Hierarchical Multiscale Recurrent Neural Networks
Learning both hierarchical and temporal representation has been among the long- standing challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can actually capture the temporal dependencies by discovering the latent hierarchical structure of the sequence. In this paper, we propose a novel multiscale approach, called the hierarchical multiscale recurrent neural network, that can capture the latent hierarchical structure in the sequence by encoding the temporal dependencies with different timescales using a novel update mechanism. We show some evidence that the proposed model can discover underlying hierarchical structure in the sequences without using explicit boundary information. We evaluate our proposed model on character-level language modelling and handwriting sequence generation.
The paper proposes a modified RNN architecture with multiple layers, where higher layers are only passed lower layer states if a FLUSH operation is predicted, consisting of passing up the state and reseting the lower layer*s state. In order to select one of three operations at each time step, the authors propose using the straight-through estimator with a slope-annealing trick during training. Empirical results and visualizations illustrate that the modified architecture performs well at boundary detection. Pros: - Paper is well-motivated, exceptionally well-composed - Provides promising initial results on learning hierarchical representations through visualizations and thorough experiments on language modeling and handwriting generation - The annealing trick with the straight-through estimator also seems potentially useful for other tasks containing discrete variables, and the trade-off in the flush operation is innovative. Cons: - In a couple cases the paper does not fully deliver. Empirical results on computational savings are not given, and hierarchy beyond a single level (where the data contains separators such as spaces and pen up/down) does not seem to be demonstrated. - It*s unclear whether better downstream performance is due to use of hierarchical information or due to the architecture changes acting as regularization, something which could hopefully be addressed.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Hierarchical Multiscale Recurrent Neural Networks
Learning both hierarchical and temporal representation has been among the long- standing challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can actually capture the temporal dependencies by discovering the latent hierarchical structure of the sequence. In this paper, we propose a novel multiscale approach, called the hierarchical multiscale recurrent neural network, that can capture the latent hierarchical structure in the sequence by encoding the temporal dependencies with different timescales using a novel update mechanism. We show some evidence that the proposed model can discover underlying hierarchical structure in the sequences without using explicit boundary information. We evaluate our proposed model on character-level language modelling and handwriting sequence generation.
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 proposes a modified RNN architecture with multiple layers, where higher layers are only passed lower layer states if a FLUSH operation is predicted, consisting of passing up the state and reseting the lower layer*s state. In order to select one of three operations at each time step, the authors propose using the straight-through estimator with a slope-annealing trick during training. Empirical results and visualizations illustrate that the modified architecture performs well at boundary detection. Pros: - Paper is well-motivated, exceptionally well-composed - Provides promising initial results on learning hierarchical representations through visualizations and thorough experiments on language modeling and handwriting generation - The annealing trick with the straight-through estimator also seems potentially useful for other tasks containing discrete variables, and the trade-off in the flush operation is innovative. Cons: - In a couple cases the paper does not fully deliver. Empirical results on computational savings are not given, and hierarchy beyond a single level (where the data contains separators such as spaces and pen up/down) does not seem to be demonstrated. - It*s unclear whether better downstream performance is due to use of hierarchical information or due to the architecture changes acting as regularization, something which could hopefully be addressed.
assistant
The paper proposes a modified RNN architecture with multiple layers, where higher layers are only passed lower layer states if a FLUSH operation is predicted, consisting of passing up the state and reseting the lower layer*s state. In order to select one of three operations at each time step, the authors propose using the straight-through estimator with a slope-annealing trick during training. Empirical results and visualizations illustrate that the modified architecture performs well at boundary detection. Pros: - Paper is well-motivated, exceptionally well-composed - Provides promising initial results on learning hierarchical representations through visualizations and thorough experiments on language modeling and handwriting generation - The annealing trick with the straight-through estimator also seems potentially useful for other tasks containing discrete variables, and the trade-off in the flush operation is innovative. Cons: - In a couple cases the paper does not fully deliver. Empirical results on computational savings are not given, and hierarchy beyond a single level (where the data contains separators such as spaces and pen up/down) does not seem to be demonstrated. - It*s unclear whether better downstream performance is due to use of hierarchical information or due to the architecture changes acting as regularization, something which could hopefully be addressed.