Publications associées à Fonction softmax

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Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Fonction softmax

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Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Generalization Properties of NAS under Activation and Skip Connection Search

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

Memory-Based Model Editing at Scale

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

Estimating an extreme Bayesian network via scalings

Generalization Properties of NAS under Activation and Skip Connection Search

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Memory-Based Model Editing at Scale

Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods

Yapa: Accelerated Proximal Algorithm For Convex Composite Problems

Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

Estimating an extreme Bayesian network via scalings