ImageNet classification with deep convolutional neural networks

Krizhevsky, Alex, Sutskever, Ilya, Hinton, Geoffrey E.·2017·Communications of the ACM

Cited by (108,466)

Items 301–400 of 108,466

  1. Fast Feature Pyramids for Object Detection
    Piotr Dollár, Ron Appel, Serge Belongie, Pietro Perona · 2014 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  2. A Critical Review of Recurrent Neural Networks for Sequence Learning
    Zachary C. Lipton, John A. Berkowitz, Charles Elkan · 2015 · arXiv (Cornell University)
  3. MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
    Guo, Yandong, Zhang, Lei, Hu, Yuxiao, He, Xiaodong, Gao, Jianfeng · 2016 · Lecture Notes in Computer Science
  4. DeViSE: A Deep Visual-Semantic Embedding Model
    Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio A, Jeff Dean, Marc’Aurelio Ranzato, Tomáš Mikolov · 2013
  5. EIE: efficient inference engine on compressed deep neural network
    Han, Song, Liu, Xingyu, Mao, Huizi, Pu, Jing, Pedram, Ardavan, Horowitz, Mark A., Dally, William J. · 2016 · ACM SIGARCH Computer Architecture News
  6. Deep Learning for Health Informatics
    Ravì, Daniele, Wong, Charence, Deligianni, Fani, Berthelot, Melissa, Andreu-Perez, Javier, Lo, Benny, Yang, Guang-Zhong · 2017 · IEEE Journal of Biomedical and Health Informatics
  7. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
    Srdjan Sladojević, Marko Arsenović, Andraš Anderla, Dubravko R. Culibrk, Darko Stefanović · 2016 · Computational Intelligence and Neuroscience
  8. Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
    Jiaxi Tang, Ke Wang · 2018 · ACM International Conference on Web Search and Data Mining (WSDM)
  9. A universal SNP and small-indel variant caller using deep neural networks
    Poplin, Ryan, Chang, Pi-Chuan, Alexander, David, Schwartz, Scott, Colthurst, Thomas, Ku, Alexander, Newburger, Dan, Dijamco, Jojo, Nguyen, Nam, Afshar, Pegah T et al. · 2018 · Nature Biotechnology
  10. Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
    Jing, Longlong, Tian, Yingli · 2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  11. Deep Image Prior
    Victor Lempitsky, Andrea Vedaldi, Dmitry Ulyanov · 2018 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  12. Understanding Convolution for Semantic Segmentation
    Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, Garrison W. Cottrell · 2018 · IEEE Winter Conference on Applications of Computer Vision
  13. Automatic Differentiation in Machine Learning: a Survey
    Atılım Güneş Baydin, Barak A. Pearlmutter, Alexey Radul, Jeffrey Mark Siskind · 2015 · Maynooth University ePrints and eTheses Archive (Maynooth University)
  14. LLNet: A deep autoencoder approach to natural low-light image enhancement
    Kin Gwn Lore, Adedotun Akintayo, Soumik Sarkar · 2016 · Pattern Recognition
  15. Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set
    Shunping Ji, Shiqing Wei, Meng Lü · 2018 · IEEE Transactions on Geoscience and Remote Sensing
  16. Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework
    Zilong Zhong, Jonathan Li, Zhiming Luo, Michael A. Chapman · 2017 · IEEE Transactions on Geoscience and Remote Sensing
  17. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
    Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Marianne Amitai, Jacob Goldberger, Hayit K. Greenspan · 2018 · Neurocomputing
  18. Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture
    Chen, C. L. Philip, Liu, Zhulin · 2018 · IEEE Transactions on Neural Networks and Learning Systems
  19. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
    Jason Wei, Kai Zou · 2019 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
  20. EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
    Helber, Patrick, Bischke, Benjamin, Dengel, Andreas, Borth, Damian · 2019 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  21. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
    Jian-Hao Luo, Jianxin Wu, Weiyao Lin · 2017 · IEEE/CVF International Conference on Computer Vision (ICCV)
  22. Deep Convolutional Neural Networks for Hyperspectral Image Classification
    Wei Hu, Yangyu Huang, Li Wei, Fan Zhang, Heng-Chao Li · 2015 · Journal of Sensors
  23. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
    Liu, Xiaoxuan, Faes, Livia, Kale, Aditya U, Wagner, Siegfried K, Fu, Dun Jack, Bruynseels, Alice, Mahendiran, Thushika, Moraes, Gabriella, Shamdas, Mohith, Kern, Christoph et al. · 2019 · The Lancet Digital Health
  24. SUN RGB-D: A RGB-D scene understanding benchmark suite
    Song, Shuran, Lichtenberg, Samuel P., Xiao, Jianxiong · 2015 · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  25. PointConv: Deep Convolutional Networks on 3D Point Clouds
    Wenxuan Wu, Zhongang Qi, Fuxin Li · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  26. Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
    Serkan Kıranyaz, Türker İnce, Moncef Gabbouj · 2015 · IEEE Transactions on Biomedical Engineering
  27. Playing for Data: Ground Truth from Computer Games
    Stephan R. Richter, Vibhav Vineet, Stefan Roth, Vladlen Koltun · 2016 · Lecture notes in computer science
  28. Return of Frustratingly Easy Domain Adaptation
    Sun, Baochen, Feng, Jiashi, Saenko, Kate · 2016 · Proceedings of the AAAI Conference on Artificial Intelligence
  29. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
    Poplin, Ryan, Varadarajan, Avinash V., Blumer, Katy, Liu, Yun, McConnell, Michael V., Corrado, Greg S., Peng, Lily, Webster, Dale R. · 2018 · Nature Biomedical Engineering
  30. DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
    Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, Xiaoou Tang · 2016 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  31. Can AI help in screening viral and COVID-19 pneumonia?
