Publications associées à Scikit-learn

Model agnostic methods meta-learn despite misspecifications

Nicolas Henri Bernard Flammarion, Oguz Kaan Yüksel, Etienne Patrice Boursier

Due to its empirical success on few shot classification and reinforcement learning, meta-learning recently received a lot of interest. Meta-learning leverages data from previous tasks to quickly learn a new task, despite limited data. In particular, model ...

2023

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

Mahsa Shoaran, Bingzhao Zhu

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

Deep Q-learning agent for building earthquake resisting wall

Katrin Beyer, Qianqing Wang, Ketson Roberto Maximiano Dos Santos

The construction of dry-joint brick masonry that is subject to seismic loading can be modelled as a strategy problem wherein bricks must be laid to form a load resisting structure. To learn how to mount bricks while considering relevant engineering criteri ...

2022

Slice Estimation in Diffusion MRI of Neonatal and Fetal Brains in Image and Spherical Harmonics Domains Using Autoencoders

Meritxell Bach Cuadra, Erick Jorge Canales Rodriguez, Gabriel Girard, Thomas Yu, Hamza Kebiri

Diffusion MRI (dMRI) of the developing brain can provide valuable insights into the white matter development. However, slice thickness in fetal dMRI is typically high (i.e., 3-5 mm) to freeze the in-plane motion, which reduces the sensitivity of the dMRI s ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Learning the Exciton Properties of Azo-dyes

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

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Michaël Unser, Shayan Aziznejad, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos

We introduce a variational framework to learn the activation functions of deep neural networks. Our aim is to increase the capacity of the network while controlling an upper-bound of the actual Lipschitz constant of the input-output relation. To that end, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Learning Graph Influence From Social Interactions

Ali H. Sayed, Virginia Bordignon, Augusto José Rabelo Almeida Santos

In social learning, agents form their opinions or beliefs about certain hypotheses by exchanging local information. This work considers the recent paradigm of weak graphs, where the network is partitioned into sending and receiving components, with the for ...

IEEE2020

Transferable Machine-Learning Model of the Electron Density

Michele Ceriotti, 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

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Scikit-learn

Model agnostic methods meta-learn despite misspecifications

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

Cellulan World: Interactive Platform to Learn Swarm Behaviors

Deep Q-learning agent for building earthquake resisting wall

Slice Estimation in Diffusion MRI of Neonatal and Fetal Brains in Image and Spherical Harmonics Domains Using Autoencoders

Learning the Exciton Properties of Azo-dyes

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Learning Graph Influence From Social Interactions

Architectural Sampling: A Formal Basis for Machine-Learnable Architecture

Transferable Machine-Learning Model of the Electron Density

Graph Chatbot

Chattez avec Graph Search

Cellulan World: Interactive Platform to Learn Swarm Behaviors

Model agnostic methods meta-learn despite misspecifications

Slice Estimation in Diffusion MRI of Neonatal and Fetal Brains in Image and Spherical Harmonics Domains Using Autoencoders

Deep Q-learning agent for building earthquake resisting wall

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

Learning Graph Influence From Social Interactions

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Learning the Exciton Properties of Azo-dyes

Transferable Machine-Learning Model of the Electron Density

Architectural Sampling: A Formal Basis for Machine-Learnable Architecture