Local Learning Rules for Deep Neural Networks with Two-State Neurons
Doktorsavhandling, 2025
Contrastive Hebbian learning
biologically inspired learning
lifted neural networks
artificial intelligence
Hopfield networks
local learning
quantized training
Författare
Rasmus Kjær Høier
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Two Tales of Single-Phase Contrastive Hebbian Learning
Proceedings of Machine Learning Research,;Vol. 235(2024)p. 18470-18488
Paper i proceeding
Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
Proceedings of Machine Learning Research,;Vol. 202(2023)p. 13141-13156
Paper i proceeding
AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,;Vol. 2022-June(2022)p. 460-469
Paper i proceeding
Lifted Regression/Reconstruction Networks
31st British Machine Vision Conference, BMVC 2020,;(2020)
Paper i proceeding
Dyadic Learning in Recurrent and Feedforward Models
NeurIPS 2024 Workshop Machine Learning with new Compute Paradigms,;(2024)
Paper i proceeding
Ämneskategorier (SSIF 2025)
Datavetenskap (datalogi)
Signalbehandling
Artificiell intelligens
ISBN
978-91-8103-176-8
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5634
Utgivare
Chalmers
EDIT-EA Lecture Hall
Opponent: Associate professor Pawel Herman, KTH