| 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 | 49253 | 2019 |
| Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks A Radford, L Metz, S Chintala International Conference on Learning Representations, 2015 | 18723 | 2015 |
| Wasserstein generative adversarial networks M Arjovsky, S Chintala, L Bottou International conference on machine learning, 214-223, 2017 | 17347 | 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 | 14239 | 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 | 3036 | 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 | 1112 | 2013 |
| Semantic segmentation using adversarial networks P Luc, C Couprie, S Chintala, J Verbeek Neural Information Processing Systems Workshop on Adversarial Training, 2016 | 1009 | 2016 |
| Convolutional neural networks applied to house numbers digit classification P Sermanet, S Chintala, Y LeCun International Conference on Pattern Recognition, 3288-3291, 2012 | 775 | 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 | 752 | 2018 |
| Automatic differentiation in pytorch.(2017) A Paszke, S Gross, S Chintala, G Chanan, E Yang, Z DeVito, Z Lin, ... | 746 | 2017 |
| 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 | 652 | 2024 |
| Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv 2015 A Radford, L Metz, S Chintala arXiv preprint arXiv:1511.06434, 2015 | 621 | 2015 |
| 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 | 600 | 2020 |
| Wasserstein gan. arXiv 2017 M Arjovsky, S Chintala, L Bottou arXiv preprint arXiv:1701.07875 30, 2017 | 528 | 2017 |
| 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 | 415 | 2015 |
| 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 | 364 | 2017 |
| 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 | 290 | 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 | 272 | 2017 |
| Pytorch: An imperative style, high-performance deep learning library, 2019 A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan, T Killeen, ... arXiv preprint arXiv:1912.01703 10, 1912 | 235 | 1912 |
| 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 | 198 | 2024 |
Adam PaszkeGoogleBestätigte E-Mail-Adresse bei students.mimuw.edu.pl