    Chowdhury, Muhammad E. H., Rahman, Tawsifur, Khandakar, Amith, Mazhar, Rashid, Kadir, Muhammad Abdul, Mahbub, Zaid Bin, Islam, Khandakar Reajul, Khan, Muhammad Salman, Iqbal, Atif, Emadi, Nasser Al et al. · 2020 · IEEE Access
  32. Digital twin: values, challenges and enablers from a modeling perspective
    Rasheed, Adil, San, Omer, Kvamsdal, Trond · 2020 · IEEE Access
  33. HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification
    Swalpa Kumar Roy, Gopal Krishna, Shiv Ram Dubey, B.B. Chaudhuri · 2019 · IEEE Geoscience and Remote Sensing Letters
  34. Parallel convolutional processing using an integrated photonic tensor core
    Feldmann, J., Youngblood, N., Karpov, M., Gehring, H., Li, X., Stappers, M., Le Gallo, M., Fu, X., Lukashchuk, A., Raja, A. S. et al. · 2021 · Nature
  35. Graph convolutional networks: a comprehensive review
    Zhang, Si, Tong, Hanghang, Xu, Jiejun, Maciejewski, Ross · 2019 · Computational Social Networks
  36. Generative adversarial network in medical imaging: A review
    Yi Xin, Ekta Walia, Paul Babyn · 2019 · Medical Image Analysis
  37. Knowledge Graphs
    Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard de Melo, Claudio Gutiérrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli A, Sebastian Neumaier et al. · 2021 · ACM Computing Surveys
  38. Deep Networks with Stochastic Depth
    Huang, Gao, Sun, Yu, Liu, Zhuang, Sedra, Daniel, Weinberger, Kilian Q. · 2016 · Lecture Notes in Computer Science
  39. A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>
    Yurtsever, Ekim, Lambert, Jacob, Carballo, Alexander, Takeda, Kazuya · 2020 · IEEE Access
  40. MemNet: A Persistent Memory Network for Image Restoration
    Ying Yu Tai, Jian Jun Yang, Xiaoming Liu, Chunyan Xu · 2017 · IEEE/CVF International Conference on Computer Vision (ICCV)
  41. Understanding deep image representations by inverting them
    Mahendran, Aravindh, Vedaldi, Andrea · 2015 · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  42. Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
    Sharma, Piyush, Ding, Nan, Goodman, Sebastian, Soricut, Radu · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  43. Hierarchical Convolutional Features for Visual Tracking
    Chao Ma, Jia‐Bin Huang, Xiaokang Yang, Ming–Hsuan Yang · 2015 · IEEE/CVF International Conference on Computer Vision (ICCV)
  44. CellProfiler 3.0: Next-generation image processing for biology
    Claire McQuin, Allen Goodman, Vasiliy S. Chernyshev, Lee D. Kamentsky, Beth A. Cimini, Kyle W. Karhohs, Minh Doan, Liya Ding, Susanne M. Rafelski, Derek J. Thirstrup et al. · 2018 · PLoS Biology
  45. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
    Weilin Xu, David Evans, Yanjun Qi · 2018 · Network and Distributed System Security Symposium
  46. GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
    Huang, Lianghua, Zhao, Xin, Huang, Kaiqi · 2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  47. EAST: An Efficient and Accurate Scene Text Detector
    Xinyu Zhou, Cong Yao, He Wen, Yuzhi Wang, Shuchang Zhou, Weiran He, Jiajun Liang · 2017 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  48. Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
    Zhaofan Qiu, Ting Yao, Tao Mei · 2017 · IEEE/CVF International Conference on Computer Vision (ICCV)
  49. Towards End-To-End Speech Recognition with Recurrent Neural Networks
    Alex Graves, Navdeep Jaitly · 2014
  50. MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
    Jun Xu, Tao Mei, Ting Yao, Yong Rui · 2016 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  51. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
    Teja Kattenborn, Jens Leitloff, Felix Schiefer, Stefan Hinz · 2021 · ISPRS Journal of Photogrammetry and Remote Sensing
  52. A survey of deep learning techniques for autonomous driving
    Grigorescu, Sorin, Trasnea, Bogdan, Cocias, Tiberiu, Macesanu, Gigel · 2020 · Journal of Field Robotics
  53. Contrastive Multiview Coding
    Tian, Yonglong, Krishnan, Dilip, Isola, Phillip · 2020 · Lecture Notes in Computer Science
  54. Time-series forecasting with deep learning: a survey
