Analytically Tractable Inference in Neural Networks - An Alternative to Backpropagation

Until now, neural networks have been predominantly relying on backpropagation and gradient descent as the inference engine in order to learn the neural network's parameters. This is primarily because closed-form Bayesian inference for neural networks has been considered to be intractable. This short paper will outline a new analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks. The method enables the analytical inference of the posterior mean vector and diagonal covariance matrix for weights and biases. One key aspect is that the method matches or exceeds the state-of-the-art performance while having the same computational complexity as current methods relying on the gradient backpropagation, i.e., linear complexity with respect to the number of parameters in the network. Performing Bayesian inference in neural networks enables several key features, such as the quantification of epistemic uncertainty associated with model parameters, the online estimation of parameters, and a reduction in the number of hyperparameters due to the absence of gradient-based optimization. Moreover, the analytical framework proposed also enables unprecedented features such as the propagation of uncertainty from the input of a network up to its output, and it allows inferring the value of hidden states, inputs, as well as latent variables. The first part covers the theoretical foundation and working principles of the analytically tractable uncertainty propagation in neural networks, as well as the parameter and hidden state inference. Then, the second part will go through benchmarks demonstrating the superiority of the approach on supervised, unsupervised, and reinforcement learning tasks. In addition, we will showcase how TAGI can be applied to reinforcement learning problems such as the Atari game environment. Finally, the last part will present how we can leverage the analytic inference capabilities of our approach to enable novel applications of neural networks such as closed-form direct adversarial attacks, and the usage of a neural network as a generic black-box optimization method.