SGDiT models MIMO detection as a noise-conditioned denoising process with a soft graph transformer and cross-entropy loss, achieving competitive bit error rates and generalization across channel conditions.
Flow matching for generative modeling,
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PO-Flow uses continuous normalizing flows trained via flow matching to jointly model potential outcome distributions and enable factual-conditioned counterfactual prediction for causal inference tasks including CATE estimation.
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Soft Graph Diffusion Transformer for MIMO Detection
SGDiT models MIMO detection as a noise-conditioned denoising process with a soft graph transformer and cross-entropy loss, achieving competitive bit error rates and generalization across channel conditions.
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Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
PO-Flow uses continuous normalizing flows trained via flow matching to jointly model potential outcome distributions and enable factual-conditioned counterfactual prediction for causal inference tasks including CATE estimation.