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

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Recreating real-world objects and scenes in a visually plausible manner and making them editable with intuitive instructions is central to Virtual and Augmented Reality (VR/AR) applications. This research lies at the intersection of neural rendering, 3D co ...

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 ...

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 ...

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 ...

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 ...

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 ...

The simplification of neural network models is described. For example, a method for simplifying a neural network model includes providing the neural network model to be simplified, defining a first temporal filter for the conveyance of input from a neuron ...

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 ...

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 ...

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. ...

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 ...

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 ...

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 ...

Predicting the effects of mutations on protein stability is a key problem in fundamental and applied biology, still unsolved even for the relatively simple case of small, soluble, globular, monomeric, two-state-folder proteins. Many articles discuss the li ...

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

Chattez avec Graph Search

Towards Visually-Plausible and Controllable 3D Representations

A clinical road map for single-cell omics

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

Boosting Protein Graph Representations through Static-Dynamic Fusion

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

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

Simplification of spiking neural network models

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

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

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

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

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

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

On double-descent in uncertainty quantification in overparametrized models

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

Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures