Cover Image

Chemical Reaction Networks Learn Better than Spiking Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jaffard, Sophie, Sbalzarini, Ivo F.
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks
by: Kothapalli, Vignesh, et al.
Published: (2024)

Neural Networks Generalize on Low Complexity Data
by: Chatterjee, Sourav, et al.
Published: (2024)

Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
by: Zhang, Bohan, et al.
Published: (2025)

Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Ability
by: Yu, Lijia, et al.
Published: (2025)

CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration
by: Jaffard, Sophie, et al.
Published: (2024)

Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks
by: Fu, Hengyu, et al.
Published: (2024)

Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon
by: Yu, Hao
Published: (2025)

Convergence of Shallow ReLU Networks on Weakly Interacting Data
by: Dana, Léo, et al.
Published: (2025)

Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions
by: He, Yixuan, et al.
Published: (2025)

Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)

Compression, Generalization and Learning
by: Campi, Marco C., et al.
Published: (2023)

Online Learning with Unknown Constraints
by: Sridharan, Karthik, et al.
Published: (2024)

Adaptive Sample Aggregation In Transfer Learning
by: Hanneke, Steve, et al.
Published: (2024)

Quantized Approximately Orthogonal Recurrent Neural Networks
by: Foucault, Armand, et al.
Published: (2024)

Conformal Prediction for Privacy-Preserving Machine Learning
by: Balinsky, Alexander David, et al.
Published: (2025)

Learning with Differentially Private (Sliced) Wasserstein Gradients
by: Rodríguez-Vítores, David, et al.
Published: (2025)

Provable Reward-Agnostic Preference-Based Reinforcement Learning
by: Zhan, Wenhao, et al.
Published: (2023)

Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits
by: Chen, Fan, et al.
Published: (2025)

Beyond identifiability: Learning causal representations with few environments and finite samples
by: Lee, Inbeom, et al.
Published: (2026)

Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
by: Shen, Zhaiming, et al.
Published: (2025)

Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
by: Foster, Dylan J., et al.
Published: (2024)

A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
by: Hazard, Christopher J., et al.
Published: (2025)

A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)

Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs
by: Dong, Zihan, et al.
Published: (2026)

Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
by: Javanmard, Adel, et al.
Published: (2024)

Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits
by: Zhao, Qingyue, et al.
Published: (2025)

Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond
by: Chen, Fan, et al.
Published: (2022)

Geometry-induced Regularization in Deep ReLU Neural Networks
by: Bona-Pellissier, Joachim, et al.
Published: (2024)

On the Exponential Convergence for Offline RLHF with Pairwise Comparisons
by: Chen, Zhirui, et al.
Published: (2024)

Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy
by: Chen, Fan, et al.
Published: (2025)

Fixed-Budget Differentially Private Best Arm Identification
by: Chen, Zhirui, et al.
Published: (2024)

Le Cam Distortion: A Decision-Theoretic Framework for Robust Transfer Learning
by: Akdemir, Deniz
Published: (2025)

Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
by: Lu, Miao, et al.
Published: (2022)

Optimal rates for density and mode estimation with expand-and-sparsify representations
by: Sinha, Kaushik, et al.
Published: (2026)

Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing
by: Xu, Danru, et al.
Published: (2026)

Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2026)

A Fine-Grained Understanding of Uniform Convergence for Halfspaces
by: Kontorovich, Aryeh, et al.
Published: (2026)

Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models
by: Balasubramanian, Krishnakumar
Published: (2026)

Low-Dimensional Adaptation of Rectified Flow: A Diffusion and Stochastic Localization Perspective
by: Roy, Saptarshi, et al.
Published: (2026)

Ordinary Least Squares is a Special Case of Transformer
by: Tan, Xiaojun, et al.
Published: (2026)