Publications

Model approximation in MDPs with unbounded per-step cost
Berk Bozkurt
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov decision process …
2024-02-13
ArXiv (prépublication)
doi.org
arxiv.org
A neuronal least-action principle for real-time learning in cortical circuits
Walter Senn
Dominik Dold
Akos F. Kungl
Benjamin Ellenberger
Jakob Jordan
Yoshua Bengio
João Sacramento
Mihai A. Petrovici
One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal lea… (voir plus)st-action principle for cortical processing of sensory streams to produce appropriate behavioural outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimize the local somato-dendritic mismatch error within individual neurons. For motor output neurons, it implies minimizing an instantaneous behavioural error. For deep network neurons, it implies a prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory inputs and the motor feedback during the whole sensory-motor trajectory. Ongoing synaptic plasticity reduces the somato-dendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic dynamics for global real-time computation and learning in the brain and in physical substrates in general.
2024-02-13
bioRxiv (prépublication)
doi.org
Regional Adaptive Metropolis Light Transport
Hisanari Otsu
Killian Herveau
Johannes Hanika
Derek Nowrouzezahrai
Carsten Dachsbacher
The design of the proposal distributions, and most notably the kernel parameters, are crucial for the performance of Markov chain Monte Carl… (voir plus)o (MCMC) rendering. A poor selection of parameters can increase the correlation of the Markov chain and result in bad rendering performance. We approach this problem by a novel path perturbation strategy for online-learning of state-dependent kernel parameters. We base our approach on the theoretical framework of regional adaptive MCMC which enables the adaptation of parameters depending on the region of the state space which contains the current sample, and on information collected from previous samples. For this, we define a partitioning of the path space on a low-dimensional canonical space to capture the characteristics of paths, with a focus on path segments closer to the sensor. Fast convergence is achieved by adaptive refinement of the partitions. Exemplarily, we present two novel regional adaptive path perturbation techniques akin to lens and multi-chain perturbations. Our approach can easily be used on top of existing path space MLT methods to improve rendering efficiency, while being agnostic to the initial choice of kernel parameters.
2024-02-13
ArXiv (prépublication)
doi.org
arxiv.org
Antagonistic AI
Alice Cai
Ian Arawjo
Elena L. Glassman
2024-02-12
ArXiv (prépublication)
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 … (voir plus)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 (publié)
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,… (voir plus) 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 (accepté)
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 … (voir 47 de plus)
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
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 (publié)
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 (prépublication)
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 (publié)
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-… (voir plus)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 (prépublication)
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 (prépublication)
doi.org
arxiv.org
On the Privacy of Selection Mechanisms with Gaussian Noise
Jonathan Lebensold
Doina Precup
Borja Balle
2024-02-09
ArXiv (prépublication)
doi.org
arxiv.org