The theory (mainly Learning theory) of Artificial neural networks. See also Deep learning theory, Mathematical modelling of neural networks, Statistical mechanics of neural networks
Neural networks class - Université de Sherbrooke (Hugo Larochelle)
Note that the notation in this paper is opposite to that standard in Machine learning. In the paper, is the input, and is the output.
See Learning theory, Artificial neural networks, Deep learning theory, Non-convex optimization...
See talk at ICLR2017 by Riccardo Zecchina
Domains of Solutions and Replica Symmetry Breaking in Multilayer Neural Networks
Computational efficiency and optimization: Unreasonable effectiveness of learning neural nets: Accessible states and robust ensembles
Theory of Deep Learning III: Generalization Properties of SGD
See Generalization, Generalization in deep learning, Statistical physics and inference
Statistical mechanics of learning
Uniqueness of the weights for minimal feedforward nets with a given input-output map,. more here
For neural networks, function determines form, for 0-hidden layer, neural nets...
Functionally Equivalent Feedforward Neural Networks
See more at Singular learning theory
Neural network dynamics, see Dynamical systems
Dynamical Systems Theory for Transparent Symbolic Computation in Neuronal Networks We show that a correspondence can be found between these networks and Finite-state transducers, and use the derived abstraction to investigate how noise affects computation in this class of systems, unveiling a surprising facilitatory effect on information transmission.