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Showing papers from Seoul National University of Science and Technology Show all papers

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NeurIPS 2026Seoul National University of SciImage editing

Identifying and Mitigating Diversity Collapse in Zero-Shot Personalization with I2I Editing Models

Uichan Lee, Jongeon Baek, Sangheum Hwang

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

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NeurIPS 2026Seoul National University of SciSeoul National University of SciVision backbones

COHE: Auditing Non-Transitivity in Sample Difficulty Proxies for Vision Models

Dayena Jeong, Sunglok Choi

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

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45%Niche pick
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NeurIPS 2026Seoul National University of SciEfficient attention & state-space models

BitShift-RoPE: Zero-FLOP Relative Positional Encoding for Spiking Neural Network Transformers

Seung-Kyu Hong, Sangheum Hwang, HYUK-YOON KWON

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

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NeurIPS 2026Seoul National University of SciQuantization

Comp$^2$VLM: A Hybrid Framework Combining Quantization and Lossless Compression for Efficient Vision-Language Models

Dahun Choi, Inseong Hwang, Hyun Kim

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

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86%Must read
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NeurIPS 2026U FloridaNorth Carolina StateWashington StateSeoul National University of SciPrivacy

Leveraging Soft Prompts for Privacy Attacks in Federated Prompt Tuning

PromptMIA uses adversarial soft prompts to exploit federated prompt-tuning updates for highly effective membership inference attacks that bypass standard defenses.

Quan M Nguyen, Min-Seon Kim, Hoang M Ngo, Nghia Hoang and 2 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 3/5
86%Must read
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NeurIPS 2026Seoul NationalSeoul National University of SciRecommender systems

What Gets Measured Gets Managed: Sign-aware Recommendation Needs Sign-aware Evaluation

Sign-aware recommender systems embed valence but rank blindly, hidden by metrics that ignore disliked items; proposed signed metrics expose poor valence protection and provide trainable fixes.

Minchan Kim, Jungmin Hwang, Hyunwoo Park

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

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 2/5