by instadatahelp | Aug 28, 2023 | AI Blogs
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval Authors: Yan Shuo Tan, Roman Vershynin; Published in 24(58):1−47, 2023. Abstract The problem of phase retrieval can be solved using a two-step procedure....
![Comparing Performance of Design Optimization and Deep Learning-based Inverse Design: A Study (arXiv:2308.13000v1 [math.OC])](https://instadatanews.com/wp-content/uploads/2023/08/arxiv-logo-fb-1080x675.png)
by instadatahelp | Aug 28, 2023 | AI Blogs
The use of surrogate model-based optimization in engineering design has been on the rise. This method involves creating a surrogate model using data from simulations or real-world experiments, and then using numerical optimization techniques to find the optimal...
by instadatahelp | Aug 28, 2023 | AI Blogs
Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing Yibo Yan, Xiaozhou Wang, Riquan Zhang; 24(245):1−49, 2023. Abstract Quantile regression with an $\ell_1$ penalty ($\ell_1$-QR) is a valuable...
by instadatahelp | Aug 28, 2023 | AI Blogs
Privacy-Aware Rejection Sampling Jordan Awan, Vinayak Rao; 24(74):1−32, 2023. Abstract In this study, we examine the potential vulnerabilities of differential privacy (DP) mechanisms to side-channel attacks, specifically timing attacks, and propose a privacy-aware...
by instadatahelp | Aug 27, 2023 | AI Blogs
Federated Learning (FL) is a method that uses locally trained models from individual clients to create a global model. FL allows for model training while preserving data privacy. However, it can suffer from performance degradation when the data distributions among...
