Deep Convolutional RL agents trained on this environment produce prefix adder circuits that Pareto-dominate existing baselines with up to 16. 0% and 30. 2% lower area for the same delay in the 32b and 64b settings respectively.
We also explore two approaches for end-to-end supervised training of the reader and retriever components in OpenQA models.
The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e. g. input-output behavior.
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers.
Multi-emotion sentiment classification is a natural language processing (NLP) problem with valuable use cases on real-world data.
Ranked #3 on Emotion Classification on SemEval 2018 Task 1E-c (Macro-F1 metric)