Learning Pattern Transformation Manifolds for Classification

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The Birman-Williams theorem gives a connection between the collection of unstable periodic orbits (UPOs) contained within a chaotic attractor and the topology of that attractor, for three-dimensional systems. In certain cases, the fractal dimension of a ch ...

Fuel performance codes are essential for predicting fuel rod behavior in nuclear reactors. These codes comprise a complex system of models with empirical constants that require calibration against experimental data. To support this calibration, this study ...

Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increas ...

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

Jointly estimating hand and object shape facilitates the grasping task in human-to-robot handovers. Relying on handcrafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to dete ...

In algorithms for solving optimization problems constrained to a smooth manifold, retractions are a well-established tool to ensure that the iterates stay on the manifold. More recently, it has been demonstrated that retractions are a useful concept for ot ...

We study the glass transition by exploring a broad class of kinetic rules that can significantly modify the normal dynamics of supercooled liquids while maintaining thermal equilibrium. Beyond the usual dynamics of liquids, this class includes dynamics in ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

We study the statistical mechanics and the equilibrium dynamics of a system of classical Heisenberg spins with frustrated interactions on a d -dimensional simple hypercubic lattice, in the limit of infinite dimensionality d -> infinity . In the analysis we ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Deep learning algorithms demonstrate a surprising ability to learn high-dimensional tasks from limited examples. This is commonly attributed to the depth of neural networks, enabling them to build a hierarchy of abstract, low-dimensional data representatio ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Learning Pattern Transformation Manifolds for Classification

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The topology of a chaotic attractor in the Kuramoto-Sivashinsky equation

Preliminary Sensitivity Analysis of Time-Dependent Code Outputs in Fuel Performance Models Using OFFBEAT for Future Calibration

Constructing multicomponent cluster expansions with machine-learning and chemical embedding

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

From low-rank retractions to dynamical low-rank approximation and back

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Aggregating Spatial and Photometric Context for Photometric Stereo

Frustrated magnets in the limit of infinite dimensions: Dynamics and disorder-free glass transition

Towards Trustworthy Deep Learning for Image Reconstruction

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Robust machine learning for neuroscientific inference

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Random matrix methods for high-dimensional machine learning models

Data and scripts for the RaFSIP scheme

Spectral Estimators for High-Dimensional Matrix Inference