IEEE

CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder With Input Convex Neural Networks

Abstract: In neural architecture search (NAS) methods based on latent space optimization (LSO), a deep generative model is trained to embed discrete neural architectures into a continuous latent space ...
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 ...