| Noisy activation functions C Gulcehre, M Moczulski, M Denil, Y Bengio Published at ICML 2016 (International conference on machine learning), 3059-3068, 2016 | 360 | 2016 |
| Deep fried convnets Z Yang, M Moczulski, M Denil, N de Freitas, A Smola, L Song, Z Wang Oral presentation at ICCV 2015 (Proceedings of the IEEE International …, 2015 | 357 | 2015 |
| Acdc: A structured efficient linear layer M Moczulski, M Denil, J Appleyard, N de Freitas Published at ICLR 2016 (International Conference on Learning Representations), 2015 | 127 | 2015 |
| Contingency-Aware Exploration in Reinforcement Learning J Choi, Y Guo, ML Moczulski, J Oh, N Wu, M Norouzi, H Lee Published at ICLR 2019, 2019 | 94 | 2019 |
| Mollifying networks C Gulcehre, M Moczulski, F Visin, Y Bengio Published at ICLR 2017, 2016 | 63 | 2016 |
| Memory based trajectory-conditioned policies for learning from sparse rewards Y Guo, J Choi, M Moczulski, S Feng, S Bengio, M Norouzi, H Lee Advances in Neural Information Processing Systems 33, 4333-4345, 2020 | 44* | 2020 |
| A robust adaptive stochastic gradient method for deep learning C Gulcehre, J Sotelo, M Moczulski, Y Bengio 2017 International Joint Conference on Neural Networks (IJCNN), 125-132, 2017 | 27 | 2017 |
| Adasecant: Robust adaptive secant method for stochastic gradient C Gulcehre, M Moczulski, Y Bengio Published at IJCNN 2017, 2014 | 25 | 2014 |
| Efficient exploration with self-imitation learning via trajectory-conditioned policy Y Guo, J Choi, M Moczulski, S Bengio, M Norouzi, H Lee arXiv preprint arXiv:1907.10247, 2019 | 11 | 2019 |
| Sparsedice: Imitation learning for temporally sparse data via regularization A Camacho, I Gur, ML Moczulski, O Nachum, A Faust ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021 | 5 | 2021 |
| A Controller-Recognizer Framework: How necessary is recognition for control? M Moczulski, K Xu, A Courville, K Cho Published at ICLR 2016 workshop, 2015 | 2 | 2015 |
Misha DenilDeepMindVerified email at google.com
