Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
ISBN 9798400701030
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
TextBridgeGNN pre-trains GNNs using text-guided hierarchical propagation to enable effective cross-domain knowledge transfer in recommendations.
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
CM-DCM jointly models direct and delayed conversions in pre-promotion e-commerce via multi-task learning, personalized gating, and counterfactual transition probabilities from add-to-cart, outperforming baselines with gains in ad revenue and GMV in A/B tests.
DNR is an adversarial denoising neural reranker that extends score error minimization with three objectives to denoise retriever scores and align them with user feedback in two-stage recommender systems.
citing papers explorer
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Limitations of LTI Koopman Modeling for Nonlinear Control Systems
Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
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TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer
TextBridgeGNN pre-trains GNNs using text-guided hierarchical propagation to enable effective cross-domain knowledge transfer in recommendations.
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DeGRe: Dense-supervised Generative Reranking for Recommendation
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
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Counterfactual Multi-task Learning for Delayed Conversion Modeling in E-commerce Sales Pre-Promotion
CM-DCM jointly models direct and delayed conversions in pre-promotion e-commerce via multi-task learning, personalized gating, and counterfactual transition probabilities from add-to-cart, outperforming baselines with gains in ad revenue and GMV in A/B tests.
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Denoising Neural Reranker for Recommender Systems
DNR is an adversarial denoising neural reranker that extends score error minimization with three objectives to denoise retriever scores and align them with user feedback in two-stage recommender systems.