Decision Making is a complex task that involves analyzing data (of different level of abstraction) from disparate sources and with different levels of certainty, merging the information by weighing in on some data source more than other, and arriving at a conclusion by exploring all possible alternatives.
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At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.
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We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes.
NAMs learn a linear combination of neural networks that each attend to a single input feature.
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL).
End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors.
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.