Bayesian Learning for Neural Networks

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

• Dropout: a simple way to prevent neural networks from overfitting→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Improving neural networks by preventing co-adaptation of feature detectors→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Natural Language Processing (almost) from Scratch→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• An Introduction to MCMC for Machine Learning→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Logistic regression and artificial neural network classification models: a methodology re…→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Variational Inference: A Review for Statisticians→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Probabilistic machine learning and artificial intelligence→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Ensemble Machine Learning→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• GAUSSIAN PROCESSES FOR MACHINE LEARNING→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Adaptive Computation and Machine Learning→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Slice sampling→Bayesian Learning for Neural NetworksLegacy edge — source detail unavailable
• Probabilistic machine learning and artificial intelligence→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→An Introduction to MCMC for Machine LearningLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Improving neural networks by preventing co-adaptation of feature detectorsLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning→Probabilistic machine learning and artificial intelligenceLegacy edge — source detail unavailable
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Gaussian Processes for Machine LearningLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?Legacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep LearningLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Variational Inference: A Review for StatisticiansLegacy edge — source detail unavailable
• A review of uncertainty quantification in deep learning: Techniques, applications and cha…→Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningLegacy edge — source detail unavailable
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning→Probabilistic machine learning and artificial intelligenceLegacy edge — source detail unavailable
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning→Dropout: a simple way to prevent neural networks from overfittingLegacy 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