Publications

An enhanced wideband tracking method for characteristic modes

Chao Huang

Chenjiang Guo

Xia Ma

Yi Yuan

An enhanced wideband tracking method for characteristic modes (CMs) is investigated in this paper. The method consists of three stages, and … (see more)its core tracking stage (CTS) is based on a classical eigenvector correlation-based algorithm. To decrease the tracking time and eliminate the crossing avoidance (CRA), we append a commonly used eigenvalue filter (EF) as the preprocessing stage and a novel postprocessing stage to the CTS. The proposed postprocessing stage can identify all CRA mode pairs by analyzing their trajectory and correlation characteristics. Subsequently, it can predict corresponding CRA frequencies and correct problematic qualities rapidly. Considering potential variations in eigenvector numbers at consecutive frequency samples caused by the EF, a new execution condition for the adaptive frequency adjustment in the CTS is introduced. Finally, CMs of a conductor plate and a fractal structure are investigated to demonstrate the performance of the proposed method, and the obtained results are discussed.

2024-02-12

International Journal of Microwave and Wireless Technologies (published)

doi.org

Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation

Priyan Vaithilingam

Ian Arawjo

Elena L. Glassman

2024-02-12

ArXiv (preprint)

doi.org

arxiv.org

Leveraging Function Space Aggregation for Federated Learning at Scale

Nikita Dhawan

Nicole Elyse Mitchell

Zachary Charles

Zachary Garrett

Gintare Karolina Dziugaite

The federated learning paradigm has motivated the development of methods for aggregating multiple client updates into a global server model,… (see more) without sharing client data. Many federated learning algorithms, including the canonical Federated Averaging (FedAvg), take a direct (possibly weighted) average of the client parameter updates, motivated by results in distributed optimization. In this work, we adopt a function space perspective and propose a new algorithm, FedFish, that aggregates local approximations to the functions learned by clients, using an estimate based on their Fisher information. We evaluate FedFish on realistic, large-scale cross-device benchmarks. While the performance of FedAvg can suffer as client models drift further apart, we demonstrate that FedFish is more robust to longer local training. Our evaluation across several settings in image and language benchmarks shows that FedFish outperforms FedAvg as local training epochs increase. Further, FedFish results in global networks that are more amenable to efficient personalization via local fine-tuning on the same or shifted data distributions. For instance, federated pretraining on the C4 dataset, followed by few-shot personalization on Stack Overflow, results in a 7% improvement in next-token prediction by FedFish over FedAvg.

2024-02-12

TMLR (accepted)

doi.org

openreview.net

Metrics reloaded: Pitfalls and recommendations for image analysis validation

Lena Maier-Hein

Annika Reinke

Evangelia Christodoulou

Ben Glocker

Patrick Godau

Fabian Isensee

Jens Kleesiek

Michal Kozubek

Mauricio Reyes

Michael A. Riegler

Manuel Wiesenfarth

Michael Baumgartner

Matthias Eisenmann

Doreen Heckmann-Notzel

A. Emre Kavu

Tim Radsch

Minu Dietlinde Tizabi

Laura C. Acion

Michela Antonelli

Tal Arbel … (see 47 more)

Spyridon Bakas

Peter Bankhead

Allison Benis

M. Cardoso

Veronika Cheplygina

Beth A. Cimini

Gary S. Collins

Keyvan Farahani

Bram van Ginneken

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Pierre Jannin

Charles E. Kahn

A. Karargyris

Alan Karthikesalingam

H. Kenngott

Annette Kopp-Schneider

Anna Kreshuk

Tahsin Kurc

Bennett Landman

G. Litjens

Amin Madani

Klaus Maier-Hein

Anne L. Martel

Peter Mattson

Erik H. W. Meijering

Bjoern Menze

David Moher

K. Moons

Henning Müller

Felix Nickel

Brennan Nichyporuk

Jens Petersen

Nasir M. Rajpoot

Nicola Rieke

Julio Saez-Rodriguez

Clarisa S'anchez Guti'errez

Shravya Jaganath Shetty

M. Smeden

Carole H. Sudre

Ronald M. Summers

Abdel Aziz Taha

Sotirios A. Tsaftaris

B. Calster

Gael Varoquaux

Paul F. Jäger

2024-02-12

Nature Methods (published)

doi.org

arxiv.org

Nearest Neighbour Score Estimators for Diffusion Generative Models

Matthew Niedoba

Dylan Green

Saeid Naderiparizi

Vasileios Lioutas

Jonathan Wilder Lavington

Xiaoxuan Liang

Yunpeng Liu

Ke Zhang

Setareh Dabiri

Adam Ścibior

Berend Zwartsenberg

Frank Wood

2024-02-12

ArXiv (preprint)

doi.org

arxiv.org

The Leukemoid Reaction in Severe Alcoholic Hepatitis: A Case Report

Siva Reddy

Sachin Agrawal

Sunil Kumar

Sourya Acharya

2024-02-11

Cureus (published)

doi.org

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Tara Akhound-Sadegh

Jarrid Rector-Brooks

Joey Bose

Sarthak Mittal

Pablo Lemos

Cheng-Hao Liu

Marcin Sendera

Siamak Ravanbakhsh

Gauthier Gidel

Yoshua Bengio

Nikolay Malkin

Alexander Tong

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (see more)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient -- and no data samples -- to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is simulation-free, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant

2024-02-09

ArXiv (preprint)

doi.org

arxiv.org

Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots

Simon Chamorro

Victor Klemm

Miguel I. Valls

Chris Pal

Roland Siegwart

2024-02-09

ArXiv (preprint)

doi.org

arxiv.org

On the Privacy of Selection Mechanisms with Gaussian Noise

Jonathan Lebensold

Doina Precup

Borja Balle

2024-02-09

ArXiv (preprint)

doi.org

arxiv.org

V-STaR: Training Verifiers for Self-Taught Reasoners

Arian Hosseini

Xingdi Yuan

Nikolay Malkin

Aaron Courville

Alessandro Sordoni

Rishabh Agarwal

Common self-improvement approaches for large language models (LLMs), such as STaR (Zelikman et al., 2022), iteratively fine-tune LLMs on sel… (see more)f-generated solutions to improve their problem-solving ability. However, these approaches discard the large amounts of incorrect solutions generated during this process, potentially neglecting valuable information in such solutions. To address this shortcoming, we propose V-STaR that utilizes both the correct and incorrect solutions generated during the self-improvement process to train a verifier using DPO that judges correctness of model-generated solutions. This verifier is used at inference time to select one solution among many candidate solutions. Running V-STaR for multiple iterations results in progressively better reasoners and verifiers, delivering a 4% to 17% test accuracy improvement over existing self-improvement and verification approaches on common code generation and math reasoning benchmarks with LLaMA2 models.

2024-02-09

ArXiv (preprint)

doi.org

arxiv.org

Deep Learning for Data-Driven Districting-and-Routing

Arthur Ferraz

Quentin Cappart

Thibaut Vidal

2024-02-08

ArXiv (preprint)

arxiv.org