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0dfa9145-5428-4c50-8b3b-97c3b66cd000 | imagebind-one-embedding-space-to-bind-them | 2305.05665 | null | https://arxiv.org/abs/2305.05665v2 | https://arxiv.org/pdf/2305.05665v2.pdf | ImageBind: One Embedding Space To Bind Them All | We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together. Imag... | ['Ishan Misra', 'Armand Joulin', 'Kalyan Vasudev Alwala', 'Mannat Singh', 'Zhuang Liu', 'Alaaeldin El-Nouby', 'Rohit Girdhar'] | 2023-05-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Girdhar_ImageBind_One_Embedding_Space_To_Bind_Them_All_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Girdhar_ImageBind_One_Embedding_Space_To_Bind_Them_All_CVPR_2023_paper.pdf | cvpr-2023-1 | ['cross-modal-retrieval'] | ['miscellaneous'] | [ 3.19850415e-01 -1.63084075e-01 -2.36943707e-01 -7.83872157e-02
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-3.39966901e-02 1.67920932e-01 2.40389153e-01 -3.91492158... | [10.4949369430542, 1.4077117443084717] |
22b116df-159b-4317-a742-66b44e615f72 | av-data2vec-self-supervised-learning-of-audio | 2302.06419 | null | https://arxiv.org/abs/2302.06419v1 | https://arxiv.org/pdf/2302.06419v1.pdf | AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations | Self-supervision has shown great potential for audio-visual speech recognition by vastly reducing the amount of labeled data required to build good systems. However, existing methods are either not entirely end-to-end or do not train joint representations of both modalities. In this paper, we introduce AV-data2vec whic... | ['Michael Auli', 'Wei-Ning Hsu', 'Alexei Baevski', 'Jiachen Lian'] | 2023-02-10 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 1.60824969e-01 1.07996345e-01 -3.55866700e-01 -6.03517354e-01
-1.35909486e+00 -3.14269364e-01 8.60084116e-01 -4.88346636e-01
-2.00567320e-01 5.30318737e-01 8.58746648e-01 -4.33375657e-01
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2.86680698e-01 4.23406720e-01 -5.45449220e-02 -6.73338026... | [14.382980346679688, 5.125270366668701] |
94e85590-c310-42ce-822b-c404304570a1 | a-general-gaussian-heatmap-labeling-for | 2109.12848 | null | https://arxiv.org/abs/2109.12848v4 | https://arxiv.org/pdf/2109.12848v4.pdf | A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection | Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many fields. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reflect the shape and direction characteristics of ar... | ['Ran Tao', 'Xiang-Gen Xia', 'Wei Li', 'Zhanchao Huang'] | 2021-09-27 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-2.65377343e-01 -2.28808165e-01 -1.33447841e-01 -4.63814408e-01
-5.00072896e-01 -1.87583014e-01 2.28379279e-01 2.06289470e-01
-2.59402186e-01 8.51401985e-02 -1.97470441e-01 -7.61767477e-02
-1.93102330e-01 -5.45750439e-01 -2.27300107e-01 -9.63468015e-01
-3.19522321e-02 3.32848310e-01 8.32336605e-01 1.68633521... | [8.758581161499023, -0.6998236179351807] |
efd32cdd-b91a-4481-8c85-ee5c209ad729 | real-time-scene-text-detection-based-on | 2203.05251 | null | https://arxiv.org/abs/2203.05251v1 | https://arxiv.org/pdf/2203.05251v1.pdf | Real-time Scene Text Detection Based on Global Level and Word Level Features | It is an extremely challenging task to detect arbitrary shape text in natural scenes on high accuracy and efficiency. In this paper, we propose a scene text detection framework, namely GWNet, which mainly includes two modules: Global module and RCNN module. Specifically, Global module improves the adaptive performance ... | ['Xue Xu', 'Wenming Song', 'Enjun Xing', 'Jionghua Yu', 'Fuqiang Zhao'] | 2022-03-10 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [-4.81044054e-02 -4.21884239e-01 9.41021964e-02 5.86660299e-03
-4.93225783e-01 -2.72620231e-01 6.30664885e-01 -5.10165747e-03
-5.15167952e-01 1.15472339e-01 1.50107250e-01 -2.03908235e-01
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1.85318008e-01 1.79111272e-01 7.40269959e-01 -1.51478440... | [12.07543659210205, 2.279555082321167] |
13ef5168-5e16-48b8-b18f-7a771db877e0 | model-based-uncertainty-in-value-functions | 2302.12526 | null | https://arxiv.org/abs/2302.12526v2 | https://arxiv.org/pdf/2302.12526v2.pdf | Model-Based Uncertainty in Value Functions | We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. In particular, we focus on characterizing the variance over values induced by a distribution over MDPs. Previous work upper bounds the posterior variance over values by solving a so-called uncertai... | ['Jan Peters', 'Felix Berkenkamp', 'Julia Vinogradska', 'Alessandro G. Bottero', 'Carlos E. Luis'] | 2023-02-24 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-2.54344791e-01 4.08598840e-01 -6.69549942e-01 -1.23392515e-01
-1.31753862e+00 -7.06732631e-01 2.65732467e-01 3.31475258e-01
-6.78068995e-01 1.54515100e+00 -2.46039834e-02 -4.60934460e-01
-5.09791672e-01 -7.61973679e-01 -9.50625658e-01 -6.40026152e-01
-4.38883275e-01 7.33448863e-01 -1.65653482e-01 -1.68187413... | [4.1533942222595215, 2.487584114074707] |
016523db-3ecd-4a7c-bc10-d7daeb06a81f | explainable-ai-algorithms-for-vibration-data | 2207.10732 | null | https://arxiv.org/abs/2207.10732v1 | https://arxiv.org/pdf/2207.10732v1.pdf | Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation | Analyzing vibration data using deep neural network algorithms is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution because the cause of classifications is not comprehensible to humans. Therefore, th... | ['Deniz Neufeld', 'Oliver Mey'] | 2022-07-21 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.48617933e-02 -2.14244485e-01 8.13053697e-02 6.85792491e-02
8.32724944e-02 -2.38102362e-01 5.38958490e-01 1.12927519e-01
6.77293306e-03 4.54193294e-01 -1.54495239e-01 -2.86659390e-01
-7.85295129e-01 -7.43620396e-01 -5.97480834e-01 -8.92495215e-01
-2.75674105e-01 2.43238688e-01 -2.41798833e-01 -7.38976061... | [6.790188312530518, 2.3957502841949463] |
e3745a8e-fd0a-4972-a5a4-43445c924d8e | towards-more-accurate-automatic-sleep-staging | 1907.13177 | null | https://arxiv.org/abs/1907.13177v3 | https://arxiv.org/pdf/1907.13177v3.pdf | Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning | Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome thes... | ['Oliver Y. Chén', 'Alfred Mertins', 'Philipp Koch', 'Zongqing Lu', 'Huy Phan', 'Maarten De Vos', 'Ian McLoughlin'] | 2019-07-30 | null | null | null | null | ['sleep-stage-detection', 'multimodal-sleep-stage-detection', 'sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical', 'medical', 'medical'] | [ 3.87622528e-02 -9.19565633e-02 -2.94980496e-01 -6.67124450e-01
-8.31846893e-01 -2.13400811e-01 1.36552334e-01 -3.11461121e-01
-7.88769960e-01 1.05172741e+00 1.83755845e-01 -7.84975290e-02
2.10103001e-02 -4.34216797e-01 -3.12610894e-01 -6.64413512e-01
9.15939882e-02 7.79043078e-01 4.12365347e-01 -2.35007912... | [13.472515106201172, 3.5094892978668213] |
5344f93c-822a-476f-a05c-5120c08558b3 | 3d-instance-segmentation-via-multi-task | 1906.08650 | null | https://arxiv.org/abs/1906.08650v2 | https://arxiv.org/pdf/1906.08650v2.pdf | 3D Instance Segmentation via Multi-Task Metric Learning | We propose a novel method for instance label segmentation of dense 3D voxel grids. We target volumetric scene representations, which have been acquired with depth sensors or multi-view stereo methods and which have been processed with semantic 3D reconstruction or scene completion methods. The main task is to learn sha... | ['Martin R. Oswald', 'Bernard Ghanem', 'Marc Pollefeys', 'Jean Lahoud'] | 2019-06-20 | 3d-instance-segmentation-via-multi-task-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Lahoud_3D_Instance_Segmentation_via_Multi-Task_Metric_Learning_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lahoud_3D_Instance_Segmentation_via_Multi-Task_Metric_Learning_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-instance-segmentation-1', '3d-semantic-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.80170888e-01 4.89574879e-01 5.56460805e-02 -5.06572306e-01
-9.48808610e-01 -3.28373671e-01 3.98964435e-01 6.57364547e-01
-2.26476535e-01 3.11029911e-01 -1.35914251e-01 3.01186293e-01
-2.30136842e-01 -8.69194448e-01 -7.50196040e-01 -9.25007403e-01
-1.79228544e-01 1.38031089e+00 3.37175161e-01 4.33766574... | [8.071526527404785, -3.0769882202148438] |
1c128806-09cb-4b6a-963d-6e9547b19adc | streaming-voice-query-recognition-using | 1812.07754 | null | http://arxiv.org/abs/1812.07754v1 | http://arxiv.org/pdf/1812.07754v1.pdf | Streaming Voice Query Recognition using Causal Convolutional Recurrent Neural Networks | Voice-enabled commercial products are ubiquitous, typically enabled by
lightweight on-device keyword spotting (KWS) and full automatic speech
recognition (ASR) in the cloud. ASR systems require significant computational
resources in training and for inference, not to mention copious amounts of
annotated speech data. KW... | ['Yajie Mao', 'Gefei Yang', 'Raphael Tang', 'Ferhan Ture', 'Jimmy Lin', 'Hong Wei'] | 2018-12-19 | null | null | null | null | ['voice-query-recognition'] | ['speech'] | [-2.26343468e-01 -2.04874307e-01 -4.22509164e-01 -4.06578660e-01
-1.24923253e+00 -6.10945642e-01 3.00340444e-01 -2.47225553e-01
-4.66440231e-01 2.95821458e-01 -6.74334243e-02 -9.54515219e-01
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3.57762873e-01 5.74987173e-01 2.98315912e-01 -1.32496804... | [14.306556701660156, 6.275557041168213] |
6aaca035-3833-45dc-b740-38e73a6e90fd | learning-guarantees-for-graph-convolutional | null | null | https://openreview.net/forum?id=dpXL6lz4mOQ | https://openreview.net/pdf?id=dpXL6lz4mOQ | LEARNING GUARANTEES FOR GRAPH CONVOLUTIONAL NETWORKS ON THE STOCHASTIC BLOCK MODEL | An abundance of neural network models and algorithms for diverse tasks on graphs have been developed in the past five years. However, very few provable guarantees have been available for the performance of graph neural network models. This state of affairs is in contrast with the steady progress on the theoretical unde... | ['Wei Lu'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['stochastic-block-model'] | ['graphs'] | [ 3.63440245e-01 4.77865756e-01 -9.07390714e-02 -5.88489249e-02
-1.54780835e-01 -5.66036642e-01 5.55867016e-01 3.28547627e-01
-2.41134822e-01 6.40055776e-01 -6.68435320e-02 -6.11208022e-01
-5.07265031e-01 -8.37258637e-01 -9.81630564e-01 -7.76446998e-01
-8.77125323e-01 6.64345264e-01 -8.86000786e-03 2.62467097... | [6.866573333740234, 6.046314716339111] |
432262ad-993b-47e5-9cd6-bc4889da86b8 | simplex-pb-2-0-a-reliable-dataset-for-lexical | null | null | https://aclanthology.org/2020.winlp-1.6 | https://aclanthology.org/2020.winlp-1.6.pdf | SIMPLEX-PB 2.0: A Reliable Dataset for Lexical Simplification in Brazilian Portuguese | Most research on Lexical Simplification (LS) addresses non-native speakers of English, since they are numerous and easy to recruit. This makes it difficult to create LS solutions for other languages and target audiences. This paper presents SIMPLEX-PB 2.0, a dataset for LS in Brazilian Portuguese that, unlike its prede... | ['ra', "S Alu{\\'\\i}sio", 'Nathan Hartmann', 'Gustavo Henrique Paetzold'] | 2020-07-01 | null | null | null | ws-2020-7 | ['lexical-simplification'] | ['natural-language-processing'] | [-3.92185777e-01 3.95328790e-01 -4.69535500e-01 -1.91055700e-01
-8.75134587e-01 -4.97769713e-01 1.89528629e-01 5.91620684e-01
-7.16646612e-01 1.16785336e+00 5.80283046e-01 -4.45685774e-01
1.29925892e-01 -7.22985804e-01 -4.77377892e-01 1.53501436e-01
3.23686481e-01 8.53058338e-01 1.38769239e-01 -6.53273404... | [10.924373626708984, 10.378673553466797] |
