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Found 5 papers, 3 papers with code
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
Algorithmic extrapolation can be achieved through recurrent systems, which can be iterated many times to solve difficult reasoning problems.
Thinking Deeper With Recurrent Networks: Logical Extrapolation Without Overthinking
Classical machine learning systems perform best when they are trained and tested on the same distribution, and they lack a mechanism to increase model power after training is complete.
Datasets for Studying Generalization from Easy to Hard Examples
We describe new datasets for studying generalization from easy to hard examples.
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models
High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL).
Understanding Generalization through Visualizations
The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive.
