| 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 | 69013* | 2019 |
| Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks A Radford, L Metz, S Chintala International Conference on Learning Representations, 2015 | 19578 | 2015 |
| Wasserstein generative adversarial networks M Arjovsky, S Chintala, L Bottou International conference on machine learning, 214-223, 2017 | 18582 | 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 | 3085 | 2015 |
| The llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 2265 | 2024 |
| 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 | 1125 | 2013 |
| Semantic segmentation using adversarial networks P Luc, C Couprie, S Chintala, J Verbeek Neural Information Processing Systems Workshop on Adversarial Training, 2016 | 1030 | 2016 |
| Convolutional neural networks applied to house numbers digit classification P Sermanet, S Chintala, Y LeCun International Conference on Pattern Recognition, 3288-3291, 2012 | 796 | 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 | 781 | 2018 |
| 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 | 700 | 2020 |
| 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 | 421 | 2015 |
| Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation J Ansel, E Yang, H He, N Gimelshein, A Jain, M Voznesensky, B Bao, ... Proceedings of the 29th ACM International Conference on Architectural …, 2024 | 393 | 2024 |
| A multipath network for object detection S Zagoruyko, A Lerer, TY Lin, PO Pinheiro, S Gross, S Chintala, P Dollár British Machine Vision Conference 2016, 2016 | 294 | 2016 |
| 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 | 290 | 2017 |
| Torchaudio: Building blocks for audio and speech processing YY Yang, M Hira, Z Ni, A Astafurov, C Chen, C Puhrsch, D Pollack, ... ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 214 | 2022 |
| Episodic exploration for deep deterministic policies for starcraft micromanagement N Usunier, G Synnaeve, Z Lin, S Chintala International Conference on Learning Representations, 2017 | 188* | 2017 |
| Generalized inner loop meta-learning E Grefenstette, B Amos, D Yarats, PM Htut, A Molchanov, F Meier, D Kiela, ... arXiv preprint arXiv:1910.01727, 2019 | 180 | 2019 |
| 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 | 145* | 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 | 142 | 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 | 135 | 2016 |
Adam PaszkeGoogle確認したメール アドレス: students.mimuw.edu.pl