approximator
ResNet with one-neuron hidden layers is a Universal Approximator
Hongzhou Lin, Stefanie Jegelka
This stands in sharp contrast to fully connected networks, which are not universal approximators if their width is the input dimensiond[21,11].
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rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions
Mathieu Fehr, Olivier Buffet, Vincent Thomas, Jilles Dibangoye
Neural Information Processing Systems http://nips.cc/
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Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems
Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P. Xing
Neural Information Processing Systems http://nips.cc/
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Non-delusionalQ-learningandValueIteration
Delusionalbias ariseswhenthe approximation architecture limits the class ofexpressible greedy policies.
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VIREL: A Variational Inference Framework for Reinforcement Learning
Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
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ResNet with one-neuron hidden layers is a Universal Approximator
Hongzhou Lin, Stefanie Jegelka
This stands in sharp contrast to fully connected networks, which are not universal approximators if their width is the input dimensiond[21,11].
Country:
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.05)
- North America > Canada > Quebec > Montreal (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.99)
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