Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
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- Type
- preprint
- Published
- 2019-06-04
- Cited by
- 356
- References
- 64
- Access
- Open access
- OpenAlex
- https://openalex.org/W2948210138
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:174798361
Keywords
Scalability, Artificial intelligence, Computer science, Uncertainty quantification, Machine learning
References
- Ensemble learning in Bayesian neural networks
- Handbook of Markov Chain Monte Carlo
- A Complete Recipe for Stochastic Gradient MCMC
- Multiple Classifier Systems
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
- Fully convolutional networks for semantic segmentation
- Keeping the neural networks simple by minimizing the description length of the weights
- Equation of State Calculations by Fast Computing Machines
- Dropout: a simple way to prevent neural networks from overfitting
- Exponentially many local minima for single neurons
- Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
- Practical Variational Inference for Neural Networks
- Vision meets robotics: The KITTI dataset
- Monte Carlo Sampling Methods Using Markov Chains and Their Applications
- Weight Uncertainty in Neural Network
- Reliability, Sufficiency, and the Decomposition of Proper Scores
- Bayesian Learning via Stochastic Gradient Langevin Dynamics
- Deep Residual Learning for Image Recognition
- Obtaining Well Calibrated Probabilities Using Bayesian Binning
- The Cityscapes Dataset for Semantic Urban Scene Understanding
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- Deep Ensembles: A Loss Landscape Perspective
- Learning Deep Conditional Target Densities for Accurate Regression
- The Case for Bayesian Deep Learning
- BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
- A General Framework for Ensemble Distribution Distillation
- DIBS: Diversity inducing Information Bottleneck in Model Ensembles
- Deep State Space Models for Nonlinear System Identification
- Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
- Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
- Neural Ensemble Search for Performant and Calibrated Predictions
- Calibrated Adversarial Refinement for Multimodal Semantic Segmentation
- A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications
- Uncertainty Prediction for Deep Sequential Regression Using Meta Models
- Energy-Based Models for Deep Probabilistic Regression
- Uncertainty with deep learning: a practical view on out of distribution detection
- Fast Uncertainty Estimation for Deep Learning Based Optical Flow
- Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
- Classification of Small Drones Using Low-Uncertainty Micro-Doppler Signature Images and Ultra-Lightweight Convolutional Neural Network
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