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19ede530-87c8-42b1-a9f4-fa1edb208b89 | variational-transformer-a-framework-beyond | 2205.14458 | null | https://arxiv.org/abs/2205.14458v2 | https://arxiv.org/pdf/2205.14458v2.pdf | Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image Captioning | Accuracy and Diversity are two essential metrizable manifestations in generating natural and semantically correct captions. Many efforts have been made to enhance one of them with another decayed due to the trade-off gap. In this work, we will show that the inferior standard of accuracy draws from human annotations (le... | ['Lianghua He', 'Yitao Peng', 'Yihang Liu', 'Longzhen Yang'] | 2022-05-28 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [-1.44306459e-02 1.91651180e-01 -9.52579007e-02 -6.19172573e-01
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9.16740149e-02 -3.81182492e-01 -8.59191895e-01 -8.02239299e-01
3.75158429e-01 6.48596525e-01 9.61727276e-02 -3.53163630... | [11.041481018066406, 0.9209818243980408] |
f4062297-a940-409c-af0f-d7b8f7b008fd | codex-hacks-hackerrank-memorization-issues | 2212.02684 | null | https://arxiv.org/abs/2212.02684v1 | https://arxiv.org/pdf/2212.02684v1.pdf | Codex Hacks HackerRank: Memorization Issues and a Framework for Code Synthesis Evaluation | The Codex model has demonstrated extraordinary competence in synthesizing code from natural language problem descriptions. However, in order to reveal unknown failure modes and hidden biases, such large-scale models must be systematically subjected to multiple and diverse evaluation studies. In this work, we evaluate t... | ['Romain Robbes', "Marco D'Ambros", 'Julian Aron Prenner', 'Anjan Karmakar'] | 2022-12-06 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.72577634e-01 3.54054570e-01 4.32919115e-02 -2.89788485e-01
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557ebe3b-aa59-4e7e-ae6c-c5db243bffd8 | storyer-automatic-story-evaluation-via | 2210.08459 | null | https://arxiv.org/abs/2210.08459v2 | https://arxiv.org/pdf/2210.08459v2.pdf | StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning | Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference. We go beyond this limitation by considering a novel \textbf{Story} \textbf{E}valuation method that mimics human preference when judging a story, namely \textbf{StoryER}, which consists of three... | ['Hideki Nakayama', 'Yusuke Miyao', 'Hiroya Takamura', 'Duc Minh Vo', 'Hong Chen'] | 2022-10-16 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 1.51960552e-01 1.25800490e-01 -3.49197149e-01 -6.00566566e-01
-1.36429775e+00 -8.09321582e-01 6.80857837e-01 1.74940020e-01
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2.47183904e-01 5.81252337e-01 3.30009796e-02 -3.55255216... | [11.627408981323242, 8.832967758178711] |
f88eaf00-210b-4c11-8105-166f52b90ca1 | dialogvcs-robust-natural-language | 2305.14751 | null | https://arxiv.org/abs/2305.14751v1 | https://arxiv.org/pdf/2305.14751v1.pdf | DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade | In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the existent data accumulated in the last updates. Within the newly added data, new intents would emerge and might have semantic entanglement wi... | ['Yunbo Cao', 'Baobao Chang', 'Binghuai Lin', 'Gang Yuan', 'Jiaqi Han', 'Haoran Meng', 'Xu Wang', 'Tianyu Liu', 'Xin Zheng', 'Zefan Cai'] | 2023-05-24 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [ 2.25841030e-01 6.79432511e-01 -8.36926848e-02 -6.26320601e-01
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3.38063091e-01 8.44768345e-01 1.17099568e-01 -7.30396032... | [12.612701416015625, 7.722309112548828] |
8312986d-090a-466f-b4a0-366e1395aaa2 | dp-kb-data-programming-with-knowledge-bases-1 | 2203.09598 | null | https://arxiv.org/abs/2203.09598v1 | https://arxiv.org/pdf/2203.09598v1.pdf | DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection | While transformers demonstrate impressive performance on many knowledge intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs) remains limited, as shown on several slot-filling, question-answering (QA), fact verification, and entity-linking tasks. In this paper, we implement an efficient, data-p... | ['Alessandro Moschitti', 'Manish Gupta', 'Thuy Vu', 'Nic Jedema'] | 2022-03-17 | dp-kb-data-programming-with-knowledge-bases | https://openreview.net/forum?id=AN4xPK0F0Fs | https://openreview.net/pdf?id=AN4xPK0F0Fs | neurips-workshop-dbai-2021-12 | ['slot-filling'] | ['natural-language-processing'] | [-1.36058526e-02 6.41246617e-01 -9.36550051e-02 -2.84943104e-01
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8.92230719e-02 7.97018170e-01 8.40066969e-01 -6.01858318... | [10.526408195495605, 8.099804878234863] |
3db57c38-c19a-47ff-85c1-cb5d67196b58 | towards-unsupervised-sketch-based-image | 2105.08237 | null | https://arxiv.org/abs/2105.08237v4 | https://arxiv.org/pdf/2105.08237v4.pdf | Towards Unsupervised Sketch-based Image Retrieval | The practical value of existing supervised sketch-based image retrieval (SBIR) algorithms is largely limited by the requirement for intensive data collection and labeling. In this paper, we present the first attempt at unsupervised SBIR to remove the labeling cost (both category annotations and sketch-photo pairings) t... | ['Yi-Zhe Song', 'Timothy M. Hospedales', 'Yunpeng Li', 'Yongxin Yang', 'Conghui Hu'] | 2021-05-18 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 4.56504673e-01 -3.35018784e-01 -4.87326771e-01 -4.02969390e-01
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-3.85039240e-01 -3.84419590e-01 -4.19788063e-01 -6.56737804e-01
2.43354410e-01 8.72060418e-01 2.64178216e-01 5.99635392... | [11.587098121643066, 0.7107003927230835] |
7447b6a9-0951-4fd5-a51b-19bdb5edef76 | a-transformer-framework-for-data-fusion-and | 2211.10506 | null | https://arxiv.org/abs/2211.10506v1 | https://arxiv.org/pdf/2211.10506v1.pdf | A Transformer Framework for Data Fusion and Multi-Task Learning in Smart Cities | Rapid global urbanization is a double-edged sword, heralding promises of economical prosperity and public health while also posing unique environmental and humanitarian challenges. Smart and connected communities (S&CCs) apply data-centric solutions to these problems by integrating artificial intelligence (AI) and the ... | ['Dwi Novitasari', 'Mochamad Donny Koerniawan', 'Rachmawan Budiarto', 'Wangda Zuo', 'Walid Saad', 'Alexander C. DeRieux'] | 2022-11-18 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 1.83621854e-01 -3.73693943e-01 -2.03809798e-01 -8.48701075e-02
-7.37209499e-01 -4.30733144e-01 8.44760537e-01 7.42526576e-02
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-1.95857197e-01 4.41210270e-01 -2.53090769e-01 -4.46190119... | [6.782973766326904, 2.413879156112671] |
522c97d4-702b-4077-b581-7fc275918805 | predictive-and-contrastive-dual-auxiliary | 2203.03982 | null | https://arxiv.org/abs/2203.03982v2 | https://arxiv.org/pdf/2203.03982v2.pdf | Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation | Self-supervised learning (SSL) recently has achieved outstanding success on recommendation. By setting up an auxiliary task (either predictive or contrastive), SSL can discover supervisory signals from the raw data without human annotation, which greatly mitigates the problem of sparse user-item interactions. However, ... | ['Xu Wang', 'Qingyu Xiong', 'Zongwei Wang', 'Junliang Yu', 'Min Gao', 'Yinghui Tao'] | 2022-03-08 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 2.04459384e-01 3.01893800e-01 -8.37737858e-01 -3.64438266e-01
-2.12158605e-01 -3.29458475e-01 6.47421658e-01 -5.37028797e-02
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-5.02686143e-01 -7.92607367e-01 -7.55137742e-01 -8.23012769e-01
-1.12910964e-01 3.57660890e-01 1.36971414e-01 -5.27551711... | [10.22691822052002, 5.612393379211426] |
5f9c0f31-b053-407e-901b-3884a2b3c1b2 | unsupervised-source-hierarchies-for-low | null | null | https://aclanthology.org/W18-2902 | https://aclanthology.org/W18-2902.pdf | Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation | Incorporating source syntactic information into neural machine translation (NMT) has recently proven successful (Eriguchi et al., 2016; Luong et al., 2016). However, this is generally done using an outside parser to syntactically annotate the training data, making this technique difficult to use for languages or domain... | ['Kenneth Heafield', 'Anna Currey'] | 2018-07-01 | null | null | null | ws-2018-7 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 4.77422029e-01 4.18035746e-01 -2.57459670e-01 -6.04194164e-01
-1.42526281e+00 -8.89794588e-01 4.38955307e-01 -1.10369489e-01
-4.99594122e-01 9.83691394e-01 4.21837002e-01 -9.53937709e-01
7.20129609e-01 -6.02145791e-01 -1.02555156e+00 -3.10827494e-01
2.63201803e-01 6.63608551e-01 -3.14124793e-01 -2.15523064... | [11.602338790893555, 10.246121406555176] |
8ded1157-92d5-4293-97c7-a108024b7cbd | enriching-object-detection-with-2d-3d | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Choy_Enriching_Object_Detection_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Choy_Enriching_Object_Detection_2015_CVPR_paper.pdf | Enriching Object Detection With 2D-3D Registration and Continuous Viewpoint Estimation | A large body of recent work on object detection has focused on exploiting 3D CAD model databases to improve detection performance. Many of these approaches work by aligning exact 3D models to images using templates generated from renderings of the 3D models at a set of discrete viewpoints. However, the training procedu... | ['Sam Corbett-Davies', 'Christopher Bongsoo Choy', 'Silvio Savarese', 'Michael Stark'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['viewpoint-estimation'] | ['computer-vision'] | [ 2.14564443e-01 -4.48380172e-01 2.51722038e-01 -1.87705040e-01
-8.53000760e-01 -7.67982423e-01 6.36562586e-01 -9.71627533e-02
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1.23471744e-01 9.48099494e-01 8.60628068e-01 1.83681056... | [7.732873439788818, -2.658701181411743] |
7bf0551f-952e-486f-87f2-0f3d7df53e11 | effseg-efficient-fine-grained-instance | 2307.01545 | null | https://arxiv.org/abs/2307.01545v1 | https://arxiv.org/pdf/2307.01545v1.pdf | EffSeg: Efficient Fine-Grained Instance Segmentation using Structure-Preserving Sparsity | Many two-stage instance segmentation heads predict a coarse 28x28 mask per instance, which is insufficient to capture the fine-grained details of many objects. To address this issue, PointRend and RefineMask predict a 112x112 segmentation mask resulting in higher quality segmentations. Both methods however have limitat... | ['Tinne Tuytelaars', 'Cédric Picron'] | 2023-07-04 | null | null | null | null | ['instance-segmentation'] | ['computer-vision'] | [ 2.59255737e-01 1.18221782e-01 2.61419248e-02 -3.44957441e-01
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15f5e3ad-568d-49ca-9191-a6336319c251 | multi-view-imputation-and-cross-attention | 2206.08019 | null | https://arxiv.org/abs/2206.08019v2 | https://arxiv.org/pdf/2206.08019v2.pdf | Multi-View Imputation and Cross-Attention Network Based on Incomplete Longitudinal and Multimodal Data for Conversion Prediction of Mild Cognitive Impairment | Predicting whether subjects with mild cognitive impairment (MCI) will convert to Alzheimer's disease is a significant clinical challenge. Longitudinal variations and complementary information inherent in longitudinal and multimodal data are crucial for MCI conversion prediction, but persistent issue of missing data in ... | ['Xiaoling Zhang', 'Xiumei Chen', 'Qianjin Feng', 'Shuoling Zhou', 'Tao Wang', 'Meiyan Huang'] | 2022-06-16 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 3.30171958e-02 -3.59612197e-01 -4.65205908e-01 -7.49572575e-01
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1.85114052e-02 4.30355757e-01 -1.89669460e-01 -1.22265913... | [14.27318000793457, -1.729067087173462] |
40f0a3e7-1663-41c9-94f5-db5ac5e2d391 | rignerf-fully-controllable-neural-3d-1 | 2206.06481 | null | https://arxiv.org/abs/2206.06481v1 | https://arxiv.org/pdf/2206.06481v1.pdf | RigNeRF: Fully Controllable Neural 3D Portraits | Volumetric neural rendering methods, such as neural radiance fields (NeRFs), have enabled photo-realistic novel view synthesis. However, in their standard form, NeRFs do not support the editing of objects, such as a human head, within a scene. In this work, we propose RigNeRF, a system that goes beyond just novel view ... | ['Zhixin Shu', 'Eli Shechtman', 'Kalyan Sunkavalli', 'Zexiang Xu', 'ShahRukh Athar'] | 2022-06-13 | rignerf-fully-controllable-neural-3d | http://openaccess.thecvf.com//content/CVPR2022/html/Athar_RigNeRF_Fully_Controllable_Neural_3D_Portraits_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Athar_RigNeRF_Fully_Controllable_Neural_3D_Portraits_CVPR_2022_paper.pdf | cvpr-2022-1 | ['face-model'] | ['computer-vision'] | [ 1.86096609e-01 4.30758387e-01 2.93490946e-01 -5.78035414e-01
-4.51600403e-01 -3.57036769e-01 5.59936404e-01 -8.46750438e-01
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2.03091577e-01 2.22894818e-01 -1.21400863e-01 -3.17415059... | [12.7859525680542, -0.42001432180404663] |
