Lie-Access Neural Turing Machines
External neural memory structures have recently become a popular tool for algorithmic deep learning (Graves et al. 2014; Weston et al. 2014). These models generally utilize differentiable versions of traditional discrete memory-access structures (random access, stacks, tapes) to provide the storage necessary for computational tasks. In this work, we argue that these neural memory systems lack specific structure important for relative indexing, and propose an alternative model, Lie-access memory, that is explicitly designed for the neural setting. In this paradigm, memory is accessed using a continuous head in a key-space manifold. The head is moved via Lie group actions, such as shifts or rotations, generated by a controller, and memory access is performed by linear smoothing in key space. We argue that Lie groups provide a natural generalization of discrete memory structures, such as Turing machines, as they provide inverse and identity operators while maintaining differentiability. To experiment with this approach, we implement a simplified Lie-access neural Turing machine (LANTM) with different Lie groups. We find that this approach is able to perform well on a range of algorithmic tasks.
The paper introduces a novel memory mechanism for NTMs based on differentiable Lie groups. This allows to place memory elements as points on a manifold, while still allowing training with backpropagation. It*s a more general version of the NTM memory, and possibly allows for training a more efficient addressing schemes. Pros: - novel and interesting idea for memory access - nicely written Cons: - need to manually specify the Lie group to use (it would be better if network could learn the best way of accessing memory) - not clear if this really works better than standard NTM (compared only to simplified version) - not clear if this is useful in practice (no comparison on real tasks)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Lie-Access Neural Turing Machines
External neural memory structures have recently become a popular tool for algorithmic deep learning (Graves et al. 2014; Weston et al. 2014). These models generally utilize differentiable versions of traditional discrete memory-access structures (random access, stacks, tapes) to provide the storage necessary for computational tasks. In this work, we argue that these neural memory systems lack specific structure important for relative indexing, and propose an alternative model, Lie-access memory, that is explicitly designed for the neural setting. In this paradigm, memory is accessed using a continuous head in a key-space manifold. The head is moved via Lie group actions, such as shifts or rotations, generated by a controller, and memory access is performed by linear smoothing in key space. We argue that Lie groups provide a natural generalization of discrete memory structures, such as Turing machines, as they provide inverse and identity operators while maintaining differentiability. To experiment with this approach, we implement a simplified Lie-access neural Turing machine (LANTM) with different Lie groups. We find that this approach is able to perform well on a range of algorithmic tasks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper introduces a novel memory mechanism for NTMs based on differentiable Lie groups. This allows to place memory elements as points on a manifold, while still allowing training with backpropagation. It*s a more general version of the NTM memory, and possibly allows for training a more efficient addressing schemes. Pros: - novel and interesting idea for memory access - nicely written Cons: - need to manually specify the Lie group to use (it would be better if network could learn the best way of accessing memory) - not clear if this really works better than standard NTM (compared only to simplified version) - not clear if this is useful in practice (no comparison on real tasks)
assistant
The paper introduces a novel memory mechanism for NTMs based on differentiable Lie groups. This allows to place memory elements as points on a manifold, while still allowing training with backpropagation. It*s a more general version of the NTM memory, and possibly allows for training a more efficient addressing schemes. Pros: - novel and interesting idea for memory access - nicely written Cons: - need to manually specify the Lie group to use (it would be better if network could learn the best way of accessing memory) - not clear if this really works better than standard NTM (compared only to simplified version) - not clear if this is useful in practice (no comparison on real tasks)