Publications related to Uncertainty in Modern Deep Learning

Related publications (13)

Uncertainty analysis of field trials of low-cost Bi-TiO_2 -P25 solar photocatalyst for sustainable water treatment

Solar photocatalysis has the potential to reduce chemical and microbial contaminants in water and make it safer for consumption in an effective and sustainable manner. This has been studied and proven well under laboratoryscale conditions. In our previous ...

2025

Partial wave analysis of $ \psi (3686)\to \Lambda {\overline{\Sigma}}^0{\pi}^0 $ + c.c

Yunxuan Song

Based on a sample of (2712 . 4 ± 14 . 3) × 10 6 ψ (3686) events collected with the BESIII detector, a partial wave analysis of the decay

\psi (3686)\to \Lambda {\overline{\Sigma}}^0{\pi}^0

+ c . c . is performed to investigate Λ * and Σ * resonances in ...

Springer2025

Integrating AI across the Chemistry Discovery Cycle: Advancing Sustainable Chemistry through Digital Methods

Oliver Tobias Schilter

The chemical industry faces mounting pressure to develop more sustainable processes while accelerating innovation to address climate challenges. Traditional discovery approaches in chemistry follow an iterative cycle: forming hypotheses about new molecules ...

EPFL2025

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

AMCG: a graph dual atomic-molecular conditional molecular generator

Andrea Cavalli

Drug design is both a time consuming and expensive endeavour. Computational strategies offer viable options to address this task; deep learning approaches in particular are indeed gaining traction for their capability of dealing with chemical structures. A ...

IOP Publishing2024

TEE4EHR: Transformer event encoder for better representation learning in electronic health records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing values in certain clinical variables is not at random but depends on the decisi ...

2024

Floorplan Isovist Dataset

Mikhael Johanes

This dataset was created as part of the PhD project Machine Understanding of Architectural Space: From Analytical to Generative Applications at EPFL Media and Design Lab (LDM). It was derived from the Cubicasa5K dataset. The dataset contains preprocessed d ...

Zenodo2024

Supplementary files for Machine learning for histological annotation and quantification of cortical layers

Olivier Burri, Julie Meystre

Creators Meystre Julie Olivier Burri Contributors Jean Jacquemier Description This dataset contains 7 QuPath projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well. The raw data contains im ...

Zenodo2024

A machine learning method for real-time numerical simulations of cardiac electromechanics

Alfio Quarteroni

We propose a machine learning-based method to build a system of differential equations that approximates the dynamics of 3D electromechanical models for the human heart, accounting for the dependence on a set of parameters. Specifically, our method permits ...

ELSEVIER SCIENCE SA2022

TwoResNet: Two-level resolution neural network for traffic forecasting on freeway networks

Nikolaos Geroliminis, Semin Kwak

Traffic forecasting problems of freeway networks are heavily tackled by deep learning methods because it requires learning highly complex correlations between variables both in time and space. Adopting a graph convolutional network (GCN) becomes a standard ...

IEEE2022

Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study

Martin Jaggi, Mary-Anne Hartley

Background Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a ...

2021

Using footstep-induced vibrations for occupant detection and recognition in buildings

Ian Smith, Yves Sylvain Gilles Reuland, Sai Ganesh Sarvotham Pai, Slah Drira

Occupant detection and recognition support functional goals such as security, healthcare, and energy management in buildings. Typical sensing approaches, such as smartphones and cameras, undermine the privacy of building occupants and inherently affect the ...

2021

Neuroevolution: from architectures to learning

Dario Floreano, Claudio Mattiussi, Peter Dürr

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

2008