15 citations · 33 across the 33 of their papers we have counts for
34 papers
Transformer Approximations from ReLUs
Jerry Yao-Chieh Hu, Mingcheng Lu, Yi-Chen Lee +1
We provide a systematic recipe for translating ReLU approximation results to softmax attention mechanism. This recipe covers many common approximation targets. Importantly, it yiel…
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…
A Theoretical Analysis of Discrete Flow Matching Generative Models
Maojiang Su, Mingcheng Lu, Jerry Yao-Chieh Hu +4
We provide a theoretical analysis for end-to-end training Discrete Flow Matching (DFM) generative models. DFM is a promising discrete generative modeling framework that learns the…
In-Context Algorithm Emulation in Fixed-Weight Transformers
Jerry Yao-Chieh Hu, Hude Liu, Jennifer Yuntong Zhang +1
We prove that a minimal Transformer with frozen weights emulates a broad class of algorithms by in-context prompting. We formalize two modes of in-context algorithm emulation. In t…
Minimalist Softmax Attention Provably Learns Constrained Boolean Functions
Jerry Yao-Chieh Hu, Xiwen Zhang, Maojiang Su +2
We study the computational limits of learning -bit Boolean functions (specifically, , , and their noisy variants), using a minimalist single-head soft…
Fast and Low-Cost Genomic Foundation Models via Outlier Removal
Haozheng Luo, Chenghao Qiu, Maojiang Su +5
To address the challenge of scarce computational resources in genomic modeling, we introduce GERM, a genomic foundation model with strong compression performance and fast adaptabil…
Attention Mechanism, Max-Affine Partition, and Universal Approximation
Hude Liu, Jerry Yao-Chieh Hu, Zhao Song +1
We establish the universal approximation capability of single-layer, single-head self- and cross-attention mechanisms with minimal attached structures. Our key insight is to interp…
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…
Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies
Yibo Wen, Chenwei Xu, Jerry Yao-Chieh Hu +2
We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design. We first pre-train a language model using millions of an…
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…
On Differentially Private String Distances
Jerry Yao-Chieh Hu, Erzhi Liu, Han Liu +2
Given a database of bit strings , a fundamental data structure task is to estimate the distances between a given query with all the st…
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…
Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
Jerry Yao-Chieh Hu, Wei-Po Wang, Ammar Gilani +3
We investigate the statistical and computational limits of prompt tuning for transformer-based foundation models. Our key contributions are prompt tuning on \emph{single-head} tran…
Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes
Jerry Yao-Chieh Hu, Dennis Wu, Han Liu
We study the optimal memorization capacity of modern Hopfield models and Kernelized Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories. We present…
Differentially Private Kernel Density Estimation
Erzhi Liu, Jerry Yao-Chieh Hu, Alex Reneau +2
We introduce a refined differentially private (DP) data structure for kernel density estimation (KDE), offering not only improved privacy-utility tradeoff but also better efficienc…
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…
Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
Jerry Yao-Chieh Hu, Maojiang Su, En-Jui Kuo +2
We study the computational limits of Low-Rank Adaptation (LoRA) for finetuning transformer-based models using fine-grained complexity theory. Our key observation is that the existe…
Decoupled Alignment for Robust Plug-and-Play Adaptation
Haozheng Luo, Jiahao Yu, Wenxin Zhang +9
We introduce a training-free safety enhancement method for aligning large language models (LLMs) without the need for supervised fine-tuning or reinforcement learning from human fe…
Mind the Inconspicuous: Revealing the Hidden Weakness in Aligned LLMs' Refusal Boundaries
Jiahao Yu, Haozheng Luo, Jerry Yao-Chieh Hu +3
Recent advances in Large Language Models (LLMs) have led to impressive alignment where models learn to distinguish harmful from harmless queries through supervised finetuning (SFT)…
Nonparametric Modern Hopfield Models
Jerry Yao-Chieh Hu, Bo-Yu Chen, Dennis Wu +2
We present a nonparametric interpretation for deep learning compatible modern Hopfield models and utilize this new perspective to debut efficient variants. Our key contribution ste…
BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
Chenwei Xu, Yu-Chao Huang, Jerry Yao-Chieh Hu +4
We introduce the \textbf{B}i-Directional \textbf{S}parse \textbf{Hop}field Network (\textbf{BiSHop}), a novel end-to-end framework for deep tabular learning. BiSHop handles the two…
Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
Jerry Yao-Chieh Hu, Pei-Hsuan Chang, Robin Luo +4
We introduce an Outlier-Efficient Modern Hopfield Model (termed ) and use it to address the outlier inefficiency problem of {training} gigantic transformer-base…