Sciweavers

NIPS
2003
14 years 28 days ago
Denoising and Untangling Graphs Using Degree Priors
This paper addresses the problem of untangling hidden graphs from a set of noisy detections of undirected edges. We present a model of the generation of the observed graph that in...
Quaid Morris, Brendan J. Frey
NIPS
2003
14 years 28 days ago
A Recurrent Model of Orientation Maps with Simple and Complex Cells
We describe a neuromorphic chip that utilizes transistor heterogeneity, introduced by the fabrication process, to generate orientation maps similar to those imaged in vivo. Our mo...
Paul Merolla, Kwabena Boahen
NIPS
2003
14 years 28 days ago
Online Learning of Non-stationary Sequences
We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. We derive upper and lower relative loss bounds for a cla...
Claire Monteleoni, Tommi Jaakkola
NIPS
2003
14 years 28 days ago
Distributed Optimization in Adaptive Networks
We develop a protocol for optimizing dynamic behavior of a network of simple electronic components, such as a sensor network, an ad hoc network of mobile devices, or a network of ...
Ciamac Cyrus Moallemi, Benjamin Van Roy
NIPS
2003
14 years 28 days ago
Identifying Structure across Pre-partitioned Data
We propose an information-theoretic clustering approach that incorporates a pre-known partition of the data, aiming to identify common clusters that cut across the given partition...
Zvika Marx, Ido Dagan, Eli Shamir
NIPS
2003
14 years 28 days ago
ARA*: Anytime A* with Provable Bounds on Sub-Optimality
Maxim Likhachev, Geoffrey J. Gordon, Sebastian Thr...
NIPS
2003
14 years 28 days ago
ICA-based Clustering of Genes from Microarray Expression Data
We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression pa...
Su-In Lee, Serafim Batzoglou
NIPS
2003
14 years 28 days 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