32ce11ed-6017-4a96-b880-81dd32b238c7 | kecp-knowledge-enhanced-contrastive-prompting | 2205.03071 | null | https://arxiv.org/abs/2205.03071v1 | https://arxiv.org/pdf/2205.03071v1.pdf | KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering | Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-trained Language Models (PLMs). However, most existing approaches for MRC may perform poorly in the few-shot learning scenario. To solve this ... | ['Ming Gao', 'Jun Huang', 'Hongbin Wang', 'Qiuhui Shi', 'Minghui Qiu', 'Chengyu Wang', 'Jianing Wang'] | 2022-05-06 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 4.08092529e-01 3.28353405e-01 -9.56552923e-02 -2.08186015e-01
-1.31671321e+00 -2.45152101e-01 6.71427071e-01 3.41378689e-01
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4.90074158e-01 3.93703043e-01 5.59304893e-01 -5.55155873... | [11.168326377868652, 8.04631519317627] |
400fcc3b-f13e-45d2-a18d-224573aaa071 | no-metrics-are-perfect-adversarial-reward | 1804.0916 | null | http://arxiv.org/abs/1804.09160v2 | http://arxiv.org/pdf/1804.09160v2.pdf | No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling | Though impressive results have been achieved in visual captioning, the task
of generating abstract stories from photo streams is still a little-tapped
problem. Different from captions, stories have more expressive language styles
and contain many imaginary concepts that do not appear in the images. Thus it
poses challe... | ['Yuan-Fang Wang', 'William Yang Wang', 'Wenhu Chen', 'Xin Wang'] | 2018-04-24 | no-metrics-are-perfect-adversarial-reward-1 | https://aclanthology.org/P18-1083 | https://aclanthology.org/P18-1083.pdf | acl-2018-7 | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.68254161e-01 3.41802448e-01 -3.08704644e-01 -1.75842881e-01
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9.44954827e-02 4.53714103e-01 -5.15015163e-02 -2.91847080... | [11.159467697143555, 0.6141761541366577] |
001972c2-82bc-4306-a46e-c235a7d5a2c5 | codeie-large-code-generation-models-are | 2305.05711 | null | https://arxiv.org/abs/2305.05711v2 | https://arxiv.org/pdf/2305.05711v2.pdf | CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors | Large language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning ability on many NLP tasks. A common practice is to recast the task into a text-to-text format such that generative LLMs of natural language (NL-LLMs) like GPT-3 can be prompted to solve it. However, it is nontrivi... | ['Xipeng Qiu', 'Xuanjing Huang', 'Yuanbin Wu', 'Hang Yan', 'Qiong Tang', 'Tianxiang Sun', 'Peng Li'] | 2023-05-09 | null | null | null | null | ['uie', 'relation-extraction'] | ['computer-vision', 'natural-language-processing'] | [ 4.75066483e-01 4.76433635e-01 -2.23792404e-01 -3.26194912e-01
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8.54251534e-02 4.79427218e-01 7.43633360e-02 -2.51933664... | [7.883000373840332, 7.918665409088135] |
96df1cad-304e-43a2-97a9-0b125d734ebf | on-learning-universal-representations-across | 2007.1596 | null | https://arxiv.org/abs/2007.15960v4 | https://arxiv.org/pdf/2007.15960v4.pdf | On Learning Universal Representations Across Languages | Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks. However, existing approaches essentially capture the co-occurrence among tokens through involving the masked language model (MLM) objective with token-le... | ['Yue Hu', 'Rongxiang Weng', 'Weihua Luo', 'Heng Yu', 'Luxi Xing', 'Xiangpeng Wei'] | 2020-07-31 | null | https://openreview.net/forum?id=Uu1Nw-eeTxJ | https://openreview.net/pdf?id=Uu1Nw-eeTxJ | iclr-2021-1 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 1.34781480e-01 -2.89091736e-01 -5.77640891e-01 -5.73781312e-01
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9.53923240e-02 5.80121040e-01 -6.31119430e-01 -3.10872018... | [11.270623207092285, 9.88017463684082] |
35cfe365-f747-4b29-a02f-0f45b45fffed | villandiffusion-a-unified-backdoor-attack | 2306.06874 | null | https://arxiv.org/abs/2306.06874v2 | https://arxiv.org/pdf/2306.06874v2.pdf | VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models | Diffusion Models (DMs) are state-of-the-art generative models that learn a reversible corruption process from iterative noise addition and denoising. They are the backbone of many generative AI applications, such as text-to-image conditional generation. However, recent studies have shown that basic unconditional DMs (e... | ['Tsung-Yi Ho', 'Pin-Yu Chen', 'Sheng-Yen Chou'] | 2023-06-12 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 6.85817838e-01 -9.96868312e-02 -1.90909822e-02 3.04945081e-01
-1.19552565e+00 -1.24487019e+00 1.29858053e+00 -5.49862921e-01
1.10068098e-01 5.31862855e-01 1.44169638e-02 -7.57740319e-01
3.18905979e-01 -1.05022633e+00 -1.02209044e+00 -1.07730389e+00
-4.66963463e-03 2.68281430e-01 2.27002650e-01 -1.13835521... | [5.760780334472656, 7.859830379486084] |
5478252d-4d76-4016-9b95-fe613f6f8fa7 | hybrur-a-hybrid-physical-neural-solution-for | 2107.0266 | null | https://arxiv.org/abs/2107.02660v1 | https://arxiv.org/pdf/2107.02660v1.pdf | HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration | Robust vision restoration for an underwater image remains a challenging problem. For the lack of aligned underwater-terrestrial image pairs, the unsupervised method is more suited to this task. However, the pure data-driven unsupervised method usually has difficulty in achieving realistic color correction for lack of o... | ['Junzhi Yu', 'Min Tan', 'Yue Lu', 'Jian Wang', 'Zhengxing Wu', 'Xingyu Chen', 'Shuaizheng Yan'] | 2021-07-06 | null | null | null | null | ['underwater-image-restoration'] | ['computer-vision'] | [ 3.43896836e-01 -2.90720463e-01 5.79524457e-01 -5.93241751e-01
-6.82715714e-01 -3.11129224e-02 1.39362872e-01 -2.18743965e-01
-6.32705092e-01 7.24996805e-01 1.84549823e-01 -2.81543117e-02
-2.05651611e-01 -7.99918890e-01 -8.98814797e-01 -1.26453173e+00
1.03381097e-01 2.36509070e-02 1.67857155e-01 -2.58542359... | [10.699172973632812, -3.5258805751800537] |
9443ea03-3e34-46ff-8254-6d9c1685c3d9 | not-all-lotteries-are-made-equal-1 | 2206.08175 | null | https://arxiv.org/abs/2206.08175v1 | https://arxiv.org/pdf/2206.08175v1.pdf | Not All Lotteries Are Made Equal | The Lottery Ticket Hypothesis (LTH) states that for a reasonably sized neural network, a sub-network within the same network yields no less performance than the dense counterpart when trained from the same initialization. This work investigates the relation between model size and the ease of finding these sparse sub-ne... | ['Somya Suhans Mahapatra', 'Sai Mitheran', 'Surya Kant Sahu'] | 2022-06-16 | null | null | null | null | ['ticket-search'] | ['methodology'] | [-9.82455090e-02 4.82701838e-01 -5.54966748e-01 -4.59958524e-01
-3.09601039e-01 -1.00261152e-01 5.81736743e-01 -3.50751162e-01
-6.89147711e-01 9.05569911e-01 1.54011399e-01 -3.86283606e-01
-1.22212335e-01 -7.17457294e-01 -1.09248352e+00 -5.22913873e-01
-3.55009466e-01 8.52636397e-01 5.21843806e-02 6.43573748... | [8.476202964782715, 3.3509488105773926] |
9fbca879-edbf-4e30-a5d0-33a7a5b08a2b | nerfinvertor-high-fidelity-nerf-gan-inversion | 2211.17235 | null | https://arxiv.org/abs/2211.17235v1 | https://arxiv.org/pdf/2211.17235v1.pdf | NeRFInvertor: High Fidelity NeRF-GAN Inversion for Single-shot Real Image Animation | Nerf-based Generative models have shown impressive capacity in generating high-quality images with consistent 3D geometry. Despite successful synthesis of fake identity images randomly sampled from latent space, adopting these models for generating face images of real subjects is still a challenging task due to its so-... | ['Yun Fu', 'Xin Tong', 'Jiaolong Yang', 'HsiangTao Wu', 'Kamran Ghasedi', 'Yu Yin'] | 2022-11-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yin_NeRFInvertor_High_Fidelity_NeRF-GAN_Inversion_for_Single-Shot_Real_Image_Animation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yin_NeRFInvertor_High_Fidelity_NeRF-GAN_Inversion_for_Single-Shot_Real_Image_Animation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-animation'] | ['computer-vision'] | [ 3.34727466e-01 4.65049654e-01 2.57621258e-01 -3.05444598e-01
-9.77855444e-01 -5.93032598e-01 7.70945191e-01 -8.90583754e-01
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2.70446151e-01 5.18780112e-01 -5.31839848e-01 -1.47542274... | [12.547532081604004, -0.3553749918937683] |
0550eae1-87d2-47c6-a30e-5d03acdc6cd7 | effective-resistance-for-pandemics-mobility | 2111.02449 | null | https://arxiv.org/abs/2111.02449v3 | https://arxiv.org/pdf/2111.02449v3.pdf | Effective Resistance for Pandemics: Mobility Network Sparsification for High-Fidelity Epidemic Simulation | Network science has increasingly become central to the field of epidemiology and our ability to respond to infectious disease threats. However, many networks derived from modern datasets are not just large, but dense, with a high ratio of edges to nodes. This includes human mobility networks where most locations have a... | ['Cristopher Moore', 'Samuel V. Scarpino', 'Alexander M. Mercier'] | 2021-11-03 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 9.76287201e-02 1.17798418e-01 -2.41014466e-01 2.52096832e-01
1.14685535e-01 -6.75187051e-01 4.92686361e-01 3.65287334e-01
-5.67054152e-01 9.85109091e-01 1.36354879e-01 -4.21796948e-01
-6.59951746e-01 -1.22098243e+00 -3.13940257e-01 -6.70665383e-01
-1.01588786e+00 9.53307867e-01 4.03724134e-01 -4.88256454... | [6.484703063964844, 5.001572608947754] |
b5ffff13-14f1-4c8e-b521-90d8514c234a | deep-gaussian-mixture-ensembles | 2306.07235 | null | https://arxiv.org/abs/2306.07235v1 | https://arxiv.org/pdf/2306.07235v1.pdf | Deep Gaussian Mixture Ensembles | This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty. By assuming the data generating process follows that of a Gaussian mixture, DGMEs are capable of approximating complex pro... | ['Svitlana Vyetrenko', 'Elizabeth Fons', 'Niccolò Dalmasso', 'Yousef El-Laham'] | 2023-06-12 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-5.03465474e-01 1.35110244e-01 2.13471442e-01 -3.65825802e-01
-9.82158601e-01 -4.13615167e-01 9.08917010e-01 -6.21855818e-02
-5.15312016e-01 8.56376350e-01 -3.59674059e-02 -5.25186360e-01
-2.23100051e-01 -9.92128372e-01 -8.21776748e-01 -9.54065144e-01
-5.51682115e-02 9.11280751e-01 -1.67444438e-01 4.46723759... | [7.2506632804870605, 3.8083291053771973] |
fb700380-58c8-48ef-a29b-5a7044a188eb | comment-on-water-sources-and-kidney-function | 2201.00399 | null | https://arxiv.org/abs/2201.00399v3 | https://arxiv.org/pdf/2201.00399v3.pdf | Comment on "Water sources and kidney function: investigating chronic kidney disease of unknown etiology in a prospective study", by P. Vlahos et al | Vlahos et al., Ref. 1, NPJ Clean water. 4, 50 (2021) have reported the presence of pesticide contamination above safe levels in a "single time-point analysis" of well water in a region in Sri Lanka where chronic kidney disease of unknown etiology (CKDu) is endemic. They conclude "that agrochemical use in paddy and othe... | ['M. W. C. Dharma-wardana'] | 2021-12-28 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 1.40761718e-01 -2.45268136e-01 3.38218398e-02 3.75812143e-01
-2.56690621e-01 -6.68622315e-01 5.08867562e-01 7.57388234e-01
-1.38749048e-01 9.18671668e-01 3.57463211e-02 -1.03088152e+00
-6.29474282e-01 -1.00753164e+00 -8.88824522e-01 -9.37925875e-01
-8.29455107e-02 2.38632713e-03 7.46984631e-02 -3.11031014... | [5.869502544403076, 4.130763530731201] |
bf56e2ce-636f-420c-91e3-1e3ba000e0c3 | multi-view-graph-structure-learning-using | 2204.05258 | null | https://arxiv.org/abs/2204.05258v1 | https://arxiv.org/pdf/2204.05258v1.pdf | Multi-view graph structure learning using subspace merging on Grassmann manifold | Many successful learning algorithms have been recently developed to represent graph-structured data. For example, Graph Neural Networks (GNNs) have achieved considerable successes in various tasks such as node classification, graph classification, and link prediction. However, these methods are highly dependent on the ... | ['Alireza Bosaghzadeh', 'Hossein Amirkhani', 'Razieh Ghiasi'] | 2022-04-11 | null | null | null | null | ['multi-view-learning', 'graph-structure-learning'] | ['computer-vision', 'graphs'] | [-4.88346070e-02 1.83404703e-02 -2.41101116e-01 -1.65680483e-01
-2.99361795e-01 -4.93234277e-01 5.99356413e-01 4.64670271e-01
6.00521937e-02 5.40390790e-01 1.14436433e-01 -2.35521331e-01
-3.47458661e-01 -1.02133524e+00 -5.14270484e-01 -6.63489401e-01
-7.55310282e-02 4.25992280e-01 2.63975233e-01 -1.78538114... | [7.344528675079346, 6.138421058654785] |
a942c6e1-3ed9-4596-8c51-0ddff2c5767c | beeds-large-scale-biomedical-event-extraction | null | null | https://aclanthology.org/2022.bionlp-1.28 | https://aclanthology.org/2022.bionlp-1.28.pdf | BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering | Automatic extraction of event structures from text is a promising way to extract important facts from the evergrowing amount of biomedical literature. We propose BEEDS, a new approach on how to mine event structures from PubMed based on a question-answering paradigm. Using a three-step pipeline comprising a document re... | ['Leon Weber', 'Ulf Leser', 'Xing David Wang'] | null | null | null | null | bionlp-acl-2022-5 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 6.03996277e-01 2.99912483e-01 -4.89509255e-01 -2.24319324e-01
-1.27375889e+00 -5.09966791e-01 5.58978081e-01 1.23899055e+00
-7.72837043e-01 1.21684861e+00 4.02616262e-01 -6.11193180e-01
-2.03608856e-01 -9.11506772e-01 -9.83324468e-01 -5.29892445e-01
1.78959548e-01 7.68446982e-01 3.95646542e-01 -6.53410181... | [8.551033020019531, 8.81286334991455] |
