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NIPS
1998
13 years 5 months ago
Bayesian Modeling of Facial Similarity
In previous work 6, 9, 10], we advanced a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measur...
Baback Moghaddam, Tony Jebara, Alex Pentland
NIPS
1998
13 years 5 months ago
Computational Differences between Asymmetrical and Symmetrical Networks
Symmetrically connected recurrent networks have recently been used as models of a host of neural computations. However, biological neural networks have asymmetrical connections, at...
Zhaoping Li, Peter Dayan
NIPS
1998
13 years 5 months ago
Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations
A common way to represent a time series is to divide it into shortduration blocks, each of which is then represented by a set of basis functions. A limitation of this approach, ho...
Michael S. Lewicki, Terrence J. Sejnowski
NIPS
1998
13 years 5 months ago
Learning a Continuous Hidden Variable Model for Binary Data
A directed generative model for binary data using a small number of hidden continuous units is investigated. A clipping nonlinearity distinguishes the model from conventional prin...
Daniel D. Lee, Haim Sompolinsky
NIPS
1998
13 years 5 months ago
A Polygonal Line Algorithm for Constructing Principal Curves
Principal curves have been defined as "self consistent" smooth curves which pass through the "middle" of a d-dimensional probability distribution or data cloud...
Balázs Kégl, Adam Krzyzak, Tam&aacut...
NIPS
1998
13 years 5 months ago
Analyzing and Visualizing Single-Trial Event-Related Potentials
Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield,...
NIPS
1998
13 years 5 months ago
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
Michael J. Kearns, Satinder P. Singh
NIPS
1998
13 years 5 months ago
Inference in Multilayer Networks via Large Deviation Bounds
We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic graphs. We show that the intractability of exact inference in such networks do...
Michael J. Kearns, Lawrence K. Saul
NIPS
1998
13 years 5 months ago
Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm
We present the CEM (Conditional Expectation Maximization) algorithm as an extension of the EM (Expectation Maximization) algorithm to conditional density estimation under missing ...
Tony Jebara, Alex Pentland