Six papers by FCAI researchers at NIPS 2017

At the Conference and Workshop on Neural Information Processing Systems NIPS 2017 in California in December, FCAI researchers presented altogether six papers. The 2017 conference broke all previous attendance records which in itself is a clear sign of the booming and wide-spread interest on artificial intelligence research.

The presented papers at NIPS 2017:

Kari Rantanen, Antti Hyttinen, Matti Järvisalo
Learning Chordal Markov Networks via Branch and Bound

Sami Remes, Markus Heinonen, Samuel Kaski
Non-Stationary Spectral Kernels

Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela
Differentially private Bayesian learning on distributed data

Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hao, Antti Rasmus, Rinu Boney, Harri Valpola
Recurrent Ladder Networks

Antti Tarvainen, Harri Valpola
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Kiran Garimella, Aristides Gionis, Nikos Parotsidis, Nikolaj Tatti
Balancing information exposure in social networks