efee2f47-9879-468f-acc5-f429d9af4b1a | learning-inr-for-event-guided-rolling-shutter | 2305.15078 | null | https://arxiv.org/abs/2305.15078v1 | https://arxiv.org/pdf/2305.15078v1.pdf | Learning INR for Event-guided Rolling Shutter Frame Correction, Deblur, and Interpolation | Images captured by rolling shutter (RS) cameras under fast camera motion often contain obvious image distortions and blur, which can be modeled as a row-wise combination of a sequence of global shutter (GS) frames within the exposure time naturally, recovering high-frame-rate GS sharp frames from an RS blur image needs... | ['Lin Wang', 'Guoqiang Liang', 'Yunfan Lu'] | 2023-05-24 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 4.97675717e-01 -5.67845464e-01 1.50494441e-01 -4.39292133e-01
-7.82830000e-01 -4.93876845e-01 3.32963765e-01 -6.50873244e-01
-4.49528605e-01 5.95965445e-01 1.26147568e-01 -1.86448246e-01
-8.57641548e-02 -4.11081582e-01 -9.99875367e-01 -7.06142783e-01
3.21598977e-01 -4.07771587e-01 3.61238033e-01 -1.14712575... | [11.246898651123047, -2.294271945953369] |
f67b5cb0-5ebd-4b60-b87c-121ea9281187 | monocular-3d-object-detection-for-autonomous | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Chen_Monocular_3D_Object_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Chen_Monocular_3D_Object_CVPR_2016_paper.pdf | Monocular 3D Object Detection for Autonomous Driving | The goal of this paper is to perform 3D object detection in single monocular images in the domain of autonomous driving. Our method first aims to generate a set of candidate class-specific object proposals, which are then run through a standard CNN pipeline to obtain high-quality object detections. The focus of this p... | ['Sanja Fidler', 'Raquel Urtasun', 'Ziyu Zhang', 'Xiaozhi Chen', 'Huimin Ma', 'Kaustav Kundu'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['object-proposal-generation', 'vehicle-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.53443977e-01 1.23448551e-01 -2.18416169e-01 -5.21946728e-01
-6.54861033e-01 -5.65095723e-01 8.30145359e-01 -1.34278670e-01
-6.64638281e-01 3.27674508e-01 -2.96099007e-01 -1.16638817e-01
4.62340236e-01 -5.38828254e-01 -9.94115949e-01 -3.17419946e-01
2.59951532e-01 7.36649156e-01 9.95315373e-01 6.61822930... | [7.702524185180664, -2.581723690032959] |
29d054ac-ae1c-49ad-8283-f9fc26b7ebfe | can-humans-do-less-than-one-shot-learning | 2202.04670 | null | https://arxiv.org/abs/2202.04670v1 | https://arxiv.org/pdf/2202.04670v1.pdf | Can Humans Do Less-Than-One-Shot Learning? | Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly {\em how} small? In this paper, we introduce a novel experimental paradigm that allows us to examine classification in an extremely data-scarce setting, asking whether humans can learn more categories than they hav... | ['Thomas L. Griffiths', 'Kerem Oktar', 'Ilia Sucholutsky', 'Maya Malaviya'] | 2022-02-09 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 1.41978592e-01 2.24632904e-01 -9.83593762e-02 -8.08103025e-01
-3.00303370e-01 -5.77534258e-01 7.23618388e-01 5.30282557e-01
-8.06444466e-01 8.43439341e-01 2.00045153e-01 -3.10887903e-01
-3.38243425e-01 -9.46061254e-01 -4.94556487e-01 -4.32831585e-01
8.18317160e-02 7.64368594e-01 -2.37145454e-01 -2.64509439... | [9.480953216552734, 6.551426887512207] |
20925217-6244-4fa6-b7c2-2c31d16d03b7 | optimal-algorithms-for-latent-bandits-with | 2301.07040 | null | https://arxiv.org/abs/2301.07040v3 | https://arxiv.org/pdf/2301.07040v3.pdf | Optimal Algorithms for Latent Bandits with Cluster Structure | We consider the problem of latent bandits with cluster structure where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \emph{latent} clusters such that the mean reward vectors of users within the same cluster are identical. At each round, a user, selected unifo... | ['Prateek Jain', 'Karthikeyan Shanmugam', 'Arun Sai Suggala', 'Soumyabrata Pal'] | 2023-01-17 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 1.82333589e-03 3.61840129e-01 -4.67773616e-01 -1.76000252e-01
-1.12321770e+00 -1.08266830e+00 -1.29252285e-01 1.30375043e-01
-5.37696421e-01 6.43478930e-01 -2.65844047e-01 -7.07579374e-01
-8.24987471e-01 -8.69816542e-01 -1.02902353e+00 -1.00944972e+00
-5.68948030e-01 8.77454758e-01 -4.06288922e-01 1.30730286... | [4.726862907409668, 3.490882396697998] |
f40cec07-51da-4ebc-a604-c10d8dc7a2e0 | conceptual-edits-as-counterfactual | null | null | http://ceur-ws.org/Vol-3121/paper6.pdf | http://ceur-ws.org/Vol-3121/paper6.pdf | Conceptual Edits as Counterfactual Explanations | We propose a framework for generating counterfactual explanations of black-box classifiers, which answer the question “What has to change for this to be classified as X instead of Y?” in terms of given domain knowledge. Specifically, we identify minimal and meaningful “concept edits” which, when applied, change the pre... | ['Giorgos Stamou1', 'Edmund Dervakos1', 'Konstantinos Thomas', 'Giorgos Filandrianos'] | 2022-03-23 | null | null | null | aaai-make-2022-3 | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.04743361e-01 1.00617468e+00 -3.42307448e-01 -6.84990227e-01
-6.38150334e-01 -6.31566942e-01 8.13472211e-01 2.25541070e-01
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-2.00628847e-01 -8.58515263e-01 -1.11154616e+00 -5.97725868e-01
1.20084308e-01 4.39937502e-01 -1.57853767e-01 2.34189600... | [8.67680835723877, 5.603013515472412] |
d298b05e-16cb-4428-98a5-e01855fc6b1a | object-adaptive-lstm-network-for-real-time | 2002.02598 | null | https://arxiv.org/abs/2002.02598v1 | https://arxiv.org/pdf/2002.02598v1.pdf | Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation | In recent years, deep learning based visual tracking methods have obtained great success owing to the powerful feature representation ability of Convolutional Neural Networks (CNNs). Among these methods, classification-based tracking methods exhibit excellent performance while their speeds are heavily limited by the ex... | ['Yang Hua', 'Yan Yan', 'Si Chen', 'Yihan Du'] | 2020-02-07 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [-0.15929487 -0.76066136 -0.45058805 -0.04235404 -0.29333937 -0.30972752
0.38818398 -0.44217598 -0.45766595 0.49329245 -0.26862985 -0.03073559
0.04021067 -0.7599547 -0.68382406 -0.9243842 0.20299062 0.14105883
0.60464317 -0.03690377 -0.04143897 0.6779602 -1.5493743 -0.21455427
0.8419731 1.3638592 0.... | [6.273575782775879, -2.165163516998291] |
c6584835-2551-46af-bed6-1b7c2c5c28e6 | revisiting-point-cloud-classification-a-new | 1908.04616 | null | https://arxiv.org/abs/1908.04616v2 | https://arxiv.org/pdf/1908.04616v2.pdf | Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data | Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classification methods have reported state-of-the-art performance on CAD model datasets such as ModelNet40 with... | ['Sai-Kit Yeung', 'Binh-Son Hua', 'Quang-Hieu Pham', 'Mikaela Angelina Uy', 'Duc Thanh Nguyen'] | 2019-08-13 | revisiting-point-cloud-classification-a-new-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Uy_Revisiting_Point_Cloud_Classification_A_New_Benchmark_Dataset_and_Classification_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Uy_Revisiting_Point_Cloud_Classification_A_New_Benchmark_Dataset_and_Classification_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-object-classification'] | ['computer-vision'] | [-3.70542333e-02 -4.52334821e-01 -1.75726414e-01 -5.68578780e-01
-6.01107895e-01 -5.64132929e-01 3.65846187e-01 -7.05518723e-02
-8.60679522e-02 4.47840504e-02 -7.23661482e-01 -5.87774813e-01
1.02559179e-01 -8.09814334e-01 -1.14610422e+00 -3.71797055e-01
-2.82645345e-01 9.68098044e-01 4.69257653e-01 -2.84110382... | [7.941969871520996, -3.3452587127685547] |
09d4ec4a-496a-4cc8-b700-ea56f6d691e7 | iss-image-as-stepping-stone-for-text-guided | 2303.15181 | null | https://arxiv.org/abs/2303.15181v1 | https://arxiv.org/pdf/2303.15181v1.pdf | ISS++: Image as Stepping Stone for Text-Guided 3D Shape Generation | In this paper, we present a new text-guided 3D shape generation approach (ISS++) that uses images as a stepping stone to bridge the gap between text and shape modalities for generating 3D shapes without requiring paired text and 3D data. The core of our approach is a two-stage feature-space alignment strategy that leve... | ['Chi-Wing Fu', 'Xiaojuan Qi', 'Ruihui Li', 'Peng Dai', 'Zhengzhe Liu'] | 2023-03-24 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 2.23702654e-01 -4.18428406e-02 1.74852923e-01 -1.05373777e-01
-6.55963898e-01 -7.77296543e-01 8.68504763e-01 -5.02880812e-01
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1.63701415e-01 -1.02967703e+00 -6.85346365e-01 -6.76043391e-01
5.95736325e-01 4.67227578e-01 1.01439111e-01 -3.85743856... | [9.079986572265625, -3.520026206970215] |
bd42418d-e2e3-4e1d-83f3-2b69110709d0 | multi-view-subspace-clustering | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Gao_Multi-View_Subspace_Clustering_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Gao_Multi-View_Subspace_Clustering_ICCV_2015_paper.pdf | Multi-View Subspace Clustering | For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the su... | ['Xuelong. Li', 'Heng Huang', 'Feiping Nie', 'Hongchang Gao'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-4.31049645e-01 -6.67203486e-01 -2.24680707e-01 -1.96409300e-01
-3.98955613e-01 -7.63650298e-01 3.00467283e-01 -3.69471371e-01
-3.32682282e-02 1.08827122e-01 2.83568323e-01 9.06329229e-02
-2.54381150e-01 -3.25474381e-01 -1.44798785e-01 -1.00893283e+00
3.66497725e-01 3.51342499e-01 2.84616530e-01 4.15778607... | [8.155718803405762, 4.625516414642334] |
d8b8a4ef-67eb-47aa-b1bf-2c41a1cffaa5 | 190909803 | 1909.09803 | null | https://arxiv.org/abs/1909.09803v4 | https://arxiv.org/pdf/1909.09803v4.pdf | Visual Odometry Revisited: What Should Be Learnt? | In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning. Most existing VO/SLAM systems with superior performance are based on geometry and have to be carefully designed for different application scenarios. Moreover, most monocular systems suffer from s... | ['Jia-Wang Bian', 'Huangying Zhan', 'Chamara Saroj Weerasekera', 'Ian Reid'] | 2019-09-21 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-4.99496371e-01 -1.61804512e-01 -8.13626796e-02 -4.69382107e-01
-2.01991096e-01 -4.41374272e-01 5.83815575e-01 -6.37686729e-01
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1.88842744e-01 4.06337917e-01 3.94426793e-01 -3.06220531... | [8.136763572692871, -2.2861247062683105] |
03e91c99-c83a-4aa1-b877-b20bd09fc8e1 | object-centric-learning-for-real-world-videos | 2306.04829 | null | https://arxiv.org/abs/2306.04829v1 | https://arxiv.org/pdf/2306.04829v1.pdf | Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities | Unsupervised video-based object-centric learning is a promising avenue to learn structured representations from large, unlabeled video collections, but previous approaches have only managed to scale to real-world datasets in restricted domains. Recently, it was shown that the reconstruction of pre-trained self-supervis... | ['Georg Martius', 'Maximilian Seitzer', 'Andrii Zadaianchuk'] | 2023-06-07 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [ 3.51128936e-01 -2.18837738e-01 -6.02126598e-01 -4.12112981e-01
-5.92297375e-01 -4.20702457e-01 7.17421949e-01 -3.84439044e-02
-5.06167710e-01 5.97515285e-01 2.74963766e-01 3.31565827e-01
-2.03898072e-01 -4.24168050e-01 -1.15995157e+00 -6.02319002e-01
-6.40177488e-01 3.66130561e-01 4.72466648e-01 1.13344088... | [8.760191917419434, 0.7470486164093018] |
ec40caa9-8b9d-4f71-8f61-ec18df58f3a2 | hyperbolic-entailment-cones-for-learning | 1804.01882 | null | http://arxiv.org/abs/1804.01882v3 | http://arxiv.org/pdf/1804.01882v3.pdf | Hyperbolic Entailment Cones for Learning Hierarchical Embeddings | Learning graph representations via low-dimensional embeddings that preserve
relevant network properties is an important class of problems in machine
learning. We here present a novel method to embed directed acyclic graphs.
