Transparency and interpretability for learned representations of artificial neural networks:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Wiesbaden
Springer Vieweg
[2022]
|
| Edition: | 1st edition 2023 |
| Series: | Research
|
| Subjects: | |
| Online Access: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Physical Description: | Illustrationen, Diagramme 21 cm x 14.8 cm |
| ISBN: | 9783658400033 365840003X |
Staff View
MARC
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| 100 | 1 | |a Meyes, Richard |d 1989- |e Verfasser |0 (DE-588)1276449453 |4 aut | |
| 245 | 1 | 0 | |a Transparency and interpretability for learned representations of artificial neural networks |c Richard Meyes |
| 263 | |a 202304 | ||
| 264 | 1 | |a Wiesbaden |b Springer Vieweg |c [2022] | |
| 264 | 4 | |c © 2022 | |
| 300 | |b Illustrationen, Diagramme |c 21 cm x 14.8 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Research | |
| 650 | 0 | 7 | |a Erklärbare künstliche Intelligenz |0 (DE-588)1263068472 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 653 | |a Transparency | ||
| 653 | |a Interpretability | ||
| 653 | |a Explainability | ||
| 653 | |a Learned Representation | ||
| 653 | |a XAI | ||
| 653 | |a Explainable AI | ||
| 653 | |a Artificial Neural Networks | ||
| 653 | |a Deep Learning | ||
| 653 | |a Digital Transformation | ||
| 653 | |a Neuroscience | ||
| 689 | 0 | 0 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 0 | 1 | |a Erklärbare künstliche Intelligenz |0 (DE-588)1263068472 |D s |
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Record in the Search Index
| _version_ | 1818330312556412928 |
|---|---|
| adam_text |
CONTENTS
1
INTRODUCTION
.
1
1.1
OBJECT
OF
INVESTIGATION
.
2
1.2
RESEARCH
QUESTIONS
.
3
1.3
STRUCTURE
OF
THE
THESIS
.
6
2
BACKGROUND
&
FOUNDATIONS
.
9
2.1
A
SHORT
HISTORY
OF
AI
RESEARCH
.
9
2.1.1
THE
EARLY
YEARS
.
10
2.1.2
THE
GOLDEN
AGES
.
11
2.1.3
THE
AL
WINTER
.
13
2.1.4
THE
AL
RENAISSANCE
.
13
2.2
THE
MODERN
ERA
OF
AI
RESEARCH
.
14
2.2.1
DEEP
LEARNING
FOR
COMPUTER
VISION
.
15
2.2.2
COMPUTER
VISION
BEYOND
THE
ILSVRC
.
17
2.2.3
FROM
SUPERVISED
LEARNING
TO
REINFORCEMENT
LEARNING
.
18
2.2.4
DEEP
REINFORCEMENT
LEARNING
BREAKTHROUGHS
FOR
VIDEO
AND
BOARD
GAMES
.
19
2.2.5
TACKLING
GAMES
WITH
IMPERFECT
INFORMATION
.
23
2.3
TOWARDS
RESEARCH
ON
TRANSPARENCY
&
INTERPRETABILITY
.
24
2.3.1
A
SHIFT
OF
PARADIGM:
FROM
OPTIMIZING
TO
UNDERSTANDING
.
25
2.3.2
INSPIRATIONS
FROM
NEUROSCIENCE
RESEARCH
.
27
3
METHODS
AND
TERMINOLOGY
.
31
3.1
LEARNED
REPRESENTATIONS
.
31
3.1.1
INVESTIGATING
LEARNED
REPRESENTATIONS
.
33
VIII
CONTENTS
3.1.1.1
STATISTICAL
MEASURES
.
34
3.1.1.2
TRANSFORMATIONS
&
EMBEDDINGS
.
35
3.1.2
VISUALIZING
STRUCTURE
OF
THE
LEARNED
REPRESENTATIONS
.
46
3.1.2.1
MAGNITUDE
OF
NEURON
ACTIVATIONS
.
47
3.1.2.2
SELECTIVITY
OF
NEURON
ACTIVATIONS
.
47
3.1.2.3
ABLATIONS
OF
INDIVIDUAL
NEURONS
.
48
3.1.2.4
GINI
IMPORTANCE
.
51
3.2
DELIMITATION
OF
THE
OBJECT
OF
INVESTIGATION
.
51
3.2.1
TRANSPARENCY
FOR
COMPUTER
VISION
MODELS
.
52
3.2.2
TRANSPARENCY
FOR
MOTION
CONTROL
MODELS
.
53
3.2.3
TRANSFER
TO
AN
INDUSTRIAL
APPLICATION
SCENARIO
.
