Search Results for author: Weizhi Li

Found 8 papers, 1 papers with code

Label efficient two-sample test

no code implementations17 Nov 2021 Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha

In the traditional formulation of this problem, the statistician has access to both the measurements (feature variables) and the group variable (label variable).

Finding the Homology of Decision Boundaries with Active Learning

1 code implementation NeurIPS 2020 Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha

We theoretically analyze the proposed framework and show that the query complexity of our active learning algorithm depends naturally on the intrinsic complexity of the underlying manifold.

Active Learning Meta-Learning +2

Regularization via Structural Label Smoothing

no code implementations7 Jan 2020 Weizhi Li, Gautam Dasarathy, Visar Berisha

Regularization is an effective way to promote the generalization performance of machine learning models.

Time-weighted Attentional Session-Aware Recommender System

no code implementations12 Sep 2019 Mei Wang, Weizhi Li, Yan Yan

Session-based Recurrent Neural Networks (RNNs) are gaining increasing popularity for recommendation task, due to the high autocorrelation of user's behavior on the latest session and the effectiveness of RNN to capture the sequence order information.

Collaborative Filtering Recommendation Systems

A novel image tag completion method based on convolutional neural network

no code implementations2 Mar 2017 Yanyan Geng, Guohui Zhang, Weizhi Li, Yi Gu, Ru-Ze Liang, Gaoyuan Liang, Jingbin Wang, Yanbin Wu, Nitin Patil, Jing-Yan Wang

In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN).

Image Retrieval

Learning convolutional neural network to maximize Pos@Top performance measure

no code implementations27 Sep 2016 Yanyan Geng, Ru-Ze Liang, Weizhi Li, Jingbin Wang, Gaoyuan Liang, Chenhao Xu, Jing-Yan Wang

The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation.


Semi-supervised structured output prediction by local linear regression and sub-gradient descent

no code implementations7 Jun 2016 Ru-Ze Liang, Wei Xie, Weizhi Li, Xin Du, Jim Jing-Yan Wang, Jingbin Wang

The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction.

Structured Prediction

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