:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Jiang, Haotian, Li, Qianxiao
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2305.18475
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Effect of Attention Head Count on Transformer Approximation
by: Yu, Penghao, et al.
Published: (2025)

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions
by: Jiang, Haotian, et al.
Published: (2025)

InfoFlow: A Framework for Multi-Layer Transformer Analysis
by: Yu, Penghao, et al.
Published: (2026)

The Effect of Depth on the Expressivity of Deep Linear State-Space Models
by: Bao, Zeyu, et al.
Published: (2025)

Allocation of Parameters in Transformers
by: Yu, Ruoxi, et al.
Published: (2025)

Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
by: Wang, Shida, et al.
Published: (2023)

From Generalization Analysis to Optimization Designs for State Space Models
by: Liu, Fusheng, et al.
Published: (2024)

Autocorrelation Matters: Understanding the Role of Initialization Schemes for State Space Models
by: Liu, Fusheng, et al.
Published: (2024)

Learning task-specific predictive models for scientific computing
by: Yin, Jianyuan, et al.
Published: (2025)

StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
by: Wang, Shida, et al.
Published: (2023)

Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving
by: Arisaka, Sohei, et al.
Published: (2024)

Unifying back-propagation and forward-forward algorithms through model predictive control
by: Ren, Lianhai, et al.
Published: (2024)

DynGMA: a robust approach for learning stochastic differential equations from data
by: Zhu, Aiqing, et al.
Published: (2024)

Learning Macroscopic Dynamics from Partial Microscopic Observations
by: Chen, Mengyi, et al.
Published: (2024)

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images
by: Zhu, Aiqing, et al.
Published: (2025)

On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
by: Li, Zhong, et al.
Published: (2020)

Learning Permutation-invariant Macroscopic Dynamics
by: Han, Zhichao, et al.
Published: (2026)

Transportation Marketplace Rate Forecast Using Signature Transform
by: Gu, Haotian, et al.
Published: (2024)

Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
by: Takakura, Shokichi, et al.
Published: (2023)

Machine Unlearning under Retain-Forget Entanglement
by: Cheng, Jingpu, et al.
Published: (2026)

Mitigating distribution shift in machine learning-augmented hybrid simulation
by: Zhao, Jiaxi, et al.
Published: (2024)

Scalable learning of macroscopic stochastic dynamics
by: Chen, Mengyi, et al.
Published: (2025)

A unified framework for establishing the universal approximation of transformer-type architectures
by: Cheng, Jingpu, et al.
Published: (2025)

Deep learning and the rate of approximation by flows
by: Cheng, Jingpu, et al.
Published: (2026)

MSign: An Optimizer Preventing Training Instability in Large Language Models via Stable Rank Restoration
by: Ren, Lianhai, et al.
Published: (2026)

Identifiable learning of dissipative dynamics
by: Zhu, Aiqing, et al.
Published: (2025)

Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling
by: Mittal, Daksh, et al.
Published: (2025)

Terminally constrained flow-based generative models from an optimal control perspective
by: Gao, Weiguo, et al.
Published: (2026)

Introduction to Sequence Modeling with Transformers
by: Kämäräinen, Joni-Kristian
Published: (2025)

Adaptive SGD with Line-Search and Polyak Stepsizes: Nonconvex Convergence and Accelerated Rates
by: Wu, Haotian
Published: (2025)

TransMamba: A Sequence-Level Hybrid Transformer-Mamba Language Model
by: Li, Yixing, et al.
Published: (2025)

An All-MLP Sequence Modeling Architecture That Excels at Copying
by: Cui, Chenwei, et al.
Published: (2024)

Local Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting
by: Aguilera-Martos, Ignacio, et al.
Published: (2024)

ELO-Rated Sequence Rewards: Advancing Reinforcement Learning Models
by: Ju, Qi, et al.
Published: (2024)

Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search
by: Salvatore, Nikolaus, et al.
Published: (2025)

Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba
by: Zhang, Runquan, et al.
Published: (2024)

Hypothesis-driven construction of mesoscopic dynamics
by: Li, Zhuoyuan, et al.
Published: (2026)

Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences
by: Ramesh, Krithik, et al.
Published: (2025)

Sequence Complementor: Complementing Transformers For Time Series Forecasting with Learnable Sequences
by: Chen, Xiwen, et al.
Published: (2025)

Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling
by: Wang, Mingze, et al.
Published: (2024)