55
4
RELATED
WORK
.
57
4.1
RELATIONSHIP
BETWEEN
A
NETWORK
'
S
INPUT
FEATURES
AND
ITS
OUTPUT
.
59
4.2
VISUALIZATION
OF
NETWORK
PROPERTIES
AND
GRAPHICAL
USER
INTERFACES
.
61
4.3
INVESTIGATING
THE
IMPORTANCE
OF
INDIVIDUAL
NETWORK
COMPONENTS
.
64
4.3.1
MISCELLANEOUS
CONTRIBUTIONS
.
65
4.3.2
ABLATIONS
STUDIES
.
67
4.3.3
REVERSE
ENGINEERING
OF
NEURAL
NETWORKS
.
69
5
RESEARCH
STUDIES
.
71
5.1
INVESTIGATING
LEARNED
REPRESENTATIONS
IN
COMPUTER
VISION
.
75
5.1.1
RESEARCH
STUDY
1:
CHARACTERIZING
SINGLE
NEURONS
IN
A
SHALLOW
MLP
.
75
5.1.1.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
75
5.1.1.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
77
5.1.1.3
RESULTS
.
78
5.1.1.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
88
5.1.2
RESEARCH
STUDY
2:
NETWORK
ABLATIONS
IN
A
DEEP
NEURAL
NETWORK
.
91
5.1.2.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
91
5.1.2.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
92
5.1.2.3
RESULTS
.
93
5.1.2.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
98
CONTENTS
IX
5.1.3
RESEARCH
STUDY
3:
FUNCTIONAL
NEURON
POPULATIONS
IN
CUSTOM-MADE
CNNS
.
100
5.1.3.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
100
5.1.3.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
101
5.1.3.3
RESULTS
.
105
5.1.3.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
113
5.2
INVESTIGATING
LEARNED
REPRESENTATIONS
IN
MOTOR
CONTROL
.
114
5.2.1
RESEARCH
STUDY
4:
INFLUENCE
OF
NETWORK
ABLATIONS
ON
ACTIVATION
PATTERNS
.
115
5.2.1.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
116
5.2.1.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
117
5.2.1.3
RESULTS
.
119
5.2.1.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
128
5.2.2
RESEARCH
STUDY
5:
RELATION
BETWEEN
NEURAL
ACTIVATIONS
AND
AGENT
BEHAVIOR
.
129
5.2.2.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
130
5.2.2.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
131
5.2.2.3
RESULTS
.
135
5.2.2.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
147
6
TRANSFER
STUDIES
.
149
6.1
TRANSFER
STUDY
1:
NETWORK
ABLATIONS
FOR
DEEP
DRAWING
.
150
6.1.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
151
6.1.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
151
6.1.3
RESULTS
.
159
6.1.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
164
6.2
TRANSFER
STUDY
2:
ATTENTION
MECHANISMS
FOR
DEEP
DRAWING
.
166
6.2.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
167
6.2.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
167
6.2.3
RESULTS
.
173
6.2.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
181
X
CONTENTS
7
CRITICAL
REFLECTION
&
OUTLOOK
.
183
7,1
REFLECTION
OF
RESULTS
&
CONTRIBUTION
TO
RESEARCH
QUESTIONS
.
183
7.1.1
RESEARCH
QUESTION
1
.
184
7.1.2
RESEARCH
QUESTION
2
.
185
7.1.3
RESEARCH
QUESTION
3
.
187
7.1.4
RESEARCH
QUESTION
4
.
189
7.2
FUTURE
RESEARCH
DIRECTIONS
.
191
8
SUMMARY
.
195
REFERENCES
.
197 |
| adam_txt |
CONTENTS
1
INTRODUCTION
.
1
1.1
OBJECT
OF
INVESTIGATION
.
2
1.2
RESEARCH
QUESTIONS
.
3
1.3
STRUCTURE
OF
THE
THESIS
.
6
2
BACKGROUND
&
FOUNDATIONS
.
9
2.1
A
SHORT
HISTORY
OF
AI
RESEARCH
.
9
2.1.1
THE
EARLY
YEARS
.
10
2.1.2
THE
GOLDEN
AGES
.
11
2.1.3
THE
AL
WINTER
.
13
2.1.4
THE
AL
RENAISSANCE
.
13
2.2
THE
MODERN
ERA
OF
AI
RESEARCH
.
14
2.2.1
DEEP
LEARNING
FOR
COMPUTER
VISION
.
15
2.2.2
COMPUTER
VISION
BEYOND
THE
ILSVRC
.
17
2.2.3
FROM
SUPERVISED
LEARNING
TO
REINFORCEMENT
LEARNING
.
