10 citations · 15 across the 4 of their papers we have counts for
5 papers
Learning Stability of Replay-Based Co-Optimization for Transmission Expansion under Strategic Bidding
Tomonari Kanazawa, Hikaru Hoshino, Eiko Furutani
This paper investigates the behavior of learning-based co-optimization for transmission expansion under strategic bidding in electricity markets. In this framework, transmission ca…
Multimodal Integrated Knowledge Transfer to Large Language Models through Preference Optimization with Biomedical Applications
Zhanliang Wang, Da Wu, Quan Nguyen +2
The scarcity of high-quality multimodal biomedical data limits the ability to effectively fine-tune pretrained Large Language Models (LLMs) for specialized biomedical tasks. To add…
Assessing the Utility of Large Language Models for Phenotype-Driven Gene Prioritization in Rare Genetic Disorder Diagnosis
Junyoung Kim, Jingye Yang, Kai Wang +2
Phenotype-driven gene prioritization is a critical process in the diagnosis of rare genetic disorders for identifying and ranking potential disease-causing genes based on observed…
GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Text
Da Wu, Zhanliang Wang, Hongzhuo Chen +12
Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests, and genetic tests over a prolonged period of time,…
Exploring the Reversal Curse and Other Deductive Logical Reasoning in BERT and GPT-Based Large Language Models
Da Wu, Jingye Yang, Kai Wang
The term "Reversal Curse" refers to the scenario where auto-regressive decoder large language models (LLMs), such as ChatGPT, trained on "A is B" fail to learn "B is A," assuming t…