Publications

Antagonistic AI

Alice Cai

Ian Arawjo

Elena L. Glassman

2024-02-12

ArXiv (preprint)

doi.org

arxiv.org

An enhanced wideband tracking method for characteristic modes

Chao Huang

Chenjiang Guo

Xia Ma

Yi Yuan

Jun Ding

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

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-NÖTZEL

A. EMRE KAVUR

TIM RÄDSCH

Minu Dietlinde Tizabi

LAURA ACION

Michela Antonelli

Tal Arbel … (see 47 more)

Spyridon Bakas

Peter Bankhead

Allison Benis

M. Jorge 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

Alexandros Karargyris

Alan Karthikesalingam

H. Kenngott

Annette Kopp-Schneider

Anna Kreshuk

Tahsin Kurc

Bennett Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne L. Martel

Peter Mattson

ERIK MEIJERING

Bjoern Menze

David Moher

KAREL G.M. MOONS

Henning Müller

Felix Nickel

Brennan Nichyporuk

Jens Petersen

NASIR RAJPOOT

Nicola Rieke

Julio Saez-Rodriguez

Clarisa S'anchez Guti'errez

SHRAVYA SHETTY

M. Smeden

Carole H. Sudre

Ronald M. Summers

Abdel Aziz Taha

Sotirios A. Tsaftaris

Ben Van 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 N. Wood

2024-02-12

ArXiv (preprint)

doi.org

arxiv.org

Understanding metric-related pitfalls in image analysis validation

Annika Reinke

Minu Dietlinde Tizabi

Michael Baumgartner

Matthias Eisenmann

DOREEN HECKMANN-NÖTZEL

A. EMRE KAVUR

TIM RÄDSCH

Carole H. Sudre

LAURA ACION

Michela Antonelli

Tal Arbel

Spyridon Bakas

Allison Benis

Arriel Benis

Matthew Blaschko

FLORIAN BUETTNER

M. Jorge Cardoso

Veronika Cheplygina

JIANXU CHEN

Evangelia Christodoulou … (see 59 more)

BETH A. CIMINI

Gary S. Collins

Keyvan Farahani

LUCIANA FERRER

Adrian Galdran

Bram van Ginneken

Ben Glocker

PATRICK GODAU

Robert Cary Haase

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Fabian Isensee

Pierre Jannin

CHARLES E. KAHN

DAGMAR KAINMUELLER

BERNHARD KAINZ

Alexandros Karargyris

Alan Karthikesalingam

H. Kenngott

Jens Kleesiek

Florian Kofler

THIJS KOOI

Annette Kopp-Schneider

Michal Kozubek

Anna Kreshuk

Tahsin Kurc

BENNETT A. LANDMAN

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne L. Martel

Peter Mattson

ERIK MEIJERING

Bjoern Menze

KAREL G.M. MOONS

Henning Müller

Brennan Nichyporuk

Felix Nickel

Jens Petersen

SUSANNE M. RAFELSKI

NASIR RAJPOOT

Mauricio Reyes

MICHAEL A. RIEGLER

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

SHRAVYA SHETTY

M. Smeden

Ronald M. Summers

Abdel Aziz Taha

ALEKSEI TIULPIN

Sotirios A. Tsaftaris

Ben Van Calster

Gael Varoquaux

Manuel Wiesenfarth

ZIV R. YANIV

PAUL F. JÄGER

Lena Maier-Hein

2024-02-12

Nature methods (published)

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

Cheng-Hao Liu

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

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Cheng-Hao Liu

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 de La Iglesia Valls

Chris Pal

Roland Siegwart

In recent years, legged and wheeled-legged robots have gained prominence for tasks in environments predominantly created for humans across v… (see more)arious domains. One significant challenge faced by many of these robots is their limited capability to navigate stairs, which hampers their functionality in multi-story environments. This study proposes a method aimed at addressing this limitation, employing reinforcement learning to develop a versatile controller applicable to a wide range of robots. In contrast to the conventional velocity-based controllers, our approach builds upon a position-based formulation of the RL task, which we show to be vital for stair climbing. Furthermore, the methodology leverages an asymmetric actor-critic structure, enabling the utilization of privileged information from simulated environments during training while eliminating the reliance on exteroceptive sensors during real-world deployment. Another key feature of the proposed approach is the incorporation of a boolean observation within the controller, enabling the activation or deactivation of a stair-climbing mode. We present our results on different quadrupeds and bipedal robots in simulation and showcase how our method allows the balancing robot Ascento to climb 15cm stairs in the real world, a task that was previously impossible for this robot.

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 de La Iglesia Valls

Chris Pal

Roland Siegwart

In recent years, legged and wheeled-legged robots have gained prominence for tasks in environments predominantly created for humans across v… (see more)arious domains. One significant challenge faced by many of these robots is their limited capability to navigate stairs, which hampers their functionality in multi-story environments. This study proposes a method aimed at addressing this limitation, employing reinforcement learning to develop a versatile controller applicable to a wide range of robots. In contrast to the conventional velocity-based controllers, our approach builds upon a position-based formulation of the RL task, which we show to be vital for stair climbing. Furthermore, the methodology leverages an asymmetric actor-critic structure, enabling the utilization of privileged information from simulated environments during training while eliminating the reliance on exteroceptive sensors during real-world deployment. Another key feature of the proposed approach is the incorporation of a boolean observation within the controller, enabling the activation or deactivation of a stair-climbing mode. We present our results on different quadrupeds and bipedal robots in simulation and showcase how our method allows the balancing robot Ascento to climb 15cm stairs in the real world, a task that was previously impossible for this robot.

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