Following prior work, we first advocate for using hyperbolic spaces which
provably model tree-li... | ['Gary Bécigneul', 'Octavian-Eugen Ganea', 'Thomas Hofmann'] | 2018-04-03 | hyperbolic-entailment-cones-for-learning-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2487 | http://proceedings.mlr.press/v80/ganea18a/ganea18a.pdf | icml-2018-7 | ['hypernym-discovery'] | ['natural-language-processing'] | [-1.22805655e-01 6.88229859e-01 -3.67340714e-01 -3.08935165e-01
-9.36785191e-02 -1.04654729e+00 8.37471366e-01 3.24519515e-01
-2.44328186e-01 2.05744997e-01 7.58773327e-01 -5.42010069e-01
-6.47730947e-01 -1.14455068e+00 -5.29949188e-01 -5.16991496e-01
-6.00028694e-01 6.60137892e-01 1.67590320e-01 -2.72607744... | [7.139491558074951, 6.040533065795898] |
9621f48e-d1ae-4094-84bb-48a52288e594 | modeling-scale-free-graphs-for-knowledge | 2108.06468 | null | https://arxiv.org/abs/2108.06468v3 | https://arxiv.org/pdf/2108.06468v3.pdf | Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation | Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention. Via unifying the KG with user-item interactions into a tripartite graph, recent works explore the graph topol... | ['Jianye Hao', 'Irwin King', 'Ziqiao Meng', 'Mengchen Zhao', 'Yingxue Zhang', 'Menglin Yang', 'Yankai Chen'] | 2021-08-14 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-6.32448018e-01 6.75325319e-02 -4.54342186e-01 -1.60292789e-01
-8.54454637e-02 -6.20110452e-01 3.31241131e-01 2.10616469e-01
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-7.57245719e-01 -1.12726367e+00 -6.47675991e-01 -7.63745666e-01
-2.45228186e-01 4.07522619e-01 1.57612950e-01 -5.58034897... | [10.237386703491211, 5.638768672943115] |
b90fd7e6-1d7a-421a-b573-14127eb84160 | an-unsupervised-approach-for-aspect-category | 1812.03361 | null | https://arxiv.org/abs/1812.03361v2 | https://arxiv.org/pdf/1812.03361v2.pdf | An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure | Aspect category detection is one of the important and challenging subtasks of aspect-based sentiment analysis. Given a set of pre-defined categories, this task aims to detect categories which are indicated implicitly or explicitly in a given review sentence. Supervised machine learning approaches perform well to accomp... | ['Heshaam Faili', 'Sajad Movahedi', 'Erfan Ghadery', 'Azadeh Shakery'] | 2018-12-08 | null | null | null | null | ['aspect-category-detection'] | ['natural-language-processing'] | [ 2.04908952e-01 -1.56763151e-01 -3.60194385e-01 -7.30701089e-01
-7.95515954e-01 -7.74583459e-01 7.64705539e-01 6.51900530e-01
-3.51608008e-01 3.69273365e-01 9.02414545e-02 -3.31701756e-01
2.52026111e-01 -6.26078427e-01 -2.94547915e-01 -4.49242592e-01
4.24095243e-01 3.02262336e-01 8.92201141e-02 -2.51605093... | [11.329687118530273, 6.678438663482666] |
a0560673-623d-4a47-9c0d-2b720714aa31 | welfare-and-fairness-in-multi-objective | 2212.01382 | null | https://arxiv.org/abs/2212.01382v3 | https://arxiv.org/pdf/2212.01382v3.pdf | Welfare and Fairness in Multi-objective Reinforcement Learning | We study fair multi-objective reinforcement learning in which an agent must learn a policy that simultaneously achieves high reward on multiple dimensions of a vector-valued reward. Motivated by the fair resource allocation literature, we model this as an expected welfare maximization problem, for some non-linear fair ... | ['Brandon Fain', 'Muhang Tian', 'Nianli Peng', 'Zimeng Fan'] | 2022-11-30 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-2.28494912e-01 1.98940799e-01 -6.53031707e-01 -2.32236236e-01
-1.23245656e+00 -5.57249367e-01 1.87669694e-01 1.27681494e-01
-1.10746014e+00 1.52415538e+00 3.74975324e-01 -3.43256742e-01
-6.32115126e-01 -6.09416902e-01 -4.97045875e-01 -7.97157109e-01
-5.23283005e-01 4.56914157e-01 -5.00274241e-01 -5.03836721... | [4.238687038421631, 2.637141466140747] |
88699d38-205e-4097-aea9-9fe4be7f0269 | an-additive-latent-feature-model-for | null | null | http://papers.nips.cc/paper/3808-an-additive-latent-feature-model-for-transparent-object-recognition | http://papers.nips.cc/paper/3808-an-additive-latent-feature-model-for-transparent-object-recognition.pdf | An Additive Latent Feature Model for Transparent Object Recognition | Existing methods for recognition of object instances and categories based on quantized local features can perform poorly when local features exist on transparent surfaces, such as glass or plastic objects. There are characteristic patterns to the local appearance of transparent objects, but they may not be well capture... | ['Sergey Karayev', 'Michael J. Black', 'Gary Bradski', 'Trevor Darrell', 'Mario Fritz'] | 2009-12-01 | null | null | null | neurips-2009-12 | ['transparent-objects'] | ['computer-vision'] | [ 2.43962258e-01 -2.96849579e-01 -1.34307235e-01 -4.37188983e-01
-3.73700023e-01 -4.67533022e-01 6.19647026e-01 -1.67765424e-01
3.94763499e-01 5.73309958e-02 3.92735809e-01 4.47662354e-01
1.99227668e-02 -8.82825196e-01 -7.91042507e-01 -1.05677402e+00
-1.06616624e-01 4.23625827e-01 5.22668183e-01 -1.81137715... | [9.823302268981934, -2.84889817237854] |
de866465-8058-4953-871b-ede0ea08e4d7 | memorization-and-generalization-in-neural | 2106.08704 | null | https://arxiv.org/abs/2106.08704v3 | https://arxiv.org/pdf/2106.08704v3.pdf | Memorization and Generalization in Neural Code Intelligence Models | Deep Neural Networks (DNNs) are increasingly being used in software engineering and code intelligence tasks. These are powerful tools that are capable of learning highly generalizable patterns from large datasets through millions of parameters. At the same time, their large capacity can render them prone to memorizing ... | ['Vincent J. Hellendoorn', 'Mohammad Amin Alipour', 'Aftab Hussain', 'Md Rafiqul Islam Rabin'] | 2021-06-16 | null | null | null | null | ['variable-misuse', 'code-documentation-generation', 'code-search', 'code-search', 'method-name-prediction', 'code-documentation-generation'] | ['computer-code', 'computer-code', 'computer-code', 'computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-1.84898302e-01 -7.45259821e-02 1.34700313e-01 -3.11801940e-01
-9.72455963e-02 -6.78309619e-01 1.81529924e-01 3.51763040e-01
-4.68628556e-01 4.36193913e-01 -1.28037766e-01 -7.01908231e-01
-1.50983721e-01 -9.61445987e-01 -1.09096253e+00 -2.59250373e-01
-8.57550576e-02 2.23030478e-01 -9.66683328e-02 -3.24589431... | [7.689231872558594, 7.724452018737793] |
2f2b79ea-4471-4429-a922-7a1d19a0ad85 | a-unified-framework-for-task-driven-data | 2106.05484 | null | https://arxiv.org/abs/2106.05484v1 | https://arxiv.org/pdf/2106.05484v1.pdf | A Unified Framework for Task-Driven Data Quality Management | High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM). Existing DQM schemes often cannot satisfactorily improve ML performance because, by design, they are oblivious to downstream ML tasks. Besides, they cannot handle various data qu... | ['Ruoxi Jia', 'Ming Jin', 'Yi Zeng', 'Tianhao Wang'] | 2021-06-10 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 1.96569756e-01 -2.26837873e-01 -4.56751674e-01 -1.48845568e-01
-1.43889213e+00 -8.64108980e-01 5.16185880e-01 4.88609850e-01
-3.48055869e-01 5.23231089e-01 1.28324568e-01 -4.61585402e-01
-3.62431973e-01 -6.85152709e-01 -1.00211537e+00 -8.75274479e-01
2.45663568e-01 3.15001220e-01 -1.99069932e-01 -1.83402315... | [8.901933670043945, 4.205723762512207] |
36f590d7-bec7-4b43-8a13-c38ddc9830f7 | local-stochastic-factored-gradient-descent | 2203.11579 | null | https://arxiv.org/abs/2203.11579v2 | https://arxiv.org/pdf/2203.11579v2.pdf | Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography | We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix over a set of local machines. QST is the canonical procedure to characterize the state of a quantum system, which we formulate as a stochastic... | ['Anastasios Kyrillidis', 'César A. Uribe', 'Mohammad Taha Toghani', 'Junhyung Lyle Kim'] | 2022-03-22 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [-2.35299632e-01 2.56733373e-02 -3.30646435e-05 -3.37185562e-01
-1.31534088e+00 -2.19095722e-01 3.43186706e-01 -1.99667603e-01
-6.27748966e-01 9.09803927e-01 1.79203972e-02 -3.15134674e-01
-3.30879927e-01 -7.84839451e-01 -9.16079283e-01 -1.22809875e+00
-2.64662027e-01 7.50912368e-01 -3.83675486e-01 -1.10593721... | [5.99885368347168, 4.780940532684326] |
889a780b-e7c8-435d-9bc5-ea5ed13b7953 | improving-the-harmony-of-the-composite-image | 1907.06406 | null | https://arxiv.org/abs/1907.06406v3 | https://arxiv.org/pdf/1907.06406v3.pdf | Improving the Harmony of the Composite Image by Spatial-Separated Attention Module | Image composition is one of the most important applications in image processing. However, the inharmonious appearance between the spliced region and background degrade the quality of the image. Thus, we address the problem of Image Harmonization: Given a spliced image and the mask of the spliced region, we try to harmo... | ['Chi-Man Pun', 'Xiaodong Cun'] | 2019-07-15 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 2.41086230e-01 -7.97984377e-02 1.37246817e-01 -1.60431534e-01
-3.34489971e-01 -1.37967810e-01 3.25308591e-01 -1.65150940e-01
-3.48886639e-01 4.31833655e-01 5.71838915e-02 4.91169915e-02
1.48280129e-01 -7.87770212e-01 -9.47567403e-01 -8.44246447e-01
6.05286002e-01 -1.56619728e-01 3.67864698e-01 -4.22447890... | [11.246770858764648, -1.205302357673645] |
9866b16f-1341-4558-bba4-14b8108dab87 | dynamic-neural-program-embeddings-for-program | null | null | https://openreview.net/forum?id=BJuWrGW0Z | https://openreview.net/pdf?id=BJuWrGW0Z | Dynamic Neural Program Embeddings for Program Repair | Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, code completion, and fault localization. However, most existing program embeddings are based on syntactic features of programs, such as token sequences or abstract syntax tree... | ['Zhendong Su', 'Ke Wang', 'Rishabh Singh'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['fault-localization', 'program-repair', 'program-repair'] | ['computer-code', 'computer-code', 'reasoning'] | [-6.75141811e-02 -3.17949653e-01 -6.67672694e-01 -5.85740685e-01
-3.65708888e-01 -5.56471646e-01 -1.87413692e-02 7.19291747e-01
-5.46511225e-02 -1.32258788e-01 3.16304922e-01 -6.94135487e-01
3.82584244e-01 -1.00099003e+00 -1.04846370e+00 -1.74308255e-01
-9.93278697e-02 -8.27102512e-02 2.30711624e-01 -2.18525365... | [7.524619102478027, 7.811716556549072] |
94fc8256-8a2e-4322-8973-8a6181f071d7 | text-only-image-captioning-with-multi-context | 2305.18072 | null | https://arxiv.org/abs/2305.18072v1 | https://arxiv.org/pdf/2305.18072v1.pdf | Text-Only Image Captioning with Multi-Context Data Generation | Text-only Image Captioning (TIC) is an approach that aims to construct a model solely based on text that can accurately describe images. Recently, diffusion models have demonstrated remarkable capabilities in generating high-quality images that are semantically coherent with given texts. This presents an opportunity to... | ['Xiaoyan Sun', 'Yueyi Zhang', 'Fengyun Rao', 'Yizhou Zhou', 'Feipeng Ma'] | 2023-05-29 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 5.80664158e-01 -2.08353940e-02 3.42518508e-01 -3.59869182e-01
-7.80426562e-01 -4.91250277e-01 1.09236634e+00 -3.61193895e-01
-2.16494694e-01 8.33362520e-01 2.71148980e-01 -1.04328024e-03
2.84028322e-01 -7.73579776e-01 -9.02601779e-01 -6.29097342e-01
5.44989347e-01 3.13515782e-01 2.54940122e-01 -1.77972287... | [11.177918434143066, 0.6114633083343506] |
1ba06dd4-b3c7-4d0b-8b5d-c5f0479ad96c | deep-temporal-contrastive-clustering | 2212.14366 | null | https://arxiv.org/abs/2212.14366v1 | https://arxiv.org/pdf/2212.14366v1.pdf | Deep Temporal Contrastive Clustering | Recently the deep learning has shown its advantage in representation learning and clustering for time series data. Despite the considerable progress, the existing deep time series clustering approaches mostly seek to train the deep neural network by some instance reconstruction based or cluster distribution based objec... | ['Chang-Dong Wang', 'Dong Huang', 'Ying Zhong'] | 2022-12-29 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-1.93020925e-01 -4.16807830e-01 1.25061601e-01 -4.79001760e-01
-7.90855527e-01 -3.93862635e-01 7.60855258e-01 2.02521265e-01
-2.68192291e-01 2.01997980e-01 -5.17511591e-02 1.06980562e-01
-4.99669224e-01 -5.94458044e-01 -6.65765524e-01 -1.16653132e+00
-6.34379864e-01 4.38398272e-01 -3.30077022e-01 8.83991942... | [7.447592258453369, 3.0917491912841797] |
94c67a23-b136-401a-aa71-bcc6b3082152 | pseudo-relevance-feedback-for-multiple | 2106.11251 | null | https://arxiv.org/abs/2106.11251v2 | https://arxiv.org/pdf/2106.11251v2.pdf | Pseudo-Relevance Feedback for Multiple Representation Dense Retrieval | Pseudo-relevance feedback mechanisms, from Rocchio to the relevance models, have shown the usefulness of expanding and reweighting the users' initial queries using information occurring in an initial set of retrieved documents, known as the pseudo-relevant set. Recently, dense retrieval -- through the use of neural con... | ['Iadh Ounis', 'Nicola Tonellotto', 'Craig Macdonald', 'Xiao Wang'] | 2021-06-21 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 2.06199646e-01 -7.35885426e-02 -6.04116693e-02 -3.90427411e-02
-1.25061524e+00 -6.77387774e-01 1.04998565e+00 8.19250405e-01
-9.84878778e-01 4.79568422e-01 7.20126212e-01 -1.99448206e-02
-9.10233021e-01 -7.05689490e-01 -4.75180656e-01 -5.56810915e-01
-2.69379884e-01 6.92696929e-01 5.66880405e-01 -6.92627668... | [11.439048767089844, 7.596195220947266] |
aef3ed15-6dbf-4b14-a1ec-78a96d0be537 | data-driven-covariance-steering-control | 2303.17675 | null | https://arxiv.org/abs/2303.17675v1 | https://arxiv.org/pdf/2303.17675v1.pdf | Data-Driven Covariance Steering Control Design | This paper studies the problem of steering the distribution of a linear time-invariant system from an initial normal distribution to a terminal normal distribution under no knowledge of the system dynamics. This data-driven control framework uses data collected from the input and the state and utilizes the seminal work... | ['Panagiotis Tsiotras', 'Joshua Pilipovsky'] | 2023-03-30 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [ 2.79938281e-01 2.15961829e-01 -1.31408378e-01 1.20142251e-01
-7.36401439e-01 -6.97452784e-01 7.30806172e-01 -1.27746388e-01
-5.16814053e-01 8.77345562e-01 7.98601005e-03 -4.54716653e-01
-8.84825349e-01 -3.00167114e-01 -5.33723533e-01 -1.17654324e+00
-1.49739340e-01 3.60397696e-01 -7.86328763e-02 -2.92691559... | [5.087167739868164, 2.524186372756958] |
575de333-d0f0-4c5a-8cdf-49a5afdab96a | multi-domain-stain-normalization-for-digital | 2301.09431 | null | https://arxiv.org/abs/2301.09431v1 | https://arxiv.org/pdf/2301.09431v1.pdf | Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images | The variation in histologic staining between different medical centers is one of the most profound challenges in the field of computer-aided diagnosis. The appearance disparity of pathological whole slide images causes algorithms to become less reliable, which in turn impedes the wide-spread applicability of downstream... | ['Titus J. Brinker', 'Tabea-Clara Bucher', 'Martin J. Hetz'] | 2023-01-23 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.23051414e-01 -2.63523050e-02 -2.07480595e-01 -2.65439063e-01
-6.65108323e-01 -7.97249794e-01 4.36414897e-01 2.58227736e-01
-7.82064974e-01 8.01228166e-01 -1.28557131e-01 -4.39956576e-01
-1.73838571e-01 -7.78534412e-01 -4.85430568e-01 -1.26031590e+00
5.03946960e-01 4.20878798e-01 3.02647501e-01 -1.36855736... | [15.016923904418945, -3.0938961505889893] |
881c2f4b-853a-4457-8e11-a60d1fab5e84 | non-log-concave-and-nonsmooth-sampling-via | 2305.15988 | null | https://arxiv.org/abs/2305.15988v1 | https://arxiv.org/pdf/2305.15988v1.pdf | Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms | We study the problem of approximate sampling from non-log-concave distributions, e.g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodality. We focus on performing this task via Markov chain Monte Carlo (MCMC) methods derived from discretizations of the overdamped Langevin dif... | ['Thomas Pock', 'Han Liu', 'Tim Tsz-Kit Lau'] | 2023-05-25 | null | null | null | null | ['image-deconvolution', 'bayesian-inference'] | ['computer-vision', 'methodology'] | [ 2.54249096e-01 -8.16475824e-02 3.73334914e-01 4.96549299e-03
-9.22240198e-01 -3.32382411e-01 8.24480951e-01 -2.99581826e-01
-5.22713959e-01 1.03166020e+00 -6.06545992e-03 -1.61786258e-01
-1.86278850e-01 -4.52891678e-01 -6.67805493e-01 -1.07582045e+00
6.44269586e-02 1.12395537e+00 6.79632723e-02 3.80605996... | [6.864263534545898, 3.84503173828125] |
c6f7e261-233f-482b-8e68-37c70f63b246 | skin-lesion-synthesis-with-generative | 1902.03253 | null | http://arxiv.org/abs/1902.03253v1 | http://arxiv.org/pdf/1902.03253v1.pdf | Skin Lesion Synthesis with Generative Adversarial Networks | Skin cancer is by far the most common type of cancer. Early detection is the
