Main Page

From ARO MURI
Main Page
Jump to: navigation, search
(Thrust 3: Non-commutative information gathering and decision making)
(Detailed Technical Agenda)
Line 58: Line 58:
 
10:30 AM: Overview of area (Vahid Tarokh) [https://drive.google.com/file/d/1qrkzy7DEZNst8ZgcB7CME87-imsDLF4K/view?usp=sharing Slides]
 
10:30 AM: Overview of area (Vahid Tarokh) [https://drive.google.com/file/d/1qrkzy7DEZNst8ZgcB7CME87-imsDLF4K/view?usp=sharing Slides]
 
   
 
   
Presentations: 
+
Presentations: (Moderator: Al Hero)
 
* UM grad student <Bob Malinas> Space-Time Adaptive Processing (STAP) with low sample support [https://drive.google.com/file/d/1Q6teftAjGPEptlmlqeCnDW98VqTVBCTa/view?usp=sharing Slides] [https://drive.google.com/file/d/1U293XvLSkvBiE4HDzvI7ykpyosF0eNWV/view?usp=sharing Highlight slide]
 
* UM grad student <Bob Malinas> Space-Time Adaptive Processing (STAP) with low sample support [https://drive.google.com/file/d/1Q6teftAjGPEptlmlqeCnDW98VqTVBCTa/view?usp=sharing Slides] [https://drive.google.com/file/d/1U293XvLSkvBiE4HDzvI7ykpyosF0eNWV/view?usp=sharing Highlight slide]
 
* Duke grad student <Mohammadreza Soltani> Deep neural compression [https://drive.google.com/file/d/1I9TZ8PJ2wyQ1vnDlvFmJ5ijvhoAMYl9E/view?usp=sharing Slides]  [https://drive.google.com/file/d/1VbDdIE6Iv_wO_gBRCl6xvYs6AEwNNCBE/view?usp=sharing Highlight slide]
 
* Duke grad student <Mohammadreza Soltani> Deep neural compression [https://drive.google.com/file/d/1I9TZ8PJ2wyQ1vnDlvFmJ5ijvhoAMYl9E/view?usp=sharing Slides]  [https://drive.google.com/file/d/1VbDdIE6Iv_wO_gBRCl6xvYs6AEwNNCBE/view?usp=sharing Highlight slide]
Line 68: Line 68:
 
12:30 AM: Overview of area (Todd Coleman) [https://drive.google.com/file/d/1SxFudr1nyRUgw7SH4lMWcw5TKi3ESBQg/view?usp=sharing Slides]
 
12:30 AM: Overview of area (Todd Coleman) [https://drive.google.com/file/d/1SxFudr1nyRUgw7SH4lMWcw5TKi3ESBQg/view?usp=sharing Slides]
 
   
 
   
Presentations:  
+
Presentations: (Moderator: Raj Nadakuditi)
 
* UM grad student <Wayne Wang> SyGlasso graphical models and clustering [https://drive.google.com/file/d/1QElOjJon31mnOxYWCNn_chQaZIqBv3-k/view?usp=sharing Slides] [https://drive.google.com/file/d/1Rmk1QkbG50eFOQyIMkSCM19bF-3h6sk2/view?usp=sharing Highlight slide]
 
* UM grad student <Wayne Wang> SyGlasso graphical models and clustering [https://drive.google.com/file/d/1QElOjJon31mnOxYWCNn_chQaZIqBv3-k/view?usp=sharing Slides] [https://drive.google.com/file/d/1Rmk1QkbG50eFOQyIMkSCM19bF-3h6sk2/view?usp=sharing Highlight slide]
 
* Stanford post-doc <Roberto Martin-Martin> JRDB - the JackRabbot Dataset and Benchmark [https://drive.google.com/file/d/1U8CHsOLb7jdv5j6_fOc9ewXjiqZpH1x3/view?usp=sharing Slides] [https://drive.google.com/file/d/1i4iBpWrQKAOu7vi8xWOZD1ttUw4_a3dD/view?usp=sharing Highlight slide]
 
* Stanford post-doc <Roberto Martin-Martin> JRDB - the JackRabbot Dataset and Benchmark [https://drive.google.com/file/d/1U8CHsOLb7jdv5j6_fOc9ewXjiqZpH1x3/view?usp=sharing Slides] [https://drive.google.com/file/d/1i4iBpWrQKAOu7vi8xWOZD1ttUw4_a3dD/view?usp=sharing Highlight slide]
Line 77: Line 77:
 
====Thrust 3: Non-commutative information gathering and decision making  ====  
 
====Thrust 3: Non-commutative information gathering and decision making  ====  
  
2:30 AM: Overview of area (Rob Nowak and Silvio Savarese) [https://drive.google.com/file/d/1wLwT9w1acrWgweFXkBbjHLZ3PYg41kuC/view?usp=sharing Slides]
+
2:30 AM: Overview of area (Rob Nowak) [https://drive.google.com/file/d/1wLwT9w1acrWgweFXkBbjHLZ3PYg41kuC/view?usp=sharing Slides]
 
   
 
   
Presentations:  
+
Presentations: (Moderator: Silvio Savarese)
 
* Stanford post-doc <Claudia D’Arpino> Reinforcement learning: Social navigation in constrained environments [https://drive.google.com/file/d/1nF5UVW05Awj1Wpn4nG5qja670REDK_iG/view?usp=sharing Highlight slide]
 
* Stanford post-doc <Claudia D’Arpino> Reinforcement learning: Social navigation in constrained environments [https://drive.google.com/file/d/1nF5UVW05Awj1Wpn4nG5qja670REDK_iG/view?usp=sharing Highlight slide]
 
