Copertina

Stochastic Multivariate Universal-Radix Finite-State Machine: a Theoretically and Practically Elegant Nonlinear Function Approximator

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Feng, Xincheng, Shen, Guodong, Hu, Jianhao, Li, Meng, Wong, Ngai
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

D-QRELO: Training- and Data-Free Delta Compression for Large Language Models via Quantization and Residual Low-Rank Approximation
di: Li, Junlin, et al.
Pubblicazione: (2026)

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary
di: Liang, Zi, et al.
Pubblicazione: (2025)

Tensor Low-rank Approximation of Finite-horizon Value Functions
di: Rozada, Sergio, et al.
Pubblicazione: (2024)

Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise
di: Blaser, Ethan, et al.
Pubblicazione: (2024)

Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
di: Zhu, Feng, et al.
Pubblicazione: (2025)

HPD: Hybrid Projection Decomposition for Robust State Space Models on Analog CIM Hardware
di: Feng, Yuannuo, et al.
Pubblicazione: (2025)

Mixture-of-Subspaces in Low-Rank Adaptation
di: Wu, Taiqiang, et al.
Pubblicazione: (2024)

Neural Networks as Universal Finite-State Machines: A Constructive Deterministic Finite Automaton Theory
di: Dhayalkar, Sahil Rajesh
Pubblicazione: (2025)

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

Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation
di: SS, Sidharth, et al.
Pubblicazione: (2024)

HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
di: Feng, Shibo, et al.
Pubblicazione: (2025)

Hierarchical Universal Value Function Approximators
di: Arora, Rushiv
Pubblicazione: (2024)

AIS: Adaptive Importance Sampling for Quantized RL
di: Zhou, Jiajun, et al.
Pubblicazione: (2026)

Universal Approximation of Nonlinear Operators and Their Derivatives
di: de Feo, Filippo
Pubblicazione: (2026)

Automated Machine Learning: From Principles to Practices
di: Shen, Zhenqian, et al.
Pubblicazione: (2018)

Adaptive Machine Learning-Driven Multi-Fidelity Stratified Sampling for Failure Analysis of Nonlinear Stochastic Systems
di: Xu, Liuyun, et al.
Pubblicazione: (2025)

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

LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
di: Yang, Yifan, et al.
Pubblicazione: (2024)

MCUBERT: Memory-Efficient BERT Inference on Commodity Microcontrollers
di: Yang, Zebin, et al.
Pubblicazione: (2024)

NLI:Non-uniform Linear Interpolation Approximation of Nonlinear Operations for Efficient LLMs Inference
di: Yu, Jiangyong, et al.
Pubblicazione: (2026)

Kernel Stochastic Configuration Networks for Nonlinear Regression
di: Chen, Yongxuan, et al.
Pubblicazione: (2024)

Heavy-Ball Momentum Accelerated Actor-Critic With Function Approximation
di: Dong, Yanjie, et al.
Pubblicazione: (2024)

Information-Theoretic Foundations for Machine Learning
di: Jeon, Hong Jun, et al.
Pubblicazione: (2024)

An Information Theoretic Approach to Machine Unlearning
di: Foster, Jack, et al.
Pubblicazione: (2024)

Tuning the Implicit Regularizer of Masked Diffusion Language Models: Enhancing Generalization via Insights from $k$-Parity
di: Huang, Jianhao, et al.
Pubblicazione: (2026)

Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks
di: Yang, Guangrui, et al.
Pubblicazione: (2024)

Generation Properties of Stochastic Interpolation under Finite Training Set
di: Li, Yunchen, et al.
Pubblicazione: (2025)

Experiment Planning with Function Approximation
di: Pacchiano, Aldo, et al.
Pubblicazione: (2024)

A Sheaf-Theoretic and Topological Perspective on Complex Network Modeling and Attention Mechanisms in Graph Neural Models
di: Hu, Chuan-Shen
Pubblicazione: (2026)

Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
di: Adibi, Arman, et al.
Pubblicazione: (2024)

The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
di: Liu, Shuze Daniel, et al.
Pubblicazione: (2024)

Reinforcement Learning with Stochastic Reward Machines
di: Corazza, Jan, et al.
Pubblicazione: (2025)

On the Mathematical Impossibility of Safe Universal Approximators
di: Yao, Jasper
Pubblicazione: (2025)

Extending Straight-Through Estimation for Robust Neural Networks on Analog CIM Hardware
di: Feng, Yuannuo, et al.
Pubblicazione: (2025)

DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDEs
di: Liu, Li, et al.
Pubblicazione: (2025)

Time Series Imputation with Multivariate Radial Basis Function Neural Network
di: Jung, Chanyoung, et al.
Pubblicazione: (2024)

Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning
di: Ren, Yutian, et al.
Pubblicazione: (2024)

Theoretical and Practical Analysis of Fréchet Regression via Comparison Geometry
di: Kimura, Masanari, et al.
Pubblicazione: (2025)

Selecting Belief-State Approximations in Simulators with Latent States
di: Jiang, Nan
Pubblicazione: (2025)

Efficient Machine Unlearning via Influence Approximation
di: Liu, Jiawei, et al.
Pubblicazione: (2025)