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Adaptive Exploitation of Non-Commutative Multimodal Information Structure
- ARMY MURI grant: W911NF-15-1-0479
- ARO Program Manager: Dr. Hamid Krim
MURI Team
- PI:
- Prof. Yoram Bresler, University of Illinois Urbana Champaign
- Co-PI's:
- Prof. Todd Coleman, University of California San Diego
- Prof. Alfred O. Hero, University of Michigan
- Prof. Raj R. Nadakuditi, University of Michigan
- Prof. Robert D. Nowak, University of Wisconsin
- Prof. Maxim Raginsky, University of Illinois Urbana Champaign
- Prof. Silvio Savarese, Stanford University
- 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
10am Overview of MURI accomplishments
Overview (Al Hero and Yoram Bresler) Slides
10:30am Thrust 1 - Non-commutative information: representation of uncertainty
Overview of area (Vahid Tarokh) Slides
Presentations: (Moderator: Al Hero)
- Duke post-doc <Mohammadreza Soltani> Deep neural compression Slides Highlight slide
- Duke post-doc <Khalil El-Khalil> Fisher-autoencoder Slides Highlight Slide
- UM grad student <Bob Malinas> Space-Time Adaptive Processing (STAP) with low sample support Slides Highlight slide
- UIUC co-PI <Max Raginsky> Neural nets, controlled diffusions, and entropic optimal transport Slides Recorded presentation Highlight slide
12pm Breakout sessions I: students will be available for Q&A and to present highlights of their work
- Session 1: Q&A for students who presented in Thrust session 1. Moderator Vahid Tarokh
- Session 2: Preview of talks for students presenting in Thrust session 2. Moderator Todd Coleman
- Session 3: Preview of talks for students presenting in Thrust session 3. Moderator Silvio Savarese
12:30pm Thrust 2: Fusion: multimodal learning in high dimensions
Overview of area (Todd Coleman) Slides
Presentations: (Moderator: Raj Nadakuditi)
- UM grad student <Wayne Wang> SyGlasso graphical models and clustering Slides Highlight slide
- Stanford post-doc <Roberto Martin-Martin> JRDB - the JackRabbot Dataset and Benchmark Slides Highlight slide
- UIUC Co-PI <Yoram Bresler> Robustness in deep learning-based image reconstruction Slides Highlight slide
- UM grad student <Rishi Sonthalia> Fusion from fragments Slides Highlight slide
- UCSD grad student <Anjulie Agrusa> Regularized Wasserstein clustering Slides Highlight slide
2pm Breakout sessions II: students will be available for Q&A and to present highlights of their work
- Session 1: Q&A for students who presented in Thrust session 1. Moderator Al Hero
- Session 2: Q&A for students who presented in Thrust session 2. Moderator Yoram Bresler
- Session 3: Preview of talks for students presenting in Thrust session 3. Moderator Rob Nowak
2:30pm Thrust 3: Non-commutative information gathering and decision making
Overview of area (Rob Nowak) Slides
Presentations: (Moderator: Silvio Savarese)
- Stanford post-doc <Claudia D’Arpino> Robot navigation in constrained pedestrian environments using RL Slides Highlight slide
- UM grad student <Neo Charalambides> Robust elastic computing Slides Highlight slide
- UW grad student <Blake Mason> A Call to All Good Arms Slides Highlight slide
- UW post-doc <Ardhendu Tripathy> Generalized Chernoff sampling SlidesHighlight slide
4pm Wrapup
Wrapup discussion: Al Hero and Yoram Bresler
4:15pm Government caucus and debrief
- Private discussion by Hamid Krim and Committee
- Feedback to MURI team
