11 citations · 29 across the 9 of their papers we have counts for
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eess.AS2024★ 1 cited
Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker Verification
Bei Liu, Yanmin Qian
Recent speaker verification (SV) systems have shown a trend toward adopting deeper speaker embedding extractors. Although deeper and larger neural networks can significantly improv…
eess.AS2024★ 8 cited
Towards Lightweight Speaker Verification via Adaptive Neural Network Quantization
Bei Liu, Haoyu Wang, Yanmin Qian
Modern speaker verification (SV) systems typically demand expensive storage and computing resources, thereby hindering their deployment on mobile devices. In this paper, we explore…
cs.LG2024★ 2 cited
CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
Haoyu Wang, Bei Liu, Hang Shao +4
Parameter quantization for Large Language Models (LLMs) has attracted increasing attentions recently in reducing memory costs and improving computational efficiency. Early approach…