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Genetic Representation of Adaptive Neural Controllers

Related publications (41)

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Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Development of a Robotic Microsurgeon for Embryonic Research

Ece Özelçi

Understanding how biological matter takes its shape is instrumental to biology, bioengineering, medicine, and bioinspired engineering. Gaining information on the principles of morphogenesis could enable clinicians to correct developmental abnormalities, ev ...

EPFL2023

Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach

Giancarlo Ferrari Trecate, Luca Furieri, Muhammad Zakwan, Clara Lucía Galimberti

Large-scale cyber-physical systems require that control policies are distributed, that is, that they only rely on local real-time measurements and communication with neighboring agents. Optimal Distributed Control (ODC) problems are, however, highly intrac ...

2022

Population pharmacokinetic model selection assisted by machine learning

Jan Sickmann Hesthaven, Nadia Terranova

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...

SPRINGER/PLENUM PUBLISHERS2021

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Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller. Evolutionary algorithms (EAs), combined with physical simulators, represent a valid tool to overcome this issue. In this work, we investi ...

Association for Computing Machinery2021

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

Harshit Gupta

In this thesis, we propose new algorithms to solve inverse problems in the context of biomedical images. Due to ill-posedness, solving these problems require some prior knowledge of the statistics of the underlying images. The traditional algorithms, in th ...

EPFL2020

We present a light field synthesis technique that achieves accurate reconstruction given a low-cost, wide-baseline camera rig. Our system integrates optical flow with methods for rectification, disparity estimation, and feature extraction, which we then fe ...

2019

Design of a Passive Flow Regulator Using a Genetic Algorithm

Harald Van Lintel

Passive flow regulators are usually intended to deliver or drain a fluid at a constant rate independently from pressure variations. Microfluidic devices made of a stack of two plates are considered here: the first plate comprises a flexible silicon membran ...

Elsevier2016

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Recently there has been a growing movement of researchers that believes innovation and novelty creation, rather than pure optimization, are the true strengths of evolutionary algorithms relative to other forms of machine learning. This idea also provides o ...

ACM Press2016

Evolutionary algorithms are heuristic methods that operate on a population of individuals, which represent candidate solutions to optimization problems. Individuals with better quality, or fitness, are reproduced at a higher rate than less fit individuals ...

EPFL2015