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5736659b-94f9-4cc8-abfd-48bf5b1b66fa | controllability-of-coarsely-measured | 2206.10569 | null | https://arxiv.org/abs/2206.10569v1 | https://arxiv.org/pdf/2206.10569v1.pdf | Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version) | We consider the controllability of large-scale linear networked dynamical systems when complete knowledge of network structure is unavailable and knowledge is limited to coarse summaries. We provide conditions under which average controllability of the fine-scale system can be well approximated by average controllabili... | ['Stark C. Draper', 'Gautam Dasarathy', 'Rajasekhar Anguluri', 'Nafiseh Ghoroghchian'] | 2022-06-21 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.76153812e-02 1.73403844e-01 5.29530644e-02 5.73378980e-01
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-7.14860320e-01 6.66841447e-01 4.34544951e-01 -5.97297609... | [6.7261786460876465, 4.960445880889893] |
6031f465-2077-4b28-93e9-589b349ea256 | a-memory-efficient-baseline-for-open-domain | 2012.15156 | null | https://arxiv.org/abs/2012.15156v1 | https://arxiv.org/pdf/2012.15156v1.pdf | A Memory Efficient Baseline for Open Domain Question Answering | Recently, retrieval systems based on dense representations have led to important improvements in open-domain question answering, and related tasks. While very effective, this approach is also memory intensive, as the dense vectors for the whole knowledge source need to be kept in memory. In this paper, we study how the... | ['Edouard Grave', 'Sebastian Riedel', 'Nicola De Cao', 'Lucas Hosseini', 'Fabio Petroni', 'Gautier Izacard'] | 2020-12-30 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [-1.41348839e-01 -1.14969373e-01 1.77488290e-02 -3.97326052e-02
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1.99037045e-01 7.89901972e-01 8.07864249e-01 -7.36001670... | [11.400516510009766, 7.703930854797363] |
f1e666c5-b9ac-4274-af90-09e70dc65d0c | affine-correspondences-between-multi-camera | 2306.12996 | null | https://arxiv.org/abs/2306.12996v1 | https://arxiv.org/pdf/2306.12996v1.pdf | Affine Correspondences between Multi-Camera Systems for Relative Pose Estimation | We present a novel method to compute the relative pose of multi-camera systems using two affine correspondences (ACs). Existing solutions to the multi-camera relative pose estimation are either restricted to special cases of motion, have too high computational complexity, or require too many point correspondences (PCs)... | ['Ji Zhao', 'Banglei Guan'] | 2023-06-22 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [-9.66660157e-02 -2.97885299e-01 -1.54961467e-01 -4.16276678e-02
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6e65ad9d-18f5-4429-843d-036a2aac9a89 | frequency-aware-face-hallucination-generative | 2110.01880 | null | https://arxiv.org/abs/2110.01880v1 | https://arxiv.org/pdf/2110.01880v1.pdf | Frequency Aware Face Hallucination Generative Adversarial Network with Semantic Structural Constraint | In this paper, we address the issue of face hallucination. Most current face hallucination methods rely on two-dimensional facial priors to generate high resolution face images from low resolution face images. These methods are only capable of assimilating global information into the generated image. Still there exist ... | ['Vinay Kumar', 'Abhinav Dhall', 'Shailza Sharma'] | 2021-10-05 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 5.32844365e-01 8.09653103e-01 2.78791159e-01 -1.35042593e-01
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4.89650220e-02 8.55699554e-02 -2.94477612e-01 -3.50831926... | [12.782869338989258, -0.09824017435312271] |
683abddb-5899-4207-bc49-f3ccfca66562 | type-prediction-with-program-decomposition | 2305.17145 | null | https://arxiv.org/abs/2305.17145v1 | https://arxiv.org/pdf/2305.17145v1.pdf | Type Prediction With Program Decomposition and Fill-in-the-Type Training | TypeScript and Python are two programming languages that support optional type annotations, which are useful but tedious to introduce and maintain. This has motivated automated type prediction: given an untyped program, produce a well-typed output program. Large language models (LLMs) are promising for type prediction,... | ['Steven Holtzen', 'Arjun Guha', 'Noah Shinn', 'Ming-Ho Yee', 'Federico Cassano'] | 2023-05-25 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-2.20919952e-01 -3.95219438e-02 -6.24135792e-01 -4.26419973e-01
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1e0340a9-02e7-483f-916b-03615b0f1bda | chatabl-abductive-learning-via-natural | 2304.11107 | null | https://arxiv.org/abs/2304.11107v1 | https://arxiv.org/pdf/2304.11107v1.pdf | ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT | Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to ... | ['Tuo Zhang', 'Tianming Liu', 'Dinggang Shen', 'Junwei Han', 'Xi Jiang', 'Dajiang Zhu', 'Xiang Li', 'Chong Ma', 'Junjie Yao', 'Wenjun Li', 'Xiaozheng Wei', 'Zhengliang Liu', 'Zihao Wu', 'Li Yang', 'Yaonai Wei', 'Tianyang Zhong'] | 2023-04-21 | null | null | null | null | ['decipherment', 'logical-reasoning'] | ['natural-language-processing', 'reasoning'] | [-2.35800356e-01 4.33033168e-01 3.36776786e-02 -1.38918087e-01
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5.47807179e-02 6.16256475e-01 2.58460402e-01 -6.29133165... | [9.546823501586914, 7.352589130401611] |
5a2386b6-a06e-4e38-ba2c-bed7ea2ce6d2 | an-automated-approach-for-the-recognition-of | 2109.00906 | null | https://arxiv.org/abs/2109.00906v1 | https://arxiv.org/pdf/2109.00906v1.pdf | An Automated Approach for the Recognition of Bengali License Plates | Automatic Number Plate Recognition (ALPR) is a system for automatically identifying the license plates of any vehicle. This process is important for tracking, ticketing, and any billing system, among other things. With the use of information and communication technology (ICT), all systems are being automated, including... | ['Faisal Muhammad Shah', 'Jannatun Naeem Muna', 'Atiqul Islam Chowdhury', 'MD Abdullah Al Nasim'] | 2021-09-01 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [-4.55945842e-02 -6.05624974e-01 -1.86036631e-01 -1.03999712e-01
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6.19372189e-01 3.90143335e-01 5.80805779e-01 -1.54728144... | [9.809768676757812, -4.980074882507324] |
fd76b4d2-9444-41a3-bf59-18b078510ede | gaitpart-temporal-part-based-model-for-gait | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Fan_GaitPart_Temporal_Part-Based_Model_for_Gait_Recognition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_GaitPart_Temporal_Part-Based_Model_for_Gait_Recognition_CVPR_2020_paper.pdf | GaitPart: Temporal Part-Based Model for Gait Recognition | Gait recognition, applied to identify individual walking patterns in a long-distance, is one of the most promising video-based biometric technologies. At present, most gait recognition methods take the whole human body as a unit to establish the spatio-temporal representations. However, we have observed that different ... | [' Zhiqiang He', ' Qing Li', ' Yongzhen Huang', ' Jiannan Chi', ' Saihui Hou', ' Xu Liu', ' Chunshui Cao', ' Yunjie Peng', 'Chao Fan'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['multiview-gait-recognition'] | ['computer-vision'] | [-6.52671531e-02 -6.54774904e-01 -3.47118467e-01 -1.39856204e-01
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-3.97060812e-01 1.50825351e-01 3.76718521e-01 -1.80299118... | [14.289898872375488, 1.4305458068847656] |
b490243d-80e8-4a20-b2ba-786d97910495 | cross-stitched-multi-task-dual-recursive | 2211.08290 | null | https://arxiv.org/abs/2211.08290v1 | https://arxiv.org/pdf/2211.08290v1.pdf | Cross-Stitched Multi-task Dual Recursive Networks for Unified Single Image Deraining and Desnowing | We present the Cross-stitched Multi-task Unified Dual Recursive Network (CMUDRN) model targeting the task of unified deraining and desnowing in a multi-task learning setting. This unified model borrows from the basic Dual Recursive Network (DRN) architecture developed by Cai et al. The proposed model makes use of cross... | ['Dimitrios Zarpalas', 'Konstantinos Konstantoudakis', 'Alexandros Doumanoglou', 'Sotiris Karavarsamis'] | 2022-11-15 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 3.87522399e-01 4.58928570e-02 5.86755797e-02 -2.57712044e-02
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4.33066159e-01 -4.57232073e-02 -2.36510709e-02 -4.33007367... | [11.228264808654785, -2.2875568866729736] |
de6ba88c-a91c-40ad-b366-93802f99b0f5 | df-platter-multi-face-heterogeneous-deepfake | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Narayan_DF-Platter_Multi-Face_Heterogeneous_Deepfake_Dataset_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Narayan_DF-Platter_Multi-Face_Heterogeneous_Deepfake_Dataset_CVPR_2023_paper.pdf | DF-Platter: Multi-Face Heterogeneous Deepfake Dataset | Deepfake detection is gaining significant importance in the research community. While most of the research efforts are focused around high-quality images and videos, deepfake generation algorithms today have the capability to generate low-resolution videos, occluded deepfakes, and multiple-subject deepfakes. In thi... | ['Richa Singh', 'Mayank Vatsa', 'Surbhi Mittal', 'Kartik Thakral', 'Harsh Agarwal', 'Kartik Narayan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [-1.50060177e-01 -2.88814634e-01 -1.46590501e-01 -2.13290602e-01
-7.15737045e-01 -5.30340374e-01 6.65334702e-01 -6.04998052e-01
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-3.20469528e-01 2.51298845e-01 1.25755280e-01 -3.73109318... | [13.033408164978027, 0.9057313203811646] |
570bdfe6-d53e-40c0-a2b2-a8a1b70158f1 | rethinking-resolution-in-the-context-of | 2209.12797 | null | https://arxiv.org/abs/2209.12797v1 | https://arxiv.org/pdf/2209.12797v1.pdf | Rethinking Resolution in the Context of Efficient Video Recognition | In this paper, we empirically study how to make the most of low-resolution frames for efficient video recognition. Existing methods mainly focus on developing compact networks or alleviating temporal redundancy of video inputs to increase efficiency, whereas compressing frame resolution has rarely been considered a pro... | ['Xiaojuan Qi', 'Ping Luo', 'Zehuan Yuan', 'Yi Jiang', 'Qiushan Guo', 'Chuofan Ma'] | 2022-09-26 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [-1.42201176e-02 -5.17663181e-01 -4.48246211e-01 -9.08040181e-02
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9.93868187e-02 -9.12570208e-02 4.36820060e-01 -2.08894163... | [9.010186195373535, 0.5332000255584717] |
328a94fe-c3eb-4682-82d9-0c1ebc340f99 | target-really-matters-target-aware | null | null | https://aclanthology.org/2022.coling-1.605 | https://aclanthology.org/2022.coling-1.605.pdf | Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection | Stance detection aims to identify the attitude from an opinion towards a certain target. Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient high-quality labeled data for every new target under fully-supervised learning, whereas unlabeled data can be... | ['Weiping Wang', 'Peng Fu', 'Jiangnan Li', 'Huishan Ji', 'Zheng Lin', 'Rui Liu'] | null | null | null | null | coling-2022-10 | ['stance-detection'] | ['natural-language-processing'] | [ 2.88512081e-01 4.50251028e-02 -7.73234546e-01 -7.76023448e-01
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ad8f80c8-3e58-4de0-b25b-231f470d1307 | learning-interpretable-causal-networks-from | 2303.06423 | null | https://arxiv.org/abs/2303.06423v1 | https://arxiv.org/pdf/2303.06423v1.pdf | Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients | Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data is available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), ba... | ['Hervé Isambert', 'Anne-Sophie Hamy', 'Liza Hettal', 'Franck Simon', 'Louise Dupuis', 'Vincent Cabeli', 'Honghao Li', 'Marcel da Câmara Ribeiro-Dantas'] | 2023-03-11 | null | null | null | null | ['causal-discovery', 'epidemiology'] | ['knowledge-base', 'medical'] | [ 5.94260216e-01 4.94201183e-01 -9.51743901e-01 -4.56099898e-01
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-8.29969883e-01 4.35216159e-01 -2.42571041e-01 3.75543386... | [7.881875514984131, 5.405465126037598] |
caa81ad4-688e-4115-bef7-ed69241f295e | gates-are-not-what-you-need-in-rnns | 2108.00527 | null | https://arxiv.org/abs/2108.00527v1 | https://arxiv.org/pdf/2108.00527v1.pdf | Gates are not what you need in RNNs | Recurrent neural networks have flourished in many areas. Consequently, we can see new RNN cells being developed continuously, usually by creating or using gates in a new, original way. But what if we told you that gates in RNNs are redundant? In this paper, we propose a new recurrent cell called Residual Recurrent Unit... | ['Karlis Freivalds', 'Emils Ozolins', 'Eliza Gaile', 'Andis Draguns', 'Ronalds Zakovskis'] | 2021-08-01 | null | null | null | null | ['music-modeling'] | ['music'] | [-8.12690880e-04 -1.81220531e-01 6.64169863e-02 -2.33453676e-01
-2.98516184e-01 -3.92777830e-01 3.94427031e-01 -4.36207056e-01
