Publications

Neural differential equations for temperature control in buildings under demand response programs

Vincent Taboga

Clement Gehring

Mathieu Le Cam

Hanane Dagdougui

Pierre-Luc Bacon

2024-08-01

Applied Energy (published)

Noise covariance estimation in multi-task high-dimensional linear models

Kai Tan

Gabriel Romon

Lune Bellec

2024-08-01

Bernoulli (published)

Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning

Georg Pichler

Marco Romanelli

Leonardo Rey Vega

Pablo Piantanida

2024-08-01

IEEE Transactions on Dependable and Secure Computing (published)

Periodic agent-state based Q-learning for POMDPs

Amit Sinha

Matthieu Geist

Aditya Mahajan

The standard approach for Partially Observable Markov Decision Processes (POMDPs) is to convert them to a fully observed belief-state MDP. H… (see more)owever, the belief state depends on the system model and is therefore not viable in reinforcement learning (RL) settings. A widely used alternative is to use an agent state, which is a model-free, recursively updateable function of the observation history. Examples include frame stacking and recurrent neural networks. Since the agent state is model-free, it is used to adapt standard RL algorithms to POMDPs. However, standard RL algorithms like Q-learning learn a stationary policy. Our main thesis that we illustrate via examples is that because the agent state does not satisfy the Markov property, non-stationary agent-state based policies can outperform stationary ones. To leverage this feature, we propose PASQL (periodic agent-state based Q-learning), which is a variant of agent-state-based Q-learning that learns periodic policies. By combining ideas from periodic Markov chains and stochastic approximation, we rigorously establish that PASQL converges to a cyclic limit and characterize the approximation error of the converged periodic policy. Finally, we present a numerical experiment to highlight the salient features of PASQL and demonstrate the benefit of learning periodic policies over stationary policies.

2024-08-01

EWRL/2024/Workshop (accepted)

openreview.net

Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping

Vishal Batchu

Alex Wilson

Betty Peng

Carl Elkin

Umangi Jain

Christopher Van Arsdale

Ross Goroshin

Varun Gulshan

The transition to renewable energy, particularly solar, is key to mitigating climate change. Google's Solar API aids this transition by esti… (see more)mating solar potential from aerial imagery, but its impact is constrained by geographical coverage. This paper proposes expanding the API's reach using satellite imagery, enabling global solar potential assessment. We tackle challenges involved in building a Digital Surface Model (DSM) and roof instance segmentation from lower resolution and single oblique views using deep learning models. Our models, trained on aligned satellite and aerial datasets, produce 25cm DSMs and roof segments. With ~1m DSM MAE on buildings, ~5deg roof pitch error and ~56% IOU on roof segmentation, they significantly enhance the Solar API's potential to promote solar adoption.

2024-08-01

arXiv (published)

The effect of gestational age on short- and long-term complications following primary esophageal atresia repair

Mathias Johansen

Samuel Wasserman

Dan Poenaru

Jean-Martin Laberge

Sam J. Daniel

Thomas Engelhardt

2024-08-01

Brazilian Journal of Anesthesiology (published)

The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups

Miguel Saavedra-Ruiz

Steven A. Parkison

Ria Arora

James Richard Forbes

Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state belief using incomplete information from nois… (see more)y sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing nonparametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. This paper introduces a novel approach to nonparametric Bayesian filtering on motion groups, designed to handle multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and asymptotically exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's performance compared with established nonparametric filtering methods across simulated and real-world localization tasks.

2024-08-01

ArXiv (preprint)

The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups

Miguel Saavedra-Ruiz

Steven A. Parkison

Ria Arora

James Richard Forbes

Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state belief using incomplete information from nois… (see more)y sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are all unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing nonparametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. This letter introduces a novel approach to nonparametric Bayesian filtering on motion groups, designed to handle multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and asymptotically exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's superior performance compared with established nonparametric filtering methods across a range of simulated and real-world localization tasks.

2024-08-01

ArXiv (preprint)

The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups

Miguel Saavedra-Ruiz

Steven A. Parkison

Ria Arora

James Richard Forbes

Bayesian estimation is a vital tool in robotics as it allows systems to update the belief of the robot state using incomplete information fr… (see more)om noisy sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are all unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing non-parametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. In this paper, we introduce a novel approach to nonparametric Bayesian filtering to cope with multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's superior performance compared with established nonparametric filtering methods across a range of simulated and real-world localization tasks.

2024-08-01

ArXiv (preprint)

The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups

Miguel Saavedra-Ruiz

Steven A. Parkison

Ria Arora

James Richard Forbes

Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state belief using incomplete information from nois… (see more)y sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing nonparametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. This paper introduces a novel approach to nonparametric Bayesian filtering on motion groups, designed to handle multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and asymptotically exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's performance compared with established nonparametric filtering methods across simulated and real-world localization tasks.

2024-08-01

ArXiv (preprint)

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team Morgane Riviere

Shreya Pathak

Pier Giuseppe Sessa

Cassidy Hardin

Surya Bhupatiraju

L'eonard Hussenot

Thomas Mesnard

Bobak Shahriari

Alexandre Ram'e

Johan Ferret

Peter Liu

Pouya Dehghani Tafti

Abe Friesen

Michelle Casbon

Sabela Ramos

Ravin Kumar

Charline Le Lan

Sammy Jerome

Anton Tsitsulin

Nino Vieillard … (see 175 more)

Piotr Stańczyk

Sertan Girgin

Nikola Momchev

Matt Hoffman

Shantanu Thakoor

Jean-Bastien Grill

Behnam Neyshabur

Alanna Walton

Aliaksei Severyn

Alicia Parrish

Aliya Ahmad

Allen Hutchison

Alvin Abdagic

Amanda Carl

Amy Shen

Andy Brock

Andy Coenen

Anthony Laforge

Antonia Paterson

Ben Bastian

Bilal Piot

Boxi Wu

Brandon Royal

Charlie Chen

Chintu Kumar

Chris Perry

Christoper A. Welty

Christopher A. Choquette-Choo

Danila Sinopalnikov

David Weinberger

Dimple Vijaykumar

Dominika Rogozi'nska

D. Herbison

Elisa Bandy

Emma Wang

Eric Noland

Erica Moreira

Evan Senter

Evgenii Eltyshev

Francesco Visin

Gabriel Rasskin

Gary Wei

Glenn Cameron

Gus Martins

Hadi Hashemi

Hanna Klimczak-Pluci'nska

Harleen Batra

Harsh Dhand

Ivan Nardini

Jacinda Mein

Jack Zhou

James Svensson

Jeff Stanway

Jetha Chan

Jin Zhou

Joana Carrasqueira

Joana Iljazi

Jocelyn Becker

Joe Fernandez

Joost Van Amersfoort

Josh Gordon

Josh Lipschultz

Joshua Newlan

Junsong Ji

Kareem Mohamed

Kartikeya Badola

Kat Black

Katie Millican

Keelin McDonell

Kelvin Nguyen

Kiranbir Sodhia

Kish Greene

Lars Lowe Sjoesund

Lauren Usui

Laurent Sifre

L. Heuermann

Leti-cia Lago

Lilly McNealus

Livio Baldini Soares

Logan Kilpatrick

Lucas Dixon

Luciano Martins

Machel Reid

Manvinder Singh

Mark Iverson

Martin Gorner

Mat Velloso

Mateo Wirth

Matt Davidow

Matt Miller

Matthew Rahtz

Matthew Watson

Meg Risdal

Mehran Kazemi

Michael Moynihan

Ming Zhang

Minsuk Kahng

Minwoo Park

Mofi Rahman

Mohit Khatwani

Natalie Dao

Nenshad Bardoliwalla

N. Devanathan

Neta Dumai

Nilay Chauhan

O. Wahltinez

Pankil Botarda

Parker Barnes

Paul R. Barham

Paul Michel

Peng-chong Jin

Petko Georgiev

Phil Culliton

Pradeep Kuppala

Ramona Comanescu

Ramona Merhej

Reena Jana

R. Rokni

Rishabh Agarwal

Ryan Mullins

Samaneh Saadat

S. M. Carthy

Sarah Perrin

S'ebastien M. R. Arnold

Se-bastian Krause

Shengyang Dai

S. Garg

Shruti Sheth

S. Ronstrom

Susan Chan

Timothy Jordan

Ting Yu

Tom Eccles

Tom Hennigan

Tomas Kocisky

Tulsee Doshi

Vihan Jain

Vikas Yadav

Vilobh Meshram

Vishal Dharmadhikari

Warren Barkley

Wei Wei

Wenming Ye

Woohyun Han

Woosuk Kwon

Xiang Xu

Zhe Shen

Zhitao Gong

Zichuan Wei

Victor Cotruta

Phoebe Kirk

Anand Rao

Minh Giang

Ludovic Peran

Tris Brian Warkentin

Eli Collins

Joelle Barral

Zoubin Ghahramani

Raia Hadsell

D. Sculley

Jeanine Banks

Anca Dragan

Slav Petrov

Oriol Vinyals

Jeffrey Dean

Demis Hassabis

Koray Kavukcuoglu

Clément Farabet

Elena Buchatskaya

Sebastian Borgeaud

Noah Fiedel

Armand Joulin

Kathleen Kenealy

Robert Dadashi

Alek Andreev

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2… (see more) billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

2024-07-31

ArXiv (preprint)