Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Carl Edward Rasmussen, Christopher K. I. Williams
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- Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning→Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)Legacy edge — source detail unavailable
- Reinforcement learning in robotics: A survey→Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)Legacy edge — source detail unavailable
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning→Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)Legacy edge — source detail unavailable
- On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation→Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)Legacy edge — source detail unavailable
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning→Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep LearningLegacy edge — source detail unavailable
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| Gaussian Processes and Bayesian Inference | Computer Science |
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