Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning
(ML) and constrained optimization to enhance decision quality by training ML models in an …

In recent years, many deep learning techniques have been applied to the image inpainting
problem: the task of filling incomplete regions of an image. However, these models struggle …

Develo** safe human-robot interaction systems is a necessary step towards the
widespread integration of autonomous agents in society. A key component of such systems …

Many real-world analytics problems involve two significant challenges: prediction and
optimization. Because of the typically complex nature of each challenge, the standard …

In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …

Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved
impressive performance on a variety of classification tasks using purely feedforward …

We introduce the value iteration network (VIN): a fully differentiable neural network with
aplanning module'embedded within. VINs can learn to plan, and are suitable for predicting …

Recent advances in semantic image segmentation have mostly been achieved by training
deep convolutional neural networks (CNNs). We show how to improve semantic …

We introduce UViM, a unified approach capable of modeling a wide range of computer
vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; …