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Showing papers from University of Sydney, University of Sydney Show all papers

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Symmetry-Guaranteed Prediction of High-Order Tensor Properties for Crystalline Materials via Irreducible Decomposition

Qiaolin Lu, Qiang Qu, Hao Jiang, Aoni Xu and 5 more

Sydney Poster Session 5, Thu, Dec 10, 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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MedHEB: Benchmarking Medical Embeddings Across Heterogeneous Clinical Evidence

Yingshu Li, Shaoyang Zhou, Zhanyu Wang, YUNYI LIU and 5 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 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
45%Niche pick
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ScopeSAE: Model-Scope Feature Discovery with Interpretable Layer Selection

Qingwen Zeng, Zehao Fu, Shuyu Meng, Linghan Huang and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
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Adaptive Multi-Frame Learning for Expressive and Stable Atomic Representations

Jun Wang, Yifan Zeng, Bo Han, Fengwang Li and 2 more

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

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
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Scalable Minimal-Change Learning for Controllable Image Editing

Shuo Chen, Fengming Huang, Yu Yao, Mingming Gong and 1 more

Sydney Poster Session 3, Wed, Dec 9, 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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Domain-Conditioned Class Imbalance: Why Global Class Balance Fails Across Domains

Yongkun Deng, NAN YANG, Xiatong Guo, Zhiyong Wang and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 1/5
80%Must read
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LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection

LOFT separates orthogonal PEFT subspaces from transformations to enable task-aware support selection, improving efficiency-performance trade-offs across language, vision, and reasoning tasks.

Lanxin Zhao, Bamdev Mishra, Pratik Kumar Jawanpuria, Lequan Lin and 3 more

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

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 0/5