e0beae2a-d7cf-4ef5-906c-0cd444224764 | paused-agent-replay-refresh | 2209.13398 | null | https://arxiv.org/abs/2209.13398v1 | https://arxiv.org/pdf/2209.13398v1.pdf | Paused Agent Replay Refresh | Reinforcement learning algorithms have become more complex since the invention of target networks. Unfortunately, target networks have not kept up with this increased complexity, instead requiring approximate solutions to be computationally feasible. These approximations increase noise in the Q-value targets and in the... | ['Benjamin Parr'] | 2022-09-26 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [ 2.15810478e-01 9.34634581e-02 -5.59770405e-01 3.48082110e-02
-4.92440194e-01 -7.16520786e-01 7.04595685e-01 -1.32894879e-02
-8.48656714e-01 1.38627303e+00 -1.89359456e-01 -4.48051363e-01
-4.46259409e-01 -9.46882606e-01 -6.74243569e-01 -9.15142179e-01
-3.99715245e-01 3.39221001e-01 2.51235276e-01 -2.70273477... | [4.103999614715576, 2.2927353382110596] |
18a2f57d-28d8-44bd-bee5-a41fa298e79e | towards-a-better-understanding-of-burrowss | null | null | https://aclanthology.org/W15-0709 | https://aclanthology.org/W15-0709.pdf | Towards a better understanding of Burrows's Delta in literary authorship attribution | null | ['Steffen Pielstr{\\"o}m', 'Fotis Jannidis', 'Christof Sch{\\"o}ch', 'Thorsten Vitt', 'Stefan Evert', 'Thomas Proisl'] | 2015-06-01 | null | null | null | ws-2015-6 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3436384201049805, 3.6421449184417725] |
9f835edb-1795-4c6f-9f8e-707619c704b8 | unsupervised-learning-of-monocular-depth-1 | 1803.03893 | null | http://arxiv.org/abs/1803.03893v3 | http://arxiv.org/pdf/1803.03893v3.pdf | Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction | Despite learning based methods showing promising results in single view depth
estimation and visual odometry, most existing approaches treat the tasks in a
supervised manner. Recent approaches to single view depth estimation explore
the possibility of learning without full supervision via minimizing photometric
error. ... | ['Chamara Saroj Weerasekera', 'Ravi Garg', 'Huangying Zhan', 'Harsh Agarwal', 'Kejie Li', 'Ian Reid'] | 2018-03-11 | unsupervised-learning-of-monocular-depth-2 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhan_Unsupervised_Learning_of_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhan_Unsupervised_Learning_of_CVPR_2018_paper.pdf | cvpr-2018-6 | ['depth-and-camera-motion'] | ['computer-vision'] | [-3.54404189e-02 4.22643796e-02 -3.10899705e-01 -5.13760448e-01
-7.60705292e-01 -5.47571540e-01 8.69545519e-01 -4.52835321e-01
-4.85797375e-01 7.00008392e-01 8.19166154e-02 -4.02378589e-02
3.31544340e-01 -6.65846765e-01 -9.05562460e-01 -6.81682408e-01
2.07225978e-01 5.68383396e-01 3.41580361e-01 -2.11720876... | [8.561073303222656, -2.3413827419281006] |
67cb7c2a-4eaf-40ef-94c2-9336c135abd9 | pretraining-for-conditional-generation-with | null | null | https://openreview.net/forum?id=H1eFXO0WpV | https://openreview.net/pdf?id=H1eFXO0WpV | Pretraining for Conditional Generation with Pseudo Self Attention | Large pretrained language representation models have changed the way researchers approach discriminative natural language understanding tasks, leading to the dominance of approaches that finetune a pretrained model. However, such transfer learning approaches have not seen the same success for natural language generatio... | ['Anonymous'] | 2019-05-21 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 4.15567845e-01 5.73782504e-01 -2.67641693e-01 -5.17527461e-01
-1.16599405e+00 -6.85525954e-01 1.13290977e+00 -1.89231932e-01
-4.24969226e-01 1.18582940e+00 7.37571061e-01 -5.07493377e-01
5.84800839e-01 -1.00864804e+00 -9.19898093e-01 -3.80432576e-01
4.70249623e-01 8.88292313e-01 7.81489983e-02 -3.91736478... | [11.612360954284668, 9.020149230957031] |
0ea3f7ee-128c-42c5-9e08-6f72ac46640e | cgt-clustered-graph-transformer-for-urban | null | null | https://openreview.net/forum?id=H1eJAANtvr | https://openreview.net/pdf?id=H1eJAANtvr | CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction | Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance. The aim of this paper is to develop a novel ... | ['Jieping Ye', 'Hongtu Zhu', 'Qiang Yang', 'Leye Wang', 'Lulu Zhang', 'Yuanbo Zhang', 'Shulin Li', 'Lingyu Zhang', 'Xu Geng'] | 2019-09-25 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-2.38863334e-01 -1.70247570e-01 -2.53884315e-01 -5.17414033e-01
-5.46963811e-01 -1.03031926e-01 6.82576656e-01 -2.25276694e-01
1.31663769e-01 4.03580576e-01 5.98613977e-01 -4.67928886e-01
-2.08811253e-01 -1.08661425e+00 -7.44978905e-01 -6.11936867e-01
-2.43046701e-01 3.43057096e-01 3.90304416e-01 -5.55024087... | [6.5302510261535645, 2.0902459621429443] |
25faf756-7ca2-4657-bc18-ccf69ab74c15 | creativegan-editing-generative-adversarial | 2103.06242 | null | https://arxiv.org/abs/2103.06242v1 | https://arxiv.org/pdf/2103.06242v1.pdf | CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis | Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which is crucial for crea... | ['Faez Ahmed', 'Muhammad Fathy Rashad', 'Amin Heyrani Nobari'] | 2021-03-10 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [ 3.67401540e-01 7.95634091e-02 -7.41341338e-02 1.51184127e-01
-4.21810269e-01 -1.00352669e+00 5.79199016e-01 -4.43021506e-01
3.46243739e-01 6.19498134e-01 5.53045757e-02 -3.84033173e-01
5.83488978e-02 -1.06877434e+00 -9.84804213e-01 -5.27635813e-01
3.16767335e-01 4.74254429e-01 -2.40219072e-01 -3.59442949... | [5.809299468994141, 3.322704792022705] |
b2616525-0510-458f-9060-aa1f54f69941 | open-domain-question-answering-over-virtual | 2110.08417 | null | https://arxiv.org/abs/2110.08417v2 | https://arxiv.org/pdf/2110.08417v2.pdf | Open Domain Question Answering with A Unified Knowledge Interface | The retriever-reader framework is popular for open-domain question answering (ODQA) due to its ability to use explicit knowledge. Although prior work has sought to increase the knowledge coverage by incorporating structured knowledge beyond text, accessing heterogeneous knowledge sources through a unified interface rem... | ['Jianfeng Gao', 'Eric Nyberg', 'Xiaodong Liu', 'Hao Cheng', 'Kaixin Ma'] | 2021-10-16 | null | https://aclanthology.org/2022.acl-long.113 | https://aclanthology.org/2022.acl-long.113.pdf | acl-2022-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [-1.55578479e-01 6.53497040e-01 -1.84608430e-01 -1.71264365e-01
-1.51982594e+00 -1.03463316e+00 6.61723554e-01 3.66072297e-01
-4.57425237e-01 7.79762506e-01 8.00568938e-01 -6.54384732e-01
-3.13516021e-01 -1.06808102e+00 -8.61220837e-01 1.24730349e-01
4.96075243e-01 1.19964647e+00 5.83009899e-01 -7.80644119... | [10.735709190368652, 7.931951522827148] |
2063082e-90ed-41c5-b08c-9d5cbf48f3c1 | character-centric-story-visualization-via | 2210.08465 | null | https://arxiv.org/abs/2210.08465v4 | https://arxiv.org/pdf/2210.08465v4.pdf | Character-Centric Story Visualization via Visual Planning and Token Alignment | Story visualization advances the traditional text-to-image generation by enabling multiple image generation based on a complete story. This task requires machines to 1) understand long text inputs and 2) produce a globally consistent image sequence that illustrates the contents of the story. A key challenge of consiste... | ['Nanyun Peng', 'Hideki Nakayama', 'Te-Lin Wu', 'Rujun Han', 'Hong Chen'] | 2022-10-16 | null | null | null | null | ['story-visualization'] | ['computer-vision'] | [ 3.42538297e-01 1.98978826e-01 1.03268586e-01 -1.26758412e-01
-6.12537503e-01 -5.09946406e-01 7.70747721e-01 -3.31361920e-01
2.50053480e-02 6.21981144e-01 4.39514220e-01 -2.51588970e-01
5.37416220e-01 -7.10825384e-01 -1.01346445e+00 -5.91194570e-01
4.36570317e-01 2.58645624e-01 4.53377627e-02 -2.14122415... | [11.178110122680664, 0.540488600730896] |
a919056c-4241-43a3-8e3d-97b55b5d9719 | part-aware-panoptic-segmentation | 2106.06351 | null | https://arxiv.org/abs/2106.06351v1 | https://arxiv.org/pdf/2106.06351v1.pdf | Part-aware Panoptic Segmentation | In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Citysc... | ['Gijs Dubbelman', 'Xiaoxiao Wen', 'Chenyang Lu', 'Panagiotis Meletis', 'Daan de Geus'] | 2021-06-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/de_Geus_Part-Aware_Panoptic_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/de_Geus_Part-Aware_Panoptic_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-parsing', 'part-level-panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.56057882e-01 -3.58140767e-02 -1.20842382e-01 -6.21525407e-01
-9.70984280e-01 -8.93038988e-01 6.44590914e-01 2.73236156e-01
-7.88067728e-02 2.09598064e-01 2.42288277e-01 -1.38905495e-01
2.10268155e-01 -8.73194695e-01 -7.49116123e-01 -4.83494967e-01
2.10678428e-01 4.07991022e-01 8.51314187e-01 3.58436108... | [9.563271522521973, 0.43641841411590576] |
3b42515b-0dd3-4ebb-a63e-c949a960c46f | video-smoke-detection-based-on-deep-saliency | 1809.02802 | null | http://arxiv.org/abs/1809.02802v2 | http://arxiv.org/pdf/1809.02802v2.pdf | Video Smoke Detection Based on Deep Saliency Network | Video smoke detection is a promising fire detection method especially in open
or large spaces and outdoor environments. Traditional video smoke detection
methods usually consist of candidate region extraction and classification, but
lack powerful characterization for smoke. In this paper, we propose a novel
video smoke... | ['Zhong Wang', 'Yongming Zhang', 'Gao Xu', 'Jinjun Wang', 'Gaohua Lin', 'Yang Jia', 'Qixing Zhang'] | 2018-09-08 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 6.78157628e-01 -7.49029160e-01 -1.34348586e-01 -8.36130381e-02
-5.48216760e-01 -3.47305089e-01 5.36521852e-01 6.09922931e-02
-3.57458621e-01 3.35252672e-01 2.34159395e-01 -6.30479380e-02
-5.73831387e-02 -9.48407352e-01 -4.38327521e-01 -7.74342895e-01
2.81460583e-01 -6.30025387e-01 9.94785547e-01 7.12932274... | [9.757658958435059, -0.4506576657295227] |
044ae7ad-4a75-435d-9096-3c11d6501956 | a-neural-framework-for-learning-subgraph-and | 2112.13143 | null | https://arxiv.org/abs/2112.13143v3 | https://arxiv.org/pdf/2112.13143v3.pdf | GREED: A Neural Framework for Learning Graph Distance Functions | Among various distance functions for graphs, graph and subgraph edit distances (GED and SED respectively) are two of the most popular and expressive measures. Unfortunately, exact computations for both are NP-hard. To overcome this computational bottleneck, neural approaches to learn and predict edit distance in polyno... | ['Sayan Ranu', 'Yogish Sabharwal', 'Venkatesan Chakaravarthy', 'Sourav Medya', 'Siddharth Grover', 'Rishabh Ranjan'] | 2021-12-24 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 6.73572272e-02 3.93418744e-02 -2.31667668e-01 -2.26919547e-01
-5.02173960e-01 -6.51870430e-01 2.76477486e-01 6.22337878e-01
-4.97100711e-01 6.58004761e-01 -2.29150075e-02 -4.00265574e-01
-4.67537820e-01 -1.13481116e+00 -8.04558516e-01 -5.59501529e-01
-2.79010028e-01 5.61032772e-01 1.56940520e-01 -1.85546771... | [7.107424736022949, 6.133922576904297] |
f9482924-e0f5-4174-926d-e8f77f13544a | hindi-history-note-generation-with | null | null | https://aclanthology.org/2020.aacl-srw.7 | https://aclanthology.org/2020.aacl-srw.7.pdf | Hindi History Note Generation with Unsupervised Extractive Summarization | In this work, the task of extractive single document summarization applied to an education setting to generate summaries of chapters from grade 10 Hindi history textbooks is undertaken. Unsupervised approaches to extract summaries are employed and evaluated. TextRank, LexRank, Luhn and KLSum are used to extract summari... | ['Meet Chetan Gadoya', 'Dhruv Mathew', 'Dhineshkumar Ramasubbu', 'Aayush Shah'] | 2020-12-01 | null | null | null | asian-chapter-of-the-association-for | ['unsupervised-extractive-summarization'] | ['natural-language-processing'] | [ 2.56340533e-01 6.19200408e-01 -4.78866786e-01 -1.81295395e-01
-1.47246575e+00 -7.33220398e-01 9.34668362e-01 1.00427318e+00
-4.48148489e-01 1.52479804e+00 1.20926654e+00 -1.86700836e-01
-7.17141628e-01 -5.23810804e-01 -4.12031233e-01 -1.98791817e-01
2.76973397e-01 4.47759509e-01 4.86908015e-03 -2.34826952... | [12.527356147766113, 9.496230125427246] |
e1335512-eaea-4ee7-8c99-118e1f5d7ded | symbolic-music-generation-conditioned-on | 2203.16165 | null | https://arxiv.org/abs/2203.16165v2 | https://arxiv.org/pdf/2203.16165v2.pdf | Symbolic music generation conditioned on continuous-valued emotions | In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset o... | ['Paula Viana', 'Matthew E. P. Davies', 'Serkan Sulun'] | 2022-03-30 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.92186612e-01 -3.80894803e-02 -8.63262564e-02 -2.28715107e-01
-1.04899693e+00 -9.27235305e-01 5.74067295e-01 1.15374528e-01
-2.41179615e-01 7.59932339e-01 2.41106614e-01 5.47442555e-01
-3.21317792e-01 -7.50783801e-01 -3.81920040e-01 -4.71293718e-01
-2.15377629e-01 5.00995517e-01 -3.04604769e-01 -5.56381881... | [15.903129577636719, 5.347635746002197] |
8ecfea2b-d4ca-4b59-92ce-56cf88876d57 | multi-view-spatial-temporal-network-for | 2204.08747 | null | https://arxiv.org/abs/2204.08747v1 | https://arxiv.org/pdf/2204.08747v1.pdf | Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition | Sign language is a beautiful visual language and is also the primary language used by speaking and hearing-impaired people. However, sign language has many complex expressions, which are difficult for the public to understand and master. Sign language recognition algorithms will significantly facilitate communication b... | ['Lu Meng', 'Ronghui Li'] | 2022-04-19 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-1.87725529e-01 -6.28208101e-01 -2.18182132e-01 -2.92761624e-01
