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

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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 ...

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 ...

Clickstream data from digital learning environments provide valuable insights into student behavior but are challenging to interpret due to their granularity. Prior methods mainly relied on handcrafted features, expert labeling, clustering, or supervised m ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 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. ...

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 ...

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

Chattez avec Graph Search

A clinical road map for single-cell omics

Boosting Protein Graph Representations through Static-Dynamic Fusion

ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMs

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

Efficient Continual Finite-Sum Minimization

On the implementation of nonlinearities in optical neural networks: opinion

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

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

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

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

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

On double-descent in uncertainty quantification in overparametrized models

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

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