They are themselves constructed from i.i.d. Gaussian processes, in analogy to the construction of Wishart or inverse Wishart random variables from i.i.d.

However, an overall selection does not properly capture the multi-faceted nature of useful rationales such as pros and cons for decisions.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The contribution of this paper is probabilistic programming language that supports parallel inference for graphical models (specifically Bayes nets). Probabilistic programming languages are powerful tools because they allow rapid development of new models without having to derive/implement new inference algorithms. Unlike most existing probabilistic programming languages, Augur produces massively parallel code that can run on a GPU (using CUDA). A unique feature of Augur is that it compiles the model (specified in the language Scala) into an intermediate representation before it's ultimately compiled into a CUDA inference algorithm for parallelization.

In this paper, we introduce the notion of the boundary thickness of a classifier, and we describe its connection with and usefulness for model robustness.

We use adversarial robustness to probe Stringer et al's theory regarding the causal role of a 1/n covariance spectrum. We empirically investigate the benefits such a neural code confers in neural networks, and illuminate its role in multi-layer architectures.

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

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper seems to essentially combine three published ideas: accelerated gradient, the stochastic gradient variance reduction technique of Johnson and Zhang, and variance reduction via minibatching. Hence, on a conceptual level at least, it's a fairly incremental paper (I don't want to minimize the effort that may have gone into developing the convergence proof). With this said, it's well-done, mostly well-written, and has good theoretical and experimental results. In terms of quality, originality and significance, it's as I said above: they're combining pre-existing ideas, but doing it well, and included a convergence proof with a slightly improved rate over the competition.

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