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References (13)

[1]
An Empirical Study of World Model Quantization
2026Zhongqian Fu, Tianyi Zhao et al.
[2]
Low-bit Model Quantization for Deep Neural Networks: A Survey
2025Kai Liu, Qian Zheng et al.
[3]
DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
2024Gaoyue Zhou, Hengkai Pan et al.
[4]
FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer
2023Zhijian Liu, Xinyu Yang et al.
[5]
Mastering Diverse Domains through World Models
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SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
2022Guangxuan Xiao, Ji Lin et al.
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GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
2022Elias Frantar, Saleh Ashkboos et al.
[8]
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
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Dream to Control: Learning Behaviors by Latent Imagination
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HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
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World Models
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