18
2.2.4
DEEP
REINFORCEMENT
LEARNING
BREAKTHROUGHS
FOR
VIDEO
AND
BOARD
GAMES
.
19
2.2.5
TACKLING
GAMES
WITH
IMPERFECT
INFORMATION
.
23
2.3
TOWARDS
RESEARCH
ON
TRANSPARENCY
&
INTERPRETABILITY
.
24
2.3.1
A
SHIFT
OF
PARADIGM:
FROM
OPTIMIZING
TO
UNDERSTANDING
.
25
2.3.2
INSPIRATIONS
FROM
NEUROSCIENCE
RESEARCH
.
27
3
METHODS
AND
TERMINOLOGY
.
31
3.1
LEARNED
REPRESENTATIONS
.
31
3.1.1
INVESTIGATING
LEARNED
REPRESENTATIONS
.
33
VIII
CONTENTS
3.1.1.1
STATISTICAL
MEASURES
.
34
3.1.1.2
TRANSFORMATIONS
&
EMBEDDINGS
.
35
3.1.2
VISUALIZING
STRUCTURE
OF
THE
LEARNED
REPRESENTATIONS
.
46
3.1.2.1
MAGNITUDE
OF
NEURON
ACTIVATIONS
.
47
3.1.2.2
SELECTIVITY
OF
NEURON
ACTIVATIONS
.
47
3.1.2.3
ABLATIONS
OF
INDIVIDUAL
NEURONS
.
48
3.1.2.4
GINI
IMPORTANCE
.
51
3.2
DELIMITATION
OF
THE
OBJECT
OF
INVESTIGATION
.
51
3.2.1
TRANSPARENCY
FOR
COMPUTER
VISION
MODELS
.
52
3.2.2
TRANSPARENCY
FOR
MOTION
CONTROL
MODELS
.
53
3.2.3
TRANSFER
TO
AN
INDUSTRIAL
APPLICATION
SCENARIO
.
55
4
RELATED
WORK
.
57
4.1
RELATIONSHIP
BETWEEN
A
NETWORK
'
S
INPUT
FEATURES
AND
ITS
OUTPUT
.
59
4.2
VISUALIZATION
OF
NETWORK
PROPERTIES
AND
GRAPHICAL
USER
INTERFACES
.
61
4.3
INVESTIGATING
THE
IMPORTANCE
OF
INDIVIDUAL
NETWORK
COMPONENTS
.
64
4.3.1
MISCELLANEOUS
CONTRIBUTIONS
.
65
4.3.2
ABLATIONS
STUDIES
.
67
4.3.3
REVERSE
ENGINEERING
OF
NEURAL
NETWORKS
.
69
5
RESEARCH
STUDIES
.
71
5.1
INVESTIGATING
LEARNED
REPRESENTATIONS
IN
COMPUTER
VISION
.
75
5.1.1
RESEARCH
STUDY
1:
CHARACTERIZING
SINGLE
NEURONS
IN
A
SHALLOW
MLP
.
75
5.1.1.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
75
5.1.1.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
77
5.1.1.3
RESULTS
.
78
5.1.1.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
88
5.1.2
RESEARCH
STUDY
2:
NETWORK
ABLATIONS
IN
A
DEEP
NEURAL
NETWORK
.
91
5.1.2.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
91
5.1.2.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
92
5.1.2.3
RESULTS
.
93
5.1.2.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
98
CONTENTS
IX
5.1.3
RESEARCH
STUDY
3:
FUNCTIONAL
NEURON
POPULATIONS
IN
CUSTOM-MADE
CNNS
.
100
5.1.3.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
100
5.1.3.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
101
5.1.3.3
RESULTS
.
105
5.1.3.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
113
5.2
INVESTIGATING
LEARNED
REPRESENTATIONS
IN
MOTOR
CONTROL
.
114
5.2.1
RESEARCH
STUDY
4:
INFLUENCE
OF
NETWORK
ABLATIONS
ON
ACTIVATION
PATTERNS
.
115
5.2.1.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
116
5.2.1.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
117
5.2.1.3
RESULTS
.
119
5.2.1.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
128
5.2.2
RESEARCH
STUDY
5:
RELATION
BETWEEN
NEURAL
ACTIVATIONS
AND
AGENT
BEHAVIOR
.
129
5.2.2.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
130
5.2.2.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
131
5.2.2.3
RESULTS
.
135
5.2.2.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
147
6
TRANSFER
STUDIES
.
149
6.1
TRANSFER
STUDY
1:
NETWORK
ABLATIONS
FOR
DEEP
DRAWING
.
