Search Results for author: Zhuang Ma

Found 6 papers, 0 papers with code

hBERT + BiasCorp - Fighting Racism on the Web

no code implementations EACL (LTEDI) 2021 Olawale Onabola, Zhuang Ma, Xie Yang, Benjamin Akera, Ibraheem Abdulrahman, Jia Xue, Dianbo Liu, Yoshua Bengio

In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer.

HBert + BiasCorp -- Fighting Racism on the Web

no code implementations6 Apr 2021 Olawale Onabola, Zhuang Ma, Yang Xie, Benjamin Akera, Abdulrahman Ibraheem, Jia Xue, Dianbo Liu, Yoshua Bengio

In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer.

Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency

no code implementations EMNLP 2018 Zhuang Ma, Michael Collins

Noise Contrastive Estimation (NCE) is a powerful parameter estimation method for log-linear models, which avoids calculation of the partition function or its derivatives at each training step, a computationally demanding step in many cases.

Classification General Classification +2

Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting

no code implementations5 May 2017 Zhuang Ma, Zongming Ma

Latent space models are effective tools for statistical modeling and exploration of network data.

Community Detection

Subspace Perspective on Canonical Correlation Analysis: Dimension Reduction and Minimax Rates

no code implementations12 May 2016 Zhuang Ma, Xiao-Dong Li

Canonical correlation analysis (CCA) is a fundamental statistical tool for exploring the correlation structure between two sets of random variables.

Dimensionality Reduction

Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis

no code implementations26 Jun 2015 Zhuang Ma, Yichao Lu, Dean Foster

In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requiring computing the product of two huge matrices and huge matrix decomposition, are computationally and storage expensive.

Stochastic Optimization

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