A clinical road map for single-cell omics

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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 ...

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 ...

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 ...

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 ...

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 ...

Optical neural networks are expected to be hybrid systems for the foreseeable future combining optics and electronics. The linear portion of the system, consisting of the weighted interconnections between the neurons, can be realized optically. The nonline ...

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 ...

We study the problems of testing and learning high-dimensional discrete convex sets. The simplest high-dimensional discrete domain where convexity is a non-Trivial property is the ternary hypercube, {-1, 0, 1}n. The goal of this work is to understand struc ...

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 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 ...

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 ...

This paper presents ALO-VC, a non-parallel low-latency one-shot phonetic posteriorgrams (PPGs) based voice conversion method. ALO-VC enables any-to-any voice conversion using only one utterance from the target speaker, with only 47.5 ms future look-ahead. ...

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

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 ...

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 ...

Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated ('replayed'), either in the same or reversed order, during bursts of activity (sharp wave-ripples [SWRs]) that occur ...

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 ...

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 ...

A clinical road map for single-cell omics

Chat with Graph Search

Boosting Protein Graph Representations through Static-Dynamic Fusion

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

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

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

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

On the implementation of nonlinearities in optical neural networks: opinion

Efficient Continual Finite-Sum Minimization

Testing and learning convex sets in the ternary hypercube

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

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

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

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

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

On double-descent in uncertainty quantification in overparametrized models

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

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

Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

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

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

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