Towards End-to-End Speech Recognition

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Atomistic machine learning has revolutionized atomistic modeling. Traditionally, molecular dynamics simulations relied on classical force fields or electronic structure calculations like Density Functional Theory. Classical force fields are fast but less p ...

Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models ...

Living systems are commonly engineered through an iterative design-build-test process. However, unlike in other engineering disciplines, the systems that synthetic biologists construct are able to evolve. On the one hand, this compromises their long-term s ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

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 ...

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 ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

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 ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

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 ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

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 ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

The increasing availability of multimodal data from electronic health records (EHR) has paved the way for deep learning methods to improve diagnosis accuracy. However, deep learning models are data-driven, requiring large-scale datasets to achieve high gen ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

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 present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Towards End-to-End Speech Recognition

Chat with Graph Search

Advancing understanding and practical performance of machine learning interatomic potentials

Practical Federated Learning without a Server

Light-directed evolution of dynamic biological systems

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Random matrix methods for high-dimensional machine learning models

Topics in statistical physics of high-dimensional machine learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Safe Deep Neural Networks

Robust machine learning for neuroscientific inference

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

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

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

CARER - ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Aggregating Spatial and Photometric Context for Photometric Stereo

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Machine Learning for Modeling Stock Returns