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d1f53c71-0c0d-4f3d-b7db-fbba6d62ca93 | multi-level-representation-learning-with | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_Multi-Level_Representation_Learning_With_Semantic_Alignment_for_Referring_Video_Object_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_Multi-Level_Representation_Learning_With_Semantic_Alignment_for_Referring_Video_Object_CVPR_2022_paper.pdf | Multi-Level Representation Learning With Semantic Alignment for Referring Video Object Segmentation | Referring video object segmentation (RVOS) is a challenging language-guided video grounding task, which requires comprehensively understanding the semantic information of both video content and language queries for object prediction. However, existing methods adopt multi-modal fusion at a frame-based spatial granul... | ['Jianbing Shen', 'Ling Shao', 'Xingping Dong', 'Dongming Wu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['video-grounding', 'referring-expression-segmentation', 'referring-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.14403342e-02 -3.89306039e-01 -6.92465663e-01 -4.54611838e-01
-9.64784443e-01 -4.80872124e-01 3.03534865e-01 7.31543377e-02
-2.75791019e-01 2.31634393e-01 4.67233390e-01 6.13038614e-02
2.18512326e-01 -5.35696208e-01 -8.65763843e-01 -4.04166877e-01
2.59098679e-01 -1.18369423e-01 8.09163332e-01 -5.86322434... | [9.733931541442871, 0.5779173374176025] |
d3162d37-60c4-4622-8351-a70571082319 | semi-supervised-learning-in-video-sequences | 2005.10266 | null | https://arxiv.org/abs/2005.10266v4 | https://arxiv.org/pdf/2005.10266v4.pdf | Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation | Supervised learning in large discriminative models is a mainstay for modern computer vision. Such an approach necessitates investing in large-scale human-annotated datasets for achieving state-of-the-art results. In turn, the efficacy of supervised learning may be limited by the size of the human annotated dataset. Thi... | ['Ekin D. Cubuk', 'Liang-Chieh Chen', 'Jonathon Shlens', 'Bowen Cheng', 'Raphael Gontijo Lopes', 'Maxwell D. Collins', 'Barret Zoph', 'Hartwig Adam'] | 2020-05-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/942_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540664.pdf | eccv-2020-8 | ['patch-matching'] | ['computer-vision'] | [ 4.34522152e-01 1.30478770e-01 -2.32766628e-01 -4.17928189e-01
-1.15548277e+00 -6.87626421e-01 6.44733548e-01 -8.85577649e-02
-6.88492060e-01 6.13646865e-01 -2.96236485e-01 -3.58699203e-01
3.57525557e-01 -6.76674843e-01 -6.97161615e-01 -7.58160174e-01
2.31502242e-02 5.88576496e-01 4.96926606e-01 -4.51490842... | [9.281479835510254, 0.5183008313179016] |
db16ef11-2b26-46bc-bc4f-c6efed34f724 | awte-bert-attending-to-wordpiece-tokenization | 2211.14829 | null | https://arxiv.org/abs/2211.14829v3 | https://arxiv.org/pdf/2211.14829v3.pdf | ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling | Natural language understanding (NLU) has two core tasks: intent classification and slot filling. The success of pre-training language models resulted in a significant breakthrough in the two tasks. One of the promising solutions called BERT can jointly optimize the two tasks. We note that BERT-based models convert each... | ['Qing Li', 'Yu Zhao', 'Shaopeng Wei', 'Huaming Du', 'Leilei Wang', 'Huangen Chen', 'Gang Wu', 'Xingyan Chen', 'Zhilong Xie', 'Yu Guo'] | 2022-11-27 | null | null | null | null | ['intent-classification', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.70245397e-01 3.28945875e-01 -5.63769281e-01 -6.41093135e-01
-8.17172170e-01 -1.38533324e-01 3.79082561e-01 2.46279433e-01
-5.51059008e-01 6.10432208e-01 4.62734431e-01 -2.75964260e-01
1.00738198e-01 -6.97282553e-01 -3.95351380e-01 -3.98496509e-01
4.75710273e-01 4.48324233e-01 1.21120550e-01 -4.07718718... | [12.515287399291992, 7.332326889038086] |
5da1bb99-f4aa-444f-aa7e-f922118cf61d | text-style-transfer-for-bias-mitigation-using | 2201.08643 | null | https://arxiv.org/abs/2201.08643v1 | https://arxiv.org/pdf/2201.08643v1.pdf | Text Style Transfer for Bias Mitigation using Masked Language Modeling | It is well known that textual data on the internet and other digital platforms contain significant levels of bias and stereotypes. Although many such texts contain stereotypes and biases that inherently exist in natural language for reasons that are not necessarily malicious, there are crucial reasons to mitigate these... | ['Toon Calders', 'Ewoenam Kwaku Tokpo'] | 2022-01-21 | null | https://aclanthology.org/2022.naacl-srw.21 | https://aclanthology.org/2022.naacl-srw.21.pdf | naacl-acl-2022-7 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 3.93844098e-01 3.31889480e-01 -6.18751466e-01 -5.37247300e-01
-5.40311456e-01 -6.81660235e-01 8.63195300e-01 2.75219142e-01
-7.05636442e-01 7.57345021e-01 6.67649448e-01 -6.54941440e-01
3.01181108e-01 -6.96774781e-01 -3.27889174e-01 -1.57106638e-01
5.86738348e-01 5.05523980e-01 1.60064638e-01 -7.22256362... | [9.1639986038208, 10.270920753479004] |
c727827f-3ce3-44ef-a58d-29ef1930640a | radar-based-materials-classification-using | 2202.05169 | null | https://arxiv.org/abs/2202.05169v1 | https://arxiv.org/pdf/2202.05169v1.pdf | Radar-based Materials Classification Using Deep Wavelet Scattering Transform: A Comparison of Centimeter vs. Millimeter Wave Units | Radar-based materials detection received significant attention in recent years for its potential inclusion in consumer and industrial applications like object recognition for grasping and manufacturing quality assurance and control. Several radar publications were developed for material classification under controlled ... | ['Andrew J. Hill', 'Rami N. Khushaba'] | 2022-02-08 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 6.48535371e-01 -3.30571920e-01 4.29696500e-01 -2.50381440e-01
-5.76497138e-01 -2.26258323e-01 4.06538486e-01 -2.48491734e-01
-2.40710378e-01 5.13659179e-01 -5.74976467e-02 -1.65506363e-01
-9.53809679e-01 -1.19183147e+00 -1.23312570e-01 -1.16370428e+00
-5.08428395e-01 4.07360435e-01 -4.93023619e-02 -4.55592215... | [6.884199619293213, 1.0778496265411377] |
72e984de-fcbd-4bae-ae66-676ae2b87599 | a-deep-knowledge-distillation-framework-for | 2112.07252 | null | https://arxiv.org/abs/2112.07252v2 | https://arxiv.org/pdf/2112.07252v2.pdf | A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging | Automatic Sleep Staging study is presently done with the help of Electroencephalogram (EEG) signals. Recently, Deep Learning (DL) based approaches have enabled significant progress in this area, allowing for near-human accuracy in automated sleep staging. However, EEG based sleep staging requires an extensive as well a... | ['Mohanasankar Sivaprakasam', 'Preejith SP', 'Sricharan Vijayarangan', 'Vaibhav Joshi'] | 2021-12-14 | null | null | null | null | ['sleep-stage-detection', 'sleep-staging', 'w-r-n-sleep-staging', 'w-r-l-d-sleep-staging', 'eeg-based-sleep-staging', 'ecg-based-sleep-staging'] | ['medical', 'medical', 'time-series', 'time-series', 'time-series', 'time-series'] | [ 1.26481026e-01 8.82723778e-02 2.76747309e-02 -5.57380080e-01
-6.68838859e-01 -1.41482353e-01 1.00256026e-01 2.73012877e-01
-7.42926896e-01 1.04672039e+00 1.07393321e-02 -3.07263762e-01
-3.57333809e-01 -4.27254945e-01 -2.22666383e-01 -8.54080439e-01
-1.44164652e-01 3.74280334e-01 2.71432623e-02 -1.34749785... | [13.466123580932617, 3.5551228523254395] |
d5683c99-b672-4755-ba31-5ba21b3d454c | deriving-neural-architectures-from-sequence | 1705.09037 | null | http://arxiv.org/abs/1705.09037v3 | http://arxiv.org/pdf/1705.09037v3.pdf | Deriving Neural Architectures from Sequence and Graph Kernels | The design of neural architectures for structured objects is typically guided
by experimental insights rather than a formal process. In this work, we appeal
to kernels over combinatorial structures, such as sequences and graphs, to
derive appropriate neural operations. We introduce a class of deep recurrent
neural oper... | ['Wengong Jin', 'Tao Lei', 'Regina Barzilay', 'Tommi Jaakkola'] | 2017-05-25 | deriving-neural-architectures-from-sequence-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=797 | http://proceedings.mlr.press/v70/lei17a/lei17a.pdf | icml-2017-8 | ['graph-regression'] | ['graphs'] | [ 3.23345989e-01 3.21187600e-02 -2.04363897e-01 -3.56222749e-01
-2.79452622e-01 -6.54202402e-01 6.57835364e-01 3.87609363e-01
-6.22575581e-01 4.32076335e-01 7.55609944e-02 -5.51740408e-01
-2.44076490e-01 -7.59523988e-01 -9.75903511e-01 -3.30281168e-01
-5.12659013e-01 8.82037953e-02 2.47893110e-01 -1.66713580... | [6.879095077514648, 6.314600944519043] |
60968768-37c3-4efc-b4df-0a287a6e9b81 | predictive-modeling-of-equine-activity | 2306.05311 | null | https://arxiv.org/abs/2306.05311v1 | https://arxiv.org/pdf/2306.05311v1.pdf | Predictive Modeling of Equine Activity Budgets Using a 3D Skeleton Reconstructed from Surveillance Recordings | In this work, we present a pipeline to reconstruct the 3D pose of a horse from 4 simultaneous surveillance camera recordings. Our environment poses interesting challenges to tackle, such as limited field view of the cameras and a relatively closed and small environment. The pipeline consists of training a 2D markerless... | ['Hedvig Kjellström', 'Pia Haubro Andersen', 'Sofia Broomé', 'Ernest Pokropek'] | 2023-06-08 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [ 4.61327881e-01 6.85367659e-02 9.04934779e-02 -2.58421272e-01
-4.50395793e-01 -5.66462338e-01 -3.39777023e-02 -1.71639740e-01
-5.83570898e-01 3.99988055e-01 1.12289533e-01 2.91526586e-01
-2.65805572e-01 2.10270453e-02 -8.84801328e-01 -3.04477245e-01
-4.35009271e-01 6.10628545e-01 5.24182558e-01 -3.24666858... | [7.298370361328125, -0.8791602849960327] |
1bf421bc-8800-4111-8de0-f543cd53a414 | cross-supervised-dual-classifiers-for-semi | 2305.16216 | null | https://arxiv.org/abs/2305.16216v1 | https://arxiv.org/pdf/2305.16216v1.pdf | Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation | Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis by significantly reducing the annotation burden while achieving comparable performance. Employing this method exhibits a high degree of potential for optimizing the segmentation process and increasing its feasi... | ['Zhicheng Jiao', 'Xin Li', 'Fan Yang', 'Heng Zhou', 'Chunna Tian', 'Ran Ran', 'Zhenxi Zhang'] | 2023-05-25 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 4.70989257e-01 4.86731708e-01 -6.46880686e-01 -7.81444609e-01
-1.29663718e+00 -3.47271383e-01 1.42928571e-01 3.84633839e-01
-4.98491943e-01 7.79778600e-01 -1.02146350e-01 -2.21281543e-01
-8.26989636e-02 -3.58550280e-01 -3.90242457e-01 -1.06487441e+00
1.26266181e-02 6.53725684e-01 2.53652692e-01 4.07731533... | [14.713275909423828, -2.1454758644104004] |
5a69aa0f-0ba5-4232-a102-ac016102b9f2 | v1net-a-computational-model-of-cortical | null | null | https://openreview.net/forum?id=Hyg4kkHKwH | https://openreview.net/pdf?id=Hyg4kkHKwH | V1Net: A computational model of cortical horizontal connections | The primate visual system builds robust, multi-purpose representations of the external world in order to support several diverse downstream cortical processes. Such representations are required to be invariant to the sensory inconsistencies caused by dynamically varying lighting, local texture distortion, etc. A key ar... | ['Virginia R. de Sa', 'Vijay Veerabadran'] | 2019-09-25 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 4.30519193e-01 -1.58649904e-03 3.05594116e-01 -1.10641479e-01
2.48952210e-01 -4.65550274e-01 6.21539831e-01 1.72686521e-02
-5.26938856e-01 2.85196483e-01 1.50069982e-01 -1.42069116e-01
-3.65080461e-02 -6.28274441e-01 -1.00751150e+00 -5.68042696e-01
-3.43789279e-01 1.23889726e-02 7.32128739e-01 -5.32458186... | [9.683012962341309, 2.434170722961426] |
467e50a1-25d7-4235-adca-9a734c9e1ad7 | off-apexnet-on-micro-expression-recognition | 1805.08699 | null | http://arxiv.org/abs/1805.08699v1 | http://arxiv.org/pdf/1805.08699v1.pdf | OFF-ApexNet on Micro-expression Recognition System | When a person attempts to conceal an emotion, the genuine emotion is manifest
as a micro-expression. Exploration of automatic facial micro-expression
recognition systems is relatively new in the computer vision domain. This is
due to the difficulty in implementing optimal feature extraction methods to
cope with the sub... | ['Yen-Chang Huang', 'Wei-Chuen Yau', 'Sze-Teng Liong', 'Y. S. Gan', 'Tan Lit Ken'] | 2018-05-10 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.63517177e-01 -3.40194225e-01 -3.19127798e-01 -4.24291968e-01
-3.26581866e-01 -1.49531022e-01 4.89064485e-01 -2.89607733e-01
-5.58901370e-01 6.52530849e-01 -7.30499923e-02 4.49497283e-01
2.25717098e-01 -3.82244498e-01 -3.17755699e-01 -1.04916179e+00
-2.32362106e-01 -5.38600504e-01 -3.98826927e-01 -2.08086684... | [13.629281997680664, 1.8062363862991333] |
0ccfd72d-92a7-413e-b33e-5af8da929db4 | swamp-swapped-assignment-of-multi-modal-pairs | 2111.05814 | null | https://arxiv.org/abs/2111.05814v2 | https://arxiv.org/pdf/2111.05814v2.pdf | SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval | We tackle the cross-modal retrieval problem, where learning is only supervised by relevant multi-modal pairs in the data. Although the contrastive learning is the most popular approach for this task, it makes potentially wrong assumption that the instances in different pairs are automatically irrelevant. To address the... | ['Minyoung Kim'] | 2021-11-10 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.69250149e-01 -8.51097256e-02 -4.19733942e-01 -4.77231741e-01
