Search Results for author:
Found 10 papers, 3 papers with code
SAIL: Self-Augmented Graph Contrastive Learning
This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario.
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS
However, the studies on learning theory for general loss functions and hypothesis spaces remain limited.
Nearly Optimal Clustering Risk Bounds for Kernel K-Means
In this paper, we study the statistical properties of kernel $k$-means and obtain a nearly optimal excess clustering risk bound, substantially improving the state-of-art bounds in the existing clustering risk analyses.
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test
DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution.
Dynamically Visual Disambiguation of Keyword-based Image Search
To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.
Deep learning in bioinformatics: introduction, application, and perspective in big data era
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics.
Efficient Cross-Validation for Semi-Supervised Learning
In this paper, we provide a method to approximate the CV for manifold regularization based on a notion of robust statistics, called Bouligand influence function (BIF).
Multi-Class Learning: From Theory to Algorithm
In this paper, we study the generalization performance of multi-class classification and obtain a shaper data-dependent generalization error bound with fast convergence rate, substantially improving the state-of-art bounds in the existing data-dependent generalization analysis.
On the Decision Boundary of Deep Neural Networks
We demonstrate, both theoretically and empirically, that the last weight layer of a neural network converges to a linear SVM trained on the output of the last hidden layer, for both the binary case and the multi-class case with the commonly used cross-entropy loss.
SupportNet: solving catastrophic forgetting in class incremental learning with support data
A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic forgetting.
