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Found 4 papers, 1 papers with code
A Hierarchical Approach to Population Training for Human-AI Collaboration
A major challenge for deep reinforcement learning (DRL) agents is to collaborate with novel partners that were not encountered by them during the training phase.
Few-Shot Regression via Learning Sparsifying Basis Functions
Recent few-shot learning algorithms have enabled models to quickly adapt to new tasks based on only a few training samples.
Few-Shot Regression via Learned Basis Functions
We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection.
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