by instadatahelp | Aug 27, 2023 | AI Blogs
Enhancing parallel computing in multiple-try Metropolis with local balancing Philippe Gagnon, Florian Maire, Giacomo Zanella; 24(248):1−59, 2023. Abstract Multiple-try Metropolis (MTM) is a widely used Markov chain Monte Carlo method that can be effectively...
by instadatahelp | Aug 27, 2023 | AI Blogs
[Submitted on 24 Aug 2023] Click here to download a PDF of the paper titled “False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations,” written by Mohammad Majid Akhtar and 3 other authors. Download PDF Abstract:...
by instadatahelp | Aug 27, 2023 | AI Blogs
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities Authors: Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock; Volume 24(122):1−77, 2023. Abstract In recent years, deep learning has made significant advancements. However, the increasing economic...
by instadatahelp | Aug 27, 2023 | AI Blogs
[Submitted on 24 Aug 2023] Download a PDF of the paper titled “Optimizing Neural Network Scale for ECG Classification” by Byeong Tak Lee and 2 other authors: Download PDF Abstract: We investigate the scaling of convolutional neural networks (CNNs),...
by instadatahelp | Aug 27, 2023 | AI Blogs
Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC Tianze Wang, Guanyang Wang; 24(249):1−40, 2023. Abstract The construction of unbiased estimators from Markov chain Monte Carlo (MCMC) outputs is a challenging problem that has...
by instadatahelp | Aug 27, 2023 | AI Blogs
This paper introduces a cost-effective and low-power approach to unintentional fall detection. The authors utilize knowledge distillation-based LSTM models to improve accuracy. The focus is on analyzing time-series data from various sensors, enabling real-time...
