ODE | Neural Ordinary Differential Equations - Best Paper Awards NeurIPS

Neural Ordinary Differential Equations at NeurIPS 2018
-----------------------------------------------------------------------------------------
By Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
-----------------------------------------------------------------------------------------
Credit to David Duvenaud and NeurIPS 2018
-----------------------------------------------------------------------------------------
Video source:   / 265425524142328  
The paper: https://arxiv.org/pdf/1806.07366.pdf
The source code: https://github.com/rtqichen/torchdiffeq
The slides: https://www.cs.toronto.edu/~duvenaud/...

A follow-up paper applying this to generative density modelling:
Paper: https://arxiv.org/abs/1810.01367
Code: https://github.com/rtqichen/ffjord

Reddit post:   / neural_ordinary_differential_equations_pdf  
-----------------------------------------------------------------------------------------
The Conference and Workshop on Neural Information Processing Systems (NeurIPS, formerly called NIPS) is a machine learning and computational neuroscience conference held every December.

Welcome to AIP.
The main focus of this channel is to publicize and promote existing SoTA AI research works presented in top conferences, removing barrier for people to access the cutting-edge AI research works.
All videos are either taken from the public internet or the Creative Common licensed, which can be accessed via the link provided in the description.
To avoid conflict of interest with the ongoing conferences, all videos are published at least 1 week after the main events. A takedown can be requested if it infringes your right via email.
If you would like your presentation to be published on AIP, feel free to drop us an email.
AI conferences covered include: NeurIPS (NIPS), AAAI, ICLR, ICML, ACL, NAACL, EMNLP, IJCAI

If you would like to support the channel, please join the membership:
   / aipursuit  

Subscribe to the channel:
https://www.youtube.com/c/AIPursuit?s...

Donation:
w/ BEP20 (BTC, ETH, USDT, SOL, BNB, Doge, Shiba) ⇢ 0x0712795299bf00eee99f13b4cda0e19dc656bf2c

USDT (TRN20) ⇢ THV9dCnGfWtGeAiZEBZVWHw8JGdGCWC4Sh

The video is reposted for educational purposes and encourages involvement in the field of AI research.

0:00 Neural Ordinary Differential Equations
0:10 Background: ODE Solvers
1:30 Resnets as Euler integrators
2:02 Related Work
3:29 How to train an ODE net?
3:58 Continuous-time Backpropagation
4:35 O(1) Memory Gradients
5:03 Drop-in replacement for Resnets
5:39 How deep are ODE-nets?
6:59 Explicit Error Control
7:41 Continuous-time models
8:36 Poisson Process Likelihoods
9:22 Instantaneous Change of Variables
9:59 Continuous Normalizing Flows Density
11:09 PyTorch Code Available