Introduction At Reverie Labs, we focus on building state-of-the-art deep learning models to predict important molecular properties. One method we use in building such models is self-supervised pre-training, which we run over tens of millions of molecules. This requires overcoming two major hurdles: Working around peculiarities in how Tensorflow handles
We're releasing a Colab notebook for training Transformer networks on a wide range of drug discovery tasks using Tensor2Tensor.
This blog post was originally published on the AWS Startups blog here. At Reverie Labs, we use computation to drive the development of therapeutics for cancer. To do this, we have built substantial cloud-based infrastructure to train machine learning models, deploy models to production, and build and ship internal-facing applications
Reverie scientists use our platform to run models with the ease of ordering online takeout.
A quick-and-dirty way to clean noisy datasets before training on them