We present a unifying framework for information theoretic feature selection, bringing almost two decades of research on heuristic filter criteria under a single theoretical inter...
Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luj...
three different levels of abstraction: detailed models including ctivity dynamics, weight dynamics that abstract from the neural activity dynamics by an adiabatic approximation, an...
This paper addresses the problem of distributed resource allocation in general fork and join processing networks. The problem is motivated by the complicated processing requiremen...
Haiquan (Chuck) Zhao, Cathy H. Xia, Zhen Liu, Dona...
This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is ge...
We study a generalized framework for structured sparsity. It extends the well known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as pa...
Luca Baldassarre, Jean Morales, Andreas Argyriou, ...