-6.35709345e-01 -1.25367507e-01 3.18961829e-01 -1.00253999e+00
-7.19725728e-01 4.82126743e-01 5.37891865e-01 -2.31653795e-01
1.20153300e-01 -7.56726325e-01 -3.44987780e-01 -8.32564294e-01
-1.00780033e-01 9.15266797e-02 3.79378468e-01 -1.98466435... | [9.207999229431152, -6.499528884887695] |
a81e95c2-06ae-4ef0-89a4-aea98ccbe28b | a-persian-asr-based-ser-modification-of | 2211.09956 | null | https://arxiv.org/abs/2211.09956v1 | https://arxiv.org/pdf/2211.09956v1.pdf | A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora | Speech Emotion Recognition (SER) is one of the essential perceptual methods of humans in understanding the situation and how to interact with others, therefore, in recent years, it has been tried to add the ability to recognize emotions to human-machine communication systems. Since the SER process relies on labeled dat... | ['Yasser Shekofteh', 'Ali Yazdani'] | 2022-11-18 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-2.16445431e-01 3.26099783e-01 4.52171415e-01 -6.70084536e-01
-5.01290858e-01 -1.93936765e-01 4.78176087e-01 -7.89893419e-02
-6.15636408e-01 6.92706466e-01 3.41872424e-01 -1.64898887e-01
2.28245690e-01 -3.47362995e-01 -1.01566479e-01 -2.15061069e-01
6.17972501e-02 4.38173324e-01 8.51436183e-02 -7.49754846... | [13.627829551696777, 5.836152076721191] |
8719ac54-9a3e-4792-95c8-df8d9087ae6c | transformer-tracking-with-cyclic-shifting | 2205.03806 | null | https://arxiv.org/abs/2205.03806v1 | https://arxiv.org/pdf/2205.03806v1.pdf | Transformer Tracking with Cyclic Shifting Window Attention | Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism. Existing transformer-based approaches adopt the pixel-to-pixel attention strategy on flattened image features and unavoidably ignore the integrity of objects. In this paper, we propose a new tr... | ['Wei Yang', 'Yi-Ping Phoebe Chen', 'Junqing Yu', 'Zikai Song'] | 2022-05-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Song_Transformer_Tracking_With_Cyclic_Shifting_Window_Attention_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Song_Transformer_Tracking_With_Cyclic_Shifting_Window_Attention_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-object-tracking'] | ['computer-vision'] | [-6.18937351e-02 -5.62800646e-01 -1.55916110e-01 -8.87851790e-02
-3.99608552e-01 -3.02419513e-01 3.62322718e-01 -2.21927956e-01
-2.97640771e-01 5.05600691e-01 2.49376241e-02 -9.20626000e-02
-3.69749926e-02 -6.73741937e-01 -6.94975555e-01 -6.44739330e-01
-5.64044192e-02 1.93407480e-02 9.68363464e-01 -2.37731442... | [6.25180721282959, -2.123166799545288] |
4e39a429-ed9c-4191-a63c-2d31a3956fd3 | learnability-and-algorithm-for-continual | 2306.12646 | null | https://arxiv.org/abs/2306.12646v1 | https://arxiv.org/pdf/2306.12646v1.pdf | Learnability and Algorithm for Continual Learning | This paper studies the challenging continual learning (CL) setting of Class Incremental Learning (CIL). CIL learns a sequence of tasks consisting of disjoint sets of concepts or classes. At any time, a single model is built that can be applied to predict/classify test instances of any classes learned thus far without p... | ['Bing Liu', 'Tatsuya Konishi', 'Changnan Xiao', 'Gyuhak Kim'] | 2023-06-22 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 5.23296595e-01 1.28471285e-01 -6.74714863e-01 -5.48612177e-01
-9.60388899e-01 -6.07826889e-01 3.53770882e-01 4.26056743e-01
-1.33243039e-01 1.02322865e+00 -5.25916338e-01 -4.02230769e-01
-3.21910858e-01 -6.19600475e-01 -1.00822222e+00 -6.60756111e-01
-3.25683445e-01 9.01912630e-01 6.70375645e-01 2.16310248... | [9.696581840515137, 3.407879590988159] |
cdbc1fed-599b-48ca-bf5f-5c2b0cc3eecc | large-scale-adversarial-training-for-vision | 2006.06195 | null | https://arxiv.org/abs/2006.06195v2 | https://arxiv.org/pdf/2006.06195v2.pdf | Large-Scale Adversarial Training for Vision-and-Language Representation Learning | We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on ima... | ['Yen-Chun Chen', 'Yu Cheng', 'Linjie Li', 'Jingjing Liu', 'Chen Zhu', 'Zhe Gan'] | 2020-06-11 | null | http://proceedings.neurips.cc/paper/2020/hash/49562478de4c54fafd4ec46fdb297de5-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/49562478de4c54fafd4ec46fdb297de5-Paper.pdf | neurips-2020-12 | ['visual-commonsense-reasoning', 'visual-entailment'] | ['reasoning', 'reasoning'] | [ 5.99325836e-01 2.47225434e-01 5.29648252e-02 -2.83096731e-01
-1.01985538e+00 -8.78446877e-01 8.69895995e-01 -1.44840464e-01
-4.23449904e-01 3.56144756e-01 4.02851105e-01 -6.47067964e-01
4.45987225e-01 -6.29306078e-01 -1.02993226e+00 -2.83525050e-01
3.32357168e-01 3.75068575e-01 -5.78731745e-02 -2.63357371... | [10.887338638305664, 1.7763948440551758] |
1f7cb39e-92e4-460b-b806-236eff40504a | decentralized-optimization-with-distributed | 2208.11224 | null | https://arxiv.org/abs/2208.11224v1 | https://arxiv.org/pdf/2208.11224v1.pdf | Decentralized Optimization with Distributed Features and Non-Smooth Objective Functions | We develop a new consensus-based distributed algorithm for solving learning problems with feature partitioning and non-smooth convex objective functions. Such learning problems are not separable, i.e., the associated objective functions cannot be directly written as a summation of agent-specific objective functions. To... | ['Stefan Werner', 'Reza Arablouei', 'Naveen K. D. Venkategowda', 'Cristiano Gratton'] | 2022-08-23 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-3.06908280e-01 -1.53931761e-02 5.98470829e-02 -2.31601402e-01
-7.54200399e-01 -5.74641705e-01 1.16920762e-01 1.35028988e-01
-6.00056946e-01 1.03582120e+00 -1.46025628e-01 -2.46461496e-01
-5.77110589e-01 -6.33967757e-01 -6.42413914e-01 -1.19638169e+00
-1.15049705e-01 6.46281898e-01 -3.23638529e-01 -2.35650409... | [6.253385066986084, 4.9205756187438965] |
9cbe0494-d4c7-494e-bbee-4da32c34cd93 | geometry-preserving-lie-group-integrators-for | 2210.08842 | null | https://arxiv.org/abs/2210.08842v4 | https://arxiv.org/pdf/2210.08842v4.pdf | Geometry-preserving Lie Group Integrators For Differential Equations On The Manifold Of Symmetric Positive Definite Matrices | In many applications, one encounters signals that lie on manifolds rather than a Euclidean space. In particular, covariance matrices are examples of ubiquitous mathematical objects that have a non Euclidean structure. The application of Euclidean methods to integrate differential equations lying on such objects does no... | ['Franck Vermet', 'Naoufal El Bekri', 'Alexandre Reiffers-Masson', 'Lucas Drumetz'] | 2022-10-17 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-1.72571987e-01 -6.11095652e-02 3.97547096e-01 -3.56609188e-02
-1.46052867e-01 -6.48838699e-01 3.56251955e-01 -6.37413442e-01
-3.64116579e-01 8.34263861e-01 -3.04234505e-01 -2.58276433e-01
-5.77365577e-01 -5.91237545e-01 -1.15435459e-01 -8.56344879e-01
-2.21259087e-01 2.89928522e-02 5.87238371e-02 -4.07503754... | [7.44998025894165, 4.174729347229004] |
02bf97bc-7ba3-41a7-a4e1-16045556207e | audio-classification-using-ml-methods | 2305.19304 | null | https://arxiv.org/abs/2305.19304v1 | https://arxiv.org/pdf/2305.19304v1.pdf | Audio classification using ML methods | Machine Learning systems have achieved outstanding performance in different domains. In this paper machine learning methods have been applied to classification task to classify music genre. The code shows how to extract features from audio files and classify them using supervised learning into 2 genres namely classical... | ['Krishna Kumar'] | 2023-05-30 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 1.73706245e-02 -2.92974323e-01 -2.26746142e-01 -4.22060817e-01
-5.82189083e-01 -9.30897236e-01 4.17357862e-01 2.38243341e-01
-4.18638825e-01 1.13227069e+00 2.47275069e-01 -3.03123027e-01
-5.84048212e-01 -5.33088565e-01 5.43236136e-02 -5.99002004e-01
-1.72630861e-01 5.21603048e-01 2.93208390e-01 -7.44744837... | [15.865103721618652, 5.214579105377197] |
5077c425-8b01-45f2-9dfb-0f0bf08ec9b9 | karsl-arabic-sign-language-database | null | null | https://dl.acm.org/doi/10.1145/3423420#:~:text=Signs%20in%20KArSL%20database%20are,language%20recognition%20using%20this%20database | https://dl.acm.org/doi/10.1145/3423420#:~:text=Signs%20in%20KArSL%20database%20are,language%20recognition%20using%20this%20database | KArSL: Arabic Sign Language Database | Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. ... | ['Mohamed Mohandes', 'Sabri Mahmoud', 'Hamzah Luqman', 'Ala Addin I. Sidig'] | 2021-01-01 | null | null | null | acm-transactions-on-asian-and-low-resource-1 | ['sign-language-recognition'] | ['computer-vision'] | [-2.34924749e-01 -5.58295071e-01 -3.72478396e-01 -4.04752105e-01
-6.50760591e-01 -5.01121223e-01 5.85810006e-01 -9.42502558e-01
-7.73628592e-01 3.37257802e-01 7.23212361e-01 6.13413788e-02
5.91562279e-02 -3.40052068e-01 -1.30731046e-01 -1.04598296e+00
7.45507851e-02 4.26256865e-01 8.90851542e-02 -2.32498720... | [9.102933883666992, -6.416418075561523] |
4449ce3c-2ed0-42df-aca7-433dc5b3be95 | ask-me-anything-a-simple-strategy-for | 2210.02441 | null | https://arxiv.org/abs/2210.02441v3 | https://arxiv.org/pdf/2210.02441v3.pdf | Ask Me Anything: A simple strategy for prompting language models | Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore ... | ['Laurel Orr', 'Christopher Ré', 'Frederic Sala', 'Ines Chami', 'Kush Bhatia', 'Neel Guha', 'Mayee F. Chen', 'Avanika Narayan', 'Simran Arora'] | 2022-10-05 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.57115412e-01 3.34370434e-01 4.48651724e-02 -5.93641400e-01
-1.47837782e+00 -7.50486135e-01 7.57209480e-01 -3.03720329e-02
-4.16686803e-01 8.17008078e-01 2.76666075e-01 -6.74022257e-01
7.09773377e-02 -5.06138206e-01 -7.59204865e-01 -4.44856822e-01
5.27086794e-01 7.46672273e-01 2.13937998e-01 -5.50231874... | [11.117913246154785, 8.491462707519531] |
37791ff1-f4a6-4e1d-85c0-bc515ced1162 | model-aggregation-for-risk-evaluation-and | 2201.0637 | null | https://arxiv.org/abs/2201.06370v2 | https://arxiv.org/pdf/2201.06370v2.pdf | Model Aggregation for Risk Evaluation and Robust Optimization | We introduce a new approach for prudent risk evaluation based on stochastic dominance, which will be called the model aggregation (MA) approach. In contrast to the classic worst-case risk (WR) approach, the MA approach produces not only a robust value of risk evaluation but also a robust distributional model which is u... | ['Qinyu Wu', 'Ruodu Wang', 'Tiantian Mao'] | 2022-01-17 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-8.86558220e-02 2.45343998e-01 1.26842305e-01 -2.61814117e-01
-9.48533654e-01 -8.03816438e-01 4.10253435e-01 4.65145648e-01
-3.11958730e-01 8.42050433e-01 7.25677460e-02 -4.34298337e-01
-1.02335620e+00 -9.71946657e-01 -3.76822710e-01 -9.18308556e-01
-2.51413137e-01 3.71556938e-01 -5.36529757e-02 -9.59181264... | [5.06982421875, 3.933800220489502] |
778a0c6c-a428-4531-8935-77bfe1f6cdbd | less-is-more-removing-text-regions-improves | 2305.05095 | null | https://arxiv.org/abs/2305.05095v1 | https://arxiv.org/pdf/2305.05095v1.pdf | Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness | The CLIP (Contrastive Language-Image Pre-training) model and its variants are becoming the de facto backbone in many applications. However, training a CLIP model from hundreds of millions of image-text pairs can be prohibitively expensive. Furthermore, the conventional CLIP model doesn't differentiate between the visua... | ['Yantao Zheng', 'Zhiyun Lu', 'Wencong Zhang', 'Xianzhi Du', 'Yinfei Yang', 'Chen Chen', 'BoWen Zhang', 'Liangliang Cao'] | 2023-05-08 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 3.05816084e-01 -3.66043538e-01 -2.92046536e-02 -1.43298909e-01
-7.94822097e-01 -7.84438372e-01 5.47395170e-01 -1.97248552e-02
-4.53109175e-01 3.85643125e-01 -1.05462102e-02 -2.28124589e-01
5.40662766e-01 -5.96566319e-01 -1.13539362e+00 -5.13801277e-01
1.52400434e-01 -4.48707156e-02 5.01142085e-01 -1.01725966... | [11.471902847290039, 0.93805330991745] |
6e3064a7-5586-4826-ab28-be30448f73b5 | multi-site-clinical-federated-learning-using | 2306.16367 | null | https://arxiv.org/abs/2306.16367v1 | https://arxiv.org/pdf/2306.16367v1.pdf | Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare | The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information. Although NLP techniques have exhibited substantial potential ... | ['Joongheon Kim', 'Samuel Kim', 'Won Joon Yun'] | 2023-06-28 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 2.86919087e-01 4.76573467e-01 -2.68949538e-01 -5.48653603e-01
-9.02161300e-01 -3.94112229e-01 2.43149742e-01 8.38453352e-01
-5.18429458e-01 7.97289491e-01 6.90091610e-01 -7.20369518e-01
-4.11246449e-01 -6.28614426e-01 -4.87392545e-01 -2.85742372e-01
-2.41643786e-01 3.90006453e-01 -7.22881377e-01 1.61410600... | [6.4742841720581055, 6.6603569984436035] |
0a6cad48-4e5d-4399-baad-5615193a669d | controlling-learned-effects-to-reduce | 2305.16863 | null | https://arxiv.org/abs/2305.16863v2 | https://arxiv.org/pdf/2305.16863v2.pdf | Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers | To address the problem of NLP classifiers learning spurious correlations between training features and target labels, a common approach is to make the model's predictions invariant to these features. However, this can be counter-productive when the features have a non-zero causal effect on the target label and thus are... | ['Amit Sharma', 'Parikshit Bansal'] | 2023-05-26 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 8.62815797e-01 4.07399595e-01 -7.24777460e-01 -5.33601046e-01
