Patent application EP21167148A (EP), filed 2021-04-07
Publication number: EP3929823A1
Title: PROGRAM, LEARNING METHOD, AND INFORMATION PROCESSING APPARATUS
Abstract: A process includes starting a learning process for building a model including multiple layers each including a parameter. The learning process executes iterations, each including calculating output error of the model using training data and updating the parameter value based on the output error. The process also includes selecting two or more candidate layers representing candidates for layers, where the updating is to be suppressed, based on results of a first iteration of the learning process. The process also includes calculating, based on the number of iterations executed up to the first iteration, a ratio value which becomes larger when the number of iterations executed is greater, and determining, amongst the candidate layers, one or more layers, where the updating is to be suppressed at a second iteration following the first iteration. The number of one or more layers is determined according to the ratio value.
IPC codes: G06N   3/    04            A I
G06N   3/    08            A I
Claims: number: 1
text: A computer program that causes a computer (10) to execute a process comprising:
number: 2
text: The computer program according to claim 1, wherein:
the number (x) of the one or more layers determined according to the ratio value is calculated by multiplying a number (N) of the two or more candidate layers by the ratio value (P).
number: 3
text: The computer program according to claim 1, wherein:
the ratio value (P) corresponding to the number of the iterations executed is calculated based on a sigmoid curve (45).
number: 4
text: The computer program according to claim 1, wherein:
the updating of the value of the parameter is performed at the second iteration in each remaining layer other than the one or more layers whose number (x) is determined according to the ratio value (P) amongst the two or more candidate layers.
number: 5
text: The computer program according to claim 1, wherein:
number: 6
text: The computer program according to claim 1, wherein:
the model is a multi-layer neural network (310).
number: 7
text: The computer program according to claim 1, wherein:
number: 8
text: The computer program according to claim 1, wherein:
number: 9
text: The computer program according to claim 1, wherein:
number: 10
text: The computer program according to claim 1, wherein:
each of the one or more layers whose number is determined according to the ratio value is determined based on spacing of the one or more layers.
number: 11
text: The computer program according to claim 1, wherein:
each of the one or more layers whose number is determined according to the ratio value is determined based on proximity thereof to an input of the model.
number: 12
text: A learning method executed by a computer (10), the learning method comprising:
number: 13
text: An information processing apparatus comprising:
Office action: search_report, dated 2021-12-29
Prior art cited: ref_id: WO2017157183A1
ref_type: patent
severity: background
ref_id: JP2019185275A
ref_type: patent
severity: background
ref_id: - ANDREW BROCK ET AL: "FREEZEOUT: ACCELERATE TRAINING BY PROGRES- SIVELY FREEZING LAYERS", 12 May 2017 (2017-05-12), pages 1 - 7, XP055642559, Retrieved from the Internet <URL:https://arxiv.org/abs/1706.04983v2> [retrieved on 20191114]
ref_type: npl
severity: background
ref_id: - Andrew Brock ET AL, "FREEZEOUT: ACCELERATE TRAINING BY PROGRES- SIVELY FREEZING LAYERS", (20170512), pages 1 - 7, URL: https://arxiv.org/abs/1706.04983v2, (20191114), XP055642559 [I] 1-13 * abstract * * 1. Introduction * * 2. FreezeOut *
ref_type: npl
severity: background
Final disposition: pending (2021-12-29)