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Showing papers from University of Athens & Archimedes AI, Athena RC Show all papers

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NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models

NeuroRVQ uses multi-scale temporal convolutions and hierarchical RVQ codebooks with a phase-aware loss to tokenize biosignals at high fidelity across modalities. NeuroRVQ-FM foundation models using these tokenizers achieve competitive or superior downstream performance, showing that high-fidelity to

Konstantinos Barmpas, Na Lee, Dimitrios Chalatsis, William Raftery and 6 more

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

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AI panel: 9 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 0/5
76%Highly rated
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GATE-AD: Graph Attention Network Encoding for Few-Shot Industrial Visual Anomaly Detection

GATE-AD employs graph attention networks with masked reconstruction to detect industrial anomalies from few normal samples, achieving state-of-the-art accuracy with faster inference across benchmarks.

Aggelos Psiris, Yannis Panagakis, Maria Vakalopoulou, Georgios Th. Papadopoulos

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

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 0/5
88%Must read
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fmxcoders: Factorized Masked Crosscoders for Cross-Layer Feature Discovery

Standard crosscoders learn layer-localized features; fmxcoders use factorized weights and layer masking to recover cross-layer features, improving coherence and reconstruction across four LLMs.

Andreas D Demou, Panagiotis Koromilas, James Oldfield, Yannis Panagakis and 1 more

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

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 3/5
medium 9/10
strict 3/5