direct optimization

play

Direct Optimization CSC2547 Adamo Young, Dami Choi, Sepehr Abbasi - PowerPoint PPT Presentation

Aug 29, 2023 •131 likes •1.2k views

Direct Optimization CSC2547 Adamo Young, Dami Choi, Sepehr Abbasi Zadeh Direct Optimization A way to obtain gradient estimates that directly optimizes a non-differentiable objective. It has first appeared in structured prediction

Gumbel Process
Gumbel Process We know:
Gumbel Process We know: Therefore:
Gumbel Process We know:
Gumbel Process A B
Gumbel Process A B
Gumbel Process A B
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value.
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value.
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value.
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value. 1.3
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value. 1.3
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value. 1.3
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value. 1.3 1.3
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value. 1.3 1.3
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value. 1.3 1.3 1.1
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value. 1.3 1.3 1.1
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value. 1.3 1.3 1.1
Trajectory Generation ● Lazily create partitions of trajectories. ● Recursion rule: ○ For , copy parent node’s value. ○ For the remaining choices of actions, group them and compute truncated value. 1.3 1.3 1.1 1.3

Download Presentation

Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

More recommend