Tag Archives: generative modeling

PhD Thesis defense of Jason Ramapuram

Jason Ramapuram will defend on Wednesday the 15th of September 2021 at 13:00 his PhD thesis entitled:  "Finding signals in the void: Improving deep latent variable generative models via supervisory signals present within data."  co-directed by Prof. Stephane Marchand-Maillet and … Continue reading

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Special session in IPIN2021

We are happy to announce that Greg is organising the Special Session "Data Compression, Data Augmentation and Generative Modeling in Indoor Positioning" in the upcoming Indoor Positioning and Indoor Navigation Conference (IPIN2021).

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PhD Thesis defense of Amina Mollaysa

Amina Mollaysa will defend her PhD Thesis entitled "Structural and Functional Regularization of Deep Learning Models" on Friday 26/02/2021, at 15h00 CET. If you are interested in attending the PhD defence of Amina Mollaysa, please send an email with the title "Thesis defense Mollaysa - Zoom" to alexandros.kalousis@hesge.ch to receive the Zoom link.

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PhD student position, University of Geneva (Computer Science) and University of Applied Sciences (Closed position)

We have an opening for a PhD position. The research target is the development of deep generative models that can incorporate strong domain knowledge within the learning process. Such domain knowledge, typically available in scientific fields, can be encoded in … Continue reading

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Eratosthenes: Deep generative modeling for indoor and outdoor positioning with fingerprinting methods

Eratosthenes is a research project funded under the Spark funding scheme of the Swiss National Science Foundation (SNSF). The aim of the Spark is to fund postdoctoral researchers to implement "projects that show unconventional thinking and introduce a unique approach". The relevant criteria for the award … Continue reading

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Lifelong Generative Modeling

The case for lifelong learning Lifelong learning (also known as continual learning) is the problem of learning multiple consecutive tasks in a sequential manner where knowledge gained from previous tasks is retained and used for future learning [1]. Living in … Continue reading

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