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3.72046858e-01 -6.12617582e-02 1.16448037e-01 -3.88523072... | [10.88154125213623, 6.362493991851807] |
15034821-ae78-4b06-aa49-111bcadf96c1 | detection-of-uncertainty-in-exceedance-of | 2303.10291 | null | https://arxiv.org/abs/2303.10291v1 | https://arxiv.org/pdf/2303.10291v1.pdf | Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer | Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patch detection by introducing an uncertainty-based adversarial patch localizer which localizes adversarial patch on an image, permitting post-p... | ['Jun Zhao', 'Wenhan Yu', 'Terence Jie Chua'] | 2023-03-18 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [ 2.69363135e-01 3.19161147e-01 1.51042953e-01 -1.08435243e-01
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-2.92121887e-01 7.19961002e-02 5.47916174e-01 -1.37116149... | [5.539936542510986, 7.9185662269592285] |
5b567c8a-f7db-4ed3-b337-4cd834007082 | e-convrec-a-large-scale-conversational | null | null | https://aclanthology.org/2022.lrec-1.622 | https://aclanthology.org/2022.lrec-1.622.pdf | E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service | There has been a growing interest in developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. Compared to the traditional recommendation, it advocates wealthier interactions and provides possibilities to obtain users’ exact preferences explicitly. Ne... | ['Xiaodong He', 'Jinhui Pang', 'Meng Chen', 'Xin Shen', 'Haobin Li', 'Zexi Xi', 'Yang song', 'Peiying Wang', 'Ruixue Liu', 'Meihuizi Jia'] | null | null | null | null | lrec-2022-6 | ['dialogue-management'] | ['natural-language-processing'] | [-1.30448937e-01 1.54798061e-01 -2.89084643e-01 -7.06926465e-01
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1.54292271e-01 4.40082639e-01 7.82351289e-03 -8.19082201... | [12.339434623718262, 7.519394874572754] |
c7a902df-bbb1-4804-a777-eb71889ae8b5 | bayesian-regression-approach-for-building-and | 2201.02034 | null | https://arxiv.org/abs/2201.02034v1 | https://arxiv.org/pdf/2201.02034v1.pdf | Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics | The paper describes the use of Bayesian regression for building time series models and stacking different predictive models for time series. Using Bayesian regression for time series modeling with nonlinear trend was analyzed. This approach makes it possible to estimate an uncertainty of time series prediction and calc... | ['Bohdan M. Pavlyshenko'] | 2022-01-06 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.70431331e-01 -1.58341572e-01 1.00389376e-01 -4.94453967e-01
-3.26035142e-01 -3.42956007e-01 6.89958513e-01 5.45015037e-01
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-2.68222570e-01 7.17344165e-01 3.57581139e-01 -1.88270122... | [6.8020477294921875, 3.3426454067230225] |
bc2f3186-c798-4606-91b7-3dd82b3afcf1 | freematch-self-adaptive-thresholding-for-semi | 2205.07246 | null | https://arxiv.org/abs/2205.07246v3 | https://arxiv.org/pdf/2205.07246v3.pdf | FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning | Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization. However, we argue that existing methods might fail to utilize the unlabeled data more effectively since they either use a pre-defined / fixe... | ['Xing Xie', 'Bernt Schiele', 'Yue Fan', 'Jindong Wang', 'Zhen Wu', 'Bhiksha Raj', 'Takahiro Shinozaki', 'Marios Savvides', 'Wenxin Hou', 'Qiang Heng', 'Hao Chen', 'Yidong Wang'] | 2022-05-15 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [-1.06495515e-01 7.91129097e-02 -6.15686893e-01 -9.77458954e-01
-8.01100194e-01 -2.64435202e-01 1.76065758e-01 1.38895735e-01
-6.10767722e-01 8.19112301e-01 -1.58774853e-01 -2.01216489e-01
-6.74397051e-02 -3.79467785e-01 -5.64943850e-01 -7.93958485e-01
2.03229338e-01 4.57229227e-01 9.34021324e-02 8.03266317... | [9.3927583694458, 3.7916150093078613] |
5b3fbc72-15a9-4b9a-a778-122d8e229047 | emotionflow-capture-the-dialogue-level | null | null | https://github.com/fpcsong/emotionflow/blob/master/EmotionFlow.pdf | https://github.com/fpcsong/emotionflow/blob/master/EmotionFlow.pdf | EmotionFlow: Capture the Dialogue Level Emotion Transitions | Emotion recognition in conversations (ERC) has attracted increasing interests in recent years, due to its wide range of applications, such as customer service analysis, health-care consultation, etc. One key challenge of ERC is that users' emotions would change due to the impact of others' emotions. That is, the emotio... | ['Longtao Huang', 'Songlin Hu', 'Rong Zhang', 'Liangjun Zang', 'Xiaohui Song'] | 2022-05-07 | null | null | null | icassp-2022-5 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.82336923e-02 -4.01226342e-01 1.92394316e-01 -7.44076610e-01
-3.09345573e-01 -2.86395073e-01 3.34815353e-01 -5.51400855e-02
-1.61510095e-01 5.91712117e-01 7.46415854e-01 2.32816726e-01
1.92743748e-01 -4.84200746e-01 -1.71181753e-01 -6.83961570e-01
1.23635128e-01 -1.47142127e-01 -2.80623376e-01 -3.75958055... | [13.024555206298828, 6.074990749359131] |
8700c64a-3de6-48fd-bb1c-6a6c83234029 | procedural-urban-environments-for-fps-games | 1604.05791 | null | http://arxiv.org/abs/1604.05791v1 | http://arxiv.org/pdf/1604.05791v1.pdf | Procedural urban environments for FPS games | This paper presents a novel approach to procedural generation of urban maps
for First Person Shooter (FPS) games. A multi-agent evolutionary system is
employed to place streets, buildings and other items inside the Unity3D game
engine, resulting in playable video game levels. A computational agent is
trained using mach... | ['Ricardo Sosa', 'Jan Kruse', 'Andy M. Connor'] | 2016-04-20 | null | null | null | null | ['fps-games'] | ['playing-games'] | [ 1.16320610e-01 3.25243056e-01 3.81476730e-01 2.55099505e-01
-2.41762906e-01 -5.05069375e-01 5.38151503e-01 -1.43903077e-01
-1.01602428e-01 5.72230041e-01 -7.80578330e-02 -3.59596670e-01
-2.45750576e-01 -1.52009583e+00 -1.53699353e-01 -3.81705523e-01
-1.19506316e-02 1.00696564e+00 5.45554638e-01 -8.08283746... | [3.5151755809783936, 1.5186182260513306] |
d0661e97-e93b-495b-afec-34444ecccedd | a-sentence-judgment-system-for-grammatical | null | null | https://aclanthology.org/C14-2015 | https://aclanthology.org/C14-2015.pdf | A Sentence Judgment System for Grammatical Error Detection | null | ['Hsin-Hsi Chen', 'Li-Ping Chang', 'Yuen-Hsien Tseng', 'Kuei-Ching Lee', 'Liang-Chih Yu', 'Lung-Hao Lee'] | 2014-08-01 | a-sentence-judgment-system-for-grammatical-1 | https://aclanthology.org/C14-2015 | https://aclanthology.org/C14-2015.pdf | coling-2014-8 | ['grammatical-error-detection'] | ['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... | [-7.263407230377197, 3.6874289512634277] |
fa95c620-7bee-4ea4-a8e3-7117c92b535a | a-sampling-theory-perspective-of-graph-based | 1705.09518 | null | http://arxiv.org/abs/1705.09518v2 | http://arxiv.org/pdf/1705.09518v2.pdf | A Sampling Theory Perspective of Graph-based Semi-supervised Learning | Graph-based methods have been quite successful in solving unsupervised and
semi-supervised learning problems, as they provide a means to capture the
underlying geometry of the dataset. It is often desirable for the constructed
graph to satisfy two properties: first, data points that are similar in the
feature space sho... | ['Aly El Gamal', 'Salman Avestimehr', 'Aamir Anis', 'Antonio Ortega'] | 2017-05-26 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 3.76902014e-01 4.70727682e-01 -3.34932327e-01 -2.94862539e-01
-1.93632767e-01 -4.96159852e-01 4.26681846e-01 2.81952232e-01
2.73964554e-01 5.90382040e-01 2.58573126e-02 -8.39053243e-02
-6.85750127e-01 -8.90421808e-01 -5.25912702e-01 -8.69878352e-01
-3.94030064e-01 3.04467916e-01 3.38161923e-02 -8.14535543... | [7.004343509674072, 5.245625019073486] |
ddec5d75-1d20-4386-8a01-8781c318ad1b | if-at-first-you-don-t-succeed-test-time-re | 2303.17703 | null | https://arxiv.org/abs/2303.17703v1 | https://arxiv.org/pdf/2303.17703v1.pdf | If At First You Don't Succeed: Test Time Re-ranking for Zero-shot, Cross-domain Retrieval | In this paper we propose a novel method for zero-shot, cross-domain image retrieval in which we make two key contributions. The first is a test-time re-ranking procedure that enables query-gallery pairs, without meaningful shared visual features, to be matched by incorporating gallery-gallery ranks into an iterative re... | ['William A. P. Smith', 'Finlay G. C. Hudson'] | 2023-03-30 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.20823878e-01 -3.39051783e-01 -2.24535748e-01 -2.74832636e-01
-1.52237856e+00 -5.46369553e-01 1.12362266e+00 2.73501985e-02
-2.59834945e-01 3.45150232e-01 1.90064996e-01 5.88732399e-02
-5.38334906e-01 -5.32513857e-01 -6.78930223e-01 -2.94336438e-01
2.60403275e-01 6.94705307e-01 2.18187779e-01 -5.02619743... | [11.423848152160645, 0.7352107763290405] |
42825fb2-02d3-4152-9a74-431a487b5b8e | learning-from-synthetic-shadows-for-shadow | 2101.01713 | null | https://arxiv.org/abs/2101.01713v2 | https://arxiv.org/pdf/2101.01713v2.pdf | Learning from Synthetic Shadows for Shadow Detection and Removal | Shadow removal is an essential task in computer vision and computer graphics. Recent shadow removal approaches all train convolutional neural networks (CNN) on real paired shadow/shadow-free or shadow/shadow-free/mask image datasets. However, obtaining a large-scale, diverse, and accurate dataset has been a big challen... | ['Toshihiko Yamasaki', 'Naoto Inoue'] | 2021-01-05 | null | null | null | null | ['shadow-removal', 'shadow-detection-and-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.71670139e-01 -1.65379077e-01 4.19374913e-01 -5.83577812e-01
-4.05434281e-01 -3.88171107e-01 3.99831474e-01 -6.39000058e-01
-6.30962402e-02 8.68659079e-01 1.30880043e-01 -4.77274030e-01
5.89422941e-01 -6.74021006e-01 -9.49007452e-01 -8.83715451e-01
4.53962743e-01 3.41811627e-01 6.65244162e-01 -4.64606673... | [10.845457077026367, -4.1003899574279785] |
0db58ee6-b785-4f13-8b2b-4d447665829f | explicit-attention-enhanced-fusion-for-rgb | 2303.15710 | null | https://arxiv.org/abs/2303.15710v1 | https://arxiv.org/pdf/2303.15710v1.pdf | Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks | Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as low light and fog. However, how to effectively fuse RGB images and thermal data remains an open challenge. Previous works involve naive fusio... | ['Tin Lun Lam', 'Fuqin Deng', 'Hua Feng', 'Chenyu Bao', 'Junjie Hu', 'Mingjian Liang'] | 2023-03-28 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 1.53097764e-01 -2.46233210e-01 1.59116283e-01 -1.44996852e-01
-6.59579396e-01 -4.67043996e-01 4.89888966e-01 1.18412133e-02
-7.28272080e-01 6.89692199e-01 -7.34795555e-02 -7.08183721e-02
1.40564770e-01 -6.96892023e-01 -7.81061828e-01 -9.43239212e-01
4.56662118e-01 -8.36540665e-03 5.85638165e-01 -3.03256392... | [9.488916397094727, -0.9647848010063171] |
c50158e5-49b8-4697-9edf-3d379b593d7f | conditional-density-estimation-with-bayesian | 1802.04908 | null | http://arxiv.org/abs/1802.04908v1 | http://arxiv.org/pdf/1802.04908v1.pdf | Conditional Density Estimation with Bayesian Normalising Flows | Modeling complex conditional distributions is critical in a variety of
settings. Despite a long tradition of research into conditional density
estimation, current methods employ either simple parametric forms or are
difficult to learn in practice. This paper employs normalising flows as a
flexible likelihood model and ... | ['Brian L. Trippe', 'Richard E. Turner'] | 2018-02-14 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [-1.77537456e-01 -1.58115417e-01 -5.85262477e-01 -7.62144625e-01
-9.49479580e-01 -3.91667247e-01 7.45555341e-01 -1.61906481e-01
-6.33382082e-01 1.11997199e+00 2.44676664e-01 -7.14280605e-01
-2.92504430e-01 -9.56439793e-01 -7.23818898e-01 -4.12474990e-01
-1.95349857e-01 7.15658069e-01 -9.00810137e-02 4.31284159... | [7.134440898895264, 3.968278646469116] |
1df3186a-f0d4-4df7-b1e8-a9b0c8401b17 | niff-alleviating-forgetting-in-generalized | 2303.04958 | null | https://arxiv.org/abs/2303.04958v1 | https://arxiv.org/pdf/2303.04958v1.pdf | NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging | Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neural networks. A promise of Generalized Few-shot Object Detection (G-FSOD), a learning paradigm in AI, is to alleviate the need for collecting ... | ['Juergen Beyerer', 'Bin Yang', 'Matthias Kayser', 'George Eskandar', 'Johannes Meier', 'Karim Guirguis'] | 2023-03-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Guirguis_NIFF_Alleviating_Forgetting_in_Generalized_Few-Shot_Object_Detection_via_Neural_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Guirguis_NIFF_Alleviating_Forgetting_in_Generalized_Few-Shot_Object_Detection_via_Neural_CVPR_2023_paper.pdf | cvpr-2023-1 | ['few-shot-object-detection'] | ['computer-vision'] | [ 3.69568676e-01 2.75671482e-01 -1.50109246e-01 -4.37982589e-01
-8.51966262e-01 -6.40122056e-01 7.23998427e-01 6.10711798e-02
-7.17053831e-01 9.70813453e-01 -1.58211365e-02 -1.36147350e-01
-4.27039191e-02 -9.44386542e-01 -1.07572663e+00 -6.42253697e-01
5.44609018e-02 3.69548321e-01 2.31133431e-01 5.24515007... | [9.79869270324707, 3.2990260124206543] |
