Search Results for author: Kaixuan Zhang

Found 12 papers, 3 papers with code

Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference

no code implementations21 Jun 2023 Boyan Li, Luziwei Leng, Ran Cheng, Shuaijie Shen, Kaixuan Zhang, JianGuo Zhang, Jianxing Liao

An expanded version of our network challenges the performance of the spiking VGG-16 network with a 71. 64% top-1 accuracy, all while operating with a model capacity 2. 1 times smaller.

Image Classification

Discrete Time Convolution for Fast Event-Based Stereo

1 code implementation CVPR 2022 Kaixuan Zhang, Kaiwei Che, JianGuo Zhang, Jie Cheng, Ziyang Zhang, Qinghai Guo, Luziwei Leng

Inspired by continuous dynamics of biological neuron models, we propose a novel encoding method for sparse events - continuous time convolution (CTC) - which learns to model the spatial feature of the data with intrinsic dynamics.

Depth Estimation Stereo Matching

A Large Dataset of Historical Japanese Documents with Complex Layouts

3 code implementations18 Apr 2020 Zejiang Shen, Kaixuan Zhang, Melissa Dell

Deep learning-based approaches for automatic document layout analysis and content extraction have the potential to unlock rich information trapped in historical documents on a large scale.

Document Layout Analysis

Connecting First and Second Order Recurrent Networks with Deterministic Finite Automata

1 code implementation12 Nov 2019 Qinglong Wang, Kaixuan Zhang, Xue Liu, C. Lee Giles

We propose an approach that connects recurrent networks with different orders of hidden interaction with regular grammars of different levels of complexity.

Shapley Homology: Topological Analysis of Sample Influence for Neural Networks

no code implementations15 Oct 2019 Kaixuan Zhang, Qinglong Wang, Xue Liu, C. Lee Giles

This has motivated different research areas such as data poisoning, model improvement, and explanation of machine learning models.

BIG-bench Machine Learning Data Poisoning

Information Extraction from Text Regions with Complex Tabular Structure

no code implementations NeurIPS Workshop Document_Intelligen 2019 Kaixuan Zhang, Zejiang Shen, Jie zhou, Melissa Dell

Recent innovations have improved layout analysis of document images, significantly improving our ability to identify text and non-text regions.

Verification of Recurrent Neural Networks Through Rule Extraction

no code implementations14 Nov 2018 Qinglong Wang, Kaixuan Zhang, Xue Liu, C. Lee Giles

The verification problem for neural networks is verifying whether a neural network will suffer from adversarial samples, or approximating the maximal allowed scale of adversarial perturbation that can be endured.

A Comparative Study of Rule Extraction for Recurrent Neural Networks

no code implementations16 Jan 2018 Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles

Then we empirically evaluate different recurrent networks for their performance of DFA extraction on all Tomita grammars.

An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks

no code implementations29 Sep 2017 Qinglong Wang, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles

Rule extraction from black-box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis.

Medical Diagnosis

Towards Interrogating Discriminative Machine Learning Models

no code implementations23 May 2017 Wenbo Guo, Kaixuan Zhang, Lin Lin, Sui Huang, Xinyu Xing

Our results indicate that the proposed approach not only outperforms the state-of-the-art technique in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of a learning model.

BIG-bench Machine Learning

Learning Adversary-Resistant Deep Neural Networks

no code implementations5 Dec 2016 Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, Xue Liu, C. Lee Giles

Despite the superior performance of DNNs in these applications, it has been recently shown that these models are susceptible to a particular type of attack that exploits a fundamental flaw in their design.

Autonomous Vehicles

Adversary Resistant Deep Neural Networks with an Application to Malware Detection

no code implementations5 Oct 2016 Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, Xue Liu

However, after a thorough analysis of the fundamental flaw in DNNs, we discover that the effectiveness of current defenses is limited and, more importantly, cannot provide theoretical guarantees as to their robustness against adversarial sampled-based attacks.

Information Retrieval Malware Detection +3

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