|Dec 2017||I will join the program committee of ICML/IJCAI 2018.|
|Dec 2017||My coordinate system is now centered at Australia.|
|Oct 2017||I won a prize.|
I study machine learning (sub-discipline of artificial intelligence). My focus is on the geometric approaches, including a geometry of the observed data, and a more intrinsic geometry of information. I try to build connections between these abstract theories and machine learning. I have recently shifted to unsupervised deep learning.
Recent Works(see my GScholar page for a manually maintained full list of publications)
- [link] F. Nielsen and K. Sun. q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators. 2018.
- [homepage][link] K. Sun and F. Nielsen. Relative Fisher Information and Natural Gradient for Learning Large Modular Models. ICML 2017. (Special thanks to IMLS for partially funding the trip.)
- [link]F. Nielsen and K. Sun. Clustering in Hilbert simplex geometry. Technical report. 2017.
- [link]K. Sun and X. Zhang. Coarse Grained Exponential Variational Autoencoders. Technical report. 2017.
- [link]F. Nielsen, K. Sun and S. Marchand-Maillet. On Hölder Projective Divergences. Technical report. 2017.
- [homepage][link] F. Nielsen and K. Sun. Guaranteed Bounds on Information-Theoretic Measures of Univariate Mixtures Using Piecewise Log-Sum-Exp Inequalities. Entropy 2016, 18(12), 442.