Boltzmann Encoded Adversarial Machines

Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-based training strategies may fail because the objective does not sufficiently penalize models that place a high probability in regions where the training data distribution has low probability. To overcome this problem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is an RBM trained against an adversary that uses the hidden layer activations of the RBM to discriminate between the training data and the probability distribution generated by the model. We present experiments demonstrating that BEAMs outperform RBMs and GANs on multiple benchmarks.

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Your Digital Twin - UnlearnAI

Podcasts

Using AI Digital Twins for Drug Testing

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Unlearn.AI nabs $12M to build “digital twins” to speed up and improve clinical trials

“Unlearn’s pioneering use of Digital Twins will limit the number of patients that need to go on placebo while also reducing overall trial enrollment time."
Dr. Charles Fisher, CEO of Unlearn AI, discusses creating digital clones by using artificial intelligence for use in clinical drug trials.
A fascinating approach to the problem of how to make clinical trials more efficient, and understand more about what may be possible with more and better patient data.

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