ICLR20 slides

1. Neural Stored-program Memory
Towards neural simulation of
Universal Turing Machines
Presented by Hung Le
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2. Motivation examples
How to teach machine to learn
complex sequential tasks?
• Algorithms
• Continual procedures
• Few-shot learning
• Natural language understanding
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3. Neural networks vs Automata
RNNs
Neural
Stacks
Neural
Turing
machines
FSM
PDA
Turing
Machine
https://en.wikipedia.org/
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4. Limitations
• All are Turing complete if properly wired
• Neural Turing Machines (NTMs) use the
most powerful model of computation (Turing
machine)
can learn atomic tasks
better than other RNNs
• However, tasks that are complicated,
consisted of multiple subtasks are far from
solvable
Graves, Alex, Greg Wayne, and Ivo Danihelka. "Neural turing machines." arXiv preprint arXiv:1410.5401 (2014).
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5. Solutions
• More powerful model: Universal
Turing Machines
• Stored-program principle
• Break a big task into subtasks,
each can be handled by a TM
stored in a program memory
5https://en.wikipedia.org/

6. Neural stored-program memory (NSM)
Principle Implementation
Program/function Weight of a neural network
Stored-program memory Memory of weights (NSM)
Universal Turing Machine = a Turing
machine that stores other Turing
machines
Neural Universal Turing Machine = a
NTM + a NSM that stores weights of
other NTMs
A UTM uses its program to query other
TMs, each of whose program is defined
for a specific task/subtask
A NUTM learns its weight to query other
NTMs each of whose weights are learnt
towards a specific task/subtask
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7. Neural Universal Turing Machine
• Stored program memory stores
key (the address) and values (the
weight)
• The weights of stored NTM and
the weight of the NUTM is learnt
end-to-end by backpropagation
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8. Experiments
• Atomic algorithms
• Compositional algorithms
• Continual algorithms
• Few-shot learning
• Natural language quesiton
answering
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9. Atomic algorithms
NTM 1
NTM 2
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10. Compositional algorithms
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11. Continual algorithms
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12. Few-shot learning
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13. Babi QA
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14. Conclusion
• We contribute a new type of external memory for neural network:
memory for weights
• The memory takes inspiration from the stored-program memory in
computer architecture
• Our model simulates Universal Turing Machine
• Experiments demonstrate the computational universality of the
approach
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15. Meet the authors
Hung Le
Associate Research Fellow
Applied Artificial Intelligence
Institute
Truyen Tran
Associate Professor
Applied Artificial
Intelligence Institute
Svetha Venkatesh
Co-Director, Applied
Artificial Intelligence
Institute (A2I2)
Alfred Deakin Professor,
Australian Laureate
Fellow
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16. Thank you
thai.le@deakin.edu.au
A²I²
Deakin University
Geelong Waurn Ponds
Campus, Geelong, VIC 3220
Hung Le
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