Concept

Rule-based machine learning

Related publications (213)

Artificial intelligence for natural product drug discovery

Philippe Schwaller, Michael Alexander Skinnider, Florian Huber, Albert Hofstetter

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to e ...

Berlin2023

A benchmark dataset for machine learning in ecotoxicology

Kristin Schirmer, Marco Baity-Jesi, Fernando Perez Cruz

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, ...

Berlin2023

Toward data-driven materials design: From atoms to pilot plants

Kevin Maik Jablonka

Discovering new materials is essential but challenging, time-consuming, and expensive.In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of p ...

EPFL2023

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Leonardo Petrini

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023

Subtleties in the trainability of quantum machine learning models

A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called quantum machine learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, it ...

SPRINGERNATURE2023

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

Michele Ceriotti, Philip Robin Loche, Kevin Kazuki Huguenin-Dumittan

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrost ...

Washington2023

Predicting Survey Response with Quotation-based Modeling: A Case Study on Favorability towards the United States

Alireza Amirshahi, Saleh Baghersalimi, Jonathan Philippe Reymond

The acquisition of survey responses is a crucial component in conducting research aimed at comprehending public opinion. However, survey data collection can be arduous, time-consuming, and expensive, with no assurance of an adequate response rate. In this ...

IEEE COMPUTER SOC2023

Blue ice in Antarctica: The gateway to travel in time and space

Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm, Veronica Tollenaar

Antarctica has unique areas that expose blue ice, which contrast to most of the continent (~98%) that is covered by snow. Some of these blue ice areas (BIAs) contain meteorite concentrations and (very) old ice, making them very valuable for understanding o ...

GFZ German Research Centre for Geosciences2023

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Nahal Mansouri, Sahand Jamal Rahi

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objecti ...

FRONTIERS MEDIA SA2022

Annotation-efficient image anomaly detection

Jean-Philippe Thiran

The present invention proposes a method for detecting anomalous or out-of-distribution images in a machine learning system (1) comprising a pre-training network with a first encoder, and an anomaly detection network with a second encoder. The system is fir ...

2022

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