Publications associées à Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

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Network Simplification: the Gaussian N-Relay Diamond Network

Christina Fragouli, Ayfer Özgür Aydin

We consider the Gaussian

N

-relay diamond network, where a source wants to communicate to a destination through a layer of

N

-relay nodes. We investigate the following question: What fraction of the capacity can we maintain by using only

k

out of the $ ...

2010

Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

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Entropy and mutual information in models of deep neural networks

Deep Learning Works in Practice. But Does it Work in Theory?

Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine

Robustness of Classifiers to Universal Perturbations: A Geometric Perspective

LIFT: Learned Invariant Feature Transform

Towards Viewpoint Invariant 3D Human Pose Estimation

Well-Posedness, Regularity, and Convergence Analysis of the Finite Element Approximation of a Generalized Robin Boundary Value Problem

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

Network Simplification: the Gaussian N-Relay Diamond Network

LIFT: Learned Invariant Feature Transform

Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine

Towards Viewpoint Invariant 3D Human Pose Estimation

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

Well-Posedness, Regularity, and Convergence Analysis of the Finite Element Approximation of a Generalized Robin Boundary Value Problem

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

Network Simplification: the Gaussian N-Relay Diamond Network

Deep Learning Works in Practice. But Does it Work in Theory?

Robustness of Classifiers to Universal Perturbations: A Geometric Perspective

Entropy and mutual information in models of deep neural networks