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@FIT, Monash Uni, August 2020
A/Prof Truyen Tran
Vuong Le, Thao Le, Hung Le,
Dung Nguyen, Tin Pham
@truyenoz
truyentran.github.io
truyen.tran@deakin.edu.au
letdataspeak.blogspot.com
goo.gl/3jJ1O0
Machine reasoning
linkedin.com/in/truyen-tran

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Dual-process theories
The problem
Neural memories
Agenda
Visual reasoning

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Among the most challenging scientific questions of our
time are the corresponding analytic and synthetic
problems:
• How does the brain function?
• Can we design a machine which will simulate a brain?
-- Automata Studies, 1956.

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#REF: A Cognitive Architecture Approach to Interactive Task Learning, John E. Laird, University of Michigan

1960s-1990s
 Hand-crafting rules,
domain-specific, logic-
based
 High in reasoning
 Can’t scale.
 Fail on unseen cases.
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2020s-2030s
 Learning + reasoning, general
purpose, human-like
 Has contextual and common-
sense reasoning
 Requires less data
 Adapt to change
 Explainable
1990s-present
 Machine learning, general
purpose, statistics-based
 Low in reasoning
 Needs lots of data
 Less adaptive
 Little explanation
Photo credit: DARPA

Learning vs Reasoning
Learning is to improve itself by experiencing ~ acquiring
knowledge & skills
Reasoning is to deduce knowledge from previously acquired
knowledge in response to a query (or a cues)
Early theories of intelligence (a) focuses solely on reasoning, (b)
learning can be added separately and later! (Khardon & Roth,
1997).
Learning precedes reasoning or the two interacting?
Can reasoning be learnt? Are learning and reasoning indeed
different facets of the same mental/computational process?
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Khardon, Roni, and Dan Roth. "Learning to reason." Journal of the ACM
(JACM) 44.5 (1997): 697-725.
(Dan Roth; ACM
Fellow; IJCAI John
McCarthy Award)

Rich Sutton’s Bitter Lesson
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“The biggest lesson that can be read from
70 years of AI research is that general
methods that leverage computation are
ultimately the most effective, and by a
large margin. ”
“The two methods that seem to scale
arbitrarily in this way
are search and learning.”
http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Bottou’s vision of reasoning
Reasoning is not necessarily achieved by
making logical inferences
There is a continuity between algebraically
rich inference and connecting together
trainable learning systems
Central to reasoning is composition rules to
guide the combinations of modules to
address new tasks
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“When we observe a visual scene, when we
hear a complex sentence, we are able to
explain in formal terms the relation of the
objects in the scene, or the precise meaning of
the sentence components. However, there is no
evidence that such a formal analysis
necessarily takes place: we see a scene, we
hear a sentence, and we just know what they
mean. This suggests the existence of a middle
layer, already a form of reasoning, but not
yet formal or logical.”
Bottou, Léon. "From machine learning to machine reasoning." Machine learning 94.2
(2014): 133-149.

Learning to reason, formal def
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Khardon, Roni, and Dan Roth. "Learning to reason." Journal of the ACM
(JACM) 44.5 (1997): 697-725.
E.g., given a video f, determines if the person with the
hat turns before singing.

10
What color is the thing with the same
size as the blue cylinder?
blue
• The network guessed the
most common color in the
image.
• Linguistic bias.
• Requires multi-step
reasoning: find blue cylinder
➔ locate other object of the
same size ➔ determine its
color (green).
A testbed: Visual QA

11Adapted from (Aditya et al., 2019)
Events,
Activities
Objects, Regions,
Trajectories
Scene Elements: Volumes, 3D
Surfaces
Image Elements: Regions, Edges,
Textures
Knowledge about
shapes
Qualitative spatial
reasoning
Relations between
objects
Commonsense knowledge
Relations between objects
Why Visual QA?

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Dual-process theories
The problem
Neural memories
Agenda
Visual reasoning

Dual-process system with memories
13
System 1:
Intuitive
System 1:
Intuitive
System 1:
Intuitive
• Fast
• Implicit/automatic
• Pattern recognition
• Multiple
System 2:
Analytical
• Slow
• Deliberate/rational
• Careful analysis
• Single, sequential
Facts
Semantics
Events and relational associations
Working space – temporal buffer
Single

System 2
Holds hypothetical thought
Decoupling from representation
Working memory size is not essential. Its
attentional control is.
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Hypotheses
Reasoning as just-in-time program synthesis.
It employs conditional computation.
Reasoning is recursive, e.g., mental travel.
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Dual-process theories
The problem
Neural memories
Agenda
Visual reasoning

Visual recognition is a (solvable) special case
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Image courtesy: https://dcist.com/
What is this?
a cat R-CNN,
YOLO,…
Object recognition Object detection
Where are objects, and what are they?

