Publication
Autonomous Detection and Deterrence of Pigeons on Buildings by Drones
Publications associées (32)
Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models
Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi
Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...
EPFL Infoscience2024Bed Topography Inference from Velocity Field Using Deep Learning.
Christophe Ancey, Mehrdad Kiani Oshtorjani
Measuring bathymetry has always been a major scientific and technological challenge. In this work, we used a deep learning technique for inferring bathymetry from the depth-averaged velocity field. The training of the neural network is based on 5742 labora ...
2023Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models
Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...
EPFL2022Distributed 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 ...
2022Predicting optical transmission through complex scattering media from reflection patterns with deep neural networks
Demetri Psaltis, Eirini Kakkava
Deep neural networks (DNNs) are used to reconstruct transmission speckle intensity patterns from the respective reflection speckle intensity patterns generated by illuminated parafilm layers. The dependence of the reconstruction accuracy on the thickness o ...
2021Determining 1D fast-ion velocity distribution functions from ion cyclotron emission data using deep neural networks
Ambrogio Fasoli, Marcelo Baquero Ruiz
The relationship between simulated ion cyclotron emission (ICE) signals s and the corresponding 1D velocity distribution function f(upsilon(perpendicular to)) of the fast ions triggering the ICE is modeled using a two-layer deep neural network. The network ...
AIP Publishing2021Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)
Clément Hongler, Franck Raymond Gabriel, Arthur Jacot
The Neural Tangent Kernel is a new way to understand the gradient descent in deep neural networks, connecting them with kernel methods. In this talk, I'll introduce this formalism and give a number of results on the Neural Tangent Kernel and explain how th ...
ASSOC COMPUTING MACHINERY2021A spiking central pattern generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards
Auke Ijspeert, Jonathan Patrick Arreguit O'Neill, Axel von Arnim, Emmanouil Angelidis
Central pattern generator (CPG) models have long been used to investigate both the neural mechanisms that underlie animal locomotion, as well as for robotic research. In this work we propose a spiking central pattern generator (SCPG) neural network and its ...
Bristol2021Maximizing Fiber Cable Capacity Under A Supply Power Constraint Using Deep Neural Networks
We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using deep neural networks in a parallel fiber context. (C) 2020 The Authors ...
IEEE2020Inverse Modelling and Predictive Inference in Continuum Mechanics: a Data-Driven Approach
Claire Marianne Charlotte Capelo
The explosive growth of machine learning in the age of data has led to a new probabilistic and data-driven approach to solving very different types of problems. In this paper we study the feasibility of using such data-driven algorithms to solve classic ph ...
2020