21195ab8-1b26-4329-a894-bfa4d010de51 | localize-me-anywhere-anytime-a-multi-task | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Lu_Localize_Me_Anywhere_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Lu_Localize_Me_Anywhere_ICCV_2015_paper.pdf | Localize Me Anywhere, Anytime: A Multi-Task Point-Retrieval Approach | Image-based localization is an essential complement to GPS localization. Current image-based localization methods are based on either 2D-to-3D or 3D-to-2D to find the correspondences, which ignore the real scene geometric attributes. The main contribution of our paper is that we use a 3D model reconstructed by a short ... | ['Nicu Sebe', 'Guoyu Lu', 'Chandra Kambhamettu', 'Jingkuan Song', 'Yan Yan', 'Li Ren'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['image-based-localization'] | ['computer-vision'] | [-8.11733156e-02 -8.97391200e-01 -3.55196118e-01 -1.92998916e-01
-1.16374683e+00 -7.04700291e-01 6.09119177e-01 -6.97710812e-02
-3.07166696e-01 1.45375699e-01 -1.42398864e-01 2.05962121e-01
-5.25509953e-01 -5.77137530e-01 -4.86513555e-01 -8.53363454e-01
6.49720132e-02 3.14867198e-01 3.38414967e-01 -3.23738128... | [7.6164937019348145, -2.195307493209839] |
28e9a1d9-db37-45d4-9afb-38a4ec4acb6b | contrastive-learning-of-global-and-local-1 | 2104.05418 | null | https://arxiv.org/abs/2104.05418v2 | https://arxiv.org/pdf/2104.05418v2.pdf | Contrastive Learning of Global-Local Video Representations | Contrastive learning has delivered impressive results for various tasks in the self-supervised regime. However, existing approaches optimize for learning representations specific to downstream scenarios, i.e., \textit{global} representations suitable for tasks such as classification or \textit{local} representations fo... | ['Yale Song', 'Daniel McDuff', 'Zhaoyang Zeng', 'Shuang Ma'] | 2021-04-07 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 3.49219441e-01 -2.94720352e-01 -3.99791121e-01 -2.74652094e-01
-1.22450972e+00 -5.82133412e-01 4.76332426e-01 8.19724724e-02
-2.23828778e-01 5.33185840e-01 4.26316559e-01 -1.27554968e-01
-9.84573215e-02 -4.04906064e-01 -8.67126226e-01 -6.61482990e-01
-1.24111928e-01 -1.44843319e-02 8.46829936e-02 6.89807534... | [14.66942024230957, 4.8974738121032715] |
9e8e8bf3-d311-4c8e-98bf-73048f5d3c9e | openstance-real-world-zero-shot-stance | 2210.14299 | null | https://arxiv.org/abs/2210.14299v1 | https://arxiv.org/pdf/2210.14299v1.pdf | OpenStance: Real-world Zero-shot Stance Detection | Prior studies of zero-shot stance detection identify the attitude of texts towards unseen topics occurring in the same document corpus. Such task formulation has three limitations: (i) Single domain/dataset. A system is optimized on a particular dataset from a single domain; therefore, the resulting system cannot work ... | ['Wenpeng Yin', 'Slobodan Vucetic', 'Hanzi Xu'] | 2022-10-25 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 3.27581853e-01 5.03989935e-01 -5.60152471e-01 -2.50767231e-01
-8.82036865e-01 -6.72661006e-01 9.05191362e-01 1.86572701e-01
-2.52532125e-01 8.48505080e-01 2.90799618e-01 -1.38623253e-01
2.32475419e-02 -7.62769341e-01 -5.90475321e-01 -5.84579706e-01
3.13308775e-01 9.40258741e-01 6.26637399e-01 -6.59673989... | [9.018631935119629, 9.944207191467285] |
f338908d-ca6f-4bca-837e-c4ade9961860 | adversarial-examples-for-electrocardiograms | 1905.05163 | null | https://arxiv.org/abs/1905.05163v2 | https://arxiv.org/pdf/1905.05163v2.pdf | Adversarial Examples for Electrocardiograms | In recent years, the electrocardiogram (ECG) has seen a large diffusion in both medical and commercial applications, fueled by the rise of single-lead versions. Single-lead ECG can be embedded in medical devices and wearable products such as the injectable Medtronic Linq monitor, the iRhythm Ziopatch wearable monitor, ... | ['Rajesh Ranganath', 'Luca Foschini', 'Yuxuan Hu', 'Xintian Han', 'Lior Jankelson', 'Larry Chinitz'] | 2019-05-13 | null | null | null | null | ['arrhythmia-detection', 'ecg-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 5.27908027e-01 3.11804503e-01 4.38649327e-01 -2.24900678e-01
-8.59072983e-01 -9.08494592e-01 -1.31351337e-01 6.10066429e-02
-1.50356099e-01 9.81241703e-01 -2.66366839e-01 -6.06915534e-01
-5.86665757e-02 -5.66999495e-01 -8.11435819e-01 -6.15188718e-01
-4.92698729e-01 2.67000586e-01 -2.76356906e-01 -1.50462165... | [14.330135345458984, 3.1059048175811768] |
8f975327-93e4-468d-86a8-7849d1823999 | peach-tree-a-multiple-sequence-alignment-and | 2112.07422 | null | https://arxiv.org/abs/2112.07422v1 | https://arxiv.org/pdf/2112.07422v1.pdf | PEACH Tree: A Multiple Sequence Alignment and Tree Display Tool for Epidemiologists | PEACH Tree is an easy-to-use, online tool for displaying multiple sequence alignments and phylogenetic trees side-by-side. PEACH Tree is powerful for rapidly tracing evolutionary and transmission histories by filtering invariant sites out of the display, and allowing samples to readily be filtered out of the display. T... | ['David Welch', 'Jordan Douglas'] | 2021-12-12 | null | null | null | null | ['multiple-sequence-alignment', 'epidemiology'] | ['medical', 'medical'] | [ 1.00035369e-01 -7.11882830e-01 -5.06523391e-03 -1.54137343e-01
-2.40117759e-01 -9.23208177e-01 2.67720193e-01 7.90648878e-01
-3.65441859e-01 6.95163608e-01 7.65871629e-03 -8.74153912e-01
-2.08647296e-01 -5.67235053e-01 -9.75782052e-02 -7.79894590e-01
-7.22945452e-01 5.58306932e-01 3.54334891e-01 -2.44066641... | [4.935035228729248, 5.168646812438965] |
4a0f30c4-ea8f-4325-b6e0-4c4dd29a37fa | clear-causal-explanations-from-attention-in | 2210.10621 | null | https://arxiv.org/abs/2210.10621v1 | https://arxiv.org/pdf/2210.10621v1.pdf | CLEAR: Causal Explanations from Attention in Neural Recommenders | We present CLEAR, a method for learning session-specific causal graphs, in the possible presence of latent confounders, from attention in pre-trained attention-based recommenders. These causal graphs describe user behavior, within the context captured by attention, and can provide a counterfactual explanation for a rec... | ['Gal Novik', 'Guy Koren', 'Yaniv Gurwicz', 'Raanan Y. Rohekar', 'Shami Nisimov'] | 2022-10-07 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.56792039e-01 8.08833957e-01 -7.21678317e-01 -3.68824065e-01
-4.54697967e-01 -5.54825425e-01 5.89470744e-01 -3.92682105e-02
2.80969590e-01 1.15316653e+00 1.33194637e+00 -6.81908309e-01
-8.81341994e-01 -6.47814214e-01 -1.16726923e+00 -2.26021186e-01
-3.43065053e-01 2.85751820e-01 -2.64923483e-01 -2.60580480... | [9.424358367919922, 5.699220180511475] |
c9d129c8-485d-4395-b8a6-b15c0f4bb6f0 | lightness-modulated-deep-inverse-tone-mapping | 2107.07907 | null | https://arxiv.org/abs/2107.07907v1 | https://arxiv.org/pdf/2107.07907v1.pdf | Lightness Modulated Deep Inverse Tone Mapping | Single-image HDR reconstruction or inverse tone mapping (iTM) is a challenging task. In particular, recovering information in over-exposed regions is extremely difficult because details in such regions are almost completely lost. In this paper, we present a deep learning based iTM method that takes advantage of the fea... | ['Guoping Qiu', 'Jiang Duan', 'Gaofeng Cao', 'Kanglin Liu'] | 2021-07-16 | null | null | null | null | ['hdr-reconstruction', 'tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.48468494e-01 1.30716309e-01 1.91256776e-01 -2.87040561e-01
-4.36154157e-01 -2.22276852e-01 3.46184134e-01 -4.80048954e-01
-8.65992084e-02 6.72287881e-01 8.73821601e-02 -1.31166011e-01
1.41013637e-01 -9.76764858e-01 -7.78552949e-01 -9.29044545e-01
1.80026397e-01 -6.50068223e-02 3.13865542e-01 -4.55702424... | [10.858057022094727, -2.2092080116271973] |
d8b9c953-61e4-4a68-91a9-aca5eb2628ef | locate-then-generate-bridging-vision-and | 2304.01603 | null | https://arxiv.org/abs/2304.01603v1 | https://arxiv.org/pdf/2304.01603v1.pdf | Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA | In this paper, we propose a novel multi-modal framework for Scene Text Visual Question Answering (STVQA), which requires models to read scene text in images for question answering. Apart from text or visual objects, which could exist independently, scene text naturally links text and visual modalities together by conve... | ['Linli Xu', 'Changcun Bao', 'Hao liu', 'Xin Li', 'Yukang Liang', 'Zhen Liu', 'Yongxin Zhu'] | 2023-04-04 | null | null | null | null | ['answer-generation'] | ['natural-language-processing'] | [ 3.63037825e-01 3.25572908e-01 2.07905412e-01 -5.10251284e-01
-8.58146489e-01 -7.95152009e-01 8.86808395e-01 2.42801741e-01
-3.78719240e-01 1.34381846e-01 2.86526829e-01 -4.21152025e-01
3.52906913e-01 -8.59663665e-01 -1.03105521e+00 -4.14071143e-01
5.77257693e-01 6.10494137e-01 6.11929893e-01 -2.73561448... | [10.81318187713623, 1.629797339439392] |
77735f7d-4d03-41ed-9ad6-3af06754f4dc | ct-image-harmonization-for-enhancing | 2107.01337 | null | https://arxiv.org/abs/2107.01337v1 | https://arxiv.org/pdf/2107.01337v1.pdf | CT Image Harmonization for Enhancing Radiomics Studies | While remarkable advances have been made in Computed Tomography (CT), capturing CT images with non-standardized protocols causes low reproducibility regarding radiomic features, forming a barrier on CT image analysis in a large scale. RadiomicGAN is developed to effectively mitigate the discrepancy caused by using non-... | ['Jin Chen', 'Guo-Qiang Zhang', 'Baowei Fei', 'Jie Zhang', 'Md Selim'] | 2021-07-03 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 1.82668313e-01 8.20660964e-02 -3.77712131e-01 -6.22130275e-01
-1.30240428e+00 -5.66694103e-02 5.47180235e-01 -2.83242583e-01
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-6.49920925e-02 -6.62300885e-01 -6.09791040e-01 -7.65765727e-01
-5.27678952e-02 5.60077667e-01 1.95887953e-01 1.83798885... | [14.19947624206543, -2.393794536590576] |
e9878def-65c6-4797-93e0-58f31b762f1c | interactive-conversational-head-generation | 2307.02090 | null | https://arxiv.org/abs/2307.02090v1 | https://arxiv.org/pdf/2307.02090v1.pdf | Interactive Conversational Head Generation | We introduce a new conversation head generation benchmark for synthesizing behaviors of a single interlocutor in a face-to-face conversation. The capability to automatically synthesize interlocutors which can participate in long and multi-turn conversations is vital and offer benefits for various applications, includin... | ['Tiejun Zhao', 'Ting Yao', 'Wei zhang', 'Yalong Bai', 'Mohan Zhou'] | 2023-07-05 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 2.59524435e-02 7.94747829e-01 3.66589695e-01 -4.00995731e-01
-6.47812247e-01 -6.39783859e-01 8.65337551e-01 -7.33898997e-01
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1.81083173e-01 8.38267088e-01 -4.81359959e-01 -6.66863084... | [12.933695793151855, 7.8924241065979] |
35981c03-00b9-4e91-9b28-39a7e0dceff7 | challenges-in-domain-specific-abstractive | 2307.00963 | null | https://arxiv.org/abs/2307.00963v1 | https://arxiv.org/pdf/2307.00963v1.pdf | Challenges in Domain-Specific Abstractive Summarization and How to Overcome them | Large Language Models work quite well with general-purpose data and many tasks in Natural Language Processing. However, they show several limitations when used for a task such as domain-specific abstractive text summarization. This paper identifies three of those limitations as research problems in the context of abstr... | ['Florian Matthes', 'Daniel Braun', 'Juraj Vladika', 'Anum Afzal'] | 2023-07-03 | null | null | null | null | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.92856228e-01 5.38390756e-01 -3.37031126e-01 -5.15772142e-02
-1.01582325e+00 -4.50870663e-01 7.36221671e-01 4.92357761e-01
-3.46331477e-01 1.18311512e+00 9.08992231e-01 -2.81597704e-01
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4.15054500e-01 9.14386749e-01 3.68299410e-02 -4.82748002... | [12.406805992126465, 9.464607238769531] |
841d1e0f-1286-4d7e-bb02-43592b685588 | face-image-retrieval-with-attribute | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zaeemzadeh_Face_Image_Retrieval_With_Attribute_Manipulation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zaeemzadeh_Face_Image_Retrieval_With_Attribute_Manipulation_ICCV_2021_paper.pdf | Face Image Retrieval With Attribute Manipulation | Current face image retrieval solutions are limited, since they treat different facial attributes the same and cannot incorporate user's preference for a subset of attributes in their search criteria. This paper introduces a new face image retrieval framework, where the input face query is augmented by both an adjus... | ['Ratheesh Kalarot', 'Mubarak Shah', 'Nazanin Rahnavard', 'Zhe Lin', 'Baldo Faieta', 'Shabnam Ghadar', 'Alireza Zaeemzadeh'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['face-image-retrieval'] | ['computer-vision'] | [ 1.49937302e-01 -3.78049016e-01 -4.27733064e-01 -7.31936157e-01
-2.76827186e-01 -7.47959852e-01 5.59012711e-01 -1.91624518e-02
-1.94164604e-01 2.61395872e-01 3.72339785e-01 3.08316946e-01
-5.61653674e-01 -6.07727766e-01 -1.49685919e-01 -7.84644127e-01
2.04137594e-01 8.14105928e-01 -3.83403838e-01 -3.28704752... | [13.1051607131958, 0.4751087427139282] |
