverting conformal prediction. Our starting point is the Conformalized Quantile Regression (CQR) method of Romano et al., which applies split-conformal prediction to the results of quantile regression. CQR is not invertible, but by using the conditional cumulative distribution function (CDF) as the non-conformity measure, we show how to obtain an invertible modification that we call \textbf{P}robability-space \textbf{C}onformalized \textbf{Q}uantile \textbf{R}egression (PCQR). Like CQR, PCQR produces well-calibrated conditional prediction intervals with finite-sample marginal guarantees. By inverting PCQR, we obtain marginal guarantees for the probability that the cumulative reward of an autonomous system will fall within an arbitrary user-specified target intervals. Experiments on two domains confirm that these probabilities are well-calibrated.	['Thomas G. Dietterich', 'Alexander Guyer']	2022-11-29	null	null	null	null	['prediction-intervals']	['miscellaneous']	[ 7.23517612e-02 3.70339096e-01 -4.34800476e-01 -3.74135256e-01 -1.07830667e+00 -4.69665945e-01 3.45112383e-01 1.70576230e-01 -4.32972699e-01 1.05382884e+00 -4.78894979e-01 -6.59713686e-01 -4.37985003e-01 -9.41627443e-01 -8.09027374e-01 -6.84102833e-01 -3.20306689e-01 3.47888112e-01 2.96358347e-01 -7.97464177e-02 3.60578746e-01 3.80272865e-01 -1.44650185e+00 -2.89188355e-01 6.77414000e-01 1.38620687e+00 -1.57998785e-01 8.51213515e-01 4.84139293e-01 5.44336677e-01 -9.13351953e-01 4.64255288e-02 4.00145978e-01 -4.58660930e-01 -3.14894348e-01 -4.26394969e-01 -3.52872342e-01 -3.06772172e-01 1.69286773e-01 9.86812294e-01 -4.06675786e-02 1.49872586e-01 9.81397748e-01 -1.78072166e+00 -1.13977700e-01 4.44666237e-01 -4.67477411e-01 4.07821462e-02 2.59415895e-01 7.85458535e-02 8.35104465e-01 -2.88020849e-01 1.04430385e-01 9.68369663e-01 4.87935036e-01 3.70501965e-01 -1.58181775e+00 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f6e77686-c852-402a-8121-7a5c4ac2001c	memvit-memory-augmented-multiscale-vision	2201.08383	null	https://arxiv.org/abs/2201.08383v2	https://arxiv.org/pdf/2201.08383v2.pdf	MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition	While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks. In this paper, we propose a new strategy to overcome this challenge. Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache "memory" at each iteration. Through the memory, the model can reference prior context for long-term modeling, with only a marginal cost. Based on this idea, we build MeMViT, a Memory-augmented Multiscale Vision Transformer, that has a temporal support 30x longer than existing models with only 4.5% more compute; traditional methods need >3,000% more compute to do the same. On a wide range of settings, the increased temporal support enabled by MeMViT brings large gains in recognition accuracy consistently. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets. Code and models are available at https://github.com/facebookresearch/memvit.	['Christoph Feichtenhofer', 'Jitendra Malik', 'Bo Xiong', 'Haoqi Fan', 'Karttikeya Mangalam', 'Yanghao Li', 'Chao-yuan Wu']	2022-01-20	null	http://openaccess.thecvf.com//content/CVPR2022/html/Wu_MeMViT_Memory-Augmented_Multiscale_Vision_Transformer_for_Efficient_Long-Term_Video_Recognition_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_MeMViT_Memory-Augmented_Multiscale_Vision_Transformer_for_Efficient_Long-Term_Video_Recognition_CVPR_2022_paper.pdf	cvpr-2022-1	['action-anticipation']	['computer-vision']	[ 7.01915547e-02 -3.10045749e-01 -3.91772181e-01 -2.49820113e-01 -7.91484594e-01 -3.69404346e-01 5.41133940e-01 -2.37681210e-01 -5.48672140e-01 2.45727375e-01 3.69673610e-01 -2.15987802e-01 3.29440236e-01 -4.90424752e-01 -6.38141394e-01 -4.16386843e-01 4.10532057e-02 1.99143797e-01 6.19882405e-01 1.89536959e-01 2.66040772e-01 1.19999804e-01 -1.45087576e+00 7.15769589e-01 3.04331630e-01 1.09607959e+00 1.75051957e-01 9.86803055e-01 1.54167088e-02 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0cb30041-f611-4fc0-b597-ddab4c8ce22d	semantic-novelty-detection-via-relational	2207.08699	null	https://arxiv.org/abs/2207.08699v2	https://arxiv.org/pdf/2207.08699v2.pdf	Semantic Novelty Detection via Relational Reasoning	Semantic novelty detection aims at discovering unknown categories in the test data. This task is particularly relevant in safety-critical applications, such as autonomous driving or healthcare, where it is crucial to recognize unknown objects at deployment time and issue a warning to the user accordingly. Despite the impressive advancements of deep learning research, existing models still need a finetuning stage on the known categories in order to recognize the unknown ones. This could be prohibitive when privacy rules limit data access, or in case of strict memory and computational constraints (e.g. edge computing). We claim that a tailored representation learning strategy may be the right solution for effective and efficient semantic novelty detection. Besides extensively testing state-of-the-art approaches for this task, we propose a novel representation learning paradigm based on relational reasoning. It focuses on learning how to measure semantic similarity rather than recognizing known categories. Our experiments show that this knowledge is directly transferable to a wide range of scenarios, and it can be exploited as a plug-and-play module to convert closed-set recognition models into reliable open-set ones.	['Tatiana Tommasi', 'Silvia Bucci', 'Francesco Cappio Borlino']	2022-07-18	null	null	null	null	['relational-reasoning']	['natural-language-processing']	[ 3.57520252e-01 3.17869484e-01 -2.28654504e-01 -5.01776695e-01 -3.45909297e-01 -4.84932959e-01 5.01427889e-01 6.05261624e-01 -4.13771272e-01 6.03496432e-01 -2.65371829e-01 -4.85094160e-01 -4.88310695e-01 -1.15448034e+00 -7.86579788e-01 -4.55858201e-01 -7.94708058e-02 5.79284489e-01 4.97142255e-01 -2.16669232e-01 3.34295422e-01 7.96491086e-01 -2.25964308e+00 4.64004129e-01 8.45345676e-01 1.39261544e+00 3.54369245e-02 2.47357741e-01 -3.08137745e-01 7.69414306e-01 -2.82996595e-01 -5.32085180e-01 4.24606025e-01 -2.76543409e-01 -6.77846789e-01 -3.09458613e-01 2.29546219e-01 -7.91867822e-02 -3.36035669e-01 1.22572899e+00 3.25258344e-01 4.14801091e-01 5.19303083e-01 -1.38717449e+00 -4.38843727e-01 3.16463202e-01 -1.07161768e-01 3.39227676e-01 1.83561027e-01 1.89679787e-02 7.75480151e-01 -6.25832975e-01 5.79126894e-01 7.95419574e-01 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