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ODE | Neural Ordinary Differential Equations - Best Paper Awards NeurIPS
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Neural Ordinary Differential Equations at NeurIPS 2018
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By Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
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Credit to David Duvenaud and NeurIPS 2018
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A follow-up paper applying this to generative density modelling:
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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.
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- 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
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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
-----------------------------------------------------------------------------------------
By Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
-----------------------------------------------------------------------------------------
Credit to David Duvenaud and NeurIPS 2018
-----------------------------------------------------------------------------------------
A follow-up paper applying this to generative density modelling:
-----------------------------------------------------------------------------------------
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:
Subscribe to the channel:
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
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