4 papers
Progressive Approximation in Deep Residual Networks: Theory and Validation
Wei Wang, Xiao-Yong Wei, Qing Li
The Universal Approximation Theorem (UAT) guarantees universal function approximation but does not explain how residual models distribute approximation across layers. We reframe re…
A General Method for Proving Networks Universal Approximation Property
Wei Wang
Deep learning architectures are highly diverse. To prove their universal approximation properties, existing works typically rely on model-specific proofs. Generally, they construct…
A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training
Mingjing Li, Huihui Zhou, Xiaofeng Xu +14
There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent…
IMPACT:InMemory ComPuting Architecture Based on Y-FlAsh Technology for Coalesced Tsetlin Machine Inference
Omar Ghazal, Wei Wang, Shahar Kvatinsky +3
The increasing demand for processing large volumes of data for machine learning models has pushed data bandwidth requirements beyond the capability of traditional von Neumann archi…