Deep learning and the information bottleneck principle
Naftali Tishby,Noga Zaslavsky +1 more
- pp 1-5
TLDR
It is argued that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer.Abstract:
Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain finite sample generalization bounds. The advantage of getting closer to the theoretical limit is quantifiable both by the generalization bound and by the network's simplicity. We argue that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer. The hierarchical representations at the layered network naturally correspond to the structural phase transitions along the information curve. We believe that this new insight can lead to new optimality bounds and deep learning algorithms.read more
Citations
More filters
Journal ArticleDOI
BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image
TL;DR: This article proposes a unified BS framework, BS Network (BS-Net), which consists of a band attention module (BAM), which aims to explicitly model the nonlinear interdependences between spectral bands, and a reconstruction network (RecNet) which is used to restore the original HSI from the learned informative bands, resulting in a flexible architecture.
Proceedings Article
Excessive Invariance Causes Adversarial Vulnerability
TL;DR: This work identifies an insufficiency of the standard cross-entropy loss as a reason for deep networks' striking failures on out-of-distribution inputs and provides the first approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities.
Journal ArticleDOI
The Information Bottleneck Problem and its Applications in Machine Learning
Ziv Goldfeld,Yury Polyanskiy +1 more
TL;DR: The information bottleneck (IB) theory recently emerged as a bold information-theoretic paradigm for analyzing DL systems, and its recent impact on DL is surveyed.
Journal ArticleDOI
Nonlinear Information Bottleneck
TL;DR: In this paper, a non-parametric upper bound for mutual information is proposed to find the optimal bottleneck variable for arbitrary-distributed discrete and/or continuous random variables X and Y with a Gaussian joint distribution.
Journal ArticleDOI
Coarse-graining as a downward causation mechanism.
TL;DR: It is suggested that in many adaptive systems components collectively compute their macroscopic worlds through coarse-graining and move from simple feedback to downward causation when components tune behaviour in response to estimates of collectively computed Macroscopic properties.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI
Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.
Book
Learning Deep Architectures for AI
TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.