ALO-VC: Any-to-any Low-latency One-shot Voice Conversion

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Machine learning for protein modeling faces significant challenges due to proteins' inherently dynamic nature, yet most graph-based machine learning methods rely solely on static structural information. Recently, the growing availability of molecular dynam ...

In a matter of years, single-cell omics has matured from a pioneering technique employed by just a handful of specialized laboratories to become a ubiquitous feature of biological research and a key driver of scientific discovery. The widespread adoption a ...

Ash dieback, caused by the fungal pathogen Hymenoscyphus fraxineus, is devastating ash tree populations across U.K. and Europe, with projections indicating that up to 80% of ash trees may die as a result of the disease. The extensive loss of this keystone ...

Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in ...

Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic fun ...

In 2019, the JPEG Standardization Committee initiated JPEG AI to define the first image coding specifications, taking advantage of an end-to-end learning-based coding approach. The JPEG AI specifications will be soon published as an international standard ...

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept—reusing prior knowledge to learn in and from novel situations—is successfully leveraged by humans to handle novel situations. In recent ...

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 ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent works mostly focus on learning a deep feature encoder by minimizing the discrepa ...

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

This thesis presents work at the junction of statistics and climate science. We first provide methodology for use by climate scientists when performing fast event attribution using extreme value theory, and then describe two interdisciplinary projects in c ...

The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which a ...

Model compression techniques have lead to a reduction of size and number of computations of Deep Learning models. However, techniques such as pruning mostly lack of a real co-optimization with hardware platforms. For instance, implementing unstructured pru ...

Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet companies to deploy machine learning as a service (MLaaS). Recently, the fi ...

Vision-based hand pose estimation is important in human-computer interaction. While many recent works focus on full degree-of-freedom hand pose estimation, robust estimation of global hand pose remains a challenging problem. This paper presents a novel alg ...

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and emplo ...

Many of the decisions we make in our everyday lives are sequential and entail sparse rewards. While sequential decision-making has been extensively investigated in theory (e.g., by reinforcement learning models) there is no systematic experimental paradigm ...

Interest in deep probabilistic graphical models has increased in recent years, due to their state-of-the-art perfor- mance on many machine learning applications. Such models are typically trained with the stochastic gradient method, which can take a signif ...

ALO-VC: Any-to-any Low-latency One-shot Voice Conversion

Chat with Graph Search

Boosting Protein Graph Representations through Static-Dynamic Fusion

A clinical road map for single-cell omics

Leaf Level Ash Dieback Disease Detection and Online Severity Estimation With UAVs

Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models

Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation

JPEG AI: The First International Standard for Image Coding Based on an End-to-End Learning-Based Approach

Efficient Continual Finite-Sum Minimization

Transfer learning in robotics: An upcoming breakthrough? A review of promises and challenges

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

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

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

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

Weakly supervised joint whole-slide segmentation and classification in prostate cancer

Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Hough Forest With Optimized Leaves for Global Hand Pose Estimation With Arbitrary Postures

Non-intrusive reduced order modeling of nonlinear problems using neural networks

What to Choose Next? A Paradigm for Testing Human Sequential Decision Making

Stochastic Spectral Descent for Discrete Graphical Models