Meta-learning with differentiable closed-form solvers
Adapting deep networks to new concepts from a few examples is challenging, due to the high computational requirements of standard fine-tuning procedures. Most work on few-shot learning has thus focused on simple learning techniques for adaptation, such as nearest neighbours or gradient descent. Nonetheless, the machine learning literature contains a wealth of methods that learn non-deep models very efficiently. In this paper, we propose to use these fast convergent methods as the main adaptation mechanism for few-shot learning. The main idea is to teach a deep network to use standard machine learning tools, such as ridge regression, as part of its own internal model, enabling it to quickly adapt to novel data. This requires back-propagating errors through the solver steps. While normally the cost of the matrix operations involved in such a process would be significant, by using the Woodbury identity we can make the small number of examples work to our advantage. We propose both closed-form and iterative solvers, based on ridge regression and logistic regression components. Our methods constitute a simple and novel approach to the problem of few-shot learning and achieve performance competitive with or superior to the state of the art on three benchmarks.
 This paper proposes a new meta-learning method based on closed-form solutions for task specific classifiers such as ridge regression and logistic regression (iterative). The idea of the paper is quite interesting, comparing to the existing metric learning based methods and optimization based methods. I have two concerns on this paper. First, the motivation and the rationale of the proposed approach is not clear. In particular, why one can simply treat .hat{Y} as a scaled and shifted version of X’W? Second, the empirical performance of the proposed approach is not very promising and it does not outperform the comparison methods, e.g., SNAIL. It is not clear what is the advantage.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Meta-learning with differentiable closed-form solvers
Adapting deep networks to new concepts from a few examples is challenging, due to the high computational requirements of standard fine-tuning procedures. Most work on few-shot learning has thus focused on simple learning techniques for adaptation, such as nearest neighbours or gradient descent. Nonetheless, the machine learning literature contains a wealth of methods that learn non-deep models very efficiently. In this paper, we propose to use these fast convergent methods as the main adaptation mechanism for few-shot learning. The main idea is to teach a deep network to use standard machine learning tools, such as ridge regression, as part of its own internal model, enabling it to quickly adapt to novel data. This requires back-propagating errors through the solver steps. While normally the cost of the matrix operations involved in such a process would be significant, by using the Woodbury identity we can make the small number of examples work to our advantage. We propose both closed-form and iterative solvers, based on ridge regression and logistic regression components. Our methods constitute a simple and novel approach to the problem of few-shot learning and achieve performance competitive with or superior to the state of the art on three benchmarks.
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 proposes a new meta-learning method based on closed-form solutions for task specific classifiers such as ridge regression and logistic regression (iterative). The idea of the paper is quite interesting, comparing to the existing metric learning based methods and optimization based methods. I have two concerns on this paper. First, the motivation and the rationale of the proposed approach is not clear. In particular, why one can simply treat .hat{Y} as a scaled and shifted version of X’W? Second, the empirical performance of the proposed approach is not very promising and it does not outperform the comparison methods, e.g., SNAIL. It is not clear what is the advantage.
assistant
 This paper proposes a new meta-learning method based on closed-form solutions for task specific classifiers such as ridge regression and logistic regression (iterative). The idea of the paper is quite interesting, comparing to the existing metric learning based methods and optimization based methods. I have two concerns on this paper. First, the motivation and the rationale of the proposed approach is not clear. In particular, why one can simply treat .hat{Y} as a scaled and shifted version of X’W? Second, the empirical performance of the proposed approach is not very promising and it does not outperform the comparison methods, e.g., SNAIL. It is not clear what is the advantage.