Search Results for author:
Found 7 papers, 3 papers with code
Information Plane Analysis Visualization in Deep Learning via Transfer Entropy
Information Plane analysis is a visualization technique used to understand the trade-off between compression and information preservation in the context of the Information Bottleneck method by plotting the amount of information in the input data against the compressed representation.
Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy
We propose a novel method to improve MI computation for CNN pruning, using the spatial aura entropy.
Pruning Convolutional Filters via Reinforcement Learning with Entropy Minimization
Structural pruning has become an integral part of neural network optimization, used to achieve architectural configurations which can be deployed and run more efficiently on embedded devices.
Semiotic Aggregation in Deep Learning
To the extent of our knowledge, this is the first application of computational semiotics in the analysis and interpretation of deep neural networks.
Weighted Random Search for Hyperparameter Optimization
We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms.
Weighted Random Search for CNN Hyperparameter Optimization
Nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters).
Big Holes in Big Data: A Monte Carlo Algorithm for Detecting Large Hyper-rectangles in High Dimensional Data
We present the first algorithm for finding holes in high dimensional data that runs in polynomial time with respect to the number of dimensions.
Computational Geometry Data Analysis, Statistics and Probability