-6.14897251e-01 -6.66396558e-01 7.60766566e-01 6.01918757e-01
-3.87127846e-01 1.20598400e+00 3.08765650e-01 -4.96811450e-01
-4.24102306e-01 -8.52345109e-01 -1.04188359e+00 -8.86010170e-01
2.28332020e-02 1.64339051e-01 8.18937644e-03 4.29935932... | [8.66940975189209, 5.503654956817627] |
295a2766-dbe7-49b5-983f-95d62fc6f5af | explanations-for-automatic-speech-recognition | 2302.14062 | null | https://arxiv.org/abs/2302.14062v1 | https://arxiv.org/pdf/2302.14062v1.pdf | Explanations for Automatic Speech Recognition | We address quality assessment for neural network based ASR by providing explanations that help increase our understanding of the system and ultimately help build trust in the system. Compared to simple classification labels, explaining transcriptions is more challenging as judging their correctness is not straightforwa... | ['Ajitha Rajan', 'Peter Bell', 'Xiaoliang Wu'] | 2023-02-27 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.99872518e-01 8.21117043e-01 -1.41088488e-02 -7.20346391e-01
-1.11213505e+00 -5.17757654e-01 3.92759502e-01 6.21386524e-03
5.10340154e-01 7.74780333e-01 6.89606190e-01 -4.34748799e-01
-3.00342679e-01 -6.44863024e-02 -1.07609761e+00 -2.70628273e-01
-7.89655093e-03 4.89487767e-01 -1.32436574e-01 -3.43953334... | [8.944230079650879, 5.694268703460693] |
6849e30f-a920-44ae-8f97-53e6bd4ed761 | dynamic-measurement-scheduling-for-event | 1901.09699 | null | https://arxiv.org/abs/1901.09699v3 | https://arxiv.org/pdf/1901.09699v3.pdf | Dynamic Measurement Scheduling for Event Forecasting using Deep RL | Imagine a patient in critical condition. What and when should be measured to forecast detrimental events, especially under the budget constraints? We answer this question by deep reinforcement learning (RL) that jointly minimizes the measurement cost and maximizes predictive gain, by scheduling strategically-timed meas... | ['Chun-Hao Chang', 'Mingjie Mai', 'Anna Goldenberg'] | 2019-01-24 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 1.48575589e-01 4.97398436e-01 -4.30501342e-01 -1.30132452e-01
-9.55486000e-01 -1.29422531e-01 -2.86748439e-01 5.39132595e-01
-7.99046576e-01 1.36123967e+00 1.87222466e-01 -7.43876755e-01
-4.48048025e-01 -6.07390165e-01 -5.42142034e-01 -5.98173738e-01
-4.70201284e-01 9.11673546e-01 -1.33871555e-01 1.97873399... | [3.9733691215515137, 2.7591826915740967] |
9134cd84-e180-45d1-a3a3-e668bdf008bb | sok-privacy-preserving-data-synthesis | 2307.02106 | null | https://arxiv.org/abs/2307.02106v1 | https://arxiv.org/pdf/2307.02106v1.pdf | SoK: Privacy-Preserving Data Synthesis | As the prevalence of data analysis grows, safeguarding data privacy has become a paramount concern. Consequently, there has been an upsurge in the development of mechanisms aimed at privacy-preserving data analyses. However, these approaches are task-specific; designing algorithms for new tasks is a cumbersome process.... | ['Dawn Song', 'Bo Li', 'David Forsyth', 'Bolin Ding', 'Chang Ge', 'Gonzalo Munilla Garrido', 'Yunhui Long', 'Qinbin Li', 'Fan Wu', 'Yuzheng Hu'] | 2023-07-05 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 5.18867493e-01 1.42711893e-01 -2.89740533e-01 -3.70322913e-01
-8.35088611e-01 -8.15547705e-01 6.74538195e-01 1.41085103e-01
-3.50687355e-01 6.86887503e-01 3.41117412e-01 -4.94452506e-01
-9.81214717e-02 -7.68303394e-01 -5.76352596e-01 -7.80076325e-01
2.18787059e-01 -1.99878603e-01 -2.89790064e-01 6.49927929... | [6.2056660652160645, 6.764781951904297] |
2305ac34-81a8-487b-b3b6-1986dab0dc1d | pseudo-lidar-for-visual-odometry | 2209.01567 | null | https://arxiv.org/abs/2209.01567v1 | https://arxiv.org/pdf/2209.01567v1.pdf | Pseudo-LiDAR for Visual Odometry | In the existing methods, LiDAR odometry shows superior performance, but visual odometry is still widely used for its price advantage. Conventionally, the task of visual odometry mainly rely on the input of continuous images. However, it is very complicated for the odometry network to learn the epipolar geometry informa... | ['Hesheng Wang', 'Yanzi Miao', 'Xinrui Wu', 'Chaokang Jiang', 'Zhiheng Feng', 'Guangming Wang', 'Huiying Deng'] | 2022-09-04 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-3.68279815e-01 -7.38909394e-02 -2.33996049e-01 -3.62729073e-01
-3.04799415e-02 -1.96006790e-01 3.03706050e-01 -2.22365394e-01
-4.69931096e-01 6.07459307e-01 -2.72544801e-01 -1.59269735e-01
1.47210717e-01 -1.24372411e+00 -7.70960271e-01 -5.61282933e-01
2.85973251e-01 1.13123918e+00 4.99514073e-01 -2.89912462... | [7.543024063110352, -2.332383871078491] |
068c88db-e491-45e4-8165-e92451e4c87a | proportional-aggregation-of-preferences-for | 2306.14858 | null | https://arxiv.org/abs/2306.14858v1 | https://arxiv.org/pdf/2306.14858v1.pdf | Proportional Aggregation of Preferences for Sequential Decision Making | We study the problem of fair sequential decision making given voter preferences. In each round, a decision rule must choose a decision from a set of alternatives where each voter reports which of these alternatives they approve. Instead of going with the most popular choice in each round, we aim for proportional repres... | ['Dominik Peters', 'Shashwat Goel', 'Nikhil Chandak'] | 2023-06-26 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 2.65125781e-01 6.90001667e-01 -4.54697758e-01 -6.19032204e-01
-6.59496903e-01 -8.94303381e-01 4.39998716e-01 2.10821316e-01
-1.04421461e+00 9.95259047e-01 1.45602167e-01 -8.62059653e-01
-5.12343466e-01 -9.99357104e-01 -3.49561244e-01 -7.05638111e-01
2.26520792e-01 1.11465478e+00 -1.14818193e-01 -4.87981945... | [8.921228408813477, 5.417226314544678] |
81c4b2f9-90b9-45b4-8a4e-0b51f609e18d | context-aware-polyunet-for-liver-and-lesion | 2106.1133 | null | https://arxiv.org/abs/2106.11330v1 | https://arxiv.org/pdf/2106.11330v1.pdf | Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images | Accurate liver and lesion segmentation from computed tomography (CT) images are highly demanded in clinical practice for assisting the diagnosis and assessment of hepatic tumor disease. However, automatic liver and lesion segmentation from contrast-enhanced CT volumes is extremely challenging due to the diversity in co... | ['Simon Chun-Ho Yu', 'Liping Zhang'] | 2021-06-21 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 5.96989170e-02 -2.46972367e-01 -2.01871529e-01 -2.34579831e-01
-9.40716088e-01 -4.45281327e-01 2.53503025e-01 4.38174039e-01
-3.38377774e-01 4.02716458e-01 1.86633021e-01 -6.00654781e-01
-3.11789513e-01 -5.18116713e-01 -6.38059974e-02 -9.47404861e-01
-3.18519086e-01 5.91030121e-01 5.34352839e-01 2.30320886... | [14.566838264465332, -2.6458566188812256] |
b850fb42-e2b1-4d92-a728-1ff1885a51af | alo-vc-any-to-any-low-latency-one-shot-voice | 2306.011 | null | https://arxiv.org/abs/2306.01100v1 | https://arxiv.org/pdf/2306.01100v1.pdf | ALO-VC: Any-to-any Low-latency One-shot Voice Conversion | This paper presents ALO-VC, a non-parallel low-latency one-shot phonetic posteriorgrams (PPGs) based voice conversion method. ALO-VC enables any-to-any voice conversion using only one utterance from the target speaker, with only 47.5 ms future look-ahead. The proposed hybrid signal processing and machine learning pipel... | ['Milos Cernak', 'Damien Ronssin', 'Bohan Wang'] | 2023-06-01 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 8.14982578e-02 1.62890956e-01 -1.70839056e-01 -2.85055816e-01
-1.38363945e+00 -5.16577601e-01 4.33346331e-01 -1.62117288e-01
-3.31626892e-01 4.59975541e-01 5.32664061e-01 -4.84850109e-01
4.40810442e-01 -4.77480501e-01 -6.91886127e-01 -3.46656442e-01
9.22340453e-02 4.56007361e-01 5.25116622e-01 2.93355696... | [14.846231460571289, 6.631141662597656] |
e905649f-d12a-4f37-9b9d-30f46c9d3cfd | cross-lingual-complex-word-identification | null | null | https://aclanthology.org/W18-0518 | https://aclanthology.org/W18-0518.pdf | Cross-lingual complex word identification with multitask learning | We approach the 2018 Shared Task on Complex Word Identification by leveraging a cross-lingual multitask learning approach. Our method is highly language agnostic, as evidenced by the ability of our system to generalize across languages, including languages for which we have no training data. In the shared task, this is... | ['Johannes Bjerva', 'Joachim Bingel'] | 2018-06-01 | null | null | null | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-2.54655480e-01 -4.65201914e-01 -1.77065790e-01 -1.22003414e-01
-1.23940635e+00 -1.07806385e+00 8.00581932e-01 9.54631343e-02
-9.23348665e-01 5.64558744e-01 4.58796114e-01 -6.37548327e-01
1.89084321e-01 -3.04313064e-01 -3.82856399e-01 -1.60200149e-01
1.10144503e-01 2.97619224e-01 -4.29211646e-01 -5.24417102... | [10.735148429870605, 10.223640441894531] |
bd34bb5e-355a-4585-8dcd-1e005c82abcf | cats-complementary-cnn-and-transformer | 2208.11572 | null | https://arxiv.org/abs/2208.11572v1 | https://arxiv.org/pdf/2208.11572v1.pdf | Cats: Complementary CNN and Transformer Encoders for Segmentation | Recently, deep learning methods have achieved state-of-the-art performance in many medical image segmentation tasks. Many of these are based on convolutional neural networks (CNNs). For such methods, the encoder is the key part for global and local information extraction from input images; the extracted features are th... | ['Ipek Oguz', 'Jiacheng Wang', 'Han Liu', 'Dewei Hu', 'Hao Li'] | 2022-08-24 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [ 1.53829336e-01 2.64049019e-03 -7.33325034e-02 -5.17876446e-01
-7.31739104e-01 -2.14598328e-01 1.72744900e-01 -7.95704573e-02
-6.70510948e-01 4.52829242e-01 -2.88648363e-02 -3.33647966e-01
2.44294778e-01 -7.19234169e-01 -7.87452877e-01 -7.52768815e-01
1.27189621e-01 2.63438791e-01 5.49350560e-01 7.68239498... | [14.595026969909668, -2.5979437828063965] |
dd052412-9bd6-43f4-adfc-9856a1b0d3e2 | cmu-net-a-strong-convmixer-based-medical | 2210.13012 | null | https://arxiv.org/abs/2210.13012v4 | https://arxiv.org/pdf/2210.13012v4.pdf | CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network | U-Net and its extensions have achieved great success in medical image segmentation. However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information. In addition, simple skip connections cannot capture salient features. In this wo... | ['Jianrui Ding', 'Min Xian', 'Chunping Ning', 'Lingtao Wang', 'Fenghe Tang'] | 2022-10-24 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 8.74938965e-02 3.46760690e-01 -3.37415844e-01 -4.99727279e-01
-8.59571218e-01 -1.59688175e-01 1.50451586e-01 1.82304844e-01
-4.01645690e-01 5.61217189e-01 3.19140673e-01 -3.82275254e-01
-4.14275266e-02 -6.92163765e-01 -6.33578062e-01 -6.38741553e-01
-4.74011377e-02 -1.07903883e-01 2.19670743e-01 4.66518216... | [14.649928092956543, -2.6139607429504395] |
c778c875-96b6-472e-9159-d8856fa44b8f | multi-view-partial-mvp-point-cloud-challenge | 2112.12053 | null | https://arxiv.org/abs/2112.12053v1 | https://arxiv.org/pdf/2112.12053v1.pdf | Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results | As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem for computer vision. With a single incomplete point cloud, it becomes the partial point cloud completion problem. Given multiple different ob... | ['Yang Yang', 'Qinlong Wang', 'Francisco Gómez-Fernández', 'Xin Li', 'Dongrui Liu', 'Changwei Lin', 'Lifa Zhu', 'Junyi An', 'Yuanjie Yan', 'Zhizhong Han', 'Yu-Shen Liu', 'Peng Xiang', 'Xin Wen', 'Junsheng Zhou', 'Yu Qiao', 'Yali Wang', 'Manning Wang', 'Peng Gao', 'Kexue Fu', 'Xiaoyuan Luo', 'Mingye Xu', 'Jie zhou', 'Ji... | 2021-12-22 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.47693098e-01 -2.00072423e-01 7.98946396e-02 -5.01107097e-01
-1.05841517e+00 -7.54924715e-01 5.36943316e-01 -1.94771111e-01
4.63611260e-02 9.05245077e-03 -1.15709022e-01 1.20249346e-01
1.91818967e-01 -4.37331110e-01 -9.26727474e-01 -2.13234812e-01
2.81388313e-01 1.32194197e+00 4.27445412e-01 -1.38745263... | [8.295578956604004, -3.1789424419403076] |
d72f408e-241e-453a-89fd-a81103e9de85 | transforming-model-prediction-for-tracking | 2203.11192 | null | https://arxiv.org/abs/2203.11192v1 | https://arxiv.org/pdf/2203.11192v1.pdf | Transforming Model Prediction for Tracking | Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function. While this inductive bias integrates valuable domain knowledge, it limits the expressivity of the tracking network. In this work, we ... | ['Luc van Gool', 'Fisher Yu', 'Danda Pani Paudel', 'Matthieu Paul', 'Goutam Bhat', 'Martin Danelljan', 'Christoph Mayer'] | 2022-03-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mayer_Transforming_Model_Prediction_for_Tracking_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mayer_Transforming_Model_Prediction_for_Tracking_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-object-tracking'] | ['computer-vision'] | [-2.89426625e-01 2.80346900e-01 -8.76707196e-01 -3.48610640e-01
-6.88985467e-01 -6.26699984e-01 7.85924852e-01 -2.19204843e-01
-2.33755246e-01 6.63507938e-01 2.64516138e-02 1.19369933e-02
-4.77733985e-02 -4.65546340e-01 -8.38912606e-01 -3.52943689e-01
-2.12332413e-01 6.95954740e-01 7.03833580e-01 1.45304576... | [6.290066242218018, -2.1356513500213623] |
