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Found 3 papers, 1 papers with code
Transfer Learning via Minimizing the Performance Gap Between Domains
We propose a new principle for transfer learning, based on a straightforward intuition: if two domains are similar to each other, the model trained on one domain should also perform well on the other domain, and vice versa.
Disentangled Deep Autoencoding Regularization for Robust Image Classification
In spite of achieving revolutionary successes in machine learning, deep convolutional neural networks have been recently found to be vulnerable to adversarial attacks and difficult to generalize to novel test images with reasonably large geometric transformations.
Reinforcement Learning applied to Single Neuron
In summary, we took the initial endeavor to study the reinforcement learning for multi-agents system.
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