39 citations · 192 across the 43 of their papers we have counts for
6 papers · 2 filters
Neuron-based Pruning of Deep Neural Networks with Better Generalization using Kronecker Factored Curvature Approximation
Abdolghani Ebrahimi, Diego Klabjan
Existing methods of pruning deep neural networks focus on removing unnecessary parameters of the trained network and fine tuning the model afterwards to find a good solution that r…
Aggregation Delayed Federated Learning
Ye Xue, Diego Klabjan, Yuan Luo
Federated learning is a distributed machine learning paradigm where multiple data owners (clients) collaboratively train one machine learning model while keeping data on their own…
Logit-based Uncertainty Measure in Classification
Huiyu Wu, Diego Klabjan
We introduce a new, reliable, and agnostic uncertainty measure for classification tasks called logit uncertainty. It is based on logit outputs of neural networks. We in particular…
A Probabilistic Approach to Neural Network Pruning
Xin Qian, Diego Klabjan
Neural network pruning techniques reduce the number of parameters without compromising predicting ability of a network. Many algorithms have been developed for pruning both over-pa…
Non-Convex Optimization with Spectral Radius Regularization
Adam Sandler, Diego Klabjan, Yuan Luo
We develop regularization methods to find flat minima while training deep neural networks. These minima generalize better than sharp minima, yielding models outperforming baselines…
Classification Models for Partially Ordered Sequences
Stephanie Ger, Diego Klabjan, Jean Utke
Many models such as Long Short Term Memory (LSTMs), Gated Recurrent Units (GRUs) and transformers have been developed to classify time series data with the assumption that events i…