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05/22/2023
On quantum backpropagation, information reuse, and cheating measurement collapse
The success of modern deep learning hinges on the ability to train neura...
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07/29/2021
Deep Networks Provably Classify Data on Curves
Data with low-dimensional nonlinear structure are ubiquitous in engineer...
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06/08/2021
Marginalizable Density Models
Probability density models based on deep networks have achieved remarkab...
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01/30/2021
Estimating the Unique Information of Continuous Variables
Partial information decompositions (PIDs) identify different modes in wh...
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08/25/2020
Deep Networks and the Multiple Manifold Problem
We study the multiple manifold problem, a binary classification task mod...
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07/02/2020
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?
Deep neural networks are typically initialized with random weights, with...
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09/25/2019
Wider Networks Learn Better Features
Transferability of learned features between tasks can massively reduce t...
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06/03/2019
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
Reducing the precision of weights and activation functions in neural net...
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01/25/2019
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs
Training recurrent neural networks (RNNs) on long sequence tasks is plag...
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09/03/2016
