We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
This paper studies constant-time approximation algorithms for problems on degree-bounded graphs. Let n and d be the number of vertices and the degree bound, respectively. This pap...
The degree of a CSP instance is the maximum number of times that a variable may appear in the scope of constraints. We consider the approximate counting problem for Boolean CSPs wi...
Martin E. Dyer, Leslie Ann Goldberg, Markus Jalsen...
We introduce several generalizations of classical computer science problems obtained by replacing simpler objective functions with general submodular functions. The new problems i...
Base station location has significant impact on network lifetime performance for a sensor network. For a multihop sensor network, this problem is particular challenging as we need ...