This paper describes an architecture based on spatiotemporal networks that identifies sequences of numbers. This architecture incorporates an input layer that transforms (by means...
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these...
Christopher J. C. Burges, Tal Shaked, Erin Renshaw...
— Handling catastrophic forgetting is an interesting and challenging topic in modeling the memory mechanisms of the human brain using machine learning models. From a more general...
In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data. In particular a ne...
— Since we can accumulate a huge amount of data including useless information in these years, it is important to investigate various extraction method of clusters from data inclu...