Sciweavers

NIPS
1997
14 years 5 days ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung
NIPS
1997
14 years 5 days ago
A Hippocampal Model of Recognition Memory
A rich body of data exists showing that recollection of specific information makes an important contribution to recognition memory, which is distinct from the contribution of fam...
Randall C. O'Reilly, Kenneth A. Norman, James L. M...
NIPS
1997
14 years 5 days ago
Learning to Schedule Straight-Line Code
Program execution speed on modern computers is sensitive, by a factor of two or more, to the order in which instructions are presented to the processor. To realize potential execu...
J. Eliot B. Moss, Paul E. Utgoff, John Cavazos, Do...
NIPS
1997
14 years 5 days ago
A Framework for Multiple-Instance Learning
Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many ins...
Oded Maron, Tomás Lozano-Pérez
NIPS
1997
14 years 5 days ago
Multi-modular Associative Memory
Recent imaging studies suggest that object knowledge is stored in the brain as a distributed network of many cortical areas. Motivated by these observations, we study a multi-modu...
Nir Levy, David Horn, Eytan Ruppin
NIPS
1997
14 years 5 days ago
A Neural Network Based Head Tracking System
We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector a...
Daniel D. Lee, H. Sebastian Seung
NIPS
1997
14 years 5 days ago
Learning Human-like Knowledge by Singular Value Decomposition: A Progress Report
Singular value decomposition (SVD) can be viewed as a method for unsupervised training of a network that associates two classes of events reciprocally by linear connections throug...
Thomas K. Landauer, Darrell Laham, Peter W. Foltz
NIPS
1997
14 years 5 days ago
Relative Loss Bounds for Multidimensional Regression Problems
We study on-line generalized linear regression with multidimensional outputs, i.e., neural networks with multiple output nodes but no hidden nodes. We allow at the final layer tra...
Jyrki Kivinen, Manfred K. Warmuth