| Pytorch: An imperative style, high-performance deep learning library A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... Advances in Neural Information Processing Systems, 8024--8035, 2019 | 24232 | 2019 |
| Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks A Radford, L Metz, S Chintala International Conference on Learning Representations, 2015 | 14043 | 2015 |
| Wasserstein generative adversarial networks M Arjovsky, S Chintala, L Bottou International conference on machine learning, 214-223, 2017 | 11959 | 2017 |
| Automatic differentiation in pytorch A Paszke, S Gross, S Chintala, G Chanan, E Yang, Z DeVito, Z Lin, ... Neural Information Processing Systems Autodiff Workshop, 2017 | 11175 | 2017 |
| Deep generative image models using a laplacian pyramid of adversarial networks E Denton, S Chintala, A Szlam, R Fergus Advances in Neural Information Processing Systems, 2015 | 2510 | 2015 |
| Pedestrian detection with unsupervised multi-stage feature learning P Sermanet, K Kavukcuoglu, S Chintala, Y LeCun Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 1044 | 2013 |
| Semantic segmentation using adversarial networks P Luc, C Couprie, S Chintala, J Verbeek Neural Information Processing Systems Workshop on Adversarial Training, 2016 | 791 | 2016 |
| Convolutional neural networks applied to house numbers digit classification P Sermanet, S Chintala, Y LeCun International Conference on Pattern Recognition, 3288-3291, 2012 | 656 | 2012 |
| Applied machine learning at facebook: A datacenter infrastructure perspective K Hazelwood, S Bird, D Brooks, S Chintala, U Diril, D Dzhulgakov, ... 2018 IEEE International Symposium on High Performance Computer Architecture …, 2018 | 532 | 2018 |
| Fast convolutional nets with fbfft: A GPU performance evaluation N Vasilache, J Johnson, M Mathieu, S Chintala, S Piantino, Y LeCun International Conference on Learning Representations, 2015 | 361 | 2015 |
| Wasserstein gan. arXiv 2017 M Arjovsky, S Chintala, L Bottou arXiv preprint arXiv:1701.07875 30, 2017 | 341 | 2017 |
| Pytorch: Tensors and dynamic neural networks in python with strong gpu acceleration A Paszke, S Gross, S Chintala, G Chanan PyTorch: Tensors and dynamic neural networks in Python with strong GPU …, 2017 | 302 | 2017 |
| A multipath network for object detection S Zagoruyko, A Lerer, TY Lin, PO Pinheiro, S Gross, S Chintala, P Dollįr British Machine Vision Conference 2016, 2016 | 259 | 2016 |
| Pytorch distributed: Experiences on accelerating data parallel training S Li, Y Zhao, R Varma, O Salpekar, P Noordhuis, T Li, A Paszke, J Smith, ... Proceedings of the VLDB Endowment, 2020 | 209 | 2020 |
| Automatic differentiation in pytorch P Adam, G Sam, C Soumith, C Gregory, Y Edward, DV Zachary, L Zeming, ... Proceedings of neural information processing systems 5, 2017 | 172 | 2017 |
| Discovering causal signals in images D Lopez-Paz, R Nishihara, S Chintala, B Scholkopf, L Bottou Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 168 | 2017 |
| Episodic exploration for deep deterministic policies: An application to starcraft micromanagement tasks N Usunier, G Synnaeve, Z Lin, S Chintala arXiv preprint arXiv:1609.02993, 2016 | 167 | 2016 |
| Torchcraft: a library for machine learning research on real-time strategy games G Synnaeve, N Nardelli, A Auvolat, S Chintala, T Lacroix, Z Lin, F Richoux, ... arXiv preprint arXiv:1611.00625, 2016 | 124 | 2016 |
| A mathematical motivation for complex-valued convolutional networks M Tygert, J Bruna, S Chintala, Y LeCun, S Piantino, A Szlam Neural computation 28 (5), 815-825, 2016 | 112* | 2016 |
| How to train a GAN? Tips and tricks to make GANs work S Chintala, E Denton, M Arjovsky, M Mathieu Neural Information Processing Systems Tutorial on Generative Adversarial …, 2016 | 112 | 2016 |
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