The paper "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" by Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell from DeepMind proposes a novel method for estimating predictive uncertainty in deep neural networks (NNs). The authors address the challenge of quantifying predictive uncertainty, which is crucial for practical applications where overconfident predictions can be harmful. Traditional Bayesian NNs, while effective, require significant modifications to the training process and are computationally expensive. The proposed method, based on deep ensembles, is simple to implement, parallelizable, and requires minimal hyperparameter tuning. It yields high-quality predictive uncertainty estimates that are well-calibrated and robust to dataset shift. The key contributions of the paper include: 1. **Simple and Scalable Method**: The method uses proper scoring rules as training criteria, ensemble model combination, and adversarial training to estimate predictive uncertainty. 2. **Evaluation Metrics**: The quality of predictive uncertainty is evaluated using calibration measures (proper scoring rules) and generalization to out-of-distribution examples. 3. **Experimental Results**: The method outperforms or matches state-of-the-art approximate Bayesian methods on various classification and regression benchmarks, including ImageNet. 4. **Scalability**: The method is evaluated on large datasets like ImageNet, demonstrating its scalability. The authors also discuss the novelty and significance of their work, highlighting that deep ensembles have been successfully used to improve predictive performance and robustness to adversarial examples. However, this is the first time they have investigated their use for predictive uncertainty estimation. The paper concludes by suggesting future directions, including further exploration of ensemble diversity and optimization techniques.The paper "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" by Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell from DeepMind proposes a novel method for estimating predictive uncertainty in deep neural networks (NNs). The authors address the challenge of quantifying predictive uncertainty, which is crucial for practical applications where overconfident predictions can be harmful. Traditional Bayesian NNs, while effective, require significant modifications to the training process and are computationally expensive. The proposed method, based on deep ensembles, is simple to implement, parallelizable, and requires minimal hyperparameter tuning. It yields high-quality predictive uncertainty estimates that are well-calibrated and robust to dataset shift. The key contributions of the paper include: 1. **Simple and Scalable Method**: The method uses proper scoring rules as training criteria, ensemble model combination, and adversarial training to estimate predictive uncertainty. 2. **Evaluation Metrics**: The quality of predictive uncertainty is evaluated using calibration measures (proper scoring rules) and generalization to out-of-distribution examples. 3. **Experimental Results**: The method outperforms or matches state-of-the-art approximate Bayesian methods on various classification and regression benchmarks, including ImageNet. 4. **Scalability**: The method is evaluated on large datasets like ImageNet, demonstrating its scalability. The authors also discuss the novelty and significance of their work, highlighting that deep ensembles have been successfully used to improve predictive performance and robustness to adversarial examples. However, this is the first time they have investigated their use for predictive uncertainty estimation. The paper concludes by suggesting future directions, including further exploration of ensemble diversity and optimization techniques.