We present a subspace learning method, called Local Discriminant Embedding with Tensor representation (LDET), that addresses simultaneously the generalization and data representat...
Graph matching is a classical problem in pattern recognition with many applications, particularly when the graphs are embedded in Euclidean spaces, as is often the case for comput...
Julian McAuley, Teofilo de Campos, Tiberio Caetano
Recursive neural networks are a powerful tool for processing structured data. According to the recursive learning paradigm, the input information consists of directed positional ac...
Monica Bianchini, Marco Gori, Lorenzo Sarti, Franc...
Hashing is fundamental to many algorithms and data structures widely used in practice. For theoretical analysis of hashing, there have been two main approaches. First, one can ass...
Over the past several years, US-CERT advisories, as well as most critical updates from software vendors, have been due to memory corruption vulnerabilities such as buffer overflo...