38a16368-e304-4d31-8c5c-a4619344b1f0 | text-sketch-image-compression-at-ultra-low | 2307.01944 | null | https://arxiv.org/abs/2307.01944v1 | https://arxiv.org/pdf/2307.01944v1.pdf | Text + Sketch: Image Compression at Ultra Low Rates | Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions. These foundation models, when pre-trained on billion-scale datasets, are effective for various downstream tasks with little or no further training. A natural question to ask is how such m... | ['Shirin Saeedi Bidokhti', 'Hamed Hassani', 'Yiğit Berkay Uslu', 'Eric Lei'] | 2023-07-04 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 8.22096050e-01 1.77231267e-01 -1.81009054e-01 -3.51881891e-01
-9.94053483e-01 -3.65745604e-01 8.06549072e-01 -7.40038678e-02
-1.05240680e-01 7.41449058e-01 5.69806397e-01 -2.06998616e-01
1.47564232e-01 -8.37114334e-01 -9.84575391e-01 -6.27708077e-01
7.78216273e-02 4.31363106e-01 5.43829910e-02 -2.45960429... | [11.358951568603516, -0.8296070098876953] |
9e8142f5-a85a-4f85-b99b-6febcfa4dcbb | faster-maximum-inner-product-search-in-high | 2212.07551 | null | https://arxiv.org/abs/2212.07551v3 | https://arxiv.org/pdf/2212.07551v3.pdf | Faster Maximum Inner Product Search in High Dimensions | Maximum Inner Product Search (MIPS) is a ubiquitous task in machine learning applications such as recommendation systems. Given a query vector and $n$ atom vectors in $d$-dimensional space, the goal of MIPS is to find the atom that has the highest inner product with the query vector. Existing MIPS algorithms scale at l... | ['DongHyun Lee', 'Martin Jinye Zhang', 'Ilan Shomorony', 'Sebastian Thrun', 'Chris Piech', 'Je-Yong Lee', 'Ryan Kang', 'Mo Tiwari'] | 2022-12-14 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-7.67357722e-02 -2.87560940e-01 -5.73877156e-01 -6.20552786e-02
-1.33499479e+00 -7.11414874e-01 -1.21908233e-01 1.49374545e-01
-4.74333078e-01 6.56457961e-01 -1.27455547e-01 -5.14240146e-01
-5.24925411e-01 -9.69696701e-01 -1.13248670e+00 -6.51372313e-01
-5.84142923e-01 8.02723169e-01 9.99601185e-02 -6.40552416... | [6.603999137878418, 4.63886833190918] |
212241de-3f9a-4036-b5b6-999b6bec1be9 | omni-gan-on-the-secrets-of-cgans-and-beyond | 2011.13074 | null | https://arxiv.org/abs/2011.13074v3 | https://arxiv.org/pdf/2011.13074v3.pdf | Omni-GAN: On the Secrets of cGANs and Beyond | The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse. This paper presents Omni-GAN, a variant of cGAN that reveals the devil in designing a proper discriminator for training... | ['Cong Geng', 'Qi Tian', 'Bingbing Ni', 'Lingxi Xie', 'Peng Zhou'] | 2020-11-26 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhou_Omni-GAN_On_the_Secrets_of_cGANs_and_Beyond_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhou_Omni-GAN_On_the_Secrets_of_cGANs_and_Beyond_ICCV_2021_paper.pdf | iccv-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 4.39357549e-01 1.42941222e-01 2.32563347e-01 -1.59596786e-01
-8.30321908e-01 -3.78172427e-01 5.57093680e-01 -9.16020155e-01
-4.82727475e-02 1.00172019e+00 6.49990514e-02 -1.92148536e-01
2.08533764e-01 -1.11871564e+00 -7.85914898e-01 -9.80881870e-01
1.37065604e-01 1.60130560e-01 -1.87116459e-01 -3.84043604... | [11.632065773010254, -0.6071309447288513] |
b45cd4be-9d0a-4165-8ba3-3e3fd244caf5 | ordnet-capturing-omni-range-dependencies-for | 2101.03929 | null | https://arxiv.org/abs/2101.03929v1 | https://arxiv.org/pdf/2101.03929v1.pdf | ORDNet: Capturing Omni-Range Dependencies for Scene Parsing | Learning to capture dependencies between spatial positions is essential to many visual tasks, especially the dense labeling problems like scene parsing. Existing methods can effectively capture long-range dependencies with self-attention mechanism while short ones by local convolution. However, there is still much gap ... | ['Shuicheng Yan', 'Jiashi Feng', 'Bo Li', 'Jizhong Han', 'Tianrui Hui', 'Si Liu', 'Shaofei Huang'] | 2021-01-11 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 8.99151936e-02 -2.85712332e-01 -8.05332363e-02 -7.49434352e-01
-1.61085159e-01 -4.55181807e-01 5.33926904e-01 -5.26024736e-02
-4.98381108e-01 5.29297829e-01 4.67537433e-01 -3.05152327e-01
-2.10467532e-01 -9.99037743e-01 -9.55741704e-01 -5.96311927e-01
-5.19244783e-02 1.94992349e-02 8.24845970e-01 -3.42758507... | [9.585773468017578, 0.36203017830848694] |
d77bb34c-47aa-423e-ac2c-1db1e5d2468b | are-neural-operators-really-neural-operators | 2305.19913 | null | https://arxiv.org/abs/2305.19913v1 | https://arxiv.org/pdf/2305.19913v1.pdf | Are Neural Operators Really Neural Operators? Frame Theory Meets Operator Learning | Recently, there has been significant interest in operator learning, i.e. learning mappings between infinite-dimensional function spaces. This has been particularly relevant in the context of learning partial differential equations from data. However, it has been observed that proposed models may not behave as operators... | ['Rima Alaifari', 'Siddhartha Mishra', 'Roberto Molinaro', 'Bogdan Raonić', 'Emmanuel de Bézenac', 'Francesca Bartolucci'] | 2023-05-31 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [ 5.81163287e-01 3.35106343e-01 9.04379264e-02 -2.57315785e-01
-2.28684247e-01 -6.74616516e-01 3.14919353e-01 2.25942001e-01
-2.79501110e-01 5.74002743e-01 -1.34781178e-03 -5.61439037e-01
-5.66635013e-01 -8.17857623e-01 -7.18225300e-01 -5.74461877e-01
-2.90473044e-01 6.65395707e-02 7.89084136e-02 -2.76937038... | [7.58817195892334, 3.6802151203155518] |
d98b5758-bc76-4b8f-b213-032cb9527350 | deep-structural-causal-models-for-tractable | 2006.06485 | null | https://arxiv.org/abs/2006.06485v2 | https://arxiv.org/pdf/2006.06485v2.pdf | Deep Structural Causal Models for Tractable Counterfactual Inference | We formulate a general framework for building structural causal models (SCMs) with deep learning components. The proposed approach employs normalising flows and variational inference to enable tractable inference of exogenous noise variables - a crucial step for counterfactual inference that is missing from existing de... | ['Daniel C. Castro', 'Nick Pawlowski', 'Ben Glocker'] | 2020-06-11 | null | http://proceedings.neurips.cc/paper/2020/hash/0987b8b338d6c90bbedd8631bc499221-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/0987b8b338d6c90bbedd8631bc499221-Paper.pdf | neurips-2020-12 | ['normalising-flows', 'counterfactual-inference'] | ['methodology', 'miscellaneous'] | [ 4.13593620e-01 4.23136026e-01 -3.67068172e-01 -4.22821045e-01
-5.94442010e-01 -1.15501493e-01 1.06366503e+00 -6.37920992e-03
-2.78985471e-01 1.25466323e+00 6.37679875e-01 -8.32515538e-01
-5.79023480e-01 -8.33707094e-01 -1.06480789e+00 -7.97375977e-01
-5.62938392e-01 3.48303318e-01 -1.97871830e-02 8.91379938... | [8.09609603881836, 5.404637336730957] |
e696039e-bb52-4a69-94d9-262fc7254e78 | evaluating-word-embeddings-in-extremely-under | null | null | https://aclanthology.org/2022.coling-1.393 | https://aclanthology.org/2022.coling-1.393.pdf | Evaluating Word Embeddings in Extremely Under-Resourced Languages: A Case Study in Bribri | Word embeddings are critical for numerous NLP tasks but their evaluation in actual under-resourced settings needs further examination. This paper presents a case study in Bribri, a Chibchan language from Costa Rica. Four experiments were adapted from English: Word similarities, WordSim353 correlations, odd-one-out task... | ['Rolando Coto-Solano'] | null | null | null | null | coling-2022-10 | ['odd-one-out'] | ['reasoning'] | [-2.23317280e-01 -1.69268072e-01 -1.99829862e-01 -1.54918686e-01
-8.98800910e-01 -9.00570214e-01 7.24734008e-01 3.31637174e-01
-1.18645239e+00 2.72452265e-01 6.12532675e-01 -8.04533601e-01
-2.51714379e-01 -7.38621473e-01 -4.95320074e-02 -5.19336283e-01
-2.95905560e-01 5.77117503e-01 8.57558772e-02 -5.43963075... | [10.755245208740234, 9.598169326782227] |
32da43d1-8b7b-432a-b62c-9bd906374635 | high-resolution-gan-inversion-for-degraded | 2302.03406 | null | https://arxiv.org/abs/2302.03406v1 | https://arxiv.org/pdf/2302.03406v1.pdf | High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets | The last decades are marked by massive and diverse image data, which shows increasingly high resolution and quality. However, some images we obtained may be corrupted, affecting the perception and the application of downstream tasks. A generic method for generating a high-quality image from the degraded one is in deman... | ['Yuan Xie', 'Zhizhong Zhang', 'Ying Tai', 'Donghao Luo', 'Chuming Lin', 'Yanbo Wang'] | 2023-02-07 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 5.79363465e-01 -1.36945531e-01 1.71165302e-01 -7.68406466e-02
-8.30475092e-01 -4.66847956e-01 3.92826289e-01 -6.97514534e-01
-8.01861808e-02 8.77051115e-01 3.08343828e-01 6.98676407e-02
1.04080759e-01 -7.54017770e-01 -7.49262810e-01 -9.96407628e-01
4.17567760e-01 2.05833297e-02 -3.07656407e-01 -1.47828609... | [11.38636302947998, -1.3780770301818848] |
4b9a6a3a-5452-4306-9cf2-7c9561014526 | vita-video-instance-segmentation-via-object | 2206.04403 | null | https://arxiv.org/abs/2206.04403v2 | https://arxiv.org/pdf/2206.04403v2.pdf | VITA: Video Instance Segmentation via Object Token Association | We introduce a novel paradigm for offline Video Instance Segmentation (VIS), based on the hypothesis that explicit object-oriented information can be a strong clue for understanding the context of the entire sequence. To this end, we propose VITA, a simple structure built on top of an off-the-shelf Transformer-based im... | ['Seon Joo Kim', 'Joon-Young Lee', 'Seoung Wug Oh', 'Sukjun Hwang', 'Miran Heo'] | 2022-06-09 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.34609300e-02 -1.06270211e-02 -3.88944358e-01 -1.02698959e-01
-6.85198605e-01 -5.52493274e-01 6.19553149e-01 8.07049721e-02
-4.92488325e-01 4.19021726e-01 7.35747954e-03 -2.14443401e-01
2.29876384e-01 -8.36870372e-01 -1.09875000e+00 -4.95915890e-01
-1.63816854e-01 2.61671066e-01 8.09209406e-01 -7.63163045... | [9.190641403198242, -0.046237919479608536] |
0f01020d-e905-4dfc-ac54-cc2d56ef751b | double-topic-shifts-in-open-domain | null | null | https://aclanthology.org/W16-4408 | https://aclanthology.org/W16-4408.pdf | Double Topic Shifts in Open Domain Conversations: Natural Language Interface for a Wikipedia-based Robot Application | The paper describes topic shifting in dialogues with a robot that provides information from Wiki-pedia. The work focuses on a double topical construction of dialogue coherence which refers to discourse coherence on two levels: the evolution of dialogue topics via the interaction between the user and the robot system, a... | ['Kristiina Jokinen', 'Graham Wilcock'] | 2016-12-01 | null | null | null | ws-2016-12 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-3.05369139e-01 1.52345252e+00 -1.80117860e-01 -2.02559270e-02
-2.73231506e-01 -5.76915741e-01 1.26498818e+00 5.55377185e-01
-1.73990905e-01 1.17815185e+00 1.07364976e+00 2.53599495e-01
-2.57542431e-01 -8.12864184e-01 -7.45219067e-02 -3.26117605e-01
-2.20890582e-01 9.76477027e-01 5.51010728e-01 -9.79069054... | [12.752128601074219, 7.942568302154541] |
126d8fb1-c39a-4c8d-bcbe-db7f787ef12f | off-policy-evaluation-in-doubly-inhomogeneous | 2306.08719 | null | https://arxiv.org/abs/2306.08719v1 | https://arxiv.org/pdf/2306.08719v1.pdf | Off-policy Evaluation in Doubly Inhomogeneous Environments | This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions -- temporal stationarity and individual homogeneity are both violated. To handle the ``double inhomogeneities", we propose a class of latent factor models for the reward and observation transition f... | ['Lan Wang', 'Zhengling Qi', 'Chengchun Shi', 'Zeyu Bian'] | 2023-06-14 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 1.35563836e-01 1.97776537e-02 -7.53633499e-01 -6.85967058e-02
-7.80670822e-01 -2.41023719e-01 3.19830090e-01 1.58324152e-01
-6.75966680e-01 1.23252952e+00 -1.58368591e-02 -5.26779711e-01
-6.48584783e-01 -2.46684268e-01 -6.65478647e-01 -1.00239289e+00
-5.07542729e-01 7.13659286e-01 1.47020400e-01 3.12468372... | [4.210254669189453, 2.515092611312866] |
73db0788-d4a7-406f-a3d3-40db6223dc4a | learning-sparse-and-low-rank-priors-for-image | 2304.10536 | null | https://arxiv.org/abs/2304.10536v1 | https://arxiv.org/pdf/2304.10536v1.pdf | Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization | We introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which we parameterize as weighted extensions of the $\ell_p^p$-vector and $\mathcal S_p^p$ Schatten-matrix quasi-norms for $0\!<p\!\le1$, respectively. Our proposed algorithm generalizes the Iteratively Reweigh... | ['Iaroslav Koshelev', 'Stamatios Lefkimmiatis'] | 2023-04-20 | null | null | null | null | ['demosaicking', 'deblurring'] | ['computer-vision', 'computer-vision'] | [ 4.42041248e-01 5.42133711e-02 2.57429350e-02 -2.00994551e-01
-9.51080918e-01 1.91544946e-02 2.76554257e-01 -5.07312894e-01
-5.38994014e-01 8.54600370e-01 3.24090004e-01 -1.09264158e-01
-5.85870445e-01 -6.00681782e-01 -9.07641828e-01 -9.03096914e-01
-2.81966478e-01 1.26973525e-01 -2.51320571e-01 -3.47382247... | [11.55685043334961, -2.29243803024292] |
d462f77b-c8d0-4409-96bc-bb680537bbe0 | unsupervised-learning-of-depth-and-ego-motion-2 | 1802.05522 | null | http://arxiv.org/abs/1802.05522v2 | http://arxiv.org/pdf/1802.05522v2.pdf | Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints | We present a novel approach for unsupervised learning of depth and ego-motion
from monocular video. Unsupervised learning removes the need for separate
supervisory signals (depth or ego-motion ground truth, or multi-view video).
