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Found 13 papers, 3 papers with code
Regularization via f-Divergence: An Application to Multi-Oxide Spectroscopic Analysis
This regularizer serves a dual purpose: on the one hand, it mitigates overfitting by enforcing a constraint on the distributional difference between predictions and noisy targets.
Adaptive Noise-Tolerant Network for Image Segmentation
Unlike image classification and annotation, for which deep network models have achieved dominating superior performances compared to traditional computer vision algorithms, deep learning for automatic image segmentation still faces critical challenges.
Advanced Tutorial: Label-Efficient Two-Sample Tests
This tutorial extends active learning concepts to two-sample testing within this \textit{label-costly} setting while maintaining statistical validity and high testing power.
Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication
Semantic communication serves as a novel paradigm and attracts the broad interest of researchers.
Active Sequential Two-Sample Testing
The classification model is adaptively updated and used to predict where the (unlabelled) features have a high dependency on labels; labeling the ``high-dependency'' features leads to the increased power of the proposed testing framework.
A label-efficient two-sample test
Two-sample tests evaluate whether two samples are realizations of the same distribution (the null hypothesis) or two different distributions (the alternative hypothesis).
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.
