1 citations · 2 across the 5 of their papers we have counts for
5 papers
Quantization Meets OOD: Generalizable Quantization-aware Training from a Flatness Perspective
Jiacheng Jiang, Yuan Meng, Chen Tang +4
Current quantization-aware training (QAT) methods primarily focus on enhancing the performance of quantized models on in-distribution (I.D) data, while overlooking the potential pe…
Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning
Yijun Liu, Yuwei Liu, Yuan Meng +8
Vision-centric hierarchical embodied models have demonstrated strong potential. However, existing methods lack spatial awareness capabilities, limiting their effectiveness in bridg…
GAQAT: gradient-adaptive quantization-aware training for domain generalization
Jiacheng Jiang, Yuan Meng, Chen Tang +4
Research on loss surface geometry, such as Sharpness-Aware Minimization (SAM), shows that flatter minima improve generalization. Recent studies further reveal that flatter minima c…
Investigating the Impact of Quantization on Adversarial Robustness
Qun Li, Yuan Meng, Chen Tang +2
Quantization is a promising technique for reducing the bit-width of deep models to improve their runtime performance and storage efficiency, and thus becomes a fundamental step for…
Retraining-free Model Quantization via One-Shot Weight-Coupling Learning
Chen Tang, Yuan Meng, Jiacheng Jiang +5
Quantization is of significance for compressing the over-parameterized deep neural models and deploying them on resource-limited devices. Fixed-precision quantization suffers from…