dogear

enter for all results · esc to close

On the information bottleneck theory of deep learning

artemyk.github.iosite

Journal of Statistical Mechanics: Theory and Experiment, 2019. [All Versions].

from
AGI & CoCoSci
added
2026-10-10
likes
0

similar

  1. Deep Learning and the Information Bottleneck Principle robotics.caltech.edu

    IEEE Information Theory Workshop'15, 2015. [All Versions]. The first paper identifying the problem of information bottleneck in representation learning.

  2. Individual differences among deep neural network models kriegeskortelab.zuckermaninstitute.columbia.edu

    Nature Communications, 2020. [All Versions].

  3. Meta-Interpretive Learning from noisy images link.springer.com

    Machine Learning, 2018. [All Versions].

  4. George Dahl cs.toronto.edu
  5. Jiquan Mgiam cs.stanford.edu
  6. Moitra. Theoretical Foundations for Deep Learning. MIT people.csail.mit.edu

    It explores theoretical foundations for deep learning, emphasizing the following themes: (1) Approximation: What sorts of functions can be represented by deep networks, and does depth provably increase the expressive power? (2) Optimization: Essentially all optimization…

AGI & CoCoSci › Papers > Complexity & Information Theory > Dimensionality Reduction: “Journal of Statistical Mechanics: Theory and Experiment, 2019. [All Versions].”