54 citations · 61 across the 12 of their papers we have counts for
12 papers
Evidence Integration in Large Language Models
Sebastien Kawada, Manolis Kellis
Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented generation, other agents, and users, how LLMs integrate such evidence…
Retrieved but not ranked: surface-form bias in structural retrieval, from mathematics to agent trajectories
Nabira Rashid, Manolis Kellis
We evaluate embedding retrieval where surface form and meaning are pulled apart on purpose: retrieving items that share underlying structure but not wording, in two unrelated domai…
Greater than the Sum of Its Parts: Building Substructure into Protein Encoding Models
Robert Calef, Arthur Liang, Manolis Kellis +1
Protein representation learning has advanced rapidly with the scale-up of sequence and structure supervision, but most models still encode proteins either as per-residue token sequ…
SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes
Zhenglun Kong, Mufan Qiu, John Boesen +5
Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology. Image-based spatial transcriptomics tec…
Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality
Zhenglun Kong, Yize Li, Fanhu Zeng +7
In Transformer architectures, tokens\textemdash discrete units derived from raw data\textemdash are formed by segmenting inputs into fixed-length chunks. Each token is then mapped…
DD-Ranking: Rethinking the Evaluation of Dataset Distillation
Zekai Li, Xinhao Zhong, Samir Khaki +49
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance co…
ProteinRPN: Towards Accurate Protein Function Prediction with Graph-Based Region Proposals
Shania Mitra, Lei Huang, Manolis Kellis
Protein function prediction is a crucial task in bioinformatics, with significant implications for understanding biological processes and disease mechanisms. While the relationship…
A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis
Lei Huang, Lei Xiong, Na Sun +3
The rapid advancement of single-cell ATAC sequencing (scATAC-seq) technologies holds great promise for investigating the heterogeneity of epigenetic landscapes at the cellular leve…
TrustLLM: Trustworthiness in Large Language Models
Yue Huang, Lichao Sun, Haoran Wang +67
Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs prese…
Contextualized Machine Learning
Benjamin Lengerich, Caleb N. Ellington, Andrea Rubbi +2
We examine Contextualized Machine Learning (ML), a paradigm for learning heterogeneous and context-dependent effects. Contextualized ML estimates heterogeneous functions by applyin…
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori +4
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components. By…
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…