BackPACK: Packing more into Backprop
Automatic differentiation frameworks are optimized for exactly one thing: computing the average mini-batch gradient. Yet, other quantities such as the variance of the mini-batch gradients or many approximations to the Hessian can, in theory, be computed efficiently, and at the same time as the gradient. While these quantities are of great interest to researchers and practitioners, current deep learning software does not support their automatic calculation. Manually implementing them is burdensome, inefficient if done naively, and the resulting code is rarely shared. This hampers progress in deep learning, and unnecessarily narrows research to focus on gradient descent and its variants; it also complicates replication studies and comparisons between newly developed methods that require those quantities, to the point of impossibility. To address this problem, we introduce BackPACK, an efficient framework built on top of PyTorch, that extends the backpropagation algorithm to extract additional information from first-and second-order derivatives. Its capabilities are illustrated by benchmark reports for computing additional quantities on deep neural networks, and an example application by testing several recent curvature approximations for optimization.
This paper adds a very interesting and useful feature to existing autodifferentiation for training neural networks. The second-order information can be backprogated just as the first-order ones, which can be used to accelerate training. This idea, although according to the paper, is developed upon existing works, still, strongly attracts as the second-order information is crucial for training and perhaps visualizing the landscape of neural networks. I vote for an acceptance as this brings a significantly important feature to PyTorch, and the author*s good experiments results and open-sourced code.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
BackPACK: Packing more into Backprop
Automatic differentiation frameworks are optimized for exactly one thing: computing the average mini-batch gradient. Yet, other quantities such as the variance of the mini-batch gradients or many approximations to the Hessian can, in theory, be computed efficiently, and at the same time as the gradient. While these quantities are of great interest to researchers and practitioners, current deep learning software does not support their automatic calculation. Manually implementing them is burdensome, inefficient if done naively, and the resulting code is rarely shared. This hampers progress in deep learning, and unnecessarily narrows research to focus on gradient descent and its variants; it also complicates replication studies and comparisons between newly developed methods that require those quantities, to the point of impossibility. To address this problem, we introduce BackPACK, an efficient framework built on top of PyTorch, that extends the backpropagation algorithm to extract additional information from first-and second-order derivatives. Its capabilities are illustrated by benchmark reports for computing additional quantities on deep neural networks, and an example application by testing several recent curvature approximations for optimization.
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 adds a very interesting and useful feature to existing autodifferentiation for training neural networks. The second-order information can be backprogated just as the first-order ones, which can be used to accelerate training. This idea, although according to the paper, is developed upon existing works, still, strongly attracts as the second-order information is crucial for training and perhaps visualizing the landscape of neural networks. I vote for an acceptance as this brings a significantly important feature to PyTorch, and the author*s good experiments results and open-sourced code.
assistant
This paper adds a very interesting and useful feature to existing autodifferentiation for training neural networks. The second-order information can be backprogated just as the first-order ones, which can be used to accelerate training. This idea, although according to the paper, is developed upon existing works, still, strongly attracts as the second-order information is crucial for training and perhaps visualizing the landscape of neural networks. I vote for an acceptance as this brings a significantly important feature to PyTorch, and the author*s good experiments results and open-sourced code.