key to increase the chances for successful treatment significantly. Currently,
Deep Neural Networks are the state-of-the-art results on automated skin cancer
classification. To push the results further, we need to address the lack of
annotated... | ['Fábio Perez', 'Alceu Bissoto', 'Sandra Avila', 'Eduardo Valle'] | 2019-02-08 | null | null | null | null | ['skin-cancer-classification', 'medical-image-generation'] | ['medical', 'medical'] | [ 4.83838379e-01 3.12594771e-01 -2.51874626e-01 -7.28598684e-02
-9.46779132e-01 -3.44446391e-01 4.72731918e-01 2.34503329e-01
-3.38203251e-01 8.55201423e-01 -5.79053313e-02 -3.14637184e-01
3.27210218e-01 -7.25288987e-01 -5.12648880e-01 -5.66446245e-01
2.21492708e-01 8.42237175e-02 3.14878911e-01 -1.70981452... | [15.377365112304688, -2.7944066524505615] |
d8b0233f-7898-416f-9ee7-1c9431f0f3a6 | learning-gait-representation-from-massive | 2206.13964 | null | https://arxiv.org/abs/2206.13964v1 | https://arxiv.org/pdf/2206.13964v1.pdf | Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark | Gait depicts individuals' unique and distinguishing walking patterns and has become one of the most promising biometric features for human identification. As a fine-grained recognition task, gait recognition is easily affected by many factors and usually requires a large amount of completely annotated data that is cost... | ['Shiqi Yu', 'Yongzhen Huang', 'Jilong Wang', 'Saihui Hou', 'Chao Fan'] | 2022-06-28 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-8.97716880e-02 -5.36168218e-01 -4.75381255e-01 -1.80836752e-01
-7.26915956e-01 -1.34256184e-01 2.93759406e-01 -4.11846548e-01
-3.20831776e-01 9.16283906e-01 2.65336633e-01 2.03154370e-01
-7.04511581e-03 -6.93509519e-01 -3.73661071e-01 -9.14059162e-01
-4.86938149e-01 7.06534266e-01 1.76624507e-01 -2.75709361... | [14.30085277557373, 1.4248937368392944] |
c580219e-0fa4-4f95-a229-1bcbefcbd63f | impact-of-microphone-position-measurement | null | null | https://aclanthology.org/2021.icon-main.23 | https://aclanthology.org/2021.icon-main.23.pdf | Impact of Microphone position Measurement Error on Multi Channel Distant Speech Recognition & Intelligibility | It was shown in (Raikar et al., 2020) that the measurement error in the microphone position affected the room impulse response (RIR) which in turn affected the single channel speech recognition. In this paper, we ex-tend this to study the more complex and realistic scenario of multi channel distant speech recognition. ... | ['Sunil Kumar Kopparapu', 'Karan Nathwani'] | null | null | null | null | icon-2021-12 | ['room-impulse-response', 'distant-speech-recognition'] | ['audio', 'speech'] | [ 2.15672582e-01 -4.16007340e-02 9.46898520e-01 -2.52841949e-01
-1.29580748e+00 -6.55441165e-01 3.36874187e-01 -2.03464687e-01
-3.44223708e-01 5.45791686e-01 4.75795567e-01 -4.98676389e-01
-8.08012262e-02 -3.32263201e-01 -7.76081741e-01 -6.73074186e-01
5.28601594e-02 1.39663190e-01 1.57176495e-01 5.34113497... | [15.014361381530762, 5.87323522567749] |
abdb7d2d-3957-4ced-8baf-df0543b6ad6a | dont-classify-translate-multi-level-e | 1812.05774 | null | http://arxiv.org/abs/1812.05774v1 | http://arxiv.org/pdf/1812.05774v1.pdf | Don't Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation | E-commerce platforms categorize their products into a multi-level taxonomy
tree with thousands of leaf categories. Conventional methods for product
categorization are typically based on machine learning classification
algorithms. These algorithms take product information as input (e.g., titles
and descriptions) to clas... | ['Maggie Yundi Li', 'Liling Tan', 'Stanley Kok'] | 2018-12-14 | null | null | null | null | ['product-categorization'] | ['miscellaneous'] | [ 9.16685387e-02 1.28668651e-01 -8.72222483e-01 -7.04300225e-01
-1.71415538e-01 -1.15160358e+00 3.72138768e-01 7.20380783e-01
2.01510563e-01 1.76120475e-01 9.76002067e-02 -1.00310183e+00
8.55666306e-03 -1.36155760e+00 -2.81362206e-01 -4.96894792e-02
-6.16677217e-02 5.72374165e-01 1.95822626e-01 -2.44116738... | [9.88339900970459, 6.276978015899658] |
a6a3aa46-c8a0-44d5-af1a-385887f70b6f | a-review-and-evaluation-of-elastic-distance | 2205.15181 | null | https://arxiv.org/abs/2205.15181v2 | https://arxiv.org/pdf/2205.15181v2.pdf | A Review and Evaluation of Elastic Distance Functions for Time Series Clustering | Time series clustering is the act of grouping time series data without recourse to a label. Algorithms that cluster time series can be classified into two groups: those that employ a time series specific distance measure; and those that derive features from time series. Both approaches usually rely on traditional clust... | ['Anthony Bagnall', 'Matthew Middlehurst', 'Chris Holder'] | 2022-05-30 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.82277071e-01 -6.44195020e-01 1.00434877e-01 -2.32255965e-01
-6.87397540e-01 -1.07203650e+00 6.47652924e-01 5.54855406e-01
-6.36337280e-01 2.88426373e-02 4.39862132e-01 -4.60926145e-01
-8.88466179e-01 -7.62199640e-01 2.72437707e-02 -8.97291601e-01
-8.31350267e-01 3.67585272e-01 2.66968697e-01 -2.63961554... | [7.247849941253662, 3.3556313514709473] |
10387f47-3cad-4026-9494-c54b31f8f8e2 | multi-stage-clarification-in-conversational | 2110.15235 | null | https://arxiv.org/abs/2110.15235v1 | https://arxiv.org/pdf/2110.15235v1.pdf | Multi-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems | Clarification resolution plays an important role in various information retrieval tasks such as interactive question answering and conversational search. In such context, the user often formulates their information needs as short and ambiguous queries, some popular search interfaces then prompt the user to confirm her ... | ['Eric Charton', 'Marc Queudot', 'Louis Marceau', 'Nada Naji', 'Hadrien Lautraite'] | 2021-10-28 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.79295206e-01 1.72910362e-01 5.41388579e-02 -3.88411790e-01
-9.24768686e-01 -9.79056299e-01 6.68422401e-01 5.65478086e-01
-7.33679235e-01 6.28922343e-01 3.02243441e-01 -8.22464347e-01
-1.51305303e-01 -3.72120231e-01 1.24540709e-01 -2.23081559e-01
6.02898598e-01 5.61870694e-01 4.33129221e-01 -7.73325264... | [12.244190216064453, 7.857144832611084] |
2c9e10bd-e115-4f6e-b3e8-ab3c59c50754 | gittables-a-large-scale-corpus-of-relational | 2106.07258 | null | https://arxiv.org/abs/2106.07258v5 | https://arxiv.org/pdf/2106.07258v5.pdf | GitTables: A Large-Scale Corpus of Relational Tables | The success of deep learning has sparked interest in improving relational table tasks, like data preparation and search, with table representation models trained on large table corpora. Existing table corpora primarily contain tables extracted from HTML pages, limiting the capability to represent offline database table... | ['Paul Groth', 'Çağatay Demiralp', 'Madelon Hulsebos'] | 2021-06-14 | null | null | null | null | ['table-annotation', 'table-annotation'] | ['knowledge-base', 'natural-language-processing'] | [-4.83296543e-01 8.47756684e-01 -5.05028665e-01 -4.76516545e-01
-1.26043177e+00 -9.11504030e-01 6.33290172e-01 1.02091014e+00
-1.10742353e-01 7.54829049e-01 5.22733986e-01 -2.81338453e-01
-3.05604279e-01 -1.18627930e+00 -1.14641690e+00 3.05441111e-01
-1.60815731e-01 1.33807516e+00 2.86506861e-01 -5.14328361... | [9.56798267364502, 7.960536479949951] |
3233cb65-d4ea-4a15-b516-1d011ba51f6a | opttyper-probabilistic-type-inference-by | 2004.00348 | null | https://arxiv.org/abs/2004.00348v3 | https://arxiv.org/pdf/2004.00348v3.pdf | OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints | We present a new approach to the type inference problem for dynamic languages. Our goal is to combine \emph{logical} constraints, that is, deterministic information from a type system, with \emph{natural} constraints, that is, uncertain statistical information about types learnt from sources like identifier names. To t... | ['Andrew D. Gordon', 'Earl T. Barr', 'Charles Sutton', 'Irene Vlassi Pandi'] | 2020-04-01 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-0.04340437 0.36808464 -0.4887595 -0.6591929 -1.029332 -0.7170144
0.5773888 0.33383736 -0.27816105 0.7951315 0.14266889 -0.6729876
0.05952645 -1.0461072 -1.3059711 -0.32822958 -0.38842875 0.38543355
0.28277433 0.09120496 0.24196438 0.09891213 -1.9565369 0.9221579
0.7243959 1.2205026 -0.009... | [8.027800559997559, 7.50396728515625] |
13e3ffa8-3f49-495a-828f-c4a395b48c36 | thundr-transformer-based-3d-human | 2106.09336 | null | https://arxiv.org/abs/2106.09336v1 | https://arxiv.org/pdf/2106.09336v1.pdf | THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers | We present THUNDR, a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people, given monocular RGB images. Key to our methodology is an intermediate 3d marker representation, where we aim to combine the predictive power of model-free-output architectures and the regularizing, ant... | ['Cristian Sminchisescu', 'Rahul Sukthankar', 'William T. Freeman', 'Eduard Gabriel Bazavan', 'Andrei Zanfir', 'Mihai Zanfir'] | 2021-06-17 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zanfir_THUNDR_Transformer-Based_3D_Human_Reconstruction_With_Markers_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zanfir_THUNDR_Transformer-Based_3D_Human_Reconstruction_With_Markers_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [-1.19210862e-01 6.60190880e-01 9.74290073e-02 -5.83398938e-01
-5.42464674e-01 1.18299499e-01 4.21942055e-01 -3.51731896e-01
-3.39268386e-01 4.77550834e-01 5.32756150e-01 3.74298215e-01
9.25146490e-02 -4.45700079e-01 -9.38380420e-01 -2.30250493e-01
-1.69348478e-01 1.36991155e+00 -1.12921812e-01 -3.39968383... | [6.977191925048828, -1.0321201086044312] |
8e364742-1749-4a1d-876a-63182fc0ef96 | microservice-deployment-in-edge-computing | null | null | https://ieeexplore.ieee.org/abstract/document/9712168 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9712168 | Microservice Deployment in Edge Computing Based on Deep Q Learning | The microservice deployment strategy is promising in reducing the overall service response time in the microservice-oriented edge computing platform. However, existing works ignore the effect of different interaction frequencies among microservices and the decrease in service execution performance caused by the increas... | ['Pengfei Yang', 'Quan Wang', 'Wenkai Lv'] | 2022-02-11 | null | null | null | ieee-transactions-on-parallel-and-distributed-2 | ['q-learning', 'edge-computing'] | ['methodology', 'time-series'] | [-9.29002583e-01 -2.15370610e-01 3.22357155e-02 -2.47063950e-01
4.48579527e-02 -3.56502146e-01 -3.85549329e-02 -3.76956582e-01
8.57018027e-03 5.25165260e-01 1.08685181e-01 -4.20705944e-01
-5.92214942e-01 -9.41553533e-01 -4.25410241e-01 -9.63700175e-01
-2.19729647e-01 5.21958768e-01 3.55253890e-02 -1.29339635... | [5.876976013183594, 1.7867498397827148] |
faf8c0a9-2495-4555-9838-f6295f589e56 | clickbait-detection-in-tweets-using-self | 1710.05364 | null | http://arxiv.org/abs/1710.05364v1 | http://arxiv.org/pdf/1710.05364v1.pdf | Clickbait Detection in Tweets Using Self-attentive Network | Clickbait detection in tweets remains an elusive challenge. In this paper, we
describe the solution for the Zingel Clickbait Detector at the Clickbait
Challenge 2017, which is capable of evaluating each tweet's level of click
baiting. We first reformat the regression problem as a multi-classification
problem, based on ... | ['Yiwei Zhou'] | 2017-10-15 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [ 1.55482953e-02 -1.88030750e-01 -4.11668390e-01 -6.20596230e-01
-1.53786099e+00 -4.28791195e-01 7.98009336e-01 4.00219336e-02
-4.44620967e-01 6.07235730e-01 2.17929170e-01 -5.31810403e-01
9.28748325e-02 -5.55800736e-01 -6.69717669e-01 -3.79532278e-01
1.98451877e-02 3.87778372e-01 4.29791063e-01 -2.28127033... | [7.750486373901367, 9.783860206604004] |
d5070f32-8c86-42cd-91ed-76077808ba68 | multi-behavior-graph-neural-networks-for | 2302.08678 | null | https://arxiv.org/abs/2302.08678v1 | https://arxiv.org/pdf/2302.08678v1.pdf | Multi-Behavior Graph Neural Networks for Recommender System | Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering archite... | ['Liefeng Bo', 'Peng Dai', 'Yong Xu', 'Chao Huang', 'Lianghao Xia'] | 2023-02-17 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.36285514e-01 -6.98393807e-02 -5.46681404e-01 -7.72936821e-01
2.14524329e-01 -2.62148470e-01 2.36485884e-01 9.94050279e-02
-3.55237499e-02 1.62742868e-01 4.56071019e-01 -4.99372751e-01
-8.13399911e-01 -1.01185751e+00 -6.20613337e-01 -2.94226915e-01
-6.42563760e-01 2.85787165e-01 -1.23363249e-01 -6.19092822... | [10.186408996582031, 5.617628574371338] |
ebcc2cc0-9550-4f4e-98a0-d674a172a5d1 | characterizing-the-efficiency-vs-accuracy | 2204.07288 | null | https://arxiv.org/abs/2204.07288v1 | https://arxiv.org/pdf/2204.07288v1.pdf | Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models | With many real-world applications of Natural Language Processing (NLP) comprising of long texts, there has been a rise in NLP benchmarks that measure the accuracy of models that can handle longer input sequences. However, these benchmarks do not consider the trade-offs between accuracy, speed, and power consumption as ... | ['Lisa Wu Wills', 'Bhuwan Dhingra', 'Phyllis Ang'] | 2022-04-15 | null | https://aclanthology.org/2022.nlppower-1.12 | https://aclanthology.org/2022.nlppower-1.12.pdf | nlppower-acl-2022-5 | ['2048'] | ['playing-games'] | [ 3.01387548e-01 -1.18282005e-01 -3.59828532e-01 -2.51302630e-01
-9.70128238e-01 -7.50886023e-01 4.55549777e-01 5.77332616e-01
-8.56096685e-01 6.57149613e-01 4.50429946e-01 -4.96379554e-01
2.87673273e-03 -8.32092643e-01 -7.10469246e-01 -2.25619614e-01
4.57683811e-03 2.52820164e-01 3.11565965e-01 5.39712049... | [11.360861778259277, 8.555187225341797] |
e5398c74-69fb-4ccf-9a0f-62c9a50c67d1 | molecular-transformer-for-chemical-reaction | 1811.02633 | null | https://arxiv.org/abs/1811.02633v2 | https://arxiv.org/pdf/1811.02633v2.pdf | Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction | Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between SMILES strings... | ['Alpha A. Lee', 'Théophile Gaudin', 'Costas Bekas', 'Teodoro Laino', 'Philippe Schwaller', 'Peter Bolgar'] | 2018-11-06 | null | null | null | null | ['chemical-reaction-prediction'] | ['medical'] | [ 6.04220688e-01 3.99153233e-01 -6.03332281e-01 -1.28117740e-01
-9.03083801e-01 -9.46438253e-01 8.60102177e-01 5.40143669e-01
-1.45882651e-01 1.23119521e+00 2.12511271e-01 -7.95053124e-01
2.81658798e-01 -6.88718855e-01 -1.16004753e+00 -8.31465483e-01
2.84766167e-01 8.77716601e-01 -6.08400144e-02 -5.72972260... | [4.546925067901611, 6.059456825256348] |
6a333742-0c4c-4534-bdcd-783773752a91 | 190107910 | 1901.07910 | null | https://arxiv.org/abs/1901.07910v3 | https://arxiv.org/pdf/1901.07910v3.pdf | NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings | Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed sema... | ['Sushma A. Akoju', 'Oscar J. Romero', 'Ankit Dangi'] | 2019-01-23 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 2.89835632e-01 3.83673877e-01 -5.89065962e-02 -8.78048241e-01
-7.74725556e-01 -7.54801095e-01 1.02675664e+00 5.20745059e-03
-8.66073593e-02 1.20045006e-01 6.76347435e-01 -6.55057728e-01
1.30589992e-01 -8.44247162e-01 -2.96117485e-01 -6.21966179e-03
-9.78578553e-02 6.28447473e-01 4.30219471e-01 -5.57878435... | [9.782556533813477, 7.94589376449585] |
1d22a4dc-d010-4334-bef6-86235def8a55 | rethinking-graph-neural-networks-for-anomaly | 2205.15508 | null | https://arxiv.org/abs/2205.15508v1 | https://arxiv.org/pdf/2205.15508v1.pdf | Rethinking Graph Neural Networks for Anomaly Detection | Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first step towards analyzing anomalies via the lens of the graph spectrum. Our crucial observation is the existence of anomalies will lead to the ... | ['Jia Li', 'Ziqi Gao', 'Jiajin Li', 'Jianheng Tang'] | 2022-05-31 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [-9.77663975e-03 -1.18806064e-01 1.39770105e-01 -6.21615024e-03
-1.82079505e-02 -3.41424555e-01 3.62354606e-01 4.76510078e-01
2.23347664e-01 2.46110588e-01 3.74366134e-01 -5.09076834e-01
-3.44811201e-01 -1.12602270e+00 -4.81255293e-01 -6.98504150e-01
-5.53225040e-01 -3.04519117e-01 2.02745348e-01 -5.51868141... | [6.643148899078369, 5.812422752380371] |
fad39c75-2716-4d91-ab7b-f758d46134dc | probabilistic-deep-learning-using-random-sum | 1806.01910 | null | http://arxiv.org/abs/1806.01910v2 | http://arxiv.org/pdf/1806.01910v2.pdf | Probabilistic Deep Learning using Random Sum-Product Networks | The need for consistent treatment of uncertainty has recently triggered
increased interest in probabilistic deep learning methods. However, most
current approaches have severe limitations when it comes to inference, since
many of these models do not even permit to evaluate exact data likelihoods.