2199282e-0e69-4697-84ba-2be6e2772a9d | bayesian-non-linear-latent-variable-modeling | 2306.08352 | null | https://arxiv.org/abs/2306.08352v1 | https://arxiv.org/pdf/2306.08352v1.pdf | Bayesian Non-linear Latent Variable Modeling via Random Fourier Features | The Gaussian process latent variable model (GPLVM) is a popular probabilistic method used for nonlinear dimension reduction, matrix factorization, and state-space modeling. Inference for GPLVMs is computationally tractable only when the data likelihood is Gaussian. Moreover, inference for GPLVMs has typically been rest... | ['Barbara E. Engelhardt', 'Gregory W. Gundersen', 'Michael Minyi Zhang'] | 2023-06-14 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 4.89965752e-02 -1.47155032e-01 -3.30799013e-01 -6.51685596e-02
-6.79205477e-01 -3.29307377e-01 8.91679168e-01 -2.61858314e-01
-1.34421706e-01 7.69715250e-01 2.65042037e-01 -2.87896872e-01
-2.08164841e-01 -6.70770764e-01 -5.61205208e-01 -9.23152506e-01
2.11242381e-02 8.44117701e-01 -9.56775844e-02 5.83698690... | [6.909604549407959, 3.833765983581543] |
53e373cc-983c-4b85-bcd1-120dde04896b | using-wiktionary-to-create-specialized | null | null | https://aclanthology.org/2022.lrec-1.370 | https://aclanthology.org/2022.lrec-1.370.pdf | Using Wiktionary to Create Specialized Lexical Resources and Datasets | This paper describes an approach aiming at utilizing Wiktionary data for creating specialized lexical datasets which can be used for enriching other lexical (semantic) resources or for generating datasets that can be used for evaluating or improving NLP tasks, like Word Sense Disambiguation, Word-in-Context challenges,... | ['Thierry Declerck', 'Lenka Bajčetić'] | null | null | null | null | lrec-2022-6 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-2.81442571e-02 4.47908759e-01 -4.07341242e-01 -4.65899020e-01
-3.20746809e-01 -6.21641636e-01 6.68437660e-01 1.17224252e+00
-9.95131195e-01 1.17908633e+00 7.02000618e-01 -2.33255818e-01
-1.93526432e-01 -9.47894573e-01 -1.38907716e-01 -1.58578679e-01
4.37483460e-01 7.87870407e-01 1.85145453e-01 -7.32770503... | [10.265355110168457, 9.318961143493652] |
9b1c40e1-dbeb-4545-945f-00bf33521040 | a-machine-learning-framework-for-neighbor | 2212.11451 | null | https://arxiv.org/abs/2212.11451v1 | https://arxiv.org/pdf/2212.11451v1.pdf | A machine learning framework for neighbor generation in metaheuristic search | This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization problems. Namely, we propose a general machine learning framework for neighbor generation in metaheuristic search. We first define an efficient neighborhood structure constructed by a... | ['Andrea Lodi', 'Alain Hertz', 'Vincent Perreault', 'Defeng Liu'] | 2022-12-22 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 5.00334144e-01 7.24908337e-02 -8.48104656e-01 -1.61592096e-01
-1.05834472e+00 -4.33593452e-01 1.87795281e-01 4.19891000e-01
-3.29408050e-01 1.15656018e+00 -3.35647553e-01 -3.60895693e-01
-7.22381592e-01 -1.19302702e+00 -5.16876459e-01 -1.15708172e+00
-1.90617949e-01 6.93924665e-01 -2.63278961e-01 -3.45432788... | [5.767823696136475, 3.619922399520874] |
def39620-422f-446a-b6d1-a47a6bb4a434 | 191013166 | 1910.13166 | null | https://arxiv.org/abs/1910.13166v2 | https://arxiv.org/pdf/1910.13166v2.pdf | Towards a Model for Spoken Conversational Search | Conversation is the natural mode for information exchange in daily life, a spoken conversational interaction for search input and output is a logical format for information seeking. However, the conceptualisation of user-system interactions or information exchange in spoken conversational search (SCS) has not been expl... | ['Damiano Spina', 'Hideo Joho', 'Paul Thomas', 'Lawrence Cavedon', 'Johanne R. Trippas', 'Mark Sanderson'] | 2019-10-29 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 3.65632862e-01 7.27111399e-01 -2.76401788e-01 -2.96497732e-01
-6.85020804e-01 -7.87589729e-01 1.15333903e+00 1.96128368e-01
-3.07718486e-01 3.33303034e-01 1.19677901e+00 -6.27790451e-01
-6.51049793e-01 -2.66133696e-01 7.71316215e-02 -5.26413880e-02
1.41833559e-01 3.32418531e-01 8.95887837e-02 -4.09229130... | [12.268839836120605, 7.761037349700928] |
731ed057-9a9e-487b-8a85-0179b2103b29 | image-and-encoded-text-fusion-for-multi-modal | 1810.02001 | null | http://arxiv.org/abs/1810.02001v1 | http://arxiv.org/pdf/1810.02001v1.pdf | Image and Encoded Text Fusion for Multi-Modal Classification | Multi-modal approaches employ data from multiple input streams such as
textual and visual domains. Deep neural networks have been successfully
employed for these approaches. In this paper, we present a novel multi-modal
approach that fuses images and text descriptions to improve multi-modal
classification performance i... | ['Ignazio Gallo', 'Alessandro Calefati', 'Shah Nawaz', 'Muhammad Kamran Janjua'] | 2018-10-03 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [ 4.26144689e-01 -1.98615640e-01 -1.95184439e-01 -4.43584144e-01
-1.56176198e+00 -5.23695827e-01 1.20902085e+00 2.60390639e-01
-4.94209230e-01 5.08262694e-01 4.53177899e-01 4.39494640e-01
9.92771834e-02 -8.11802149e-01 -7.51450658e-01 -4.11269128e-01
5.47101676e-01 4.28819329e-01 2.26371348e-01 -3.75598431... | [10.768221855163574, 1.516209363937378] |
05074f29-808b-435c-99c1-32ae4fe94d94 | towards-learning-discrete-representations-via | 2306.01108 | null | https://arxiv.org/abs/2306.01108v1 | https://arxiv.org/pdf/2306.01108v1.pdf | Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition | Human activity recognition (HAR) in wearable computing is typically based on direct processing of sensor data. Sensor readings are translated into representations, either derived through dedicated preprocessing, or integrated into end-to-end learning. Independent of their origin, for the vast majority of contemporary H... | ['Thomas Ploetz', 'Irfan Essa', 'Harish Haresamudram'] | 2023-06-01 | null | null | null | null | ['human-activity-recognition', 'quantization', 'human-activity-recognition'] | ['computer-vision', 'methodology', 'time-series'] | [ 7.45448828e-01 6.92314729e-02 -2.82798409e-01 -4.75384235e-01
-8.52086484e-01 -5.81306696e-01 4.90094841e-01 5.91496646e-01
-3.77135515e-01 8.23617995e-01 3.39814574e-01 -5.76201454e-02
-3.46493691e-01 -8.79207671e-01 -6.80536032e-01 -5.88323295e-01
-3.59325290e-01 -1.02976738e-02 -8.72008428e-02 -1.45905733... | [7.702676296234131, 1.0347967147827148] |
b8073561-3e69-4ba9-86e4-c5710bed09a1 | romanization-based-large-scale-adaptation-of | 2304.08865 | null | https://arxiv.org/abs/2304.08865v1 | https://arxiv.org/pdf/2304.08865v1.pdf | Romanization-based Large-scale Adaptation of Multilingual Language Models | Large multilingual pretrained language models (mPLMs) have become the de facto state of the art for cross-lingual transfer in NLP. However, their large-scale deployment to many languages, besides pretraining data scarcity, is also hindered by the increase in vocabulary size and limitations in their parameter budget. In... | ['Ivan Vulić', 'Iryna Gurevych', 'Jonas Pfeiffer', 'Sebastian Ruder', 'Sukannya Purkayastha'] | 2023-04-18 | null | null | null | null | ['transliteration', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-9.96497273e-02 -3.60754758e-01 -2.94601500e-01 -2.18807235e-01
-1.19900477e+00 -1.11675060e+00 5.35475910e-01 -2.59568632e-01
-9.88696098e-01 1.02060008e+00 8.62614587e-02 -7.98290253e-01
3.48743677e-01 -3.83063883e-01 -8.74434710e-01 -2.46897563e-01
3.17412853e-01 9.78468955e-01 -2.25957185e-01 -5.27428567... | [11.053694725036621, 10.050030708312988] |
7392cc2e-74d6-45ae-ac99-2a33be0a6c6d | parallel-detection-and-segmentation-learning | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Shen_Parallel_Detection-and-Segmentation_Learning_for_Weakly_Supervised_Instance_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Shen_Parallel_Detection-and-Segmentation_Learning_for_Weakly_Supervised_Instance_Segmentation_ICCV_2021_paper.pdf | Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation | Weakly supervised instance segmentation (WSIS) with only image-level labels has recently drawn much attention. To date, bottom-up WSIS methods refine discriminative cues from classifiers with sophisticated multi-stage training procedures, which also suffer from inconsistent object boundaries. And top-down WSIS meth... | ['Rongrong Ji', 'Feiyue Huang', 'Yongjian Wu', 'Chi Su', 'Baochang Zhang', 'Zhiwei Chen', 'Liujuan Cao', 'Yunhang Shen'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['weakly-supervised-instance-segmentation'] | ['computer-vision'] | [ 7.62046337e-01 2.97800988e-01 -6.32790506e-01 -4.71414298e-01
-1.02573442e+00 -5.65390110e-01 6.03339553e-01 1.29095092e-01
-5.07660031e-01 3.03763717e-01 -4.61591750e-01 -7.70858377e-02
4.70628738e-01 -5.39091468e-01 -9.41183448e-01 -7.93314457e-01
3.79963428e-01 3.39154363e-01 1.20343721e+00 1.39971644... | [9.542442321777344, 0.6353179812431335] |
5de0d5c1-7249-4d1c-bd26-ebd06b37ebb4 | the-user-behind-the-abuse-a-position-on | 2103.17191 | null | https://arxiv.org/abs/2103.17191v2 | https://arxiv.org/pdf/2103.17191v2.pdf | Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability | Abuse on the Internet is an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse across various platforms. The psychological effects of abuse on individuals can be profound and lasting. Consequently, over the past few years, there has bee... | ['Ekaterina Shutova', 'Helen Yannakoudakis', 'Pushkar Mishra'] | 2021-03-31 | null | https://aclanthology.org/2021.findings-emnlp.287 | https://aclanthology.org/2021.findings-emnlp.287.pdf | findings-emnlp-2021-11 | ['abuse-detection'] | ['natural-language-processing'] | [-4.88041714e-02 1.85930252e-01 -4.90618974e-01 -3.73293281e-01
-2.49365702e-01 -6.99545801e-01 3.07277650e-01 2.79185742e-01
-2.55277812e-01 7.96952009e-01 2.42714614e-01 -3.55464876e-01
-8.37797001e-02 -5.15737593e-01 -4.25777696e-02 2.90124100e-02
-1.86966017e-01 8.20784569e-02 -3.32887799e-01 -2.28572026... | [8.638358116149902, 10.415502548217773] |
c604f661-5b21-4be5-8a9e-26b5d2d9b0b4 | differentially-private-algorithms-for-the | 2302.12909 | null | https://arxiv.org/abs/2302.12909v2 | https://arxiv.org/pdf/2302.12909v2.pdf | Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap | We show that convex-concave Lipschitz stochastic saddle point problems (also known as stochastic minimax optimization) can be solved under the constraint of $(\epsilon,\delta)$-differential privacy with \emph{strong (primal-dual) gap} rate of $\tilde O\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$, where $n... | ['Michael Menart', 'Cristóbal Guzmán', 'Raef Bassily'] | 2023-02-24 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 7.16555789e-02 3.63510668e-01 6.96850894e-03 -1.51397049e-01
-1.42242396e+00 -7.26487279e-01 -3.98931503e-01 3.37111324e-01
-8.91681671e-01 8.40649307e-01 -4.25244391e-01 -6.42770946e-01
-4.71814871e-01 -7.86592603e-01 -8.01964164e-01 -1.05164385e+00
-5.27699351e-01 1.52731776e-01 -1.75553858e-01 -2.60881066... | [6.394703388214111, 4.522167205810547] |
6e34c00d-b595-4efb-b5b3-0e4ee96aa468 | multi-scale-sparse-convolution-point-cloud | 2205.01550 | null | https://arxiv.org/abs/2205.01550v6 | https://arxiv.org/pdf/2205.01550v6.pdf | Point Cloud Semantic Segmentation using Multi Scale Sparse Convolution Neural Network | In recent years, with the development of computing resources and LiDAR, point cloud semantic segmentation has attracted many researchers. For the sparsity of point clouds, although there is already a way to deal with sparse convolution, multi-scale features are not considered. In this letter, we propose a feature extra... | ['Jie Cao', 'Lei Jiang', 'Yunzheng Su'] | 2022-05-03 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-8.66712108e-02 -6.83587790e-01 3.10912371e-01 -6.62627041e-01
-1.25752077e-01 -2.22271815e-01 1.88890606e-01 -9.56310779e-02
-4.50246871e-01 3.30438763e-01 -6.91686496e-02 -7.97727704e-02
-2.50271380e-01 -1.34005964e+00 -7.77091622e-01 -4.16887164e-01
-6.80872947e-02 2.25723118e-01 4.89302427e-01 -7.47699663... | [7.943401336669922, -3.4353489875793457] |
3cb30562-e939-4aa2-8f8e-7b7caff4ecd2 | aragpt2-pre-trained-transformer-for-arabic | 2012.15520 | null | https://arxiv.org/abs/2012.15520v2 | https://arxiv.org/pdf/2012.15520v2.pdf | AraGPT2: Pre-Trained Transformer for Arabic Language Generation | Recently, pre-trained transformer-based architectures have proven to be very efficient at language modeling and understanding, given that they are trained on a large enough corpus. Applications in language generation for Arabic are still lagging in comparison to other NLP advances primarily due to the lack of advanced ... | ['Hazem Hajj', 'Fady Baly', 'Wissam Antoun'] | 2020-12-31 | null | https://aclanthology.org/2021.wanlp-1.21 | https://aclanthology.org/2021.wanlp-1.21.pdf | eacl-wanlp-2021-4 | ['news-generation'] | ['natural-language-processing'] | [-1.22055992e-01 4.74928737e-01 -4.08067834e-03 1.38661014e-02
-1.28468442e+00 -7.04660356e-01 1.11176252e+00 1.39519885e-01
-1.69155106e-01 1.02916157e+00 4.19862390e-01 -5.44516802e-01
5.96769273e-01 -9.51433539e-01 -6.11626387e-01 -2.30991200e-01
1.00781389e-01 1.23661280e+00 2.82523278e-02 -1.02453232... | [11.691597938537598, 9.480245590209961] |
