Monografie
Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence
- Sprache
-
Englisch
- Identifier
-
1278956646
Thema
Neuronales Netz; Bayes-Verfahren; Gauß-Prozess; Maschinelles Lernen; Bayes-Netz; Maschinelles Lernen ; Neuronales Netz
Beteiligte Personen und Organisationen
Kristiadi, Agustinus
Hennig, Philipp
Hennig, Philipp
- URN
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
02.05.2023, 09:12 MESZ
Objekttyp
- Monografie
Beteiligte
- Kristiadi, Agustinus
- Hennig, Philipp
Ähnliche Objekte (12)
Probabilistic Numerical Linear Algebra for Machine Learning
Backpropagation Beyond the Gradient
Probabilistic Linear Algebra for Stochastic Optimization
Image Representations in Deep Neural Networks and their Applications to Neural Data Modelling
Improving the automated search of neural network architectures
Understanding Deep Learning Optimization via Benchmarking and Debugging
Deep Neural Networks and Tabular Data : Inference, Generation, and Explainability
Verification of unsupervised neural networks
Robust Speech Enhancement Using Statistical Signal Processing and Machine-Learning
Nature-Inspired Inductive Biases in Learning Robots
GANs schön kompliziert : Applications of Generative Adversarial Networks
Empirics-based Line Searches for Deep Learning
Probabilistic Numerical Linear Algebra for Machine Learning
Backpropagation Beyond the Gradient
Probabilistic Linear Algebra for Stochastic Optimization
Image Representations in Deep Neural Networks and their Applications to Neural Data Modelling
Improving the automated search of neural network architectures
Understanding Deep Learning Optimization via Benchmarking and Debugging
Deep Neural Networks and Tabular Data : Inference, Generation, and Explainability
Verification of unsupervised neural networks
Robust Speech Enhancement Using Statistical Signal Processing and Machine-Learning
Nature-Inspired Inductive Biases in Learning Robots
GANs schön kompliziert : Applications of Generative Adversarial Networks
Empirics-based Line Searches for Deep Learning
Probabilistic Numerical Linear Algebra for Machine Learning
Backpropagation Beyond the Gradient
Probabilistic Linear Algebra for Stochastic Optimization
Image Representations in Deep Neural Networks and their Applications to Neural Data Modelling
Improving the automated search of neural network architectures
Understanding Deep Learning Optimization via Benchmarking and Debugging
Deep Neural Networks and Tabular Data : Inference, Generation, and Explainability
Verification of unsupervised neural networks
Robust Speech Enhancement Using Statistical Signal Processing and Machine-Learning
Nature-Inspired Inductive Biases in Learning Robots
GANs schön kompliziert : Applications of Generative Adversarial Networks