activity
20172019
most citedRecombination of Artificial Neural Networks

4 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

cs.NE20194 cited

Recombination of Artificial Neural Networks

Aaron Vose, Jacob Balma, Alex Heye +5

We propose a genetic algorithm (GA) for hyperparameter optimization of artificial neural networks which includes chromosomal crossover as well as a decoupling of parameters (i.e.,…

cs.LG2018

Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction

Garrett B. Goh, Khushmeen Sakloth, Charles Siegel +2

Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications. H…

cs.AI2018

ColdRoute: Effective Routing of Cold Questions in Stack Exchange Sites

Jiankai Sun, Abhinav Vishnu, Aniket Chakrabarti +2

Routing questions in Community Question Answer services (CQAs) such as Stack Exchange sites is a well-studied problem. Yet, cold-start -- a phenomena observed when a new question i…

cs.DC2018

GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent

Jeff Daily, Abhinav Vishnu, Charles Siegel +2

In this paper, we present GossipGraD - a gossip communication protocol based Stochastic Gradient Descent (SGD) algorithm for scaling Deep Learning (DL) algorithms on large-scale sy…

cs.DC20171 cited

What does fault tolerant Deep Learning need from MPI?

Vinay Amatya, Abhinav Vishnu, Charles Siegel +1

Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed…