Paolo Frasconi

Learning long-term dependencies with gradient descent is difficult

Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties hav...

The problem of learning long-term dependencies in recurrent networks

The authors seek to train recurrent neural networks in order to map input sequences to output sequences, for applications in sequence recognition or production. Results are pres...

Local Feedback Multilayered Networks

In this paper, we investigate the capabilities of local feedback multilayered networks, a particular class of recurrent networks, in which feedback connections are only allowed ...

Unified integration of explicit knowledge and learning by example in recurrent networks

We propose a novel unified approach for integrating explicit knowledge and learning by example in recurrent networks. The explicit knowledge is represented by automaton rules, w...

Hidden Markov Models for Text Categorization in Multi-Page Documents