A mean-pool deep set trained on sets of size at most two produces an encoder that generalizes to arbitrary sizes, decoupling representation learning from posterior modeling and making training cost independent of deployment set size N.
Learning from many collider events at once
2 Pith papers cite this work. Polarity classification is still indexing.
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Context-aware stress testing reveals that the local assumption fails for Z→ℓℓ reconstruction at HL-LHC, producing bias and degraded resolution that an unsupervised regime-mapping framework then corrects.
citing papers explorer
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It Just Takes Two: Scaling Amortized Inference to Large Sets
A mean-pool deep set trained on sets of size at most two produces an encoder that generalizes to arbitrary sizes, decoupling representation learning from posterior modeling and making training cost independent of deployment set size N.
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Stress testing of fast reconstruction pipelines using machine learning
Context-aware stress testing reveals that the local assumption fails for Z→ℓℓ reconstruction at HL-LHC, producing bias and degraded resolution that an unsupervised regime-mapping framework then corrects.