Publications related to Measurement-based optimization of batch processes with terminal constraint

Scalable constrained optimization

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Mesh-based topology, shape and sizing optimization of ribbed plates

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

Springer2024

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

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Touradj Ebrahimi, Yuhang Lu, Zewei Xu

Recent years have witnessed significant advance- ment in face recognition (FR) techniques, with their applications widely spread in people’s lives and security-sensitive areas. There is a growing need for reliable interpretations of decisions of such syste ...

2024

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

Silvestro Micera, Simone Romeni

A method for optimizing at least one of a geometry, an implantation procedure, and/or stimulation protocol of one or more electrodes for an electrical stimulation of a target structure in a nervous system of a living being by a computer device, the method ...

2024

Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization

Siang-Yun Lee

Electronic devices play an irreplaceable role in our lives. With the tightening time to market, exploding demand for computing power, and continuous desire for smaller, faster, less energy-consuming, and lower-cost chips, computer-aided design for electron ...

EPFL2024

Learning to Remove Cuts in Integer Linear Programming

Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...

2024

Measurement-based optimization of batch processes with terminal constraint

Graph Chatbot

Chat with Graph Search

Scalable constrained optimization

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Mesh-based topology, shape and sizing optimization of ribbed plates

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

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Explainable Face Verification via Feature-Guided Gradient Backpropagation

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

Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization

Learning to Remove Cuts in Integer Linear Programming

Scalable constrained optimization

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Mesh-based topology, shape and sizing optimization of ribbed plates

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

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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

Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization

Learning to Remove Cuts in Integer Linear Programming

Explainable Face Verification via Feature-Guided Gradient Backpropagation