23 citations · 47 across the 14 of their papers we have counts for
16 papers · 1 filter
Use of Deterministic Transforms to Design Weight Matrices of a Neural Network
Pol Grau Jurado, Xinyue Liang, Alireza M. Javid +1
Self size-estimating feedforward network (SSFN) is a feedforward multilayer network. For the existing SSFN, a part of each weight matrix is trained using a layer-wise convex optimi…
Normalizing Flow based Hidden Markov Models for Classification of Speech Phones with Explainability
Anubhab Ghosh, Antoine Honoré, Dong Liu +2
In pursuit of explainability, we develop generative models for sequential data. The proposed models provide state-of-the-art classification results and robust performance for speec…
Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows
Anubhab Ghosh, Antoine Honoré, Dong Liu +2
We test the robustness of a maximum-likelihood (ML) based classifier where sequential data as observation is corrupted by noise. The hypothesis is that a generative model, that com…
Statistical model-based evaluation of neural networks
Sandipan Das, Prakash B. Gohain, Alireza M. Javid +2
Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs). The setup helps to benchmark a set of NNs vis-a-vis m…
A ReLU Dense Layer to Improve the Performance of Neural Networks
Alireza M. Javid, Sandipan Das, Mikael Skoglund +1
We propose ReDense as a simple and low complexity way to improve the performance of trained neural networks. We use a combination of random weights and rectified linear unit (ReLU)…
A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning
Xinyue Liang, Alireza M. Javid, Mikael Skoglund +1
We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers). We assume the communication…