Mixup [28] is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pairs of images and their associated labels. While simple to implement, it has shown to be a surprisingly effective method of data augmentation for image classification; DNNs trained with mixup show noticeable gains in classification performance on a number of image classification benchmarks. In this work, we discuss a hitherto untouched aspect of mixup training -the calibration and predictive uncertainty of models trained with mixup. We find that DNNs trained with mixup are significantly better calibrated -i.e the predicted softmax scores are much better indicators of the actual likelihood of a correct prediction -than DNNs trained in the regular fashion. We conduct experiments on a number of image classification architectures and datasets -including large-scale datasets like ImageNet -and find this to be the case. Additionally, we find that merely mixing features does not result in the same calibration benefit and that the label smoothing in mixup training plays a significant role in improving calibration. Finally, we also observe that mixuptrained DNNs are less prone to over-confident predictions on out-of-distribution and random-noise data. We conclude that the typical overconfidence seen in neural networks, even on in-distribution data is likely a consequence of training with hard labels, suggesting that mixup training be employed for classification tasks where predictive uncertainty is a significant concern.1 Introduction: Overconfidence and Uncertainty in Deep Learning Machine learning algorithms are replacing or expected to increasingly replace humans in decisionmaking pipelines. With the deployment of AI-based systems in high risk fields such as medical diagnosis [18], autonomous vehicle control [16] and the legal sector [1], the major challenges of the upcoming era are thus going to be in issues of uncertainty and trust-worthiness of a classifier. With deep neural networks having established supremacy in many pattern recognition tasks, it is the predictive uncertainty of these types of classifiers that will be of increasing importance. The DNN must not only be accurate, but also indicate when it is likely to get the wrong answer. This allows the decision-making to be routed to a human or another more accurate, but possibly more expensive, classifier, with the assumption being that the additional cost incurred is greatly surpassed by the consequences of a wrong prediction.Preprint. Under review.

Abstract-We give efficient sequential and distributed approximation algorithms for strong edge coloring graphs modeling wireless networks. Strong edge coloring is equivalent to computing a conflict-free assignment of channels or frequencies to pairwise links between transceivers in the network.

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We propose a new methodology, RESTORED, for model-based storage and regeneration of TCP traces. RESTORED provides significant data compression by exploiting semantics of TCP. Experiments show that RESTORED can achieve over 10,000-fold compression ratios for some really large input connections, while still being able to recover several structural and QoS measures.

We present a new approach for solving the all-pairs shortest-path (APSP) problem for planar graphs that exploits the massive on-chip parallelism available in today's Graphics Processing Units (GPUs). We describe two new algorithms based on our approach. Both algorithms use Floyd-Warshall method, have near optimal complexity in terms of the total number of operations, while their matrix-based structure is regular enough to allow for efficient parallel implementation on the GPUs. By applying a divide-and-conquer approach, we are able to make use of multi-node GPU clusters, resulting in more than an order of magnitude speedup over fastest known Dijkstra-based GPU implementation and a twofold speedup over a parallel Dijkstra-based CPU implementation.