31 results on '"Andrew Y. Ng"'
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2. Deep Voice: Real-time Neural Text-to-Speech.
3. Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin.
4. Parsing Natural Scenes and Natural Language with Recursive Neural Networks.
5. On Random Weights and Unsupervised Feature Learning.
6. On optimization methods for deep learning.
7. Learning Deep Energy Models.
8. Multimodal Deep Learning.
9. The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization.
10. Near-Bayesian exploration in polynomial time.
11. A majorization-minimization algorithm for (multiple) hyperparameter learning.
12. Large-scale deep unsupervised learning using graphics processors.
13. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations.
14. Regularization and feature selection in least-squares temporal difference learning.
15. Space-indexed dynamic programming: learning to follow trajectories.
16. Learning for control from multiple demonstrations.
17. Self-taught learning: transfer learning from unlabeled data.
18. Constructing informative priors using transfer learning.
19. Using inaccurate models in reinforcement learning.
20. High speed obstacle avoidance using monocular vision and reinforcement learning.
21. Exploration and apprenticeship learning in reinforcement learning.
22. Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection.
23. Algorithms for Inverse Reinforcement Learning.
24. Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping.
25. Improving Text Classification by Shrinkage in a Hierarchy of Classes.
26. On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples.
27. Preventing 'Overfitting' of Cross-Validation Data.
28. Building high-level features using large scale unsupervised learning.
29. Online and batch learning of pseudo-metrics.
30. Learning random walk models for inducing word dependency distributions.
31. Apprenticeship learning via inverse reinforcement learning.
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