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Found 8 papers, 2 papers with code
Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances
To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumulate.
Meta-Reinforcement Learning via Exploratory Task Clustering
In this paper, we explore the structured heterogeneity among tasks via clustering to improve meta-RL.
MiddleGAN: Generate Domain Agnostic Samples for Unsupervised Domain Adaptation
We extend the theory of GAN to show that there exist optimal solutions for the parameters of the two discriminators and one generator in MiddleGAN, and empirically show that the samples generated by the MiddleGAN are similar to both samples from the source domain and samples from the target domain.
Meta Policy Learning for Cold-Start Conversational Recommendation
We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations.
Improve Learning from Crowds via Generative Augmentation
This creates a sparsity issue and limits the quality of machine learning models trained on such data.
Learning from Crowds by Modeling Common Confusions
Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost.
Ranked #1 on
Image Classification
on LabelMe
InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction
We see information sources as multiple views and fusing them to construct an intact space with sufficient information.
CN-Probase: A Data-driven Approach for Large-scale Chinese Taxonomy Construction
Taxonomies play an important role in machine intelligence.
