Statistical Learning
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
Lin Chen, Hossein Esfandiari, Gang Fu, Vahab Mirrokni
Neural Information Processing Systems http://nips.cc/
Shaping the distribution of neural responses with interneurons in a recurrent circuit model
Neural circuits are typically comprised of populations of primary (excitatory) neurons and local (inhibitory) interneurons.
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves
Stefan Meintrup, Alexander Munteanu, Dennis Rohde
Neural Information Processing Systems http://nips.cc/
ae07d152c51ea2ddae65aa7192eb5ff7-Supplemental-Conference.pdf
Recent work has shown that a much simpler model, simple graph convolution (SGC) (Wu et al., 2019),iscompetitivewithGCNs incommon graph machine learning benchmarks.
AdaptiveProximalGradientMethodsforStructured NeuralNetworks
The technique of regularization is ubiquitous in machine learning as it can effectively prevent overfitting and yield better generalization.