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Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Scott Reed, Yutian Chen, Thomas Paine +5
Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require m…
Qualitatively characterizing neural network optimization problems
Ian J. Goodfellow, Oriol Vinyals, Andrew M. Saxe
Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and ot…
Recurrent Neural Network Regularization
Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing…