PonderNet: Learning to PonderDownload PDF

May 20, 2021 (edited Jul 19, 2021)AutoML@ICML2021 PosterReaders: Everyone
  • Keywords: adaptive computation, probabilistic, recurrent network, transformer, ponder
  • TL;DR: PonderNet is a new algorithm that learns to adapt the amount of computation in a probabilist fashion, using end-to-end learning.
  • Abstract: In standard neural networks the amount of computation used grows with the size of the inputs, but not with the complexity of the problem being learnt. To overcome this limitation we introduce PonderNet, a new algorithm that learns to adapt the amount of computation based on the complexity of the problem at hand. PonderNet learns end-to-end the number of computational steps to achieve an effective compromise between training prediction accuracy, computational cost and generalization. On a complex synthetic problem, PonderNet dramatically improves performance over previous adaptive computation methods and additionally succeeds at extrapolation tests where traditional neural networks fail. Also, our method matched the current state of the art results on a real world question and answering dataset, but using less compute. Finally, PonderNet reached state of the art results on a complex task designed to test the reasoning capabilities of neural networks.
  • Ethics Statement: In this work we introduced PonderNet, a new method that enables neural networks to adapt their computational complexity to the task they are trying to solve. Neural networks achieve state of the art in a wide range of applications, including natural language processing, reinforcement learning, computer vision and more. Currently, they require much time, expensive hardware and energy to train and to deploy. They also often fail to generalize and to extrapolate to conditions beyond their training. PonderNet expands the capabilities of neural networks, by letting them decide to ponder for an indefinite amount of time (analogous to how both humans and computers think). This can be used to reduce the amount of compute and energy at inference time, which makes it particularly well suited for platforms with limited resources such as mobile phones. Additionally, our experiments show that enabling neural networks to adapt their computational complexity has also benefits for their performance (beyond the computational requirements) when evaluating outside of the training distribution, which is one of the limiting factors when applying neural networks for real-world problems. We encourage other researchers to pursue the questions we have considered on this work. We believe that biasing neural network architectures to behave more like algorithms, and less like ``flat mappings, will help develop deep learning methods to their the full potential.
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