✳flâneur — a map of the web's best reading

Bayesian Neural Networks

cs.toronto.edu · 5,346 words · saved by 7 readers

Bayesian inference allows us to learn a probability distribution over possible neural networks. We can approximately solve inference with a simple modification to standard neural network tools. The resulting algorithm mitigates overfitting, enables learning from small datasets, and tells us how uncertain our predictions are. You may have heard deep neural networks described as powerful function approximators. Their power is due to the extreme flexibility of having many model parameters (the weights and biases) whose values can be learned from data via gradient-based optimization. Because they are good at approximating functions (input-output relationships) when lots of data are available, neural networks are well-suited to artificial intelligence tasks like speech recognition and image classification. But the extreme flexibility of neural networks has a downside: they are particularly vulnerable to overfitting. Overfitting happens when the learning algorithm does such a good job of tun

Bayesian Neural Networks Bayesian Neural Networks Bayesian inference allows us to learn a probability distribution over possible neural networks. We can approximately solve inference with a simple modification to standard neural network tools. The resulting algorithm mitigates overfitting, enables learning from small datasets, and tells us how uncertain our predictions are. Train What's wrong with neural networks? You may have heard deep neural networks described as powerful function approximators . Their power is due to the extreme flexibility of having many model parameters (the weights and

Explore this link on the map →

saved by

related reading