16 citations · 47 across the 11 of their papers we have counts for
11 papers
PokeBNN: A Binary Pursuit of Lightweight Accuracy
Yichi Zhang, Zhiru Zhang, Lukasz Lew
Optimization of Top-1 ImageNet promotes enormous networks that may be impractical in inference settings. Binary neural networks (BNNs) have the potential to significantly lower the…
Unrestricted Adversarial Attacks on ImageNet Competition
Yuefeng Chen, Xiaofeng Mao, Yuan He +34
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-wo…
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework
Lifan Yuan, Yichi Zhang, Yangyi Chen +1
Despite recent success on various tasks, deep learning techniques still perform poorly on adversarial examples with small perturbations. While optimization-based methods for advers…
Adversarial Semantic Contour for Object Detection
Yichi Zhang, Zijian Zhu, Xiao Yang +1
Modern object detectors are vulnerable to adversarial examples, which brings potential risks to numerous applications, e.g., self-driving car. Among attacks regularized by …
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Weizhe Hua, Yichi Zhang, Chuan Guo +2
Neural network robustness has become a central topic in machine learning in recent years. Most training algorithms that improve the model's robustness to adversarial and common cor…
U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations
Yichi Zhang, Jesper Kers, Clarissa A. Cassol +8
Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in th…
Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation
Zhe Xie, Chengxuan Liu, Yichi Zhang +3
Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism…
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations
Yichi Zhang, Junhao Pan, Xinheng Liu +3
Binary neural networks (BNNs) have 1-bit weights and activations. Such networks are well suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory requ…
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
Shubham Rai, Walter Lau Neto, Yukio Miyasaka +37
Logic synthesis is a fundamental step in hardware design whose goal is to find structural representations of Boolean functions while minimizing delay and area. If the function is c…
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
Yichi Zhang, Ritchie Zhao, Weizhe Hua +3
We propose precision gating (PG), an end-to-end trainable dynamic dual-precision quantization technique for deep neural networks. PG computes most features in a low precision and o…
Sound Search by Text Description or Vocal Imitation?
Yichi Zhang, Yiting Zhang, Zhiyao Duan
Searching sounds by text labels is often difficult, as text descriptions cannot describe the audio content in detail. Query by vocal imitation bridges such gap and provides a novel…