90 citations · 256 across the 21 of their papers we have counts for
23 papers
Fast Black-Box Quantum State Preparation Based on Linear Combination of Unitaries
Shengbin Wang, Zhimin Wang, Guolong Cui +6
Black-box quantum state preparation is a fundamental primitive in quantum algorithms. Starting from Grover, a series of techniques have been devised to reduce the complexity. In th…
Ternary Hashing
Chang Liu, Lixin Fan, Kam Woh Ng +5
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the…
Quantum Amplitude Arithmetic
Shengbin Wang, Zhimin Wang, Guolong Cui +6
Quantum algorithm involves the manipulation of amplitudes and computational basis, of which manipulating basis is largely a quantum analogue of classical computing that is always a…
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness
Yilun Jin, Lixin Fan, Kam Woh Ng +2
Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed. While adversarial training (AT) is regarded as the most robust defen…
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
Lixin Fan, Kam Woh Ng, Ce Ju +4
This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks. First, we propose to quantitatively measure the…
Quantum circuits design for evaluating transcendental functions based on a function-value binary expansion method
Shengbin Wang, Zhimin Wang, Wendong Li +4
Quantum arithmetic in the computational basis constitutes the fundamental component of many circuit-based quantum algorithms. There exist a lot of studies about reversible implemen…
[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
Object Detection in Equirectangular Panorama
Wenyan Yang, Yanlin Qian, Francesco Cricri +2
We introduced a high-resolution equirectangular panorama (360-degree, virtual reality) dataset for object detection and propose a multi-projection variant of YOLO detector. The mai…
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure
Caglar Aytekin, Francesco Cricri, Lixin Fan +1
For a graph representation of a dataset, a straightforward normality measure for a sample can be its graph degree. Considering a weighted graph, degree of a sample is the sum of th…
Depth Masked Discriminative Correlation Filter
Uğur Kart, Joni-Kristian Kämäräinen, Jiří Matas +2
Depth information provides a strong cue for occlusion detection and handling, but has been largely omitted in generic object tracking until recently due to lack of suitable benchma…
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
Caglar Aytekin, Xingyang Ni, Francesco Cricri +2
Computer vision algorithms with pixel-wise labeling tasks, such as semantic segmentation and salient object detection, have gone through a significant accuracy increase with the in…
Deep Epitome for Unravelling Generalized Hamming Network: A Fuzzy Logic Interpretation of Deep Learning
Lixin Fan
This paper gives a rigorous analysis of trained Generalized Hamming Networks(GHN) proposed by Fan (2017) and discloses an interesting finding about GHNs, i.e., stacked convolution…
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network
Lixin Fan
We revisit fuzzy neural network with a cornerstone notion of generalized hamming distance, which provides a novel and theoretically justified framework to re-interpret many useful…
Happy Travelers Take Big Pictures: A Psychological Study with Machine Learning and Big Data
Xuefeng Liang, Lixin Fan, Yuen Peng Loh +2
In psychology, theory-driven researches are usually conducted with extensive laboratory experiments, yet rarely tested or disproved with big data. In this paper, we make use of 418…
A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds
Xiaoshui Huang, Jian Zhang, Qiang Wu +2
With the development of numerous 3D sensing technologies, object registration on cross-source point cloud has aroused researchers' interests. When the point clouds are captured fro…
A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structures
Xiaoshui Huang, Jian Zhang, Lixin Fan +2
We propose a systematic approach for registering cross-source point clouds. The compelling need for cross-source point cloud registration is motivated by the rapid development of a…