
Flowbased sampling for fermionic lattice field theories
Algorithms based on normalizing flows are emerging as promising machine ...
read it

Introduction to Normalizing Flows for Lattice Field Theory
This notebook tutorial demonstrates a method for sampling Boltzmann dist...
read it

Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
Recent work in deep reinforcement learning (RL) has produced algorithms ...
read it

Sampling using SU(N) gauge equivariant flows
We develop a flowbased sampling algorithm for SU(N) lattice gauge theor...
read it

Disentangling by Subspace Diffusion
We present a novel nonparametric algorithm for symmetrybased disentangl...
read it

Equivariant flowbased sampling for lattice gauge theory
We define a class of machinelearned flowbased sampling algorithms for ...
read it

Targeted free energy estimation via learned mappings
Free energy perturbation (FEP) was proposed by Zwanzig more than six dec...
read it

Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributi...
read it

Hamiltonian Generative Networks
The Hamiltonian formalism plays a central role in classical and quantum ...
read it

Equivariant Hamiltonian Flows
This paper introduces equivariant hamiltonian flows, a method for learni...
read it

Automated curricula through settersolver interactions
Reinforcement learning algorithms use correlations between policies and ...
read it

Differentiable Game Mechanics
Deep learning is built on the foundational guarantee that gradient desce...
read it

An investigation of modelfree planning
The field of reinforcement learning (RL) is facing increasingly challeng...
read it

Towards a Definition of Disentangled Representations
How can intelligent agents solve a diverse set of tasks in a dataeffici...
read it

Woulda, Coulda, Shoulda: CounterfactuallyGuided Policy Search
Learning policies on data synthesized by models can in principle quench ...
read it

The Mechanics of nPlayer Differentiable Games
The cornerstone underpinning deep learning is the guarantee that gradien...
read it

Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in modelbased reinforcement learning (RL) is to synthes...
read it

ImaginationAugmented Agents for Deep Reinforcement Learning
We introduce ImaginationAugmented Agents (I2As), a novel architecture f...
read it

Learning modelbased planning from scratch
Conventional wisdom holds that modelbased planning is a powerful approa...
read it

Recurrent Environment Simulators
Models that can simulate how environments change in response to actions ...
read it
Sebastien Racanière
is this you? claim profile