# Stanford AI Lab Papers and Talks at NeurIPS 2021

DevFeed: [Stanford AI Lab Papers and Talks at NeurIPS 2021](<https://devfeed.tech/articles/stanford-ai-lab-papers-and-talks-at-neurips-2021-7586.md>)

Original publisher: [Read original article](<https://ai.stanford.edu/blog/neurips-2021/>)

Author: Compiled by Drew A. Hudson

Published: 2021-12-06T08:00:00Z

Content type: article

Language: en

Sources: [The Stanford AI Lab Blog](<https://devfeed.tech/sources/the-stanford-ai-lab-blog.md>)

Topics: [NeurIPS](<https://devfeed.tech/topics/neurips.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Reverse Engineering](<https://devfeed.tech/topics/reverse-engineering.md>), [data](<https://devfeed.tech/topics/data.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [generative](<https://devfeed.tech/tags/generative.md>), [imitation](<https://devfeed.tech/tags/imitation.md>), [learning](<https://devfeed.tech/tags/learning.md>), [models](<https://devfeed.tech/tags/models.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

## AI overview

Stanford AI Lab presents its research at NeurIPS 2021, including work on generative models, recurrent and transformer-based architectures, state-space models, emergent communication, reinforcement learning, imitation learning, and neural coding.

## Source excerpt

The thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021 is being hosted virtually from Dec 6th - 14th. We're excited to share all the work from SAIL that's being presented at the main conference, at the Datasets and Benchmarks track and the various workshops, and you'll find links to papers, videos and blogs below. Some of the members in our SAIL community also serve as co-organizers of several exciting workshops that will take place on Dec 13-14, so we hope you will check them out! Feel free to reach out to the contact authors and the workshop organizers directly to learn more about the work that's happening at Stanford! Main Conference Improving Compositionality of Neural Networks by Decoding Representations to Inputs Authors: Mike Wu, Noah Goodman, Stefano Ermon Contact: wumike@stanford.edu Links: Paper Keywords: generative models, compositionality, decoder Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems Authors: Jimmy T.H. Smith, Scott W. Linderman, David Sussillo Contact: jsmith14@stanford.edu Links: Paper | Website Keywords: recurrent neural networks, switching linear dynamical systems, interpretability, fixed points Compositional Transformers for Scene Generation Authors: Drew A. Hudson, C. Lawrence Zitnick Contact: dorarad@cs.stanford.edu Links: Paper | Github Keywords: GANs, transformers, compositionality, scene synthesis Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers Authors: Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Chris Ré Contact: albertgu@stanford.edu Links: Paper Keywords: recurrent neural networks, rnn, continuous models, state space, long range dependencies, sequence modeling Emergent Communication of Generalizations Authors: Jesse Mu, Noah Goodman Contact: muj@stanford.edu Links: Paper | Video Keywords: emergent communication, multi-agent communication, language grounding, compositionality Deep Lear