85 citations · 86 across the 6 of their papers we have counts for
7 papers
Rethinking Molecular Text Representations for LLMs: An Empirical Study
Arun Raja, Garrett M. Morris, Kian Ming A. Chai
Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use. We present a systematic benchmark, MolRepBench…
Variational Learning of Fractional Posteriors
Kian Ming A. Chai, Edwin V. Bonilla
We introduce a novel one-parameter variational objective that lower bounds the data evidence and enables the estimation of approximate fractional posteriors. We extend this framewo…
Thompson Sampling in Function Spaces via Neural Operators
Rafael Oliveira, Xuesong Wang, Kian Ming A. Chai +1
We propose an extension of Thompson sampling to optimization problems over function spaces where the objective is a known functional of an unknown operator's output. We assume that…
Brain-Inspired Stochastic Joint Embedding Representation Learning
Makoto Yamada, Kian Ming A. Chai, Ayoub Rhim +3
Representation learning is one of the key research topics in machine learning, and the framework of self-supervised learning (SSL) has revolutionized computer vision. However, thes…
BOSPHORUS: Bridging ANF and CNF Solvers
Davin Choo, Mate Soos, Kian Ming A. Chai +1
Algebraic Normal Form (ANF) and Conjunctive Normal Form (CNF) are commonly used to encode problems in Boolean algebra. ANFs are typically solved via Gr"obner basis algorithms, ofte…
Optimizing F-measure: A Tale of Two Approaches
Ye Nan, Kian Ming Chai, Wee Sun Lee +1
F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical ut…
A Split-Merge Framework for Comparing Clusterings
Qiaoliang Xiang, Qi Mao, Kian Ming Chai +3
Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the…