5 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.LG2021
Self-learn to Explain Siamese Networks Robustly
Chao Chen, Yifan Shen, Guixiang Ma +4
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and i…
physics.data-an2019★ 2 cited
Designing compact training sets for data-driven molecular property prediction
Bowen Li, Srinivas Rangarajan
In this paper, we consider the problem of designing a training set using the most informative molecules from a specified library to build data-driven molecular property models. Spe…
physics.data-an2019★ 5 cited
On Deriving Probabilistic Models for Adsorption Energy on Transition Metals using Multi-level Ab initio and Experimental Data
Huijie Tian, Srinivas Rangarajan
In this paper, we apply multi-task Gaussian Process (MT-GP) to show that the adsorption energy of small adsorbates on transition metal surfaces can be modeled to a high level of fi…