Sum-product networks (... | ['Kristian Kersting', 'Alejandro Molina', 'Martin Trapp', 'Karl Stelzner', 'Robert Peharz', 'Antonio Vergari', 'Zoubin Ghahramani'] | 2018-06-05 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-1.23444065e-01 1.32400677e-01 8.74662772e-03 -5.70943773e-01
-7.19594598e-01 -4.02009904e-01 1.01577830e+00 2.13704184e-01
-3.89940977e-01 1.03972232e+00 -4.54098135e-02 -1.53546721e-01
-3.16142827e-01 -9.08553839e-01 -9.52291727e-01 -9.50170875e-01
7.98326433e-02 9.77105677e-01 1.07610993e-01 2.15076655... | [7.2564239501953125, 3.7992563247680664] |
d40e737a-b607-4d81-bfb8-82a02cd89711 | hallucinated-heartbeats-anomaly-aware-remote | 2303.06452 | null | https://arxiv.org/abs/2303.06452v1 | https://arxiv.org/pdf/2303.06452v1.pdf | Hallucinated Heartbeats: Anomaly-Aware Remote Pulse Estimation | Camera-based physiological monitoring, especially remote photoplethysmography (rPPG), is a promising tool for health diagnostics, and state-of-the-art pulse estimators have shown impressive performance on benchmark datasets. We argue that evaluations of modern solutions may be incomplete, as we uncover failure cases fo... | ['Adam Czajka', 'Patrick Flynn', 'Lu Niu', 'Benjamin Sporrer', 'Nathan Vance', 'Jeremy Speth'] | 2023-03-11 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 1.71704501e-01 4.19090651e-02 3.42986852e-01 -2.20963314e-01
-4.70089942e-01 -2.14496881e-01 1.76401302e-01 -3.44692260e-01
-8.33362043e-02 7.84905493e-01 6.14134297e-02 -2.84606099e-01
4.45078835e-02 -2.31203198e-01 -6.20711923e-01 -9.22769785e-01
-7.33930290e-01 1.06050216e-01 -8.69268253e-02 1.03999883... | [13.810835838317871, 2.72556734085083] |
8a5bd8ee-fff0-4d82-9abc-bf87993a5412 | a-language-independent-and-compositional | 1610.04345 | null | http://arxiv.org/abs/1610.04345v1 | http://arxiv.org/pdf/1610.04345v1.pdf | A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts | Many methods have been used to recognize author personality traits from text,
typically combining linguistic feature engineering with shallow learning
models, e.g. linear regression or Support Vector Machines. This work uses
deep-learning-based models and atomic features of text, the characters, to
build hierarchical, ... | ['Fei Liu', 'Julien Perez', 'Scott Nowson'] | 2016-10-14 | a-language-independent-and-compositional-1 | https://aclanthology.org/E17-1071 | https://aclanthology.org/E17-1071.pdf | eacl-2017-4 | ['personality-trait-recognition'] | ['computer-vision'] | [-3.16248804e-01 1.17558464e-01 -2.92812377e-01 -7.21114218e-01
-3.98982942e-01 -3.20075423e-01 1.04464912e+00 6.26749694e-01
-4.97184008e-01 5.25348663e-01 7.49818325e-01 -1.58739328e-01
-1.59496129e-01 -6.51298642e-01 2.74989784e-01 -1.09600358e-01
-1.57863319e-01 6.62321091e-01 -3.70054722e-01 -3.39917421... | [9.53443717956543, 10.310481071472168] |
3150c44d-b825-4a84-90fe-46ea72465579 | diversity-in-machine-learning | 1807.01477 | null | https://arxiv.org/abs/1807.01477v2 | https://arxiv.org/pdf/1807.01477v2.pdf | Diversity in Machine Learning | Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machine learning system is composed of plentiful training data, a good model training p... | ['Weidong Hu', 'Ping Zhong', 'Zhiqiang Gong'] | 2018-07-04 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 2.39002593e-02 -4.25991446e-01 -6.49738312e-01 -4.12261486e-01
-3.61256182e-01 -2.40889683e-01 2.71551877e-01 -3.05106729e-01
-2.47578397e-02 7.49959290e-01 -1.73559323e-01 -1.88686877e-01
-4.94149983e-01 -8.94014180e-01 -4.74466950e-01 -1.34208977e+00
2.79515475e-01 7.58715451e-01 2.63994157e-01 -8.14183056... | [9.873506546020508, 0.09700153023004532] |
ccbbc147-a3f5-4710-a885-151edb67ba7b | storytrans-non-parallel-story-author-style | 2208.13423 | null | https://arxiv.org/abs/2208.13423v2 | https://arxiv.org/pdf/2208.13423v2.pdf | StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing | Non-parallel text style transfer is an important task in natural language generation. However, previous studies concentrate on the token or sentence level, such as sentence sentiment and formality transfer, but neglect long style transfer at the discourse level. Long texts usually involve more complicated author lingui... | ['Juan Liu', 'Minlie Huang', 'Jian Guan', 'Xuekai Zhu'] | 2022-08-29 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 4.16517258e-01 2.97661722e-01 -2.23638222e-01 -6.26025200e-01
-7.66917467e-01 -7.75043547e-01 9.28513587e-01 -2.08666176e-01
-3.15431476e-01 9.95738268e-01 8.96589935e-01 -1.52148694e-01
5.15031159e-01 -8.95988643e-01 -7.53445685e-01 -4.09102887e-01
7.87569642e-01 3.72384548e-01 -2.28281066e-01 -5.32254398... | [11.709510803222656, 9.464496612548828] |
86adcaad-4a0e-4f8f-8e16-f4a89112c16f | extending-rnn-t-based-speech-recognition | 2207.13965 | null | https://arxiv.org/abs/2207.13965v1 | https://arxiv.org/pdf/2207.13965v1.pdf | Extending RNN-T-based speech recognition systems with emotion and language classification | Speech transcription, emotion recognition, and language identification are usually considered to be three different tasks. Each one requires a different model with a different architecture and training process. We propose using a recurrent neural network transducer (RNN-T)-based speech-to-text (STT) system as a common ... | ['George Saon', 'Samuel Thomas', 'Hong-Kwang Kuo', 'Matheus Damasceno', 'Edmilson Morais', 'Hagai Aronowitz', 'Zvi Kons'] | 2022-07-28 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 1.31978855e-01 -2.94285685e-01 1.43282086e-01 -6.61736071e-01
-9.31977451e-01 -5.04744649e-01 4.88618314e-01 -1.36384934e-01
-4.55594212e-01 1.28019929e-01 2.36333251e-01 -4.45003361e-01
5.47656119e-01 3.94074507e-02 -1.31823555e-01 -5.27415156e-01
3.62035543e-01 4.70209509e-01 -2.34399602e-01 -3.25507849... | [13.899874687194824, 6.207845211029053] |
ddf56dd3-9480-4a9c-bddb-d95cfbb279b7 | native-language-identification-using-large-1 | null | null | https://aclanthology.org/L14-1051 | https://aclanthology.org/L14-1051.pdf | Native Language Identification Using Large, Longitudinal Data | Native Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts. To date, research on NLI has focused on relatively small corpora. We apply NLI to the recently released EFCamDat corpus which is not only multiple times l... | ['Dora Alexopoulou', 'Xiao Jiang', 'Yufan Guo', 'Anna Korhonen', 'Lin Sun', 'Jeroen Geertzen'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['native-language-identification'] | ['natural-language-processing'] | [ 1.90509427e-02 -1.62436888e-01 -9.55156326e-01 -3.75836134e-01
-1.00768495e+00 -7.54384339e-01 8.57934356e-01 4.91578609e-01
-8.13639522e-01 5.67845106e-01 5.33854663e-01 -7.64137447e-01
-1.84904858e-01 -2.20432699e-01 -2.88040638e-01 -1.30776212e-01
9.72522795e-02 4.96973962e-01 1.68666020e-01 -1.97815329... | [10.664031982421875, 10.17415714263916] |
73c02674-8398-434a-964e-60607f1b3ebf | disentangling-propagation-and-generation-for | 1812.00452 | null | https://arxiv.org/abs/1812.00452v2 | https://arxiv.org/pdf/1812.00452v2.pdf | Disentangling Propagation and Generation for Video Prediction | A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated. Prior approaches to video prediction typically learn either to warp or to hallucinate future pixels, but not both. In this paper, we describ... | ['Fisher Yu', 'Ruth Wang', 'Qi-Zhi Cai', 'Hang Gao', 'Huazhe Xu', 'Trevor Darrell'] | 2018-12-02 | disentangling-propagation-and-generation-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Gao_Disentangling_Propagation_and_Generation_for_Video_Prediction_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Gao_Disentangling_Propagation_and_Generation_for_Video_Prediction_ICCV_2019_paper.pdf | iccv-2019-10 | ['predict-future-video-frames'] | ['computer-vision'] | [ 5.59560478e-01 4.34476286e-01 -2.83502787e-01 -4.60801780e-01
-5.28896332e-01 -4.16527778e-01 9.76644218e-01 -2.50291109e-01
-1.32304104e-03 1.09032834e+00 8.47412407e-01 3.71798202e-02
5.78125596e-01 -7.58448422e-01 -1.13314426e+00 -4.98788118e-01
-1.29118934e-01 2.72694677e-01 4.74625796e-01 -5.70533648... | [10.755776405334473, -1.0561455488204956] |
ea7cbaa0-8047-4346-b56d-f8385fe9ef43 | a-study-of-autoregressive-decoders-for-multi | 2303.17376 | null | https://arxiv.org/abs/2303.17376v1 | https://arxiv.org/pdf/2303.17376v1.pdf | A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision | There has been a recent explosion of computer vision models which perform many tasks and are composed of an image encoder (usually a ViT) and an autoregressive decoder (usually a Transformer). However, most of this work simply presents one system and its results, leaving many questions regarding design decisions and tr... | ['Xiaohua Zhai', 'Liang-Chieh Chen', 'Qihang Yu', 'Xiao Wang', 'Emanuele Bugliarello', 'André Susano Pinto', 'Alexander Kolesnikov', 'Andreas Steiner', 'Filip Pavetic', 'Gagan Madan', 'Bo Wan', 'Lucas Beyer'] | 2023-03-30 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 4.76486236e-01 1.86224639e-01 1.49894908e-01 -5.40380061e-01
-1.08139515e+00 -7.10180223e-01 9.77674365e-01 -2.78449237e-01
-5.57597101e-01 1.85967252e-01 4.12021041e-01 -4.06485736e-01
2.93141276e-01 -1.14739232e-01 -1.19536889e+00 -6.68749571e-01
5.85384786e-01 5.74181616e-01 1.91920549e-01 6.34363713... | [10.939334869384766, 1.61617910861969] |
91c989a8-7693-48b1-ad77-2edf77a8a432 | hybridnets-end-to-end-perception-network-1 | 2203.09035 | null | https://arxiv.org/abs/2203.09035v1 | https://arxiv.org/pdf/2203.09035v1.pdf | HybridNets: End-to-End Perception Network | End-to-end Network has become increasingly important in multi-tasking. One prominent example of this is the growing significance of a driving perception system in autonomous driving. This paper systematically studies an end-to-end perception network for multi-tasking and proposes several key optimizations to improve ac... | ['Hung Phan', 'Bao Ngo', 'Dat Vu'] | 2022-03-17 | hybridnets-end-to-end-perception-network | https://arxiv.org/abs/2203.09035 | https://arxiv.org/ftp/arxiv/papers/2203/2203.09035.pdf | null | ['lane-detection', 'drivable-area-detection'] | ['computer-vision', 'computer-vision'] | [-1.87321469e-01 -1.23026185e-01 -4.07413661e-01 -6.54460907e-01
-8.25524390e-01 -4.01003271e-01 1.22286521e-01 -2.06414163e-01
-6.75487041e-01 5.27841866e-01 -2.56892473e-01 -5.82851708e-01
5.60383797e-02 -7.69627512e-01 -8.96033347e-01 -3.75882626e-01
1.88513830e-01 3.20132643e-01 7.31604695e-01 -3.56077552... | [8.053057670593262, -1.3039683103561401] |
06091c2c-7149-4bbb-b3bc-e46fd8ddc7f5 | distributed-feature-selection-for-high | 2205.07932 | null | https://arxiv.org/abs/2205.07932v2 | https://arxiv.org/pdf/2205.07932v2.pdf | DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation | Distributed statistical learning has become a popular technique for large-scale data analysis. Most existing work in this area focuses on dividing the observations, but we propose a new algorithm, DDAC-SpAM, which divides the features under a high-dimensional sparse additive model. Our approach involves three steps: di... | ['Ruiyang Wu', 'Yang Feng', 'Yong Zhou', 'Yifan He'] | 2022-05-16 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.42216519e-01 -4.93908584e-01 -2.25503474e-01 -3.80296290e-01
-1.01805913e+00 -2.33179182e-01 1.74206078e-01 4.57328819e-02
1.78786114e-01 7.72485912e-01 2.50032425e-01 2.37312183e-01
-5.64291954e-01 -5.09385467e-01 -5.69202542e-01 -1.16436803e+00
-3.30250800e-01 2.49144137e-01 -4.76827584e-02 3.30767661... | [7.089601039886475, 4.513828754425049] |
426ed98f-5a65-479f-a698-de3376ce18c7 | non-deterministic-behavior-of-ranking-based | 1806.07171 | null | http://arxiv.org/abs/1806.07171v2 | http://arxiv.org/pdf/1806.07171v2.pdf | Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings | Embedding data into vector spaces is a very popular strategy of pattern
recognition methods. When distances between embeddings are quantized,
performance metrics become ambiguous. In this paper, we present an analysis of
the ambiguity quantized distances introduce and provide bounds on the effect.