-1.45528913e+00 -6.41920686e-01 6.70592546e-01 3.62368256e-01
-2.83722579e-01 6.68213189e-01 5.96201159e-02 3.79813612e-01
-3.39698076e-01 -5.84996700e-01 -7.29565620e-01 -8.93969536e-01
2.19000876e-01 8.00104380e-01 1.42806306e-01 4.50135358... | [11.11589241027832, 1.1065013408660889] |
deb1b04e-0929-49a9-a233-2b3d4fda5da5 | gradient-imitation-reinforcement-learning-for-1 | 2211.06014 | null | https://arxiv.org/abs/2211.06014v2 | https://arxiv.org/pdf/2211.06014v2.pdf | Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction | Information Extraction (IE) aims to extract structured information from heterogeneous sources. IE from natural language texts include sub-tasks such as Named Entity Recognition (NER), Relation Extraction (RE), and Event Extraction (EE). Most IE systems require comprehensive understandings of sentence structure, implied... | ['Philip S. Yu', 'Irwin King', 'Lijie Wen', 'Xiangli Yang', 'Chenwei Zhang', 'Shiao Meng', 'Xuming Hu'] | 2022-11-11 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.76557049e-01 4.57395285e-01 -5.41752398e-01 -4.70573187e-01
-8.52824509e-01 -6.19704366e-01 4.53954965e-01 9.60601270e-02
-8.92543375e-01 1.01559973e+00 2.45078817e-01 -1.39290467e-01
1.72252178e-01 -6.33816540e-01 -7.71978855e-01 -1.35129884e-01
9.88328010e-02 4.02960271e-01 -1.65721536e-01 1.12086786... | [10.021318435668945, 8.562751770019531] |
ead9e257-c783-4d70-a72e-77339748ab18 | unsupervised-part-of-speech-tagging-with | null | null | https://aclanthology.org/Q16-1018 | https://aclanthology.org/Q16-1018.pdf | Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models | We tackle unsupervised part-of-speech (POS) tagging by learning hidden Markov models (HMMs) that are particularly well-suited for the problem. These HMMs, which we call anchor HMMs, assume that each tag is associated with at least one word that can have no other tag, which is a relatively benign condition for POS taggi... | ['Daniel Hsu', 'Michael Collins', 'Karl Stratos'] | 2016-01-01 | null | null | null | tacl-2016-1 | ['unsupervised-part-of-speech-tagging'] | ['natural-language-processing'] | [ 6.58853212e-03 4.86094594e-01 -3.98466825e-01 -3.10056299e-01
-7.89466918e-01 -9.23362195e-01 5.09095013e-01 1.42002106e-01
-4.16118860e-01 6.12924159e-01 2.95846373e-01 -7.04778790e-01
3.01753879e-01 -4.95568573e-01 -6.79172456e-01 -8.21283162e-01
-1.86895430e-01 6.29592717e-01 3.61736745e-01 6.56492859... | [10.337764739990234, 9.731013298034668] |
4cc6b953-0e5a-41e5-b0db-65ca50f7b10b | deepco3-deep-instance-co-segmentation-by-co | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Hsu_DeepCO3_Deep_Instance_Co-Segmentation_by_Co-Peak_Search_and_Co-Saliency_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Hsu_DeepCO3_Deep_Instance_Co-Segmentation_by_Co-Peak_Search_and_Co-Saliency_Detection_CVPR_2019_paper.pdf | DeepCO3: Deep Instance Co-Segmentation by Co-Peak Search and Co-Saliency Detection | In this paper, we address a new task called instance co-segmentation. Given a set of images jointly covering object instances of a specific category, instance co-segmentation aims to identify all of these instances and segment each of them, i.e. generating one mask for each instance. This task is important since instan... | [' Yung-Yu Chuang', ' Yen-Yu Lin', 'Kuang-Jui Hsu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['co-saliency-detection'] | ['computer-vision'] | [ 5.28810501e-01 1.51715234e-01 -2.73093998e-01 -2.61199355e-01
-8.81602466e-01 -3.72292608e-01 3.88855100e-01 3.72959942e-01
-3.42352152e-01 4.57337528e-01 -3.43765765e-01 2.06085160e-01
1.84028428e-02 -6.07399583e-01 -8.69034410e-01 -7.42685854e-01
-2.46862005e-02 4.79981989e-01 8.28731179e-01 2.24765748... | [9.809576034545898, -0.07218162715435028] |
3d0675d3-ff2d-4ea2-8f32-b27351225cdf | cost-effective-training-in-low-resource-1 | 2201.05700 | null | https://arxiv.org/abs/2201.05700v1 | https://arxiv.org/pdf/2201.05700v1.pdf | Cost-Effective Training in Low-Resource Neural Machine Translation | While Active Learning (AL) techniques are explored in Neural Machine Translation (NMT), only a few works focus on tackling low annotation budgets where a limited number of sentences can get translated. Such situations are especially challenging and can occur for endangered languages with few human annotators or having ... | ['Jan Niehues', 'Danni Liu', 'Sai Koneru'] | 2022-01-14 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 4.02797610e-01 3.38778734e-01 -6.05315030e-01 -4.89531249e-01
-1.36600089e+00 -6.08278811e-01 5.92393935e-01 2.04091221e-01
-8.98276687e-01 1.26802349e+00 -8.16240557e-04 -4.03592885e-01
2.66147852e-01 -4.78037447e-01 -1.05542254e+00 -3.64262819e-01
3.15062374e-01 1.16354990e+00 4.76227626e-02 -5.21116197... | [11.523652076721191, 10.256119728088379] |
ea287f43-e018-4c26-8aae-88d9f597b0e1 | learning-graph-neural-networks-for-image | 2207.11681 | null | https://arxiv.org/abs/2207.11681v2 | https://arxiv.org/pdf/2207.11681v2.pdf | Learning Graph Neural Networks for Image Style Transfer | State-of-the-art parametric and non-parametric style transfer approaches are prone to either distorted local style patterns due to global statistics alignment, or unpleasing artifacts resulting from patch mismatching. In this paper, we study a novel semi-parametric neural style transfer framework that alleviates the de... | ['DaCheng Tao', 'Xinchao Wang', 'Mingli Song', 'Yibing Zhan', 'Yiding Yang', 'Yining Mao', 'Yongcheng Jing'] | 2022-07-24 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 3.05733293e-01 -1.50879724e-02 4.59735915e-02 -4.79475051e-01
-5.39247394e-01 -5.35235763e-01 7.06793308e-01 -2.60253072e-01
3.28085646e-02 6.40722394e-01 -1.25063062e-02 2.59578675e-01
-8.25594738e-02 -9.81289566e-01 -1.04797137e+00 -7.52852380e-01
5.19486368e-01 6.33627713e-01 1.85771987e-01 -3.88756603... | [11.58558177947998, -0.6166903972625732] |
cfd3aad7-92e3-4ee5-bd53-e46f05f3f615 | adversarial-speaker-disentanglement-using | 2305.09167 | null | https://arxiv.org/abs/2305.09167v1 | https://arxiv.org/pdf/2305.09167v1.pdf | Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion | Nowadays, recognition-synthesis-based methods have been quite popular with voice conversion (VC). By introducing linguistics features with good disentangling characters extracted from an automatic speech recognition (ASR) model, the VC performance achieved considerable breakthroughs. Recently, self-supervised learning ... | ['Helen Meng', 'Zhiyong Wu', 'Yang Chao', 'Shuai Wang', 'Xintao Zhao'] | 2023-05-16 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 3.74375701e-01 1.23796232e-01 -2.81270109e-02 -2.72037297e-01
-8.98121178e-01 -5.83898067e-01 7.32894897e-01 -2.08185673e-01
-4.31685418e-01 6.29079044e-01 5.34935355e-01 -2.95801491e-01
5.48821747e-01 -7.31627941e-01 -4.36992317e-01 -6.99542403e-01
2.24984378e-01 6.14382438e-02 8.50930437e-02 -4.82021242... | [14.821115493774414, 6.583869457244873] |
7c36d378-cdc0-42b5-81e3-6ed2095251c1 | layer-wise-cross-view-decoding-for-sequence | 2005.08081 | null | https://arxiv.org/abs/2005.08081v7 | https://arxiv.org/pdf/2005.08081v7.pdf | Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding | In sequence-to-sequence learning, e.g., natural language generation, the decoder relies on the attention mechanism to efficiently extract information from the encoder. While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder... | ['Xuewei Ma', 'Xu sun', 'Xian Wu', 'Chenyu You', 'Guangxiang Zhao', 'Xuancheng Ren', 'Fenglin Liu'] | 2020-05-16 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 8.48657250e-01 4.20960188e-01 -1.47864580e-01 -1.88368604e-01
-1.31427562e+00 -4.98927623e-01 5.31310380e-01 -1.59406699e-02
7.86539074e-03 1.07137430e+00 7.96466410e-01 -2.81418353e-01
5.88611841e-01 -6.38326406e-01 -9.37602937e-01 -5.67777812e-01
5.19696534e-01 3.39796185e-01 -1.02789126e-01 -2.35643610... | [10.958041191101074, 0.8731665015220642] |
7078c5b5-5e1c-4aa4-a82a-b812d3712512 | fast-moving-object-counting-with-an-event | 2212.08384 | null | https://arxiv.org/abs/2212.08384v1 | https://arxiv.org/pdf/2212.08384v1.pdf | Fast-moving object counting with an event camera | This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects - in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct ... | ['Tomasz Kryjak', 'Krzysztof Blachut', 'Marcin Kowalczyk', 'Kamil Bialik'] | 2022-12-16 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 4.23295468e-01 -3.35356921e-01 4.22082752e-01 2.94037014e-02
5.44050276e-01 -6.67681396e-01 5.55074930e-01 3.45795333e-01
-6.51703775e-01 4.52972084e-01 -8.03043425e-01 -2.66076446e-01
-1.57919854e-01 -8.97616088e-01 -4.55760360e-01 -6.99185014e-01
1.36126027e-01 3.42960387e-01 6.08246326e-01 1.73573121... | [9.149149894714355, -1.4198837280273438] |
37fa7c87-6a6f-4052-b3cf-0d9d72a68787 | joint-inference-for-fine-grained-opinion | null | null | https://aclanthology.org/P13-1161 | https://aclanthology.org/P13-1161.pdf | Joint Inference for Fine-grained Opinion Extraction | null | ['Bishan Yang', 'Claire Cardie'] | 2013-08-01 | null | null | null | acl-2013-8 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.308376789093018, 3.708082914352417] |
99337b04-4777-483f-b402-ca450d5b15c0 | reduce-communication-costs-and-preserve | 2208.12268 | null | https://arxiv.org/abs/2208.12268v3 | https://arxiv.org/pdf/2208.12268v3.pdf | FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning | Federated learning (FL) has enabled global model training on decentralized data in a privacy-preserving way by aggregating model updates. However, for many natural language processing (NLP) tasks that utilize pre-trained language models (PLMs) with large numbers of parameters, there are considerable communication costs... | ['Gongshen Liu', 'Peixuan Li', 'Fangqi Li', 'Wei Du', 'Haodong Zhao'] | 2022-08-25 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-1.82457626e-01 -3.45493294e-02 -2.89244831e-01 -2.62813121e-01
-6.98455632e-01 -1.19391608e+00 6.26730561e-01 2.22046927e-01
-6.52129531e-01 7.39427090e-01 -5.33027016e-02 -7.15805531e-01
1.03472829e-01 -8.90302777e-01 -8.85833025e-01 -7.64974535e-01
-2.07524151e-01 1.40198588e-01 3.47493589e-01 -5.37226573... | [5.819021224975586, 6.834571361541748] |
63a50dbd-b53f-4a8c-9d5a-818fc428431d | dual-skip-connections-minimize-the-false | 2110.13036 | null | https://arxiv.org/abs/2110.13036v1 | https://arxiv.org/pdf/2110.13036v1.pdf | Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images | Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography. Automated pulmonary nodule detection is an essential part of computer-aided diagnosis, which is still facing great challenges and difficulties to quickly and accurately locate t... | ['Andreas Nürnberger', 'Tung Lung Liu', 'Philipp Ernst', 'Jiahua Xu'] | 2021-10-25 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 1.29864946e-01 2.64421314e-01 -3.46154004e-01 1.73433408e-01
-6.49652541e-01 -2.63630971e-02 2.36121908e-01 -1.39915124e-01
-3.38508815e-01 5.41492939e-01 -4.22171466e-02 -6.11927211e-01
-1.13940343e-01 -9.66233730e-01 -2.12515175e-01 -5.22150695e-01
-1.46761566e-01 3.73297095e-01 6.30720019e-01 8.29424784... | [15.441780090332031, -2.1471214294433594] |
2057a0c4-5fbd-4af7-9ce6-ab6c3cc2f23e | lexicon-based-graph-convolutional-network-for | null | null | https://aclanthology.org/2021.findings-emnlp.248 | https://aclanthology.org/2021.findings-emnlp.248.pdf | Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation | Precise information of word boundary can alleviate the problem of lexical ambiguity to improve the performance of natural language processing (NLP) tasks. Thus, Chinese word segmentation (CWS) is a fundamental task in NLP. Due to the development of pre-trained language models (PLM), pre-trained knowledge can help neura... | ['Degen Huang', 'Jingxiang Cao', 'Wei Liu', 'Junpeng Liu', 'Hao Yu', 'Kaiyu Huang'] | null | null | null | null | findings-emnlp-2021-11 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [-2.33783070e-02 -1.71308368e-01 -3.17319781e-01 -3.53550106e-01
-7.40409911e-01 -5.78121960e-01 2.56476253e-01 1.33155473e-02
-6.20610535e-01 6.76568031e-01 1.53538167e-01 -5.59799373e-01
1.73632368e-01 -9.45784807e-01 -5.83708346e-01 -3.36254895e-01
2.43535578e-01 2.72368312e-01 6.23686433e-01 -4.98846471... | [9.96081256866455, 10.029912948608398] |
6beab1d3-47c1-4eac-bd55-d1ed2840bb35 | hier-metric-learning-beyond-class-labels-via | 2212.14258 | null | https://arxiv.org/abs/2212.14258v3 | https://arxiv.org/pdf/2212.14258v3.pdf | HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization | Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances in the field. In this regard, we propose a new regularization method, dubbed HIER, to... | ['Boseung Jeong', 'Suha Kwak', 'Sungyeon Kim'] | 2022-12-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_HIER_Metric_Learning_Beyond_Class_Labels_via_Hierarchical_Regularization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_HIER_Metric_Learning_Beyond_Class_Labels_via_Hierarchical_Regularization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-1.10480838e-01 2.93047190e-01 -2.75310844e-01 -5.31848431e-01
-8.15564752e-01 -5.50527990e-01 5.73586285e-01 4.54716235e-01
-3.58070165e-01 5.93278825e-01 4.55583483e-01 -1.33361015e-02
-5.10375321e-01 -7.64495730e-01 -4.86607909e-01 -9.20870543e-01
-1.00190386e-01 7.45897889e-01 2.80699819e-01 -1.47348627... | [9.24476146697998, 3.155038356781006] |
