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P.F.J. Lermusiaux, P. Lu, M.P. Ueckermann, P.J. Haley, Jr., T. Lolla, W.G. Leslie, C. Mirabito Massachusetts Institute of Technology
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Ongoing MIT research Additional ATL Links Presentations Background information ATL Wiki Web Site |
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| This research sponsored by the Office of Naval Research. | ||
Ongoing MIT research
Our main objective is to research ATL schemes and techniques to be applied to the modeling of ocean processes.
Goals - The specific goals are to:
- Research, apply and implement learning theory and principles to ocean modeling
- Develop idealized and realistic ocean modeling tests beds for ATL research
- Incubate new (machine) learning schemes based on the idealized set-ups and test the best ones in realistic simulations.
- Provide ocean data, simulations, software and methodology to the ATL project research teams
- Collaborate and transfer of expertise, approaches, algorithms and software to and from naval laboratories and centers.
Applications - There are three types of ATL learning applications and scenarios that we are researching using realistic ocean simulations and idealized simulations:
- ATL directly from ocean measurements (either simulated or real): learning science/processes from data alone
- ATL within realistic complex simulations (as complex as real-ocean)
- Science challenge: find new processes in these complex simulations
- Naval challenge: what are the keys for the Naval operator
- ATL from mismatch between models and measurements
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Additional ATL Links
Documents below from ATL meetings are password protected.
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Presentations
Documents below are password protected.
- Tutorial on Ocean Modeling with Active Transfer Learning Focus Presented on 7 January 2011 [PDF - 43MB]
- Active Transfer Learning for Ocean Process Modeling - Presented on 10 February 2011 [PDF - 3.2MB]
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Background information
The overall objective of this program is to conduct basic research that will help enable robust autonomy and automation in dynamic, unconstrained environments and contexts. The two science problems of interest are (i) how a learning machine may leverage all relevant prior knowledge; and, (ii) how it may leverage occasional in situ availability of a subject matter expert (SME). This leads naturally to the existing research of Transfer Learning and Active Learning. The intent of the ATL program is to improve upon these two existing areas of research and combine them to produce a novel, powerful learning capability. The primary deficiency of active learning is that it typically involves only labeling exemplars and does not allow the SME to fully impart his/her rich domain knowledge as they would to a human student. A deficiency of transfer learning is that when it fails it is typically not possible for an SME to repair or complete the transfer in situ. By exploiting both of these deficiencies, this ATL rogram seeks to fundamentally extend the scope of active learning and incorporate it into the knowledge transfer process. The two specific technical goals are to capitalize on the occasional availability of an SME to enable 1) the robust transfer of knowledge from existing sources and 2) the injection of new knowledge in situ. This first technical goal includes both machine-initiated and human-guided exploration of existing knowledge sources as well as machine-based reasoning on knowledge sufficiency for prompting SME queries. This second technical goal includes both machine-initiated queries of target knowledge as well as SME injection of new, rich domain knowledge into the target.
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