Thread by @ZetaVector

Want to dive into #NeurIPS2021 but don't know where to start?

Here're some ideas! A thread🧵👇

1. "A 3D Generative Model for Structure-Based Drug Design" is one of the multiple papers at NeurIPS about drug discovery using neural networks.

This model generates molecules that bind to a specific protein binding site.

By Shitong Luo et al.

papers.nips.cc/paper/2021/has…

2. "The Emergence of Objectness: Learning Zero-shot Segmentation from Videos" by Runtao Liu et al.

Leveraging clever self supervision with videos to segment objects without labels.

papers.nips.cc/paper/2021/has…

3. "Multimodal Few-Shot Learning with Frozen Language Models" by @jacobmenick @serkancabi @arkitus @OriolVinyalsML @FelixHill84 .

Freeze a pre-trained LM and train a vision encoder for prompting the LM to perform vision/language tasks.

papers.nips.cc/paper/2021/has…

4. "Efficient Training of Retrieval Models using Negative Cache" by Erik Lindgren et al.

A proposal to train dense retrieval without needing huge batches and a lot of memory.

papers.nips.cc/paper/2021/has…

5. "VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text" by @AkbariH70 Liangzhe Yuan @RuiQian3 Wei-Hong Chuang, Shih-Fu Chang, @YinCui1 @BoqingGo.

The promised multimodal future is here!

papers.nips.cc/paper/2021/has…

6. "Robust Predictable Control" by @ben_eysenbach, @rsalakhu and @svlevine.

Seing RL through the lens of compression. How do agents behave when they favor policies that are compressible (i.e. easy to predict)?

papers.nips.cc/paper/2021/has…

7. "FLEX: Unifying Evaluation for Few-Shot NLP" by Jonathan Bragg et al.

Now we can apples-to-apples comparisons of few-shot performance!

papers.nips.cc/paper/2021/has…

8. "Partition and Code: learning how to compress graphs" by @gbouritsas @loukasa_tweet @AspectStalence @mmbronstein

How would you build a native compression algorithm for graphs? -> partition and code

papers.nips.cc/paper/2021/has…

9. "Learning to Draw: Emergent Communication through Sketching" by @DanielaMihai13 and @jon_hare

How two models pretty much learn to play Pictionary.

papers.nips.cc/paper/2021/has…

10. "Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems" by Ruihan Wu et al.

Watch out! Improving parts of an ML model might lead to worse overall performance...😔

papers.nips.cc/paper/2021/has…

Read our comments on this selection and more interesting papers on our blog zeta-alpha.com/post/neurips-2…