a46e0427-47e4-4ca9-a9d0-421e362cb84d | supervised-and-unsupervised-speech | 1709.05362 | null | http://arxiv.org/abs/1709.05362v1 | http://arxiv.org/pdf/1709.05362v1.pdf | Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization | Reducing the interference noise in a monaural noisy speech signal has been a
challenging task for many years. Compared to traditional unsupervised speech
enhancement methods, e.g., Wiener filtering, supervised approaches, such as
algorithms based on hidden Markov models (HMM), lead to higher-quality enhanced
speech sig... | ['Paris Smaragdis', 'Arne Leijon', 'Nasser Mohammadiha'] | 2017-09-15 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 4.03085023e-01 -2.98142582e-01 4.55645323e-01 -2.68417627e-01
-8.47137034e-01 -8.00774172e-02 3.00338686e-01 -1.05018236e-01
-5.45334339e-01 6.15294635e-01 3.13421309e-01 -2.52867609e-01
-3.27878177e-01 -4.51807439e-01 -3.79765451e-01 -1.15468633e+00
3.21358860e-01 -2.91365921e-01 1.21717170e-01 -3.02141637... | [15.005529403686523, 5.820611953735352] |
44a3f073-c03f-4416-a048-c6691b6b6271 | extracting-temporal-and-causal-relations | 1604.08120 | null | http://arxiv.org/abs/1604.08120v1 | http://arxiv.org/pdf/1604.08120v1.pdf | Extracting Temporal and Causal Relations between Events | Structured information resulting from temporal information processing is
crucial for a variety of natural language processing tasks, for instance to
generate timeline summarization of events from news documents, or to answer
temporal/causal-related questions about some events. In this thesis we present
a framework for ... | ['Paramita Mirza'] | 2016-04-27 | extracting-temporal-and-causal-relations-1 | https://aclanthology.org/P14-3002 | https://aclanthology.org/P14-3002.pdf | acl-2014-6 | ['temporal-relation-extraction', 'timeline-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.92834640e-01 3.53037357e-01 -4.45179760e-01 -5.31062484e-01
-4.26036745e-01 -7.73355782e-01 1.15464640e+00 8.76960933e-01
-4.06444609e-01 1.21196413e+00 1.21565974e+00 -4.57840055e-01
-5.23555636e-01 -8.79314423e-01 -2.24996641e-01 -3.68272454e-01
-8.29374135e-01 5.44472635e-01 3.18477303e-01 -3.02759200... | [9.065596580505371, 9.246320724487305] |
025fa920-3bc0-47cb-8f55-9d74f8e17db5 | citations-as-queries-source-attribution-using | 2306.17322 | null | https://arxiv.org/abs/2306.17322v1 | https://arxiv.org/pdf/2306.17322v1.pdf | Citations as Queries: Source Attribution Using Language Models as Rerankers | This paper explores new methods for locating the sources used to write a text, by fine-tuning a variety of language models to rerank candidate sources. After retrieving candidates sources using a baseline BM25 retrieval model, a variety of reranking methods are tested to see how effective they are at the task of source... | ['David Smith', 'Ryan Muther'] | 2023-06-29 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 8.58686939e-02 1.10578731e-01 -8.92860532e-01 -2.18449030e-02
-1.42149532e+00 -1.01246476e+00 1.45164955e+00 7.02060521e-01
-6.76704645e-01 8.19885790e-01 9.24007595e-01 -3.89084876e-01
-2.36763582e-01 -4.82344717e-01 -3.61304611e-01 -4.60573519e-03
3.31268698e-01 8.15863848e-01 4.70994055e-01 -3.54122788... | [12.011384010314941, 8.581085205078125] |
df872e89-696a-460f-877e-6d7a8eee2c46 | scalable-3d-captioning-with-pretrained-models | 2306.07279 | null | https://arxiv.org/abs/2306.07279v2 | https://arxiv.org/pdf/2306.07279v2.pdf | Scalable 3D Captioning with Pretrained Models | We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects. This approach utilizes pretrained models from image captioning, image-text alignment, and LLM to consolidate captions from multiple views of a 3D asset, completely side-stepping the time-consuming and costly process of manual anno... | ['Justin Johnson', 'Honglak Lee', 'Chris Rockwell', 'Tiange Luo'] | 2023-06-12 | null | null | null | null | ['image-captioning', 'text-to-3d', 'prompt-engineering'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.44708473e-02 3.91071528e-01 2.31858954e-01 -4.76136595e-01
-1.43397510e+00 -9.69832599e-01 7.85987735e-01 -9.91983935e-02
-1.87076315e-01 3.09220225e-01 6.08103275e-01 -6.95320517e-02
5.84335029e-01 -1.32103816e-01 -1.08390892e+00 -2.63832379e-02
2.91187763e-01 1.06649101e+00 1.58230036e-01 -5.91125749... | [8.199196815490723, -3.289128303527832] |
bc8f4206-79cb-46be-b912-0655391ecefb | cst-yolo-a-novel-method-for-blood-cell | 2306.14590 | null | https://arxiv.org/abs/2306.14590v1 | https://arxiv.org/pdf/2306.14590v1.pdf | CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer | Blood cell detection is a typical small-scale object detection problem in computer vision. In this paper, we propose a CST-YOLO model for blood cell detection based on YOLOv7 architecture and enhance it with the CNN-Swin Transformer (CST), which is a new attempt at CNN-Transformer fusion. We also introduce three other ... | ['Raphaël Phan', 'Fung Fung Ting', 'Chee-Ming Ting', 'Ming Kang'] | 2023-06-26 | null | null | null | null | ['cell-detection', 'blood-cell-detection'] | ['computer-vision', 'medical'] | [-6.03152871e-01 -4.54628468e-01 2.28784323e-01 -2.92238616e-03
-4.40690488e-01 -6.11759685e-02 3.70666414e-01 2.64410406e-01
-5.75562119e-01 5.49846292e-01 -6.62114471e-02 -1.57852117e-02
6.39298320e-01 -9.39644098e-01 -3.59466106e-01 -8.95168364e-01
3.51422280e-02 1.95073247e-01 7.60019124e-01 4.50882092... | [14.77100944519043, -3.100222587585449] |
cc6ce6ee-261c-4c16-b734-f55f888a21c2 | cross-lingual-speaker-identification-using | 2210.05780 | null | https://arxiv.org/abs/2210.05780v1 | https://arxiv.org/pdf/2210.05780v1.pdf | Cross-Lingual Speaker Identification Using Distant Supervision | Speaker identification, determining which character said each utterance in literary text, benefits many downstream tasks. Most existing approaches use expert-defined rules or rule-based features to directly approach this task, but these approaches come with significant drawbacks, such as lack of contextual reasoning an... | ['Dan Roth', 'Dong Yu', 'Dian Yu', 'Ben Zhou'] | 2022-10-11 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.70674592e-01 -1.10996313e-01 -3.47410530e-01 -7.36231267e-01
-1.48642230e+00 -8.49612474e-01 6.73959792e-01 -1.90609723e-01
-4.12708163e-01 6.65779650e-01 3.00847828e-01 -5.19785285e-01
7.63513744e-02 -2.02064067e-01 -5.67127228e-01 -5.49112439e-01
3.38919371e-01 6.23971939e-01 1.72403976e-01 -2.47141734... | [14.179730415344238, 6.737485885620117] |
d2d8ec2c-6e60-4342-a557-79189f1ccfc8 | histgnn-hierarchical-spatio-temporal-graph | 2201.09101 | null | https://arxiv.org/abs/2201.09101v2 | https://arxiv.org/pdf/2201.09101v2.pdf | HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting | Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time series data, the task is suitable for data-driven approaches, especially deep neural networks. Recently, the Graph Neural Networks (GNNs) based m... | ['Junbo Zhang', 'Shenggong Ji', 'Bin Wang', 'Tianrui Li', 'Fei Teng', 'Peng Xie', 'Minbo Ma'] | 2022-01-22 | null | null | null | null | ['self-learning', 'spatio-temporal-forecasting'] | ['natural-language-processing', 'time-series'] | [-6.14552021e-01 -3.09633315e-01 2.64915854e-01 -2.39324808e-01
5.42019963e-01 -3.71230006e-01 6.44332051e-01 2.60525793e-01
-7.70007372e-02 6.62230909e-01 1.10860363e-01 -8.73069763e-01
-3.96868110e-01 -1.40840864e+00 -5.52115798e-01 -9.28407371e-01
-9.16985512e-01 9.01826769e-02 5.04463375e-01 -7.59347260... | [6.608173847198486, 2.7861597537994385] |
c2008a86-19df-461e-9249-10f47cf33df8 | multi-attention-multi-class-constraint-for | 1806.05372 | null | http://arxiv.org/abs/1806.05372v1 | http://arxiv.org/pdf/1806.05372v1.pdf | Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition | Attention-based learning for fine-grained image recognition remains a
challenging task, where most of the existing methods treat each object part in
isolation, while neglecting the correlations among them. In addition, the
multi-stage or multi-scale mechanisms involved make the existing methods less
efficient and hard ... | ['Feng Zhou', 'Yuchen Yuan', 'Errui Ding', 'Ming Sun'] | 2018-06-14 | multi-attention-multi-class-constraint-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Ming_Sun_Multi-Attention_Multi-Class_Constraint_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Sun_Multi-Attention_Multi-Class_Constraint_ECCV_2018_paper.pdf | eccv-2018-9 | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 4.92001697e-03 -1.83362067e-01 -2.77763575e-01 -7.53209651e-01
-5.78601778e-01 -1.86666280e-01 2.10349157e-01 -1.95414349e-01
-4.40339267e-01 3.81534606e-01 9.06812623e-02 1.37329876e-01
-1.58251673e-01 -6.27248645e-01 -8.86902213e-01 -8.63455892e-01
1.85371250e-01 2.90976614e-01 2.62259603e-01 -4.02928516... | [9.59341049194336, 2.0033609867095947] |
72b5089a-4eaf-4ac5-a922-0763c7db50a3 | graph-constrained-data-representation | 2107.13362 | null | https://arxiv.org/abs/2107.13362v2 | https://arxiv.org/pdf/2107.13362v2.pdf | Graph Constrained Data Representation Learning for Human Motion Segmentation | Recently, transfer subspace learning based approaches have shown to be a valid alternative to unsupervised subspace clustering and temporal data clustering for human motion segmentation (HMS). These approaches leverage prior knowledge from a source domain to improve clustering performance on a target domain, and curren... | ['Herwig Wendt', 'Guillem Rodriguez-Corominas', 'Lluís Garrido', 'Mariella Dimiccoli'] | 2021-07-28 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Dimiccoli_Graph_Constrained_Data_Representation_Learning_for_Human_Motion_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Dimiccoli_Graph_Constrained_Data_Representation_Learning_for_Human_Motion_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['motion-segmentation'] | ['computer-vision'] | [ 2.01623127e-01 -7.65819699e-02 -3.13453734e-01 -1.90042138e-01
-7.10695744e-01 -5.94229698e-01 4.53476191e-01 -2.70930618e-01
-4.90949690e-01 2.46001601e-01 4.06658262e-01 1.60717711e-01
-1.63602993e-01 -2.43346572e-01 -5.25705636e-01 -1.23308384e+00
-1.86704081e-02 6.90956533e-01 2.68989444e-01 6.71561109... | [7.9792866706848145, 4.3353047370910645] |
dfbbb243-dc27-4471-a7e9-35750d529e71 | weakly-supervised-generative-network-for | 2008.05770 | null | https://arxiv.org/abs/2008.05770v1 | https://arxiv.org/pdf/2008.05770v1.pdf | Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses | 3D human pose estimation from a single image is an inverse problem due to the inherent ambiguity of the missing depth. Several previous works addressed the inverse problem by generating multiple hypotheses. However, these works are strongly supervised and require ground truth 2D-to-3D correspondences which can be diffi... | ['Chen Li', 'Gim Hee Lee'] | 2020-08-13 | null | null | null | null | ['multi-hypotheses-3d-human-pose-estimation'] | ['computer-vision'] | [ 6.10034503e-02 4.04629976e-01 2.02005845e-03 -4.55392867e-01
-1.00866497e+00 -3.05492043e-01 5.26008308e-01 -4.40332860e-01
-4.03072894e-01 7.90066719e-01 1.29637584e-01 2.59175211e-01
3.25956829e-02 -6.43506289e-01 -9.59520578e-01 -5.86402714e-01
3.79362494e-01 1.17161739e+00 1.88962519e-01 -4.10869718... | [7.101036071777344, -1.11672842502594] |
fba84173-7a9e-4afc-a4f3-b2d8151dd9a8 | improving-cross-lingual-transfer-learning-for | 2006.05474 | null | https://arxiv.org/abs/2006.05474v2 | https://arxiv.org/pdf/2006.05474v2.pdf | Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation | Transfer learning from high-resource languages is known to be an efficient way to improve end-to-end automatic speech recognition (ASR) for low-resource languages. Pre-trained or jointly trained encoder-decoder models, however, do not share the language modeling (decoder) for the same language, which is likely to be in... | ['Jiatao Gu', 'Juan Pino', 'Changhan Wang'] | 2020-06-09 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 5.32289803e-01 4.44640279e-01 -2.30361938e-01 -3.23441595e-01
-1.85483396e+00 -5.50455570e-01 5.75419664e-01 -4.93033797e-01
-5.57866037e-01 8.43451798e-01 4.20496076e-01 -8.59987140e-01
8.46148908e-01 -2.38670051e-01 -9.87289131e-01 -3.30911309e-01
4.48622048e-01 7.77778983e-01 -1.65420443e-01 -2.97098517... | [14.52535629272461, 7.185739040374756] |
6b2104ee-9105-465c-b504-4297a696316c | physics-constrained-unsupervised-deep | 2306.11014 | null | https://arxiv.org/abs/2306.11014v1 | https://arxiv.org/pdf/2306.11014v1.pdf | Physics Constrained Unsupervised Deep Learning for Rapid, High Resolution Scanning Coherent Diffraction Reconstruction | By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervised deep learning strategies h... | ['Apurva Mehta', 'Aashwin Ananda Mishra', 'Oliver Hoidn'] | 2023-06-19 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 5.92276692e-01 -3.42174143e-01 1.60517752e-01 -4.42195147e-01
-9.92801726e-01 -2.22473606e-01 4.40788001e-01 -3.59402210e-01
-9.34266627e-01 8.68334889e-01 -1.27084740e-02 -3.00008237e-01
-4.35926169e-01 -5.12941599e-01 -5.12026906e-01 -1.07640648e+00
1.00660674e-01 6.64514840e-01 -1.90970138e-01 4.81875390... | [12.832693099975586, -2.7858383655548096] |
