Efficiency Analysis in Distributed Machine Learning
Analyzing the stochastic runtime of synchronous, asynchronous, and K-worker SGD, and discussing the time to reach target accuracy and …
machine-learning
Convergence Analysis in Federated Learning (Part 5)
Deriving non-convex stationary point bounds and strongly convex error bounds under full participation from FedAvg's local updates, …
Convergence Analysis in Distributed Machine Learning (Part 4)
Deriving strong convexity convergence bounds for synchronous, asynchronous, and K-Async SGD, clarifying assumptions of independent sampling, …
Convergence Analysis in Deep Learning (Part 3)
Deriving strong convex error bounds and non-convex stationary point bounds for mini-batch SGD under assumptions of conditionally unbiased …
Convergence Analysis in Deep Learning (Part 2)
Derive first-order method convergence bounds under smooth/non-smooth and convex/non-convex conditions, clarifying step sizes, output points, …
Convergence Analysis in Deep Learning (Part 1)
Unify definitions and assumptions regarding convexity, strong convexity, smoothness, and stochastic gradients, and derive inequalities …
Deep Learning for Pixel Art: Research, Datasets, and Tools
Organizing the stylistic features, galleries, datasets, and drawing tools of pixel art, introducing research and projects on deep learning …