Publications related to Object code optimizer

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

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

Colin Neil Jones, Bratislav Svetozarevic, Wenjie Xu

We study the problem of performance optimization of closed -loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed -loop performance by automatically tuning controller gains or re ...

Elsevier Sci Ltd2024

Stable Nonconvex-Nonconcave Training via Linear Interpolation

Volkan Cevher, Thomas Michaelsen Pethick, Wanyun Xie

This paper presents a theoretical analysis of linear interpolation as a principled method for stabilizing (large-scale) neural network training. We argue that instabilities in the optimization process are often caused by the nonmonotonicity of the loss lan ...

2023

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

A general framework to evaluate different rebalancing operations strategies in one-way car sharing systems

Michel Bierlaire, Nikola Obrenovic, Selin Ataç

Car sharing systems (CSSs) are one of the environmentally beneficial solutions in urban transportation. However, the operators still struggle to make these systems profitable. One of the main contributors in operational cost is rebalancing operations. Ther ...

Springer2023

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

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

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

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

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Object code optimizer

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

Stable Nonconvex-Nonconcave Training via Linear Interpolation

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

A general framework to evaluate different rebalancing operations strategies in one-way car sharing systems

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

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

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

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

Graph Chatbot

Chat with Graph Search

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

A general framework to evaluate different rebalancing operations strategies in one-way car sharing systems

Stable Nonconvex-Nonconcave Training via Linear Interpolation

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

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

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

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

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