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Ji-Bing Wu

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Conference Aug 2026

Multi-Metric Prototypical Networks for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) requires a model to absorb new visual classes from a handful of labeled examples while preserving decisions for previously seen classes. Prototype insertion is attractive because it avoids incremental optimization, yet a single embedding geometry can make few-shot prototypes...

Jibing Wu, Z. Mao, Hang Zhang et al. · 0 citations
Conference Aug 2026

MDIE-AutoML: Multi-Detector Drift Detection with Dynamic Weighted Ensemble for Streaming AutoML

Non-stationary data streams suffer from simultaneous data and concept drifts that degrade model generalization. Conventional Automated Machine Learning for data streams, AML4S, relies on single-pipeline architecture with univariate ADWIN detection and exhaustive post-drift reconstruction, causing knowledge waste and in...

Jia-Qiang Zhang, Hang Zhang, Ning-Chao Ge et al. · 0 citations
Conference Aug 2026

"Question→SQL→Wiki" Dynamic Wiki Graph for NL2SQL

To bridge the semantic gap in NL2SQL (Natural Language to SQL) tasks, this study proposes a "Question→SQL→Wiki" framework that leverages a dynamic Wiki Graph as an intermediate reasoning layer. Departing from conventional NL2SQL approaches that rely solely on end-to-end mapping, our method utilizes Large Language Model...

Jia-Xuan Liu, Shi-Yu Fang, Ji-Bing Wu et al. · 0 citations
Conference Aug 2026

A Template-Driven Multi-Source Benchmark Generation Method for Heterogeneous Data Lakes

Large language models (LLMs) have rapidly advanced natural-language-to-query (Text-to-Query) capabilities, yet existing public benchmarks remain confined to single database paradigms such as Text-to-SQL or Text-to-KG. They do not capture real-world settings where relational databases, graph databases, document database...

Guo-Shen Li, Hang Zhang, Ying-Jun Liu et al. · 0 citations

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