| Feature Extraction and Learning for Visual Data PS Chandakkar, R Venkatesan, B Li, G Dong, Liu, Huang Feature Engineering for Machine Learning and Data Analytics, 55-85, 2018 | 406* | 2018 |
| Convolutional Neural Networks in Visual Computing: A Concise Guide R Venkatesan, B Li CRC Press, Taylor and Francis Group 1, 226, 2017 | 253 | 2017 |
| dsne: Domain adaptation using stochastic neighborhood embedding. X Xu, X Zhou, R Venkatesan, G Swaminathan, O Majumder IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2492-2501, 2019 | 170 | 2019 |
| A strategy for an uncompromising incremental learner R Venkatesan, H Venkateswara, B Li, S Panchanathan arXiv preprint arXiv:1705.00744 3 (6), 2017 | 65 | 2017 |
| Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features R Venkatesan, P Chandakkar, B Li, HK Li IEEE Annual Conference on Engineering in Medicine and Biology, 1462-1465, 2012 | 64 | 2012 |
| Trending chic: Analyzing the influence of social media on fashion brands L Manikonda, R Venkatesan, S Kambhampati, B Li arXiv preprint arXiv:1512.01174, 2015 | 45* | 2015 |
| Simpler non-parametric methods provide as good or better results to multiple-instance learning. R Venkatesan, P Chandakkar, B Li IEEE International Conference on Computer Vision, 2015 | 26 | 2015 |
| Diving deeper into mentee networks R Venkatesan, B Li arXiv preprint arXiv:1604.08220, 2016 | 21 | 2016 |
| Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework PS Chandakkar, R Venkatesan, B Li SPIE Conference on Medical Imaging, 86700Q--86700Q, 2013 | 16 | 2013 |
| On the generality of neural image features R Venkatesan, V Gatupalli, B Li IEEE International Conference on Image Processing, 2016 | 8* | 2016 |
| MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images PS Chandakkar, R Venkatesan, B Li Journal of Medical Imaging 4 (3), 034003-034003, 2017 | 6 | 2017 |
| Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks A MacLaughlin, J Dhamala, A Kumar, S Venkatapathy, R Venkatesan, ... arXiv preprint arXiv:2010.04249, 2020 | 5 | 2020 |
| Searching compression profiles for trained neural networks R Venkatesan, G Swaminathan, Z Xiong, R Luo, V Khare US Patent 11,755,603, 2023 | 4 | 2023 |
| Video Deinterlacing with Control Grid Interpolation Frameworks R Venkatesan, C Zwart, D Frakes IEEE International Conference on Image Processing, 861-864, 2012 | 4 | 2012 |
| A machine-learning approach to retrieving diabetic retinopathy images PS Chandakkar, R Venkatesan, B Li, HK Li Proceedings of the ACM Conference on Bioinformatics, Computational Biology …, 2012 | 3 | 2012 |
| Out-of-the-box channel pruned networks R Venkatesan, G Swaminathan, X Zhou, A Luo arXiv preprint arXiv:2004.14584, 2020 | 2 | 2020 |
| Decomposed multidimensional control grid interpolation for common consumer electronic image processing applications CM Zwart, R Venkatesan, DH Frakes Journal of Electronic Imaging 21 (4), 043012-043012, 2012 | 2 | 2012 |
| Decoupled machine learning training S Gupta, B Balaji, LP Dirac, S Genc, V Khare, R Venkatesan, ... US Patent 11,861,490, 2024 | 1 | 2024 |
| Applying compression profiles across similar neural network architectures G Swaminathan, R Venkatesan, Z Xiong, R Luo, V Khare US Patent 11,809,992, 2023 | 1 | 2023 |
| Reinforcement learning for training compression policies for machine learning models G Swaminathan, R Venkatesan, Z Xiong, R Luo, V Khare US Patent 11,501,173, 2022 | 1 | 2022 |
Baoxin LiArizona State University在 asu.edu 的电子邮件经过验证