A Practical Bayesian Framework for Backpropagation Networks
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- Deep learning in neural networks: An overview→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- Overcoming catastrophic forgetting in neural networks→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- Kalman Filtering and Neural Networks→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- On Calibration of Modern Neural Networks→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- A Tutorial on Learning with Bayesian Networks→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- Neural networks for classification: a survey→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- A continual learning survey: Defying forgetting in classification tasks→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and …→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
- A Practical Bayesian Framework for Backpropagation Networks→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
- A Practical Bayesian Framework for Backpropagation Networks→Optimal Brain DamageLegacy edge — source detail unavailable
- Deep learning in neural networks: An overview→Kalman Filtering and Neural NetworksLegacy edge — source detail unavailable
- Deep learning in neural networks: An overview→Optimal Brain DamageLegacy edge — source detail unavailable
- Neural networks for classification: a survey→Optimal Brain DamageLegacy edge — source detail unavailable
- On Calibration of Modern Neural Networks→Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep LearningLegacy edge — source detail unavailable
- A continual learning survey: Defying forgetting in classification tasks→Overcoming catastrophic forgetting in neural networksLegacy 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
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Learning representations by back-propagating errors | 1986 | 31,688 |
| Optimal Brain Damage | 1989 | 2,606 |
What cites it, inside the corpus
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Topics
| Machine Learning and Algorithms | Computer Science |
| Bayesian Modeling and Causal Inference | Computer Science |
| Neural Networks and Applications | Computer Science |
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