# Notes from NIPS 2017 on geometric deep learning, GAN theory, reinforcement learning, fairness, and Bayesian deep learning

DevFeed: [Notes from NIPS 2017 on geometric deep learning, GAN theory, reinforcement learning, fairness, and Bayesian deep learning](<https://devfeed.tech/articles/neurips-notes-40108.md>)

Original publisher: [Read original article](<https://korbonits.com/blog/2017-12-04-nips/>)

Published: 2017-12-04T12:00:00Z

Content type: opinion

Language: en

Sources: [Alex Korbonits](<https://devfeed.tech/sources/alex-korbonits.md>)

Topics: [NeurIPS](<https://devfeed.tech/topics/neurips.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [bias](<https://devfeed.tech/tags/bias.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [fairness](<https://devfeed.tech/tags/fairness.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [ml](<https://devfeed.tech/tags/ml.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>)

## AI overview

Personal notes from attending NIPS 2017 cover geometric deep learning on manifolds and graphs, Bayesian deep learning, fairness and bias, theory, deep reinforcement learning, and GANs. The article also reflects on how those research themes developed over the following years.

## Source excerpt

Notes from NIPS 2017 -- covering geometric deep learning, GAN theory, reinforcement learning, fairness in ML, Bayesian deep learning, and more.