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Found 24 papers, 0 papers with code
Learning Efficient Markov Networks
We present an algorithm for learning high-treewidth Markov networks where inference is still tractable.
Lifted Inference Seen from the Other Side : The Tractable Features
Lifted inference algorithms for representations that combine first-order logic and probabilistic graphical models have been the focus of much recent research.
On Lifting the Gibbs Sampling Algorithm
Statistical relational learning models combine the power of first-order logic, the de facto tool for handling relational structure, with that of probabilistic graphical models, the de facto tool for handling uncertainty.
Dynamic Blocking and Collapsing for Gibbs Sampling
Our dynamic algorithm periodically updates the partitioning into blocked and collapsed variables by leveraging correlation statistics gathered from the generated samples and enables rapid mixing by blocking together and collapsing highly correlated variables.
Structured Message Passing
In this paper, we present structured message passing (SMP), a unifying framework for approximate inference algorithms that take advantage of structured representations such as algebraic decision diagrams and sparse hash tables.
Join-Graph Propagation Algorithms
The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP).
Probabilistic Inference Modulo Theories
We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, propositional, equalities on discrete sorts, and inequalities, more specifically difference arithmetic, on bounded integers).
Lifted Region-Based Belief Propagation
Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Relational Models (SRMs), such as Lifted First-Order Belief Propagation.
Joint Inference for Event Coreference Resolution
Event coreference resolution is a challenging problem since it relies on several components of the information extraction pipeline that typically yield noisy outputs.
Automatic Parameter Tying in Neural Networks
Recently, there has been growing interest in methods that perform neural network compression, namely techniques that attempt to substantially reduce the size of a neural network without significant reduction in performance.
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization
We propose a simple and easy to implement neural network compression algorithm that achieves results competitive with more complicated state-of-the-art methods.
Lifted Marginal MAP Inference
Lifted inference reduces the complexity of inference in relational probabilistic models by identifying groups of constants (or atoms) which behave symmetric to each other.
Domain Aware Markov Logic Networks
Experiments on the benchmark Friends & Smokers domain show that our ap- proach results in significantly higher accuracies compared to existing methods when testing on domains whose sizes different from those seen during training.
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition
The explanations generated by these simplified models, however, might not accurately justify and be truthful to the model.
A Novel Approach for Constrained Optimization in Graphical Models
Given two (possibly identical) PGMs $M_1$ and $M_2$ defined over the same set of variables and a real number $q$, find an assignment of values to all variables such that the probability of the assignment is maximized w. r. t.
Novel Upper Bounds for the Constrained Most Probable Explanation Task
We propose several schemes for upper bounding the optimal value of the constrained most probable explanation (CMPE) problem.
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.
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.
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.
Towards Scene Graph Anticipation
In SceneSayer, we leverage object-centric representations of relationships to reason about the observed video frames and model the evolution of relationships between objects.
Grasping Trajectory Optimization with Point Clouds
The task space of a robot is represented by a point cloud that can be obtained from depth sensors.
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 $g(\mathbf{x}, \mathbf{y})\leq q$.
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.
