HyperMixer: An MLP-based Low Cost Alternative to Transformers

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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 ...

To protect both local gradients and estimated parameters in distributed learning, this paper introduces a masked diffusion (MD) strategy, leading to two algorithms: the MD stochastic gradient (MD-SG) and the MD primal-dual stochastic gradient (MPD-SG). The ...

A first-principles approach for calculating ion separation in solution through two-dimensional (2D) membranes is proposed and applied. Ionic energy profiles across the membrane are obtained first, where solvation effects are simulated explicitly with machi ...

We propose a machine learning-based estimator of the hand state for rehabilitation purposes, using light exoskeletons. These devices are easy to use and useful for delivering domestic and frequent therapies. We build a supervised approach using information ...

Virtual reality (VR) environments simulating height offer a unique platform to investigate neural adaptations to emotionally salient contexts during locomotion. These simulations allow for controlled analysis of motor‐cognitive interactions under perceived ...

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 ...

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 ...

Electrochemical devices and systems are significant for the development of therapeutic drug monitoring (TDM) and personalized therapy. However, electrochemical sensors are usually not that much selective. Therefore, innovative machine learning (ML) approac ...

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 ...

Stochastic Nonlinear Optimal Control (SNOC) involves minimizing a cost function that averages out the random uncertainties affecting the dynamics of nonlinear systems. For tractability reasons, this problem is typically addressed by minimizing an empirical ...

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have ...

We study robust linear regression in highdimension, when both the dimension d and the number of data points n diverge with a fixed ratio α = n/d, and study a data model that includes outliers. We provide exact asymptotics for the performances of the empiri ...

Social robots are widely used for educational activities, especially to attract children's attention. As a side effect, pupils' excitement can suppress their focus on the tutors and the content being approached. A potential solution to tackle this issue is ...

Recent efforts in natural language processing (NLP) commonsense reasoning research have yielded a considerable number of new datasets and benchmarks. However, most of these datasets formulate commonsense reasoning challenges in artificial scenarios that ar ...

This work presents the characterization results and methods used to determine the reconstruction of a 22Na point source placed in a coincidence setup. For this purpose, a novel, fully-integrated, reconfigurable feed-forward artificial neural network was im ...

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 ...

Physics-based representations constructed using only atomic positions and nuclear charges (also known as quantum machine learning, QML) allow for the reliable and efficient inference of molecular properties from training data. Chemistry is a science rooted ...

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 ...

HyperMixer: An MLP-based Low Cost Alternative to Transformers

Chattez avec Graph Search

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

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

Ion Sieving in Two-Dimensional Membranes from First Principles

Learning Hand State Estimation for a Light Exoskeleton

Walking on the Edge: Brain Connectivity Changes in Response to Virtual Height Challenges

Optimization frameworks for bespoke sensory encoding in neuroprosthetics

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

Simultaneous Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors

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

A PAC-Bayesian Framework for Optimal Control with Stability Guarantees

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Asymptotic Characterisation of the Performance of Robust Linear Regression in the Presence of Outliers

Teachers, Take Care of the Essential. the Rest Is Story: Using LLM and Social Robots for Content Approaching by Storytelling

CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks

On-chip artificial neural network for PET source position reconstruction

Overcoming the Domain Gap in Neural Action Representations

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

Physics-based representations for machine learning properties of chemical reactions

Advanced Techniques in Optical Diffraction Tomography