27777c98-7c37-43e2-9f4b-7428fb162155 | ad-nerf-audio-driven-neural-radiance-fields | 2103.11078 | null | https://arxiv.org/abs/2103.11078v3 | https://arxiv.org/pdf/2103.11078v3.pdf | AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis | Generating high-fidelity talking head video by fitting with the input audio sequence is a challenging problem that receives considerable attentions recently. In this paper, we address this problem with the aid of neural scene representation networks. Our method is completely different from existing methods that rely on... | ['Yong-Jin Liu', 'Juyong Zhang', 'Hujun Bao', 'Sen Liang', 'Keyu Chen', 'Yudong Guo'] | 2021-03-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Guo_AD-NeRF_Audio_Driven_Neural_Radiance_Fields_for_Talking_Head_Synthesis_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Guo_AD-NeRF_Audio_Driven_Neural_Radiance_Fields_for_Talking_Head_Synthesis_ICCV_2021_paper.pdf | iccv-2021-1 | ['talking-face-generation'] | ['computer-vision'] | [ 3.40680599e-01 1.33641928e-01 4.05527771e-01 -3.63777310e-01
-7.96282291e-01 -2.75514394e-01 3.07875037e-01 -5.78667462e-01
2.25476816e-01 5.20859063e-01 4.52898651e-01 1.04586974e-01
4.46554780e-01 -7.26326585e-01 -8.56767118e-01 -6.79520071e-01
3.17414939e-01 -1.88082889e-01 1.26631428e-02 -2.04584211... | [13.16397762298584, -0.4364239275455475] |
4c3bc958-d439-4e5a-879f-2fcac664d693 | korean-timebank-including-relative-temporal | null | null | https://aclanthology.org/L18-1326 | https://aclanthology.org/L18-1326.pdf | Korean TimeBank Including Relative Temporal Information | null | ['Ho-Jin Choi', 'Young-Seob Jeong', 'Chae-Gyun Lim'] | 2018-05-01 | korean-timebank-including-relative-temporal-1 | https://aclanthology.org/L18-1326 | https://aclanthology.org/L18-1326.pdf | lrec-2018-5 | ['temporal-information-extraction'] | ['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... | [-7.358663558959961, 3.7095444202423096] |
54baea31-30b2-4fb5-bc11-4b7e6cf1ff09 | variable-complexity-weighted-tempered-gibbs | 2304.02899 | null | https://arxiv.org/abs/2304.02899v1 | https://arxiv.org/pdf/2304.02899v1.pdf | Variable-Complexity Weighted-Tempered Gibbs Samplers for Bayesian Variable Selection | Subset weighted-Tempered Gibbs Sampler (wTGS) has been recently introduced by Jankowiak to reduce the computation complexity per MCMC iteration in high-dimensional applications where the exact calculation of the posterior inclusion probabilities (PIP) is not essential. However, the Rao-Backwellized estimator associated... | ['Lan V. Truong'] | 2023-04-06 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 2.60935158e-01 -1.86679431e-03 3.46768707e-01 -2.04437539e-01
-1.37806594e+00 -2.04263628e-01 2.80927807e-01 2.95225065e-02
-7.65163898e-01 7.39971697e-01 -3.08796644e-01 -3.42294961e-01
-2.05848530e-01 -9.68964219e-01 -6.36683524e-01 -9.49802697e-01
-4.68946517e-01 7.30449438e-01 5.61086237e-01 3.87226641... | [6.704499244689941, 4.191630840301514] |
214e038c-b4f5-4a6d-aaed-63b882c28efd | reverse-kl-divergence-training-of-prior | 1905.13472 | null | https://arxiv.org/abs/1905.13472v2 | https://arxiv.org/pdf/1905.13472v2.pdf | Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness | Ensemble approaches for uncertainty estimation have recently been applied to the tasks of misclassification detection, out-of-distribution input detection and adversarial attack detection. Prior Networks have been proposed as an approach to efficiently \emph{emulate} an ensemble of models for classification by paramete... | ['Andrey Malinin', 'Mark Gales'] | 2019-05-31 | reverse-kl-divergence-training-of-prior-1 | http://papers.nips.cc/paper/9597-reverse-kl-divergence-training-of-prior-networks-improved-uncertainty-and-adversarial-robustness | http://papers.nips.cc/paper/9597-reverse-kl-divergence-training-of-prior-networks-improved-uncertainty-and-adversarial-robustness.pdf | neurips-2019-12 | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 3.73632014e-01 6.62076324e-02 2.13819429e-01 -4.32838500e-01
-8.87842953e-01 -9.13846850e-01 6.80844188e-01 -1.44401446e-01
-5.57211876e-01 9.97415662e-01 -2.68567204e-01 -6.12457633e-01
-2.14330465e-01 -8.42661619e-01 -7.34799445e-01 -8.54211092e-01
-9.02718380e-02 6.01673424e-01 1.83374077e-01 -1.53177902... | [5.735271453857422, 7.700480937957764] |
5aa4bccd-dea3-4d4d-9049-0d05ecad174b | a-neural-pairwise-ranking-model-for | 2203.07450 | null | https://arxiv.org/abs/2203.07450v1 | https://arxiv.org/pdf/2203.07450v1.pdf | A Neural Pairwise Ranking Model for Readability Assessment | Automatic Readability Assessment (ARA), the task of assigning a reading level to a text, is traditionally treated as a classification problem in NLP research. In this paper, we propose the first neural, pairwise ranking approach to ARA and compare it with existing classification, regression, and (non-neural) ranking me... | ['Sowmya Vajjala', 'Justin Lee'] | 2022-03-14 | null | https://aclanthology.org/2022.findings-acl.300 | https://aclanthology.org/2022.findings-acl.300.pdf | findings-acl-2022-5 | ['cross-corpus'] | ['computer-vision'] | [ 2.89338946e-01 6.02648668e-02 -1.23968706e-01 -5.72179556e-01
-1.52670455e+00 -6.58723056e-01 9.24538493e-01 5.99124968e-01
-6.68105304e-01 9.39702749e-01 6.45722449e-01 -3.69545221e-01
-4.46844846e-01 -7.10072875e-01 -6.93773031e-01 -1.50689960e-01
2.56560832e-01 9.70057964e-01 -1.87482461e-01 -2.77623475... | [11.04572582244873, 10.137849807739258] |
ab8e8880-ac3b-4fab-ba3c-ae72635bc867 | an-improved-baseline-framework-for-pose | 2303.07141 | null | https://arxiv.org/abs/2303.07141v1 | https://arxiv.org/pdf/2303.07141v1.pdf | An Improved Baseline Framework for Pose Estimation Challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop | This technical report describes our first-place solution to the pose estimation challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop. In this challenge, we aim to estimate human poses from in-the-wild stitched panoramic images. Our method is built based on Faster R-CNN for human detect... | ['Jianqin Yin', 'Wending Zhao', 'Feng Zhou', 'Shaojie Zhang', 'Ruoqi Yin', 'Yonghao Dang', 'Jiajun Fu'] | 2023-03-13 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [-1.20410673e-01 7.42969364e-02 3.68706584e-01 -3.31160367e-01
-9.49723780e-01 -3.85813534e-01 1.08152144e-01 -4.09798652e-01
-1.07165360e+00 6.00993454e-01 -3.61393504e-02 -2.79544234e-01
1.78765386e-01 -4.31814522e-01 -8.41538668e-01 -2.43530273e-01
-3.32817882e-01 3.99479538e-01 3.96859974e-01 -5.41744590... | [7.179612636566162, -0.8098330497741699] |
31d9fcf6-418b-44ab-b7d3-a518a2de77b9 | voting-for-deceptive-opinion-spam-detection | 1409.4504 | null | http://arxiv.org/abs/1409.4504v1 | http://arxiv.org/pdf/1409.4504v1.pdf | Voting for Deceptive Opinion Spam Detection | Consumers' purchase decisions are increasingly influenced by user-generated
online reviews. Accordingly, there has been growing concern about the potential
for posting deceptive opinion spam fictitious reviews that have been
deliberately written to sound authentic, to deceive the readers. Existing
approaches mainly foc... | ['Tao Wang', 'Hua Zhu'] | 2014-09-16 | null | null | null | null | ['deception-detection', 'spam-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 1.13937743e-01 1.59200773e-01 -2.04195101e-02 -7.35211790e-01
-5.12959421e-01 -7.57442296e-01 7.76532531e-01 3.47821377e-02
9.78065655e-02 8.25566471e-01 9.48100090e-02 -4.33137119e-01
1.94958687e-01 -6.09951138e-01 -2.28840709e-01 -6.13708854e-01
4.32950974e-01 -9.50401574e-02 2.66434234e-02 -1.94674030... | [7.860377311706543, 10.047374725341797] |
8eb32046-4375-4f31-adc4-41f875680af3 | towards-generating-diverse-audio-captions-via | 2212.02033 | null | https://arxiv.org/abs/2212.02033v1 | https://arxiv.org/pdf/2212.02033v1.pdf | Towards Generating Diverse Audio Captions via Adversarial Training | Automated audio captioning is a cross-modal translation task for describing the content of audio clips with natural language sentences. This task has attracted increasing attention and substantial progress has been made in recent years. Captions generated by existing models are generally faithful to the content of audi... | ['Wenwu Wang', 'Mark D. Plumbley', 'Jianyuan Sun', 'Xubo Liu', 'Xinhao Mei'] | 2022-12-05 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.75005329e-01 4.62923981e-02 2.39063352e-01 -4.28594410e-01
-1.45756829e+00 -8.73518169e-01 3.73942763e-01 -2.82432318e-01
1.66687220e-01 8.77329469e-01 5.39678395e-01 2.99393028e-01
5.64254403e-01 -5.76642990e-01 -1.00145376e+00 -4.86004442e-01
2.42849633e-01 6.61515534e-01 -4.84638438e-02 -3.41284901... | [15.269145011901855, 4.918099403381348] |
5aca7a86-642a-4ba9-bb05-1c897f4f5c9e | perceiving-3d-human-object-spatial | 2007.15649 | null | https://arxiv.org/abs/2007.15649v2 | https://arxiv.org/pdf/2007.15649v2.pdf | Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild | We present a method that infers spatial arrangements and shapes of humans and objects in a globally consistent 3D scene, all from a single image in-the-wild captured in an uncontrolled environment. Notably, our method runs on datasets without any scene- or object-level 3D supervision. Our key insight is that considerin... | ['Jason Y. Zhang', 'Hanbyul Joo', 'Deva Ramanan', 'Sam Pepose', 'Angjoo Kanazawa', 'Jitendra Malik'] | 2020-07-30 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1474_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570035.pdf | eccv-2020-8 | ['3d-shape-reconstruction-from-a-single-2d'] | ['computer-vision'] | [ 5.36916889e-02 -8.93136561e-02 2.13320881e-01 -4.98701125e-01
-4.51982915e-01 -8.48926961e-01 4.00528312e-01 5.74470088e-02
-4.34600711e-01 2.04064935e-01 5.02078570e-02 -3.80245224e-02
-1.02029137e-01 -3.05089474e-01 -1.03616309e+00 -1.64436370e-01
-9.52608287e-02 9.12458599e-01 2.87213415e-01 2.39906795... | [6.912295341491699, -1.0810794830322266] |
fd7fca6b-35ae-4bf0-82e6-acd9ad13a21b | modelps-an-interactive-and-collaborative | 2105.08275 | null | https://arxiv.org/abs/2105.08275v3 | https://arxiv.org/pdf/2105.08275v3.pdf | ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale | AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background. In particular, altering these DNN models in the deployment stage posits a tremendous challenge. In this research, we propose and develop a low-code solution, ModelPS (a... | ['Yong Luo', 'Yonggang Wen', 'Fan Yang', 'Shanshan Jiang', 'Huaizheng Zhang', 'Yuanming Li'] | 2021-05-18 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [-1.51638076e-01 -6.25711530e-02 1.55044362e-01 -5.37623048e-01
1.37155995e-01 -7.68278837e-01 3.03342640e-01 -2.93662071e-01
-2.05847561e-01 1.00417092e-01 -2.97528327e-01 -5.99341750e-01
-1.75755352e-01 -5.58236539e-01 -5.64041495e-01 3.75233404e-02
4.93979901e-01 3.28988045e-01 -3.05574443e-02 -3.35972577... | [7.927487850189209, 7.620371341705322] |
4eca24a2-f36c-406d-ba81-2f61c1d2deb9 | panoptic-video-scene-graph-generation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Panoptic_Video_Scene_Graph_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Panoptic_Video_Scene_Graph_Generation_CVPR_2023_paper.pdf | Panoptic Video Scene Graph Generation | Towards building comprehensive real-world visual perception systems, we propose and study a new problem called panoptic scene graph generation (PVSG). PVSG is related to the existing video scene graph generation (VidSGG) problem, which focuses on temporal interactions between humans and objects localized with bound... | ['Ziwei Liu', 'Chen Change Loy', 'Wayne Zhang', 'Kaiyang Zhou', 'Zheng Ma', 'Bo Li', 'Liangyu Chen', 'Zujin Guo', 'Xiangtai Li', 'Wenxuan Peng', 'Jingkang Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['panoptic-segmentation', 'scene-graph-generation', 'video-understanding', 'panoptic-scene-graph-generation', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.23568100e-01 -1.09811488e-03 2.17639860e-02 -2.49503717e-01
-3.06610763e-01 -7.22315073e-01 6.17840469e-01 -2.33495444e-01
3.58449034e-02 3.96504968e-01 5.26638567e-01 2.95039285e-02
1.37541100e-01 -5.38078785e-01 -8.66956294e-01 -4.35097128e-01
-1.14874721e-01 -1.09508000e-02 6.91871762e-01 -8.66568610... | [9.570330619812012, -0.1118595078587532] |
2ec80593-29f8-4fa2-9499-70ef93e467f1 | towards-a-unified-compositional-model-for | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Tang_Towards_a_Unified_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Tang_Towards_a_Unified_ICCV_2017_paper.pdf | Towards a Unified Compositional Model for Visual Pattern Modeling | Compositional models represent visual patterns as hierarchies of meaningful and reusable parts. They are attractive to vision modeling due to their ability to decompose complex patterns into simpler ones and resolve the low-level ambiguities in high-level image interpretations. However, current compositional models sep... | ['Jiahuan Zhou', 'Wei Tang', 'Ying Wu', 'Pei Yu'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 5.55640876e-01 1.92597762e-01 -1.52685165e-01 -5.20097017e-01
-1.61683872e-01 -5.07510900e-01 7.29527950e-01 8.91058054e-03
1.71738967e-01 2.58053452e-01 1.78824529e-01 -4.07485396e-01
-5.34069166e-02 -7.10144222e-01 -1.06084502e+00 -6.80641413e-01
8.85112211e-02 6.47897422e-01 3.11411232e-01 1.59698233... | [9.952221870422363, 0.921543538570404] |
3293e11b-75c2-4b47-968d-c3d8918ce2a2 | a-comprehensive-overview-and-comparative | 2305.17473 | null | https://arxiv.org/abs/2305.17473v2 | https://arxiv.org/pdf/2305.17473v2.pdf | A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU | Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecuri... | ['Raihani Mohamed', 'Norwati Mustapha', 'Thinagaran Perumal', 'Farhad MortezaPour Shiri'] | 2023-05-27 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-1.55539781e-01 -4.92222309e-02 -3.41326594e-01 -1.88951626e-01
-3.54516059e-01 -6.18542917e-02 7.42989540e-01 -2.19097912e-01
-1.68902338e-01 8.17545056e-01 2.24503711e-01 -7.39266515e-01
6.45160303e-02 -9.25836980e-01 -7.33850956e-01 -7.23161399e-01
-1.14078790e-01 4.08044100e-01 -8.24935287e-02 -3.01545709... | [10.859955787658691, 6.290380477905273] |
03ebc842-09dc-436b-81f3-a6e399eb56cd | taches-auxiliaires-pour-lanalyse-biaffine-en | null | null | https://aclanthology.org/2022.jeptalnrecital-taln.42 | https://aclanthology.org/2022.jeptalnrecital-taln.42.pdf | Tâches auxiliaires pour l’analyse biaffine en graphes de dépendances (Auxiliary tasks to boost Biaffine Semantic Dependency Parsing) | L’analyseur biaffine de Dozat & Manning (2017), qui produit des arbres de dépendances syntaxiques, a été étendu avec succès aux graphes de dépendances syntaxico-sémantiques (Dozat & Manning, 2018). Ses performances sur les graphes sont étonnamment hautes étant donné que, sans la contrainte de devoir produire un arbre, ... | ['Marie Candito'] | null | null | null | null | jep-taln-recital-2022-6 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-2.86921769e-01 -5.28180739e-03 4.39911991e-01 -5.20349383e-01
-6.30848110e-02 -1.12228394e+00 1.07644117e+00 9.16079283e-01
-5.12698472e-01 6.44682825e-01 1.03316501e-01 -3.17427427e-01
5.39094657e-02 -1.10825908e+00 -1.04126060e+00 -1.38529912e-01
-3.20802510e-01 3.78135949e-01 8.33446980e-02 -7.44420826... | [14.101972579956055, 13.316201210021973] |
955a7564-0444-4275-8c3e-bcc705d8d99f | graph-to-tree-neural-networks-for-learning | 2004.13781 | null | https://arxiv.org/abs/2004.13781v2 | https://arxiv.org/pdf/2004.13781v2.pdf | Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem | The celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, semantic parsing, and math word problem solving. However, these models either only consider input objects as sequences while ignoring the important structural information for encodi... | ['Lingfei Wu', 'Shucheng Li', 'Fengyuan Xu', 'Fangli Xu', 'Sheng Zhong', 'Shiwei Feng'] | 2020-04-07 | null | https://aclanthology.org/2020.findings-emnlp.255 | https://aclanthology.org/2020.findings-emnlp.255.pdf | findings-of-the-association-for-computational | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 6.61629975e-01 5.02552450e-01 -2.01245546e-01 -4.93235648e-01
-6.13559008e-01 -7.52160370e-01 1.19561397e-01 2.59087056e-01
-2.00141817e-01 6.14477456e-01 2.83807009e-01 -7.72192776e-01
4.24115658e-01 -1.19087005e+00 -1.09886777e+00 -2.35937700e-01
1.47385215e-02 3.85666519e-01 9.21935141e-02 1.83661282... | [10.068853378295898, 8.072834968566895] |
b90e760c-f5ec-4657-aa2c-82b40494a476 | some-algorithms-on-exact-approximate-and | 2012.15279 | null | https://arxiv.org/abs/2012.15279v1 | https://arxiv.org/pdf/2012.15279v1.pdf | Some Algorithms on Exact, Approximate and Error-Tolerant Graph Matching | The graph is one of the most widely used mathematical structures in engineering and science because of its representational power and inherent ability to demonstrate the relationship between objects. The objective of this work is to introduce the novel graph matching techniques using the representational power of the g... | ['Shri Prakash Dwivedi'] | 2020-12-30 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 3.23066652e-01 3.45273525e-01 2.89822936e-01 -3.07222456e-01
2.52992921e-02 -8.06319356e-01 3.98078382e-01 8.46875608e-01
-2.32625216e-01 2.15169057e-01 -3.33723456e-01 -2.02466130e-01
-6.92400336e-01 -1.42781878e+00 -4.91071224e-01 -3.20167869e-01
-3.97773802e-01 4.85209674e-01 4.79163259e-01 -4.79209483... | [7.2398858070373535, 5.7393412590026855] |
30101149-d295-44ae-85b8-f916d38fc477 | ai-marker-based-large-scale-ai-literature | 2011.00518 | null | https://arxiv.org/abs/2011.00518v2 | https://arxiv.org/pdf/2011.00518v2.pdf | AI Marker-based Large-scale AI Literature Mining | The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets and metrics are used as AI markers for AI literature. These entities can be used to trace the research process described ... | ['Ou wu', 'Shuxiao Li', 'Ji Zhang', 'Yingchun Ye', 'Rujing Yao'] | 2020-11-01 | null | null | null | null | ['literature-mining'] | ['natural-language-processing'] | [ 5.89694232e-02 -2.42832378e-01 -3.26554388e-01 1.90732315e-01
-6.28535151e-02 -3.27082753e-01 7.44762361e-01 5.32584190e-01
-4.95555699e-01 8.93251956e-01 8.56458768e-02 1.84191782e-02
-5.17930090e-01 -8.52986038e-01 -5.84063053e-01 -8.55304062e-01
-3.63730788e-02 1.47542715e-01 2.12494597e-01 2.02807084... | [9.4851713180542, 8.141538619995117] |
141480d0-1193-46f7-944c-64125e1a4e4b | unsupervised-opinion-summarization-with | 2004.10150 | null | https://arxiv.org/abs/2004.10150v1 | https://arxiv.org/pdf/2004.10150v1.pdf | Unsupervised Opinion Summarization with Noising and Denoising | The supervised training of high-capacity models on large datasets containing hundreds of thousands of document-summary pairs is critical to the recent success of deep learning techniques for abstractive summarization. Unfortunately, in most domains (other than news) such training data is not available and cannot be eas... | ['Mirella Lapata', 'Reinald Kim Amplayo'] | 2020-04-21 | unsupervised-opinion-summarization-with-1 | https://aclanthology.org/2020.acl-main.175 | https://aclanthology.org/2020.acl-main.175.pdf | acl-2020-6 | ['unsupervised-opinion-summarization'] | ['natural-language-processing'] | [ 5.57315707e-01 6.97757602e-01 -1.11042909e-01 -4.57298249e-01
-1.59739828e+00 -7.61258364e-01 9.72655237e-01 6.25973403e-01
-4.32067931e-01 1.08145976e+00 9.96153474e-01 -3.48429084e-01
4.25445586e-01 -6.27289474e-01 -8.28152776e-01 -3.24638516e-01
5.12225270e-01 5.97749710e-01 -2.94655383e-01 -2.68389940... | [12.492395401000977, 9.373167991638184] |
e607d219-dfbe-43cb-8ea7-a00796a58634 | towards-language-models-that-can-see-computer | 2306.16410 | null | https://arxiv.org/abs/2306.16410v1 | https://arxiv.org/pdf/2306.16410v1.pdf | Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language | We propose LENS, a modular approach for tackling computer vision problems by leveraging the power of large language models (LLMs). Our system uses a language model to reason over outputs from a set of independent and highly descriptive vision modules that provide exhaustive information about an image. We evaluate the a... | ['Amanpreet Singh', 'Douwe Kiela', 'Tristan Thrush', 'Gautam Mittal', 'William Berrios'] | 2023-06-28 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-9.86425057e-02 -4.59248051e-02 -2.33255699e-02 -4.54880387e-01
-9.44120169e-01 -8.57439935e-01 9.19515014e-01 -9.56704170e-02
-6.26285374e-01 3.62339453e-03 2.90337265e-01 -2.30602771e-01
2.05457553e-01 -3.47214311e-01 -7.57829487e-01 -4.23626840e-01
2.50470072e-01 4.00197446e-01 2.43965566e-01 -3.94651759... | [10.79066276550293, 1.6278600692749023] |
77ed2fb6-a141-4577-81d2-69017f96c124 | ds-at-semeval-2019-task-9-from-suggestion | null | null | https://aclanthology.org/S19-2209 | https://aclanthology.org/S19-2209.pdf | DS at SemEval-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classification | Suggestion Mining is the task of classifying sentences into suggestions or non-suggestions. SemEval-2019 Task 9 sets the task to mine suggestions from online texts. For each of the two subtasks, the classification has to be applied on a different domain. Subtask A addresses the domain of posts in suggestion online foru... | ['Tobias Cabanski'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 7.64813200e-02 6.13441288e-01 -2.39998132e-01 -6.09553814e-01
-6.26328647e-01 -3.50095779e-01 8.46733034e-01 3.42936456e-01
-5.62722325e-01 7.47526228e-01 3.31407487e-01 -6.04100227e-01
1.05539873e-01 -5.65685332e-01 -4.06668544e-01 -4.43165988e-01
3.25019896e-01 4.05004263e-01 5.90215735e-02 -2.33002275... | [10.895408630371094, 7.5550007820129395] |
abdfa1b6-69f2-46bb-bdd3-ff61480c4395 | minkloc-lidar-and-monocular-image-fusion-for | 2104.05327 | null | https://arxiv.org/abs/2104.05327v2 | https://arxiv.org/pdf/2104.05327v2.pdf | MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition | We introduce a discriminative multimodal descriptor based on a pair of sensor readings: a point cloud from a LiDAR and an image from an RGB camera. Our descriptor, named MinkLoc++, can be used for place recognition, re-localization and loop closure purposes in robotics or autonomous vehicles applications. We use late f... | ['Tomasz Trzcinski', 'Monika Wysoczanska', 'Jacek Komorowski'] | 2021-04-12 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 8.89987424e-02 -1.16062969e-01 3.20798680e-02 -5.75229287e-01
-1.19644058e+00 -7.39213705e-01 9.87218857e-01 4.50255096e-01
-7.93503881e-01 3.67522717e-01 -1.29792213e-01 3.40559594e-02
-6.21265545e-03 -5.06661892e-01 -9.09909487e-01 -7.07747996e-01
2.00975135e-01 5.31639338e-01 2.74856180e-01 -6.64872006... | [7.60085391998291, -1.9898031949996948] |
b75f98a9-7b11-4e00-8fff-069e576ba1ce | scene-text-image-super-resolution-via | null | null | https://dl.acm.org/doi/10.1145/3474085.3475469 | https://dl.acm.org/doi/10.1145/3474085.3475469 | Scene Text Image Super-Resolution via Parallelly Contextual Attention Network | Optical degradation makes text shapes and edges blurred, so the existing scene text recognition methods are difficult to achieve desirable results on low-resolution (LR) scene text images acquired in natural scenes. Therefore, efficiently extracting the sequential information for reconstructing super-resolution (SR) te... | ['Shen Heng Tao', 'Shen Fumin', 'Wu Jun', 'Ding Zhijun', 'Zhao Brian Nlong', 'Feng Shuyang', 'Zhao Cairong'] | 2021-10-17 | null | null | null | acm-international-conference-on-multimedia-2 | ['scene-text-recognition'] | ['computer-vision'] | [ 6.94352031e-01 -6.65468097e-01 1.84875187e-02 -2.94137504e-02
-6.72237098e-01 -4.82459813e-02 3.87495518e-01 -8.49744558e-01
-1.43187135e-01 5.79644501e-01 4.51953530e-01 2.82422174e-02
-2.82888919e-01 -6.17160380e-01 -6.11997604e-01 -1.12370145e+00
6.43232703e-01 3.89049798e-02 2.27093726e-01 -2.41600797... | [11.357799530029297, -1.8216440677642822] |
0a11b164-1c7b-419a-88c5-482825ec8085 | opinion-spam-detection-a-new-approach-using | 2205.13422 | null | https://arxiv.org/abs/2205.13422v1 | https://arxiv.org/pdf/2205.13422v1.pdf | Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms | E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming more prevalent and negatively impacting customers and service providers. There are many reasons why it is ha... | ['Dan Vilenchik', 'Michael Segal', 'Kiril Danilchenko'] | 2022-05-26 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 5.05552441e-02 3.36148053e-01 -6.39400780e-01 -5.70254922e-01
-6.16064727e-01 -6.40727520e-01 7.23021150e-01 6.25191808e-01
-3.93154800e-01 5.04739165e-01 -1.08825058e-01 -5.93429029e-01
2.65396923e-01 -9.14830744e-01 -4.91911292e-01 -3.87197971e-01
1.85152486e-01 8.74383926e-01 6.88568056e-01 -2.93417603... | [7.8513617515563965, 10.03599739074707] |
5c6aa70c-74ed-4e2e-a1f9-def53a4f91db | aircraft-engines-remaining-useful-life | null | null | https://www.sciencedirect.com/science/article/abs/pii/S095219762030258X | https://www.sciencedirect.com/science/article/pii/S095219762030258X?casa_token=sl4Tdv5eIjYAAAAA:91VC-_dXZoqUR4iKbieXHebBKvqb7yZAoOdQCGnYfsD14jqwCS3ZJxb5kZZBiVO41wWzgqErPGI | Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine | Remaining Useful Life (RUL) prediction for aircraft engines based on the available run-to-failure measurements of similar systems becomes more prevalent in Prognostic Health Management (PHM) thanks to the new advanced methods of estimation. However, feature extraction and RUL prediction are challenging tasks, especiall... | ['Mohamed Benbouzid', 'Lotfi Saïdi', 'Ouahab Kadri', 'Leïla-Haye Mouss', 'Tarek Berghout'] | 2020-09-08 | null | null | null | null | ['exponential-degradation'] | ['time-series'] | [-5.90487979e-02 -3.91017675e-01 3.98831695e-01 -2.43804932e-01
-6.65004402e-02 9.83668268e-02 4.17056903e-02 2.77074635e-01
-4.03306514e-01 8.15169930e-01 -3.90089482e-01 -1.45151347e-01
-8.83191466e-01 -6.22783720e-01 -4.72478509e-01 -1.08755910e+00
-2.85028666e-01 4.89706099e-01 1.40590698e-01 -4.27173942... | [6.766389846801758, 2.4263031482696533] |
