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ORCID27th AAAI Workshop: Learning Rich Representations from Low-Level Sensors 2013: Bellevue, WA, USA
- Learning Rich Representations from Low-Level Sensors, Papers from the 2013 AAAI Workshop, Bellevue, Washington, USA, July 15, 2013. AAAI Technical Report WS-13-12, AAAI 2013
- Marc Pickett:
Organizers. - Marc Pickett, Benjamin Kuipers, Yann LeCun, Clayton T. Morrison:
Preface. - Adrian Barbu, Maria Pavlovskaia, Song Chun Zhu:
Rates for Inductive Learning of Compositional Models. - George Dimitri Konidaris, Leslie Pack Kaelbling, Tomás Lozano-Pérez:
Symbol Acquisition for Task-Level Planning. - Ashique Rupam Mahmood, Richard S. Sutton:
Representation Search through Generate and Test. - Amy Sue Fire, Song-Chun Zhu:
Learning Perceptual Causality from Video. - Simon D. Levy, Suraj Bajracharya, Ross W. Gayler:
Learning Behavior Hierarchies via High-Dimensional Sensor Projection. - Michael S. P. Miller:
The Construction of Reality in a Cognitive System. - Joseph Modayil:
Two Perspectives on Learning Rich Representations from Robot Experience. - Jonathan Mugan:
Top-Down Abstraction Learning Using Prediction as a Supervisory Signal. - Marc Pickett:
Building on Deep Learning. - Richard James Rohwer:
Events, Interest, Segmentation, Binding and Hierarchy. - Shawn Squire, Marie desJardins:
Autonomous Hierarchical POMDP Planning from Low-Level Sensors.
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