83.4k citations · 96.3k across the 51 of their papers we have counts for
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Reversible Recurrent Neural Networks
Matthew MacKay, Paul Vicol, Jimmy Ba +1
Recurrent neural networks (RNNs) provide state-of-the-art performance in processing sequential data but are memory intensive to train, limiting the flexibility of RNN models which…
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Yeming Wen, Paul Vicol, Jimmy Ba +2
Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Un…
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn +1
Generative Adversarial Networks (GANs) are one of the most practical methods for learning data distributions. A popular GAN formulation is based on the use of Wasserstein distance…