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 …
deep-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, …
Privacy Leakage in Deep Learning: What Models, Predictions, and Gradients Reveal
Reviewing membership inference, attribute inference, model inversion, and training data extraction; deriving the recoverability of …
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 …
Will AI Replace Artists' Jobs?
Combining the development of diffusion models and AI art, this article discusses which tasks in artistic creation might be automated, along …
Trial Run of Diffusion Models
Reviewing the basic concepts and development history of diffusion models, and recording trial experiences and generation results of image …
hCaptcha Seaplane Recognition: Model Training and Testing
Records the training and testing practices for the hCaptcha seaplane recognition task, discussing sample annotation, overfitting issues, and …