7 papers
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…
Cost-effective Reduced-Order Modeling via Bayesian Active Learning
Amir Hossein Rahmati, Nathan M. Urban, Byung-Jun Yoon +1
Machine Learning surrogates have been developed to accelerate solving systems dynamics of complex processes in different science and engineering applications. To faithfully capture…
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…
Hyperparameter Tuning Through Pessimistic Bilevel Optimization
Meltem Apaydin Ustun, Liang Xu, Bo Zeng +1
Automated hyperparameter search in machine learning, especially for deep learning models, is typically formulated as a bilevel optimization problem, with hyperparameter values dete…
Path-Guided Particle-based Sampling
Mingzhou Fan, Ruida Zhou, Chao Tian +1
Particle-based Bayesian inference methods by sampling from a partition-free target (posterior) distribution, e.g., Stein variational gradient descent (SVGD), have attracted signifi…