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Found 9 papers, 2 papers with code
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
We present a simple approach to incorporate prior knowledge in BNNs based on external summary information about the predicted classification probabilities for a given dataset.
Understanding Learning Dynamics of Binary Neural Networks via Information Bottleneck
Compact neural networks are essential for affordable and power efficient deep learning solutions.
Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment
Directional beamforming is a crucial component for realizing robust wireless communication systems using millimeter wave (mmWave) technology.
Beyond 5G: Leveraging Cell Free TDD Massive MIMO using Cascaded Deep learning
This paper deals with the calibration of Time Division Duplexing (TDD) reciprocity in an Orthogonal Frequency Division Multiplexing (OFDM) based Cell Free Massive MIMO system where the responses of the (Radio Frequency) RF chains render the end to end channel non-reciprocal, even though the physical wireless channel is reciprocal.
Design of Communication Systems using Deep Learning: A Variational Inference Perspective
However, in communication systems, the receiver has to work with noise corrupted versions of transmit symbols.
A Centralized Multi-stage Non-parametric Learning Algorithm for Opportunistic Spectrum Access
Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio (CR) was introduced as a technique to attain high spectral efficiency.
An aggregating strategy for shifting experts in discrete sequence prediction
We study how we can adapt a predictor to a non-stationary environment with advises from multiple experts.
Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach
Here, we propose an algorithm to not only select a channel for data transmission but also to predict how long the channel will remain unoccupied so that the time spent on channel sensing can be minimized.
Taming Non-stationary Bandits: A Bayesian Approach
We consider the multi armed bandit problem in non-stationary environments.
