Meta-learning a Convolutional Neural Process to infer neural architecture performance from context-target splits on synthesized tasks improves top-K ranking and achieves state-of-the-art selection on NAS-Bench-101 and NAS-Bench-201 with limited samples.
Introduction to information retrieval, volume 39
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A hierarchical QA framework converts RST discourse trees into enhanced sentence representations for structure-guided retrieval and reports consistent gains over baselines on four datasets across genres and languages.
The paper formalizes query answering with soft constraints on knowledge graphs and introduces two lightweight methods (parameter tuning or small neural network) to incorporate them while preserving original rankings.
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From Regression to Inference: Meta-Learning Predictors for Neural Architecture Search
Meta-learning a Convolutional Neural Process to infer neural architecture performance from context-target splits on synthesized tasks improves top-K ranking and achieves state-of-the-art selection on NAS-Bench-101 and NAS-Bench-201 with limited samples.
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Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering
A hierarchical QA framework converts RST discourse trees into enhanced sentence representations for structure-guided retrieval and reports consistent gains over baselines on four datasets across genres and languages.
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Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
The paper formalizes query answering with soft constraints on knowledge graphs and introduces two lightweight methods (parameter tuning or small neural network) to incorporate them while preserving original rankings.