We present a general machine learning framework for modelling the phenomenon of missing information in data. We propose a masking process model to capture the stochastic nature of...
We propose a new class of consistency constraints for Linear Programming (LP) relaxations for finding the most probable (MAP) configuration in graphical models. Usual cluster-base...
Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stag...
Jonathan Pillow, Liam Paninski, Eero P. Simoncelli
A data structure called PC-tree is introduced as a generalization of PQ-trees. PC-trees were originally introduced in a planarity test of Shih and Hsu [7] where they represent par...
Reversibility is of interest in achieving extremely low power dissipation; it is also an inherent design requirement of quantum computation. Logical fault models for conventional ...
Ilia Polian, Thomas Fiehn, Bernd Becker, John P. H...