b24848e7-a13b-456a-9376-39efa0138236 | toward-fast-and-accurate-neural-chinese-word | 1903.04190 | null | https://arxiv.org/abs/1903.04190v2 | https://arxiv.org/pdf/1903.04190v2.pdf | Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning | The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities. Multi-criteria Chinese word segmentation aims to capture various annotation criteria among datasets and leverage their common underlying knowledge. In this paper, we propose a domain adaptive segme... | ['Wei Chu', 'Kunlong Chen', 'Taifeng Wang', 'Xingyi Cheng', 'Weipeng Huang'] | 2019-03-11 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 2.57413507e-01 -6.97161034e-02 -6.43831849e-01 -5.30908287e-01
-9.90815818e-01 -9.63522255e-01 9.04852003e-02 -1.95203274e-01
-6.53718650e-01 6.15403652e-01 5.52755892e-01 -6.67111456e-01
1.27998710e-01 -6.12872720e-01 -5.43922663e-01 -2.48072982e-01
5.80304742e-01 5.12384892e-01 4.93372709e-01 -1.19055100... | [9.963030815124512, 10.11161994934082] |
863a84b3-f1fe-4670-a7ad-c8dde0c56bdb | audio-visual-speech-enhancement-with | 2306.06495 | null | https://arxiv.org/abs/2306.06495v1 | https://arxiv.org/pdf/2306.06495v1.pdf | Audio-Visual Speech Enhancement With Selective Off-Screen Speech Extraction | This paper describes an audio-visual speech enhancement (AV-SE) method that estimates from noisy input audio a mixture of the speech of the speaker appearing in an input video (on-screen target speech) and of a selected speaker not appearing in the video (off-screen target speech). Although conventional AV-SE methods h... | ['Shigeo Morishima', 'Keitaro Tanaka', 'Tomoya Yoshinaga'] | 2023-06-10 | null | null | null | null | ['speech-enhancement', 'speech-extraction'] | ['speech', 'speech'] | [ 0.3264381 -0.07979473 0.00756943 -0.16222504 -1.1460321 -0.31054142
0.2491533 0.09201301 -0.29537877 0.5333175 0.27621046 -0.22826807
0.24561931 -0.30495888 -0.4147279 -0.7607765 0.1883137 -0.03896622
0.5558414 0.1446196 0.1776544 0.36807662 -1.8397728 0.63629985
0.6386946 1.0169967 0.... | [14.535749435424805, 5.272113800048828] |
e2cec697-cca3-419b-aacc-9ce8cd741273 | sifter-a-task-specific-alignment-strategy-for | 2306.12280 | null | https://arxiv.org/abs/2306.12280v1 | https://arxiv.org/pdf/2306.12280v1.pdf | SIFTER: A Task-specific Alignment Strategy for Enhancing Sentence Embeddings | The paradigm of pre-training followed by fine-tuning on downstream tasks has become the mainstream method in natural language processing tasks. Although pre-trained models have the advantage of generalization, their performance may still vary significantly across different domain tasks. This is because the data distrib... | ['Qiuhong zhai', 'XiaoYu Zhang', 'Chaoming Liu', 'Wenhao Zhu', 'Chao Yu'] | 2023-06-21 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'sentiment-analysis'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.17888689e-01 -1.21008821e-01 4.22720760e-02 -7.60642648e-01
-3.85988444e-01 -4.76596355e-01 4.62385088e-01 4.73138630e-01
-7.53566265e-01 5.63533783e-01 8.00822794e-01 -2.81965077e-01
6.55851364e-02 -9.00551140e-01 -4.57561851e-01 -6.49863422e-01
3.05482209e-01 1.97015226e-01 1.63850725e-01 -9.81229186... | [11.084193229675293, 9.089661598205566] |
0899687e-6c17-4c4f-b83a-6ba2c84d6d51 | behavior-retrieval-few-shot-imitation | 2304.08742 | null | https://arxiv.org/abs/2304.08742v2 | https://arxiv.org/pdf/2304.08742v2.pdf | Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets | Enabling robots to learn novel visuomotor skills in a data-efficient manner remains an unsolved problem with myriad challenges. A popular paradigm for tackling this problem is through leveraging large unlabeled datasets that have many behaviors in them and then adapting a policy to a specific task using a small amount ... | ['Chelsea Finn', 'Dorsa Sadigh', 'Suraj Nair', 'Maximilian Du'] | 2023-04-18 | null | null | null | null | ['few-shot-imitation-learning', 'open-question'] | ['methodology', 'natural-language-processing'] | [ 2.87533879e-01 2.25481749e-01 -3.79765898e-01 -2.88269579e-01
-9.47513938e-01 -9.56876993e-01 5.24232030e-01 -4.77220640e-02
-8.39767158e-01 9.50238883e-01 8.83295983e-02 -1.05777629e-01
-2.37489238e-01 -3.41993272e-01 -1.11671114e+00 -8.04696500e-01
-8.90595280e-03 9.15780365e-01 3.98259491e-01 -2.15393096... | [4.320559024810791, 1.2315562963485718] |
c675e770-70dd-4158-a808-81a324866828 | view-consistent-metal-segmentation-in-the | 2112.02101 | null | https://arxiv.org/abs/2112.02101v1 | https://arxiv.org/pdf/2112.02101v1.pdf | View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT -- An Investigation of Potential Improvement | The positive outcome of a trauma intervention depends on an intraoperative evaluation of inserted metallic implants. Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR). The majority of these MAR methods require prior se... | ['Björn W. Kreher', 'Florian Kordon', 'Andreas Maier', 'Tristan M. Gottschalk'] | 2021-12-03 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 1.73618838e-01 2.67322689e-01 4.96720970e-01 -7.17710704e-02
-8.58364403e-01 -1.46931127e-01 2.35286802e-01 3.98500293e-01
-8.36051047e-01 6.00638509e-01 -7.98465125e-03 -1.56548470e-01
-4.30336267e-01 -6.29563093e-01 -7.16991842e-01 -9.46778834e-01
-9.56577882e-02 3.34022671e-01 5.27688622e-01 -3.24355587... | [13.608577728271484, -2.700577735900879] |
1f509ae2-e329-48b3-a242-2e4b7bf63d68 | game-theoretic-mixed-experts-for | 2211.14669 | null | https://arxiv.org/abs/2211.14669v2 | https://arxiv.org/pdf/2211.14669v2.pdf | Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning | Recent advances in adversarial machine learning have shown that defenses considered to be robust are actually susceptible to adversarial attacks which are specifically customized to target their weaknesses. These defenses include Barrage of Random Transforms (BaRT), Friendly Adversarial Training (FAT), Trash is Treasur... | ['Marten van Dijk', 'Caiwen Ding', 'Sohaib Ahmad', 'Kaleel Mahmood', 'Ethan Rathbun'] | 2022-11-26 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 4.92451400e-01 2.32034802e-01 4.42182958e-01 1.81805402e-01
-6.43212616e-01 -1.56698632e+00 8.96595597e-01 -4.35470074e-01
-1.81270003e-01 4.96393055e-01 -3.76392692e-01 -7.34757841e-01
-4.09582406e-01 -1.27318883e+00 -6.82652652e-01 -8.69971573e-01
-3.30169499e-01 2.82964110e-01 3.13357174e-01 -8.15630734... | [5.5822577476501465, 7.724758148193359] |
94f7e20c-6a68-4c47-9615-a6135b45eb62 | multiplier-bootstrap-based-exploration | 2302.01543 | null | https://arxiv.org/abs/2302.01543v1 | https://arxiv.org/pdf/2302.01543v1.pdf | Multiplier Bootstrap-based Exploration | Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncertainty. In this paper, we propose Multiplier Bootstrap-based Exploration (MBE), a novel exploration strategy that is applicable to any reward ... | ['Rui Song', 'Branislav Kveton', 'Haoyu Wei', 'Runzhe Wan'] | 2023-02-03 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-2.06561431e-01 -1.85312331e-02 -8.22395921e-01 -3.55204731e-01
-1.29905570e+00 -6.38062596e-01 2.34406039e-01 3.42608541e-02
-3.77603740e-01 1.59259152e+00 -1.38669536e-01 -8.52394640e-01
-9.93866086e-01 -7.04517484e-01 -7.84129024e-01 -6.46358073e-01
-4.96564656e-01 7.57066131e-01 -1.87421188e-01 2.04643250... | [4.513027667999268, 3.2638907432556152] |
7a18994c-5f5a-4a10-8ac3-f1a251c9800d | sentiment-analysis-for-arabic-in-social-media | 1911.05483 | null | https://arxiv.org/abs/1911.05483v1 | https://arxiv.org/pdf/1911.05483v1.pdf | Sentiment Analysis for Arabic in Social Media Network: A Systematic Mapping Study | With the expansion in tenders on the Internet and social media, Arabic Sentiment Analysis (ASA) has assumed a significant position in the field of text mining study and has since remained used to explore the sentiments of users about services, various products or topics conversed over the Internet. This mapping paper d... | ['Mohamed Elhag M. Abo', 'Ram Gopal Raj', 'Atika Qazi', 'Abubakar Zakari'] | 2019-10-26 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.91548541e-01 1.80838704e-01 -7.61096060e-01 -1.03897057e-01
-1.46660671e-01 -6.97665811e-01 4.81950194e-01 3.28685164e-01
-3.17267179e-01 3.66973877e-01 3.31010580e-01 -3.06637496e-01
-3.69021803e-01 -5.00349820e-01 -2.85831213e-01 -4.71923172e-01
2.49873653e-01 2.01214343e-01 -1.72851011e-01 -7.55959034... | [10.882221221923828, 6.876919746398926] |
70125e2e-5eb9-49ad-a7ec-657a8ad40a18 | simulating-realistic-mri-variations-to | 2111.00837 | null | https://arxiv.org/abs/2111.00837v1 | https://arxiv.org/pdf/2111.00837v1.pdf | Simulating Realistic MRI variations to Improve Deep Learning model and visual explanations using GradCAM | In the medical field, landmark detection in MRI plays an important role in reducing medical technician efforts in tasks like scan planning, image registration, etc. First, 88 landmarks spread across the brain anatomy in the three respective views -- sagittal, coronal, and axial are manually annotated, later guidelines ... | ['Srinivasa Rao Kundeti', 'Deepam Gautam', 'Sumit Sharma', 'Razeem Ahmad Ali Mattathodi', 'Shrey Singla', 'Muhammad Ilyas Patel'] | 2021-11-01 | null | null | null | null | ['brain-landmark-detection'] | ['computer-vision'] | [ 1.2377848e-01 5.7651818e-01 -3.4001213e-02 -6.1622161e-01
-7.4493873e-01 -4.9066198e-01 2.5982437e-01 3.7341487e-01
-5.2661830e-01 5.3527546e-01 2.4072768e-01 -3.3208624e-01
3.4236345e-02 -4.6924940e-01 -4.8063794e-01 -4.5393810e-01
-3.2730669e-01 7.7564830e-01 4.6853742e-01 -1.7473189e-02
3.2752949e-01... | [14.489715576171875, -2.4153785705566406] |
f49bb10a-76e5-4d0b-a72d-ab6860c19852 | comprehensive-time-series-regression-models | 1412.5397 | null | http://arxiv.org/abs/1412.5397v3 | http://arxiv.org/pdf/1412.5397v3.pdf | Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013 | Using Gretl, I apply ARMA, Vector ARMA, VAR, state-space model with a Kalman
filter, transfer-function and intervention models, unit root tests,
cointegration test, volatility models (ARCH, GARCH, ARCH-M, GARCH-M,
Taylor-Schwert GARCH, GJR, TARCH, NARCH, APARCH, EGARCH) to analyze quarterly
time series of GDP and Gover... | [] | 2019-08-17 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.09070337e+00 -7.22477632e-03 1.25443965e-01 1.54190615e-01
-2.25826293e-01 -6.53778851e-01 6.87912107e-01 -9.32625756e-02
-1.07298516e-01 8.35642278e-01 6.20076299e-01 -1.24681532e+00
-3.13840061e-01 -9.40454006e-01 -2.16050103e-01 -7.81463683e-01
-8.60058963e-02 2.73429662e-01 -2.40396857e-01 -2.97992259... | [5.514752388000488, 4.0551533699035645] |
be3280b7-a933-4bd2-a82f-917212627ecc | deep-features-for-cbir-with-scarce-data-using | 2205.08935 | null | https://arxiv.org/abs/2205.08935v1 | https://arxiv.org/pdf/2205.08935v1.pdf | Deep Features for CBIR with Scarce Data using Hebbian Learning | Features extracted from Deep Neural Networks (DNNs) have proven to be very effective in the context of Content Based Image Retrieval (CBIR). In recent work, biologically inspired \textit{Hebbian} learning algorithms have shown promises for DNN training. In this contribution, we study the performance of such algorithms ... | ['Giuseppe Amato', 'Claudio Gennaro', 'Fabrizio Falchi', 'Claudio Gallicchio', 'Davide Bacciu', 'Gabriele Lagani'] | 2022-05-18 | null | null | null | null | ['content-based-image-retrieval', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 1.94387183e-01 -1.38177797e-01 -5.26470691e-02 -4.03849304e-01
-4.36310291e-01 -3.10171604e-01 9.13725674e-01 8.91913921e-02
-9.63429570e-01 6.95758641e-01 5.07559590e-02 -3.60593125e-02
-5.18087566e-01 -7.06520200e-01 -7.56831765e-01 -1.10607088e+00
2.72902906e-01 7.49743283e-01 1.60071731e-01 -1.92312643... | [9.30739688873291, 2.877955675125122] |
4857e4b6-a449-4226-a47a-5ef0bd1b2deb | convolutional-neural-networks-demystified-a | 2108.11663 | null | https://arxiv.org/abs/2108.11663v3 | https://arxiv.org/pdf/2108.11663v3.pdf | Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial | Deep Neural Networks (DNN) and especially Convolutional Neural Networks (CNN) are a de-facto standard for the analysis of large volumes of signals and images. Yet, their development and underlying principles have been largely performed in an ad-hoc and black box fashion. To help demystify CNNs, we revisit their operati... | ['Danilo Mandic', 'Ljubisa Stankovic'] | 2021-08-26 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [ 2.99315065e-01 -7.03818426e-02 4.26932305e-01 -2.99644321e-01
1.27623960e-01 -2.46429473e-01 4.69887376e-01 -1.60959139e-01
-3.17268729e-01 3.94949853e-01 -1.36204258e-01 -4.18303251e-01
-4.16951120e-01 -7.93671966e-01 -5.19770265e-01 -8.66331637e-01
-6.64212108e-01 -4.58881855e-01 1.36232138e-01 -3.67848217... | [8.993414878845215, 2.3539631366729736] |
797320c7-4c03-4927-ba96-2d01584eb81e | multimodal-joint-attribute-prediction-and | 2009.07162 | null | https://arxiv.org/abs/2009.07162v1 | https://arxiv.org/pdf/2009.07162v1.pdf | Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product | Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product recommendations, and product retrieval. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, we ... | ['Bo-Wen Zhou', 'Tiangang Zhu', 'Yue Wang', 'Xiaodong He', 'Youzheng Wu', 'Haoran Li'] | 2020-09-15 | null | https://aclanthology.org/2020.emnlp-main.166 | https://aclanthology.org/2020.emnlp-main.166.pdf | emnlp-2020-11 | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 2.44620964e-01 -1.61567897e-01 -7.87863553e-01 -9.24734294e-01
-3.04662526e-01 -8.43897760e-01 3.63374472e-01 4.04405862e-01
-2.09703550e-01 4.68416274e-01 2.39632964e-01 6.82001486e-02
-2.31462494e-01 -8.23112607e-01 -3.84855241e-01 -7.70604968e-01
1.90361053e-01 4.90723610e-01 -3.74128520e-01 -2.91049778... | [10.65604019165039, 1.9439187049865723] |
