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Showing papers from Jinan University Show all papers

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Learning Event-to-Field Operators Without Interpolation

xingyu sha

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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Adversarial Attack and Defense for Machine Learning in Statistical Physics

Zhao-Rong Lai, Qiantong Liang, Jian Weng

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
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pyCD: A Unified Benchmark for Reliable Evaluation of Cognitive Diagnosis Models

Youheng Bai, Xueyi Li, Tengteng Cheng, Mingliang Hou and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
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strict 1/5
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MathCD: A Benchmark Dataset for Cognitive Diagnosis with Semantic Information

Xueyi Li, Youheng Bai, Tengteng Cheng, Mingliang Hou and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing

Houru Jiang, Zixi Wang, Tengteng Cheng, Xueyi Li and 5 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
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Cross-Domain Knowledge Separation and Positive Transmission for Noisy Domain Incremental Learning

Kunlun Xu, Zhengyuan Cai, Jiahuan Zhou

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning

UFO proposes a flow-oriented continual graph learning framework that combats catastrophic forgetting and noisy-label-induced catastrophic remembering via generative replay and instance reliability scoring, outperforming baselines across benchmarks.

Danhui Zhang, Zhe Wang, Qing Qing, Jiarui Liu and 5 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 0/5