181 citations · 1.2k across the 132 of their papers we have counts for
13 papers · 1 filter
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization
Mahdi Haghifam, Borja Rodríguez-Gálvez, Ragnar Thobaben +3
To date, no "information-theoretic" frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic…
An Information-Theoretic Analysis of Bayesian Reinforcement Learning
Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering +1
Building on the framework introduced by Xu and Raginksy [1] for supervised learning problems, we study the best achievable performance for model-based Bayesian reinforcement learni…
Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning
Hao Chen, Yu Ye, Ming Xiao +1
Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increas…
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT
Wanlu Lei, Yu Ye, Ming Xiao +2
Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasi…
A ReLU Dense Layer to Improve the Performance of Neural Networks
Alireza M. Javid, Sandipan Das, Mikael Skoglund +1
We propose ReDense as a simple and low complexity way to improve the performance of trained neural networks. We use a combination of random weights and rectified linear unit (ReLU)…
A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning
Xinyue Liang, Alireza M. Javid, Mikael Skoglund +1
We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers). We assume the communication…