We demonstrate that i... | ['Vincent Christlein', 'Anguelos Nicolaou', 'Sounak Dey', 'Andreas Maier', 'Dimosthenis Karatzas'] | 2018-06-19 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 2.08875701e-01 6.63690343e-02 -1.45238414e-01 -3.54070485e-01
-8.24348927e-01 -9.19994235e-01 7.76496470e-01 3.94103765e-01
-8.28015387e-01 5.44228196e-01 -9.01806802e-02 -6.12200022e-01
-1.05785877e-01 -7.74170041e-01 -5.95656574e-01 -7.16400743e-01
-4.10116732e-01 2.99485773e-01 3.84772629e-01 -3.56363684... | [8.227996826171875, 4.149080276489258] |
4cef6651-abac-4cc9-9f6e-478ab188b339 | mcmc-guided-cnn-training-and-segmentation-for | 2003.03938 | null | https://arxiv.org/abs/2003.03938v1 | https://arxiv.org/pdf/2003.03938v1.pdf | MCMC Guided CNN Training and Segmentation for Pancreas Extraction | Efficient organ segmentation is the precondition of various quantitative analysis. Segmenting the pancreas from abdominal CT images is a challenging task because of its high anatomical variability in shape, size and location. What's more, the pancreas only occupies a small portion in abdomen, and the organ border is ve... | ['Chudong Cai', 'Yi Gao', 'Xiaxia Yu', 'Jinchan He'] | 2020-03-09 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-1.22145779e-01 -1.54531002e-01 -1.60302848e-01 -4.35908943e-01
-5.62079668e-01 -3.76962721e-01 1.74327597e-01 4.09067005e-01
-4.07816678e-01 4.86561030e-01 -6.44192174e-02 -4.23448347e-03
4.34417129e-02 -7.56232381e-01 -5.08675992e-01 -1.12899888e+00
-7.69025609e-02 6.87505662e-01 2.80855715e-01 3.85729790... | [14.521695137023926, -2.6666057109832764] |
4c87a80e-ee2c-47b5-9841-f5184360e2ca | ensemble-conformalized-quantile-regression | 2202.08756 | null | https://arxiv.org/abs/2202.08756v2 | https://arxiv.org/pdf/2202.08756v2.pdf | Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting | This paper presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data. EnCQR can be applied on top... | ['Stian Norman Anfinsen', 'Filippo Maria Bianchi', 'Vilde Jensen'] | 2022-02-17 | null | null | null | null | ['prediction-intervals', 'probabilistic-time-series-forecasting'] | ['miscellaneous', 'time-series'] | [-5.23264766e-01 -2.79084742e-01 -2.38016799e-01 -8.22171450e-01
-8.44660819e-01 -5.41551530e-01 5.54574847e-01 -1.64654836e-01
-3.80701050e-02 9.93751585e-01 1.98094249e-01 -6.00299358e-01
-4.71420288e-01 -1.04578245e+00 -7.91255593e-01 -8.30584168e-01
-4.32657987e-01 5.64590394e-01 -2.35463604e-01 -8.15472230... | [7.142037868499756, 3.5163469314575195] |
9affd78d-f5bb-4c50-9656-a186895b0277 | dasha-distributed-nonconvex-optimization-with | 2202.01268 | null | https://arxiv.org/abs/2202.01268v2 | https://arxiv.org/pdf/2202.01268v2.pdf | DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization | We develop and analyze DASHA: a new family of methods for nonconvex distributed optimization problems. When the local functions at the nodes have a finite-sum or an expectation form, our new methods, DASHA-PAGE and DASHA-SYNC-MVR, improve the theoretical oracle and communication complexity of the previous state-of-the-... | ['Peter Richtárik', 'Alexander Tyurin'] | 2022-02-02 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.11669847e-01 1.61486030e-01 -7.47695491e-02 5.57101108e-02
-1.06804454e+00 -8.34290087e-01 -1.46121820e-02 7.33574629e-02
-8.65144372e-01 1.10322011e+00 -4.08173472e-01 -7.35286772e-01
-7.56098330e-01 -8.98969948e-01 -1.05562997e+00 -1.34800816e+00
-7.43665040e-01 2.95307964e-01 -1.09698139e-01 -1.27016991... | [6.373004913330078, 4.621700286865234] |
44a6ac7f-d39e-4965-9694-b5d29a61f0f7 | leveraging-mocap-data-for-human-mesh-recovery | 2110.09243 | null | https://arxiv.org/abs/2110.09243v1 | https://arxiv.org/pdf/2110.09243v1.pdf | Leveraging MoCap Data for Human Mesh Recovery | Training state-of-the-art models for human body pose and shape recovery from images or videos requires datasets with corresponding annotations that are really hard and expensive to obtain. Our goal in this paper is to study whether poses from 3D Motion Capture (MoCap) data can be used to improve image-based and video-b... | ['Grégory Rogez', 'Yannis Kalantidis', 'Romain Brégier', 'Philippe Weinzaepfel', 'Thibault Groueix', 'Fabien Baradel'] | 2021-10-18 | null | null | null | null | ['3d-human-reconstruction', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [-9.10491198e-02 -1.95647940e-01 8.52208585e-03 -2.15556383e-01
-7.66251743e-01 -1.35094985e-01 3.25426817e-01 -5.92253685e-01
-4.20895785e-01 3.37476283e-01 1.68762848e-01 2.73006439e-01
3.20191324e-01 -6.48436546e-01 -1.07461190e+00 -4.15005356e-01
-9.68028232e-02 7.55044460e-01 7.54266381e-01 -5.44885218... | [7.075483798980713, -0.911954402923584] |
8506d3a9-47fe-4ccf-b2ce-0dd1f444cf11 | logical-activation-functions-logit-space-1 | 2110.11940 | null | https://arxiv.org/abs/2110.11940v2 | https://arxiv.org/pdf/2110.11940v2.pdf | Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators | The choice of activation functions and their motivation is a long-standing issue within the neural network community. Neuronal representations within artificial neural networks are commonly understood as logits, representing the log-odds score of presence of features within the stimulus. We derive logit-space operators... | ['Sageev Oore', 'Thomas Trappenberg', "Jason d'Eon", 'Robert Earle', 'Scott C. Lowe'] | 2021-10-22 | logical-activation-functions-logit-space | https://openreview.net/forum?id=Ck_iw4jMC4l | https://openreview.net/pdf?id=Ck_iw4jMC4l | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 4.91023183e-01 -9.36624706e-02 1.96476594e-01 -4.82361794e-01
-2.20950067e-01 -6.40112400e-01 5.95509470e-01 4.49698299e-01
-8.64981472e-01 8.19063902e-01 -4.05837238e-01 -6.44110918e-01
-5.43203533e-01 -1.20370197e+00 -9.74111736e-01 -8.61163855e-01
-4.87898409e-01 1.51300684e-01 3.06254834e-01 -1.90961212... | [8.423966407775879, 3.172776460647583] |
bf134129-bf45-44ba-a7fc-c73971436b5a | avtpnet-convolutional-autoencoder-for-avtp | 2202.00045 | null | https://arxiv.org/abs/2202.00045v2 | https://arxiv.org/pdf/2202.00045v2.pdf | Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks | Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep lear... | ['Jean-Luc Danger', 'Hadi Ghauch', 'Maria Mushtaq', 'Natasha Alkhatib'] | 2022-01-31 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-2.46454716e-01 -3.18898648e-01 -2.40030661e-02 -3.89015704e-01
6.17236085e-02 -2.97569185e-01 7.88303733e-01 4.68463600e-01
-4.02767181e-01 5.96397400e-01 -6.39600098e-01 -1.01215434e+00
-4.11423832e-01 -7.77390480e-01 -5.82944632e-01 -7.21994817e-01
-4.90612149e-01 6.61274970e-01 5.98217666e-01 -4.75850314... | [5.259235858917236, 7.279350280761719] |
3f9fb7aa-1061-4991-aaa8-cbf8c37098a4 | ischemic-stroke-lesion-segmentation-using | 2204.04993 | null | https://arxiv.org/abs/2204.04993v1 | https://arxiv.org/pdf/2204.04993v1.pdf | Ischemic Stroke Lesion Segmentation Using Adversarial Learning | Ischemic stroke occurs through a blockage of clogged blood vessels supplying blood to the brain. Segmentation of the stroke lesion is vital to improve diagnosis, outcome assessment and treatment planning. In this work, we propose a segmentation model with adversarial learning for ischemic lesion segmentation. We adopt ... | ['Hongliang Ren', 'V Jeya Maria Jose', 'N Rajiv Vaidyanathan', 'Mobarakol Islam'] | 2022-04-11 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 3.24366689e-01 3.05767000e-01 -1.43295810e-01 -5.90127468e-01
-7.85382092e-01 -6.92793489e-01 2.03683242e-01 -1.74287006e-01
-7.05057621e-01 9.65194106e-01 3.47056836e-01 -5.65199852e-01
3.83068949e-01 -1.03695560e+00 -7.45206892e-01 -6.36096895e-01
-2.29686216e-01 5.51171780e-01 5.42452812e-01 3.28471214... | [14.24228572845459, -2.0783257484436035] |
1c508d28-dc9b-40b6-934d-97860a2500b2 | viseret-a-simple-yet-effective-approach-to | 2110.05146 | null | https://arxiv.org/abs/2110.05146v2 | https://arxiv.org/pdf/2110.05146v2.pdf | ViSeRet: A simple yet effective approach to moment retrieval via fine-grained video segmentation | Video-text retrieval has many real-world applications such as media analytics, surveillance, and robotics. This paper presents the 1st place solution to the video retrieval track of the ICCV VALUE Challenge 2021. We present a simple yet effective approach to jointly tackle two video-text retrieval tasks (video retrieva... | ['Minjoon Seo', 'Hanseok Oh', 'Aiden Seungjoon Lee'] | 2021-10-11 | null | null | null | null | ['moment-retrieval', 'video-text-retrieval'] | ['computer-vision', 'computer-vision'] | [-1.00337565e-01 -8.95182610e-01 -4.76420164e-01 1.49433557e-02
-1.17881906e+00 -4.94747132e-01 1.03132808e+00 -1.32847711e-01
-6.62491798e-01 1.50766939e-01 3.47811878e-01 8.24973825e-03
-2.43793070e-01 -1.98909909e-01 -5.09673178e-01 -3.56999874e-01
-3.99800390e-02 1.63468987e-01 5.31930089e-01 -3.38348866... | [10.382040023803711, 0.836878776550293] |
e8cd254a-cadb-45b0-8760-e09446ce9be6 | ssn-mlrg1-dravidianlangtech-acl2022-troll | null | null | https://aclanthology.org/2022.dravidianlangtech-1.21 | https://aclanthology.org/2022.dravidianlangtech-1.21.pdf | SSN_MLRG1@DravidianLangTech-ACL2022: Troll Meme Classification in Tamil using Transformer Models | The ACL shared task of DravidianLangTech-2022 for Troll Meme classification is a binary classification task that involves identifying Tamil memes as troll or not-troll. Classification of memes is a challenging task since memes express humour and sarcasm in an implicit way. Team SSN_MLRG1 tested and compared results obt... | ['Angel S', 'Rajalakshmi Sivanaiah', 'Saritha Madhavan', 'Sarika Esackimuthu', 'Shruthi Hariprasad'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['meme-classification'] | ['natural-language-processing'] | [-5.58252931e-01 -5.07808268e-01 4.60443236e-02 1.19947188e-01
-5.56283593e-01 -4.27035123e-01 1.03092313e+00 4.67117399e-01
-9.11704183e-01 1.23014915e+00 4.27012533e-01 -2.15839565e-01
1.09873101e-01 -5.57968616e-01 -3.08820903e-01 -2.78575361e-01
1.83956549e-01 5.56870639e-01 3.12130541e-01 -6.54194832... | [8.634638786315918, 10.847969055175781] |
94977595-3adf-48f9-9cb3-f36bce03bef9 | natcs-eliciting-natural-customer-support | 2305.03007 | null | https://arxiv.org/abs/2305.03007v1 | https://arxiv.org/pdf/2305.03007v1.pdf | NatCS: Eliciting Natural Customer Support Dialogues | Despite growing interest in applications based on natural customer support conversations, there exist remarkably few publicly available datasets that reflect the expected characteristics of conversations in these settings. Existing task-oriented dialogue datasets, which were collected to benchmark dialogue systems main... | ['Saab Mansour', 'Yi Zhang', 'Arshit Gupta', 'Wesley Rose', 'Emily Moeng', 'James Gung'] | 2023-05-04 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 9.50098932e-02 6.28223062e-01 3.88329215e-02 -8.55658770e-01
-7.18623757e-01 -8.60581160e-01 1.42263341e+00 -1.57012284e-01
-2.37732634e-01 1.04711509e+00 8.55938315e-01 -3.76625508e-01
3.99687320e-01 -4.40943688e-01 8.13444927e-02 -1.45728961e-01
1.47110477e-01 1.50669932e+00 5.73231131e-02 -9.44089055... | [12.833279609680176, 8.039114952087402] |
d2567f19-6969-4656-9582-08cd20818e34 | adapt-and-align-to-improve-zero-shot-sketch | 2305.05144 | null | https://arxiv.org/abs/2305.05144v2 | https://arxiv.org/pdf/2305.05144v2.pdf | Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval | Zero-shot sketch-based image retrieval (ZS-SBIR) is challenging due to the cross-domain nature of sketches and photos, as well as the semantic gap between seen and unseen image distributions. Previous methods fine-tune pre-trained models with various side information and learning strategies to learn a compact feature s... | ['Xinbo Gao', 'Heng Yang', 'Nannan Wang', 'Mingrui Zhu', 'Shiyin Dong'] | 2023-05-09 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.31830323e-01 -4.36066180e-01 -4.08892721e-01 -4.19992864e-01
-9.43283975e-01 -9.55060184e-01 8.90815973e-01 -2.37357318e-01
-7.61425495e-02 4.35137033e-01 3.97570044e-01 3.47295731e-01
-2.48414889e-01 -7.19606936e-01 -6.80180371e-01 -4.75639969e-01
4.31913793e-01 4.82437283e-01 2.30059385e-01 -2.51510262... | [11.561333656311035, 0.6827563047409058] |
