Idiap has a new opening for 2 PhD positions in machine learning
The Idiap Research Institute, affiliated with École Polytechnique Fédérale de Lausanne, seeks two PhD students in machine learning to develop new techniques to speed up the training of deep architectures using importance sampling, and to learn automatically network architectures from data. The starting date is early 2017.
These positions are funded by the Swiss National Science Foundation, and the candidates will be doctoral students at EPFL. Research will be conducted in the Computer Vision and Learning group at the Idiap research institute, under the supervision of Dr. François Fleuret.
Large neural networks demonstrate excellent performances for applications such as image classification, object detection, speech processing, and natural language processing. They are currently the standard machine-learning tool to deal with such problems when large training sets are available.
Two key issues remain. The first is the computational effort during training which is often the limiting factor in practice. The second is the need for a careful design of the architecture, for which very few heuristics exist. The two lines of research we will pursue aim at addressing the first through the development of novel importance-sampling strategies that focus the computational effort over the samples which influence the parameter optimization the most. The second point will be addressed by revisiting variants of the Boosting algorithm in the context of deep architectures.
This work will mix theoretical developments in machine learning with the implementation and benchmarking of algorithms on real-world data.
Applicants must imperatively be self-sufficient programmers and have a strong background in mathematics. They should be familiar with several of the following topics: probabilities, applied statistics, information theory, signal processing, optimization, algorithmic, and development with some of the modern "deep learning" frameworks (e.g. Torch, Theano, TensorFlow)
To apply for this position, click on the following link: PhD position in machine learning
Please contact firstname.lastname@example.org for additional information.