| Pruning convolutional neural networks for resource efficient inference P Molchanov, S Tyree, T Karras, T Aila, J Kautz arXiv preprint arXiv:1611.06440, 2016 | 2488 | 2016 |
| Compressing neural networks with the hashing trick W Chen, J Wilson, S Tyree, K Weinberger, Y Chen International conference on machine learning, 2285-2294, 2015 | 1445 | 2015 |
| Importance estimation for neural network pruning P Molchanov, A Mallya, S Tyree, I Frosio, J Kautz Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 1043 | 2019 |
| Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural network P Molchanov, X Yang, S Gupta, K Kim, S Tyree, J Kautz Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 758 | 2016 |
| Pruning convolutional neural networks for resource efficient transfer learning P Molchanov, S Tyree, T Karras, T Aila, J Kautz arXiv preprint arXiv:1611.06440 3, 2016 | 354 | 2016 |
| Reinforcement learning through asynchronous advantage actor-critic on a gpu M Babaeizadeh, I Frosio, S Tyree, J Clemons, J Kautz arXiv preprint arXiv:1611.06256, 2016 | 341 | 2016 |
| Non-linear metric learning D Kedem, S Tyree, F Sha, G Lanckriet, KQ Weinberger Advances in neural information processing systems 25, 2012 | 275 | 2012 |
| Exact Gaussian processes on a million data points K Wang, G Pleiss, J Gardner, S Tyree, KQ Weinberger, AG Wilson Advances in neural information processing systems 32, 2019 | 274 | 2019 |
| Parallel boosted regression trees for web search ranking S Tyree, KQ Weinberger, K Agrawal, J Paykin Proceedings of the 20th international conference on World wide web, 387-396, 2011 | 217 | 2011 |
| Learning with marginalized corrupted features L Maaten, M Chen, S Tyree, K Weinberger International Conference on Machine Learning, 410-418, 2013 | 205 | 2013 |
| Improving landmark localization with semi-supervised learning S Honari, P Molchanov, S Tyree, P Vincent, C Pal, J Kautz Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 203 | 2018 |
| Compressing convolutional neural networks in the frequency domain W Chen, J Wilson, S Tyree, KQ Weinberger, Y Chen Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016 | 141 | 2016 |
| Learning robotic tasks using one or more neural networks J Tremblay, S Birchfield, S Tyree, T To, J Kautz, A Molchanov US Patent 11,941,719, 2024 | 123 | 2024 |
| Megapose: 6d pose estimation of novel objects via render & compare Y Labbé, L Manuelli, A Mousavian, S Tyree, S Birchfield, J Tremblay, ... arXiv preprint arXiv:2212.06870, 2022 | 94 | 2022 |
| Online detection and classification of dynamic gestures with recurrent convolutional neural networks P Molchanov, X Yang, S De Mello, K Kim, SW Tyree, J Kautz US Patent 10,157,309, 2018 | 94 | 2018 |
| GA3C: GPU-based A3C for deep reinforcement learning M Babaeizadeh, I Frosio, S Tyree, J Clemons, J Kautz CoRR abs/1611.06256, 2016 | 93 | 2016 |
| Stochastic neighbor compression M Kusner, S Tyree, K Weinberger, K Agrawal International conference on machine learning, 622-630, 2014 | 90 | 2014 |
| Bundlesdf: Neural 6-dof tracking and 3d reconstruction of unknown objects B Wen, J Tremblay, V Blukis, S Tyree, T Müller, A Evans, D Fox, J Kautz, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 89 | 2023 |
| 6-DoF pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark S Tyree, J Tremblay, T To, J Cheng, T Mosier, J Smith, S Birchfield 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 76 | 2022 |
| Compressing convolutional neural networks W Chen, JT Wilson, S Tyree, KQ Weinberger, Y Chen arXiv preprint arXiv:1506.04449, 2015 | 74 | 2015 |
