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Found 9 papers, 4 papers with code
Structural perspective on constraint-based learning of Markov networks
When it comes to the number of tests, our upper bound on the sizes of conditioning sets implies that every $n$-vertex graph can be learned by at most $n^{\kappa}$ tests with conditioning sets of sizes at most $\kappa$.
Inspecting class hierarchies in classification-based metric learning models
In addition to the standard classification accuracy, we evaluate the hierarchical inference performance by inspecting learned class representatives and the hierarchy-informed performance, i. e., the classification performance, and the metric learning performance by considering predefined hierarchical structures.
Realistic mask generation for matter-wave lithography via machine learning
This approximation is then used to generate the initial population of the genetic optimisation algorithm that can converge to arbitrary precision.
Distance-Ratio-Based Formulation for Metric Learning
We propose a distance-ratio-based (DR) formulation for metric learning.
Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics
Bayesian networks represent relations between variables using a directed acyclic graph (DAG).
Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary
As a result, adversarial accuracy based on this adversary avoids a tradeoff between accuracy and adversarial accuracy on training data even when $\epsilon$ is large.
Distributed Bayesian Matrix Factorization with Limited Communication
Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals.
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number
Both learning and inference tasks on Bayesian networks are NP-hard in general.
Learning Structures of Bayesian Networks for Variable Groups
We show that for dependency structures between groups to be expressible exactly, the data have to satisfy the so-called groupwise faithfulness assumption.
