So far in this chapter, we have studied two kinds of generative models—GANs and VAEs—but there is also another kind, known as flow-based generative models, which directly learn the probability density function of the data distribution, which is something that the previous models do not do. Flow-based models make use of normalizing flows, which overcomes the difficulty that GANs and VAEs face in trying to learn the distribution. This approach can transform a simple distribution into a more complex one through a series of invertible mappings. We repeatedly apply the change of variables rule, which allows the initial probability density to flow through the series of invertible mappings, and at the end, we get the target probability distribution.
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You're reading from Hands-On Mathematics for Deep Learning
Jay Dawani is a former professional swimmer turned mathematician and computer scientist. He is also a Forbes 30 Under 30 Fellow. At present, he is the Director of Artificial Intelligence at Geometric Energy Corporation (NATO CAGE) and the CEO of Lemurian Labs - a startup he founded that is developing the next generation of autonomy, intelligent process automation, and driver intelligence. Previously he has also been the technology and R&D advisor to Spacebit Capital. He has spent the last three years researching at the frontiers of AI with a focus on reinforcement learning, open-ended learning, deep learning, quantum machine learning, human-machine interaction, multi-agent and complex systems, and artificial general intelligence.
Read more about Jay Dawani
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Jay Dawani is a former professional swimmer turned mathematician and computer scientist. He is also a Forbes 30 Under 30 Fellow. At present, he is the Director of Artificial Intelligence at Geometric Energy Corporation (NATO CAGE) and the CEO of Lemurian Labs - a startup he founded that is developing the next generation of autonomy, intelligent process automation, and driver intelligence. Previously he has also been the technology and R&D advisor to Spacebit Capital. He has spent the last three years researching at the frontiers of AI with a focus on reinforcement learning, open-ended learning, deep learning, quantum machine learning, human-machine interaction, multi-agent and complex systems, and artificial general intelligence.
Read more about Jay Dawani