Publications related to Technology Legalization and Optimization for Adiabatic Quantum-Flux Parametron

Related publications (17)

Deep Neural Networks: Large-Width Behavior and Generalization Bounds

During several decades, neural network architectures have undergone considerable evolution: from shallow to deep, from fully connected to structured (e.g. convolutional, recurrent, or residual), from unnormalized to normalized. The present thesis contribut ...

EPFL2025

Metacognition meets AI: Empowering reflective writing with large language models

Tanja Christina Käser Jacober, Paola Mejia Domenzain, Seyed Parsa Neshaei

Reflective writing is known as a useful method in learning sciences to improve the metacognitive skills of students. However, students struggle to structure their reflections properly, limiting the possible learning gains. Previous works in educational tec ...

Wiley2025

Thermal Energy Storage in Multi-Energy System Optimization: How to Consider Temperatures?

Ueli Schilt

The transition from fossil-based to renewable energy sources requires the adoption of intermittent, decentralized energy generation technologies. Therefore, the integration of energy storage technologies is becoming increasingly important. The impact of op ...

IEEE2024

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Marc Conrad Russwurm, Thiên-Anh Claris Nguyen, Gaston Jean Lenczner

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Olga Fink, Hao Lu

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

One-Bit Byzantine-Tolerant Distributed Learning via Over-The-Air Computation

Yuning Jiang

Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies distributed learnin ...

2024

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Melanie Nicole Zeilinger, Roland Schwan, Xuan Truong Nghiem

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Lenka Zdeborová, Florent Gérard Krzakala, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

Robotic automation and unsupervised cluster assisted modeling for solving the forward and reverse design problem of paper airplanes

Josephine Anna Eleanor Hughes, Kai Christian Junge, Nana Obayashi, Stefan Ilic

Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory f ...

Nature Research2022

Learning of physical systems: from inference to control

Babak Rahmani

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

EPFL2022

Predicting chemical hazard across taxa through machine learning

Kristin Schirmer, Julita Maria Stadnicka-Michalak

We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in the cl ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Bayesian approach for validation of runaway electron simulations

Carl Mathias Paul Hoppe

Plasma-terminating disruptions in future fusion reactors may result in conversion of the initial current to a relativistic runaway electron beam. Validated predictive tools are required to optimise the scenarios and mitigation actuators to avoid the excess ...

Cambridge University Press2022

Dual Consensus Proximal Algorithm for Multi-Agent Sharing Problems

Ali H. Sayed, Sulaiman A S A E Alghunaim

This work considers multi-agent sharing optimization problems, where each agent owns a local smooth function plus a non-smooth function, and the network seeks to minimize the sum of all local functions plus a coupling composite function (possibly non-smoot ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes

Michaël Unser, Anaïs Laure Marie-Thérèse Badoual

We present a new family of active surfaces for the semiautomatic segmentation of volumetric objects in 3D biomedical images. We represent our deformable model by a subdivision surface encoded by a small set of control points and generated through a geometr ...

2021

Towards a pragmatist dealing with algorithmic bias in medical machine learning

Georg Michael Joachim Starke

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One ...

2021

Scalable Bayesian optimization for model calibration: Case study on coupled building and HVAC dynamics

Emilio Maddalena

Model calibration for building systems is a key step to achieving accurate and reliable predictions that reflect the dynamics of real systems under study. Calibration becomes particularly challenging when integrating building and HVAC dynamics, due to larg ...

2021

Modeling and Assessment of Urban Energy Systems

Amarasinghage Tharindu Dasun Perera

Improving the energy sustainability of our cities involves the integration of multiple renewable energy technologies into existing energy infrastructure, stretching the capabilities of traditional energy systems to the limit. To consider the transition tak ...

EPFL2019