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New methods for Deep Learning

In deep learning, hundreds of successive computations are combined together to learn very complex tasks. This how computers and phones now recognize faces in images or translate languages. In the new paper by the FCAI team, combining all the computations together is replaced with a continuous transforming flow of inputs, which are used to perform the learning task in way that’s easier to interpret. The work also presents a new connection between deep learning and a group of mathematical models called “stochastic dynamical systems”. This connection means that, compared to common neural networks, the new method can understand how much uncertainty there is in the prediction process. This understanding of uncertainty means the new method excels at learning models where there are smaller amounts of data – potentially useful for future applications like personalized medicine or drug design.

Researchers from Aalto also presented the following talks and posters:

Talks

• Deep learning with differential Gaussian process flows

  • Pashupati Hegde,  Markus Heinonen, Harri Lähdesmäki, Samuel Kaski

Posters

• Analysis of Network Lasso for Semi-Supervised Regression

  • Alexander Jung, Natalia Vesselinova,

• Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution

  • Topi Paananen, Juho Piironen (Curious AI); Michael Andersen, Aki Vehtari  

• Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

  • Arno Solin  

• Harmonizable mixture kernels with variational Fourier features

  • Zheyang Shen, Markus Heinonen, Samuel Kaski