111 citations · 1k across the 103 of their papers we have counts for
8 papers · 1 filter
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking
Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura +1
In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. As…
Enabling Machine Learning-Ready HPC Ensembles with Merlin
J. Luc Peterson, Ben Bay, Joe Koning +17
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensembl…
Deep Kernels with Probabilistic Embeddings for Small-Data Learning
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura +2
Gaussian Processes (GPs) are known to provide accurate predictions and uncertainty estimates even with small amounts of labeled data by capturing similarity between data points thr…
Generative Counterfactual Introspection for Explainable Deep Learning
Shusen Liu, Bhavya Kailkhura, Donald Loveland +1
In this work, we propose an introspection technique for deep neural networks that relies on a generative model to instigate salient editing of the input image for model interpretat…
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
Pu Zhao, Sijia Liu, Pin-Yu Chen +4
Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average c…
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li +1
This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifi…
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
Yunhui Long, Boxin Wang, Zhuolin Yang +4
Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this w…
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery
Bhavya Kailkhura, Brian Gallagher, Sookyung Kim +2
Material scientists are increasingly adopting the use of machine learning (ML) for making potentially important decisions, such as, discovery, development, optimization, synthesis…