Concept

Meta-learning (computer science)

Related publications (158)

AdaptHD: Adaptive Efficient Training for Brain-Inspired Hyperdimensional Computing

Giovanni De Micheli, Samuel Bosch, Mohsen Imani

Brain-inspired Hyperdimensional (HD) computing is a promising solution for energy-efficient classification. However, the existing HD computing algorithms have a lack of controllability on the training iterations which often results in slow training or dive ...

2019

Exploring Neural Network Models for the Classification of Students in Highly Interactive Environments

Open-ended learning environments (OELEs) allow students to freely interact with the content and to discover important principles and concepts of the learning domain on their own. However, only some students possess the necessary skills for efficient and ef ...

International Educational Data Mining Society2019

Robust Distributed Learning and Robust Learning Machines (Research Statement)

El Mahdi El Mhamdi

Whether it occurs in artificial or biological substrates, {\it learning} is a {distributed} phenomenon in at least two aspects. First, meaningful data and experiences are rarely found in one location, hence {\it learners} have a strong incentive to work to ...

2019

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks

Maksym Andriushchenko

The problem of adversarial robustness has been studied extensively for neural networks. However, for boosted decision trees and decision stumps there are almost no results, even though they are widely used in practice (e.g. XGBoost) due to their accuracy, ...

2019

Model-based reinforcement learning and navigation in animals and machines

Dane Sterling Corneil

For decades, neuroscientists and psychologists have observed that animal performance on spatial navigation tasks suggests an internal learned map of the environment. More recently, map-based (or model-based) reinforcement learning has become a highly activ ...

EPFL2018

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

Meta-Modeling to Assess the Possible Future of Paris Agreement

Marc Vielle, Frédéric Louis François Babonneau, Alain Haurie, Alain Bernard

In the meta-modeling approach one builds a numerically tractable dynamic optimization or game model in which the parameters are identifed through statistical emulation of a detailed large scale numerical simulation model. In this paper we show how this app ...

2018

Meta-Modeling to Assess the Possible Future of Paris Agreement

Marc Vielle, Frédéric Louis François Babonneau, Alain Haurie, Alain Bernard

In the meta-modeling approach, one builds a numerically tractable dynamic optimization or game model in which the parameters are identified through statistical emulation of a detailed large scale numerical simulation model. In this paper, we show how this ...

SPRINGER2018

Knowledge Transfer with Jacobian Matching

François Fleuret, Suraj Srinivas

Classical distillation methods transfer representations from a “teacher” neural network to a “student” network by matching their output activations. Recent methods also match the Jacobians, or the gradient of output activations with the input. However, thi ...

2018

A Multilevel Monte Carlo Evolutionary Algorithm for Robust Aerodynamic Shape Design

Fabio Nobile, Pénélope Leyland, Michele Pisaroni

The majority of problems in aircraft production and operation require decisions made in the presence of uncertainty. For this reason aerodynamic designs obtained with traditional deterministic optimization techniques seeking only optimality in a specific s ...

2017

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