Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling
We propose a generic confidence-based approximation that can be plugged in and simplify an auto-regressive generation process with a proved convergence. We first assume that the priors of future samples can be generated in an independently and identically distributed (i.i.d.) manner using an efficient predictor. Given the past samples and future priors, the mother AR model can post-process the priors while the accompanied confidence predictor decides whether the current sample needs a resampling or not. Thanks to the i.i.d. assumption, the post-processing can update each sample in a parallel way, which remarkably accelerates the mother model. Our experiments on different data domains including sequences and images show that the proposed method can successfully capture the complex structures of the data and generate the meaningful future samples with lower computational cost while preserving the sequential relationship of the data.}
The paper presents a technique for approximately sampling from autoregressive models using something like a a proposal distribution and a critic. The idea is to chunk the output into blocks and, for each block, predict each element in the block independently from a proposal network, ask a critic network whether the block looks sensible and, if not, resampling the block using the autoregressive model itself. In broad strokes the approach makes sense. It assumes, essentially, that parts of the sequence are hard to predict and parts are easy and, if there are enough easy parts, this procedure should lead to faster inference. The paper*s writing is not ideal. There are some grammatical mistakes that harm reading (for example, the second paragraph of the introduction says *However, these models must infer each element of the data x ? RN step by step in a serial manner, requiring O(N) times more than other non-sequential estimators*, where it is unclear what is O(N) more than what, how is this measured, etc). That said I was mostly able to follow all key points. The authors do not point out the obvious connection to GANs, which also rely on a critic network to decide whether a sample looks like it comes from the correct distribution, except in GANs the critic is jointly trained with the generator (as opposed to here where it*s trained after) and in GANs the critic is only used at training time, while here the critic is used to accelerate sampling (the better the critic the faster this method can sample). I wish the experimental results were a little more explicit about the time vs quality tradeoff; I expected to see more plots with pareto curves, since as-is it*s hard to judge the magnitude of the tradeoffs involved. I*d also like a more thorough analysis on why there is a non-monotonic tradeoff in some experiments (table 1, figure 2(b)) between the amount of approximation and the sample quality; this makes me think something else is going on here as this approximate inference method should just decrease quality, never increase it. Overall I lean towards accepting the paper, but I encourage the authors to revise the writing and to add a few plots explicitly showing the time vs quality tradeoff both in likelihood (wrt the full model) and in downstream metrics like FID.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling
We propose a generic confidence-based approximation that can be plugged in and simplify an auto-regressive generation process with a proved convergence. We first assume that the priors of future samples can be generated in an independently and identically distributed (i.i.d.) manner using an efficient predictor. Given the past samples and future priors, the mother AR model can post-process the priors while the accompanied confidence predictor decides whether the current sample needs a resampling or not. Thanks to the i.i.d. assumption, the post-processing can update each sample in a parallel way, which remarkably accelerates the mother model. Our experiments on different data domains including sequences and images show that the proposed method can successfully capture the complex structures of the data and generate the meaningful future samples with lower computational cost while preserving the sequential relationship of the data.}
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 presents a technique for approximately sampling from autoregressive models using something like a a proposal distribution and a critic. The idea is to chunk the output into blocks and, for each block, predict each element in the block independently from a proposal network, ask a critic network whether the block looks sensible and, if not, resampling the block using the autoregressive model itself. In broad strokes the approach makes sense. It assumes, essentially, that parts of the sequence are hard to predict and parts are easy and, if there are enough easy parts, this procedure should lead to faster inference. The paper*s writing is not ideal. There are some grammatical mistakes that harm reading (for example, the second paragraph of the introduction says *However, these models must infer each element of the data x ? RN step by step in a serial manner, requiring O(N) times more than other non-sequential estimators*, where it is unclear what is O(N) more than what, how is this measured, etc). That said I was mostly able to follow all key points. The authors do not point out the obvious connection to GANs, which also rely on a critic network to decide whether a sample looks like it comes from the correct distribution, except in GANs the critic is jointly trained with the generator (as opposed to here where it*s trained after) and in GANs the critic is only used at training time, while here the critic is used to accelerate sampling (the better the critic the faster this method can sample). I wish the experimental results were a little more explicit about the time vs quality tradeoff; I expected to see more plots with pareto curves, since as-is it*s hard to judge the magnitude of the tradeoffs involved. I*d also like a more thorough analysis on why there is a non-monotonic tradeoff in some experiments (table 1, figure 2(b)) between the amount of approximation and the sample quality; this makes me think something else is going on here as this approximate inference method should just decrease quality, never increase it. Overall I lean towards accepting the paper, but I encourage the authors to revise the writing and to add a few plots explicitly showing the time vs quality tradeoff both in likelihood (wrt the full model) and in downstream metrics like FID.
assistant
The paper presents a technique for approximately sampling from autoregressive models using something like a a proposal distribution and a critic. The idea is to chunk the output into blocks and, for each block, predict each element in the block independently from a proposal network, ask a critic network whether the block looks sensible and, if not, resampling the block using the autoregressive model itself. In broad strokes the approach makes sense. It assumes, essentially, that parts of the sequence are hard to predict and parts are easy and, if there are enough easy parts, this procedure should lead to faster inference. The paper*s writing is not ideal. There are some grammatical mistakes that harm reading (for example, the second paragraph of the introduction says *However, these models must infer each element of the data x ? RN step by step in a serial manner, requiring O(N) times more than other non-sequential estimators*, where it is unclear what is O(N) more than what, how is this measured, etc). That said I was mostly able to follow all key points. The authors do not point out the obvious connection to GANs, which also rely on a critic network to decide whether a sample looks like it comes from the correct distribution, except in GANs the critic is jointly trained with the generator (as opposed to here where it*s trained after) and in GANs the critic is only used at training time, while here the critic is used to accelerate sampling (the better the critic the faster this method can sample). I wish the experimental results were a little more explicit about the time vs quality tradeoff; I expected to see more plots with pareto curves, since as-is it*s hard to judge the magnitude of the tradeoffs involved. I*d also like a more thorough analysis on why there is a non-monotonic tradeoff in some experiments (table 1, figure 2(b)) between the amount of approximation and the sample quality; this makes me think something else is going on here as this approximate inference method should just decrease quality, never increase it. Overall I lean towards accepting the paper, but I encourage the authors to revise the writing and to add a few plots explicitly showing the time vs quality tradeoff both in likelihood (wrt the full model) and in downstream metrics like FID.