Finally, inspired by our superior selection mechanism, we propose to further regularize the objective function with entropy-minimization.
We study settings where gradient penalties are used alongside risk minimization with the goal of obtaining predictors satisfying different notions of monotonicity.
We study the setting where risk minimization is performed over general classes of models and consider two cases where monotonicity is treated as either a requirement to be satisfied everywhere or a useful property.
We propose TD-GEN, a graph generation framework based on tree decomposition, and introduce a reduced upper bound on the maximum number of decisions needed for graph generation.
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types.
Then, we develop an efficient weight-transfer method to explain decisions for any image without back-propagation.
Despite promising progress on unimodal data imputation (e. g. image inpainting), models for multimodal data imputation are far from satisfactory.
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes.
Learning from only partially-observed data for imputation has been an active research area.
This paper presents HCRF-Boost, a novel and general framework for learning HCRFs in functional space.
We propose a novel approach to identify one of the most significant dermoscopic criteria in the diagnosis of Cutaneous Melanoma: the Blue-whitish structure.
We present a novel approach for discovering human interactions in videos.
Many visual recognition problems can be approached by counting instances.
We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks.