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Showing papers from Ben Gurion University of the Negev Show all papers

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SemGeo-Gen: Unsupervised Generation of Approximate Cross-Instance Semantic-Geometric Correspondences

Roy Amoyal, Shira Ifergane, Oren Freifeld

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

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medium 0/10
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57%Worth a look
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Local–Global Sparse Autoencoders for Multiscale Interpretability in Vision Models

Itay Benou, Tammy Riklin Raviv

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
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Video Stitching from Multiple Moving Cameras

Shira Ifergane, Roy Amoyal, Shahar Benishay, Oren Freifeld

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

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AI panel: 0 of 20 reviewers recommend it
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medium 0/10
strict 0/5
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Pareto DNN Verification: Fast for Most Queries

Yizhak Y. Elboher, Avraham Raviv, Amihay Elboher, Zhouxing Shi and 2 more

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

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strict 0/5
57%Worth a look
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Fairness in limited resource prediction-driven decisions

Inbal Livni Navon, Eitan Bachmat

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 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
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Module-Aware Optimization for Graph Neural Networks

Guy Hadad, Haggai Roitman, Moshe Eliasof, Bracha Shapira

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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AudioGS: High-Fidelity Neural Audio Compression via Continuous Gaussian Splatting

Ron Aluf, Alon Canfi, Eliya Nachmani

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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AI panel: 0 of 20 reviewers recommend it
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71%Highly rated
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Transfer Learning of Linear Regression with Multiple Pretrained Models: Benefiting from More Pretrained Models via Overparameterization Debiasing

Overparameterized pretrained linear models risk transfer learning bias, but combining many with a multiplicative debiasing correction improves target predictions.

Daniel Boharon, Yehuda Dar

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

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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 3/5
medium 3/10
strict 1/5