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Found 11 papers, 0 papers with code
Deep conv-attention model for diagnosing left bundle branch block from 12-lead electrocardiograms
This study presents a deep learning model for the detection of LBBB arrhythmia from 12-lead ECG data.
Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks
Later, we build on this robust framework to design robust semi-supervised learning over graph methods.
Distributionally Robust Semi-Supervised Learning Over Graphs
The data distribution is considered unknown, but lies within a Wasserstein ball centered around empirical data distribution.
Heavy Ball Momentum for Conditional Gradient
Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documented merits in machine learning and signal processing applications.
Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data
This robust learning task entails an infinite-dimensional optimization problem, which is challenging.
Reinforcement Learning for Caching with Space-Time Popularity Dynamics
With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks.
Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness
Numerical tests using real load data on the IEEE $118$-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives.
A Statistical Learning Approach to Reactive Power Control in Distribution Systems
Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs.
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks
To handle the large continuous state space, a scalable deep reinforcement learning approach is pursued.
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing
Under this generic formulation, first by considering stationary distributions for the costs and file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem.
Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularities
To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests.
Networking and Internet Architecture
