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Found 8 papers, 1 papers with code
Label efficient two-sample test
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
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.
Regularization via Structural Label Smoothing
Regularization is an effective way to promote the generalization performance of machine learning models.
Time-weighted Attentional Session-Aware Recommender System
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.
A novel image tag completion method based on convolutional neural network
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).
Learning convolutional neural network to maximize Pos@Top performance measure
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.
A novel transfer learning method based on common space mapping and weighted domain matching
We map the data of two domains to one single common space, and learn a classifier in this common space.
Semi-supervised structured output prediction by local linear regression and sub-gradient descent
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.