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Found 4 papers, 1 papers with code
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs
Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e. g., computation, I/O, and memory-bound) edge computing systems.
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning
Minimizing a non-smooth function over the Grassmannian appears in many applications in machine learning.
Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms
However, its geometric analysis is based on quantities that are difficult to interpret and are not amenable to statistical analysis.
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Recently, there has been an increasing interest in using tools from dynamical systems to analyze the behavior of simple optimization algorithms such as gradient descent and accelerated variants.
