90 citations · 256 across the 21 of their papers we have counts for
8 papers · 1 filter
[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
Lixin Fan, Kam Woh Ng, Chee Seng Chan
With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect t…
L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition
Yuanfeng Song, Di Jiang, Xuefang Zhao +4
Modern Automatic Speech Recognition (ASR) systems primarily rely on scores from an Acoustic Model (AM) and a Language Model (LM) to rescore the N-best lists. With the abundance of…
Quantum Fast Poisson Solver: the algorithm and modular circuit design
Shengbin Wang, Zhimin Wang, Wendong Li +3
The Poisson equation has applications across many areas of physics and engineering, such as the dynamic process simulation of ocean current. Here we present a quantum Fast Poisson…
Simultaneously Learning Architectures and Features of Deep Neural Networks
Tinghuai Wang, Lixin Fan, Huiling Wang
This paper presents a novel method which simultaneously learns the number of filters and network features repeatedly over multiple epochs. We propose a novel pruning loss to explic…
Digital Passport: A Novel Technological Strategy for Intellectual Property Protection of Convolutional Neural Networks
Lixin Fan, KamWoh Ng, Chee Seng Chan
In order to prevent deep neural networks from being infringed by unauthorized parties, we propose a generic solution which embeds a designated digital passport into a network, and…
Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement
Xiaoshui Huang, Lixin Fan, Qiang Wu +2
Many types of 3D acquisition sensors have emerged in recent years and point cloud has been widely used in many areas. Accurate and fast registration of cross-source 3D point clouds…
A Universal Logic Operator for Interpretable Deep Convolution Networks
KamWoh Ng, Lixin Fan, Chee Seng Chan
Explaining neural network computation in terms of probabilistic/fuzzy logical operations has attracted much attention due to its simplicity and high interpretability. Different cho…
Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning
Quanshi Zhang, Lixin Fan, Bolei Zhou
This is the Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning