Author

B. Kailkhura

3 papers indexed here

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#machine learning Open access Jan 2025

Active learning enables generation of molecules that advance the known Pareto front

This work creates a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data and generates molecules with properties extending beyond the training distribution and achieves a 79% improvement in out-of-distribution molecule classification accuracy.

Evan R. Antoniuk, Peggy Li, N. Keilbart et al. · npj Computational Materials · 11 citations · ⚡1

#artificial intelligence May 2025

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models

No existing model achieves strong generalization across all tasks: even the top-performing model exhibited an average OOD error 3x higher than in-distribution, so developing models with strong OOD generalization is a new frontier challenge in chemical ML.

Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun et al. · Neural Information Processin... · 14 citations · ⚡1

#artificial intelligence Oct 2023

Instance-wise Linearization of Neural Network for Model Interpretation

This work proposes an instance-wise linearization approach to reformulates the forward computation process of a neural network prediction and discusses the application of this technique in both supervise classification and unsupervised neural network learning parametric t-SNE dimension reduction.

Zhi-Min Li, Shu-Sen Liu, B. Kailkhura et al. · arXiv.org · 0 citations