Publications associées à Optimisation de boucle

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

Silvestro Micera, Simone Romeni, Laura Toni, Fiorenzo Artoni

Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. ...

Aip Publishing2024

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

Felix Schürmann, Pramod Shivaji Kumbhar, Omar Awile, Ioannis Magkanaris

Advances in computational capabilities and large volumes of experimental data have established computer simulations of brain tissue models as an important pillar in modern neuroscience. Alongside, a variety of domain specific languages (DSLs) have been dev ...

ACM2023

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Martin Jaggi, Thijs Vogels, Hadrien Hendrikx

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

Brookline2023

Machine-learning predictor of nerve fiber firing rate allows the automatic optimization of electrical stimulation protocols

Silvestro Micera, Simone Romeni, Luca Pierantoni, Gabriele Marino

Computational models have been widely employed to study the electrical stimulation of the nervous system. Still, most applications either study fundamental mechanisms underlying stimulation, or address qualitative scientific questions. When quantitative qu ...

IEEE2023

An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization

Jürg Alexander Schiffmann, Soheyl Massoudi, Cyril Picard

Designing turbocompressors is a complex and challenging task, as it involves balancing conflicting objectives such as efficiency, stability, and robustness against manufacturing deviations. This paper proposes an integrated design methodology for turbocomp ...

2023

Silent Bugs Matter: A Study of Compiler-Introduced Security Bugs

Mathias Josef Payer, Jianhao Xu

Compilers assure that any produced optimized code is semantically equivalent to the original code. However, even "correct" compilers may introduce security bugs as security properties go beyond translation correctness. Security bugs introduced by such corr ...

Berkeley2023

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

François Maréchal, Julia Granacher

In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

Oxford2023

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Flavio Ponzina

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

2022

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Optimisation de boucle

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

Learning-based techniques for lensless reconstruction

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Machine-learning predictor of nerve fiber firing rate allows the automatic optimization of electrical stimulation protocols

RMAML: Riemannian meta-learning with orthogonality constraints

An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization

Silent Bugs Matter: A Study of Compiler-Introduced Security Bugs

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

Graph Chatbot

Chattez avec Graph Search

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

Machine-learning predictor of nerve fiber firing rate allows the automatic optimization of electrical stimulation protocols

RMAML: Riemannian meta-learning with orthogonality constraints

An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization

Silent Bugs Matter: A Study of Compiler-Introduced Security Bugs

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

Learning-based techniques for lensless reconstruction

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering