Deep Learning

Predicting signals like stress and health depends on collecting noisy data from a number of modalities, e.g. smartphone data, or physiological data from a wrist-worn sensor. Our method can continue making accurate predictions even when a modality goes missing; for example, if the person forgets to wear their sensor.

Traditional, one-size-fits-all machine learning models fail to account for individual differences in predicting wellbeing outcomes like stress, mood, and health. Instead, we personalize models to the individual using multi-task learning (MTL), employing hierarchical Bayes, kernel-based and deep neural network MTL models to improve prediction accuracy by 13-23%.

Modeling measures like mood, stress, and health using a monolithic machine learning model leads to low prediction accuracy. Instead, we develop personalized regression models using multi-task learning and Gaussian Processes, leading to dramatic improvements in next-day predictions.

To combine supervised learning on data with reinforcement learning, we pre-train a supervised data prior, and penalize KL-divergence from this model using RL training. This enables effective learning of complex sequence-modeling problems for which we wish to match the data while optimizing external metrics like drug effectiveness. The approach produces compelling results in the disparate domains of music generation and drug discovery.

This demo deployed RL Tuner and other Magenta music generation models into an interactive interface in which users can collaborate creatively with a machine learning model. The interface supports call and response interaction, automatically generating an accompaniment to the user’s melody, or melody morphing: responding both with variations on the user’s melody and a bass accompaniment.

Generating music using traditional supervised sequence models suffers from known failure modes, including the inability to produce coherent global structure. Music is an interesting sequence generation problem, because musical compositions adhere to known rules. We impose these rules with a novel algorithm combining RL and supervised learning.

Given only one-minute slices of facial expressions and body language, we use machine learning to accurately predict whether two humans having a conversation will bond with each other. We analyze factors which lead to bonding and discover that synchrony in body language and appropriate, empathetic facial expressions lead to higher bonding.

We conduct a study in which participants form American Sign Language hand signs while being recorded with a Microsoft Kinect. The resulting infra-red distance data are used to train both neural networks with dropout (dropout NN) and Random Forests; dropout NN perform significantly better.