Deep Learning (DL)

The summary for the Deep Learning (DL) Federal Grant is detailed below. It contains information such as the Catalog of Federal Domestic Assistance (CFDA) number, the government agency offering the grant, funding award amounts, and important deadlines. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed in the Grant Announcement Contact section.

Federal Grant Title:	Deep Learning (DL)
CFDA Number:	12.910
CFDA Description:	Research and Technology Development
Federal Agency Name:	DARPA Information Processing Technology Office
Category of Funding Activity:	Science and Technology
Category Explanation:	Information not provided
Opportunity Category:	Discretionary
Funding Opportunity Number:	DARPA-BAA-09-40
Document Type:	Modification to Previous Grants Notice
Funding Instrument Type:	Cooperative Agreement Grant Other Procurement Contract
Posted Date:	Apr 15, 2009
Creation Date:	May 06, 2009
Original Closing Date for Applications:	Apr 14, 2010
Current Closing Date for Applications:	Apr 14, 2010
Archive Date:	May 14, 2010
Expected Number of Awards:	Information not provided
Estimated Total Program Funding:	Information not provided
Federal Grant Award Ceiling:	Information not provided
Federal Grant Award Floor:	Information not provided
Cost Sharing or Matching Requirement:	No

Applicants Eligible for this Grant: Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled "Additional Information on Eligibility"
Additional Information on Eligibility: Information not provided
Grant Description: DARPA is soliciting innovative research proposals in the area of deeply layered machine learning or, simply, Deep Learning (DL). Over the course of the envisioned program, performer teams will build a universal machine learning engine that uses a single set of methods in multiple layers (at least three internally) to generate progressively more sophisticated representations of patterns, invariants, and correlations from data inputs. The engine is expected to be applicable to multiple input modalities given only changes to the inputs' preprocessing, and is expected to be able to learn important characteristics of the inputs and produce useful representations solely on the basis of unlabeled inputs. Accomplishing many of the tasks set by the program will require that the engine be able to produce and utilize sophisticated spatio-temporal representations.
Link to Full Grant Announcement: https://www.fbo.gov/index?s=opportunity&mode=form&id=eb010502ff9dc69e92b86d3edd1b8ae9&tab=core&_cview=1
Grant Announcement Contact: Josh Alspector, Program Manager BAA Coordinator [DARPA-BAA-09-40@darpa.mil]
Similar Government Grants: • Tactical Expandable Maritime Platform (TEMP); • Antenna Element for Volumetric Interferometry (AEVI); • Oh By The Way (OBTW); • Clean-slate design of Resilient, Adaptive, Secure Hosts (CRASH); • Maximum Mobility and Manipulation (M3); • Wide Band Gap Semiconductors for RF Applications (WBGS-RF); • Robust Integrated Power Electronics (RIPE); • Microantenna Arrays:Technology and Applications (MIATA); • Imaging Coherent Optical Radar (ICHOR); • Terahertz Imaging Focal Plane Array Technology (TIFT)