GATED FAST WEIGHTS FOR ASSOCIATIVE RETRIEVAL
We improve previous end-to-end differentiable neural networks (NNs) with fast weight memories. A gate mechanism updates fast weights at every time step of a sequence through two separate outer-product-based matrices generated by slow parts of the net. The system is trained on a complex sequence to sequence variation of the Associative Retrieval Problem with roughly 70 times more temporal memory (i.e. time-varying variables) than similar-sized standard recurrent NNs (RNNs). In terms of accuracy and number of parameters, our architecture outperforms a variety of RNNs, including Long Short-Term Memory, Hypernetworks, and related fast weight architectures.
 The authors present an evolution of the idea of fast weights: training a double recurrent neural network, one "slow" trained as usual and one "fast" that gets updated in every time-step based on the slow network. The authors generalize this idea in a nice way and present results on 1 experiment. On the positive side, the paper is clearly written and while the fast-weights are not new, the details of the presented method are original. On the negative side, the experimental results are presented on only 1 experiment with a data-set and task made up by the authors. The results are good but the improvements are not too large, and they are measured over weak baselines implemented by the authors. For a convincing result, one would require an evaluation on a number of tasks, including long-studied ones like language modeling, and comparison to stronger related models, such as the Neural Turing Machine or the Transformer (from "Attention is All You Need"). Without comparison to stronger baselines and with results only on 1 task constructed by the authors, we have to recommend rejection.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
GATED FAST WEIGHTS FOR ASSOCIATIVE RETRIEVAL
We improve previous end-to-end differentiable neural networks (NNs) with fast weight memories. A gate mechanism updates fast weights at every time step of a sequence through two separate outer-product-based matrices generated by slow parts of the net. The system is trained on a complex sequence to sequence variation of the Associative Retrieval Problem with roughly 70 times more temporal memory (i.e. time-varying variables) than similar-sized standard recurrent NNs (RNNs). In terms of accuracy and number of parameters, our architecture outperforms a variety of RNNs, including Long Short-Term Memory, Hypernetworks, and related fast weight architectures.
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 authors present an evolution of the idea of fast weights: training a double recurrent neural network, one "slow" trained as usual and one "fast" that gets updated in every time-step based on the slow network. The authors generalize this idea in a nice way and present results on 1 experiment. On the positive side, the paper is clearly written and while the fast-weights are not new, the details of the presented method are original. On the negative side, the experimental results are presented on only 1 experiment with a data-set and task made up by the authors. The results are good but the improvements are not too large, and they are measured over weak baselines implemented by the authors. For a convincing result, one would require an evaluation on a number of tasks, including long-studied ones like language modeling, and comparison to stronger related models, such as the Neural Turing Machine or the Transformer (from "Attention is All You Need"). Without comparison to stronger baselines and with results only on 1 task constructed by the authors, we have to recommend rejection.
assistant
 The authors present an evolution of the idea of fast weights: training a double recurrent neural network, one "slow" trained as usual and one "fast" that gets updated in every time-step based on the slow network. The authors generalize this idea in a nice way and present results on 1 experiment. On the positive side, the paper is clearly written and while the fast-weights are not new, the details of the presented method are original. On the negative side, the experimental results are presented on only 1 experiment with a data-set and task made up by the authors. The results are good but the improvements are not too large, and they are measured over weak baselines implemented by the authors. For a convincing result, one would require an evaluation on a number of tasks, including long-studied ones like language modeling, and comparison to stronger related models, such as the Neural Turing Machine or the Transformer (from "Attention is All You Need"). Without comparison to stronger baselines and with results only on 1 task constructed by the authors, we have to recommend rejection.