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Incorporation of Deep Neural Network & Reinforcement Learning with Domain Knowledge

arXiv 2107.14613 ↗First posted 29 Jul 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

We present a study of the manners by which Domain information has been incorporated when building models with Neural Networks. Integrating space data is uniquely important to the development of Knowledge understanding model, as well as other fields that aid in understanding information by utilizing the human-machine interface and Reinforcement Learning. On numerous such occasions, machine-based model development may profit essentially from the human information on the world encoded in an adequately exact structure. This paper inspects expansive ways to affect encode such information as sensible and mathematical limitations and portrays methods and results that came to a couple of subcategories under all of those methodologies.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 54 references that are in the map. First posted on arXiv 29 Jul 2021.

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References · 23

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  3. 2014How transferable are features in deep neural networks?cited by 1,216
  4. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  5. 2016The Mythos of Model Interpretabilitycited by 716
  6. 2019Neural Probabilistic Logic Programming in DeepProbLogcited by 389
  7. 2017A Semantic Loss Function for Deep Learning with Symbolic Knowledgecited by 267
  8. 2017Learning Explanatory Rules from Noisy Datacited by 210
  9. 2019Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systemscited by 203
  10. 2017Neural-Symbolic Learning and Reasoning: A Survey and Interpretationcited by 195
  11. 2016Harnessing Deep Neural Networks with Logic Rulescited by 135
  12. 2019Knowledge Graph Convolutional Networks for Recommender Systemscited by 121
11 more
  1. 2019Towards Knowledge-Based Recommender Dialog Systemcited by 112
  2. 2018Imitating Latent Policies from Observationcited by 101
  3. 2017Regularization for Deep Learning: A Taxonomycited by 91
  4. 2019Embedding Symbolic Knowledge into Deep Networkscited by 49
  5. 2019Estimating Node Importance in Knowledge Graphs Using Graph Neural Networkscited by 43
  6. 2020Turning 30: New Ideas in Inductive Logic Programmingcited by 42
  7. 2018Improving the Interpretability of Deep Neural Networks with Knowledge Distillationcited by 37
  8. 2019Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learningcited by 19
  9. 2020Incorporating Symbolic Domain Knowledge into Graph Neural Networkscited by 13
  10. 2018Logical Explanations for Deep Relational Machines Using Relevance Informationcited by 9
  11. 2020Propositionalization and Embeddings: Two Sides of the Same Coincited by 8

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