TY - JOUR
TI - Derivatives of Turing machines in Linear Logic
AU - Murfet, Daniel
AU - Clift, James
T2 - arXiv:1805.11813 [math]
AB - We calculate denotations under the Sweedler semantics of the Ehrhard-Regnier derivatives of various encodings of Turing machines into linear logic. We show that these derivatives calculate the rate of change of probabilities naturally arising in the Sweedler semantics of linear logic proofs. The resulting theory is applied to the problem of synthesising Turing machines by gradient descent.
DA - 2019/01/28/
PY - 2019
DP - arXiv.org
UR - http://arxiv.org/abs/1805.11813
Y2 - 2019/11/21/20:33:27
KW - Abstract machines
KW - Categorical ML
KW - Differentiation
KW - Linear logic
KW - Machine learning
ER -
TY - COMP
TI - dmurfet/deeplinearlogic
AU - Murfet, Daniel
AB - Deep learning and linear logic. Contribute to dmurfet/deeplinearlogic development by creating an account on GitHub.
DA - 2018/07/14/T01:08:44Z
PY - 2018
DP - GitHub
LA - Jupyter Notebook
UR - https://github.com/dmurfet/deeplinearlogic
Y2 - 2019/11/22/16:44:43
KW - Categorical ML
KW - Implementation
KW - Linear logic
KW - Machine learning
KW - Semantics
ER -
TY - COMP
TI - dmurfet/polysemantics
AU - Murfet, Daniel
AB - Polynomial semantics of linear logic. Contribute to dmurfet/polysemantics development by creating an account on GitHub.
DA - 2018/04/29/T20:41:43Z
PY - 2018
DP - GitHub
LA - Python
UR - https://github.com/dmurfet/polysemantics
Y2 - 2019/11/22/16:45:35
KW - Categorical ML
KW - Implementation
KW - Linear logic
KW - Machine learning
KW - Semantics
ER -
TY - SLIDE
TI - Linear logic and deep learning
A2 - Murfet, Daniel
A2 - Hu, Huiyi
LA - en
KW - Categorical ML
KW - Linear logic
KW - Machine learning
KW - Semantics
ER -