Learning Rich Representations from Low-Level Sensors
Papers from the 2013 AAAI Workshop
Marc Pickett, Benjamin Kuipers, Yann LeCun, Clayton Morrison Organizers
AAAI Technical Report WS-13-12
published by The AAAI Press, Palo Alto, California
This technical report is also available in book format.
Contents
Organizers
Marc Pickett
Preface
Marc Pickett, Benjamin Kuipers, Yann LeCun, Clayton Morrison
Rates for Inductive Learning of Compositional Models
Adrian Barbu, Maria Pavlovskaia, Song Chun Zhu
Symbol Acquisition for Task-Level Planning
George Konidaris, Leslie Pack Kaelbling, Tomas Lozano-Perez
Representation Search through Generate and Test
Ashique Rupam Mahmood, Richard S. Sutton
Learning Perceptual Causality from Video
Amy Sue Fire, Song-Chun Zhu
Learning Behavior Hierarchies via High-Dimensional Sensor Projection
Simon D. Levy, Suraj Bajracharya, Ross W. Gayler
The Construction of Reality in a Cognitive System
Michael S. P. Miller
Two Perspectives on Learning Rich Representations from Robot Experience
Joseph Modayil
Top-Down Abstraction Learning Using Prediction as a Supervisory Signal
Jonathan Mugan
Building on Deep Learning
Marc Pickett
Events, Interest, Segmentation, Binding and Hierarchy
Richard James Rohwer
Autonomous Hierarchical POMDP Planning from Low-Level Sensors
Shawn Squire, Marie desJardins
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