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Information Plane

To obtain the Information Plane (IP) of deep neural networks, which shows the trajectories of the hidden layers during training in a 2D plane using as coordinate axes the mutual information between the input and the hidden layer, and the mutual information between the output and the hidden layer.

Papers

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Showing 1–25 of 30 papers

TitleStatusHype
Cauchy-Schwarz Divergence Information Bottleneck for RegressionCode1
Understanding Autoencoders with Information Theoretic Concepts—0
Information plane and compression-gnostic feedback in quantum machine learning—0
Lightweight Conceptual Dictionary Learning for Text Classification Using Information Compression—0
Mutual information estimation for graph convolutional neural networks—0
On Information Plane Analyses of Neural Network Classifiers -- A Review—0
On Predictive Information Sub-optimality of RNNs—0
On Predictive Information in RNNs—0
On the Trajectory of Stochastic Gradient Descent in the Information Plane—0
REPRESENTATION COMPRESSION AND GENERALIZATION IN DEEP NEURAL NETWORKS—0
SoFaiR: Single Shot Fair Representation Learning—0
A Comparative Genomic Analysis of Coronavirus Families Using Chaos Game Representation and Fisher-Shannon Complexity—0
Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph—0
Enhancing Neural Network Interpretability Through Conductance-Based Information Plane Analysis—0
Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane—0
Fundamental Limits and Tradeoffs in Invariant Representation Learning—0
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training—0
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels—0
Information Plane Analysis of Deep Neural Networks via Matrix--Based Renyi's Entropy and Tensor Kernels—0
Information Plane Analysis Visualization in Deep Learning via Transfer Entropy—0
A Provably Convergent Information Bottleneck Solution via ADMMCode0
Information flows of diverse autoencodersCode0
Scalable Mutual Information Estimation using Dependence GraphsCode0
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise TrainingCode0
Opening the Black Box of Deep Neural Networks via InformationCode0
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