Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.47)
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
Neural Information Processing Systems
Empirically, the low-variance baseline of POMO makes RL training fast and stable, and it is more resistant to local minima compared to previous approaches.
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Meta-Learning to Improve Pre-Training
Neural Information Processing Systems
Choosing optimal PT hyperparameter values is challenging, and existing methods do not work well.
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Meta-Learning to Improve Pre-Training
Neural Information Processing Systems
Choosing optimal PT hyperparameter values is challenging, and existing methods do not work well.
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- North America > Canada > Ontario > Toronto (0.14)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
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- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
On Convergence and Generalization of Dropout Training
Neural Information Processing Systems
We study dropout in two-layer neural networks with rectified linear unit (ReLU) activations.
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.31)