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Found 8 papers, 4 papers with code
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions.
GC-Flow: A Graph-Based Flow Network for Effective Clustering
Graph convolutional networks (GCNs) are \emph{discriminative models} that directly model the class posterior $p(y|\mathbf{x})$ for semi-supervised classification of graph data.
Alleviating Label Switching with Optimal Transport
Label switching is a phenomenon arising in mixture model posterior inference that prevents one from meaningfully assessing posterior statistics using standard Monte Carlo procedures.
Hierarchical Optimal Transport for Document Representation
The ability to measure similarity between documents enables intelligent summarization and analysis of large corpora.
BreGMN: scaled-Bregman Generative Modeling Networks
Well-definedness of f-divergences, however, requires the distributions of the data and model to overlap completely in every time step of training.
Learning Embeddings into Entropic Wasserstein Spaces
Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions.
Learning Entropic Wasserstein Embeddings
Despite their prevalence, Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions.
Embedding Inference for Structured Multilabel Prediction
A key bottleneck in structured output prediction is the need for inference during training and testing, usually requiring some form of dynamic programming.