efeaeb76-ca9b-4c86-bdb7-965ebaa5ec0c | neural-airport-ground-handling | 2303.02442 | null | https://arxiv.org/abs/2303.02442v1 | https://arxiv.org/pdf/2303.02442v1.pdf | Neural Airport Ground Handling | Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, exist... | ['Jie Zhang', 'Zhiguang Cao', 'Xianli Zhang', 'Yunwen Xia', 'Jianan Zhou', 'Yaoxin Wu'] | 2023-03-04 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [-3.26876640e-01 -2.55400501e-02 -1.07565127e-01 -1.27889752e-01
-6.53244913e-01 -7.51714706e-01 -2.43464291e-01 1.26832142e-01
-2.31347680e-01 9.19847608e-01 -2.76201010e-01 -8.27249706e-01
-7.35580206e-01 -1.19316304e+00 -9.12899613e-01 -6.04486823e-01
-4.35376018e-01 7.63848662e-01 -1.64479315e-01 -6.77125275... | [5.098991394042969, 2.780911445617676] |
39fd5392-7e99-4141-867e-579de580bdd5 | efficient-video-object-segmentation-via | 1802.01218 | null | http://arxiv.org/abs/1802.01218v1 | http://arxiv.org/pdf/1802.01218v1.pdf | Efficient Video Object Segmentation via Network Modulation | Video object segmentation targets at segmenting a specific object throughout
a video sequence, given only an annotated first frame. Recent deep learning
based approaches find it effective by fine-tuning a general-purpose
segmentation model on the annotated frame using hundreds of iterations of
gradient descent. Despite... | ['Aggelos K. Katsaggelos', 'Yanran Wang', 'Xuehan Xiong', 'Linjie Yang', 'Jianchao Yang'] | 2018-02-04 | efficient-video-object-segmentation-via-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Efficient_Video_Object_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Efficient_Video_Object_CVPR_2018_paper.pdf | cvpr-2018-6 | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.16283081e-01 5.10999300e-02 -4.12401944e-01 -5.21901786e-01
-5.10210156e-01 -5.54524958e-01 3.46345663e-01 -1.15405925e-01
-5.63553274e-01 5.56292355e-01 -3.21597785e-01 -7.24173784e-02
4.08026695e-01 -5.34669816e-01 -1.07620573e+00 -5.44632077e-01
8.23332146e-02 4.93447036e-01 9.00618434e-01 3.68076339... | [9.230489730834961, 0.06667330116033554] |
124233ee-2a00-442f-8fc2-d39f144c06ee | tanimoto-random-features-for-scalable | 2306.14809 | null | https://arxiv.org/abs/2306.14809v1 | https://arxiv.org/pdf/2306.14809v1.pdf | Tanimoto Random Features for Scalable Molecular Machine Learning | The Tanimoto coefficient is commonly used to measure the similarity between molecules represented as discrete fingerprints, either as a distance metric or a positive definite kernel. While many kernel methods can be accelerated using random feature approximations, at present there is a lack of such approximations for t... | ['José Miguel Hernández-Lobato', 'Sukriti Singh', 'Sergio Bacallado', 'Austin Tripp'] | 2023-06-26 | null | null | null | null | ['property-prediction', 'molecular-property-prediction'] | ['medical', 'miscellaneous'] | [ 1.62191495e-01 -3.27780694e-01 -3.96688253e-01 -4.48465049e-01
-3.59373778e-01 -7.47310340e-01 4.46667820e-01 5.18660545e-01
-3.61016244e-01 8.90615106e-01 -2.40777478e-01 -3.69387597e-01
-6.25379860e-01 -8.34636152e-01 -4.05544728e-01 -8.20637286e-01
-6.31331146e-01 1.94941744e-01 5.02826691e-01 3.42088081... | [7.57961893081665, 4.063313961029053] |
8e5b4ed5-6f59-4878-bb29-9373db7d3f4b | developing-all-skyrmion-spiking-neural | 1705.02995 | null | http://arxiv.org/abs/1705.02995v1 | http://arxiv.org/pdf/1705.02995v1.pdf | Developing All-Skyrmion Spiking Neural Network | In this work, we have proposed a revolutionary neuromorphic computing
methodology to implement All-Skyrmion Spiking Neural Network (AS-SNN). Such
proposed methodology is based on our finding that skyrmion is a topological
stable spin texture and its spatiotemporal motion along the magnetic nano-track
intuitively interp... | ['Deliang Fan', 'Zhezhi He'] | 2017-05-08 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 4.04978305e-01 -2.04296127e-01 3.19287121e-01 -5.55797890e-02
4.06175256e-01 -4.73525017e-01 6.42997205e-01 -1.37214944e-01
-5.02758443e-01 1.20160890e+00 -2.28476882e-01 -2.09470943e-01
2.01175790e-02 -7.62551904e-01 -1.21921790e+00 -1.12937498e+00
3.59505624e-01 3.30096424e-01 7.53467619e-01 -3.65363747... | [8.217039108276367, 2.4637224674224854] |
f43813df-f2d8-4c0b-ad69-fcd07d4590af | performance-optimized-deep-neural-networks | 2306.03779 | null | https://arxiv.org/abs/2306.03779v1 | https://arxiv.org/pdf/2306.03779v1.pdf | Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex | One of the most impactful findings in computational neuroscience over the past decade is that the object recognition accuracy of deep neural networks (DNNs) correlates with their ability to predict neural responses to natural images in the inferotemporal (IT) cortex. This discovery supported the long-held theory that o... | ['Thomas Serre', 'Margaret Livingstone', 'Saloni Sharma', 'Michael Arcaro', 'Thomas Fel', 'Ivan F. Rodriguez', 'Drew Linsley'] | 2023-06-06 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 1.94641471e-01 -1.50374938e-02 1.45811617e-01 -4.05715346e-01
-5.58831841e-02 -5.83895445e-01 6.89818442e-01 -2.81909645e-01
-9.04555798e-01 3.70940536e-01 1.57615408e-01 -2.09501714e-01
7.25603700e-02 -4.60910887e-01 -9.17913735e-01 -6.95801675e-01
1.38989478e-01 3.40197980e-01 4.96735930e-01 -3.45739350... | [9.633954048156738, 2.4523041248321533] |
976912f6-04b6-4f6d-8b13-b5c67eadfabb | one-shot-to-weakly-supervised-relation | null | null | https://openreview.net/forum?id=W0mr06PxTHp | https://openreview.net/pdf?id=W0mr06PxTHp | One-shot to Weakly-Supervised Relation Classification using Language Models | Relation classification aims at detecting a particular relation type between two entities in text, whose methods mostly requires annotated data. Data annotation is either a manual process for supervised learning, or automated, using knowledge bases for distant learning. Unfortunately, both annotation methodologies are ... | ['Sophia Ananiadou', 'Phong Le', 'Thy Thy Tran'] | 2021-06-22 | null | null | null | akbc-2021-10 | ['relation-classification'] | ['natural-language-processing'] | [ 1.82756558e-01 6.62981749e-01 -6.31843686e-01 -4.46781576e-01
-8.11058521e-01 -5.56485593e-01 7.83223391e-01 8.51000726e-01
-4.27621812e-01 1.12529719e+00 -7.32220560e-02 -2.89444387e-01
-1.83442682e-01 -1.14237392e+00 -7.75802672e-01 -3.29865485e-01
-2.79386615e-04 9.88503397e-01 4.03348446e-01 -2.64208287... | [9.414453506469727, 8.496979713439941] |
0bfe6a6c-8b31-4b65-8c46-807d599a7acf | general-board-game-concepts | 2107.01078 | null | https://arxiv.org/abs/2107.01078v1 | https://arxiv.org/pdf/2107.01078v1.pdf | General Board Game Concepts | Many games often share common ideas or aspects between them, such as their rules, controls, or playing area. However, in the context of General Game Playing (GGP) for board games, this area remains under-explored. We propose to formalise the notion of "game concept", inspired by terms generally used by game players and... | ['Cameron Browne', 'Dennis J. N. J. Soemers', 'Matthew Stephenson', 'Éric Piette'] | 2021-07-02 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.83563703e-01 2.83215612e-01 2.68328816e-01 1.03328384e-01
8.73354301e-02 -6.53546393e-01 8.67897809e-01 1.16850525e-01
-1.95570841e-01 4.26424146e-01 1.90375343e-01 -3.92165899e-01
-6.41549826e-01 -1.28590024e+00 -9.49632674e-02 -5.25428236e-01
-1.81703001e-01 6.66197002e-01 6.54650629e-01 -1.17394423... | [3.4425103664398193, 1.4869986772537231] |
ecabae3d-8ec6-4ce1-89d4-f3341c10708a | reside-improving-distantly-supervised-neural | 1812.04361 | null | http://arxiv.org/abs/1812.04361v2 | http://arxiv.org/pdf/1812.04361v2.pdf | RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information | Distantly-supervised Relation Extraction (RE) methods train an extractor by
automatically aligning relation instances in a Knowledge Base (KB) with
unstructured text. In addition to relation instances, KBs often contain other
relevant side information, such as aliases of relations (e.g., founded and
co-founded are alia... | ['Chiranjib Bhattacharyya', 'Rishabh Joshi', 'Shikhar Vashishth', 'Sai Suman Prayaga', 'Partha Talukdar'] | 2018-12-11 | reside-improving-distantly-supervised-neural-1 | https://aclanthology.org/D18-1157 | https://aclanthology.org/D18-1157.pdf | emnlp-2018-10 | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [ 8.39523152e-02 7.78185964e-01 -7.61628747e-01 -5.81352770e-01
-4.71746445e-01 -5.68446875e-01 3.52966875e-01 6.44300461e-01
-3.12861323e-01 1.15310264e+00 3.35309893e-01 -7.13354468e-01
-2.92316496e-01 -1.30278480e+00 -9.15733218e-01 -1.41498014e-01
-2.53698081e-01 6.36307180e-01 8.05179104e-02 -3.51751715... | [9.30806827545166, 8.538050651550293] |
edd2e6d2-6a3b-4206-901c-5b0c7cc801c0 | compositional-3d-human-object-neural | 2304.14070 | null | https://arxiv.org/abs/2304.14070v1 | https://arxiv.org/pdf/2304.14070v1.pdf | Compositional 3D Human-Object Neural Animation | Human-object interactions (HOIs) are crucial for human-centric scene understanding applications such as human-centric visual generation, AR/VR, and robotics. Since existing methods mainly explore capturing HOIs, rendering HOI remains less investigated. In this paper, we address this challenge in HOI animation from a co... | ['DaCheng Tao', 'Baosheng Yu', 'Zhi Hou'] | 2023-04-27 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 2.54395068e-01 1.19234882e-02 1.81165323e-01 -1.36389777e-01
2.27380600e-02 -2.42054060e-01 9.05342877e-01 -4.61930573e-01
1.76882669e-01 4.97947723e-01 4.06807810e-01 2.02176884e-01
8.98507312e-02 -7.01718330e-01 -9.70435619e-01 -5.57865322e-01
1.05108932e-01 4.40093309e-01 -5.60973547e-02 -1.17773890... | [10.927425384521484, -0.7304286956787109] |
d1462e87-76c4-4b97-b34a-3a5ea3f8c32c | p-vectors-a-parallel-coupled-tdnn-transformer | 2305.14778 | null | https://arxiv.org/abs/2305.14778v2 | https://arxiv.org/pdf/2305.14778v2.pdf | P-vectors: A Parallel-Coupled TDNN/Transformer Network for Speaker Verification | Typically, the Time-Delay Neural Network (TDNN) and Transformer can serve as a backbone for Speaker Verification (SV). Both of them have advantages and disadvantages from the perspective of global and local feature modeling. How to effectively integrate these two style features is still an open issue. In this paper, we... | ['Jing Xiao', 'Liang Xu', 'Bo Xu', 'Fangyuan Wang', 'Xiyuan Wang'] | 2023-05-24 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-1.58410043e-01 -5.45585081e-02 8.78261309e-03 -7.29023874e-01
-7.18461752e-01 -2.54883468e-01 4.38782662e-01 -2.96129346e-01
-1.44794717e-01 3.74330223e-01 4.68668282e-01 -2.33707786e-01
-8.29783529e-02 -3.55977058e-01 -3.80794168e-01 -8.17811131e-01
-1.28692016e-01 -2.61616051e-01 2.08370779e-02 -2.45083243... | [14.40996265411377, 6.0098395347595215] |
ab475380-80c1-4052-845e-8190b3324351 | recurrent-transformer-for-dynamic-graph | 2304.10079 | null | https://arxiv.org/abs/2304.10079v1 | https://arxiv.org/pdf/2304.10079v1.pdf | Recurrent Transformer for Dynamic Graph Representation Learning with Edge Temporal States | Dynamic graph representation learning is growing as a trending yet challenging research task owing to the widespread demand for graph data analysis in real world applications. Despite the encouraging performance of many recent works that build upon recurrent neural networks (RNNs) and graph neural networks (GNNs), they... | ['Yixin Chen', 'Bofeng Zhang', 'Chenyang Zhou', 'Liangrui Wu', 'Shiyi Lin', 'Guobing Zou', 'Shengxiang Hu'] | 2023-04-20 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [ 1.88672066e-01 -8.00918415e-02 -5.51813543e-01 4.02987078e-02
1.91458070e-03 -1.91628516e-01 7.89838254e-01 2.12643668e-01
1.54148862e-01 3.88212740e-01 3.52660626e-01 -5.88083327e-01
-3.94131869e-01 -9.28355932e-01 -4.18180346e-01 -6.35491908e-01
-6.27744019e-01 2.56959140e-01 2.32866451e-01 -2.63739824... | [7.199073314666748, 5.973312854766846] |
5fa11ae2-3632-48be-8e26-66d1660b8d84 | fun2vec-a-contrastive-learning-framework-of | 2209.02442 | null | https://arxiv.org/abs/2209.02442v1 | https://arxiv.org/pdf/2209.02442v1.pdf | Fun2Vec:a Contrastive Learning Framework of Function-level Representation for Binary | Function-level binary code similarity detection is essential in the field of cyberspace security. It helps us find bugs and detect patent infringements in released software and plays a key role in the prevention of supply chain attacks. A practical embedding learning framework relies on the robustness of vector represe... | ['Pan ZhiSong', 'Sun Meng', 'Guo JinHong', 'Guo ShiZe', 'Sun RuiJin'] | 2022-09-06 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 9.91792083e-02 -3.51911724e-01 -4.65602905e-01 -2.14367613e-01
-5.96115768e-01 -1.12602806e+00 2.19217449e-01 5.38112581e-01
-1.30469605e-01 3.26134413e-01 -1.06288463e-01 -9.32031274e-01
3.97504903e-02 -7.55238652e-01 -5.89504600e-01 -4.68472749e-01
-1.43569976e-01 -8.09816718e-02 1.40077427e-01 -3.64167333... | [7.172255992889404, 7.806069374084473] |
c0da5dd2-3831-412f-a256-5b39781dd8e6 | caponimage-context-driven-dense-captioning-on | 2204.12974 | null | https://arxiv.org/abs/2204.12974v1 | https://arxiv.org/pdf/2204.12974v1.pdf | CapOnImage: Context-driven Dense-Captioning on Image | Existing image captioning systems are dedicated to generating narrative captions for images, which are spatially detached from the image in presentation. However, texts can also be used as decorations on the image to highlight the key points and increase the attractiveness of images. In this work, we introduce a new ta... | ['Peng Wang', 'Yuning Jiang', 'Tiezheng Ge', 'Yuanmeng Zhang', 'Xinglin Hou', 'Yiqi Gao'] | 2022-04-27 | null | null | null | null | ['dense-captioning'] | ['computer-vision'] | [ 4.03583258e-01 2.47089684e-01 -1.49031326e-01 -3.70129198e-01
