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03/31/2023
Generalized Information Bottleneck for Gaussian Variables
The information bottleneck (IB) method offers an attractive framework fo...
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08/08/2022
Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality
Avoiding overfitting is a central challenge in machine learning, yet man...
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05/28/2021
Perturbation Theory for the Information Bottleneck
Extracting relevant information from data is crucial for all forms of le...
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03/23/2021
Leveraging background augmentations to encourage semantic focus in self-supervised contrastive learning
Unsupervised representation learning is an important challenge in comput...
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10/13/2020
Are all negatives created equal in contrastive instance discrimination?
Self-supervised learning has recently begun to rival supervised learning...
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09/27/2020
Learning Optimal Representations with the Decodable Information Bottleneck
We address the question of characterizing and finding optimal representa...
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07/29/2020
Theory of gating in recurrent neural networks
RNNs are popular dynamical models, used for processing sequential data. ...
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02/29/2020
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
Batch normalization (BatchNorm) has become an indispensable tool for tra...
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02/24/2020
The Early Phase of Neural Network Training
Recent studies have shown that many important aspects of neural network ...
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01/31/2020
Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs
Recurrent neural networks (RNNs) are powerful dynamical models for data ...
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10/01/2019
How noise affects the Hessian spectrum in overparameterized neural networks
Stochastic gradient descent (SGD) forms the core optimization method for...
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03/06/2019
Mean-field Analysis of Batch Normalization
Batch Normalization (BatchNorm) is an extremely useful component of mode...
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03/23/2018
A high-bias, low-variance introduction to Machine Learning for physicists
Machine Learning (ML) is one of the most exciting and dynamic areas of m...
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12/27/2017
The information bottleneck and geometric clustering
The information bottleneck (IB) approach to clustering takes a joint dis...
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09/12/2016
Comment on "Why does deep and cheap learning work so well?" [arXiv:1608.08225]
In a recent paper, "Why does deep and cheap learning work so well?", Lin...
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05/18/2016
Supervised Learning with Quantum-Inspired Tensor Networks
Tensor networks are efficient representations of high-dimensional tensor...
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04/01/2016
The deterministic information bottleneck
Lossy compression and clustering fundamentally involve a decision about ...
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10/14/2014