Neural Turing Machines: Perils and Promise of Memory-Augmented Neural Networks
•Download as PPTX, PDF•
3 likes•1,793 views
This document discusses neural Turing machines, which are neural networks combined with external memory systems that allow them to be trained end-to-end using backpropagation. Neural Turing machines can learn simple algorithms and generalize well for tasks like language modeling and question answering. However, they are difficult to train due to numerical instability and optimizing memory usage. The document recommends techniques like gradient clipping, loss clipping, and curriculum learning to improve training. It also covers developments like dynamic neural computers that can allocate and deallocate memory.
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Neural Turing Machines: Perils and Promise of Memory-Augmented Neural Networks
Similar to Neural Turing Machines: Perils and Promise of Memory-Augmented Neural Networks (20)
More from MLconf
More from MLconf (20)
Recently uploaded
Recently uploaded (20)
Neural Turing Machines: Perils and Promise of Memory-Augmented Neural Networks
- 1. Neural Turing Machines: Perils and Promise Daniel Shank
- 2. Overview 1.Neural Turing Machines 2.Applications and Performance 3.Challenges and Recommendations 4.Dynamic Neural Computers
- 4. 3 28 What’s a Turing Machine? Model of a computer Memory tape Read and write heads
- 5. 4 28 What’s a Neural Turing Machine? Neural Network “Controller” Memory Learns from sequence Graves et al 2014, arXiv:1410.5401v2
- 6. 5 28 Neural Turing Machines are Differentiable Turing Machines ‘Sharp’ functions made smooth Can train with backpropagation
- 8. 7 28 Neural Turing Machines can… Learn simple algorithms (Copy, repeat, recognize simple formal languages...) Generalize Do well at language modeling Do well at bAbI
- 9. 8 28 Generalization on Copy/Repeat task Graves et al 2014
- 10. 9 28 Neural Turing Machines Outperform LSTMs Graves et al 2014
- 11. 10 28 Balanced Parenthesis Tristan Deleu https://medium.com/snips-ai/
- 12. 11 28 bAbI dataset 1 Mary moved to the bathroom. 2 John went to the hallway. 3 Where is Mary? bathroom 1 4 Daniel went back to the hallway. 5 Sandra moved to the garden. 6 Where is Daniel? hallway 4 7 John moved to the office. 8 Sandra journeyed to the bathroom. 9 Where is Daniel? hallway 4 10 Mary moved to the hallway. 11 Daniel travelled to the office. 12 Where is Daniel? office 11 13 John went back to the garden. 14 John moved to the bedroom. 15 Where is Sandra? bathroom 8 1 Sandra travelled to the office. 2 Sandra went to the bathroom. 3 Where is Sandra? bathroom 2 Small vocabulary Stories Context https://research.facebook.com/research/babi/
- 13. 12 28 bAbI results Empirical Study on Deep Learning Models for Question Answering Yu et al. 2015
- 15. 14 28 Problems Architecture dependent Large number of parameters Doesn’t benefit much from GPU acceleration Hard to train
- 16. 15 28 Hard to train Numerical Instability Using memory is hard Needs smart optimization Difficult to use in practice
- 17. 16 28 Combating Numerical Instability: Gradient clipping Limits training speed of parameters Particularly helpful for learning long range dependencies
- 18. 17 28 Loss clipping Cap total response to a given training batch Helpful in addition to gradient clipping
- 19. 18 28 Graves’ RMSprop A version of back propagation used to train the network Used in many of Graves’ RNN papers: 𝑛𝑖 = 𝛼 + 1 − 𝛼 𝜖𝑖 2 𝑔𝑖 = 𝛼𝑔𝑖 + 1 − 𝛼 𝜖𝑖 Δ𝑖 = 𝛽Δ𝑖 − 𝛾 𝜖𝑖 𝑛𝑖 − 𝑔𝑖 2 + 𝛾 + 𝛿 𝑤𝑖 = 𝑤𝑖 + Δ𝑖 Similar to normalizing gradient updates by their variance, important for the NTM’s high-variability changes in loss.
- 20. 19 28 Adam Optimizer Works well for many tasks Comes pre-loaded in most ML frameworks Like Graves’ RMSprop, smooths gradients
- 21. 20 28 Attention to initialization Memory initialization extremely important Poor initialization can prevent convergence Pay particularly close attention to the starting value of the memory
- 22. 21 28 Short sequences first (“Curriculum Learning”) 1) Feed in short training data 2) When loss hits a target, increase the size of the input 3) Repeat
- 24. 23 28 Neural Turing Machines “V2” Similar to NTMs, except… No index shift based addressing Can ‘allocate’ and ‘deallocate’ memory Remembers recent memory use
- 25. 24 28 Architecture updates(1) Graves et al. 2016
- 26. 25 28 Architecture updates(2) Graves et al. 2016
- 27. 26 28 Dynamic Neural Computer Performance on Inference Tasks Graves et al. 2016
- 28. 27 28 Dynamic Neural Computer bAbI Results Graves et al. 2016
- 29. 28 28 References Implementations: Tensorflow: https://github.com/carpedm20/NTM-tensorflow Go: https://github.com/fumin/ntm Torch: https://github.com/kaishengtai/torch-ntm Node.JS: https://github.com/gcgibson/NTM Lasagne: https://github.com/snipsco/ntm-lasagne Theano: https://github.com/shawntan/neural-turing-machines Papers: Graves et al. 2016 – Hybrid computing using a neural network with dynamic external memory Graves et al. 2014 – Neural Turing Machines Yu et al. 2015 – Empirical Study on Deep Learning Models for Question Answering Rae et al. 2016 – Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
- 30. 29 28 NTM operations The Convolutional Shift parameter has proven to be one of if not the most problematic.
- 31. 30 28
Editor's Notes
- A turing machine is a simplified model of a computer. Instead of RAM or a hard drive, it has only a ‘tape’ with symbols written on it. The ‘head’ of the machine can read a symbol in a given location, and decide whether to write a new symbol or move up and down the the tape. This models a program that makes use of an external memory source
- A Neural Turing Machine is a kind of neural network with an architecture or underlying structure that is similar to that of a turing machine. Unlike the classical turing machine, a neural turing machine has access to external input as well as producing external output. There is an internal memory component or ‘tape’ that the turing machine can move up and down. The main achievement of the NTM is in representing the usual operations of a turing machine with differentiable functions, so that it can be trained like any other sequence to sequence RNN with backpropagation.
- NTMs can learn discrete processes and (in theory) extrapolate them over variable inputs or data.
- Empirical Study on Deep Learning Models for Question Answering (2015)
- Don’t need, put at the end. Make the points shorter. Highlight key aspect of sentence, use that as bullet point.
- BYRON NOTES(from memory): Compare to the *standard* RMSprop algorithm
- BYRON NOTES (from memory): Describe what is happening in the slide before I launch into what it means. “As you can see…” means I haven’t explained enough.
- These are the operations that take finite step procedures like accessing memory and make them understandable in terms of backpropagation. In other words, they let us understand choices such as "which location of memory should I access" in terms of relative 'loss' or cost of that decision.