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Michael James

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3 papers

1 author row

NeurIPS Conference 2019 Conference Paper

Online Normalization for Training Neural Networks

Vitaliy Chiley
Ilya Sharapov
Atli Kosson
Urs Koster
Ryan Reece
Sofia Samaniego de la Fuente
Vishal Subbiah
Michael James

Online Normalization is a new technique for normalizing the hidden activations of a neural network. Like Batch Normalization, it normalizes the sample dimension. While Online Normalization does not use batches, it is as accurate as Batch Normalization. We resolve a theoretical limitation of Batch Normalization by introducing an unbiased technique for computing the gradient of normalized activations. Online Normalization works with automatic differentiation by adding statistical normalization as a primitive. This technique can be used in cases not covered by some other normalizers, such as recurrent networks, fully connected networks, and networks with activation memory requirements prohibitive for batching. We show its applications to image classification, image segmentation, and language modeling. We present formal proofs and experimental results on ImageNet, CIFAR, and PTB datasets.

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NeurIPS Conference 2011 Conference Paper

A rational model of causal inference with continuous causes

Thomas Griffiths
Michael James

Rational models of causal induction have been successful in accounting for people's judgments about the existence of causal relationships. However, these models have focused on explaining inferences from discrete data of the kind that can be summarized in a 2 ✕ 2 contingency table. This severely limits the scope of these models, since the world often provides non-binary data. We develop a new rational model of causal induction using continuous dimensions, which aims to diminish the gap between empirical and theoretical approaches and real-world causal induction. This model successfully predicts human judgments from previous studies better than models of discrete causal inference, and outperforms several other plausible models of causal induction with continuous causes in accounting for people's inferences in a new experiment.

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AAMAS Conference 2008 Conference Paper

Approximate Predictive State Representations

Britton Wolfe
Michael James
Satinder Singh

Predictive state representations (PSRs) are models that represent the state of a dynamical system as a set of predictions about future events. The existing work with PSRs focuses on trying to learn exact models, an approach that cannot scale to complex dynamical systems. In contrast, our work takes the first steps in developing a theory of approximate PSRs. We examine the consequences of using an approximate predictive state representation, bounding the error of the approximate state under certain conditions. We also introduce factored PSRs, a class of PSRs with a particular approximate state representation. We show that the class of factored PSRs allow one to tune the degree of approximation by trading off accuracy for compactness. We demonstrate this trade-off empirically on some example systems, using factored PSRs that were learned from data.

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