    Bryan Y. Lim, Stefan Zohren · 2021 · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
  55. Deep Learning in Mobile and Wireless Networking: A Survey
    Chaoyun Zhang, Paul Patras, Hamed Haddadi · 2019 · IEEE Communications Surveys & Tutorials
  56. BinaryConnect: Training Deep Neural Networks with binary weights during propagations
    Courbariaux, Matthieu, Bengio, Yoshua, David, Jean-Pierre · 2015 · arXiv
  57. MesoNet: a Compact Facial Video Forgery Detection Network
    Afchar, Darius, Nozick, Vincent, Yamagishi, Junichi, Echizen, Isao · 2018 · 2018 IEEE International Workshop on Information Forensics and Security (WIFS)
  58. Learning to Adapt Structured Output Space for Semantic Segmentation
    Yi–Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming–Hsuan Yang, Manmohan Krishna Chandraker · 2018 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  59. Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images
    Cheng, Gong, Zhou, Peicheng, Han, Junwei · 2016 · IEEE Transactions on Geoscience and Remote Sensing
  60. Efficient Multi-Scale Attention Module with Cross-Spatial Learning
    Daliang Ouyang, Su He, Guozhong Zhang, Mingzhu Luo, Huaiyong Guo, Jianming Zhan, Zhijie Huang · 2023 · IEEE International Conference on Acoustics Speech and Signal Processing
  61. YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
    Li, Chuyi, Li, Lulu, Jiang, Hongliang, Weng, Kaiheng, Geng, Yifei, Li, Liang, Ke, Zaidan, Li, Qingyuan, Cheng, Meng, Nie, Weiqiang et al. · 2022 · arXiv
  62. DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
    Chen, Chenyi, Seff, Ari, Kornhauser, Alain, Xiao, Jianxiong · 2015 · 2015 IEEE International Conference on Computer Vision (ICCV)
  63. Deep Learning: Methods and Applications
    Li Deng · 2014 · now publishers, Inc. eBooks
  64. Dilated Residual Networks
    Fisher Yu, Vladlen Koltun, Thomas A. Funkhouser · 2017 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  65. Can we open the black box of AI?
    Castelvecchi, Davide · 2016 · Nature
  66. Deep Facial Expression Recognition: A Survey
    Shan Li, Weihong Deng · 2020 · IEEE Transactions on Affective Computing
  67. Deep Learning Face Representation by Joint Identification-Verification
    Sun, Yi, Wang, Xiaogang, Tang, Xiaoou · 2014 · arXiv
  68. Semantic Understanding of Scenes Through the ADE20K Dataset
    Zhou, Bolei, Zhao, Hang, Puig, Xavier, Xiao, Tete, Fidler, Sanja, Barriuso, Adela, Torralba, Antonio · 2019 · International Journal of Computer Vision
  69. Adversarial Examples: Attacks and Defenses for Deep Learning
    Yuan, Xiaoyong, He, Pan, Zhu, Qile, Li, Xiaolin · 2019 · IEEE Transactions on Neural Networks and Learning Systems
  70. BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation
    Yu, Changqian, Gao, Changxin, Wang, Jingbo, Yu, Gang, Shen, Chunhua, Sang, Nong · 2021 · International Journal of Computer Vision
  71. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals
    Zhang, Wei, Peng, Gaoliang, Li, Chuanhao, Chen, Yuanhang, Zhang, Zhujun · 2017 · Sensors
  72. Introduction to Radiomics
    Mayerhoefer, Marius E., Materka, Andrzej, Langs, Georg, Häggström, Ida, Szczypiński, Piotr, Gibbs, Peter, Cook, Gary · 2020 · Journal of Nuclear Medicine
  73. Remaining useful life estimation in prognostics using deep convolution neural networks
    Xiang Li, Qian Ding, Jian‐Qiao Sun · 2017 · Reliability Engineering & System Safety
  74. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
    Bera, Kaustav, Schalper, Kurt A., Rimm, David L., Velcheti, Vamsidhar, Madabhushi, Anant · 2019 · Nature Reviews Clinical Oncology
  75. Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network
    Peng, Chao, Zhang, Xiangyu, Yu, Gang, Luo, Guiming, Sun, Jian · 2017 · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  76. Image Captioning with Semantic Attention
    Quanzeng You, Hailin Jin, Zhaowen Wang, Fang Chen, Jiebo Luo · 2016 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  77. Image reconstruction by domain-transform manifold learning
    Zhu, Bo, Liu, Jeremiah Z., Cauley, Stephen F., Rosen, Bruce R., Rosen, Matthew S. · 2018 · Nature
  78. Deep Learning for Anomaly Detection: A Review
