Imagen de Portada

Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Cao, Yang, Chen, Bo, Li, Xiaoyu, Liang, Yingyu, Sha, Zhizhou, Shi, Zhenmei, Song, Zhao, Wan, Mingda
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
por: Liang, Yingyu, et al.
Publicado: (2025)

High-Order Matching for One-Step Shortcut Diffusion Models
por: Chen, Bo, et al.
Publicado: (2025)

On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis
por: Ke, Yekun, et al.
Publicado: (2025)

RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation
por: Cao, Yuefan, et al.
Publicado: (2025)

Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers
por: Li, Xiaoyu, et al.
Publicado: (2024)

On Computational Limits of FlowAR Models: Expressivity and Efficiency
por: Cao, Yang, et al.
Publicado: (2025)

HSR-Enhanced Sparse Attention Acceleration
por: Chen, Bo, et al.
Publicado: (2024)

Looped ReLU MLPs May Be All You Need as Practical Programmable Computers
por: Liang, Yingyu, et al.
Publicado: (2024)

Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time
por: Liang, Yingyu, et al.
Publicado: (2024)

Differential Privacy Mechanisms in Neural Tangent Kernel Regression
por: Gu, Jiuxiang, et al.
Publicado: (2024)

Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies
por: Cao, Yuefan, et al.
Publicado: (2025)

Visual Autoregressive Transformers Must Use $Ω(n^2 d)$ Memory
por: Cao, Yang, et al.
Publicado: (2025)

Universal Approximation of Visual Autoregressive Transformers
por: Chen, Yifang, et al.
Publicado: (2025)

DPBloomfilter: Securing Bloom Filters with Differential Privacy
por: Ke, Yekun, et al.
Publicado: (2025)

Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective
por: Liang, Yingyu, et al.
Publicado: (2024)

Training Tensor Attention Efficiently: From Cubic to Almost Linear Time
por: Cao, Yang, et al.
Publicado: (2024)

Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent
por: Chen, Bo, et al.
Publicado: (2024)

Theoretical Guarantees for High Order Trajectory Refinement in Generative Flows
por: Gong, Chengyue, et al.
Publicado: (2025)

A Tighter Complexity Analysis of SparseGPT
por: Li, Xiaoyu, et al.
Publicado: (2024)

Exploring the Frontiers of Softmax: Provable Optimization, Applications in Diffusion Model, and Beyond
por: Cao, Yang, et al.
Publicado: (2024)

Modern Hopfield Networks Require Chain-of-Thought to Solve $\mathsf{NC}^1$-Hard Problems
por: Cao, Yang, et al.
Publicado: (2024)

Scaling Law Phenomena Across Regression Paradigms: Multiple and Kernel Approaches
por: Chen, Yifang, et al.
Publicado: (2025)

Towards Infinite-Long Prefix in Transformer
por: Liang, Yingyu, et al.
Publicado: (2024)

The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity
por: Chen, Yifang, et al.
Publicado: (2024)

Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study
por: Ke, Yekun, et al.
Publicado: (2024)

Fast John Ellipsoid Computation with Differential Privacy Optimization
por: Li, Xiaoyu, et al.
Publicado: (2024)

Circuit Complexity Bounds for Visual Autoregressive Model
por: Ke, Yekun, et al.
Publicado: (2025)

Circuit Complexity Bounds for RoPE-based Transformer Architecture
por: Chen, Bo, et al.
Publicado: (2024)

RoPE Attention Can Be Trained in Almost Linear Time
por: Cao, Yang, et al.
Publicado: (2024)

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond
por: Ke, Yekun, et al.
Publicado: (2024)

Text-to-Image Diffusion Models Cannot Count, and Prompt Refinement Cannot Help
por: Guo, Xuyang, et al.
Publicado: (2025)

Can You Count to Nine? A Human Evaluation Benchmark for Counting Limits in Modern Text-to-Video Models
por: Guo, Xuyang, et al.
Publicado: (2025)

Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix
por: Liang, Yingyu, et al.
Publicado: (2024)

When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time?
por: Li, Chenyang, et al.
Publicado: (2025)

On Fine-Grained I/O Complexity of Attention Backward Passes
por: Li, Xiaoyu, et al.
Publicado: (2024)

Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding
por: Chen, Yifang, et al.
Publicado: (2025)

On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective
por: Li, Xiaoyu, et al.
Publicado: (2025)

Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
por: Xu, Zhuoyan, et al.
Publicado: (2024)

Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers
por: Liang, Yingyu, et al.
Publicado: (2024)

Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers
por: Huang, Zekai, et al.
Publicado: (2025)