by GCBC Ventures | Apr 3, 2020 | Machine Learning, Papers

This paper provides a useful overview of Bayesian optimization methods that are currently in use as well as more speculative methods. While this is interesting, from the perspective of using Bayesian optimization hyperparameter search for machine learning algorithms,...
by GCBC Ventures | Apr 3, 2020 | Machine Learning

GPyOpt Python Package Using GPyOpt Best Result and Ensembling Results Code 1. GPyOpt Python Package GPyOpt is a Python open-source library for Bayesian Optimization developed by the Machine Learning group of the University of Sheffield. It is based on GPy, a Python...
by GCBC Ventures | Mar 22, 2020 | Machine Learning, Papers

In this work, the authors present a simpler alternative, “a simple linear model w.r.t. the adjacency matrix of the graph”, to graph convolution encoders for graph autoencoders. Embedding vectors are obtained by multiplying the n × n normalized adjacency...
by GCBC Ventures | Mar 15, 2020 | Machine Learning

Paper: Optuna: A Next-generation Hyperparameter Optimization Framework – Akiba et al 2019 Hyperparameter Search With Optuna: Part 1 – Scikit-learn Classification and Ensembling Hyperparameter Search With Optuna: Part 2 – XGBoost Classification and Ensembling...
by GCBC Ventures | Feb 28, 2020 | Machine Learning, Papers

A new message passing graph neural network with attention is presented here as applied to small molecule predictive machine learning tasks. In this paper, we describe a self-attention-based message-passing neural network (SAMPN) model, which is a modification of...