    Guansong Pang, Chunhua Shen, Longbing Cao · 2020 · Singapore Management University Institutional Knowledge (InK) (Singapore Management University)
  79. OctNet: Learning Deep 3D Representations at High Resolutions
    Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger · 2017 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  80. Towards Open Set Deep Networks
    Bendale, Abhijit, Boult, Terrance E. · 2016 · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  81. AutoML: A survey of the state-of-the-art
    Xin James He, Kaiyong Zhao, Xiaowen Chu · 2020 · Knowledge-Based Systems
  82. Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
    Katz, Guy, Barrett, Clark, Dill, David L., Julian, Kyle, Kochenderfer, Mykel J. · 2017 · Lecture Notes in Computer Science
  83. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
    Shuai Wang, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, Mengjiao Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng et al. · 2021 · European Radiology
  84. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
    Yuhong Li, Xiaofan Zhang, Deming Chen · 2018 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  85. Combining satellite imagery and machine learning to predict poverty
    Jean, Neal, Burke, Marshall, Xie, Michael, Davis, W. Matthew, Lobell, David B., Ermon, Stefano · 2016 · Science
  86. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
    Karpatne, Anuj, Atluri, Gowtham, Faghmous, James H., Steinbach, Michael, Banerjee, Arindam, Ganguly, Auroop, Shekhar, Shashi, Samatova, Nagiza, Kumar, Vipin · 2017 · IEEE Transactions on Knowledge and Data Engineering
  87. Using goal-driven deep learning models to understand sensory cortex
    Yamins, Daniel L K, DiCarlo, James J · 2016 · Nature Neuroscience
  88. Collaborative Deep Learning for Recommender Systems
    Wang, Hao, Wang, Naiyan, Yeung, Dit-Yan · 2015 · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
  89. A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning
    Junho Yim, Donggyu Joo, Ji‐Hoon Bae, Junmo Kim · 2017 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  90. Moment Matching for Multi-Source Domain Adaptation
    Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, Bo Wang · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  91. A Continual Learning Survey: Defying Forgetting in Classification Tasks
    Delange, Matthias, Aljundi, Rahaf, Masana, Marc, Parisot, Sarah, Jia, Xu, Leonardis, Ales, Slabaugh, Greg, Tuytelaars, Tinne · 2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  92. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
    U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Hojjat Adeli · 2017 · Computers in Biology and Medicine
  93. Domain Adaptive Faster R-CNN for Object Detection in the Wild
    Chen, Yuhua, Li, Wen, Sakaridis, Christos, Dai, Dengxin, Van Gool, Luc · 2018 · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  94. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices
    Abràmoff, Michael D., Lavin, Philip T., Birch, Michele, Shah, Nilay, Folk, James C. · 2018 · npj Digital Medicine
  95. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning
    Siyu Shao, Stephen McAleer, Ruqiang Yan, Pierre Baldi · 2018 · IEEE Transactions on Industrial Informatics
  96. NetVLAD: CNN architecture for weakly supervised place recognition
    Relja Arandjelović, Petr Gronát, Akihiko Torii, Tomáš Pajdla, Josef Šivic · 2015 · arXiv (Cornell University)
  97. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
    Nasr, Milad, Shokri, Reza, Houmansadr, Amir · 2019 · 2019 IEEE Symposium on Security and Privacy (SP)
  98. A review of affective computing: From unimodal analysis to multimodal fusion
    Poria, Soujanya, Cambria, Erik, Bajpai, Rajiv, Hussain, Amir · 2017 · Information Fusion
  99. End-to-end training of deep visuomotor policies
    Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel · 2016 · Journal of Machine Learning Research
  100. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
    Cai, Zhaowei, Fan, Quanfu, Feris, Rogerio S., Vasconcelos, Nuno · 2016 · Lecture Notes in Computer Science