189 citations · 391 across the 11 of their papers we have counts for
12 papers
Structural phase transitions between layered Indium Selenide for inte-grated photonic memory
Tiantian Li, Yong Wang, Wei Li +10
The primary mechanism of optical memristive devices relies on the phase transitions between amorphous-crystalline states. The slow or energy hungry amorphous-crystalline transition…
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
Logan Ward, J. Gregory Pauloski, Valerie Hayot-Sasson +7
Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supe…
Acoustic-to-articulatory Speech Inversion with Multi-task Learning
Yashish M. Siriwardena, Ganesh Sivaraman, Carol Espy-Wilson
Multi-task learning (MTL) frameworks have proven to be effective in diverse speech related tasks like automatic speech recognition (ASR) and speech emotion recognition. This paper…
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
Yashish M. Siriwardena, Ahmed Adel Attia, Ganesh Sivaraman +1
Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developin…
Acoustic To Articulatory Speech Inversion Using Multi-Resolution Spectro-Temporal Representations Of Speech Signals
Rahil Parikh, Nadee Seneviratne, Ganesh Sivaraman +2
Multi-resolution spectro-temporal features of a speech signal represent how the brain perceives sounds by tuning cortical cells to different spectral and temporal modulations. Thes…
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing
Logan Ward, Ganesh Sivaraman, J. Gregory Pauloski +9
Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform. Methods that use…
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
Jenna Bilbrey, Logan Ward, Sutanay Choudhury +2
We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of urgency, we chose well-valid…
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Logan Ward, Jenna A. Bilbrey, Sutanay Choudhury +2
Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative mod…
An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide
Ganesh Sivaraman, Leighanne Gallington, Anand Narayanan Krishnamoorthy +4
Understanding the structure and properties of refractory oxides are critical for high temperature applications. In this work, a combined experimental and simulation approach uses a…
Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide
Ganesh Sivaraman, Anand Narayanan Krishnamoorthy, Matthias Baur +5
We propose a novel active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our act…
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
Emre Yılmaz, Vikramjit Mitra, Ganesh Sivaraman +1
The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies. One promi…
Adversarial Auto-encoders for Speech Based Emotion Recognition
Saurabh Sahu, Rahul Gupta, Ganesh Sivaraman +2
Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks su…