This is a PyTorch implementation/tutorial of Deep Q Networks (DQN) from paper Playing Atari with Deep Reinforcement Learning. This includes dueling network architecture, a prioritized replay buffer and double-Q-network training.
Graph neural networks are intimately related to partial differential equations governing information diffusion on graphs. Thinking of GNNs as PDEs leads to a new broad class of graph ML methods.
Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from the realm of research to engineering. Unfortunately, this also means that most…
Have you ever wondered how will the machine learning frameworks of the '20s look like? In this essay, I examine the directions AI research might take and the requirements they impose on the tools at our disposal, concluding with an overview of what I believe to be the two strong candidates: `JAX` and `S4TF`.
Ever since graduation, people have been asking me: “What’s now?” My answer has been an unequivocal: “I don’t know.” I used to think that by the time I finish...
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