Max Ku

According to our database1, Max Ku authored at least 13 papers between 2023 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2025
DisProtEdit: Exploring Disentangled Representations for Multi-Attribute Protein Editing.
CoRR, June, 2025

TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding.
CoRR, February, 2025

TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025

2024
AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks.
Trans. Mach. Learn. Res., 2024

Mantis: Interleaved Multi-Image Instruction Tuning.
Trans. Mach. Learn. Res., 2024

AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks.
CoRR, 2024

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

GenAI Arena: An Open Evaluation Platform for Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

ImagenHub: Standardizing the evaluation of conditional image generation models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023
DreamEdit: Subject-driven Image Editing.
Trans. Mach. Learn. Res., 2023

TheoremQA: A Theorem-driven Question Answering Dataset.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023


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