Publications associées à Designed Semiconductor Network Random Lasers

Publications associées (24)

Reweighting simulated events using machine-learning techniques in the CMS experiment

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk, Paul Théodore Gabriel Pablo De Bryas Dexmiers d'Archiac

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detect ...

2025

Deep Neural Networks: Large-Width Behavior and Generalization Bounds

Evgenii Golikov

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

Source-Free Domain-Invariant Performance Prediction

Mathieu Salzmann

Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their ...

Springer Nature2025

Broadband and turnkey Pockels micro-comb with Monolithic Microwave Resonator

Tobias Kippenberg, Jianqi Hu, Mikhail Churaev, Chengli Wang, Junyin Zhang

We demonstrate a fully hybrid integrated electrooptic comb generator based on a dispersion engineered thin-film Lithium Tantalate microresonator featuring robust turnkey operation and record-broad >200nm span covering the entire telecommunications S, C, an ...

Institute of Electrical and Electronics Engineers Inc.2024

Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems

Pascal Frossard, Javier Alejandro Maroto Morales

Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming i ...

2024

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

AdaNCA: Neural Cellular Automata as Adaptors for More Robust Vision Transformer

Sabine Süsstrunk, Tong Zhang, Yitao Xu

Vision Transformers (ViTs) demonstrate remarkable performance in image classification through visual-token interaction learning, particularly when equipped with local information via region attention or convolutions. Although such architectures improve the ...

2024

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

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Guillaume Marc Georges Vray, Devavrat Tomar

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

Los Alamitos2023

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

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

Single-photon nonlinearities and blockade from a strongly driven photonic molecule

Camille Sophie Brès, Marco Clementi

Achieving the regime of single-photon nonlinearities in photonic devices by just exploiting the intrinsic high-order susceptibilities of conventional materials would open the door to practical semiconductor-based quantum photonic technologies. Here we show ...

Optica Publishing Group2022

Nanoscale Mapping of Light Emission in Nanospade-Based InGaAs Quantum Wells Integrated on Si(100): Implications for Dual Light-Emitting Devices

Anna Fontcuberta i Morral, Wonjong Kim, Jean-Baptiste Leran, Nicolas Tappy, Didem Dede, Nicholas Paul Morgan, Valerio Piazza, Akshay Balgarkashi, Raphaël François Lemerle

III-V semiconductors outperform Si in many optoelectronics applications due to their high carrier mobility, efficient light emission and absorption processes, and the possibility to engineer their band gap through alloying. However, complementing Si techno ...

2022

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

Predictive Analysis of Errors During Robot-Mediated Gamified Training

Pierre Dillenbourg, Arzu Güneysu Özgür

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

IEEE2022

A tunable femtosecond fiber laser source for multiphoton emission microscopy

Pierre-Marc Jean Marie Dansette

Second harmonic generation microscopy is used to image interfaces or non-centrosymmetric structures and molecules. Two-photon fluorescence microscopy can image molecules within cells without using markers. Thus those technics have lead to advances in our u ...

EPFL2021

Machine learning with kernels for portfolio valuation and risk management

Damir Filipovic, Lotfi Boudabsa

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is ...

SPRINGER HEIDELBERG2021

Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference

Jean-Louis Scartezzini, Nahid Mohajeri Pour Rayeni, Roberto Castello, Alina Ursula Rosa Walch

The increasing use of ground-source heat pumps (GSHPs) for heating and cooling of buildings raises questions regarding the technical potential of GSHPs and their impact on the temperature in the shallow subsurface. In this paper, we develop a method using ...

IOP PUBLISHING LTD2021