Copertina

In-Context Algorithm Emulation in Fixed-Weight Transformers

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hu, Jerry Yao-Chieh, Liu, Hude, Zhang, Jennifer Yuntong, Liu, Han
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Are Hallucinations Bad Estimations?
di: Liu, Hude, et al.
Pubblicazione: (2025)

Attention Mechanism, Max-Affine Partition, and Universal Approximation
di: Liu, Hude, et al.
Pubblicazione: (2025)

Universal Approximation with Softmax Attention
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2025)

On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)

Transformer Approximations from ReLUs
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2026)

Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)

On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2025)

Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models
di: Wu, Dennis, et al.
Pubblicazione: (2024)

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)

Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)

Differentially Private Kernel Density Estimation
di: Liu, Erzhi, et al.
Pubblicazione: (2024)

On Flow Matching KL Divergence
di: Su, Maojiang, et al.
Pubblicazione: (2025)

Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
di: Yao, Xinhao, et al.
Pubblicazione: (2024)

On Differentially Private String Distances
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)

A Theoretical Analysis of Discrete Flow Matching Generative Models
di: Su, Maojiang, et al.
Pubblicazione: (2025)

TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations
di: Zhang, Zheng, et al.
Pubblicazione: (2024)

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)

Nonparametric Modern Hopfield Models
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)

Fast and Low-Cost Genomic Foundation Models via Outlier Removal
di: Luo, Haozheng, et al.
Pubblicazione: (2025)

BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model
di: Xu, Chenwei, et al.
Pubblicazione: (2024)

POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization
di: Wang, Ziqing, et al.
Pubblicazione: (2025)

In-Context Deep Learning via Transformer Models
di: Wu, Weimin, et al.
Pubblicazione: (2024)

How Well Can Transformers Emulate In-context Newton's Method?
di: Giannou, Angeliki, et al.
Pubblicazione: (2024)

STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction
di: Wu, Dennis, et al.
Pubblicazione: (2023)

Understanding Fairness Surrogate Functions in Algorithmic Fairness
di: Yao, Wei, et al.
Pubblicazione: (2023)

Multimodal Representation Learning Conditioned on Semantic Relations
di: Qiao, Yang, et al.
Pubblicazione: (2025)

Discrete Flow Matching Policy Optimization
di: Su, Maojiang, et al.
Pubblicazione: (2026)

GradientStabilizer:Fix the Norm, Not the Gradient
di: Huang, Tianjin, et al.
Pubblicazione: (2025)

NdLinear: Preserving Multi-Dimensional Structure for Parameter-Efficient Neural Networks
di: Reneau, Alex, et al.
Pubblicazione: (2025)

Robust Weight Initialization for Tanh Neural Networks with Fixed Point Analysis
di: Lee, Hyunwoo, et al.
Pubblicazione: (2024)

On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
di: Liu, Renpu, et al.
Pubblicazione: (2024)

CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling
di: Zhao, Runsong, et al.
Pubblicazione: (2026)

Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix
di: Zhang, Jinhao, et al.
Pubblicazione: (2026)

Graph Diffusion Transformers are In-Context Molecular Designers
di: Liu, Gang, et al.
Pubblicazione: (2025)

AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures
di: Gao, Yihang, et al.
Pubblicazione: (2024)

Reconciling In-Context and In-Weight Learning via Dual Representation Space Encoding
di: Chen, Guanyu, et al.
Pubblicazione: (2026)

Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models
di: Huang, Jerry, et al.
Pubblicazione: (2024)

How Well Can a Long Sequence Model Model Long Sequences? Comparing Architechtural Inductive Biases on Long-Context Abilities
di: Huang, Jerry
Pubblicazione: (2024)