5 papers · 1 filter
Quantifying Depth Sufficiency in Residual Neural Networks: A First-Order Criterion
Zeyu Liu, Jinhao Zhang, Yunquan Zhang +4
How can we determine whether a trained neural network is already deep enough? We study this under a fixed function-preserving residual-growth protocol specifying insertion location…
Mechanisms of Width Scaling in Normalized Residual Networks: The Effective Alignment Dimension
Jinhao Zhang, Zeyu Liu, Zicheng Yan +4
Existing theories of neural-network width characterize asymptotic limits, but provide limited guidance on whether an expansion direction identified from finite training data remain…
A General Error-Theoretical Analysis Framework for Constructing Compression Strategies
Boyang Zhang, Daning Cheng, Yunquan Zhang +3
The exponential growth in parameter size and computational complexity of deep models poses significant challenges for efficient deployment. The core problem of existing compression…
Lossless Model Compression via Joint Low-Rank Factorization Optimization
Boyang Zhang, Daning Cheng, Yunquan Zhang +2
Low-rank factorization is a popular model compression technique that minimizes the error between approximated and original weight matrices. Despite achieving performances close…
FP=xINT:Representing Neural Networks via Low-Bit Series Basis Functions
Boyang Zhang, Daning Cheng, Yunquan Zhang +3
Post-Training Quantization (PTQ) converts pre-trained Full-Precision (FP) models into quantized versions without training. While existing methods reduce size and computational cost…