But Image QA is challenging
18
(CLERV, Johnson et al., 2017)
(Q) How many objects are either
small cylinders or metal things?
(Q) Are there an equal number of
large things and metal spheres?
(GQA, Hudson et al., 2019)
(Q) What is the brown animal sitting
inside of?
(Q) Is there a bag to the right of the
green door?

And Video QA is even more challenging
19(Data: TGIF-QA)

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Reasoning
Computer Vision
Natural Language
Processing
Machine
learning
Visual Reasoning
Visual QA is a great
testbed

Reasoning over visual modality
• Context:
 Videos/Images: compositional data
 Hierarchy of visual information: objects, actions, attributes,
relations, commonsense knowledge.
 Relation of abstract objects: spatial, temporal, semantic aspects.
• Research questions:
 How to represent object/action relations in images and videos?
 How to reason with object/action relations?
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A simple VQA framework that works surprisingly well
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Visual
representation
Question
What is the brown
animal sitting inside of?
Textual
representation
Fusion
Predict
answer
Answer
box
CNN
Word embedding + LSTM

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A Prelim Dual-System Model
Le, Thao Minh, Vuong Le, Svetha Venkatesh, and Truyen Tran. "Neural
Reasoning, Fast and Slow, for Video Question Answering." IJCNN (2020).

Separate reasoning process from perception
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 Video QA: inherent dynamic nature of visual content over time.
 Recent success in visual reasoning with multi-step inference and
handling of compositionality.
System 1: visual
representation
System 2: High-
level reasoning

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Separate reasoning process from perception (2)

System 1: Clip-based Relation Network
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For 𝑘𝑘 = 2,3, … , 𝐾𝐾 where ℎ𝜙𝜙 and
𝑔𝑔𝜃𝜃 are linear transformations
with parameters 𝜙𝜙 and 𝜃𝜃,
respectively, for feature fusion.
Why temporal relations?
• Situate an event/action in relation to
events/actions in the past and
formulate hypotheses on future
events.
• Long-range sequential modeling.

System 2: MAC Net
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Hudson, Drew A., and Christopher D. Manning. "Compositional attention
networks for machine reasoning." ICLR 2018.

Results on SVQA dataset.
28
Model Exist Count
Integer
Comparison
Attribute Comparison Query
All
More Equal Less Color Size Type Dir Shape Color Size Type Dir Shape
SA(S) 51.7 36.3 72.7 54.8 58.6 52.2 53.6 52.7 53.0 52.3 29.0 54.0 55.7 38.1 46.3 43.1
TA-
GRU(T)
54.6 36.6 73.0 57.3 57.7 53.8 53.4 54.8 55.1 52.4 22.0 54.8 55.5 41.7 42.9 44.2
SA+TA 52.0 38.2 74.3 57.7 61.6 56.0 55.9 53.4 57.5 53.0 23.4 63.3 62.9 43.2 41.7 44.9
CRN+M
AC
72.8 56.7 84.5 71.7 75.9 70.5 76.2 90.7 75.9 57.2 76.1 92.8 91.0 87.4 85.4 75.8

Results on TGIF-QA dataset.
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Model Action Trans. FrameQA Count
ST-TP 62.9 69.4 49.5 4.32
Co-Mem 68.2 74.3 51.5 4.10
PSAC 70.4 76.9 55.7 4.27
CRN+MAC 71.3 78.7 59.2 4.23

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A System 1 for Video
Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran, “Hierarchical
conditional relation networks for video question answering”, CVPR'20.
System 1: visual
representation
System 2: High-level
reasoning

Conditional Relation Network Unit
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Problems:
• Lack of a generic mechanism for
modeling the interaction of
multimodal inputs.
• Flaws in temporal relations of
breaking local motion in videos.
Inputs:
• An array of 𝑛𝑛 objects
• Conditioning feature
Output:
• An array of 𝑚𝑚 objects

Hierarchical
Conditional Relation
Networks
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Results
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Model Action Trans. FrameQA Count
ST-TP 62.9 69.4 49.5 4.32
Co-Mem 68.2 74.3 51.5 4.10
PSAC 70.4 76.9 55.7 4.27
HME 73.9 77.8 53.8 4.02
HCRN 75.0 81.4 55.9 3.82
TGIF-QA dataset MSVD-QA and MSRVTT-QA datasets.

