23 citations · 56 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
Alireza M. Javid, Xinyue Liang, Arun Venkitaraman +1
We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University. Our main obj…
Asynchronous Decentralized Learning of a Neural Network
Xinyue Liang, Alireza M. Javid, Mikael Skoglund +1
In this work, we exploit an asynchronous computing framework namely ARock to learn a deep neural network called self-size estimating feedforward neural network (SSFN) in a decentra…
High-dimensional Neural Feature Design for Layer-wise Reduction of Training Cost
Alireza M. Javid, Arun Venkitaraman, Mikael Skoglund +1
We design a ReLU-based multilayer neural network by mapping the feature vectors to a higher dimensional space in every layer. We design the weight matrices in every layer to ensure…