49 citations · 64 across the 7 of their papers we have counts for
7 papers
Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows
Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han
In this paper, we leverage predictive uncertainty of deep neural networks to answer challenging questions material scientists usually encounter in machine learning based materials…
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura +2
While Deep Neural Networks (DNNs) achieve state-of-the-art accuracy in various applications, they often fall short in accurately estimating their predictive uncertainty and, in tur…
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design
Shusen Liu, Bhavya Kailkhura, Jize Zhang +4
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in buildin…
Actionable Attribution Maps for Scientific Machine Learning
Shusen Liu, Bhavya Kailkhura, Jize Zhang +4
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in buildin…
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han
This paper studies the problem of post-hoc calibration of machine learning classifiers. We introduce the following desiderata for uncertainty calibration: (a) accuracy-preserving,…
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…
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…