Correction to "Linear and logarithmic capacities in associative neural networks" (vol 35, pg 558, 1989)

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In this thesis, we study how combinatorial structures and symmetries emerge from analytic properties of graph polynomials and neural networks. Graph polynomials encode combinatorial aspects of the underlying graph in their coefficients and are remarkably u ...

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Correction to "Linear and logarithmic capacities in associative neural networks" (vol 35, pg 558, 1989)

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Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

Optimization frameworks for bespoke sensory encoding in neuroprosthetics

Surface distributions and vertical profiles of trace gases (CO, O3, NO, NO2) in the Arctic wintertime boundary layer using low-cost sensors during ALPACA-2022

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

Euclid preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons

Overcoming the Domain Gap in Neural Action Representations

The dynamics of representation learning in shallow, non-linear autoencoders

UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs

Advanced Techniques in Optical Diffraction Tomography

Align, then memorise: the dynamics of learning with feedback alignment

How isotropic kernels perform on simple invariants

Perceptual Quality of Point Clouds with application to Compression

Real-time neural network prediction for handling two-hands mutual occlusions