-8.68967474e-01 -6.51789904e-01 7.03513861e-01 3.98147218e-02
-1.24530882e-01 6.21644378e-01 5.56192160e-01 -8.39026272e-02
5.66776633e-01 -6.33421361e-01 -1.20846021e+00 -4.65553880e-01
4.00838435e-01 1.66127741e-01 1.88130841e-01 -3.38519186... | [10.97808837890625, 0.9605631828308105] |
06e42eb6-538a-438c-b62c-1461c3a0f6e9 | the-text-anonymization-benchmark-tab-a | 2202.00443 | null | https://arxiv.org/abs/2202.00443v2 | https://arxiv.org/pdf/2202.00443v2.pdf | The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization | We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods. Text anonymization, defined as the task of editing a text document to prevent the disclosure of personal information, currently suffers from a shortage of privacy-oriented annotated text resources... | ['Montserrat Batet', 'David Sánchez', 'Anthi Papadopoulou', 'Lilja Øvrelid', 'Pierre Lison', 'Ildikó Pilán'] | 2022-01-25 | null | null | null | null | ['text-anonymization'] | ['natural-language-processing'] | [ 4.01855081e-01 3.50806952e-01 -1.69698521e-01 -4.07290846e-01
-7.91610122e-01 -1.14122796e+00 7.25083351e-01 8.28125954e-01
-6.52822733e-01 7.53243268e-01 7.18881130e-01 -2.84218520e-01
-3.80395800e-01 -5.29415727e-01 -2.19670549e-01 -2.75885373e-01
2.93806076e-01 5.96721590e-01 -3.77703190e-01 2.65762955... | [6.180400371551514, 6.981301784515381] |
fe913b34-68d8-4e82-a5f0-2f3b13d157f2 | hcld-a-hierarchical-framework-for-zero-shot | null | null | https://aclanthology.org/2022.coling-1.396 | https://aclanthology.org/2022.coling-1.396.pdf | HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System | Recently, many task-oriented dialogue systems need to serve users in different languages. However, it is time-consuming to collect enough data of each language for training. Thus, zero-shot adaptation of cross-lingual task-oriented dialog systems has been studied. Most of existing methods consider the word-level alignm... | ['Jianfeng Liu', 'Xurui Yang', 'Jian Ye', 'Zhanyu Ma'] | null | null | null | null | coling-2022-10 | ['intent-detection', 'slot-filling', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.91148907e-01 -1.05514698e-01 -2.53188640e-01 -6.10694051e-01
-7.75750995e-01 -4.22172129e-01 5.44454873e-01 -3.53077576e-02
-5.37357688e-01 6.73219323e-01 5.77498078e-01 -4.54304338e-01
3.87449712e-01 -4.49568927e-01 3.82493258e-01 -3.47815096e-01
3.37632686e-01 7.99339831e-01 5.45901299e-01 -7.89949894... | [12.721749305725098, 7.754916667938232] |
a2bd64d2-ca0b-4cce-b71b-86ba5baf9374 | ilgnet-inception-modules-with-connected-local | 1610.02256 | null | http://arxiv.org/abs/1610.02256v3 | http://arxiv.org/pdf/1610.02256v3.pdf | ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation | In this paper, we address a challenging problem of aesthetic image
classification, which is to label an input image as high or low aesthetic
quality. We take both the local and global features of images into
consideration. A novel deep convolutional neural network named ILGNet is
proposed, which combines both the Incep... | ['Xiao-Dong Li', 'Geng Zhao', 'Xin Jin', 'Xiaokun Zhang', 'Le Wu', 'Siwei Peng', 'Shuying Li', 'Shiming Ge', 'Jingying Chi'] | 2016-10-07 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [-1.54692784e-01 -8.45957324e-02 -1.28777400e-02 -4.90164161e-01
-4.09116775e-01 -3.54668140e-01 2.76570112e-01 -2.40247205e-01
-2.10877851e-01 1.68831781e-01 -8.94445255e-02 -2.42320728e-02
-6.52597025e-02 -1.20472300e+00 -6.06230795e-01 -4.11292374e-01
3.14429671e-01 1.62615836e-01 -3.20796743e-02 -3.74279171... | [11.504947662353516, -1.052346110343933] |
9101afd7-701a-4500-bb92-63fc8610d036 | efficient-anomaly-detection-with-budget | 2306.03492 | null | https://arxiv.org/abs/2306.03492v1 | https://arxiv.org/pdf/2306.03492v1.pdf | Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer | Anomaly Detection is challenging as usually only the normal samples are seen during training and the detector needs to discover anomalies on-the-fly. The recently proposed deep-learning-based approaches could somehow alleviate the problem but there is still a long way to go in obtaining an industrial-class anomaly dete... | ['Chunhua Shen', 'Mingwen Wang', 'Hao Chen', 'Jingqi Wu', 'Hanxi Li'] | 2023-06-06 | null | null | null | null | ['supervised-anomaly-detection', 'unsupervised-anomaly-detection'] | ['computer-vision', 'methodology'] | [ 3.97955775e-01 1.32194057e-01 1.27322719e-01 -3.22254449e-01
-9.10362065e-01 -1.81144387e-01 4.31564450e-01 2.40472749e-01
-3.41960460e-01 4.47467357e-01 -6.86521113e-01 -1.37423500e-01
-3.48724760e-02 -6.34388149e-01 -5.22701502e-01 -1.13559484e+00
2.13090181e-01 3.95548254e-01 6.84117556e-01 -4.00042944... | [7.626263618469238, 2.0777740478515625] |
bb9aad67-db13-4218-b5ac-43d3d5036fed | multi-scale-prototypical-transformer-for | 2307.02308 | null | https://arxiv.org/abs/2307.02308v1 | https://arxiv.org/pdf/2307.02308v1.pdf | Multi-Scale Prototypical Transformer for Whole Slide Image Classification | Whole slide image (WSI) classification is an essential task in computational pathology. Despite the recent advances in multiple instance learning (MIL) for WSI classification, accurate classification of WSIs remains challenging due to the extreme imbalance between the positive and negative instances in bags, and the co... | ['Jun Shi', 'Juncheng Li', 'Jun Wang', 'Saisai Ding'] | 2023-07-05 | null | null | null | null | ['classification-1', 'multiple-instance-learning'] | ['methodology', 'methodology'] | [ 2.92386889e-01 -8.63205492e-02 -7.33595863e-02 -3.11585397e-01
-1.14686203e+00 4.37339582e-02 3.71256053e-01 3.92525345e-01
-2.91280270e-01 6.11146450e-01 6.71394691e-02 1.07754529e-01
-4.84106421e-01 -6.04591191e-01 -5.76368570e-01 -1.28819025e+00
2.10129961e-01 4.77796316e-01 3.16968948e-01 -1.33833051... | [15.091320991516113, -2.8070013523101807] |
83c39907-a4fe-436e-b486-2c23c12441f2 | fine-grained-visual-categorization-via-multi | 1402.0453 | null | http://arxiv.org/abs/1402.0453v2 | http://arxiv.org/pdf/1402.0453v2.pdf | Fine-Grained Visual Categorization via Multi-stage Metric Learning | Fine-grained visual categorization (FGVC) is to categorize objects into
subordinate classes instead of basic classes. One major challenge in FGVC is
the co-occurrence of two issues: 1) many subordinate classes are highly
correlated and are difficult to distinguish, and 2) there exists the large
intra-class variation (e... | ['Yuanqing Lin', 'Shenghuo Zhu', 'Qi Qian', 'Rong Jin'] | 2014-02-03 | fine-grained-visual-categorization-via-multi-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Qian_Fine-Grained_Visual_Categorization_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Qian_Fine-Grained_Visual_Categorization_2015_CVPR_paper.pdf | cvpr-2015-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-8.70048851e-02 -3.69627297e-01 6.06552437e-02 -4.22496378e-01
-6.13783896e-01 -5.04851580e-01 2.61977553e-01 3.93285900e-01
-4.26480860e-01 5.00935555e-01 -1.75177559e-01 4.05642949e-02
-5.21517515e-01 -8.95657420e-01 -3.31119925e-01 -8.88904870e-01
-2.74274379e-01 3.16544980e-01 4.63578671e-01 -1.77559648... | [9.635170936584473, 2.2147717475891113] |
a107f398-7a9e-46ce-b0e8-bbcb7b4b9cc9 | analyzing-assumptions-in-conversation | 1810.11118 | null | https://arxiv.org/abs/1810.11118v2 | https://arxiv.org/pdf/1810.11118v2.pdf | A Large-Scale Corpus for Conversation Disentanglement | Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation str... | ['Walter S. Lasecki', 'Chulaka Gunasekara', 'Sai R. Gouravajhala', 'Lazaros Polymenakos', 'Jonathan K. Kummerfeld', 'Siva Sankalp Patel', 'Vignesh Athreya', 'Joseph Peper', 'Jatin Ganhotra'] | 2018-10-25 | a-large-scale-corpus-for-conversation | https://aclanthology.org/P19-1374 | https://aclanthology.org/P19-1374.pdf | acl-2019-7 | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 4.11847413e-01 6.77732527e-01 -3.54947001e-01 -5.20593047e-01
-9.97397661e-01 -1.15919673e+00 1.11895108e+00 4.65586960e-01
-1.51280984e-01 1.09172869e+00 1.34411037e+00 -7.25615025e-01
2.15499736e-02 -3.35695982e-01 2.86551006e-02 -2.26641297e-01
8.49128589e-02 7.53944337e-01 -9.06881616e-02 -5.15063763... | [12.501896858215332, 8.000479698181152] |
e322565d-e5d7-4fba-bb35-fb9abee156ff | adversarially-robust-neural-architecture | 2304.04168 | null | https://arxiv.org/abs/2304.04168v1 | https://arxiv.org/pdf/2304.04168v1.pdf | Adversarially Robust Neural Architecture Search for Graph Neural Networks | Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk-sensitive domains. Existing defensive methods neither guarantee performance facing new data/tasks or adversarial attacks nor provide insigh... | ['Wenwu Zhu', 'Rex Ying', 'Zhiqiang Zhang', 'Daixin Wang', 'Xin Wang', 'Ziwei Zhang', 'Heng Chang', 'Beini Xie'] | 2023-04-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xie_Adversarially_Robust_Neural_Architecture_Search_for_Graph_Neural_Networks_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xie_Adversarially_Robust_Neural_Architecture_Search_for_Graph_Neural_Networks_CVPR_2023_paper.pdf | cvpr-2023-1 | ['architecture-search', 'robust-design'] | ['methodology', 'miscellaneous'] | [ 9.77715552e-02 -7.23290443e-02 -4.49450351e-02 -5.80831952e-02
-5.12965083e-01 -1.20446730e+00 5.69342375e-01 -5.94851049e-03
-7.04438984e-02 1.52713448e-01 3.77803333e-02 -7.31136918e-01
-2.71372229e-01 -1.14187825e+00 -9.58752394e-01 -5.41226447e-01
-2.31044620e-01 1.54637024e-01 3.87451440e-01 -7.08820522... | [6.072975158691406, 7.380462646484375] |
9cab8284-8403-4764-a135-99b93dc367f7 | adapting-deep-learning-for-sentiment | 2001.01047 | null | https://arxiv.org/abs/2001.01047v1 | https://arxiv.org/pdf/2001.01047v1.pdf | Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text | Nowadays, an abundance of short text is being generated that uses nonstandard writing styles influenced by regional languages. Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification. In this work, we (1) present a... | ['Asim Karim', 'Muhammad Haroon Shakeel'] | 2020-01-04 | null | null | null | null | ['lexical-normalization'] | ['natural-language-processing'] | [-6.49534911e-02 -3.40732068e-01 -4.33352232e-01 -5.02193987e-01
-6.74768150e-01 -7.57788122e-01 6.42165840e-01 5.37954628e-01
-7.87394524e-01 6.79203808e-01 4.61410999e-01 -5.91071129e-01
2.59863228e-01 -5.18793583e-01 -2.74800032e-01 -2.96680391e-01
4.32749301e-01 4.27223802e-01 -9.65550989e-02 -5.47106981... | [9.8646821975708, 10.052085876464844] |
07619254-5fbd-4bf8-856b-a4b629624fdd | external-knowledge-selection-with-weighted | 2209.02251 | null | https://arxiv.org/abs/2209.02251v1 | https://arxiv.org/pdf/2209.02251v1.pdf | External Knowledge Selection with Weighted Negative Sampling in Knowledge-grounded Task-oriented Dialogue Systems | Constructing a robust dialogue system on spoken conversations bring more challenge than written conversation. In this respect, DSTC10-Track2-Task2 is proposed, which aims to build a task-oriented dialogue (TOD) system incorporating unstructured external knowledge on a spoken conversation, extending DSTC9-Track1. This p... | ['Stanley Jungkyu Choi', 'Yireun Kim', 'Gyeonghun Kim', 'Hyunjik Jo', 'Hosung Song', 'Joongbo Shin', 'Janghoon Han'] | 2022-09-06 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 1.95701480e-01 5.68022311e-01 4.67588186e-01 -7.76864648e-01
-8.23854089e-01 -5.49284220e-01 9.10787582e-01 -3.60217065e-01
-6.94948673e-01 1.07347536e+00 7.79351175e-01 -4.34229560e-02
3.12716186e-01 -3.61047834e-01 1.08652189e-01 -3.17758054e-01
4.06694859e-01 9.64553595e-01 2.48055160e-01 -8.07579219... | [12.812052726745605, 8.064651489257812] |
4b998537-c4a6-4f2f-ae21-4b6a09272a63 | on-the-identifiability-of-markov-switching | 2305.15925 | null | https://arxiv.org/abs/2305.15925v2 | https://arxiv.org/pdf/2305.15925v2.pdf | On the Identifiability of Markov Switching Models | Identifiability of latent variable models has recently gained interest in terms of its applications to interpretability or out of distribution generalisation. In this work, we study identifiability of Markov Switching Models as a first step towards extending recent results to sequential latent variable models. We prese... | ['Yingzhen Li', 'Yixin Wang', 'Carles Balsells-Rodas'] | 2023-05-25 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.12689805e-01 8.74642730e-02 -3.70560229e-01 -3.90046299e-01
-5.57997525e-01 -6.76594436e-01 8.92862737e-01 -8.76774415e-02
9.10854563e-02 6.92332864e-01 2.52281785e-01 -7.66784906e-01
-7.60474682e-01 -3.41706097e-01 -3.65084261e-01 -8.15167904e-01
-4.93794918e-01 1.07032681e+00 -6.78938031e-02 3.60453367... | [7.097782611846924, 3.935403347015381] |
eafd190c-715c-4ded-b012-30a6fd4b4a34 | using-meta-knowledge-mined-from-identifiers | 2012.09005 | null | https://arxiv.org/abs/2012.09005v1 | https://arxiv.org/pdf/2012.09005v1.pdf | Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Neuro-Symbolic Algorithms | In this paper we explore the use of meta-knowledge embedded in intent identifiers to improve intent recognition in conversational systems. As evidenced by the analysis of thousands of real-world chatbots and in interviews with professional chatbot curators, developers and domain experts tend to organize the set of chat... | ['Gabriel Malfatti', 'Henrique Ferreira', 'Melina Guerra', 'Maira Gatti de Bayser', 'Mauro Pichiliani', 'Ana Appel', 'Julio Nogima', 'Heloisa Candello', 'Victor Ribeiro', 'Paulo Cavalin', 'Claudio Pinhanez'] | 2020-12-16 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 2.24163562e-01 8.06163371e-01 1.54854983e-01 -4.19024020e-01
