maximizing a tree series in the representation space
play

Maximizing a Tree Series in the Representation Space Guillaume - PowerPoint PPT Presentation

Maximizing a Tree Series in the Representation Space Guillaume Rabusseau, Fran cois Denis ICGI 2014 September 19, 2014 Overview Starting Point: Metropolis Procedural Modeling 1 Problem Formulation 2 Working in the Representation Space 3


  1. Maximizing a Tree Series in the Representation Space Guillaume Rabusseau, Fran¸ cois Denis ICGI 2014 September 19, 2014

  2. Overview Starting Point: Metropolis Procedural Modeling 1 Problem Formulation 2 Working in the Representation Space 3 Experiments 4 Conclusion 5 Guillaume Rabusseau, Fran¸ cois Denis (Qarma) Maximization in the Representation Space September 19, 2014 2 / 23

  3. Overview Starting Point: Metropolis Procedural Modeling 1 Problem Formulation 2 Preliminaries: Tree Series and the Representation Space Motivations and Problematic Working in the Representation Space 3 Complexity Study Metropolis-Hastings in the Representation Space Experiments 4 Conclusion 5 Guillaume Rabusseau, Fran¸ cois Denis (Qarma) Maximization in the Representation Space September 19, 2014 3 / 23

  4. Metropolis Procedural Modeling [Talton et al., 2011] PCFG G PCFG G , I = · · · t 1 ∈ T G t 2 ∈ T G ˆ t ∈ T G · · · 2 steps: Define a posterior distribution p ( t | I ) ∝ π ( t ) L ( I | t ) on T G Find ˆ t ∈ T G maximizing p ( ·| I ) ⇒ Metropolis-Hastings Guillaume Rabusseau, Fran¸ cois Denis (Qarma) Maximization in the Representation Space September 19, 2014 4 / 23

  5. Metropolis-Hastings Algorithm � p : X → R + such that Z = X p ( x ) d x < ∞ ⇒ ˆ p : x �→ p ( x ) / Z is a probability distribution on X . Guillaume Rabusseau, Fran¸ cois Denis (Qarma) Maximization in the Representation Space September 19, 2014 5 / 23

  6. Metropolis-Hastings Algorithm � p : X → R + such that Z = X p ( x ) d x < ∞ ⇒ ˆ p : x �→ p ( x ) / Z is a probability distribution on X . To sample from ˆ p : (i) Choose a jump distribution q x ( · ) (distribution on X for each x ∈ X ). (ii) Build a Markov chain in X : x n ∈ X Input : Returns : x n+1 ∈ X 1: Draw a candidate x ∗ ∼ q x n ( · ) 2: Accept x ∗ (i.e. x n+1 ← x ∗ , otherwise x n+1 ← x n ) with probability � 1 , p ( x ∗ ) q x ∗ ( x n ) � α ( x n , x ∗ ) = min p ( x n ) q xn ( x ∗ ) Guillaume Rabusseau, Fran¸ cois Denis (Qarma) Maximization in the Representation Space September 19, 2014 5 / 23

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