by GCBC Ventures | Feb 21, 2020 | Machine Learning, Papers
This is a very interesting paper in which the authors use a message passing neural network on a carefully selected data set to predict antibacterial activity against E. coli. Then they apply their model to other data sets, while also prioritizing molecules that are...
by GCBC Ventures | Feb 15, 2020 | Machine Learning, Papers
SMILES (Simplified Molecular Input Line Entry System) representations of molecules have found many uses in machine learning algorithms, especially those derived from natural language processing techniques. However, they were not designed for machine learning and thus...
by GCBC Ventures | Feb 13, 2020 | Machine Learning, Papers
In this paper, the authors use a genetic algorithm operating on the SELFIES (SELF-referencIng Embedded Strings) representation of molecules to explore the vast space of small molecules. A neural network is used to guide the exploration process. Also, fitness functions...
by GCBC Ventures | Feb 11, 2020 | Machine Learning, Papers
This paper introduces Optuna, a Python package for performing hyperparameter optimization and pruning for machine learning algorithms. It is the creation of Preferred Networks who have kindly released what had been an internal project as open source code. This should...
by GCBC Ventures | Jan 19, 2020 | Machine Learning, Papers
In this paper, the authors demonstrate a feasible and promising approach to automatically constructing machine learning pipelines. We formulate the AutoML problem of pipeline synthesis as a single-player game, in which the player starts from an empty pipeline, and in...
by GCBC Ventures | Jan 16, 2020 | Machine Learning, Papers
This is a monograph (it is 86 pages) on variational autoencoders. The goal of this work is: The framework of variational autoencoders (VAEs) (Kingma and Welling, 2014; Rezende et al., 2014) provides a principled method for jointly learning deep latent-variable models...
