In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three ma...
Shawn Martin, George Davidson, Elebeoba E. May, Je...
This paper is devoted to the analysis of network approximation in the framework of approximation and regularization theory. It is shown that training neural networks and similar n...
We propose, and justify, an economic theory to guide memory system design, operation, and analysis. Our theory treats memory random-access latency, and its cost per installed mega...
This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet-networks are inspired by both feed-forward neural networks and the theo...
This paper presents a robust scalable video coding scheme with leaky prediction, suitable for time-varying error-prone channels, such as the Internet or wireless channels. The pro...