Avogadro-Scale Engineering:

Slides:



Advertisements
Similar presentations
Mean-Field Theory and Its Applications In Computer Vision1 1.
Advertisements

Bayesian Belief Propagation
Tapestry: Decentralized Routing and Location SPAM Summer 2001 Ben Y. Zhao CS Division, U. C. Berkeley.
Bozidar Stojadinovic Gilberto Mosqueda UC Berkeley NEES FAST-MOST.
Join-graph based cost-shifting Alexander Ihler, Natalia Flerova, Rina Dechter and Lars Otten University of California Irvine Introduction Mini-Bucket Elimination.
Junction Trees And Belief Propagation. Junction Trees: Motivation What if we want to compute all marginals, not just one? Doing variable elimination for.
Discrete Optimization in Computer Vision Nikos Komodakis Ecole des Ponts ParisTech, LIGM Traitement de l’information et vision artificielle.
Loopy Belief Propagation a summary. What is inference? Given: –Observabled variables Y –Hidden variables X –Some model of P(X,Y) We want to make some.
Cambridge, Massachusetts Analog Logic Ben Vigoda.
Convergent Message-Passing Algorithms for Inference over General Graphs with Convex Free Energies Tamir Hazan, Amnon Shashua School of Computer Science.
Randomized Accuracy Aware Program Transformations for Efficient Approximate Computations Sasa Misailovic Joint work with Zeyuan Allen ZhuJonathan KelnerMartin.
Introduction to Belief Propagation and its Generalizations. Max Welling Donald Bren School of Information and Computer and Science University of California.
3 March, 2003University of Glasgow1 Statistical-Mechanical Approach to Probabilistic Inference --- Cluster Variation Method and Generalized Loopy Belief.
Belief Propagation by Jakob Metzler. Outline Motivation Pearl’s BP Algorithm Turbo Codes Generalized Belief Propagation Free Energies.
What Are Partially Observable Markov Decision Processes and Why Might You Care? Bob Wall CS 536.
Belief Propagation on Markov Random Fields Aggeliki Tsoli.
CS774. Markov Random Field : Theory and Application Lecture 04 Kyomin Jung KAIST Sep
Approximation Algoirthms: Semidefinite Programming Lecture 19: Mar 22.
AAMAS 2009, Budapest1 Analyzing the Performance of Randomized Information Sharing Prasanna Velagapudi, Katia Sycara and Paul Scerri Robotics Institute,
Distributed Message Passing for Large Scale Graphical Models Alexander Schwing Tamir Hazan Marc Pollefeys Raquel Urtasun CVPR2011.
Yashar Ganjali Computer Systems Laboratory Stanford University February 13, 2003 Optimal Routing in the Internet.
Semidefinite Programming
Recent Development on Elimination Ordering Group 1.
Two Approaches to Multiphysics Modeling Sun, Yongqi FAU Erlangen-Nürnberg.
Learning Low-Level Vision William T. Freeman Egon C. Pasztor Owen T. Carmichael.
. Applications and Summary. . Presented By Dan Geiger Journal Club of the Pharmacogenetics Group Meeting Technion.
Chapter 3 Parallel Algorithm Design. Outline Task/channel model Task/channel model Algorithm design methodology Algorithm design methodology Case studies.
Probabilistic Graphical Models
Physics Fluctuomatics / Applied Stochastic Process (Tohoku University) 1 Physical Fluctuomatics Applied Stochastic Process 9th Belief propagation Kazuyuki.
Tokyo Institute of Technology, Japan Yu Nishiyama and Sumio Watanabe Theoretical Analysis of Accuracy of Gaussian Belief Propagation.
Physics Fluctuomatics (Tohoku University) 1 Physical Fluctuomatics 12th Bayesian network and belief propagation in statistical inference Kazuyuki Tanaka.
Readings: K&F: 11.3, 11.5 Yedidia et al. paper from the class website
Teacher: Chun-Yuan Lin
Learning With Bayesian Networks Markus Kalisch ETH Zürich.
