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Poster
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
Behnam Neyshabur · Yuhuai Wu · Russ Salakhutdinov · Nati Srebro
We investigate the parameter-space geometry of recurrent neural networks (RNNs), and develop an adaptation of path-SGD optimization method, attuned to this geometry, that can learn plain RNNs with ReLU activations. On several datasets that require capturing long-term dependency structure, we show that path-SGD can significantly improve trainability of ReLU RNNs compared to RNNs trained with SGD, even with various recently suggested initialization schemes.
Author Information
Behnam Neyshabur (TTI-Chicago)
Yuhuai Wu (University of Toronto)
Russ Salakhutdinov (University of Toronto)
Nati Srebro (TTI-Chicago)
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Russ Salakhutdinov · Geoffrey E Hinton -
2009 Spotlight: Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data »
Boaz Nadler · Nati Srebro · Xueyuan Zhou -
2009 Poster: Learning in Markov Random Fields using Tempered Transitions »
Russ Salakhutdinov -
2009 Poster: Modelling Relational Data using Bayesian Clustered Tensor Factorization »
Ilya Sutskever · Russ Salakhutdinov · Josh Tenenbaum -
2008 Poster: Fast Rates for Regularized Objectives »
Karthik Sridharan · Shai Shalev-Shwartz · Nati Srebro -
2008 Poster: Evaluating probabilities under high-dimensional latent variable models »
Iain Murray · Russ Salakhutdinov -
2008 Spotlight: Evaluating probabilities under high-dimensional latent variable models »
Iain Murray · Russ Salakhutdinov -
2007 Poster: Probabilistic Matrix Factorization »
Russ Salakhutdinov · Andriy Mnih -
2007 Oral: Probabilistic Matrix Factorization »
Russ Salakhutdinov · Andriy Mnih -
2007 Poster: Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes »
Russ Salakhutdinov · Geoffrey E Hinton