Prior work in unsupervised depth learning uses pixel-wise or gradient-based
losses, which o... | ['Anelia Angelova', 'Reza Mahjourian', 'Martin Wicke'] | 2018-02-15 | unsupervised-learning-of-depth-and-ego-motion-3 | http://openaccess.thecvf.com/content_cvpr_2018/html/Mahjourian_Unsupervised_Learning_of_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Mahjourian_Unsupervised_Learning_of_CVPR_2018_paper.pdf | cvpr-2018-6 | ['depth-and-camera-motion'] | ['computer-vision'] | [ 2.30116665e-01 -1.25718728e-01 -9.35227349e-02 -4.91353929e-01
-8.74598145e-01 -6.03872240e-01 4.56065029e-01 -3.92258853e-01
-6.89945281e-01 7.59358943e-01 2.14165583e-01 2.21389830e-01
6.61299005e-02 -7.09658742e-01 -1.09266555e+00 -8.09544683e-01
2.70960126e-02 4.13157046e-01 3.95377189e-01 3.84315342... | [8.590929985046387, -2.360869884490967] |
5f73452f-2e98-4d8f-bf93-876c87b54632 | moviepuzzle-visual-narrative-reasoning | 2306.02252 | null | https://arxiv.org/abs/2306.02252v2 | https://arxiv.org/pdf/2306.02252v2.pdf | MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning | We introduce MoviePuzzle, a novel challenge that targets visual narrative reasoning and holistic movie understanding. Despite the notable progress that has been witnessed in the realm of video understanding, most prior works fail to present tasks and models to address holistic video understanding and the innate visual ... | ['Zilong Zheng', 'Dongyan Zhao', 'Yuxuan Wang', 'Jianghui Wang'] | 2023-06-04 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 2.20536128e-01 -1.08731762e-01 -3.25788677e-01 -2.52732188e-01
-2.23643586e-01 -1.05613756e+00 7.84924090e-01 -7.08409250e-02
-4.90966439e-02 1.39504299e-01 7.62191355e-01 -2.71462470e-01
-1.75972849e-01 -2.51572847e-01 -8.52513909e-01 -3.04187328e-01
-3.81606743e-02 3.05343240e-01 1.68604776e-01 -1.70722753... | [10.222158432006836, 0.8335487842559814] |
7bd4ac95-fb4e-48c8-9e3a-119f1547248b | comparison-and-analysis-of-deep-audio | 2104.06517 | null | https://arxiv.org/abs/2104.06517v1 | https://arxiv.org/pdf/2104.06517v1.pdf | Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition | Emotion is a complicated notion present in music that is hard to capture even with fine-tuned feature engineering. In this paper, we investigate the utility of state-of-the-art pre-trained deep audio embedding methods to be used in the Music Emotion Recognition (MER) task. Deep audio embedding methods allow us to effic... | ['Shlomo Dubnov', 'Eunjeong Koh'] | 2021-04-13 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [-2.03776807e-01 -2.24294081e-01 2.25498229e-01 -2.25928113e-01
-7.64189243e-01 -5.47001958e-01 2.14082435e-01 -2.10124720e-02
-2.83019662e-01 9.10514668e-02 5.29547930e-01 3.56139779e-01
-2.62068331e-01 -5.98389983e-01 -4.64706063e-01 -5.10964990e-01
-2.40404427e-01 3.25477034e-01 -2.82648265e-01 -4.10410315... | [15.729960441589355, 5.194106578826904] |
26a67488-33b4-4915-8a98-0e35e7e3b559 | multi-tasks-retinanet-for-mitosis-detection | 2208.12657 | null | https://arxiv.org/abs/2208.12657v1 | https://arxiv.org/pdf/2208.12657v1.pdf | Multi tasks RetinaNet for mitosis detection | The account of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, it is a highly challenging task to detect mitotic cells in tumor tissues. At the same time, although advanced deep learning method have achieved great success in cell detection, the performance ... | ['Zhang Yongbing', 'Bian Hao', 'Fang Zijie', 'Wang Ziyue', 'Chen Yang'] | 2022-08-26 | null | null | null | null | ['cell-detection', 'mitosis-detection'] | ['computer-vision', 'medical'] | [ 2.05327168e-01 -3.04124206e-01 -2.19537437e-01 1.34597421e-01
-9.57531869e-01 -3.75744104e-01 6.09094977e-01 1.86060846e-01
-5.58095634e-01 8.78160596e-01 -1.59805030e-01 -3.37553769e-01
4.17845786e-01 -4.50591952e-01 -3.62168789e-01 -1.33868694e+00
5.52188337e-01 6.11293316e-01 6.56293571e-01 1.22670449... | [15.098042488098145, -3.127023696899414] |
231d435a-41e3-4668-a4fe-db0365dd9da2 | memorizing-complementation-network-for-few | 2208.0561 | null | https://arxiv.org/abs/2208.05610v1 | https://arxiv.org/pdf/2208.05610v1.pdf | Memorizing Complementation Network for Few-Shot Class-Incremental Learning | Few-shot Class-Incremental Learning (FSCIL) aims at learning new concepts continually with only a few samples, which is prone to suffer the catastrophic forgetting and overfitting problems. The inaccessibility of old classes and the scarcity of the novel samples make it formidable to realize the trade-off between retai... | ['Xuelong Li', 'Yanwei Pang', 'Xiyao Liu', 'Zhishen Hou', 'Zhong Ji'] | 2022-08-11 | null | null | null | null | ['few-shot-class-incremental-learning', 'novel-concepts'] | ['methodology', 'reasoning'] | [ 1.30708411e-01 -1.00711100e-01 1.01913884e-01 -2.33903781e-01
-9.50520560e-02 1.42188175e-02 3.49088371e-01 2.02440605e-01
-7.28483915e-01 1.32993698e+00 -2.32320696e-01 1.59637645e-01
-2.26644978e-01 -7.95732677e-01 -7.20913410e-01 -9.27239537e-01
-3.47106233e-02 4.02939230e-01 7.21573293e-01 -1.37319833... | [9.840677261352539, 3.404435634613037] |
73037ab5-66c6-4a7e-8a71-82fb889111b8 | dual-skipping-networks | 1710.10386 | null | http://arxiv.org/abs/1710.10386v3 | http://arxiv.org/pdf/1710.10386v3.pdf | Dual Skipping Networks | Inspired by the recent neuroscience studies on the left-right asymmetry of
the human brain in processing low and high spatial frequency information, this
paper introduces a dual skipping network which carries out coarse-to-fine
object categorization. Such a network has two branches to simultaneously deal
with both coar... | ['xiangyang xue', 'Jianfeng Feng', 'Yu-Gang Jiang', 'Yanwei Fu', 'Wenlian Lu', 'Wei Liu', 'Changmao Cheng'] | 2017-10-28 | dual-skipping-networks-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Cheng_Dual_Skipping_Networks_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Cheng_Dual_Skipping_Networks_CVPR_2018_paper.pdf | cvpr-2018-6 | ['object-categorization'] | ['computer-vision'] | [ 9.57576036e-02 -1.41122192e-01 -3.84492964e-01 -8.87448967e-01
6.92316666e-02 -4.35472786e-01 6.18410408e-01 -8.27718824e-02
-6.02170885e-01 5.21726012e-01 1.47258818e-01 -1.24806173e-01
-4.27543253e-01 -8.47965419e-01 -3.15683246e-01 -5.31000257e-01
-2.39542991e-01 1.81933135e-01 3.74693483e-01 -5.11714704... | [9.55135440826416, 2.1599888801574707] |
2f332aac-3cbe-424a-aadc-5c3e0f1ed0d9 | hypergraph-and-protein-function-prediction | 1212.0388 | null | http://arxiv.org/abs/1212.0388v1 | http://arxiv.org/pdf/1212.0388v1.pdf | Hypergraph and protein function prediction with gene expression data | Most network-based protein (or gene) function prediction methods are based on
the assumption that the labels of two adjacent proteins in the network are
likely to be the same. However, assuming the pairwise relationship between
proteins or genes is not complete, the information a group of genes that show
very similar p... | ['Loc Tran'] | 2012-12-03 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 4.26628888e-01 4.26981270e-01 -2.53523201e-01 -4.13503349e-01
-4.98560257e-02 -5.11879563e-01 9.09825936e-02 2.71789670e-01
-1.21935792e-02 1.13938415e+00 -3.11760455e-01 -8.61648843e-02
-5.85384786e-01 -8.54239523e-01 -6.41314387e-01 -1.15054846e+00
-4.79521424e-01 6.03756785e-01 3.71412516e-01 7.03486204... | [6.655246734619141, 5.479518413543701] |
037d906a-e48c-4c64-8c27-4d762a2f5652 | activity-auto-completion-predicting-human | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Xu_Activity_Auto-Completion_Predicting_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Xu_Activity_Auto-Completion_Predicting_ICCV_2015_paper.pdf | Activity Auto-Completion: Predicting Human Activities From Partial Videos | In this paper, we propose an activity auto-completion (AAC) model for human activity prediction by formulating activity prediction as a query auto-completion (QAC) problem in information retrieval. First, we extract discriminative patches in frames of videos. A video is represented based on these patches and divided in... | ['Zhen Xu', 'Laiyun Qing', 'Jun Miao'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 6.03260815e-01 -9.05638486e-02 -6.16486669e-01 -2.83016145e-01
-8.75608087e-01 -2.89828509e-01 5.02625942e-01 6.67877942e-02
-3.24483931e-01 5.90211153e-01 5.35394609e-01 4.30440426e-01
1.70560658e-03 -2.08081678e-01 -5.91052651e-01 -5.03362656e-01
-4.32346761e-01 3.26069176e-01 5.56361437e-01 3.62949252... | [8.573851585388184, 0.5433752536773682] |
fb4e5fd7-b40e-4465-9470-ac95b29b5e0d | economic-impacts-of-ai-augmented-r-d | 2212.08198 | null | https://arxiv.org/abs/2212.08198v2 | https://arxiv.org/pdf/2212.08198v2.pdf | Economic impacts of AI-augmented R&D | Since its emergence around 2010, deep learning has rapidly become the most important technique in Artificial Intelligence (AI), producing an array of scientific firsts in areas as diverse as protein folding, drug discovery, integrated chip design, and weather prediction. As more scientists and engineers adopt deep lear... | ['Neil Thompson', 'Nicholas Emery-Xu', 'Tamay Besiroglu'] | 2022-12-15 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [-3.24418783e-01 1.94009498e-01 -2.19117522e-01 2.20006138e-01
-1.32103398e-01 -5.02681732e-01 5.83949804e-01 2.08766103e-01
-4.95026231e-01 6.63185716e-01 5.38801849e-02 -8.04766953e-01
4.10706066e-02 -1.00730562e+00 -9.11983609e-01 -3.85557175e-01
1.44987591e-02 4.15784746e-01 -4.79310334e-01 -5.02201617... | [8.90942668914795, 6.343876361846924] |
3cc64475-e3d1-4a26-8e84-196eb48dd9da | pareto-policy-pool-for-model-based-offline | null | null | https://openreview.net/forum?id=OqcZu8JIIzS | https://openreview.net/pdf?id=OqcZu8JIIzS | Pareto Policy Pool for Model-based Offline Reinforcement Learning | Online reinforcement learning (RL) can suffer from poor exploration, sparse reward, insufficient data, and overhead caused by inefficient interactions between an immature policy and a complicated environment. Model-based offline RL instead trains an environment model using a dataset of pre-collected experiences so onli... | ['Yuhui Shi', 'Jie Ma', 'Tianyi Zhou', 'Jing Jiang', 'Yijun Yang'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['d4rl'] | ['robots'] | [-2.26849258e-01 -2.42119223e-01 -3.26960623e-01 3.46909091e-02
-9.79849219e-01 -9.23182547e-01 2.95491844e-01 1.93104178e-01
-8.04005086e-01 9.53251123e-01 8.30569863e-03 -1.70258433e-01
-2.98991948e-01 -6.05219901e-01 -9.52147424e-01 -8.41088176e-01
-3.28761935e-01 6.94242597e-01 3.36312805e-03 -1.60979047... | [4.128280162811279, 2.1908161640167236] |
f79248f7-5d87-48ea-922d-424cff0c8f93 | updated-version-a-video-anomaly-detection | 2303.05109 | null | https://arxiv.org/abs/2303.05109v1 | https://arxiv.org/pdf/2303.05109v1.pdf | Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency | Video anomaly detection is an essential but challenging task. The prevalent methods mainly investigate the reconstruction difference between normal and abnormal patterns but ignore the semantics consistency between appearance and motion information of behavior patterns, making the results highly dependent on the local ... | ['Zhiqiang Wu', 'Caidan Zhao', 'Xiangyu Huang'] | 2023-03-09 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 7.61755109e-02 -4.97509539e-01 -8.39104652e-02 -4.35115278e-01
3.79049569e-01 -1.66652799e-01 3.65218163e-01 9.59832743e-02
4.61514182e-02 2.09133383e-02 1.52992144e-01 2.08377942e-01
-7.68464431e-03 -6.65220022e-01 -2.81141669e-01 -6.84935212e-01
-1.83321834e-02 -5.09172380e-01 8.27413380e-01 -1.99917585... | [7.899126052856445, 1.5612841844558716] |
a170c41f-b214-46af-b5f7-a9c846400b76 | source-free-domain-adaptation-requires | 2304.02798 | null | https://arxiv.org/abs/2304.02798v2 | https://arxiv.org/pdf/2304.02798v2.pdf | Source-free Domain Adaptation Requires Penalized Diversity | While neural networks are capable of achieving human-like performance in many tasks such as image classification, the impressive performance of each model is limited to its own dataset. Source-free domain adaptation (SFDA) was introduced to address knowledge transfer between different domains in the absence of source d... | ['Mohammad Havaei', 'Thomas Fevens', 'Samira Ebrahimi Kahou', 'Alexandre See', 'Farhood Farahnak', 'Ivaxi Sheth', 'Laya Rafiee Sevyeri'] | 2023-04-06 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 6.59391820e-01 1.20380864e-01 -3.00612986e-01 -5.07864356e-01