9fa456dd-be6b-47c7-b72a-6a57e0e7af8c | automatic-photo-adjustment-using-deep-neural | 1412.7725 | null | http://arxiv.org/abs/1412.7725v2 | http://arxiv.org/pdf/1412.7725v2.pdf | Automatic Photo Adjustment Using Deep Neural Networks | Photo retouching enables photographers to invoke dramatic visual impressions
by artistically enhancing their photos through stylistic color and tone
adjustments. However, it is also a time-consuming and challenging task that
requires advanced skills beyond the abilities of casual photographers. Using an
automated algor... | ['Hao Zhang', 'Zhicheng Yan', 'Yizhou Yu', 'Sylvain Paris', 'Baoyuan Wang'] | 2014-12-24 | null | null | null | null | ['photo-retouching'] | ['computer-vision'] | [ 4.65152681e-01 -2.21349508e-01 8.40989202e-02 -4.36801523e-01
-1.60274267e-01 -8.01726341e-01 8.05536926e-01 -1.11033417e-01
-3.83226156e-01 5.69671988e-01 5.30140400e-02 -4.50998247e-02
1.68993603e-03 -5.83714008e-01 -6.57975614e-01 -5.33511758e-01
4.58324492e-01 2.53390998e-01 1.44212052e-01 -5.01355946... | [11.41006851196289, -0.6946847438812256] |
aeef3325-f14b-421b-96e9-66caa212c56d | physically-primed-deep-neural-networks-for | 2209.00462 | null | https://arxiv.org/abs/2209.00462v1 | https://arxiv.org/pdf/2209.00462v1.pdf | Physically-primed deep-neural-networks for generalized undersampled MRI reconstruction | A plethora of deep-neural-networks (DNN) based methods were proposed over the past few years to address the challenging ill-posed inverse problem of MRI reconstruction from undersampled "k-space" (Fourier domain) data. However, instability against variations in the acquisition process and the anatomical distribution, i... | ['Moti Freiman', 'Nitzan Avidan'] | 2022-08-31 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 4.77852315e-01 2.30958924e-01 2.23268956e-01 -4.08532679e-01
-4.29478258e-01 -1.39507458e-01 5.21254122e-01 -4.64383453e-01
-6.10114455e-01 7.26313889e-01 2.14610115e-01 -3.62257361e-01
-6.24391377e-01 -3.97085637e-01 -7.74648130e-01 -8.30068588e-01
-4.64666307e-01 4.67582822e-01 1.48647711e-01 -3.28560442... | [13.533040046691895, -2.4218926429748535] |
8e653daf-8a28-41dd-9b14-7d6162766c37 | highly-accurate-dichotomous-image | 2203.03041 | null | https://arxiv.org/abs/2203.03041v4 | https://arxiv.org/pdf/2203.03041v4.pdf | Highly Accurate Dichotomous Image Segmentation | We present a systematic study on a new task called dichotomous image segmentation (DIS) , which aims to segment highly accurate objects from natural images. To this end, we collected the first large-scale DIS dataset, called DIS5K, which contains 5,470 high-resolution (e.g., 2K, 4K or larger) images covering camouflage... | ['and Luc Van Gool', 'Ling Shao', 'Deng-Ping Fan', 'Xiaobin Hu', 'Hang Dai', 'Xuebin Qin'] | 2022-03-06 | null | null | null | null | ['dichotomous-image-segmentation'] | ['computer-vision'] | [ 4.38223243e-01 -8.44170973e-02 -3.04335833e-01 -2.41055101e-01
-7.96647012e-01 -5.31515956e-01 2.79603601e-01 -4.12109375e-01
-3.40480477e-01 6.01431429e-01 -1.38121441e-01 -2.71442264e-01
2.38320053e-01 -5.18844783e-01 -1.00897133e+00 -6.59183800e-01
2.47205943e-01 2.80029178e-01 5.83899796e-01 1.74851641... | [9.630363464355469, -0.10245904326438904] |
334087a0-348a-4a48-a4d9-2b65cb50fa8f | bilex-rx-lexical-data-augmentation-for | 2303.15265 | null | https://arxiv.org/abs/2303.15265v1 | https://arxiv.org/pdf/2303.15265v1.pdf | Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation | Neural machine translation (NMT) has progressed rapidly over the past several years, and modern models are able to achieve relatively high quality using only monolingual text data, an approach dubbed Unsupervised Machine Translation (UNMT). However, these models still struggle in a variety of ways, including aspects of... | ['Orhan Firat', 'Ishank Saxena', 'Isaac Caswell', 'Alex Jones'] | 2023-03-27 | null | null | null | null | ['nmt', 'unsupervised-machine-translation'] | ['computer-code', 'natural-language-processing'] | [ 5.71023114e-02 -1.86274111e-01 -5.60949385e-01 -1.81438699e-01
-1.49269962e+00 -9.64303076e-01 8.67684424e-01 8.83834288e-02
-4.92375433e-01 1.09255040e+00 5.62337041e-01 -7.57546067e-01
3.87625784e-01 -5.27807355e-01 -9.10218537e-01 -3.31080437e-01
2.72672057e-01 9.03440237e-01 -3.47103357e-01 -6.48731768... | [11.513226509094238, 10.301836013793945] |
6d6be8a0-2da2-4f09-b69c-e00ba27f40b6 | making-the-invisible-visible-action | 1909.09300 | null | https://arxiv.org/abs/1909.09300v1 | https://arxiv.org/pdf/1909.09300v1.pdf | Making the Invisible Visible: Action Recognition Through Walls and Occlusions | Understanding people's actions and interactions typically depends on seeing them. Automating the process of action recognition from visual data has been the topic of much research in the computer vision community. But what if it is too dark, or if the person is occluded or behind a wall? In this paper, we introduce a n... | ['Ming-Min Zhao', 'Lijie Fan', 'Tianhong Li', 'Yingcheng Liu', 'Dina Katabi'] | 2019-09-20 | making-the-invisible-visible-action-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Making_the_Invisible_Visible_Action_Recognition_Through_Walls_and_Occlusions_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Making_the_Invisible_Visible_Action_Recognition_Through_Walls_and_Occlusions_ICCV_2019_paper.pdf | iccv-2019-10 | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 6.99339390e-01 -3.02512925e-02 -1.10696837e-01 -2.80698866e-01
-2.20215231e-01 -3.61554027e-01 6.12692475e-01 -4.71499354e-01
-4.74185497e-01 5.11537135e-01 5.39130569e-01 -1.81614801e-01
3.20675403e-01 -6.76688790e-01 -4.15521532e-01 -4.54455733e-01
1.74148217e-01 2.38523290e-01 4.29956228e-01 5.06340265... | [8.05561351776123, 0.3702184855937958] |
9618451a-e878-4e49-944b-78bb4e1d2f9b | constraint-translation-candidates-a-bridge | 2010.13658 | null | https://arxiv.org/abs/2010.13658v1 | https://arxiv.org/pdf/2010.13658v1.pdf | Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval | Query translation (QT) is a key component in cross-lingual information retrieval system (CLIR). With the help of deep learning, neural machine translation (NMT) has shown promising results on various tasks. However, NMT is generally trained with large-scale out-of-domain data rather than in-domain query translation pai... | ['Boxing Chen', 'Weihua Luo', 'Haibo Zhang', 'Baosong Yang', 'Liang Yao', 'Tianchi Bi'] | 2020-10-26 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 2.29111180e-01 -3.52958679e-01 -6.62216723e-01 -2.14742109e-01
-1.68845642e+00 -6.83099627e-01 7.09911466e-01 -1.77261844e-01
-7.03605413e-01 7.55205154e-01 3.02570373e-01 -5.14458299e-01
-7.71632269e-02 -7.41392732e-01 -6.51367605e-01 -5.05888164e-01
6.20719850e-01 1.07087326e+00 1.48827927e-02 -6.31412685... | [11.565766334533691, 10.009098052978516] |
e314bd0c-23e6-4ac2-91df-cbed759b1578 | temporal-roi-align-for-video-object | 2109.03495 | null | https://arxiv.org/abs/2109.03495v2 | https://arxiv.org/pdf/2109.03495v2.pdf | Temporal RoI Align for Video Object Recognition | Video object detection is challenging in the presence of appearance deterioration in certain video frames. Therefore, it is a natural choice to aggregate temporal information from other frames of the same video into the current frame. However, RoI Align, as one of the most core procedures of video detectors, still rema... | ['Huamin Feng', 'Nenghai Yu', 'Dahua Lin', 'Feng Zhu', 'Qi Chu', 'Xinjiang Wang', 'Kai Chen', 'Tao Gong'] | 2021-09-08 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.11476237e-02 -5.15159905e-01 -4.13302749e-01 -2.93269694e-01
-4.93814081e-01 -4.73175615e-01 1.53255731e-01 -2.01954674e-02
-4.79865491e-01 3.16867769e-01 -1.23187862e-01 1.50612429e-01
9.99784097e-02 -5.04652560e-01 -6.04694545e-01 -7.30694354e-01
4.08263020e-02 -3.83731037e-01 1.11093223e+00 1.37771562... | [9.143132209777832, -0.29532381892204285] |
283be3e4-9c2b-49a2-a945-c5d7155e5e00 | hybrid-indoor-localization-via-reinforcement | 2210.15132 | null | https://arxiv.org/abs/2210.15132v1 | https://arxiv.org/pdf/2210.15132v1.pdf | Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion | The paper is motivated by the importance of the Smart Cities (SC) concept for future management of global urbanization. Among all Internet of Things (IoT)-based communication technologies, Bluetooth Low Energy (BLE) plays a vital role in city-wide decision making and services. Extreme fluctuations of the Received Signa... | ['Arash Mohammadi', 'Mohammad Salimibeni'] | 2022-10-27 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-2.14126810e-01 -4.12540644e-01 -2.00806230e-01 -1.28989816e-01
-6.29471600e-01 -4.60476428e-02 7.97879815e-01 2.28938252e-01
-5.44105232e-01 1.36462653e+00 1.57823935e-01 -5.00018179e-01
-5.46597064e-01 -1.34976423e+00 -3.76813769e-01 -9.85944808e-01
-1.71762947e-02 2.39472508e-01 3.05796593e-01 -4.41974044... | [6.342438220977783, 1.0813220739364624] |
f69a8b2d-dc44-45c1-83a5-dac14d714ffb | a-negation-detection-assessment-of-gpts | 2306.16638 | null | https://arxiv.org/abs/2306.16638v1 | https://arxiv.org/pdf/2306.16638v1.pdf | A negation detection assessment of GPTs: analysis with the xNot360 dataset | Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension. Our study assesses the negation detection performance of Generative Pre-trained Transformer (GPT) models, specifically GPT-2, GPT-3, GPT-3.5, and GPT-4. We focus on the identification of negation in natural... | ['Ken Satoh', 'Kostas Stathis', 'Francesca Toni', 'Randy Goebel', 'Ha Thanh Nguyen'] | 2023-06-29 | null | null | null | null | ['negation-detection', 'natural-language-understanding'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.36768591e-01 8.86193216e-01 -3.55475806e-02 -4.68145847e-01
-7.76029527e-01 -6.43586278e-01 6.39752746e-01 7.09176242e-01
-5.10813236e-01 7.21058071e-01 6.15426362e-01 -8.84976327e-01
-9.20518786e-02 -8.77147615e-01 -6.96684420e-01 1.07959904e-01
2.21968308e-01 5.00423789e-01 -5.66429608e-02 -9.48765516... | [10.108912467956543, 8.129762649536133] |
3d552716-f55f-46bb-9a78-9dca645c397e | what-my-motion-tells-me-about-your-pose-self | 2007.14812 | null | https://arxiv.org/abs/2007.14812v2 | https://arxiv.org/pdf/2007.14812v2.pdf | What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector | The estimation of the orientation of an observed vehicle relative to an Autonomous Vehicle (AV) from monocular camera data is an important building block in estimating its 6 DoF pose. Current Deep Learning based solutions for placing a 3D bounding box around this observed vehicle are data hungry and do not generalize w... | ['Cédric Picron', 'Tinne Tuytelaars', 'Punarjay Chakravarty', 'Tom Roussel'] | 2020-07-29 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-4.24653053e-01 2.39889428e-01 -2.39501044e-01 -6.69930637e-01
-6.31578863e-01 -9.98712361e-01 6.37158334e-01 -3.09321672e-01
-4.54704672e-01 2.13004962e-01 -3.08575720e-01 -5.10193646e-01
6.59006596e-01 -3.88298780e-01 -1.36264837e+00 -3.65404725e-01
7.86575601e-02 9.68761146e-01 4.01080281e-01 -1.68123338... | [7.897592544555664, -2.2931013107299805] |
877b9ad0-35d6-4afe-aa86-671446965844 | collection-and-validation-of | 2011.00958 | null | https://arxiv.org/abs/2011.00958v2 | https://arxiv.org/pdf/2011.00958v2.pdf | Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset | Proper training and analytics in eSports require accurately collected and annotated data. Most eSports research focuses exclusively on in-game data analysis, and there is a lack of prior work involving eSports athletes' psychophysiological data. In this paper, we present a dataset collected from professional and amateu... | ['Andrey Somov', 'Paul Lukowicz', 'Bo Zhou', 'Anton Smerdov'] | 2020-11-02 | null | null | null | null | ['physiological-computing', 'sensor-modeling', 'skills-evaluation', 'skills-assessment', 'real-time-strategy-games'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'playing-games'] | [-2.31220990e-01 -1.29955485e-01 -2.93593794e-01 -1.46098062e-01
-4.86447692e-01 -6.35594249e-01 -2.57721901e-01 6.32292747e-01
-7.34088480e-01 6.98168278e-01 -7.38413706e-02 3.52040052e-01
-2.77960777e-01 -5.66910446e-01 -5.94584107e-01 -3.37335914e-01
-2.03924015e-01 2.93572903e-01 4.42535847e-01 -5.45782030... | [6.85168981552124, 0.37554827332496643] |
