5 citations · 6 across the 4 of their papers we have counts for
7 papers
Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models
Eric Hanchen Jiang, Mengting Li, Guancheng Wan +8
The efficiency of multi-agent systems driven by large language models (LLMs) largely hinges on their communication topology. However, designing an optimal topology is a non-trivial…
Multimodal Function Vectors for Visual Relations
Shuhao Fu, Esther Goldberg, Ying Nian Wu +1
Large Multimodal Models (LMMs) demonstrate impressive in-context learning abilities from few multimodal demonstrations, yet the internal mechanisms supporting such task learning re…
MLZero: A Multi-Agent System for End-to-end Machine Learning Automation
Haoyang Fang, Boran Han, Nick Erickson +10
Existing AutoML systems have advanced the automation of machine learning (ML); however, they still require substantial manual configuration and expert input, particularly when hand…
Watermarking Continuous Tabular Data Generation
Hengzhi He, Peiyu Yu, Junpeng Ren +2
In this paper, we introduce a simple yet effective tabular data watermarking mechanism with statistical guarantees. We show theoretically that the proposed watermark can be effecti…
Efficient and Scalable Fine-Tune of Language Models for Genome Understanding
Huixin Zhan, Ying Nian Wu, Zijun Zhang
Although DNA foundation models have advanced the understanding of genomes, they still face significant challenges in the limited scale and diversity of genomic data. This limitatio…
SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data
Cristian Challu, Peihong Jiang, Ying Nian Wu +1
In this work, we tackle two widespread challenges in real applications for time-series forecasting that have been largely understudied: distribution shifts and missing data. We pro…
Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge
Zijun Zhang, Linqi Zhou, Liangke Gou +1
We report a neural architecture search framework, BioNAS, that is tailored for biomedical researchers to easily build, evaluate, and uncover novel knowledge from interpretable deep…