IEEE

A Neural-Network-Based Statistical Compact Model Using a Variational Autoencoder for Emerging Transistors

Abstract: Emerging transistors lack the statistical compact models needed to evaluate the yield of integrated circuits. To address this, we propose a novel framework combining a variational ...
GitHub

This repo describes python code and input data files for "Forecasting stock return distributions around the globe with quantile neural networks".

Inputs: describes all the raw inputs to run the code Code description: describes the python code and how to run it Code to figure mapping: provides mapping of figures in the paper to code used to ...
IEEE

Autoencoder-Augmented Graph Neural Networks for Accurate and Scalable Structure Recognition in Analog/Mixed-Signal Schematics

Abstract: The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, ...