5 papers
Can Local Learning Match Self-Supervised Backpropagation?
Wu S. Zihan, Ariane Delrocq, Wulfram Gerstner +1
While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local self-supervised learning (local-…
Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data
Ariane Delrocq, Wu S. Zihan, Guillaume Bellec +1
The brain learns abstract representations of high-dimensional sensory input, but the plasticity rules that enable such learning are unknown. We study biologically plausible algorit…
Flat Channels to Infinity in Neural Loss Landscapes
Flavio Martinelli, Alexander Van Meegen, Berfin ÅimÅek +2
The loss landscapes of neural networks contain minima and saddle points that may be connected in flat regions or appear in isolation. We identify and characterize a special structu…
Data Augmentation Techniques to Reverse-Engineer Neural Network Weights from Input-Output Queries
Alexander Beiser, Flavio Martinelli, Wulfram Gerstner +1
Network weights can be reverse-engineered given enough informative samples of a network's input-output function. In a teacher-student setup, this translates into collecting a datas…
Emergent rate-based dynamics in duplicate-free populations of spiking neurons
Valentin Schmutz, Johanni Brea, Wulfram Gerstner
Can Spiking Neural Networks (SNNs) approximate the dynamics of Recurrent Neural Networks (RNNs)? Arguments in classical mean-field theory based on laws of large numbers provide a p…