Deep learning frameworks have often focused on either usability or speed, but not both.
In this work we introduce a new optimisation method called SAGA in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast linear convergence rates.
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
In this paper, we address the over-confidence issue and the over-sensitivity issue existing in current RC models simultaneously with the help of external linguistic knowledge.
We describe efforts to adapt the Tesseract open source OCR engine for multiple scripts and languages.