19 citations · 60 across the 15 of their papers we have counts for
8 papers · 1 filter
Compressed Active Subspaces for Scalable Bayesian Inference
Thomas Flynn, Sanket Jantre, Byung-Jun Yoon +1
Active subspace methods provide a framework for quantifying predictive uncertainty in high-dimensional models by identifying and performing inference along parameter directions tha…
Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
Sanket Jantre, Deepak Akhare, Zhiyuan Wang +2
Partial differential equations (PDEs) underpin the modeling of many natural and engineered systems. It can be convenient to express such models as neural PDEs rather than using tra…
C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
Amir Hossein Rahmati, Sanket Jantre, Weifeng Zhang +4
Low-Rank Adaptation (LoRA) offers a cost-effective solution for fine-tuning large language models (LLMs), but it often produces overconfident predictions in data-scarce few-shot se…
Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Sanket Jantre, Tianle Wang, Gilchan Park +5
Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative d…
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban +1
In recent years, deep generative models have been successfully adopted for various molecular design tasks, particularly in the life and material sciences. A critical challenge for…
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design
A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban +1
Deep generative models have been accelerating the inverse design process in material and drug design. Unlike their counterpart property predictors in typical molecular design frame…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness
Sumegha Premchandar, Sandeep Madireddy, Sanket Jantre +1
Robust machine learning models with accurately calibrated uncertainties are crucial for safety-critical applications. Probabilistic machine learning and especially the Bayesian for…