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NIPS
1997
14 years 11 months 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
AGENTS
2001
Springer
15 years 2 months ago
Using background knowledge to speed reinforcement learning in physical agents
This paper describes Icarus, an agent architecture that embeds a hierarchical reinforcement learning algorithm within a language for specifying agent behavior. An Icarus program e...
Daniel G. Shapiro, Pat Langley, Ross D. Shachter
CVPR
2004
IEEE
15 years 11 months ago
High-Zoom Video Hallucination by Exploiting Spatio-Temporal Regularities
In this paper, we consider the problem of super-resolving a human face video by a very high (?16) zoom factor. Inspired by recent literature on hallucination and examplebased lear...
Göksel Dedeoglu, Jonas August, Takeo Kanade
CORR
2011
Springer
181views Education» more  CORR 2011»
14 years 1 months ago
SmartInt: Using Mined Attribute Dependencies to Integrate Fragmented Web Databases
Many web databases can be seen as providing partial and overlapping information about entities in the world. To answer queries effectively, we need to integrate the information ab...
Ravi Gummadi, Anupam Khulbe, Aravind Kalavagattu, ...
ICPR
2008
IEEE
15 years 4 months ago
A 2D model for face superresolution
Traditional face superresolution methods treat face images as 1D vectors and apply PCA on the set of these 1D vectors to learn the face subspace. Zhang et al [7] proposed Two-dire...
B. G. Vijay Kumar, Rangarajan Aravind