8 papers
LICO: Large Language Models for In-Context Molecular Optimization
Tung Nguyen, Aditya Grover
Optimizing black-box functions is a fundamental problem in science and engineering. To solve this problem, many approaches learn a surrogate function that estimates the underlying…
IndiaWeatherBench: A Dataset and Benchmark for Data-Driven Regional Weather Forecasting over India
Tung Nguyen, Harkanwar Singh, Nilay Naharas +2
Regional weather forecasting is a critical problem for localized climate adaptation, disaster mitigation, and sustainable development. While machine learning has shown impressive p…
Iceberg: Enhancing HLS Modeling with Synthetic Data
Zijian Ding, Tung Nguyen, Weikai Li +3
Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap…
PhysiX: A Foundation Model for Physics Simulations
Tung Nguyen, Arsh Koneru, Shufan Li +1
Foundation models have achieved remarkable success across video, image, and language domains. By scaling up the number of parameters and training datasets, these models acquire gen…
MedMax: Mixed-Modal Instruction Tuning for Training Biomedical Assistants
Hritik Bansal, Daniel Israel, Siyan Zhao +3
Recent advancements in mixed-modal generative have opened new avenues for developing unified biomedical assistants capable of analyzing biomedical images, answering complex questio…
Probing the Decision Boundaries of In-context Learning in Large Language Models
Siyan Zhao, Tung Nguyen, Aditya Grover
In-context learning is a key paradigm in large language models (LLMs) that enables them to generalize to new tasks and domains by simply prompting these models with a few exemplars…