Publication

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

Publications associées (30)

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

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 ...

EPFL2024

Glucose Enhances Salinity-Driven Sea Spray Aerosol Production in Eastern Arctic Waters

Guangyu Li, Julia Schmale, Athanasios Nenes, André Welti

Sea spray aerosols (SSA) greatly affect the climate system by scattering solar radiation and acting as seeds for cloud droplet formation. The ecosystems in the Arctic Ocean are rapidly changing due to global warming, and the effects these changes have on t ...

2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Partial discharge localization in power transformer tanks using machine learning methods

Farhad Rachidi-Haeri, Marcos Rubinstein, Hamidreza Karami

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Nature Publishing Group2024

Adsorption of Copper by Naturally and Artificially Aged Polystyrene Microplastics and Subsequent Release in Simulated Gastrointestinal Fluid

Florian Frédéric Vincent Breider, Thibault Béranger Masset, Lu Zhou

Microplastics, especially aged microplastics can become vectors of metals from environment to organisms with potential negative effects on food chain. However, a few studies focused on the bioavailability of adsorbed metals and most studies related to aged ...

2024

Photo-Fenton inactivation of MS2 bacteriophage at alkaline pH by Fe salts or nm to μm-sized oxides, and the Janus-faced effects of natural organic matter in surface waters

César Pulgarin, Stefanos Giannakis, Ogadimma Cassandra Oji-Okoro, Giulio Farinelli

In this study, MS2 bacteriophage was inactivated by homogeneous and heterogeneous photo-Fenton processes in an alkaline matrix (pH 8) using low concentrations of H2O2 and iron forms (1 mg/L), including Fe(II), Fe(III), and Fe (hydr)oxides. As a reference, ...

Elsevier2024

On the Generalization of Stochastic Gradient Descent with Momentum

Volkan Cevher, Kimon Antonakopoulos

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Microtome Publishing2024

Organic matters, but inorganic matters too: column examination of elevated mercury sorption on low organic matter aquifer material using concentrations and stable isotope ratios

Lorenz Schwab

Sorption of mercury (Hg) in soils is suggested to be predominantly associated with organic matter (OM). However, there is a growing collection of research that suggests that clay minerals and oxides are also important solid phases for the sorption of solu ...

2024

Understanding generalization and robustness in modern deep learning

Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024