no code implementations • 17 Apr 2024 • Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
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 $g(\mathbf{x}, \mathbf{y})\leq q$.
no code implementations • 17 Apr 2024 • Shivvrat Arya, Yu Xiang, Vibhav Gogate
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.
no code implementations • 6 Feb 2024 • Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
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.
no code implementations • 22 Dec 2023 • Rohith Peddi, Shivvrat Arya, Bharath Challa, Likhitha Pallapothula, Akshay Vyas, Jikai Wang, Qifan Zhang, Vasundhara Komaragiri, Eric Ragan, Nicholas Ruozzi, Yu Xiang, Vibhav Gogate
Following step-by-step procedures is an essential component of various activities carried out by individuals in their daily lives.
no code implementations • 1 Feb 2023 • Shivvrat Arya, Yu Xiang, Vibhav Gogate
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.