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Linearly Decomposing and Recomposing Vision Transformers for Diverse-Scale Models Shuxia Lin

Neural Information Processing Systems

Vision Transformers (ViTs) are widely used in a variety of applications, while they usually have a fixed architecture that may not match the varying computational resources of different deployment environments.


Graph-based Uncertainty Metrics for Long-form Language Model Outputs

Neural Information Processing Systems

Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities, but these systems are still known to hallucinate, and granular uncertainty estimation for long-form LLM generations remains challenging.


Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare

Neural Information Processing Systems

While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to transfer reliable relative quality comparison outputs to continuous perceptual quality scores remains largely unexplored.