AbductiveLearning
/
ABLkit
Not watched
1
0
Fork 0
Code
Releases 0 Wiki evaluate Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain HPC
You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.
575 Commits
4 Branches
27 MB
79 Download
Tree: b00ac95d34
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from 'b00ac95d34'
${ noResults }
ABLkit/docs/References.rst

References.rst 665 B
Raw Normal View History

[ENH] build docs structures
2 years ago
[DOC] add references
2 years ago
​
[DOC] add color in basics
2 years ago
[DOC] add references
2 years ago
​
​
​
[ENH] build docs structures
2 years ago
​
 12345678910 
  1. References

  2. ==========

  3. 

  4. Zhi-Hua Zhou. `Abductive learning: Towards bridging machine learning and logical reasoning. <https://link.springer.com/epdf/10.1007/s11432-018-9801-4?author_access_token=jgJe1Ox3Mk-K7ORSnX7jtfe4RwlQNchNByi7wbcMAY7_PxTx-xNLP7Lp0mIZ04ORp3VG4wioIBHSCIAO3B_TBJkj87YzapmdnYVSQvgBIO3aEpQWppxZG25KolINetygc2W_Cj2gtoBdiG_J1hU3pA==>`_. **Science China Information Sciences**, 2019, 62: 076101.

  5. 

  6. Zhi-Hua Zhou and Yu-Xuan Huang. `Abductive learning <https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf>`_. In P. Hitzler and M. K. Sarker eds., **Neuro-Symbolic Artificial Intelligence: The State of the Art**, IOP Press, Amsterdam, 2022, p.353-379

  7. 

  8. 

  9. 

  10.

An efficient Python toolkit for Abductive Learning (ABL), a novel paradigm that integrates machine learning and logical reasoning in a unified framework.