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141
Voted
NIPS
2003
15 years 2 months ago
Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
In this paper we introduce a new underlying probabilistic model for principal component analysis (PCA). Our formulation interprets PCA as a particular Gaussian process prior on a ...
Neil D. Lawrence
NIPS
2000
15 years 2 months ago
An Information Maximization Approach to Overcomplete and Recurrent Representations
The principle of maximizing mutual information is applied to learning overcomplete and recurrent representations. The underlying model consists of a network of input units driving...
Oren Shriki, Haim Sompolinsky, Daniel D. Lee
110
Voted
NIPS
1997
15 years 2 months ago
Hybrid NN/HMM-Based Speech Recognition with a Discriminant Neural Feature Extraction
In thispaper, we present a novelhybridarchitecture forcontinuousspeech recognition systems. It consists of a continuous HMM system extended by an arbitrary neural network that is ...
Daniel Willett, Gerhard Rigoll
118
Voted
NIPS
2004
15 years 2 months ago
Euclidean Embedding of Co-Occurrence Data
Embedding algorithms search for low dimensional structure in complex data, but most algorithms only handle objects of a single type for which pairwise distances are specified. Thi...
Amir Globerson, Gal Chechik, Fernando C. Pereira, ...
114
Voted
NIPS
2004
15 years 2 months ago
Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization
An auditory "scene", composed of overlapping acoustic sources, can be viewed as a complex object whose constituent parts are the individual sources. Pitch is known to be...
Fei Sha, Lawrence K. Saul