Objective
In machine learning, deep neural networks are powerful computer-based models that use layers of computational units. Current commercial applications for these models include a wide array of software tasks such as image classification, identification of potential drugs, market predictions and speech recognition. Network models must be ‘trained’ using data, and their success hinges critically on the quality of the learning algorithm that is employed. We have recently discovered a novel, biologically inspired algorithm for training deep neural networks that is simpler to implement, more flexible and finds better solutions than existing techniques on bench-mark tests. Thus, our system has the potential to improve performance widely across the many fields that make use of machine learning in software tasks. Furthermore, the simplicity and flexibility of our method means that it could be more easily exploited in hardware devices such as mobile phones and cameras. The central aim of this proposal is to move our new algorithm to a stage where it is ready for commercialization. To do this we plan to accomplish two main areas of work. First, we will research the optimal way to employ our algorithm, establish its performance on a comprehensive set of industry-accepted bench-mark tasks, and compile our research into a manuscript for publication in a leading machine learning journal. Second, we will secure any arising intellectual property in line with the preliminary US patent application that we have already filed, assess application of the algorithm to the different commercial sectors identified through market research, and generate commercial interest in the technology through targeted marketing to relevant companies. This plan of work will confirm the innovation potential of our new algorithm and will establish the technical and commercial feasibility of our discovery.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors optical sensors
- natural sciences computer and information sciences artificial intelligence computer vision image recognition
- engineering and technology electrical engineering, electronic engineering, information engineering information engineering telecommunications mobile phones
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
ERC-2013-PoC
See other projects for this call
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Host institution
OX1 2JD Oxford
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.