—Previous vector architectures divided the available register file space in a fixed number of registers of equal sizes and shapes. We propose a register file organization whic...
Catalin Bogdan Ciobanu, Georgi Kuzmanov, Georgi Ga...
In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion...
Raquel Urtasun, David J. Fleet, Andreas Geiger, Jo...
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...
Background: The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard super...
In this paper we explore avenues for improving the reliability of dimensionality reduction methods such as Non-Negative Matrix Factorization (NMF) as interpretive exploratory data...
Nikolaos Vasiloglou, Alexander G. Gray, David V. A...