In the context of single-label classification, despite the huge success of deep learning, the commonly used cross-entropy loss function ignores the i…

We present a visualization framework for annotating and comparing colonoscopy videos, where these annotations can then be used for semi-automatic rep…

In Neural Networks (NN), Adaptive Activation Functions (AAF) have parameters that control the shapes of activation functions. These parameters are tr…

Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inf…

Online consumer reviews reflect the testimonials of real people, unlike advertisements. As such, they have critical impact on potential consumers, an…

In this paper we study the Airspace Sectorization Problem (ASP) where the goal is to find an optimal partition (sectorization) of the airspace into a…

To cope with the increasing demand and computational intensity of deep neural networks (DNNs), industry and academia have turned to accelerator techn…

We propose a new graph kernel for graph classification and comparison using Ollivier Ricci curvature. The Ricci curvature of an edge in a graph descr…

The formalism herein presents a new paradigm where data incorporate a quantification of own uncertainty. We concentrated on binary classification and the case of “Gaussian points” to present a proof of concept, in the form of a suitable kernel in Th…

We propose a probabilistic enhancement of standard {\em kernel Support Vector Machines} for binary classification, in order to address the case when,…

In this paper, we implement the Stochastic Damped LBFGS (SdLBFGS) for stochastic non-convex optimization. We make two important modifications to the …

We consider the task of training a neural network to anticipate human actions in video. This task is challenging given the complexity of video data, …

We discuss related work on Datalog evaluation, applications of rules to pointer analysis, precise complexities for pointer analyses, and directions for future work.

Pointer analysis is a fundamental static program analysis for computing the set of objects that an expression can refer to. Decades of research has g…

Robustness is a critical measure of the resilience of large networked systems, such as transportation and communication networks. Most prior works fo…

Graph-regularized semi-supervised learning has been used effectively for classification when (i) instances are connected through a graph, and (ii) la…

Technical Support Scams (TSS), which combine online abuse with social engineering over the phone channel, have persisted despite several law enforcem…

Given a stream of heterogeneous graphs containing different types of nodes and edges, how can we spot anomalous ones in real-time while consuming bou…

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process…

We consider learning, from strictly behavioral data, the structure and parameters of linear influence games (LIGs), a class of parametric graphical g…

Online content analysis employs algorithmic methods to identify entities in unstructured text. Both machine learning and knowledge-base approaches li…

Full projector compensation aims to modify a projector input image such that it can compensate for both geometric and photometric disturbance of the …

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