Real-time sensing and modeling of the human body, especially the hands, is an important
research endeavor for various applicative purposes such as in natural human computer …

Unveiling feeder topologies from data is of paramount importance to advance situational
awareness and proper utilization of smart resources in power distribution grids. This tutorial …

We design a predictive layer for structured-output prediction (SOP) that can be plugged into
any neural network guaranteeing its predictions are consistent with a set of predefined …

We establish conditions under which latent causal graphs are nonparametrically identifiable
and can be reconstructed from unknown interventions in the latent space. Our primary focus …

In this paper we present the Latent Regression Forest (LRF), a novel framework for real-
time, 3D hand pose estimation from a single depth image. In contrast to prior forest-based …

Most causal discovery procedures assume that there are no latent confounders in the
system, which is often violated in real-world problems. In this paper, we consider a …

Sum-product networks (SPNs) are a deep probabilistic representation that allows for
efficient, exact inference. SPNs generalize many other tractable models, including thin …

Simple tree models for articulated objects prevails in the last decade. However, it is also
believed that these simple tree models are not capable of capturing large variations in many …

The context of an image encapsulates rich information about how natural scenes and
objects are related to each other. Such contextual information has the potential to enable a …

Distribution network models are often inaccurate or nonexistent. This work considers the
problem of estimating the impedance and topology of distribution networks from noisy …