​​ODE DL paper with overview

This paper recieved award at #NeurIPS2018. Main idea: defining a deep residual network as a continuously evolving system & instead of updating the hidden units layer by layer, define their derivative with respect to depth instead.

ArXiV: https://arxiv.org/pdf/1806.07366.pdf
GitHub: https://github.com/rtqichen/torchdiffeq
Overview: https://rkevingibson.github.io/blog/neural-networks-as-ordinary-differential-equations/

#ODE #DL #NeurIPS

Function-Space Distributions over Kernels

With a function-space approach to kernel learning helps to incorporate interpretable inductive biases, manage uncertainty, and discover rich representations of data.

ArXiV: https://arxiv.org/abs/1910.13565

#gaussianprocess #NeurIPS #NeurIPS2019 #FKL #kernellearning

​​What we learned from NeurIPS 2019 data

x4 growth since 2014
21.6% acceptance rate

Takeaways:

1. No free-loader problem: Relatively few papers are submitted where none of the authors invited to participate in the review process accepted the invitation
2. Unclear how to rapidly filter papers prior to full review: Allowing for early desk rejects by ACs is unlikely to have a significant impact on reviewer load without producing inappropriate decisions. Likewise, the eagerness of reviewers to review a particular paper is not a strong signal, either.
3. No clear evidence that review quality as measured by length is lower for NeurIPS: NeurIPS is surprisingly not much different from other conferences of smaller sizes when it comes to review length.
4. Impact of engagement in rebuttal/discussion period: Overall engagement seemed to be higher than in 2018.

#Nips #NeurIPS #NIPS2019 #conference #meta

NeurIPS slides and presentations link

Link: https://slideslive.com/neurips/
Brief paper overview videos: https://nips.cc/Conferences/2019/Videos

#NeurIPS #NIPS #NIPS2019

#NLP #News (by Sebastian Ruder):
* 2020 NLP wish lists
* #HuggingFace + #fastai
* #NeurIPS 2019
* #GPT2 things
* #ML Interviews

blog post: http://newsletter.ruder.io/archive/211277

​​Few-shot Video-to-Video Synthesis

it's the pytorch implementation for few-shot photorealistic video-to-video (vid2vid) translation.
it can be used for generating human motions from poses, synthesizing people talking from edge maps, or turning semantic label maps into photo-realistic videos.
the core of vid2vid translation is image-to-image translation.

blog post: https://nvlabs.github.io/few-shot-vid2vid/
paper: https://arxiv.org/abs/1910.12713
youtube: https://youtu.be/8AZBuyEuDqc
github: https://github.com/NVlabs/few-shot-vid2vid

#cv #nips #neurIPS #pattern #recognition #vid2vid #synthesis

Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020

Good thread about what ML scientists do experiments on their papers


twitter: https://twitter.com/deliprao/status/1235697595919421440
report: https://hal.archives-ouvertes.fr/hal-02447823/document

#Survey #NeurIPS #ICLR #Experiments #ml