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Found 11 papers, 4 papers with code
3D Graph Contrastive Learning for Molecular Property Prediction
Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data.
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
Our method is learning to learn a primary task with various auxiliary tasks to improve generalization performance.
M2FN: Multi-step Modality Fusion for Advertisement Image Assessment
Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry.
Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty
Label noise is a critical factor that degrades the generalization performance of deep neural networks, thus leading to severe issues in real-world problems.
Ranked #23 on
Image Classification
on Clothing1M
(using extra training data)
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Our proposed method is learning to learn a primary task by predicting meta-paths as auxiliary tasks.
Multi-Manifold Learning for Large-scale Targeted Advertising System
The key to the success of precise user targeting lies in learning the accurate user and ad representation in the embedding space.
Graphs, Entities, and Step Mixture
Existing approaches for graph neural networks commonly suffer from the oversmoothing issue, regardless of how neighborhoods are aggregated.
Which Ads to Show? Advertisement Image Assessment with Auxiliary Information via Multi-step Modality Fusion
Despite crucial influences of image quality, auxiliary information of ad images such as tags and target subjects can also determine image preference.
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction
To guarantee the commutative property for homogeneous interaction, we apply model sharing and hidden representation merging techniques.
DNA Steganalysis Using Deep Recurrent Neural Networks
We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework.
NASCUP: Nucleic Acid Sequence Classification by Universal Probability
Motivated by the need for fast and accurate classification of unlabeled nucleotide sequences on a large scale, we developed NASCUP, a new classification method that captures statistical structures of nucleotide sequences by compact context-tree models and universal probability from information theory.
Genomics Information Theory Information Theory
