Tim Salimans
TitleCited byYear
Improved techniques for training gans
T Salimans, I Goodfellow, W Zaremba, V Cheung, A Radford, X Chen
Advances in neural information processing systems, 2234-2242, 2016
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
T Salimans, DP Kingma
Advances in Neural Information Processing Systems, 901-909, 2016
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, 4743-4751, 2016
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
Improving language understanding by generative pre-training
A Radford, K Narasimhan, T Salimans, I Sutskever
URL https://s3-us-west-2. amazonaws. com/openai-assets/researchcovers …, 2018
Variational dropout and the local reparameterization trick
DP Kingma, T Salimans, M Welling
Advances in Neural Information Processing Systems, 2575-2583, 2015
Markov chain monte carlo and variational inference: Bridging the gap
T Salimans, D Kingma, M Welling
International Conference on Machine Learning, 1218-1226, 2015
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
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
Fixed-form variational posterior approximation through stochastic linear regression
T Salimans, DA Knowles
Bayesian Analysis 8 (4), 837-882, 2013
Improving language understanding with unsupervised learning
A Radford, K Narasimhan, T Salimans, I Sutskever
Technical report, OpenAI, 2018
Improving GANs Using Optimal Transport
T Salimans, H Zhang, A Radford, D Metaxas
International Conference on Learning Representations (ICLR), 2018
Variable selection and functional form uncertainty in cross-country growth regressions
T Salimans
Journal of Econometrics 171 (2), 267-280, 2012
Learning Montezuma’s Revenge from a single demonstration
T Salimans, R Chen
Deep RL Workshop, Neural Information Processing Systems (NeurIPS), 2018
On using control variates with stochastic approximation for variational bayes and its connection to stochastic linear regression
T Salimans, DA Knowles
arXiv preprint arXiv:1401.1022, 2014
OpenAI Post on Generative Models
A Karpathy, P Abbeel, G Brockman, P Chen, V Cheung, R Duan, ...
URL https://blog. openai. com/generative-models, 2016
The likelihood of mixed hitting times
JH Abbring, T Salimans
arXiv preprint arXiv:1905.03463, 2019
Collaborative learning of preference rankings
T Salimans, U Paquet, T Graepel
Proceedings of the sixth ACM conference on Recommender systems, 261-264, 2012
A structured variational auto-encoder for learning deep hierarchies of sparse features
T Salimans
arXiv preprint arXiv:1602.08734, 2016
Observing Dark Worlds: A crowdsourcing experiment for dark matter mapping
D Harvey, TD Kitching, J Noah-Vanhoucke, B Hamner, T Salimans, ...
Astronomy and Computing 5, 35-44, 2014
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Articles 1–20