1ef2ef44-3f27-4ac1-bf56-a403affdcd71 | implicit-dual-domain-convolutional-network | 1810.08042 | null | https://arxiv.org/abs/1810.08042v3 | https://arxiv.org/pdf/1810.08042v3.pdf | Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction | Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they suffer from handling color images because the compression processes for gray-scale and color images are completely different. Moreover, these methods train a specific model ... | ['Xuesong Liu', 'Yaowu Chen', 'Xiang Tian', 'Fan Zhou', 'Bolun Zheng'] | 2018-10-18 | null | null | null | null | ['color-image-compression-artifact-reduction', 'jpeg-artifact-correction', 'image-compression-artifact-reduction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.20552135e-01 -7.63793886e-01 -2.45475009e-01 -2.90682822e-01
-6.86411679e-01 -1.49371609e-01 3.43956441e-01 -2.91108966e-01
-4.75695878e-01 2.50707358e-01 1.17211670e-01 -1.67562187e-01
1.23436959e-03 -1.00564635e+00 -7.03569174e-01 -6.24308407e-01
1.35219365e-01 -6.55252486e-02 1.99487060e-01 -2.73381352... | [11.320850372314453, -1.6525719165802002] |
25ec00e0-7e4b-405f-baba-35fe35a897b0 | can-machine-learning-discover-the-determining | 2212.03092 | null | https://arxiv.org/abs/2212.03092v3 | https://arxiv.org/pdf/2212.03092v3.pdf | Can Machine Learning discover the determining factors in participation in insurance schemes? A comparative analysis | Identifying factors that affect participation is key to a successful insurance scheme. This study's challenges involve using many factors that could affect insurance participation to make a better forecast.Huge numbers of factors affect participation, making evaluation difficult. These interrelated factors can mask the... | ['Simone Severini', 'Luigi Biagini'] | 2022-12-06 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 3.33566874e-01 -9.83567256e-03 -1.17513800e+00 -4.02661294e-01
-2.35030919e-01 -2.79288411e-01 -3.52208912e-02 7.27644563e-02
-7.08015338e-02 8.83922160e-01 5.26077032e-01 -1.06442904e+00
-2.62724280e-01 -1.11808205e+00 -9.53429103e-01 -4.52075303e-01
-1.32801384e-02 1.07616596e-01 -4.15959507e-01 -5.79835892... | [7.818584442138672, 5.032001972198486] |
9e42e065-e235-498c-9b62-4c1837af519a | visual-exploration-and-knowledge-discovery | 2009.13059 | null | https://arxiv.org/abs/2009.13059v1 | https://arxiv.org/pdf/2009.13059v1.pdf | Visual Exploration and Knowledge Discovery from Biomedical Dark Data | Data visualization techniques proffer efficient means to organize and present data in graphically appealing formats, which not only speeds up the process of decision making and pattern recognition but also enables decision-makers to fully understand data insights and make informed decisions. Over time, with the rise in... | ['Shashwat Aggarwal', 'Ramesh Singh'] | 2020-09-28 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [ 1.43152068e-03 3.02229505e-02 -2.31304973e-01 7.55034313e-02
1.30708925e-02 -6.54021978e-01 5.50275743e-01 1.32910466e+00
-3.38229328e-01 5.95694244e-01 4.67156857e-01 -8.42363775e-01
-4.22503889e-01 -8.54803741e-01 2.18922906e-02 -4.11052346e-01
-2.05332890e-01 1.70946985e-01 1.69499665e-01 -7.18232617... | [9.560006141662598, 7.948827743530273] |
3dc116b8-cc1f-4b66-a2b6-206b5c381519 | monoise-modeling-noise-using-a-modular | 1710.03476 | null | http://arxiv.org/abs/1710.03476v1 | http://arxiv.org/pdf/1710.03476v1.pdf | MoNoise: Modeling Noise Using a Modular Normalization System | We propose MoNoise: a normalization model focused on generalizability and
efficiency, it aims at being easily reusable and adaptable. Normalization is
the task of translating texts from a non- canonical domain to a more canonical
domain, in our case: from social media data to standard language. Our proposed
model is ba... | ['Rob van der Goot', 'Gertjan van Noord'] | 2017-10-10 | null | null | null | null | ['lexical-normalization'] | ['natural-language-processing'] | [ 3.16476852e-01 1.93293408e-01 -3.39940488e-01 -3.14578980e-01
-6.46463215e-01 -8.10657024e-01 1.04672647e+00 7.83172905e-01
-8.32937360e-01 5.65879405e-01 5.49129248e-01 -1.60470784e-01
-1.42544627e-01 -9.58325744e-01 -4.83471930e-01 -5.43165922e-01
3.02683353e-01 8.55854213e-01 4.33167845e-01 -8.63849103... | [10.260762214660645, 9.85561752319336] |
dc1dc959-6bec-4f97-ba5b-e2b090ff12bf | coherent-online-video-style-transfer | 1703.09211 | null | http://arxiv.org/abs/1703.09211v2 | http://arxiv.org/pdf/1703.09211v2.pdf | Coherent Online Video Style Transfer | Training a feed-forward network for fast neural style transfer of images is
proven to be successful. However, the naive extension to process video frame by
frame is prone to producing flickering results. We propose the first end-to-end
network for online video style transfer, which generates temporally coherent
stylize... | ['Dongdong Chen', 'Jing Liao', 'Nenghai Yu', 'Lu Yuan', 'Gang Hua'] | 2017-03-27 | coherent-online-video-style-transfer-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Chen_Coherent_Online_Video_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Coherent_Online_Video_ICCV_2017_paper.pdf | iccv-2017-10 | ['video-style-transfer', 'image-stylization'] | ['computer-vision', 'computer-vision'] | [ 1.98700443e-01 -1.06331825e-01 5.54477647e-02 -3.14932406e-01
-7.57707953e-01 -6.46706939e-01 7.33609140e-01 -6.75984740e-01
-3.62102509e-01 9.71427321e-01 4.95712578e-01 -2.16081947e-01
4.98510987e-01 -6.27970815e-01 -1.14165139e+00 -4.90055799e-01
5.20205013e-02 4.24257368e-02 2.98484623e-01 -9.27066654... | [10.997218132019043, -0.8071110844612122] |
963b909e-3655-4b4a-82ed-8fb1b4fcd319 | quantum-contextual-bandits-and-recommender | 2301.13524 | null | https://arxiv.org/abs/2301.13524v1 | https://arxiv.org/pdf/2301.13524v1.pdf | Quantum contextual bandits and recommender systems for quantum data | We study a recommender system for quantum data using the linear contextual bandit framework. In each round, a learner receives an observable (the context) and has to recommend from a finite set of unknown quantum states (the actions) which one to measure. The learner has the goal of maximizing the reward in each round,... | ['Marco Tomamichel', 'Josep Lumbreras', 'Shrigyan Brahmachari'] | 2023-01-31 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.92077780e-01 2.83990979e-01 -4.53476816e-01 -2.33841464e-01
-5.26196718e-01 -4.15786743e-01 6.47031426e-01 1.99666858e-01
-4.63962793e-01 5.05472183e-01 2.72554696e-01 -4.17672634e-01
-5.39193392e-01 -1.22734523e+00 -7.74201274e-01 -1.29000962e+00
3.84291977e-01 7.57491708e-01 -1.44700661e-01 -2.56660372... | [5.630544185638428, 4.9417338371276855] |
0952acb7-df08-4a36-9ce5-36f9ec9a9a80 | semantic-parsing-for-text-to-3d-scene | null | null | https://aclanthology.org/W14-2404 | https://aclanthology.org/W14-2404.pdf | Semantic Parsing for Text to 3D Scene Generation | null | ['Angel Chang', 'Christopher Manning', 'Manolis Savva'] | 2014-06-01 | null | null | null | ws-2014-6 | ['scene-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.259825229644775, 3.746004104614258] |
ea2ac90b-3533-492f-9ec5-b9260a30ad92 | new-students-on-sesame-street-what-order | 2109.08449 | null | https://arxiv.org/abs/2109.08449v2 | https://arxiv.org/pdf/2109.08449v2.pdf | General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings | Large pretrained language models (PreLMs) are revolutionizing natural language processing across all benchmarks. However, their sheer size is prohibitive for small laboratories or for deployment on mobile devices. Approaches like pruning and distillation reduce the model size but typically retain the same model archite... | ['Ansgar Scherp', 'Angelina Sonderecker', 'Henrik Ferdinand Nölscher', 'Christoph Meyer', 'Isabelle Cuber', 'Lukas Galke'] | 2021-09-17 | null | null | null | null | ['linguistic-acceptability', 'question-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.62670180e-01 2.57304639e-01 -6.09633327e-02 -6.83041334e-01
-9.77143645e-01 -7.17292249e-01 5.75005889e-01 6.74484074e-01
-1.07331204e+00 2.11205408e-01 3.82546961e-01 -9.69314039e-01
3.29551727e-01 -7.67178297e-01 -7.21405745e-01 -1.81882292e-01
1.37475237e-01 5.76295435e-01 -8.56085345e-02 -6.45561218... | [10.790111541748047, 8.714434623718262] |
17ab0007-3d1a-4075-ae99-e26935d72079 | learning-latent-representations-in-neural | 1802.03063 | null | http://arxiv.org/abs/1802.03063v1 | http://arxiv.org/pdf/1802.03063v1.pdf | Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization | In this paper, we propose a novel unsupervised clustering approach exploiting
the hidden information that is indirectly introduced through a pseudo
classification objective. Specifically, we randomly assign a pseudo
parent-class label to each observation which is then modified by applying the
domain specific transforma... | ['Ozsel Kilinc', 'Ismail Uysal'] | 2018-02-08 | learning-latent-representations-in-neural-1 | https://openreview.net/forum?id=HkMvEOlAb | https://openreview.net/pdf?id=HkMvEOlAb | iclr-2018-1 | ['unsupervised-image-classification'] | ['computer-vision'] | [ 6.21617079e-01 4.48621571e-01 -4.76675600e-01 -6.68099344e-01
-6.35904312e-01 -3.56228858e-01 7.44309783e-01 3.27367276e-01
-3.49389017e-01 5.09383678e-01 3.74039710e-02 2.27105856e-01
-3.03577870e-01 -6.00280881e-01 -7.76126802e-01 -1.17330635e+00
-1.36682972e-01 6.40168548e-01 -4.27228361e-02 5.73666096... | [9.321417808532715, 3.1222541332244873] |
ef8d62d3-5c77-4ddf-a451-cc883766758a | reference-based-image-super-resolution-with | 2207.11938 | null | https://arxiv.org/abs/2207.11938v2 | https://arxiv.org/pdf/2207.11938v2.pdf | Reference-based Image Super-Resolution with Deformable Attention Transformer | Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images. Recently, RefSR has been attracting great attention as it provides an alternative way to surpass single image SR. However, addressing the RefSR problem has two critical challenges... | ['Luc van Gool', 'Wenguan Wang', 'Yulun Zhang', 'Yawei Li', 'Kai Zhang', 'Jingyun Liang', 'JieZhang Cao'] | 2022-07-25 | null | null | null | null | ['reference-based-super-resolution'] | ['computer-vision'] | [ 7.04618335e-01 -2.66427368e-01 3.50741297e-03 -3.75205129e-01
-1.21299100e+00 -2.27556288e-01 4.02790606e-01 -6.33452475e-01
-8.74541700e-04 7.56042778e-01 2.94172704e-01 2.28818744e-01
-1.39055684e-01 -7.29154587e-01 -7.79054046e-01 -9.83719110e-01
5.67517698e-01 -3.85072008e-02 5.86373568e-01 -5.75173974... | [10.99864673614502, -2.059796094894409] |
a18c714b-2d1b-40f2-b7ac-4a51706fa2aa | current-trends-in-deep-learning-for-earth | 2207.07189 | null | https://arxiv.org/abs/2207.07189v2 | https://arxiv.org/pdf/2207.07189v2.pdf | Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification | We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures... | ['Nikola Simidjievski', 'Dragi Kocev', 'Ivan Kitanovski', 'Ivica Dimitrovski'] | 2022-07-14 | null | null | null | null | ['satellite-image-classification', 'remote-sensing-image-classification'] | ['computer-vision', 'miscellaneous'] | [ 1.95506424e-01 -4.53625679e-01 1.13077879e-01 -6.92642570e-01
-7.31730282e-01 -6.37699127e-01 6.45256221e-01 3.10251713e-01
-5.53065181e-01 5.79914808e-01 -2.32757881e-01 -3.98744851e-01
-3.21648121e-01 -7.32265294e-01 -5.99195659e-01 -1.03764451e+00
-6.56118631e-01 5.68810999e-01 -1.49984971e-01 -1.90579742... | [9.534760475158691, -1.4226665496826172] |
2515f787-658c-4015-a18b-5e57203516b5 | generating-scenes-with-latent-object-models | null | null | https://openreview.net/forum?id=WTXMNULQ3Uu | https://openreview.net/pdf?id=WTXMNULQ3Uu | Generating Scenes with Latent Object Models | We introduce a structured latent variable model that learns the underlying data-generating process for a dataset of scenes. Our goals are to obtain a compositional scene representation and to perform scene generation by modeling statistical relationships between scenes as well as between objects within a scene. To make... | ['Anand Rangarajan', 'Sanjay Ranka', 'Pan He', 'Patrick Emami'] | 2021-09-29 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 5.30800104e-01 3.00202638e-01 -1.01448961e-01 -4.81212735e-01
-6.22165143e-01 -6.98451221e-01 1.09231198e+00 4.78005297e-02
1.16641343e-01 3.30387414e-01 4.53307509e-01 -2.51947194e-01
-2.43032560e-01 -1.09858906e+00 -9.84588206e-01 -6.61095679e-01
1.08815446e-01 8.68587077e-01 1.05312802e-01 1.37375474... | [10.285720825195312, 0.21697717905044556] |
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