International Business Machines Corporation

Trong Nghia Hoang

Yasaman Khazaeni

Subhro Das

Mayank Agarwal

Mikhail Yurochkin

Soumya Ghosh

Kristjan Herbert Greenewald

Nathan Fulton

Nathan Hunt

In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. A Bayesian nonparametric framework is presented for federated learning with neural networks. Each data server is assumed to provide local neural network weights, which are modeled through our framework. An inference approach is presented that allows us to synthesize a more expressive global network without additional supervision, data pooling and with as few as a single communication round. The efficacy of the present invention on federated learning problems simulated from two popular image classification datasets is shown.

Bayesian nonparametric learning of neural networks

A method for training control software to reinforce safety constraints using visual inputs includes performing template matching for each object in an image of a reinforcement learning (RL) agent's action space using a visual template for each object wherein each object in the RL agent's action space is detected, mapping each detected object to a set of planar coordinates for each object in the RL agent's action space, determining a set of safe actions for the RL agent by applying a safety specification for the RL agent's action space to the set of variables for coordinates for each object in the RL agent's action space, outputting the set of safe actions to the RL agent for a current state of a RL procedure, and preventing the RL agent from executing an action that is unsafe, before the RL agent takes an action.

Formally safe symbolic reinforcement learning on visual inputs

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Trong Nghia Hoang