* UM grad student <Neo Charalambides> Robust elastic computing [https://drive.google.com/file/d/1D4bPtc-v4fnInJL1CoeEuHH-g9CGBIeS/view?usp=sharing Slides] [https://drive.google.com/file/d/1-v2n-doBey0fItACSU3t66ZKDFeFjqNG/view?usp=sharing Highlight slide]
 
* UM grad student <Neo Charalambides> Robust elastic computing [https://drive.google.com/file/d/1D4bPtc-v4fnInJL1CoeEuHH-g9CGBIeS/view?usp=sharing Slides] [https://drive.google.com/file/d/1-v2n-doBey0fItACSU3t66ZKDFeFjqNG/view?usp=sharing Highlight slide]
 
* UW grad student <Blake Mason> A Call to All Good Arms [https://drive.google.com/file/d/1St2s9MzGbYoMrhnEiw9fdNZ_SHRl1vry/view?usp=sharing Slides] [https://drive.google.com/file/d/1Lac4E42CW-q7v1iCeNfIzb3wwjjb5RI_/view?usp=sharing Highlight slide]
 
* UW grad student <Blake Mason> A Call to All Good Arms [https://drive.google.com/file/d/1St2s9MzGbYoMrhnEiw9fdNZ_SHRl1vry/view?usp=sharing Slides] [https://drive.google.com/file/d/1Lac4E42CW-q7v1iCeNfIzb3wwjjb5RI_/view?usp=sharing Highlight slide]
 
* UW post-doc <Ardhendu Tripathy> Generalized Chernoff sampling [https://drive.google.com/file/d/1KN29_Kj6ABwfQ-_MJLMkRP4Qdf22YKHU/view?usp=sharing Highlight slide]
 
* UW post-doc <Ardhendu Tripathy> Generalized Chernoff sampling [https://drive.google.com/file/d/1KN29_Kj6ABwfQ-_MJLMkRP4Qdf22YKHU/view?usp=sharing Highlight slide]

Revision as of 18:50, October 20, 2020

Contents

Adaptive Exploitation of Non-Commutative Multimodal Information Structure

MURI Team

  1. Prof. Yoram Bresler, University of Illinois Urbana Champaign
  1. Prof. Todd Coleman, University of California San Diego
  2. Prof. Alfred O. Hero, University of Michigan
  3. Prof. Raj R. Nadakuditi, University of Michigan
  4. Prof. Robert D. Nowak, University of Wisconsin
  5. Prof. Maxim Raginsky, University of Illinois Urbana Champaign
  6. Prof. Silvio Savarese, Stanford University
  7. Prof. Vahid Tarokh, Duke University

A complete roster and web page links may be found at People.

Executive Summary

Non-commutativity is intrinsic to emerging complex sensing and processing systems. The performance of a distributed sensor network depends on the ordering or partial ordering of the sequence of information sharing actions taken across the network. Multiuser brain-computer interfaces provide directed channels of neural communication from human to machine and between humans. Understanding human activities from video requires differentiating between different ordered or partially ordered sequences of gestures and actions. Hierarchical models for signals, images, and videos, such as convolutional networks and HDP representations, are directed graphs that may link multiple layers of data types including real-valued scalars, vectors, and matrices; categorical and ordinal variables (ranks); graphs and hypergraphs; and symbolic categories with complicated spatial and temporal co-dependencies. Extracting useful information from such complex data sources is always constrained by limited resources, such as energy or computation power, limited sensing opportunity (i.e., transient visibility), and limited transmission capacity. The powerful toolboxes of convex optimization, information theory, and statistical machine learning have enabled breakthroughs in computer vision, speech recognition, and database indexing, for which algorithms can be designed offine from large amounts of training data. A grand challenge is to move beyond offine static designs to online, sequential, and adaptive designs of data collection and analysis. To do so, we must grapple with the increased design complexity associated with adaptive procedures. Principal among the reasons for increased complexity is non-commutativity of the sequence of sensing actions: the optimal action depends critically on the result of previous actions. The objective of our proposed effort is to address this challenge by developing new tools for the next generation of adaptive online sensing and processing systems.

Publications

publications: publications resulting from this program.

Annual Review

The 4th annual review of the MURI will take place on Oct 21 2020.

The meeting will be virtual and will take place in the Zoom room https://umich.zoom.us/j/97652740954.

The high level agenda is below. All times are EDT.

10:00 - Introduction (Bresler, Hero and Krim).

10:05 - MURI overview of accomplishments (Al and Yoram)

10:30 - Technical talks

12:00 - East coast lunch break & Poster Session (in Zoom breakout rooms)

12:30 - Technical talks

14:00 - West coast lunch break & Poster Session (in Zoom breakout rooms)

14:30 - Technical talks

16:00 - Wrapup (Al and Yoram)

16:15 - Govt Caucus

17:30 - Adjourn

Detailed Technical Agenda

10 AM: Overview of MURI accomplishments (Al and Yoram)

Thrust 1: Non-commutative information: representation of uncertainty

10:30 AM: Overview of area (Vahid Tarokh) Slides

Presentations: (Moderator: Al Hero)

Thrust 2: Fusion: multimodal learning in high dimensions

12:30 AM: Overview of area (Todd Coleman) Slides

Presentations: (Moderator: Raj Nadakuditi)

Thrust 3: Non-commutative information gathering and decision making

2:30 AM: Overview of area (Rob Nowak) Slides

Presentations: (Moderator: Silvio Savarese)

Personal tools
Namespaces
Variants
Actions
Navigation
Toolbox
EECS @ UM
Tools