Statistical Model Aggregation via Parameter Matching
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics, we develop a general meta-modeling framework that learns shared global latent structures by identifying correspondences among local model parameterizations. Our proposed framework is model-independent and is applicable to a wide range of model types. After verifying our approach on simulated data, we demonstrate its utility in aggregating Gaussian topic models, hierarchical Dirichlet process based hidden Markov models, and sparse Gaussian processes with applications spanning text summarization, motion capture analysis, and temperature forecasting.
Code (1)
Tasks
Gaussian ProcessesmodelText SummarizationTopic ModelsSimilar Papers 제목 키워드 기반
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo
We present a new deep learning-based approach for dense stereo matching. Compared to previous works, our approach does not use deep learning of pixel appearance descriptors, employing very fast classical matching scores …
Deep LearningGPUStereo MatchingStereo Matching HandSpectral State Compression of Markov Processes
Model reduction of Markov processes is a basic problem in modeling state-transition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a discrete-…
ClusteringDeep Stereo Matching with Explicit Cost Aggregation Sub-Architecture
Deep neural networks have shown excellent performance for stereo matching. Many efforts focus on the feature extraction and similarity measurement of the matching cost computation step while less attention is paid on cos…
Stereo MatchingStereo Matching HandAttention Concatenation Volume for Accurate and Efficient Stereo Matching
Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper…
Patch MatchingStereo Depth EstimationStereo MatchingFederated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation
In the federated learning paradigm, multiple mobile clients train local models independently based on datasets generated by edge devices, and the server aggregates parameters/gradients from local models to form a global…
Federated LearningVariational Inference