-2.04755932e-01 -3.82031262e-01 6.83902383e-01 -9.26983505e-02
-3.83154333e-01 7.76382685e-01 8.15305933e-02 -2.34553367e-01
-3.14510703e-01 -5.76049924e-01 -3.32010180e-01 -2.69814461e-01
-6.79386705e-02 7.12989569e-01 2.45825365e-01 -7.03279972... | [12.44571590423584, 7.813089847564697] |
28777994-0b53-4e7f-bc50-0390d601261c | are-natural-language-inference-models | 2004.03066 | null | https://arxiv.org/abs/2004.03066v2 | https://arxiv.org/pdf/2004.03066v2.pdf | Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition | Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMP... | ['Paloma Jeretic', 'Suvrat Bhooshan', 'Alex Warstadt', 'Adina Williams'] | 2020-04-07 | are-natural-language-inference-models-1 | https://aclanthology.org/2020.acl-main.768 | https://aclanthology.org/2020.acl-main.768.pdf | acl-2020-6 | ['implicatures'] | ['natural-language-processing'] | [ 3.45650733e-01 1.02567863e+00 -2.62844115e-01 -6.49825931e-01
-8.07906568e-01 -7.53134012e-01 9.44128811e-01 6.90057799e-02
-2.69602567e-01 7.47047603e-01 9.46052432e-01 -8.31058443e-01
-3.80683213e-01 -4.83763844e-01 -7.91129589e-01 -2.57987171e-01
1.33963495e-01 8.45926940e-01 -7.76600242e-02 -2.97768414... | [10.489751815795898, 8.693599700927734] |
a846f1fb-cae4-4e94-a75b-d7e57f3039ef | human-eyes-inspired-recurrent-neural-networks | 2206.07282 | null | https://arxiv.org/abs/2206.07282v1 | https://arxiv.org/pdf/2206.07282v1.pdf | Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises | Compared to human vision, computer vision based on convolutional neural networks (CNN) are more vulnerable to adversarial noises. This difference is likely attributable to how the eyes sample visual input and how the brain processes retinal samples through its dorsal and ventral visual pathways, which are under-explore... | ['Zhongming Liu', 'Xiaokai Wang', 'Kuan Han', 'Yizhen Zhang', 'Minkyu Choi'] | 2022-06-15 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 4.19613123e-01 1.31249741e-01 2.84556150e-01 7.70599395e-02
3.16518806e-02 -6.26099586e-01 6.42379642e-01 -3.81221235e-01
-5.91341555e-01 1.55559897e-01 2.37162754e-01 -3.30445170e-01
2.51870722e-01 -7.96934366e-01 -8.99339497e-01 -8.09025824e-01
3.52112919e-01 -7.25878417e-01 2.67345846e-01 -1.02086775... | [10.094368934631348, 2.3083271980285645] |
8feebf71-cde2-40f4-9607-e5847d98f65b | learning-attention-propagation-for | 2210.11557 | null | https://arxiv.org/abs/2210.11557v1 | https://arxiv.org/pdf/2210.11557v1.pdf | Learning Attention Propagation for Compositional Zero-Shot Learning | Compositional zero-shot learning aims to recognize unseen compositions of seen visual primitives of object classes and their states. While all primitives (states and objects) are observable during training in some combination, their complex interaction makes this task especially hard. For example, wet changes the visua... | ['Muhammad Zeshan Afzal', 'Didier Stricker', 'Alain Pagani', 'Luc van Gool', 'Muhammad Ferjad Naeem', 'Muhammad Gul Zain Ali Khan'] | 2022-10-20 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.90477061e-01 1.88477989e-02 -8.38100389e-02 -2.76750982e-01
-1.98059008e-01 -6.62923634e-01 1.08691728e+00 2.15629399e-01
-2.06160042e-02 2.47126877e-01 6.20824993e-01 -6.84162974e-02
1.21551275e-01 -8.00773561e-01 -1.19656610e+00 -8.66682589e-01
2.80235875e-02 7.99641967e-01 6.52231634e-01 -2.84420371... | [10.267924308776855, 2.1391336917877197] |
bdef150e-0853-4849-aaf3-02400acac815 | modanet-a-large-scale-street-fashion-dataset | 1807.01394 | null | http://arxiv.org/abs/1807.01394v4 | http://arxiv.org/pdf/1807.01394v4.pdf | ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations | Understanding clothes from a single image has strong commercial and cultural
impacts on modern societies. However, this task remains a challenging computer
vision problem due to wide variations in the appearance, style, brand and
layering of clothing items. We present a new database called ModaNet, a
large-scale collec... | ['Shuai Zheng', 'M. Hadi Kiapour', 'Fan Yang', 'Robinson Piramuthu'] | 2018-07-03 | null | null | null | null | ['fashion-understanding'] | ['computer-vision'] | [-5.79559878e-02 -4.55393374e-01 -5.73364571e-02 -3.80797923e-01
-5.16676307e-01 -9.08996999e-01 2.93130130e-01 1.94104090e-01
-2.16405503e-02 3.50498021e-01 -1.35901853e-01 2.75507480e-01
3.23119849e-01 -7.99440503e-01 -1.16311538e+00 -4.06414241e-01
1.44984528e-01 6.66001379e-01 3.01241308e-01 -5.00017166... | [9.961243629455566, 0.3327944576740265] |
397ba321-677a-4b00-b25e-6766b2538038 | wavelet-channel-attention-module-with-a | 2007.09163 | null | https://arxiv.org/abs/2007.09163v1 | https://arxiv.org/pdf/2007.09163v1.pdf | Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining | Single image deraining is a crucial problem because rain severely degenerates the visibility of images and affects the performance of computer vision tasks like outdoor surveillance systems and intelligent vehicles. In this paper, we propose the new convolutional neural network (CNN) called the wavelet channel attentio... | ['Yu-Chiang Frank Wang', 'Chao-Han Huck Yang', 'Hao-Hsiang Yang'] | 2020-07-17 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.79767281e-01 -4.18914676e-01 2.49191806e-01 -5.75337529e-01
-4.50675845e-01 -6.72483072e-02 2.00209692e-01 -5.47529817e-01
-5.36741972e-01 8.08877110e-01 6.00727350e-02 -4.80305962e-02
-9.20047238e-02 -1.00731039e+00 -7.17775464e-01 -1.12062383e+00
-8.76932889e-02 -6.02447748e-01 3.27306777e-01 -2.25994959... | [10.909921646118164, -2.9692516326904297] |
917c12bf-fdd8-4bbb-b47c-84e0da99c574 | term-sets-can-be-strong-document-identifiers | 2305.13859 | null | https://arxiv.org/abs/2305.13859v2 | https://arxiv.org/pdf/2305.13859v2.pdf | Term-Sets Can Be Strong Document Identifiers For Auto-Regressive Search Engines | Auto-regressive search engines emerge as a promising paradigm for next-gen information retrieval systems. These methods work with Seq2Seq models, where each query can be directly mapped to the identifier of its relevant document. As such, they are praised for merits like being end-to-end differentiable. However, auto-r... | ['Zhao Cao', 'Zhicheng Dou', 'Yujia Zhou', 'Zheng Liu', 'Peitian Zhang'] | 2023-05-23 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [ 3.20951879e-01 -1.23678610e-01 -2.41394266e-01 -9.78754908e-02
-1.03380477e+00 -7.62195706e-01 8.10744703e-01 3.60213369e-02
-3.97862107e-01 5.72477460e-01 2.61455774e-01 -3.97565395e-01
-5.71759582e-01 -8.09528768e-01 -4.45853055e-01 -6.26677334e-01
1.11818075e-01 7.79877961e-01 8.59047696e-02 -6.55479491... | [11.529601097106934, 7.595560073852539] |
20d47ebd-d55b-4ba0-baf4-57669ee12a05 | clifford-neural-layers-for-pde-modeling | 2209.04934 | null | https://arxiv.org/abs/2209.04934v2 | https://arxiv.org/pdf/2209.04934v2.pdf | Clifford Neural Layers for PDE Modeling | Partial differential equations (PDEs) see widespread use in sciences and engineering to describe simulation of physical processes as scalar and vector fields interacting and coevolving over time. Due to the computationally expensive nature of their standard solution methods, neural PDE surrogates have become an active ... | ['Jayesh K. Gupta', 'Max Welling', 'Rianne van den Berg', 'Johannes Brandstetter'] | 2022-09-08 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-5.84137082e-01 -8.70588422e-01 6.10637009e-01 -6.23798557e-02
2.40875334e-01 -6.41897440e-01 9.19652462e-01 -6.77052001e-03
-5.86384237e-01 8.61819029e-01 -2.15556324e-01 -4.81379330e-01
-1.44867614e-01 -9.33336496e-01 -6.96295142e-01 -8.07422280e-01
-5.31343699e-01 3.16225380e-01 -1.36491045e-01 -5.88920295... | [6.522068023681641, 3.3594846725463867] |
1c4d7019-60c1-46cf-8bf5-60862580e8c9 | visual-transformers-with-primal-object | 2112.05485 | null | https://arxiv.org/abs/2112.05485v2 | https://arxiv.org/pdf/2112.05485v2.pdf | Visual Transformers with Primal Object Queries for Multi-Label Image Classification | Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at set prediction. The first vision-based transformer model, which was proposed for the object detection... | ['LongLong Yu', 'Joost Van de Weijer', 'Vacit Oguz Yazici'] | 2021-12-10 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 0.59241545 -0.06540931 -0.22908238 -0.5646367 -1.0390016 -0.628068
0.5434346 0.45356372 -0.46180272 0.294161 -0.18605775 -0.28565636
0.13675247 -0.66526634 -1.0219728 -0.62266684 0.45923638 0.73499537
0.57515526 0.15163817 0.37443495 0.08282934 -1.9029074 0.98847884
0.52509874 1.5366338 0.60... | [9.79572868347168, 3.9635047912597656] |
dd5006ef-4060-463f-8890-0ea37480eef0 | multi-scale-temporal-network-for-continuous | 2204.03864 | null | https://arxiv.org/abs/2204.03864v2 | https://arxiv.org/pdf/2204.03864v2.pdf | Multi-scale temporal network for continuous sign language recognition | Continuous Sign Language Recognition (CSLR) is a challenging research task due to the lack of accurate annotation on the temporal sequence of sign language data. The recent popular usage is a hybrid model based on "CNN + RNN" for CSLR. However, when extracting temporal features in these works, most of the methods using... | ['Quan Gan', 'Fei Yuan', 'Jing Li', 'Qidan Zhu'] | 2022-04-08 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-2.40215380e-02 -7.89813995e-01 -3.52403760e-01 -3.55837464e-01
-6.68998837e-01 -1.69123396e-01 4.27334189e-01 -7.66238809e-01
-8.57994378e-01 3.84329051e-01 2.79765666e-01 -4.68100794e-02
-1.31730050e-01 -3.56667548e-01 -3.57986838e-01 -9.48656976e-01
-5.18494770e-02 -2.52790660e-01 8.67815912e-01 -1.16022579... | [9.20715618133545, -6.473455429077148] |
7115507e-e403-4a3a-9efe-53def9e1d161 | funcgnn-a-graph-neural-network-approach-to | 2007.13239 | null | https://arxiv.org/abs/2007.13239v3 | https://arxiv.org/pdf/2007.13239v3.pdf | funcGNN: A Graph Neural Network Approach to Program Similarity | Program similarity is a fundamental concept, central to the solution of software engineering tasks such as software plagiarism, clone identification, code refactoring and code search. Accurate similarity estimation between programs requires an in-depth understanding of their structure, semantics and flow. A control flo... | ['Avijit Roy', 'Karl Meinke', 'Aravind Nair'] | 2020-07-26 | null | null | null | null | ['code-search', 'code-search', 'graph-similarity'] | ['computer-code', 'computer-vision', 'graphs'] | [ 3.05481344e-01 1.56121895e-01 -2.09502473e-01 -1.24156721e-01
-1.13515005e-01 -6.25800252e-01 3.18476975e-01 9.08951104e-01
-2.45362595e-02 -9.80363563e-02 -4.74726669e-02 -8.84120643e-01
-1.14527173e-01 -1.01937151e+00 -8.02091479e-01 -7.44994730e-03
-4.20038581e-01 3.26665230e-02 2.75442690e-01 -2.07797468... | [7.254534721374512, 7.803074836730957] |
7ed83ad7-eb24-41e4-9234-ba59b8cf2127 | unsupervised-word-segmentation-from-speech | 1806.06734 | null | http://arxiv.org/abs/1806.06734v1 | http://arxiv.org/pdf/1806.06734v1.pdf | Unsupervised Word Segmentation from Speech with Attention | We present a first attempt to perform attentional word segmentation directly
from the speech signal, with the final goal to automatically identify lexical
units in a low-resource, unwritten language (UL). Our methodology assumes a
pairing between recordings in the UL with translations in a well-resourced
language. It u... | ['François Yvon', 'Marcely Zanon-Boito', 'Laurent Besacier', 'Alexandre Berard', 'Lucas Ondel', 'Aline Villavicencio', 'Pierre Godard'] | 2018-06-18 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 6.89032376e-01 3.59789342e-01 -1.66356951e-01 -1.91269338e-01
-1.55890739e+00 -7.69840062e-01 3.96766663e-01 -2.85924345e-01
-5.98408639e-01 7.70136893e-01 5.42608738e-01 -8.19939613e-01
5.25796294e-01 -1.70561507e-01 -6.42200649e-01 -2.44622096e-01
3.39108497e-01 8.46047699e-01 -2.69645810e-01 -1.04178652... | [14.366455078125, 6.97222375869751] |
2733e118-6fb3-4ea7-a271-3749bedfa77e | provably-efficient-adversarial-imitation | 2306.06563 | null | https://arxiv.org/abs/2306.06563v1 | https://arxiv.org/pdf/2306.06563v1.pdf | Provably Efficient Adversarial Imitation Learning with Unknown Transitions | Imitation learning (IL) has proven to be an effective method for learning good policies from expert demonstrations. Adversarial imitation learning (AIL), a subset of IL methods, is particularly promising, but its theoretical foundation in the presence of unknown transitions has yet to be fully developed. This paper exp... | ['Zhi-Quan Luo', 'Yang Yu', 'Ziniu Li', 'Tian Xu'] | 2023-06-11 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [ 1.99688986e-01 5.53105116e-01 -3.80828559e-01 3.95072460e-01
-1.08469963e+00 -8.12022448e-01 3.37906122e-01 4.22321595e-02
-8.35070193e-01 9.45396423e-01 -5.58276415e-01 -7.43885994e-01
-6.12707376e-01 -6.08368039e-01 -1.13309288e+00 -7.59213626e-01
-7.25570560e-01 3.69443208e-01 1.22313559e-01 -1.60632074... | [4.328333854675293, 2.7797763347625732] |
