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...
With resemblance of finite-state machines to some biological mechanisms in cells and numerous applications of finite automata in different fields, this paper uses analogies an...
An essential step in designing a new computer architecture is the careful examination of different design options. It is critical that computer architects have efficient means by ...
Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Cald...
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...
Automatically acquiring control-knowledge for planning, as it is the case for Machine Learning in general, strongly depends on the training examples. In the case of planning, examp...