18 citations · 67 across the 12 of their papers we have counts for
13 papers
SIMA 2: A Generalist Embodied Agent for Virtual Worlds
SIMA team, Adrian Bolton, Alexander Lerchner +63
We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…
Vision-Language Models as a Source of Rewards
Kate Baumli, Satinder Baveja, Feryal Behbahani +24
Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning. A key limiting factor for bui…
In-context Reinforcement Learning with Algorithm Distillation
Michael Laskin, Luyu Wang, Junhyuk Oh +11
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal seque…
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Zhaocheng Zhu, Xinyu Yuan, Mikhail Galkin +4
Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capacity that embeddings lack, their scalabil…
Higher Order Generalization Error for First Order Discretization of Langevin Diffusion
Mufan Bill Li, Maxime Gazeau
We propose a novel approach to analyze generalization error for discretizations of Langevin diffusion, such as the stochastic gradient Langevin dynamics (SGLD). For an toleranc…
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise
Yeming Wen, Kevin Luk, Maxime Gazeau +3
The choice of batch-size in a stochastic optimization algorithm plays a substantial role for both optimization and generalization. Increasing the batch-size used typically improves…
A general system of differential equations to model first order adaptive algorithms
André Belotto da Silva, Maxime Gazeau
First order optimization algorithms play a major role in large scale machine learning. A new class of methods, called adaptive algorithms, were recently introduced to adjust iterat…
Scalable Recommender Systems through Recursive Evidence Chains
Elias Tragas, Calvin Luo, Maxime Gazeau +2
Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors. Optimizing these parameters by subsampling data becom…
Implicit Manifold Learning on Generative Adversarial Networks
Kry Yik Chau Lui, Yanshuai Cao, Maxime Gazeau +1
This paper raises an implicit manifold learning perspective in Generative Adversarial Networks (GANs), by studying how the support of the learned distribution, modelled as a subman…
Bloch theory and spectral gaps for linearized water waves
Walter Craig, Maxime Gazeau, Christophe Lacave +1
The system of equations for water waves, when linearized about equilibrium of a fluid body with a varying bottom boundary, is described by a spectral problem for the Dirichlet -- N…
Analysis and simulation of rare events for SPDE
Charles-Edouard Bréhier, Maxime Gazeau, Ludovic Goudenège +1
In this work, we consider the numerical estimation of the probability for a stochastic process to hit a set B before reaching another set A. This event is assumed to be rare. We co…
Strong order of convergence of a semidiscrete scheme for the stochastic Manakov equation
Maxime Gazeau
It is well accepted by physicists that the Manakov PMD equation is a good model to describe the evolution of nonlinear electric fields in optical fibers with randomly varying biref…
A diffusion approximation theorem for a nonlinear PDE with application to random birefringent optical fibers
A. de Bouard, M. Gazeau
In this article we propose a generalization of the theory of diffusion approximation for random ODE to a nonlinear system of random Schrödinger equations. This system arises in the…