This paper introduces a learning method for two-layer feedforward neural networks based on sensitivity analysis, which uses a linear training algorithm for each of the two layers....
This work presents a proposal to diagnose distributed systems utilizing model-based diagnosis using distributed databases. In order to improve aspects as versatility, persistence,...
Abstract. Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) are local in space and time and closely related to a biological model of memory in the prefrontal cortex. N...
We present a technique for coupling simulated fluid phenomena that interact with real dynamic scenes captured as a binocular video sequence. We first process the binocular video s...
Time synchronization is necessary in many distributed systems, but achieving synchronization in sensornets, which combine stringent precision requirements with severe resource con...
Jeremy Elson, Richard M. Karp, Christos H. Papadim...