by GCBC Ventures | Nov 20, 2020 | Machine Learning, Papers
In this paper the authors present a Python version of the Self-Organizing Map (SOM) algorithm that can run in parallel on CPUs and GPUs (via CuPy). Additionally, they speed up the algorithm by: heavily exploiting higher-dimensional operations (e.g., matrix-matrix) to...
by GCBC Ventures | Apr 28, 2020 | Machine Learning, Papers
Deep Learning for Toxicity and Disease Prediction is a collection of 12 articles published as a free ebook. Below is an excerpt.
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 | 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...
