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» Learning to Generate Fast Signal Processing Implementations
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109
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DATE
2009
IEEE
135views Hardware» more  DATE 2009»
15 years 9 months ago
Heterogeneous coarse-grained processing elements: A template architecture for embedded processing acceleration
Reconfigurable Architectures are good candidates for application accelerators that cannot be set in stone at production time. FPGAs however, often suffer from the area and perfor...
Giovanni Ansaloni, Paolo Bonzini, Laura Pozzi
119
Voted
ICASSP
2010
IEEE
15 years 2 months ago
High frame rate Motion Compensated Frame Interpolation in High-Definition video processing
Numerous MCFI methods have been proposed to increase the frame rate in the past ten years. However, these methods usually focus on how to double the frame rate and involve complex...
Yen-Lin Lee, Truong Nguyen
91
Voted
ACL
2006
15 years 3 months ago
FAST - An Automatic Generation System for Grammar Tests
This paper introduces a method for the semi-automatic generation of grammar test items by applying Natural Language Processing (NLP) techniques. Based on manually-designed pattern...
Chia-Yin Chen, Hsien-Chin Liou, Jason S. Chang
ICRA
2010
IEEE
137views Robotics» more  ICRA 2010»
15 years 25 days ago
Robot reinforcement learning using EEG-based reward signals
Abstract— Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These r...
Iñaki Iturrate, Luis Montesano, Javier Ming...
121
Voted
NIPS
2007
15 years 3 months ago
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
Ruslan Salakhutdinov, Geoffrey E. Hinton