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Deep Learning has given us a new way to think about things. Partially, the reason is that they can be made arbitrarily big, allowing them to have immense capacity, and they can be regularised, preventing overfitting. However, it is not very easy to understand how these models work. In this post, I will show a simple technique that allows somebody to somewhat see what the model is doing.
And scoring 350+ by implementing extensions such as double dueling DQN and prioritized experience replay A while ago, DeepMind released Agent57, a new benchmark for Atari AI. It is an incredible achievement — one I would like to talk about in depth in the future — but neither it, nor nearly any of great advances we have seen in RL would have been possible without two key papers, Mnih et al. 2013 and Mnih et al.
A guide to the paper “Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis” Nobody wants to listen to a robotic text-to-speech (TTS) program drone on about whatever. With the paper, Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis by Jia, Zhang, and Weiss et al., we don’t have to anymore.
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