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Transformation to simplify classification functions

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Start date : 12 March 2020

offer n° SL-DRT-20-0871

Many of the problems addressed by Artificial Intelligence are problems of classifying complex input data into different classes. The functions transforming the complex input space into a simpler, linearly separable space are done either by learning (deep convolutional networks) or by projecting input data into a high-dimensional space in order to obtain a “rich” non-linear representation of the inputs, then having a linear matching between the high-dimensional space and the output units (the “reservoir computing” approach). These concepts are also linked to the Support Vector Machines (work of Vapnik 1966-1995). The objective of the thesis is to study this type of transformations that can be applicable for real applications, and to define an optimized, generic architecture for a given application domain, allowing data to be pre-processed in order to prepare them for a classification requiring a minimum of operations and which can, for example, be done on the fly (continuous learning).

The targeted research results are multiple:

– From a theoretical point of view, an approach unifying the transformations done by deep learning networks, “reservoir computing” and approaches that transform a complex input space into an essentially linearly separable space.

– Define which transformations should be done in practice for a given class of problems (e. g. object recognition) by simplifying them as much as possible (depending on the error rate, false positives, etc.).

– Propose optimized architectures, making the best use of advanced technologies (semiconductor, 3D stacking, photonics, etc.).

The final result would be the proposal of an optimized module, which could be used as preprocessing unit, to help efficiently perform transfer learning, one shot learning and continuous learning functions for example.

  • Keywords : Engineering sciences, Technological challenges, Artificial intelligence & Data intelligence, Computer science and software, DACLE, Leti
  • Laboratory : DACLE / Leti
  • CEA code : SL-DRT-20-0871
  • Contact : marc.duranton@cea.fr
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