Cover Image

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Walker, James A., Khajehnejad, Moein, Razi, Adeel
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

BrainSymphony: A parameter-efficient multimodal foundation model for brain dynamics with limited data
by: Khajehnejad, Moein, et al.
Published: (2025)

Graph-Based Representation Learning of Neuronal Dynamics and Behavior
by: Khajehnejad, Moein, et al.
Published: (2024)

Brain-MGF: Multimodal Graph Fusion Network for EEG-fMRI Brain Connectivity Analysis Under Psilocybin
by: Yap, Sin-Yee, et al.
Published: (2025)

Hi-DREAM: Brain Inspired Hierarchical Diffusion for fMRI Reconstruction via ROI Encoder and visuAl Mapping
by: Zhang, Guowei, et al.
Published: (2025)

Simulating Biological Intelligence: Active Inference with Experiment-Informed Generative Model
by: Paul, Aswin, et al.
Published: (2025)

Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld
by: Khajehnejad, Moein, et al.
Published: (2024)

Loss Spike in Training Neural Networks
by: Li, Xiaolong, et al.
Published: (2023)

Comment on "A Note on Over-Smoothing for Graph Neural Networks"
by: Hasson, Razi, et al.
Published: (2025)

On Predictive planning and counterfactual learning in active inference
by: Paul, Aswin, et al.
Published: (2024)

A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
by: Meng, Qingyan, et al.
Published: (2025)

Exploring Spiking Neural Networks for Binary Classification in Multivariate Time Series at the Edge
by: Ghawaly, James, et al.
Published: (2025)

Optimal Decision Making Under Strategic Behavior
by: Tsirtsis, Stratis, et al.
Published: (2019)

Training of Spiking Neural Networks with Expectation-Propagation
by: Yao, Dan, et al.
Published: (2025)

Provable Generalization Bounds for Deep Neural Networks with Momentum-Adaptive Gradient Dropout
by: Safder, Adeel
Published: (2025)

A Closer Look at Knowledge Distillation in Spiking Neural Network Training
by: Liu, Xu, et al.
Published: (2025)

Active Continual Learning with Metaplastic Binary Bayesian Neural Networks
by: Cottart, Kellian, et al.
Published: (2026)

Sharpness Aware Surrogate Training for Spiking Neural Networks
by: Nicholson, Maximilian
Published: (2026)

Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
by: Katti, Prabodh, et al.
Published: (2023)

Bullet Trains: Parallelizing Training of Temporally Precise Spiking Neural Networks
by: Morrill, Todd, et al.
Published: (2026)

Spiking Brain Compression: Post-Training Second-order Compression for Spiking Neural Networks
by: Shi, Lianfeng, et al.
Published: (2025)

Training Feedforward Neural Networks with Bayesian Hyper-Heuristics
by: Schreuder, Arné, et al.
Published: (2023)

On efficient computation in active inference
by: Paul, Aswin, et al.
Published: (2023)

A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
by: Mentzelopoulos, Georgios, et al.
Published: (2025)

Improving Neural Additive Models with Bayesian Principles
by: Bouchiat, Kouroche, et al.
Published: (2023)

Dynamic Spiking Framework for Graph Neural Networks
by: Yin, Nan, et al.
Published: (2023)

On Training a Neural Network to Explain Binaries
by: Interrante-Grant, Alexander, et al.
Published: (2024)

ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs
by: Jahin, Md Abrar, et al.
Published: (2026)

Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
by: Carrasquilla, Juan, et al.
Published: (2023)

SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks
by: Venkatesh, Sreyes, et al.
Published: (2024)

Training Bayesian Neural Networks with Sparse Subspace Variational Inference
by: Li, Junbo, et al.
Published: (2024)

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks
by: Baronig, Maximilian, et al.
Published: (2025)

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks
by: Hossain, Forsad Al, et al.
Published: (2025)

On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning
by: Lin, Jiaqi, et al.
Published: (2025)

ADMM-Based Training for Spiking Neural Networks
by: Perin, Giovanni, et al.
Published: (2025)

SpikingGamma: Surrogate-Gradient Free and Temporally Precise Online Training of Spiking Neural Networks with Smoothed Delays
by: Koopman, Roel, et al.
Published: (2026)

Spiking Graph Neural Network on Riemannian Manifolds
by: Sun, Li, et al.
Published: (2024)

Quantization of Spiking Neural Networks Beyond Accuracy
by: Smith, Evan Gibson, et al.
Published: (2026)

A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification
by: Yap, Sin-Yee, et al.
Published: (2023)

A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties
by: Schodt, David J., et al.
Published: (2024)

Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization
by: Beckers, Jim, et al.
Published: (2022)