How are we changing clinical trials?

How are we changing clinical trials?

How are we changing clinical trials?

Publications

Boltzmann Encoded Adversarial Machines

The deep learning revolution has driven tremendous advances on supervised learning problems, and a primary outcome is that feed-forward neural networks have become a powerful tool.
Blog

Uncertainty is More Important to AI than Explainability

Blog

The Travails of Comparing Generative Models

Blog

Introducing Paysage

Paysage is a powerful library for training RBMs — and more-generally, energy-based neural network models.
Increasingly, the task of machine learning will be to explore the formidable frontier of unsupervised learning.
Uncertainty is an alternative to explainability that places no constraints on the complexity of the underlying algorithm.

Machine learning for comprehensive forecasting of Alzheimer's Disease progression

Don't Model Humans with SNPs

Publications

Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks

Using a dataset of subjects enrolled in the placebo arms of MS clinical trials, we trained a Conditional Restricted Boltzmann Machine to generate digital subjects.
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