Cover Image

Determinism in the Undetermined: Deterministic Output in Charge-Conserving Continuous-Time Neuromorphic Systems with Temporal Stochasticity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yan, Jing, You, Kang, He, Zhezhi, Zhang, Yaoyu
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs
by: Yu, He, et al.
Published: (2025)

The Extrapolation Cliff in On-Policy Distillation of Near-Deterministic Structured Outputs
by: Li, Xin, et al.
Published: (2026)

A Temporal Difference Method for Stochastic Continuous Dynamics
by: Settai, Haruki, et al.
Published: (2025)

Conservative Continuous-Time Treatment Optimization
by: Schneider, Nora, et al.
Published: (2026)

Deterministic Policy Gradient for Reinforcement Learning with Continuous Time and State
by: Cheng, Ziheng, et al.
Published: (2025)

Deterministic Decomposition of Stochastic Generative Dynamics
by: Song, Xingyu, et al.
Published: (2026)

Learning Optimal Deterministic Policies with Stochastic Policy Gradients
by: Montenegro, Alessandro, et al.
Published: (2024)

Interpretable Spatial-Temporal Fusion Transformers: Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputs
by: Sun, Shuwen, et al.
Published: (2025)

A Contrastive Diffusion-based Network (CDNet) for Time Series Classification
by: Zhang, Yaoyu, et al.
Published: (2025)

PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers
by: Bastian, Julien, et al.
Published: (2026)

Embedding principle of homogeneous neural network for classification problem
by: Zhang, Jiahan, et al.
Published: (2025)

Uncovering Critical Sets of Deep Neural Networks via Sample-Independent Critical Lifting
by: Zhang, Leyang, et al.
Published: (2025)

Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
by: Jiang, Xiaoyu, et al.
Published: (2024)

Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion
by: Bai, Zhiwei, et al.
Published: (2024)

Disentangle Sample Size and Initialization Effect on Perfect Generalization for Single-Neuron Target
by: Zhao, Jiajie, et al.
Published: (2024)

Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
by: Ling, Zhenqing, et al.
Published: (2025)

Breaking Determinism: Stochastic Modeling for Reliable Off-Policy Evaluation in Ad Auctions
by: Yeom, Hongseon, et al.
Published: (2025)

Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
by: Grazzi, Riccardo, et al.
Published: (2024)

ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations
by: Gong, Mingze, et al.
Published: (2024)

Stochastic Sampling from Deterministic Flow Models
by: Singh, Saurabh, et al.
Published: (2024)

Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization
by: Zhang, Yaoyu, et al.
Published: (2024)

DNNs May Determine Major Properties of Their Outputs Early, with Timing Possibly Driven by Bias
by: Park, Song, et al.
Published: (2025)

Adjacent Leader Decentralized Stochastic Gradient Descent
by: He, Haoze, et al.
Published: (2024)

Geometry and Local Recovery of Global Minima of Two-layer Neural Networks at Overparameterization
by: Zhang, Leyang, et al.
Published: (2023)

Geometry of Critical Sets and Existence of Saddle Branches for Two-layer Neural Networks
by: Zhang, Leyang, et al.
Published: (2024)

Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems
by: Roy, Samrendra, et al.
Published: (2026)

Non-Determinism of "Deterministic" LLM Settings
by: Atil, Berk, et al.
Published: (2024)

Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance
by: Beyler, Eliot, et al.
Published: (2025)

Deterministic Reservoir Computing for Chaotic Time Series Prediction
by: Viehweg, Johannes, et al.
Published: (2025)

A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From Deterministic to Stochastic Iterative Algorithms
by: Chen, Zaiwei, et al.
Published: (2025)

Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models
by: Keane, Ronan
Published: (2024)

Towards Continuous-Time Approximations for Stochastic Gradient Descent without Replacement
by: Perko, Stefan
Published: (2025)

Overview frequency principle/spectral bias in deep learning
by: Xu, Zhi-Qin John, et al.
Published: (2022)

An overview of condensation phenomenon in deep learning
by: Xu, Zhi-Qin John, et al.
Published: (2025)

Sequence Diffusion Model for Temporal Link Prediction in Continuous-Time Dynamic Graph
by: Duc, Nguyen Minh, et al.
Published: (2026)

Action-Driven Processes for Continuous-Time Control
by: He, Ruimin, et al.
Published: (2025)

Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network
by: Ding, Yongqi, et al.
Published: (2024)

Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems
by: Sheng, Zhi, et al.
Published: (2025)

Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
by: Ablin, Pierre, et al.
Published: (2023)

Single-Loop Deterministic and Stochastic Interior-Point Algorithms for Nonlinearly Constrained Optimization
by: Curtis, Frank E., et al.
Published: (2024)