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Found 6 papers, 1 papers with code
Joint User and Beam Selection in Millimeter Wave Networks
We study the problem of selecting a user equipment (UE) and a beam for each access point (AP) for concurrent transmissions in a millimeter wave (mmWave) network, such that the sum of weighted rates of UEs is maximized.
Efficient Coding Approach Towards Non-Linear Spectro-Temporal Receptive Fields
Linear Non-Linear(LN) models are widely used to characterize the receptive fields of early-stage auditory processing.
Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes
To overcome the curses of dimensionality and modeling of Dynamic Programming (DP) methods to solve Markov Decision Process (MDP) problems, Reinforcement Learning (RL) methods are adopted in practice.
MIST: A Novel Training Strategy for Low-latencyScalable Neural Net Decoders
The enhanced decoding speed is due to the use of convolutional neural network (CNN) as opposed to recurrent neural network (RNN) used in the best known neural net based decoders.
KLUCB Approach to Copeland Bandits
Multi-armed bandit(MAB) problem is a reinforcement learning framework where an agent tries to maximise her profit by proper selection of actions through absolute feedback for each action.
A Structure-aware Online Learning Algorithm for Markov Decision Processes
In this paper, we propose a new RL algorithm which utilizes the known threshold structure of the optimal policy while learning by reducing the feasible policy space.
