In Algorithm 1 we give the full clustering algorithm used for each of the T fixing iterations. In Figure 1 we show how the layers' In Figure 2 we show the impact of increasing the regularisation strength.

Weight-sharing quantization has emerged as a technique to reduce energy expenditure during inference in large neural networks by constraining their weights to a limited set of values.

Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone. In this paper, we propose a strategy for automating such derivations. Unlike previous systems which focus on relationships between pairs of random variables, our system (which we call Autoconj) operates directly on Python functions that compute log-joint distribution functions. Autoconj provides support for conjugacy-exploiting algorithms in any Python-embedded PPL. This paves the way for accelerating development of novel inference algorithms and structure-exploiting modeling strategies.

Although dropout is widely used as a regularization technique for fully connected layers, it is often less effective for convolutional layers.

Neural Information Processing Systems http://nips.cc/

Although dropout is widely used as a regularization technique for fully connected layers, it is often less effective for convolutional layers.

The first category of methods assumes that the spurious attributes are known a priori.

What uncertainties do we need in bayesian deep learning for computer vision?

Upper bound Sigmoid (4) γ = 0 .8 We thank all reviewers and ACs. We appreciate the many positive and constructive comments. We will update our paper and supp. Unfortunately, [28] was lost from the citations in L80, which may have made this unclear.