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Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
We present a unified framework called deep dependency networks (DDNs) that combines dependency networks and deep learning architectures for multi-label classification, with a particular emphasis on image and video data.
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
Given an assignment $\mathbf{x}$ to all variables in $\mathbf{X}$ (evidence) and a real number $q$, the constrained most-probable explanation (CMPE) task seeks to find an assignment $\mathbf{y}$ to all variables in $\mathbf{Y}$ such that $f(\mathbf{x}, \mathbf{y})$ is maximized and $g(\mathbf{x}, \mathbf{y})\leq q$.
Neural Network Approximators for Marginal MAP in Probabilistic Circuits
We evaluate our new approach on several benchmark datasets and show that it outperforms three competing linear time approximations, max-product inference, max-marginal inference and sequential estimation, which are used in practice to solve MMAP tasks in PCs.
CaptainCook4D: A dataset for understanding errors in procedural activities
Following step-by-step procedures is an essential component of various activities carried out by individuals in their daily lives.
Deep Dependency Networks for Multi-Label Classification
We propose a simple approach which combines the strengths of probabilistic graphical models and deep learning architectures for solving the multi-label classification task, focusing specifically on image and video data.
