11 citations · 11 across the 11 of their papers we have counts for
13 papers
DiMOS: Doob-Guided Inference-Time Multi-Objective Search for Scientific Design
Ziqing Wang, Qijie Zhu, Weimin Wu +4
Scientific design often requires jointly satisfying multiple objectives and constraints. Pretrained masked diffusion models provide a generative foundation for this task, but fine-…
D-DOIT: Training-free Adaptation of Discrete Diffusion via Doob's h-Transform
Jieke Wu, Qijie Zhu, Weimin Wu +3
We propose D-DOIT (Discrete Doob-Oriented Inference-time Transformation), a training-free and efficient adaptation method for discrete diffusion models with generic rewards. D-DOIT…
Discrete Flow Matching Policy Optimization
Maojiang Su, Po-Chung Hsieh, Weimin Wu +4
We introduce Discrete flow Matching policy Optimization (DoMinO), a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matching (DFM) models under a broad…
Cell-JEPA: Latent Representation Learning for Single-Cell Transcriptomics
Ali ElSheikh, Rui-Xi Wang, Weimin Wu +9
Single-cell foundation models learn by reconstructing masked gene expression, implicitly treating technical noise as signal. With dropout rates exceeding 90%, reconstruction object…
On Structured State-Space Duality
Jerry Yao-Chieh Hu, Xiwen Zhang, Ali ElSheikh +2
Structured State-Space Duality (SSD) [Dao & Gu, ICML 2024] is an equivalence between a simple Structured State-Space Model (SSM) and a masked attention mechanism. In particular, a…
POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization
Ziqing Wang, Yibo Wen, William Pattie +6
Lead optimization in drug discovery requires efficiently navigating vast chemical space through iterative cycles to enhance molecular properties while preserving structural similar…
Sci2Pol: Evaluating and Fine-tuning LLMs on Scientific-to-Policy Brief Generation
Weimin Wu, Alexander C. Furnas, Eddie Yang +5
We propose Sci2Pol-Bench and Sci2Pol-Corpus, the first benchmark and training dataset for evaluating and fine-tuning large language models (LLMs) on policy brief generation from a…
Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
Weimin Wu, Xuefeng Song, Yibo Wen +5
We introduce Genome-Factory, the first integrated Python library for tuning, deploying, and interpreting genomic foundation models. Our core contribution is to simplify and unify t…
Universal Approximation with Softmax Attention
Jerry Yao-Chieh Hu, Hude Liu, Hong-Yu Chen +2
We prove that with linear transformations, both (i) two-layer self-attention and (ii) one-layer self-attention followed by a softmax function are universal approximators for contin…
On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality
Jerry Yao-Chieh Hu, Weimin Wu, Yi-Chen Lee +3
We investigate the approximation and estimation rates of conditional diffusion transformers (DiTs) with classifier-free guidance. We present a comprehensive analysis for ``in-conte…
In-Context Deep Learning via Transformer Models
Weimin Wu, Maojiang Su, Jerry Yao-Chieh Hu +2
We investigate the transformer's capability to simulate the training process of deep models via in-context learning (ICL), i.e., in-context deep learning. Our key contribution is p…
On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
Jerry Yao-Chieh Hu, Weimin Wu, Zhao Song +1
We investigate the statistical and computational limits of latent Diffusion Transformers (DiTs) under the low-dimensional linear latent space assumption. Statistically, we study th…
DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA Embeddings
Zhihan Zhou, Weimin Wu, Harrison Ho +5
We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space.…