From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

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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 ...

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

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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 ...

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 paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

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 ...

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

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 ...

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

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The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem. We introduce a statistical learning approach that makes the optimization trivial by using kernel me ...

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, ca ...

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Chattez avec Graph Search

Robust machine learning for neuroscientific inference

Unlabeled Principal Component Analysis and Matrix Completion

Topics in statistical physics of high-dimensional machine learning

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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

Random matrix methods for high-dimensional machine learning models

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

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

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Aggregating Spatial and Photometric Context for Photometric Stereo

4M: Massively Multimodal Masked Modeling

Sparse Autoencoders for Speech Modeling and Recognition

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

Neural Distributed Image Compression with Cross-Attention Feature Alignment

Supervised learning and inference of spiking neural networks with temporal coding

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Scalable and Privacy-Preserving Federated Principal Component Analysis

Leveraging Unlabeled Data to Track Memorization