cf9e31a7-945f-4f79-832c-46c3648f2063 | an-unbiased-transformer-source-code-learning | 2304.11072 | null | https://arxiv.org/abs/2304.11072v1 | https://arxiv.org/pdf/2304.11072v1.pdf | An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph | Over the years, open-source software systems have become prey to threat actors. Even as open-source communities act quickly to patch the breach, code vulnerability screening should be an integral part of agile software development from the beginning. Unfortunately, current vulnerability screening techniques are ineffec... | ['Peyman Najafirad', 'Elias Bou-Harb', 'Dylan Manuel', 'Gonzalo De La Torre Parra', 'Nafis Tanveer Islam'] | 2023-04-17 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-8.99339616e-02 -2.25263406e-02 -1.87014669e-01 -6.40056655e-02
-9.44833040e-01 -9.23902392e-01 1.45600900e-01 4.15146470e-01
1.52955905e-01 3.42836976e-01 -1.91316858e-01 -7.05591261e-01
5.11736348e-02 -9.86683249e-01 -8.56976390e-01 -3.04482996e-01
-5.24190426e-01 -2.71273315e-01 4.36119705e-01 -2.50945687... | [7.083827018737793, 7.771669864654541] |
39cb17c0-bb06-4bec-b7c2-da768f3f70ee | formality-style-transfer-with-shared-latent | null | null | https://aclanthology.org/2020.coling-main.203 | https://aclanthology.org/2020.coling-main.203.pdf | Formality Style Transfer with Shared Latent Space | Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training. However, the dataset for formality style transfer is considerably smaller than translation corpora. Moreover, we observe that informal and formal sentences clo... | ['WenHan Chao', 'Zhoujun Li', 'Lili Mou', 'Yu Wu', 'Yunli Wang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 4.34530616e-01 1.56491533e-01 -4.48259354e-01 -4.89278048e-01
-1.19345117e+00 -8.63866031e-01 9.72306550e-01 -5.04956245e-01
-3.22371930e-01 1.22892249e+00 5.14881134e-01 -6.34156287e-01
4.06589299e-01 -6.55284822e-01 -1.13107884e+00 -5.19637525e-01
6.71332061e-01 7.66671956e-01 -1.83674783e-01 -5.02405226... | [11.644266128540039, 9.872642517089844] |
31064e65-375d-4e77-a926-77344d73f4a1 | 3d-matting-a-benchmark-study-on-soft | 2210.05104 | null | https://arxiv.org/abs/2210.05104v1 | https://arxiv.org/pdf/2210.05104v1.pdf | 3D Matting: A Benchmark Study on Soft Segmentation Method for Pulmonary Nodules Applied in Computed Tomography | Usually, lesions are not isolated but are associated with the surrounding tissues. For example, the growth of a tumour can depend on or infiltrate into the surrounding tissues. Due to the pathological nature of the lesions, it is challenging to distinguish their boundaries in medical imaging. However, these uncertain r... | ['ZongYuan Ge', 'Xin Zhao', 'Kaimin Song', 'Wei Feng', 'Xin Wang', 'Huan Luo', 'Yi Luo', 'Lie Ju', 'Wanji He', 'Donghao Zhang', 'Xiufen Ye', 'Lin Wang'] | 2022-10-11 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 0.37847474 0.4111595 -0.2355155 -0.36500698 -0.7181227 -0.2740386
0.3617241 -0.04723692 -0.12009643 0.48680165 0.012515 -0.4813982
0.12493899 -0.782377 -0.44466418 -1.0439912 0.31737867 0.74700254
0.4195925 0.17087118 -0.30678195 0.5561162 -0.9795513 0.6377139
1.1102868 1.0168098 0.514... | [14.604718208312988, -2.196493148803711] |
7722c34c-a25c-4f34-b4db-7fba579f0952 | ai-based-arabic-language-and-speech-tutor | 2210.12346 | null | https://arxiv.org/abs/2210.12346v1 | https://arxiv.org/pdf/2210.12346v1.pdf | AI-based Arabic Language and Speech Tutor | In the past decade, we have observed a growing interest in using technologies such as artificial intelligence (AI), machine learning, and chatbots to provide assistance to language learners, especially in second language learning. By using AI and natural language processing (NLP) and chatbots, we can create an intellig... | ['Abdessamad Mbarki', 'Sonia Shiri', 'Pratik Satam', 'Salim Hariri', 'Saleem Alharir', 'Sicong Shao'] | 2022-10-22 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-2.29183912e-01 1.22803502e-01 1.86555460e-01 -2.33512536e-01
-6.95896924e-01 -6.23424709e-01 1.07375994e-01 5.47090113e-01
-2.66469747e-01 7.88754761e-01 2.51870714e-02 -8.84364605e-01
-3.43776375e-01 -7.75195122e-01 -5.20669639e-01 -3.58462632e-01
-2.65052728e-02 4.41260397e-01 2.74917722e-01 -8.31326783... | [11.85006332397461, 8.391040802001953] |
e6876080-18b7-474d-8187-b0cb49517b60 | dynamic-mixed-membership-stochastic-block | 2304.05894 | null | https://arxiv.org/abs/2304.05894v1 | https://arxiv.org/pdf/2304.05894v1.pdf | Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks | Most real-world networks evolve over time. Existing literature proposes models for dynamic networks that are either unlabeled or assumed to have a single membership structure. On the other hand, a new family of Mixed Membership Stochastic Block Models (MMSBM) allows to model static labeled networks under the assumption... | ['Sabine Loudcher', 'Julien Velcin', 'Gaël Poux-Médard'] | 2023-04-12 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.80334181e-01 2.74847955e-01 -5.41290164e-01 -2.98794150e-01
1.29936844e-01 -7.08822489e-01 9.85766053e-01 -5.75779472e-03
-2.73646444e-01 9.03155744e-01 -4.73373353e-01 -5.69804192e-01
-4.94279265e-01 -8.02707434e-01 -7.23054647e-01 -8.97890389e-01
-3.95013422e-01 8.92855644e-01 7.02149928e-01 -2.38996625... | [6.90802001953125, 5.190961837768555] |
3b5b042b-ca82-4819-a998-9cf5788ac290 | upc-s-speech-translation-system-for-iwslt | 2105.04512 | null | https://arxiv.org/abs/2105.04512v2 | https://arxiv.org/pdf/2105.04512v2.pdf | End-to-End Speech Translation with Pre-trained Models and Adapters: UPC at IWSLT 2021 | This paper describes the submission to the IWSLT 2021 offline speech translation task by the UPC Machine Translation group. The task consists of building a system capable of translating English audio recordings extracted from TED talks into German text. Submitted systems can be either cascade or end-to-end and use a cu... | ['Marta R. Costa-jussà', 'José A. R. Fonollosa', 'Carlos Escolano', 'Ioannis Tsiamas', 'Gerard I. Gállego'] | 2021-05-10 | null | https://aclanthology.org/2021.iwslt-1.11 | https://aclanthology.org/2021.iwslt-1.11.pdf | acl-iwslt-2021-8 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 7.28270337e-02 3.07616532e-01 2.14948416e-01 -3.15185875e-01
-1.67845476e+00 -8.33934128e-01 5.89763582e-01 -2.71775067e-01
-4.20111150e-01 5.97863615e-01 3.53331834e-01 -9.01027143e-01
6.01003051e-01 -2.57349461e-01 -7.83584058e-01 -3.46108288e-01
2.39573345e-01 9.32881117e-01 2.00337470e-01 -5.45884907... | [14.486565589904785, 7.128199100494385] |
3a2d3a04-2673-43dd-8323-b10cbdee78a7 | adaptive-deep-pyramid-matching-for-remote | 1611.03589 | null | http://arxiv.org/abs/1611.03589v1 | http://arxiv.org/pdf/1611.03589v1.pdf | Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification | Convolutional neural networks (CNNs) have attracted increasing attention in
the remote sensing community. Most CNNs only take the last fully-connected
layers as features for the classification of remotely sensed images, discarding
the other convolutional layer features which may also be helpful for
classification purpo... | ['Fuping Zhu', 'Renlong Hang', 'Javier Plaza', 'Qingshan Liu', 'Huihui Song', 'Antonio Plaza'] | 2016-11-11 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 2.90334672e-01 -4.81100053e-01 1.08818263e-01 -5.14390528e-01
-2.90937960e-01 -2.14122057e-01 2.67558664e-01 1.75944343e-01
-4.70407546e-01 4.03275281e-01 8.71728659e-02 -1.20408446e-01
-3.17719370e-01 -1.33473408e+00 -4.83428091e-01 -8.04207742e-01
-3.10240865e-01 -3.65004689e-01 4.67932105e-01 -1.54155359... | [9.756077766418457, -1.4288630485534668] |
ced701b2-f9c8-448e-897f-93d30b0576c8 | 2-5d-visual-relationship-detection | 2104.12727 | null | https://arxiv.org/abs/2104.12727v1 | https://arxiv.org/pdf/2104.12727v1.pdf | 2.5D Visual Relationship Detection | Visual 2.5D perception involves understanding the semantics and geometry of a scene through reasoning about object relationships with respect to the viewer in an environment. However, existing works in visual recognition primarily focus on the semantics. To bridge this gap, we study 2.5D visual relationship detection (... | ['Boqing Gong', 'Ming-Hsuan Yang', 'Matthew Brown', 'Hartwig Adam', 'Radu Soricut', 'Lior Shapira', 'Cho-Jui Hsieh', 'Sathish Thoppay', 'Xiangning Chen', 'Soravit Changpinyo', 'Yu-Chuan Su'] | 2021-04-26 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [-2.19580561e-01 -3.87120210e-02 -1.47545725e-01 -6.14952326e-01
-1.62877321e-01 -7.87993073e-01 6.20921791e-01 2.82432109e-01
-1.12231923e-02 -1.66450313e-03 2.79984236e-01 -2.90444404e-01
1.34214178e-01 -5.51073790e-01 -6.33397937e-01 -1.78573892e-01
-4.33146320e-02 5.35393715e-01 5.15657246e-01 -1.35085255... | [10.288493156433105, 1.6002289056777954] |
76a046c3-198c-417f-b333-65e07a715f70 | cross-sensor-adversarial-domain-adaptation-of | 2006.05923 | null | https://arxiv.org/abs/2006.05923v1 | https://arxiv.org/pdf/2006.05923v1.pdf | Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection | The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor independently. Differences in retrieved radiances lead to significant ... | ['Luis Gómez-Chova', 'Dan López-Puigdollers', 'Gonzalo Mateo-García', 'Valero Laparra'] | 2020-06-10 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 5.75261414e-01 -4.78047490e-01 1.72201231e-01 -1.58524737e-01
-6.72285557e-01 -8.92499566e-01 6.18037879e-01 3.54651883e-02
-4.84018117e-01 8.96298885e-01 -5.00513554e-01 -2.48740777e-01
-1.79594561e-01 -1.14665592e+00 -6.99558318e-01 -1.18035102e+00
-2.17953417e-02 5.16806655e-02 1.10674366e-01 -3.47705811... | [9.923925399780273, -1.8303279876708984] |
63258a8e-9452-49a0-887e-f1901af9c5d6 | how-can-subgroup-discovery-help-aiops | 2109.04909 | null | https://arxiv.org/abs/2109.04909v1 | https://arxiv.org/pdf/2109.04909v1.pdf | How Can Subgroup Discovery Help AIOps? | The genuine supervision of modern IT systems brings new challenges as it requires higher standards of scalability, reliability and efficiency when analysing and monitoring big data streams. Rule-based inference engines are a key component of maintenance systems in detecting anomalies and automating their resolution. Ho... | ['Youcef Remil'] | 2021-09-10 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 1.82171494e-01 2.65155017e-01 -1.61203846e-01 -1.03845306e-01
3.37120354e-01 -1.83632046e-01 3.56812418e-01 8.25322866e-01
4.60122935e-02 6.26214981e-01 -2.31928095e-01 -6.17853880e-01
-9.35518146e-01 -9.34072316e-01 -6.96722195e-02 -4.80770528e-01
-4.22900319e-01 6.41404152e-01 3.72105777e-01 -4.01309252... | [8.458593368530273, 5.935868263244629] |
d6c6f0a8-600e-45c2-b685-57645f2b01a5 | inside-out-visual-place-recognition | 2111.13546 | null | https://arxiv.org/abs/2111.13546v1 | https://arxiv.org/pdf/2111.13546v1.pdf | Inside Out Visual Place Recognition | Visual Place Recognition (VPR) is generally concerned with localizing outdoor images. However, localizing indoor scenes that contain part of an outdoor scene can be of large value for a wide range of applications. In this paper, we introduce Inside Out Visual Place Recognition (IOVPR), a task aiming to localize images ... | ['Marcel Worring', 'Tim Alpherts', 'Nanne van Noord', 'Sarah Ibrahimi'] | 2021-11-26 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.68856621e-01 -3.62255871e-01 -1.21861277e-02 -4.12246078e-01
-7.01387823e-01 -7.77180791e-01 7.74968743e-01 2.20065013e-01
-5.92126250e-01 6.44183338e-01 2.68295854e-01 -2.98515141e-01
2.31919765e-01 -7.87337184e-01 -1.08719838e+00 -4.50499266e-01
-8.98116305e-02 -6.66038543e-02 1.42609522e-01 -1.76580533... | [7.604981899261475, -1.8918046951293945] |
3907b64d-cc23-4f9d-b383-bf70367806ce | fine-grained-image-text-matching-by-cross | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pan_Fine-Grained_Image-Text_Matching_by_Cross-Modal_Hard_Aligning_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pan_Fine-Grained_Image-Text_Matching_by_Cross-Modal_Hard_Aligning_Network_CVPR_2023_paper.pdf | Fine-Grained Image-Text Matching by Cross-Modal Hard Aligning Network | Current state-of-the-art image-text matching methods implicitly align the visual-semantic fragments, like regions in images and words in sentences, and adopt cross-attention mechanism to discover fine-grained cross-modal semantic correspondence. However, the cross-attention mechanism may bring redundant or irreleva... | ['BaiLing Zhang', 'Fangyu Wu', 'Zhengxin Pan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['semantic-correspondence', 'text-matching'] | ['computer-vision', 'natural-language-processing'] | [ 1.41570941e-01 -4.35140431e-01 -5.28703451e-01 -4.15181905e-01
-7.91593611e-01 -3.79633874e-01 5.24140298e-01 -7.08803441e-03
-4.57310706e-01 1.81587502e-01 5.33433855e-01 -1.04461201e-01
-2.22039342e-01 -5.79063177e-01 -6.90380394e-01 -6.31528914e-01
5.71457207e-01 2.08211944e-01 8.90037343e-02 -1.04446948... | [10.785484313964844, 1.2691649198532104] |
da57e3bc-e039-40c9-8261-a0da6f35dbba | bdanet-multiscale-convolutional-neural | 2105.07364 | null | https://arxiv.org/abs/2105.07364v1 | https://arxiv.org/pdf/2105.07364v1.pdf | BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images | Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before relief effort is deployed. With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent ... | ['Qian Du', 'Liang Xiao', 'Jianyu Chen', 'Delu Pan', 'Chen Chen', 'Taojiannan Yang', 'Sijie Zhu', 'Yu Shen'] | 2021-05-16 | null | null | null | null | ['2d-semantic-segmentation', 'extracting-buildings-in-remote-sensing-images'] | ['computer-vision', 'miscellaneous'] | [ 2.10876942e-01 -3.39488089e-01 2.84617066e-01 -3.57311189e-01
