Unbiasing Truncated Backpropagation Through Time
emph{Truncated Backpropagation Through Time} (truncated BPTT, cite{jaeger2002tutorial}) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of emph{Backpropagation Through Time} (BPTT cite{werbos:bptt}) while relieving the need for a complete backtrack through the whole data sequence at every step. However, truncation favors short-term dependencies: the gradient estimate of truncated BPTT is biased, so that it does not benefit from the convergence guarantees from stochastic gradient theory. We introduce emph{Anticipated Reweighted Truncated Backpropagation} (ARTBP), an algorithm that keeps the computational benefits of truncated BPTT, while providing unbiasedness. ARTBP works by using variable truncation lengths together with carefully chosen compensation factors in the backpropagation equation. We check the viability of ARTBP on two tasks. First, a simple synthetic task where careful balancing of temporal dependencies at different scales is needed: truncated BPTT displays unreliable performance, and in worst case scenarios, divergence, while ARTBP converges reliably. Second, on Penn Treebank character-level language modelling cite{ptb_proc}, ARTBP slightly outperforms truncated BPTT.
 This paper introduces a new approximation to backpropagation through time (BPTT) to overcome the computational and memory load that arise when having to learn from long sequences. Rather than chopping the sequence into subsequences of equal length as in truncated BPTT, the authors suggest to segment the sequence into subsequences of differing lengths according to an a priori specified distribution for the segment length. The gradient estimator is made unbiased through a weighting procedure. Whilst the proposed method is interesting and relevant, I find the analysis quite superficial and limited. 1) The distribution for the segment length is fully specified a priori. Depending on the problem at hand, different specifications could give rise to very different results. It would be good to suggest an approach for more automatically determine the (parameters of the) distribution. 2) Whilst unbiased, the proposed estimator could have high variance. This point is not investigated in the experimental section. 3) For an experimental paper as this one, it would be good to have many more problems analysed and a deeper analysis than the one given for the language problem.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unbiasing Truncated Backpropagation Through Time
emph{Truncated Backpropagation Through Time} (truncated BPTT, cite{jaeger2002tutorial}) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of emph{Backpropagation Through Time} (BPTT cite{werbos:bptt}) while relieving the need for a complete backtrack through the whole data sequence at every step. However, truncation favors short-term dependencies: the gradient estimate of truncated BPTT is biased, so that it does not benefit from the convergence guarantees from stochastic gradient theory. We introduce emph{Anticipated Reweighted Truncated Backpropagation} (ARTBP), an algorithm that keeps the computational benefits of truncated BPTT, while providing unbiasedness. ARTBP works by using variable truncation lengths together with carefully chosen compensation factors in the backpropagation equation. We check the viability of ARTBP on two tasks. First, a simple synthetic task where careful balancing of temporal dependencies at different scales is needed: truncated BPTT displays unreliable performance, and in worst case scenarios, divergence, while ARTBP converges reliably. Second, on Penn Treebank character-level language modelling cite{ptb_proc}, ARTBP slightly outperforms truncated BPTT.
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 new approximation to backpropagation through time (BPTT) to overcome the computational and memory load that arise when having to learn from long sequences. Rather than chopping the sequence into subsequences of equal length as in truncated BPTT, the authors suggest to segment the sequence into subsequences of differing lengths according to an a priori specified distribution for the segment length. The gradient estimator is made unbiased through a weighting procedure. Whilst the proposed method is interesting and relevant, I find the analysis quite superficial and limited. 1) The distribution for the segment length is fully specified a priori. Depending on the problem at hand, different specifications could give rise to very different results. It would be good to suggest an approach for more automatically determine the (parameters of the) distribution. 2) Whilst unbiased, the proposed estimator could have high variance. This point is not investigated in the experimental section. 3) For an experimental paper as this one, it would be good to have many more problems analysed and a deeper analysis than the one given for the language problem.
assistant
 This paper introduces a new approximation to backpropagation through time (BPTT) to overcome the computational and memory load that arise when having to learn from long sequences. Rather than chopping the sequence into subsequences of equal length as in truncated BPTT, the authors suggest to segment the sequence into subsequences of differing lengths according to an a priori specified distribution for the segment length. The gradient estimator is made unbiased through a weighting procedure. Whilst the proposed method is interesting and relevant, I find the analysis quite superficial and limited. 1) The distribution for the segment length is fully specified a priori. Depending on the problem at hand, different specifications could give rise to very different results. It would be good to suggest an approach for more automatically determine the (parameters of the) distribution. 2) Whilst unbiased, the proposed estimator could have high variance. This point is not investigated in the experimental section. 3) For an experimental paper as this one, it would be good to have many more problems analysed and a deeper analysis than the one given for the language problem.