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Yuxuan Yue

2 accepted papers

2025

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods

ICLR 2025poster

Mamba is an efficient sequence model that rivals Transformers and demonstrates significant potential as a foundational architecture for various tasks. Quantization is commonly used in neural networks to reduce model size and computational latency. However, applying quantization to Mamba remains unde…

Cited by 3SourcePDFScholar
2025

RWKVQuant: Quantizing the RWKV Family with Proxy Guided Hybrid of Scalar and Vector Quantization

ICML 2025poster

RWKV is a modern RNN architecture with comparable performance to Transformer, but still faces challenges when deployed to resource-constrained devices. Post Training Quantization (PTQ), which is a an essential technique to reduce model size and inference latency, has been widely used in Transformer…

Cited by 0SourcePDFScholar