Seuraa
Tim Salimans
Tim Salimans
Google Brain Amsterdam
Vahvistettu sähköpostiosoite verkkotunnuksessa google.com
Nimike
Viittaukset
Viittaukset
Vuosi
Improved techniques for training gans
T Salimans, I Goodfellow, W Zaremba, V Cheung, A Radford, X Chen
Advances in neural information processing systems 29, 2016
72222016
Improving language understanding by generative pre-training
A Radford, K Narasimhan, T Salimans, I Sutskever
4622*2018
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T Salimans, DP Kingma
Advances in neural information processing systems 29, 2016
15882016
Improved variational inference with inverse autoregressive flow
DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling
Advances in neural information processing systems 29, 2016
15022016
Evolution strategies as a scalable alternative to reinforcement learning
T Salimans, J Ho, X Chen, S Sidor, I Sutskever
arXiv preprint arXiv:1703.03864, 2017
12522017
Variational dropout and the local reparameterization trick
DP Kingma, T Salimans, M Welling
Advances in neural information processing systems 28, 2015
11872015
Dota 2 with large scale deep reinforcement learning
C Berner, G Brockman, B Chan, V Cheung, P Dębiak, C Dennison, ...
arXiv preprint arXiv:1912.06680, 2019
8932019
Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications
T Salimans, A Karpathy, X Chen, DP Kingma
arXiv preprint arXiv:1701.05517, 2017
7732017
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
6212016
Markov chain monte carlo and variational inference: Bridging the gap
T Salimans, D Kingma, M Welling
International conference on machine learning, 1218-1226, 2015
5482015
Improving GANs Using Optimal Transport
T Salimans, H Zhang, A Radford, D Metaxas
International Conference on Learning Representations (ICLR), 2018
2302018
Fixed-form variational posterior approximation through stochastic linear regression
T Salimans, DA Knowles
Bayesian Analysis 8 (4), 837-882, 2013
2222013
Axial attention in multidimensional transformers
J Ho, N Kalchbrenner, D Weissenborn, T Salimans
arXiv preprint arXiv:1912.12180, 2019
2032019
How good is the bayes posterior in deep neural networks really?
F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ...
arXiv preprint arXiv:2002.02405, 2020
1812020
Metnet: A neural weather model for precipitation forecasting
CK Sønderby, L Espeholt, J Heek, M Dehghani, A Oliver, T Salimans, ...
arXiv preprint arXiv:2003.12140, 2020
1152020
Learning Montezuma’s Revenge from a single demonstration
T Salimans, R Chen
Deep RL Workshop, Neural Information Processing Systems (NeurIPS), 2018
952018
Image super-resolution via iterative refinement
C Saharia, J Ho, W Chan, T Salimans, DJ Fleet, M Norouzi
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
862022
Variational diffusion models
D Kingma, T Salimans, B Poole, J Ho
Advances in neural information processing systems 34, 21696-21707, 2021
862021
Advances in neural information processing systems
T Salimans, I Goodfellow, W Zaremba, V Cheung, A Radford, X Chen
Improved techniques for training gans, 2234-2242, 2016
662016
Cascaded Diffusion Models for High Fidelity Image Generation.
J Ho, C Saharia, W Chan, DJ Fleet, M Norouzi, T Salimans
J. Mach. Learn. Res. 23, 47:1-47:33, 2022
642022
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