![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...
by instadatahelp | Aug 27, 2023 | AI Blogs
Graph Attention Retrospective Authors: Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath; Volume 24, Issue 246, Pages 1-52, 2023. Abstract Graph-based learning is a rapidly growing sub-field of machine learning that finds applications in...
by instadatahelp | Aug 27, 2023 | AI Blogs
arXivLabs is a platform where collaborators can create and share new features for the arXiv website. Both individuals and organizations that collaborate with arXivLabs align with our principles of openness, community, excellence, and user data privacy. We are...
