To bridge this gap, we develop Differentiable ArchiT ecture Approximation (DA T A) with an Ensemble Gumbel-Softmax (EGS) estimator to automatically approximate architectures during searching and validating in a differentiable manner.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. In this paper the authors propose a flexible RBM choice model that can be used to learn the typical choice phenomena, including the similarity effect, the attraction effect, and the compromise effect. The author also show that their choice model is equivalent to a restricted Boltzmann machine whose parameters can be learned efficiently. Quality: The paper is technically sound. It would be nice if the author could discuss more limitations of this work.

Many learning tasks can find their counterpart problems in control fields.

These certificates map out-of-distribution examples to non-zero values, signaling epistemic uncertainty.

We find that GCNs are rather restrictive in learning graph moments. Without careful design, GCNs can fail miserably even with multiple layers and nonlinear activation functions.