Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks

1. 1Learn a prior distribution over neural network weights using a scalable and structured posterior
2. 2Use the learned prior as a regularizer to improve generalization guarantees
3. 3Evaluate the uncertainty of the model using the learned prior

ML researchers and engineers can benefit from this method to improve the performance and reliability of their models, particularly in applications where uncertainty estimation is crucial

💡 Learning expressive priors can provide informative and generalizable representations for neural networks

🤖 Learn expressive priors for neural networks to boost generalization & uncertainty estimation!