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 ...

Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain–machine interface with sensory feedback would provide the user with sensation about movement, pressure, curv ...

Electrochemical gas sensors (EGSs) have been used to measure the surface distributions and vertical profiles of trace gases in the wintertime Arctic boundary layer during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) field experiment in Fair ...

For high-dimensional Gaussian data, we investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large batch gradient descent steps, leading to an improvement in the approximation c ...

Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-le ...

Virtual reality (VR) technology revitalises rehabilitation training by creating rich, interactive virtual rehabilitation scenes and tasks that deeply engage patients. Robotics with immersive VR environments have the potential to significantly enhance the s ...

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, int ...

Relating behavior to brain activity in animals is a fundamental goal in neuroscience, with practical applications in building robust brain-machine interfaces. However, the domain gap between individuals is a major issue that prevents the training of genera ...

Autoencoders are the simplest neural network for unsupervised learning, and thus an ideal framework for studying feature learning. While a detailed understanding of the dynamics of linear autoencoders has recently been obtained, the study of non-linear aut ...

We present an approach to quantifying both aleatoric and epistemic uncertainty for deep neural networks in image classification, based on generative adversarial networks (GANs). While most works in the literature that use GANs to generate out-of-distributi ...

In this thesis, we study the 3 challenges described above. First, we study different reconstruction techniques and assess the fidelity of each reconstruction results by means of structured illumination and phase conjugation. By reconstructing the 3D refrac ...

Direct Feedback Alignment (DFA) is emerging as an efficient and biologically plausible alternative to backpropagation for training deep neural networks. Despite relying on random feedback weights for the backward pass, DFA successfully trains state-of-the- ...

We investigate how the training curve of isotropic kernel methods depends on the symmetry of the task to be learned, in several settings. (i) We consider a regression task, where the target function is a Gaussian random field that depends only on d(paralle ...

Modern information technologies and human-centric communication systems employ advanced content representations for richer portrayals of the real world. The newly adopted imaging modalities offer additional information cues and permit the depiction of real ...

Hands deserve particular attention in virtual reality (VR) applications because they represent our primary means for interacting with the environment. Although marker-based motion capture works adequately for full body tracking, it is less reliable for sma ...

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

Chattez avec Graph Search

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