by GCBC Ventures | Apr 9, 2020 | Machine Learning, Papers

In this paper, the authors modified a seq2seq RNN constructed for language translation to a seq2seq RNN autoencoder (specifically for SMILES input-output) so that the resultant latent data space could be used as molecular fingerprints for subsequent machine learning...
by GCBC Ventures | Apr 7, 2020 | Machine Learning, Papers

In this interesting paper, the authors use a multi input neural network to predict various small molecule properties in which one branch is a multilayer perceptron with MACCS fingerprints and the other is one of a RNN, 1D CNN, 1D CNN-RNN with SMILES. On various data...
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...