Showing papers from UNSW Sydney Show all papers
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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FOGO detects and resolves gradient interference via spectral orthogonalization and compact codebook memory to prevent dominant directions from suppressing rare updates, improving convergence and retention across continual and standard training.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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GlucoFM decomposes CGM data into dual slow and short-term streams for pretraining, improving linear-probe phenotype classification and postprandial response prediction over prior models.
Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026 · ▲ 8 on Hugging Face
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TRACE introduces tourism dialogues pairing multi-turn recommendations with review citations and rejection turns to expose the Three-Competency Gap across accuracy, grounding, and recovery.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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MAGE externalizes self-knowledge into co-evolutionary knowledge graphs that guide frozen-learner agents, achieving strong multi-benchmark gains via complementary success and correction memories.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Look-Before-Move separates observation specification from motion execution for narrative-grounded camera planning, improving subject perception, intent consistency, and trajectory quality over baselines.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Root cause analysis benchmarks conflate retrieval and reranking failures, revealing graph methods rarely beat statistical baselines; a two-stage retriever-LLM reranker matches or exceeds all baselines without causal graphs or labels.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Gaussian processes are recast as linear diffusion models to enable conditioning on arbitrary likelihoods, including language and physics, via ODE sampling without bespoke derivations.
Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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