2 citations · 5 across the 5 of their papers we have counts for
5 papers · 1 filter
Why "classic" Transformers are shallow and how to make them go deep
Yueyao Yu, Yin Zhang
Since its introduction in 2017, Transformer has emerged as the leading neural network architecture, catalyzing revolutionary advancements in many AI disciplines. The key innovation…
POViT: Vision Transformer for Multi-objective Design and Characterization of Nanophotonic Devices
Xinyu Chen, Renjie Li, Yueyao Yu +4
We solve a fundamental challenge in semiconductor IC design: the fast and accurate characterization of nanoscale photonic devices. Much like the fusion between AI and EDA, many eff…
A Lightweight and Gradient-Stable Neural Layer
Yueyao Yu, Yin Zhang
To enhance resource efficiency and model deployability of neural networks, we propose a neural-layer architecture based on Householder weighting and absolute-value activating, call…
Multi-layer Perceptron Trainability Explained via Variability
Yueyao Yu, Yin Zhang
Despite the tremendous successes of deep neural networks (DNNs) in various applications, many fundamental aspects of deep learning remain incompletely understood, including DNN tra…
AuxBlocks: Defense Adversarial Example via Auxiliary Blocks
Yueyao Yu, Pengfei Yu, Wenye Li
Deep learning models are vulnerable to adversarial examples, which poses an indisputable threat to their applications. However, recent studies observe gradient-masking defenses are…