Search Results for author: Zhaohui Wu

Found 10 papers, 1 papers with code

SPAIC: A Spike-based Artificial Intelligence Computing Framework

1 code implementation26 Jul 2022 Chaofei Hong, Mengwen Yuan, Mengxiao Zhang, Xiao Wang, Chegnjun Zhang, Jiaxin Wang, Gang Pan, Zhaohui Wu, Huajin Tang

In this work, we present a Python based spiking neural network (SNN) simulation and training framework, aka SPAIC that aims to support brain-inspired model and algorithm researches integrated with features from both deep learning and neuroscience.

Spectral-graph Based Classifications: Linear Regression for Classification and Normalized Radial Basis Function Network

no code implementations19 May 2017 Zhenfang Hu, Gang Pan, Zhaohui Wu

The spectral graph theory provides us with a new insight into a fundamental aspect of classification: the tradeoff between fitting error and overfitting risk.

General Classification Model Selection +1

Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices

no code implementations2 Jan 2017 Wenjia Meng, Zonghua Gu, Ming Zhang, Zhaohui Wu

With the rapid proliferation of Internet of Things and intelligent edge devices, there is an increasing need for implementing machine learning algorithms, including deep learning, on resource-constrained mobile embedded devices with limited memory and computation power.

General Classification Model Compression

A neural probabilistic model for context based citation recommendation

no code implementations AAAI 2015 Wenyi Huang, Zhaohui Wu, Chen Liang, Prasenjit Mitra, C. Lee Giles

It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context.

Citation Recommendation

Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut

no code implementations25 Mar 2014 Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu

The methods include PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method developed by us, called spectral sparse representation (SSR).

Dimensionality Reduction

Sparse Principal Component Analysis via Rotation and Truncation

no code implementations6 Mar 2014 Zhenfang Hu, Gang Pan, Yueming Wang, Zhaohui Wu

In contrast to most of existing work which deal with the problem by adding some sparsity penalties on various objectives of PCA, in this paper, we propose a new method SPCArt, whose motivation is to find a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation.

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