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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…

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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 0Source ↗PDF ↗Scholar ↗