2 citations · 13 across the 26 of their papers we have counts for
13 papers · 1 filter
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…
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…
Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models
Dennis Wu, Jerry Yao-Chieh Hu, Teng-Yun Hsiao +1
We propose a two-stage memory retrieval dynamics for modern Hopfield models, termed , with enhanced memory capacity. Our key contribution is a learnable feat…
On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song +1
We investigate the computational limits of the memory retrieval dynamics of modern Hopfield models from the fine-grained complexity analysis. Our key contribution is the characteri…