Physics Fluctuomatics (Tohoku University) 1 Physical Fluctuomatics 7th~10th Belief propagation Kazuyuki Tanaka Graduate School of Information Sciences,
Belief Propagation and its Generalizations Shane Oldenburger.
Inference for Learning Belief Propagation. So far... Exact methods for submodular energies Approximations for non-submodular energies Move-making ( N_Variables.
Practical Message-passing Framework for Large-scale Combinatorial Optimization Inho Cho, Soya Park, Sejun Park, Dongsu Han, and Jinwoo Shin KAIST 2015.
Join-graph based cost-shifting Alexander Ihler, Natalia Flerova, Rina Dechter and Lars Otten University of California Irvine Introduction Mini-Bucket Elimination.
@ 15/7/2003 Tokyo Institute of Technology 1 Propagating beliefs in spin- glass models Yoshiyuki Kabashima Dept. of Compt. Intel. & Syst.
Analog Logic and Differential Belief Propagation Benjamin Vigoda, Ph.D. NSF CCR Physics and Media Group MIT Media Laboratory Benjamin Vigoda, November,
1 Relational Factor Graphs Lin Liao Joint work with Dieter Fox.
Daphne Koller Overview Maximum a posteriori (MAP) Probabilistic Graphical Models Inference.
Bayesian Belief Propagation for Image Understanding David Rosenberg.
30 November, 2005 CIMCA2005, Vienna 1 Statistical Learning Procedure in Loopy Belief Propagation for Probabilistic Image Processing Kazuyuki Tanaka Graduate.
ベーテ自由エネルギーに対するCCCPアルゴリズムの拡張
Graduate School of Information Sciences, Tohoku University
Introduction of BP & TRW-S
Distributed Vehicle Routing Approximation
Today.
StatSense In-Network Probabilistic Inference over Sensor Networks
MULTISCALE OPTIMIZATION Desired Multiscale Objectives
Towards Next Generation Panel at SAINT 2002
Mathematical Programming (towards programming with math)
Generalized Belief Propagation
Graduate School of Information Sciences, Tohoku University
Physical Fluctuomatics 7th~10th Belief propagation
Expectation-Maximization & Belief Propagation
CSC4005 – Distributed and Parallel Computing
Graduate School of Information Sciences, Tohoku University
Probabilistic image processing and Bayesian network
Avogadro-Scale Engineering:
Clique Tree Algorithm: Computation
Readings: K&F: 11.3, 11.5 Yedidia et al. paper from the class website
Graduate School of Information Sciences, Tohoku University
Graduate School of Information Sciences, Tohoku University
Mean Field and Variational Methods Loopy Belief Propagation
Generalized Belief Propagation
MULTISCALE OPTIMIZATION Desired Multiscale Objectives
Kazuyuki Tanaka Graduate School of Information Sciences
Presentation transcript:

Avogadro-Scale Engineering: Form and Function http://cba.mit.edu NSF CCR-0122419

Statistical Mechanics macroscopic state internal configuration microscopic degrees of freedom

Statistical-Mechanical Engineering? macroscopic goal internal configuration microscopic degrees of freedom

Programming Distributed Systems problem algorithm program executable protocol messages

Graphical Message-Passing problem algorithm program executable protocol messages

Graphical Networks

trees: exact loops: approximations Sum Product Belief propagation, …, (Loeliger, …) fm xi fn xj trees: exact loops: approximations

Bethe Approximation (Jonathan Yedidia) fn xi

CDMA LFSR code acquisition Noise-Locked Loops (Ben Vigoda) CDMA LFSR code acquisition

Local Search

Global Convex Optimization (Pablo Parrilo et al.) Interior point solution to a semidefinite program on a relaxed problem

programming computer programming linear programming semidefinite programming integer programming dynamic progamming toolpath programming fault-tolerant programming developmental programming programming