Learning Efficient Algorithms with Hierarchical Attentive Memory
In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM). It is based on a binary tree with leaves corresponding to memory cells. This allows HAM to perform memory access in O(log n) complexity, which is a significant improvement over the standard attention mechanism that requires O(n) operations, where n is the size of the memory. We show that an LSTM network augmented with HAM can learn algorithms for problems like merging, sorting or binary searching from pure input-output examples. In particular, it learns to sort n numbers in time O(n log n) and generalizes well to input sequences much longer than the ones seen during the training. We also show that HAM can be trained to act like classic data structures: a stack, a FIFO queue and a priority queue.
This paper introduces a novel hierarchical memory architecture for neural networks, based on a binary tree with leaves corresponding to memory cells. This allows for O(log n) memory access, and experiments additionally demonstrate ability to solve more challenging tasks such as sorting from pure input-output examples and dealing with longer sequences. The idea of the paper is novel and well-presented, and the memory structure seems reasonable to have advantages in practice. However, the main weakness of the paper is the experiments. There is no experimental comparison with other external memory-based approaches (e.g. those discussed in Related Work), or experimental analysis of computational efficiency given overhead costs (beyond just computational complexity) despite that being one of the main advantages. Furthermore, the experimental setups are relatively weak, all on artificial tasks with moderate increases in sequence length. Improving on these would greatly strengthen the paper, as the core idea is interesting.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning Efficient Algorithms with Hierarchical Attentive Memory
In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM). It is based on a binary tree with leaves corresponding to memory cells. This allows HAM to perform memory access in O(log n) complexity, which is a significant improvement over the standard attention mechanism that requires O(n) operations, where n is the size of the memory. We show that an LSTM network augmented with HAM can learn algorithms for problems like merging, sorting or binary searching from pure input-output examples. In particular, it learns to sort n numbers in time O(n log n) and generalizes well to input sequences much longer than the ones seen during the training. We also show that HAM can be trained to act like classic data structures: a stack, a FIFO queue and a priority queue.
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>



This paper introduces a novel hierarchical memory architecture for neural networks, based on a binary tree with leaves corresponding to memory cells. This allows for O(log n) memory access, and experiments additionally demonstrate ability to solve more challenging tasks such as sorting from pure input-output examples and dealing with longer sequences. The idea of the paper is novel and well-presented, and the memory structure seems reasonable to have advantages in practice. However, the main weakness of the paper is the experiments. There is no experimental comparison with other external memory-based approaches (e.g. those discussed in Related Work), or experimental analysis of computational efficiency given overhead costs (beyond just computational complexity) despite that being one of the main advantages. Furthermore, the experimental setups are relatively weak, all on artificial tasks with moderate increases in sequence length. Improving on these would greatly strengthen the paper, as the core idea is interesting.
assistant
This paper introduces a novel hierarchical memory architecture for neural networks, based on a binary tree with leaves corresponding to memory cells. This allows for O(log n) memory access, and experiments additionally demonstrate ability to solve more challenging tasks such as sorting from pure input-output examples and dealing with longer sequences. The idea of the paper is novel and well-presented, and the memory structure seems reasonable to have advantages in practice. However, the main weakness of the paper is the experiments. There is no experimental comparison with other external memory-based approaches (e.g. those discussed in Related Work), or experimental analysis of computational efficiency given overhead costs (beyond just computational complexity) despite that being one of the main advantages. Furthermore, the experimental setups are relatively weak, all on artificial tasks with moderate increases in sequence length. Improving on these would greatly strengthen the paper, as the core idea is interesting.