-8.18265498e-01 6.48079589e-02 4.05262560e-01 4.56802785e-01
-6.32803500e-01 4.66770828e-01 4.87086982e-01 -1.64096579e-01
-1.42569378e-01 -1.47857845e+00 -4.59136367e-01 -9.72901583e-01
-2.33318001e-01 1.69987008e-01 1.50275389e-02 -5.58338046... | [9.649314880371094, -1.3308225870132446] |
74d051e5-95bc-4d2b-9596-a26326d5b85f | single-shot-multi-person-3d-pose-estimation | 1712.03453 | null | http://arxiv.org/abs/1712.03453v3 | http://arxiv.org/pdf/1712.03453v3.pdf | Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB | We propose a new single-shot method for multi-person 3D pose estimation in
general scenes from a monocular RGB camera. Our approach uses novel
occlusion-robust pose-maps (ORPM) which enable full body pose inference even
under strong partial occlusions by other people and objects in the scene. ORPM
outputs a fixed numbe... | ['Christian Theobalt', 'Gerard Pons-Moll', 'Weipeng Xu', 'Oleksandr Sotnychenko', 'Franziska Mueller', 'Dushyant Mehta', 'Srinath Sridhar'] | 2017-12-09 | null | null | null | null | ['3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative'] | ['computer-vision', 'computer-vision'] | [-2.30776116e-01 2.52312403e-02 3.21043342e-01 -3.92641246e-01
-8.34682107e-01 -5.68031549e-01 5.12665749e-01 -3.68285328e-01
-4.45851415e-01 6.05772138e-01 3.86692882e-01 6.31638169e-01
3.46790463e-01 -3.62964541e-01 -7.84053266e-01 -2.77451694e-01
-2.54295822e-02 1.33036697e+00 3.78579646e-01 -5.93991838... | [7.0341362953186035, -0.9356746077537537] |
074b6642-e373-45fe-8f50-43dae1ac21f2 | arraybot-reinforcement-learning-for | 2306.16857 | null | https://arxiv.org/abs/2306.16857v1 | https://arxiv.org/pdf/2306.16857v1.pdf | ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch | We present ArrayBot, a distributed manipulation system consisting of a $16 \times 16$ array of vertically sliding pillars integrated with tactile sensors, which can simultaneously support, perceive, and manipulate the tabletop objects. Towards generalizable distributed manipulation, we leverage reinforcement learning (... | ['Huazhe Xu', 'Gu Zhang', 'Changyi Lin', 'Yuanchen Ju', 'Zhengmao He', 'Jingwen Cheng', 'Han Zhang', 'Zhengrong Xue'] | 2023-06-29 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [ 2.64474005e-01 2.11505204e-01 2.95448187e-03 3.39753628e-01
-5.11662900e-01 -1.05352414e+00 1.36657104e-01 -3.30733322e-02
-3.02736044e-01 9.57223773e-01 -2.10931510e-01 1.18248269e-01
-7.31738150e-01 -7.31346428e-01 -9.92746890e-01 -8.81331980e-01
-5.38883388e-01 6.03746831e-01 4.50648844e-01 -2.78016150... | [4.654242515563965, 0.6777402758598328] |
c2c40e37-85d1-4b17-a735-2fa65f36a510 | deep-multi-task-multi-label-cnn-for-effective | 2002.03683 | null | https://arxiv.org/abs/2002.03683v1 | https://arxiv.org/pdf/2002.03683v1.pdf | Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification | Facial Attribute Classification (FAC) has attracted increasing attention in computer vision and pattern recognition. However, state-of-the-art FAC methods perform face detection/alignment and FAC independently. The inherent dependencies between these tasks are not fully exploited. In addition, most methods predict all ... | ['Jing-Hao Xue', 'Longbiao Mao', 'Yan Yan', 'Hanzi Wang'] | 2020-02-10 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 3.36245745e-02 -4.10707921e-01 -2.34751418e-01 -9.67251301e-01
-6.88187599e-01 7.30769187e-02 2.27448896e-01 -1.16273286e-02
-4.58113253e-01 4.61786270e-01 -6.42605200e-02 2.64907837e-01
-3.22567552e-01 -5.97652674e-01 -3.94956887e-01 -9.64592516e-01
2.76267111e-01 4.95670706e-01 -2.70282447e-01 -8.09729248... | [13.531457901000977, 0.842373251914978] |
fa7f2f6b-5bac-46f4-a8bd-61632a78cd3f | multi-view-and-cross-view-brain-decoding | null | null | https://aclanthology.org/2022.coling-1.10 | https://aclanthology.org/2022.coling-1.10.pdf | Multi-view and Cross-view Brain Decoding | Can we build multi-view decoders that can decode concepts from brain recordings corresponding to any view (picture, sentence, word cloud) of stimuli? Can we build a system that can use brain recordings to automatically describe what a subject is watching using keywords or sentences? How about a system that can automati... | ['Raju S. Bapi', 'Manish Gupta', 'Jashn Arora', 'Subba Reddy Oota'] | null | null | null | null | coling-2022-10 | ['brain-decoding', 'brain-decoding', 'keyword-extraction'] | ['medical', 'miscellaneous', 'natural-language-processing'] | [ 3.57398778e-01 2.41167113e-01 2.94680834e-01 -5.86387277e-01
-9.73525703e-01 -7.87045181e-01 8.77758145e-01 -2.81302333e-02
-4.05782819e-01 2.79002637e-01 4.40101862e-01 -2.49977648e-01
4.86791641e-01 -4.29858238e-01 -9.83281732e-01 -4.96622175e-01
3.81194383e-01 3.91115606e-01 1.19042639e-02 -8.42287168... | [10.94041633605957, 1.688606858253479] |
f4556680-7f2c-4ebe-9b17-cad2ff90d619 | 3d-molecule-generation-by-denoising-voxel | 2306.07473 | null | https://arxiv.org/abs/2306.07473v1 | https://arxiv.org/pdf/2306.07473v1.pdf | 3D molecule generation by denoising voxel grids | We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. First, we train a denoising neural network that learns to map from a smooth distribution of noisy molecules to the distribution of real molecules. Then, we follow the neural empirical Bayes framework [Saremi... | ['Saeed Saremi', 'Vishnu Sresht', 'Stephen Ra', 'Andrew Martin Watkins', 'Omar Mahmood', 'Michael Maser', 'Joseph Kleinhenz', 'Joshua Rackers', 'Pedro O. Pinheiro'] | 2023-06-13 | null | null | null | null | ['3d-molecule-generation'] | ['medical'] | [ 4.79216635e-01 1.79547831e-01 2.40437463e-01 -1.23580560e-01
-8.06701660e-01 -6.63931906e-01 9.45920110e-01 3.33006121e-02
-3.76302630e-01 1.37608492e+00 -6.26443475e-02 -2.54666239e-01
3.02500695e-01 -1.30380785e+00 -1.06615901e+00 -1.06487370e+00
2.75156468e-01 1.20642483e+00 -1.73116308e-02 -5.26870377... | [5.052623271942139, 5.486171722412109] |
556fe568-4993-4340-8097-d4cca9a0b782 | deepxml-scalable-accurate-deep-extreme | null | null | https://openreview.net/forum?id=SJlWyerFPS | https://openreview.net/pdf?id=SJlWyerFPS | DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases | The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set. Unfortunately, state-of-the-art deep extreme classifiers are either not scalable or inaccurate for sh... | ['Manik Varma', 'Sumeet Agarwal', 'Himanshu Jain', 'Kushal Dave', 'Deepak Saini', 'Anshul Mittal', 'Kunal Dahiya'] | 2019-09-25 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [ 1.85074918e-02 1.84313253e-01 -7.26131201e-01 -6.95915878e-01
-1.25014126e+00 -8.81915390e-01 6.46251440e-01 3.38344127e-01
-6.65762603e-01 4.84124213e-01 -4.04877663e-02 -3.85402799e-01
-1.29069924e-01 -7.06055641e-01 -5.16238213e-01 -4.36485380e-01
5.63571639e-02 1.18999350e+00 1.38642639e-02 -6.04243129... | [9.605245590209961, 4.434901714324951] |
afdce437-093f-4624-8c1c-8777938dce24 | detect-anticipate-and-generate-semi | 1809.07075 | null | http://arxiv.org/abs/1809.07075v1 | http://arxiv.org/pdf/1809.07075v1.pdf | Detect, anticipate and generate: Semi-supervised recurrent latent variable models for human activity modeling | Successful Human-Robot collaboration requires a predictive model of human
behavior. The robot needs to be able to recognize current goals and actions and
to predict future activities in a given context. However, the spatio-temporal
sequence of human actions is difficult to model since latent factors such as
intention, ... | ['Judith Bütepage', 'Danica Kragic'] | 2018-09-19 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 4.07402575e-01 2.25884631e-01 -4.93933380e-01 -4.88132119e-01
-4.14463550e-01 -2.75571764e-01 9.31443989e-01 -5.73489554e-02
-4.91913915e-01 8.34889889e-01 7.26824462e-01 1.30009830e-01
-1.27610546e-02 -3.44929367e-01 -6.30431771e-01 -3.77323210e-01
-4.95818883e-01 8.70761335e-01 7.39741325e-02 1.66959047... | [7.798001766204834, 0.5155978798866272] |
4a455cc6-ab1b-4013-a30b-ad715282008e | matching-thermal-to-visible-face-images-using | 1903.00963 | null | http://arxiv.org/abs/1903.00963v1 | http://arxiv.org/pdf/1903.00963v1.pdf | Matching Thermal to Visible Face Images Using a Semantic-Guided Generative Adversarial Network | Designing face recognition systems that are capable of matching face images
obtained in the thermal spectrum with those obtained in the visible spectrum is
a challenging problem. In this work, we propose the use of semantic-guided
generative adversarial network (SG-GAN) to automatically synthesize visible
face images f... | ['Cunjian Chen', 'Arun Ross'] | 2019-03-03 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 8.52988601e-01 2.88136117e-02 1.59147695e-01 -7.36125886e-01
-8.10675383e-01 -6.45378590e-01 5.80880642e-01 -9.42086041e-01
1.78476095e-01 3.55409145e-01 -1.36534616e-01 8.73037577e-02
1.29883870e-01 -8.96306574e-01 -8.30218732e-01 -9.69151437e-01
5.55342376e-01 -8.23304430e-02 -6.77288592e-01 -7.64292926... | [12.895934104919434, 0.11870142817497253] |
e18745df-b8d0-4b2a-a066-5911701ec01d | ask2transformers-zero-shot-domain-labelling | 2101.02661 | null | https://arxiv.org/abs/2101.02661v2 | https://arxiv.org/pdf/2101.02661v2.pdf | Ask2Transformers: Zero-Shot Domain labelling with Pre-trained Language Models | In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, the system is not restricted to use a particular set of domain labels. We exploit the knowledge encoded within different off-the-shelf pre-tr... | ['German Rigau', 'Oscar Sainz'] | 2021-01-07 | null | null | null | null | ['domain-labelling'] | ['natural-language-processing'] | [ 1.05611585e-01 4.08288166e-02 -5.41941881e-01 -6.20445907e-01
-3.96326125e-01 -6.19562447e-01 7.32581437e-01 4.00188297e-01
-9.75023091e-01 8.84219229e-01 -1.02732129e-01 -1.79072097e-01
5.70961982e-02 -9.12268400e-01 4.95789805e-03 -1.12894788e-01
3.57770056e-01 8.82821798e-01 5.84559262e-01 -6.78857982... | [10.466891288757324, 8.859926223754883] |
d1f36538-07e9-4509-a639-3f08c67fb809 | boosting-multiple-sclerosis-lesion-1 | null | null | https://www.sciencedirect.com/science/article/pii/S0010482523004869 | https://www.sciencedirect.com/science/article/pii/S0010482523004869 | Boosting multiple sclerosis lesion segmentation through attention mechanism | Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully automated analysis is not yet available. State-of-the-art methods rely on slight... | ['S. Battiato', 'F. Pappalardo', 'D. Maimone', 'C. Di Lorenzo', 'G. Russo', 'A. Ortis', 'O. Giudice', 'F. Guarnera', 'E. Crispino', 'A. Rondinella'] | 2023-07-01 | null | null | null | computers-in-biology-and-medicine-2023-7 | ['brain-image-segmentation', 'lesion-segmentation'] | ['medical', 'medical'] | [ 4.43452060e-01 2.58780401e-02 -1.16009563e-01 -4.30761844e-01
-9.21674848e-01 -1.49514705e-01 4.93807107e-01 1.86548874e-01
-7.24278867e-01 6.64349496e-01 -1.53373331e-01 -3.89456861e-02
-4.88045841e-01 -4.99778032e-01 -4.32533026e-01 -5.17188489e-01
-6.70994699e-01 9.03222620e-01 6.15595579e-01 -1.70100741... | [14.239950180053711, -2.063462257385254] |
1afcce53-c5ac-49a2-bda4-f1268d0138fb | weakly-supervised-deep-learning-model-for | 2212.12844 | null | https://arxiv.org/abs/2212.12844v1 | https://arxiv.org/pdf/2212.12844v1.pdf | Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images | Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) staine... | ['Sheida Nabavi', 'Ga Hie Nam', 'Harold Yamase', 'Anna Tarakanova', 'Ankit Bhardwaj', 'Jun Bai', 'Hanzhang Wang', 'Mohammad Madani', 'Mohammad Mahdi Behzadi'] | 2022-12-25 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 3.06086361e-01 2.74320275e-01 -2.21674144e-01 -6.53193951e-01
-1.17234576e+00 -5.19584298e-01 4.17196393e-01 6.71573937e-01
-4.53900665e-01 5.92283607e-01 -1.69220448e-01 -1.51896223e-01
-1.33333743e-01 -1.10277498e+00 -3.84164929e-01 -1.07357347e+00
-2.40977496e-01 7.82443166e-01 1.24896660e-01 1.93006098... | [14.963743209838867, -2.776460647583008] |
da269988-947e-49a7-bf82-9ec5d3760aaa | deep-learning-based-inverse-method-for-layout | 1806.03182 | null | http://arxiv.org/abs/1806.03182v1 | http://arxiv.org/pdf/1806.03182v1.pdf | Deep learning based inverse method for layout design | Layout design with complex constraints is a challenging problem to solve due
to the non-uniqueness of the solution and the difficulties in incorporating the
constraints into the conventional optimization-based methods. In this paper, we
propose a design method based on the recently developed machine learning
technique,... | ['Yu-Jie Zhang', 'Wenjing Ye'] | 2018-06-07 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [ 6.45188242e-02 1.87924922e-01 3.80765080e-01 -1.10600203e-01
1.99221715e-01 -3.97259772e-01 2.80388355e-01 -3.49406213e-01
-1.12565085e-01 8.91790509e-01 -2.34339356e-01 -2.98902780e-01
-5.12513340e-01 -8.47867906e-01 -7.59013772e-01 -8.48641992e-01
3.75957459e-01 5.14068484e-01 -2.60783315e-01 -2.26827756... | [5.9222517013549805, 3.4329161643981934] |
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