63432856-6852-42fb-b776-22b6c65c973d | hpointloc-point-based-indoor-place | 2212.14649 | null | https://arxiv.org/abs/2212.14649v1 | https://arxiv.org/pdf/2212.14649v1.pdf | HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images | We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment and loop detection in simultaneous localization and mapping. The loop detection sub-task is especially relevant when a robot with an on-board RGB-D camera can drive past the sam... | ['Aleksandr I. Panov', 'Aleksei Staroverov', 'Ruslan Musaev', 'Yaroslav Solomentsev', 'Dmitry Yudin'] | 2022-12-30 | null | null | null | null | ['simultaneous-localization-and-mapping', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [-3.23162615e-01 -1.95555165e-01 2.53950149e-01 -2.50914961e-01
-5.52642226e-01 -9.02833402e-01 7.51612306e-01 3.85174602e-02
-6.59264684e-01 6.78448200e-01 -3.18818778e-01 -2.26975143e-01
-1.49575114e-01 -7.09190130e-01 -9.38616097e-01 -6.22135520e-01
-3.44871789e-01 5.86942315e-01 2.66863286e-01 -3.90792161... | [7.358870029449463, -2.054351568222046] |
18cca702-24d0-4ef8-a633-a6bad2a8cc56 | detecting-gender-bias-in-transformer-based | 2110.15733 | null | https://arxiv.org/abs/2110.15733v1 | https://arxiv.org/pdf/2110.15733v1.pdf | Detecting Gender Bias in Transformer-based Models: A Case Study on BERT | In this paper, we propose a novel gender bias detection method by utilizing attention map for transformer-based models. We 1) give an intuitive gender bias judgement method by comparing the different relation degree between the genders and the occupation according to the attention scores, 2) design a gender bias detect... | ['Caiwen Ding', 'Hang Liu', 'Binghui Wang', 'Weiwen Jiang', 'Yueying Liang', 'Lei Yang', 'Junhuan Yang', 'Rajat Sainju', 'Hongwu Peng', 'Bingbing Li'] | 2021-10-15 | null | null | null | null | ['gender-bias-detection', 'gender-bias-detection'] | ['miscellaneous', 'natural-language-processing'] | [-2.79587001e-01 4.09650564e-01 -2.66580969e-01 -4.11534399e-01
3.59666407e-01 -1.85041532e-01 6.20977819e-01 -9.99644049e-04
-6.15646839e-01 4.43180114e-01 3.84237647e-01 -1.27002046e-01
4.74650264e-02 -1.15972841e+00 -5.12180507e-01 -6.22763872e-01
1.90116420e-01 3.81329954e-01 2.15039000e-01 -4.63956863... | [9.403170585632324, 10.213443756103516] |
86d81b65-96ca-4825-b8ca-a48dd384be17 | spherical-transformer-adapting-spherical | 2101.03848 | null | https://arxiv.org/abs/2101.03848v3 | https://arxiv.org/pdf/2101.03848v3.pdf | Spherical Transformer: Adapting Spherical Signal to CNNs | Convolutional neural networks (CNNs) have been widely used in various vision tasks, e.g. image classification, semantic segmentation, etc. Unfortunately, standard 2D CNNs are not well suited for spherical signals such as panorama images or spherical projections, as the sphere is an unstructured grid. In this paper, we ... | ['Haikuan Du', 'Yin Wang', 'Yuqi Liu', 'Shen Cai'] | 2021-01-11 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [ 1.61014527e-01 -2.28110738e-02 2.61307180e-01 -6.37515545e-01
-3.42414498e-01 -5.25072217e-01 8.23978782e-01 -5.99565744e-01
-5.19509971e-01 2.51331609e-02 -2.75026672e-02 -2.48103812e-01
1.62008643e-01 -1.05747128e+00 -1.10051548e+00 -6.94820404e-01
3.41391444e-01 5.08221865e-01 5.94085634e-01 -1.18299626... | [8.081743240356445, -3.342715263366699] |
5e01ae74-5b00-47ca-85af-03de165494e3 | evaluation-of-chatgpt-as-a-question-answering | 2303.07992 | null | https://arxiv.org/abs/2303.07992v1 | https://arxiv.org/pdf/2303.07992v1.pdf | Evaluation of ChatGPT as a Question Answering System for Answering Complex Questions | ChatGPT is a powerful large language model (LLM) that has made remarkable progress in natural language understanding. Nevertheless, the performance and limitations of the model still need to be extensively evaluated. As ChatGPT covers resources such as Wikipedia and supports natural language question answering, it has ... | ['Guilin Qi', 'Yongrui Chen', 'Nan Hu', 'Wenbo Li', 'Yu Li', 'Dehai Min', 'Yiming Tan'] | 2023-03-14 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [-6.28801525e-01 3.23509425e-01 3.17731470e-01 -4.89803880e-01
-1.27291751e+00 -7.57998168e-01 3.35291982e-01 2.67650425e-01
-3.32025290e-01 6.93376184e-01 1.97063029e-01 -5.59935510e-01
-5.32046139e-01 -1.02392936e+00 -4.92041051e-01 3.84010859e-02
3.05453658e-01 1.14839995e+00 6.58606529e-01 -7.76340723... | [10.607220649719238, 7.974024772644043] |
c19b2e26-6470-403f-b53e-bf2ab98b9a79 | representation-learning-to-classify-and | 2107.04448 | null | https://arxiv.org/abs/2107.04448v1 | https://arxiv.org/pdf/2107.04448v1.pdf | Representation Learning to Classify and Detect Adversarial Attacks against Speaker and Speech Recognition Systems | Adversarial attacks have become a major threat for machine learning applications. There is a growing interest in studying these attacks in the audio domain, e.g, speech and speaker recognition; and find defenses against them. In this work, we focus on using representation learning to classify/detect attacks w.r.t. the ... | ['Najim Dehak', 'Piotr Żelasko', 'Sonal Joshi', 'Jesús Villalba'] | 2021-07-09 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 4.68851209e-01 3.97478119e-02 2.19214156e-01 -2.72529632e-01
-1.05056834e+00 -1.10218692e+00 6.96997285e-01 2.73092836e-01
-1.67229310e-01 2.62729466e-01 1.22742960e-02 -6.98282421e-01
6.70415834e-02 -6.96890593e-01 -6.04819894e-01 -6.66465700e-01
-3.59355271e-01 2.03554422e-01 1.85727775e-01 -3.81540716... | [13.957962989807129, 5.807216167449951] |
68dc4fb4-1755-4e35-b765-63335f91f8c1 | recognition-of-handwritten-digit-using | 1909.08490 | null | https://arxiv.org/abs/1909.08490v1 | https://arxiv.org/pdf/1909.08490v1.pdf | Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers | In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, heal... | ['Fathma Siddique', 'Md. Abu Bakr Siddique', 'Shadman Sakib'] | 2019-09-12 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.56872153e-01 -4.19384569e-01 -4.59707044e-02 -5.37650764e-01
4.22991306e-01 -1.36139929e-01 8.09825778e-01 1.86684132e-01
-5.61104476e-01 6.07737243e-01 -6.98440615e-03 -4.86061037e-01
-2.16203466e-01 -8.45668018e-01 -4.62298840e-01 -5.60296118e-01
4.00043055e-02 5.70889190e-02 1.78314909e-01 -3.68685603... | [11.600064277648926, 2.7044730186462402] |
e0d1ea22-2bd1-472f-8569-84bce7914f1a | bonsai-diverse-and-shallow-trees-for-extreme | 1904.08249 | null | https://arxiv.org/abs/1904.08249v2 | https://arxiv.org/pdf/1904.08249v2.pdf | Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification | Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space... | ['Sujay Khandagale', 'Rohit Babbar', 'Han Xiao'] | 2019-04-17 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 2.84694880e-01 3.34796379e-04 -3.97889823e-01 -4.85116482e-01
-1.19181669e+00 -7.38121569e-01 5.11605322e-01 2.53029704e-01
-1.22869268e-01 5.63296556e-01 -1.98897552e-02 -4.58227664e-01
-1.70781255e-01 -6.37776494e-01 -4.11691785e-01 -8.00979614e-01
1.13054834e-01 1.04814076e+00 2.76429169e-02 1.24880694... | [9.53431224822998, 4.387480735778809] |
77335cd8-0053-4596-b99c-a032f53fc977 | simsc-a-simple-framework-for-semantic | 2305.02385 | null | https://arxiv.org/abs/2305.02385v1 | https://arxiv.org/pdf/2305.02385v1.pdf | SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning | We propose SimSC, a remarkably simple framework, to address the problem of semantic matching only based on the feature backbone. We discover that when fine-tuning ImageNet pre-trained backbone on the semantic matching task, L2 normalization of the feature map, a standard procedure in feature matching, produces an overl... | ['Victor Adrian Prisacariu', 'Xingchen Wan', 'Kai Han', 'Xinghui Li'] | 2023-05-03 | null | null | null | null | ['semantic-correspondence'] | ['computer-vision'] | [ 3.18970799e-01 1.08488694e-01 -3.73690516e-01 -6.09593332e-01
-1.15673518e+00 -7.88662732e-01 5.19863725e-01 -1.19237632e-01
-5.40543139e-01 1.88522309e-01 2.38235295e-01 1.36042148e-01
-5.99512421e-02 -7.46353924e-01 -1.04881239e+00 -5.62477827e-01
2.19013706e-01 4.93478626e-01 4.29076314e-01 -2.68619418... | [8.262397766113281, -1.8707923889160156] |
0af73d7a-6bc4-424f-a8de-791912cc592a | robust-image-stitching-with-multiple-1 | 2011.11784 | null | https://arxiv.org/abs/2011.11784v1 | https://arxiv.org/pdf/2011.11784v1.pdf | Robust image stitching with multiple registrations | Panorama creation is one of the most widely deployed techniques in computer vision. In addition to industry applications such as Google Street View, it is also used by millions of consumers in smartphones and other cameras. Traditionally, the problem is decomposed into three phases: registration, which picks a single t... | ['Ramin Zabih', 'Ce Liu', 'Michael Krainin', 'Emil Keyder', 'Richard Strong Bowen', 'Chen Wang', 'Charles Herrmann'] | 2020-11-23 | robust-image-stitching-with-multiple | http://openaccess.thecvf.com/content_ECCV_2018/html/Charles_Herrmann_Robust_image_stitching_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Charles_Herrmann_Robust_image_stitching_ECCV_2018_paper.pdf | eccv-2018-9 | ['image-stitching'] | ['computer-vision'] | [ 8.28441441e-01 -2.01882794e-01 3.61366905e-02 -1.97738007e-01
-5.92184782e-01 -7.04390764e-01 7.13234007e-01 -1.86661541e-01
-3.10518652e-01 4.27353829e-01 4.56188731e-02 -1.84163228e-01
1.17599726e-01 -6.85805857e-01 -8.42640221e-01 -6.78152621e-01
2.37434000e-01 1.17281131e-01 7.28399515e-01 -2.43483588... | [9.303654670715332, -2.4975831508636475] |
957ba3d8-a865-4480-a965-9b3828678e57 | voice2series-reprogramming-acoustic-models | 2106.09296 | null | https://arxiv.org/abs/2106.09296v3 | https://arxiv.org/pdf/2106.09296v3.pdf | Voice2Series: Reprogramming Acoustic Models for Time Series Classification | Learning to classify time series with limited data is a practical yet challenging problem. Current methods are primarily based on hand-designed feature extraction rules or domain-specific data augmentation. Motivated by the advances in deep speech processing models and the fact that voice data are univariate temporal s... | ['Pin-Yu Chen', 'Yun-Yun Tsai', 'Chao-Han Huck Yang'] | 2021-06-17 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 3.98892015e-01 -6.62744939e-02 7.95130953e-02 -5.13608992e-01
-1.07057953e+00 -7.57241786e-01 7.18112767e-01 1.30948022e-01
-4.24100131e-01 2.54816443e-01 1.97455212e-01 -6.93975389e-01
-3.01797569e-01 -3.09127897e-01 -5.94806790e-01 -5.54269314e-01
-6.80344224e-01 6.19895346e-02 -7.24394396e-02 -1.27163127... | [15.090948104858398, 5.68483304977417] |
815de829-e5c5-40d7-a900-77a9ccc47586 | virus2vec-viral-sequence-classification-using | 2304.12328 | null | https://arxiv.org/abs/2304.12328v1 | https://arxiv.org/pdf/2304.12328v1.pdf | Virus2Vec: Viral Sequence Classification Using Machine Learning | Understanding the host-specificity of different families of viruses sheds light on the origin of, e.g., SARS-CoV-2, rabies, and other such zoonotic pathogens in humans. It enables epidemiologists, medical professionals, and policymakers to curb existing epidemics and prevent future ones promptly. In the family Coronavi... | ['Murray Patterson', 'Imdad Ullah Khan', 'Pin-Yu Chen', 'Ria Thazhe Punathil', 'Prakash Chourasia', 'Babatunde Bello', 'Sarwan Ali'] | 2023-04-24 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 2.36234367e-01 -6.45357788e-01 -3.04039627e-01 -2.71872580e-01
-5.01104221e-02 -9.14148390e-01 5.84632933e-01 3.05508077e-01
-5.57381749e-01 6.08292580e-01 1.27835125e-01 -5.61039448e-01
2.63414472e-01 -5.70843279e-01 -4.62124735e-01 -8.41032267e-01
-5.22393346e-01 8.14701080e-01 -2.60222614e-01 -3.49250019... | [4.950372219085693, 5.278842926025391] |
e540419c-9324-409d-8ce7-2b3f35f13db6 | are-you-stealing-my-model-sample-correlation | 2210.15427 | null | https://arxiv.org/abs/2210.15427v1 | https://arxiv.org/pdf/2210.15427v1.pdf | Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks | An off-the-shelf model as a commercial service could be stolen by model stealing attacks, posing great threats to the rights of the model owner. Model fingerprinting aims to verify whether a suspect model is stolen from the victim model, which gains more and more attention nowadays. Previous methods always leverage the... | ['Ran He', 'Jian Liang', 'Jiyang Guan'] | 2022-10-21 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 4.68152642e-01 -2.07099125e-01 -3.92548531e-01 -3.93384755e-01
-9.75718141e-01 -1.15504670e+00 5.76590478e-01 -3.57094914e-01
-9.49742720e-02 7.02735841e-01 -6.95562780e-01 -5.84674656e-01
1.94936782e-01 -8.44379723e-01 -1.03917503e+00 -7.03375041e-01
8.10248107e-02 4.33685005e-01 1.55002698e-01 1.29613891... | [5.769282817840576, 7.444635391235352] |
717e10f9-2192-4237-945b-29be058506e6 | document-based-recommender-system-for-job | null | null | https://aclanthology.org/N18-3027 | https://aclanthology.org/N18-3027.pdf | Document-based Recommender System for Job Postings using Dense Representations | Job boards and professional social networks heavily use recommender systems in order to better support users in exploring job advertisements. Detecting the similarity between job advertisements is important for job recommendation systems as it allows, for example, the application of item-to-item based recommendations. ... | ['Martin Riedl', 'Chris Biemann', 'Ahmed Elsafty'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['document-embedding'] | ['methodology'] | [-4.94443029e-02 8.29230249e-02 -2.71535784e-01 -4.96438712e-01
-3.49386185e-01 -3.80022556e-01 8.16884100e-01 5.13211071e-01
-7.33310521e-01 3.92347217e-01 4.97832566e-01 -2.89250582e-01
-5.27720094e-01 -8.16433132e-01 -2.81902313e-01 -2.47233853e-01
1.09387875e-01 9.10479844e-01 3.00107628e-01 -4.90217626... | [10.207293510437012, 5.778411865234375] |
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