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A System 2 for Relational Reasoning
Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran, “Dynamic
Language Binding in Relational Visual Reasoning”, IJCAI’20.
System 1: visual
representation
System 2: High-level
reasoning

Reasoning with structured representation of
spatial relation
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• Key insight: Reasoning is chaining of relational predicates
to arrive at a final conclusion
→ Needs to uncover spatial relations, conditioned on query
→ Chaining is query-driven
→ Objects/language needs binding
→ Object semantics is query-dependent
→ Very thing is end-to-end differentiable

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Language-binding Object Graph Network for VQA

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Language-binding Object Graph Unit (LOG)

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Results in CLEVR dataset
39

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Comparison with the on CLEVR dataset of
different data fractions.
Performance comparison on
VQA v2 subset of long questions.
Model Action
XNM(Objects) 43.4
MAC(ResNet) 40.7
MAC(Objects) 45.5
LOGNet(Objects) 46.8

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Dual-process theories
The problem
Neural memoriesVisual reasoning
Agenda

“[…] one cannot hope to understand
reasoning without understanding the memory
processes […]”
(Thompson and Feeney, 2014)
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Dual-process system with memories
44
System 1:
Intuitive
System 1:
Intuitive
System 1:
Intuitive
• Fast
• Implicit/automatic
• Pattern recognition
• Multiple
System 2:
Analytical
• Slow
• Deliberate/rational
• Careful analysis
• Single, sequential
Facts
Semantics
Events and relational associations
Working space – temporal buffer
Single

From psychology & cognitive science
Reasoning and memory are studied separately, but …
 Analytic reasoning requires working memory
 Spatial reasoning requires place memory
 Inferential reasoning requires episodic memory
Dual-processes in memory:
 Fast retrieval/recognition (using similarity as criteria)
 Slow item recollection
Dual-processes in reasoning
 Induction as fast (using similarity as criteria)
 Deduction as slow

Reasoning as memory
Expert reasoning was enabled by a large long-term memory,
acquired through experience
Working memory for analytic reasoning
 WM capacity predicts IQ
 Higher order cognition = creating & manipulating relations  representation of
premises, temporarily stored in WM
 WM is a system to support information binding to a coordinate system
 Reasoning as deliberative hypothesis testing  memory-retrieval based hypothesis
generation
Knowledge structures support reasoning
Fuzzy-trace theory: gist ( intuition) and verbatim memory (
analytic processing)
Memory is critical for episodic future thinking (mental simulation)
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Learning a Turing machine
 Can we learn a (neural)
program that learns to
program from data?
Visual reasoning is a
specific program of two
inputs (visual, linguistic)

Neural Turing machine (NTM)
A memory-augmented neural network (MANN)
A controller that takes
input/output and talks to an
external memory module.
Memory has read/write
operations.
The main issue is where to write,
and how to update the memory
state.
All operations are differentiable.
Source: rylanschaeffer.github.io

MANN for reasoning
Three steps:
 Store data into memory
 Read query, process sequentially, consult memory
 Output answer
Behind the scene:
 Memory contains data & results of intermediate steps
 LOGNet does the same, memory consists of object representations
Drawbacks of current MANNs:
 No memory of controllers  Less modularity and compositionality when
query is complex
 No memory of relations  Much harder to chain predicates.
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Source: rylanschaeffer.github.io

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Universal Neural Turing Machines
Hung Le, Truyen Tran, Svetha Venkatesh, “Neural stored-program
memory”, ICLR'20.
Hung Le, Truyen Tran, Svetha Venkatesh, “Neurocoder: Learning General-
Purpose Computation Using Stored Neural Programs”, Work in progress.

Computing devices vs neural counterparts
FSM (1943) ↔ RNNs (1982)
PDA (1954) ↔ Stack RNN (1993)
TM (1936) ↔ NTM (2014)
The missing piece: A memory to store programs
Neural stored-program memory
UTM/VNA (1936/1945) ↔ NUTM--ours (2019)

NUTM = NTM + NSM

Question answering (bAbI dataset)
Credit: hexahedria

Neurocoder
Flexible programs are essential for intelligence
Cognitive science: Meta-level process controls object-level process
Instance-specific program vs Task-specific program
In QA, program is query-specific
 On-demand program synthesis
Idea: A program to generate programs
Previous works: Fast weights, HyperNetworks, Neural Module
Networks, Adaptive Mixture of Experts
Current work: Programs are generated by combining basis sub-
programs
 The hope: Basis programs span the space of all possible programs
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Neurocoder (cont.)
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Neural relational memory
Hung Le, Truyen Tran, Svetha Venkatesh, “Self-attentive associative
memory”, ICML'20.

Failures of current MANNs for reasoning
Relational representationis NOT stored  Can’t reuse later in the
chain
A single memory of items and relations  Can’t understand how
relational reasoning occurs
The memory-memory relationship is coarse since it is represented as
either dot product, or weighted sum.
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Self-attentive associative memories (SAM)
Learning relations automatically over time
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Multi-step reasoning over graphs
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Reasoning on text
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Thank you Truyen Tran
@truyenoz
truyentran.github.io
truyen.tran@deakin.edu.au
letdataspeak.blogspot.com
goo.gl/3jJ1O0
linkedin.com/in/truyen-tran