150
6.1.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
151
6.1.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
151
6.1.3
RESULTS
.
159
6.1.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
164
6.2
TRANSFER
STUDY
2:
ATTENTION
MECHANISMS
FOR
DEEP
DRAWING
.
166
6.2.1
KEY
CONTRIBUTIONS
OF
THE
STUDY
.
167
6.2.2
METHODS
AND
EXPERIMENTAL
DESIGN
.
167
6.2.3
RESULTS
.
173
6.2.4
SUMMARY
AND
CONTRIBUTION
OF
THE
RESULTS
TO
THE
RESEARCH
QUESTIONS
.
181
X
CONTENTS
7
CRITICAL
REFLECTION
&
OUTLOOK
.
183
7,1
REFLECTION
OF
RESULTS
&
CONTRIBUTION
TO
RESEARCH
QUESTIONS
.
183
7.1.1
RESEARCH
QUESTION
1
.
184
7.1.2
RESEARCH
QUESTION
2
.
185
7.1.3
RESEARCH
QUESTION
3
.
187
7.1.4
RESEARCH
QUESTION
4
.
189
7.2
FUTURE
RESEARCH
DIRECTIONS
.
191
8
SUMMARY
.
195
REFERENCES
.
197 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Meyes, Richard 1989- |
| author_GND | (DE-588)1276449453 |
| author_facet | Meyes, Richard 1989- |
| author_role | aut |
| author_sort | Meyes, Richard 1989- |
| author_variant | r m rm |
| building | Verbundindex |
| bvnumber | BV049452636 |
| classification_rvk | ST 301 |
| ctrlnum | (OCoLC)1349705092 (DE-599)DNB1271554054 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | 1st edition 2023 |
| format | Book |
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| id | DE-604.BV049452636 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:13:17Z |
| indexdate | 2024-12-13T13:01:57Z |
| institution | BVB |
| institution_GND | (DE-588)1043386068 |
| isbn | 9783658400033 365840003X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034798497 |
| oclc_num | 1349705092 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | Illustrationen, Diagramme 21 cm x 14.8 cm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Vieweg |
| record_format | marc |
| series2 | Research |
| spelling | Meyes, Richard 1989- Verfasser (DE-588)1276449453 aut Transparency and interpretability for learned representations of artificial neural networks Richard Meyes 202304 Wiesbaden Springer Vieweg [2022] © 2022 Illustrationen, Diagramme 21 cm x 14.8 cm txt rdacontent n rdamedia nc rdacarrier Research Erklärbare künstliche Intelligenz (DE-588)1263068472 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Transparency Interpretability Explainability Learned Representation XAI Explainable AI Artificial Neural Networks Deep Learning Digital Transformation Neuroscience Neuronales Netz (DE-588)4226127-2 s Erklärbare künstliche Intelligenz (DE-588)1263068472 s DE-604 Springer Fachmedien Wiesbaden (DE-588)1043386068 pbl Erscheint auch als Online-Ausgabe 9783658400040 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=810da808ab594ca7819a0d5c28ee39a4&prov=M&dok_var=1&dok_ext=htm Inhaltstext X:MVB http://www.springer.com/ DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034798497&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20221029 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Meyes, Richard 1989- Transparency and interpretability for learned representations of artificial neural networks Erklärbare künstliche Intelligenz (DE-588)1263068472 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)1263068472 (DE-588)4226127-2 |
| title | Transparency and interpretability for learned representations of artificial neural networks |
| title_auth | Transparency and interpretability for learned representations of artificial neural networks |
| title_exact_search | Transparency and interpretability for learned representations of artificial neural networks |
| title_exact_search_txtP | Transparency and Interpretability for Learned Representations of Artificial Neural Networks |
| title_full | Transparency and interpretability for learned representations of artificial neural networks Richard Meyes |
| title_fullStr | Transparency and interpretability for learned representations of artificial neural networks Richard Meyes |
| title_full_unstemmed | Transparency and interpretability for learned representations of artificial neural networks Richard Meyes |
| title_short | Transparency and interpretability for learned representations of artificial neural networks |
| title_sort | transparency and interpretability for learned representations of artificial neural networks |
| topic | Erklärbare künstliche Intelligenz (DE-588)1263068472 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Erklärbare künstliche Intelligenz Neuronales Netz |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=810da808ab594ca7819a0d5c28ee39a4&prov=M&dok_var=1&dok_ext=htm http://www.springer.com/ http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034798497&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT meyesrichard transparencyandinterpretabilityforlearnedrepresentationsofartificialneuralnetworks AT springerfachmedienwiesbaden transparencyandinterpretabilityforlearnedrepresentationsofartificialneuralnetworks |