Publications associées à Scikit-learn

Identifying uncertainty states during wayfinding in indoor environments: An EEG classification Study

The researchers used a machine-learning classification approach to better understand neurological features associated with periods of wayfinding uncertainty. The participants (n = 30) were asked to complete wayfinding tasks of varying difficulty in a virtu ...

ELSEVIER SCI LTD2022

Cellulan World: Interactive Platform to Learn Swarm Behaviors

Pierre Dillenbourg, Barbara Bruno, Hala Khodr, Aditi Kothiyal

Swarming behaviors are ubiquitous in nature, and an important topic to teach and learn. In the learning sciences domain of complex systems understanding, studies have shown that novices tend to assume that centralized control and leadership should exist wh ...

IFAAMAS2022

Learning the Exciton Properties of Azo-dyes

Anne-Clémence Corminboeuf, Ksenia Briling, Alberto Fabrizio, Sergio Vela Llausi

The ab initio determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine l ...

AMER CHEMICAL SOC2021

Architectural Sampling: A Formal Basis for Machine-Learnable Architecture

Immanuel Chee Beng Koh

The spatial and formal conception of architecture, and thus its modes of design perception and representation, directly contributes to its machine-learnability; and consequently, its capacity in leveraging today's machine learning apparatus for design inno ...

EPFL2019

Transferable Machine-Learning Model of the Electron Density

Michele Ceriotti, Anne-Clémence Corminboeuf, Alberto Fabrizio, David Mark Wilkins, Benjamin André René Meyer, Andrea Grisafi

The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework t ...

2019

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

François Maréchal, Nils Christian Schüler, Sébastien Cajot

Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice ...

2019

Non-Linear Domain Adaptation with Boosting

Pascal Fua, Carlos Joaquin Becker, Christos Marios Christoudias

A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can gen ...

2013