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Found 6 papers, 1 papers with code
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise
In this study, we propose a new method for constructing UCB-type algorithms for stochastic multi-armed bandits based on general convex optimization methods with an inexact oracle.
Memory-efficient particle filter recurrent neural network for object localization
This study proposes a novel memory-efficient recurrent neural network (RNN) architecture specified to solve the object localization problem.
Multiparticle Kalman filter for object localization in symmetric environments
Two well-known classes of filtering algorithms to solve the localization problem are Kalman filter-based methods and particle filter-based methods.
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction
Since the data about user experience are distributed among devices, the federated learning setup is used to train the proposed sequential matrix factorization model.
NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer
Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms.
Extension of Dynamic Mode Decomposition for dynamic systems with incomplete information based on t-model of optimal prediction
The Dynamic Mode Decomposition has proved to be a very efficient technique to study dynamic data.
