US 12,175,347 B2
Neural trees
Aditya Vithal Nori, Cambridge (GB); Antonio Criminisi, Cambridge (GB); and Ryutaro Tanno, London (GB)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Apr. 25, 2023, as Appl. No. 18/306,888.
Application 18/306,888 is a continuation of application No. 16/043,131, filed on Jul. 23, 2018.
Claims priority of application No. 1810736 (GB), filed on Jun. 29, 2018.
Prior Publication US 2023/0267381 A1, Aug. 24, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 20/20 (2019.01); G06F 17/18 (2006.01); G06F 18/243 (2023.01); G06N 3/04 (2023.01); G06N 3/082 (2023.01); G06N 5/01 (2023.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC G06N 20/20 (2019.01) [G06F 17/18 (2013.01); G06F 18/24323 (2023.01); G06N 3/04 (2013.01); G06N 3/082 (2013.01); G06N 5/01 (2023.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a memory storing instructions;
a processor configured to execute the instructions stored in the memory to:
create nodes connected by edges at least by computing parameterized, differential operations;
construct, in parallel:
a first model via a growing process dependent on training data; and
a second model by increasing a depth of an incoming edge of at least one of the nodes;
remove at least one of the nodes based on comparing values of the differentiable operations with validation data, resulting in a pruned decision tree; and
apply the pruned decision tree to an input example resulting in a predicted output.