-5.92517793e-01 -3.45429391e-01 6.33698583e-01 2.62191385e-01
-3.99758011e-01 1.07550275e+00 2.69232541e-01 -4.21158299e-02
-3.80587965e-01 -7.10186005e-01 -5.98853290e-01 -9.88283336e-01
1.00597747e-01 8.35446790e-02 -2.96087209e-02 -1.52039722... | [10.356512069702148, 3.239576816558838] |
33bd9b0b-a740-45d1-af3b-55a2d2c2825e | graph-sequential-neural-ode-process-for-link | 2211.08568 | null | https://arxiv.org/abs/2211.08568v1 | https://arxiv.org/pdf/2211.08568v1.pdf | Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs | Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks (DGNNs) typically require a significant amount of historical data (interactions over time), which is not always available in practice. The missing links over time, which is a common phenome... | ['Shirui Pan', 'Reza Haffari', 'Linhao Luo'] | 2022-11-15 | null | null | null | null | ['graph-mining'] | ['graphs'] | [-2.77969420e-01 6.17089942e-02 -9.88565236e-02 9.26361233e-02
2.33640894e-01 -2.36212865e-01 4.39262092e-01 3.11508566e-01
2.22928584e-01 6.70111477e-01 -1.01667121e-01 -5.66692412e-01
-4.86706227e-01 -1.46828210e+00 -6.23280108e-01 -6.61774635e-01
-4.33385134e-01 7.80217826e-01 4.57796425e-01 -3.50901097... | [7.234675884246826, 5.931657314300537] |
b33fd621-848a-43f2-b951-7bfa3567ccdb | a-multi-domain-vne-algorithm-based-on-multi | 2202.1283 | null | https://arxiv.org/abs/2202.12830v1 | https://arxiv.org/pdf/2202.12830v1.pdf | A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0 | Unmanned aerial vehicle (UAV) has a broad application prospect in the future, especially in the Industry 4.0. The development of Internet of Drones (IoD) makes UAV operation more autonomous. Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a cruc... | ['Haotong Cao', 'Zeyu Qin', 'Chao Wang', 'Peiying Zhang'] | 2022-02-08 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-2.42465660e-01 -4.06526268e-01 -1.86075434e-01 4.55720305e-01
3.90137672e-01 -2.71943092e-01 -8.87112841e-02 -6.82566315e-02
-4.25561011e-01 9.15384352e-01 -5.02486408e-01 -2.64891684e-01
-8.27684045e-01 -1.15963399e+00 -2.25152429e-02 -9.38189089e-01
-5.33699542e-02 3.89626175e-01 4.64443833e-01 -4.39204872... | [5.877322673797607, 1.713700532913208] |
444d0599-7610-4409-be5e-140a399d0686 | gapoera-application-programming-interface-for | 2110.11924 | null | https://arxiv.org/abs/2110.11924v1 | https://arxiv.org/pdf/2110.11924v1.pdf | Gapoera: Application Programming Interface for AI Environment of Indonesian Board Game | Currently, the development of computer games has shown a tremendous surge. The ease and speed of internet access today have also influenced the development of computer games, especially computer games that are played online. Internet technology has allowed computer games to be played in multiplayer mode. Interaction be... | ['Galang Prihadi Mahardhika', 'Rian Adam Rajagede'] | 2021-10-22 | null | null | null | null | ['board-games'] | ['playing-games'] | [-5.59602380e-01 -7.88319930e-02 3.90794396e-01 1.70730859e-01
1.36138275e-01 -6.81396663e-01 1.78882569e-01 -1.62165090e-01
-6.20975614e-01 6.36743426e-01 -4.78957593e-01 -7.12751567e-01
-1.52505636e-01 -1.34809709e+00 2.89642299e-03 -5.33657134e-01
1.33333296e-01 7.22082257e-01 6.47151113e-01 -8.92088711... | [3.4727137088775635, 1.4863237142562866] |
2c17d207-42cd-49ad-b3b7-bb7e0d4eff26 | actrce-augmenting-experience-via-teachers | 1902.04546 | null | http://arxiv.org/abs/1902.04546v1 | http://arxiv.org/pdf/1902.04546v1.pdf | ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning | Sparse reward is one of the most challenging problems in reinforcement
learning (RL). Hindsight Experience Replay (HER) attempts to address this issue
by converting a failed experience to a successful one by relabeling the goals.
Despite its effectiveness, HER has limited applicability because it lacks a
compact and un... | ['Sanja Fidler', 'Jimmy Ba', 'Yuhuai Wu', 'Jamie Kiros', 'Harris Chan'] | 2019-02-12 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-1.32594451e-01 4.52181041e-01 3.95815521e-02 -1.37948647e-01
-8.27545464e-01 -7.80971110e-01 4.23899531e-01 -3.32849286e-02
-6.52958572e-01 1.11839151e+00 2.58176118e-01 -4.70603228e-01
-1.76311627e-01 -6.61562622e-01 -6.28661931e-01 -6.57373130e-01
-3.28565389e-01 4.83504385e-01 1.25991315e-01 -8.30417097... | [4.091815948486328, 1.4609720706939697] |
74f558b9-884f-419b-a0ec-646d48963fe1 | video-cloze-procedure-for-self-supervised | 2001.00294 | null | https://arxiv.org/abs/2001.00294v1 | https://arxiv.org/pdf/2001.00294v1.pdf | Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning | We propose a novel self-supervised method, referred to as Video Cloze Procedure (VCP), to learn rich spatial-temporal representations. VCP first generates "blanks" by withholding video clips and then creates "options" by applying spatio-temporal operations on the withheld clips. Finally, it fills the blanks with "optio... | ['Can Ma', 'Yu Zhou', 'Chang Liu', 'Qixiang Ye', 'Dongbao Yang', 'Dezhao Luo', 'Weiping Wang'] | 2020-01-02 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 2.85385847e-01 -1.25312909e-01 -7.39422739e-01 -5.01644671e-01
-6.73108816e-01 -4.20781583e-01 6.58474684e-01 -1.53120264e-01
-7.18110204e-02 3.50128889e-01 5.76925814e-01 -3.28681134e-02
1.84211716e-01 -6.16366386e-01 -9.64593649e-01 -5.02802730e-01
-1.09049082e-01 2.81203896e-01 1.42890483e-01 6.64658099... | [8.692266464233398, 0.7276906371116638] |
ae30e49c-38d0-4892-8e25-4500ab6bd6fe | neural-user-simulation-for-corpus-based | null | null | https://aclanthology.org/W18-5007 | https://aclanthology.org/W18-5007.pdf | Neural User Simulation for Corpus-based Policy Optimisation of Spoken Dialogue Systems | User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and its output is in semantic form. Issues arise from both properties such as limited diversity and the i... | ["Milica Ga{\\v{s}}i{\\'c}", 'Pawe{\\l} Budzianowski', 'I{\\~n}igo Casanueva', 'Florian Kreyssig'] | 2018-07-01 | null | null | null | ws-2018-7 | ['user-simulation'] | ['natural-language-processing'] | [ 2.57411867e-01 7.06141710e-01 1.91203237e-01 -4.57350850e-01
-5.40338218e-01 -5.61143160e-01 9.51532841e-01 -4.16317210e-03
-7.83686996e-01 1.19130623e+00 2.07486212e-01 -5.25910497e-01
3.05522114e-01 -4.85322446e-01 -5.33426642e-01 -3.87716085e-01
9.94253010e-02 1.09118462e+00 4.04363722e-01 -7.39436388... | [13.050532341003418, 8.027019500732422] |
cffbccb1-a4fb-4b80-a3d1-df7680ba523f | deep-convolutional-neural-network-for-multi | 1910.04066 | null | https://arxiv.org/abs/1910.04066v1 | https://arxiv.org/pdf/1910.04066v1.pdf | Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion | In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different from other methods based on deep learning, our network architecture is designed by drawing inspirations from a new proposed multi-modal conv... | ['Pier Luigi Dragotti', 'Xin Deng'] | 2019-10-09 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 3.81344765e-01 -2.38322034e-01 1.29543439e-01 -5.65855391e-02
-9.23722267e-01 1.03515036e-01 2.58027494e-01 -4.08027321e-01
-1.86696827e-01 6.51662052e-01 5.43751359e-01 2.12058425e-01
-2.55850405e-01 -6.86519921e-01 -6.61612391e-01 -1.09792066e+00
4.70298856e-01 -2.98289299e-01 1.92999333e-01 -3.51059943... | [10.601655960083008, -1.863613486289978] |
63d1b273-c8e8-4767-a276-5998aa418620 | data-driven-computational-imaging-for | 2210.16709 | null | https://arxiv.org/abs/2210.16709v1 | https://arxiv.org/pdf/2210.16709v1.pdf | Data-Driven Computational Imaging for Scientific Discovery | In computational imaging, hardware for signal sampling and software for object reconstruction are designed in tandem for improved capability. Examples of such systems include computed tomography (CT), magnetic resonance imaging (MRI), and superresolution microscopy. In contrast to more traditional cameras, in these dev... | ['Vidya Ganapati', 'Yolanda Hu', 'Andrew Olsen'] | 2022-10-29 | null | null | null | null | ['transparent-objects', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 5.36905766e-01 -2.93289185e-01 1.39916837e-01 -1.65621862e-01
-6.07444763e-01 -3.56801271e-01 3.21343333e-01 -5.69584109e-02
-4.76857126e-01 7.46803045e-01 -9.94148254e-02 -2.26077959e-01
1.14581786e-01 -5.62202096e-01 -4.08635914e-01 -1.06327701e+00
1.54420047e-03 2.93458760e-01 4.55072135e-01 2.89901942... | [12.913479804992676, -2.7983791828155518] |
65b1b70c-2b66-4a88-a83a-3a7bf6b6329f | video-face-manipulation-detection-through | 2004.07676 | null | https://arxiv.org/abs/2004.07676v1 | https://arxiv.org/pdf/2004.07676v1.pdf | Video Face Manipulation Detection Through Ensemble of CNNs | In the last few years, several techniques for facial manipulation in videos have been successfully developed and made available to the masses (i.e., FaceSwap, deepfake, etc.). These methods enable anyone to easily edit faces in video sequences with incredibly realistic results and a very little effort. Despite the usef... | ['Nicolò Bonettini', 'Luca Bondi', 'Paolo Bestagini', 'Stefano Tubaro', 'Sara Mandelli', 'Edoardo Daniele Cannas'] | 2020-04-16 | null | null | null | null | ['localization-in-video-forgery', 'image-manipulation-detection', 'gan-image-forensics', 'video-forensics', 'fake-image-detection', 'detecting-image-manipulation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.12635472e-01 7.13380799e-02 6.73535392e-02 -4.04846296e-02
-8.63377098e-03 -4.42430913e-01 8.62863958e-01 -2.73176432e-01
-4.57250953e-01 8.07924688e-01 -2.28733763e-01 2.62176059e-02
1.82085127e-01 -7.48312294e-01 -1.07438612e+00 -6.50478125e-01
-3.91959362e-02 7.98406452e-02 1.55647367e-01 -4.30007935... | [12.60118293762207, 1.1074293851852417] |
43a6b002-647f-4b8e-8955-74b782ca0500 | online-to-pac-conversions-generalization | 2305.19674 | null | https://arxiv.org/abs/2305.19674v1 | https://arxiv.org/pdf/2305.19674v1.pdf | Online-to-PAC Conversions: Generalization Bounds via Regret Analysis | We present a new framework for deriving bounds on the generalization bound of statistical learning algorithms from the perspective of online learning. Specifically, we construct an online learning game called the "generalization game", where an online learner is trying to compete with a fixed statistical learning algor... | ['Gergely Neu', 'Gábor Lugosi'] | 2023-05-31 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [-1.04213424e-01 2.88205445e-01 -7.17951804e-02 -2.94412971e-01
-1.18675804e+00 -8.45439374e-01 -8.81459787e-02 3.82841855e-01
-6.21588051e-01 6.42058909e-01 -5.59656739e-01 -4.92457598e-01
-7.13695824e-01 -7.09386408e-01 -1.03462648e+00 -9.54003513e-01
-5.36687553e-01 4.66534674e-01 3.74999344e-01 1.55432075... | [4.848182678222656, 3.5158028602600098] |
3f7b0f10-fdcc-4936-a490-5f62d3ee56f0 | discord-questions-a-computational-approach-to | 2211.05007 | null | https://arxiv.org/abs/2211.05007v1 | https://arxiv.org/pdf/2211.05007v1.pdf | Discord Questions: A Computational Approach To Diversity Analysis in News Coverage | There are many potential benefits to news readers accessing diverse sources. Modern news aggregators do the hard work of organizing the news, offering readers a plethora of source options, but choosing which source to read remains challenging. We propose a new framework to assist readers in identifying source differenc... | ['Caiming Xiong', "Xiang 'Anthony' Chen", "Lidiya Murakhovs'ka", 'Chien-Sheng Wu', 'Philippe Laban'] | 2022-11-09 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-8.72753337e-02 6.74938321e-01 -9.94363353e-02 -2.61647850e-01
-1.86830521e+00 -1.06176388e+00 8.39447558e-01 6.37702227e-01
-1.54443756e-01 9.12045956e-01 1.17601871e+00 -3.22780371e-01
-6.82238489e-02 -7.83433914e-01 -6.35544181e-01 1.44854724e-01
5.61879992e-01 7.67547846e-01 4.99343097e-01 -7.92483926... | [11.586124420166016, 8.198283195495605] |
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