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Differentiable programming: Generalization, characterization and limitations of deep learning

arXiv 2205.06898 ↗First posted 13 May 2022cs.LGcited by 0

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Abstract (arXiv)

In the past years, deep learning models have been successfully applied in several cognitive tasks. Originally inspired by neuroscience, these models are specific examples of differentiable programs. In this paper we define and motivate differentiable programming, as well as specify some program characteristics that allow us to incorporate the structure of the problem in a differentiable program. We analyze different types of differentiable programs, from more general to more specific, and evaluate, for a specific problem with a graph dataset, its structure and knowledge with several differentiable programs using those characteristics. Finally, we discuss some inherent limitations of deep learning and differentiable programs, which are key challenges in advancing artificial intelligence, and then analyze possible solutions

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 13 May 2022.

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References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
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  11. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  12. 2018Ontology Reasoning with Deep Neural Networkscited by 40
2 more
  1. 2018Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagatorcited by 14
  2. 2013Multi-GPU Training of ConvNetscited by 6

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