CAML: Fast Context Adaptation via Meta-Learning
We propose CAML, a meta-learning method for fast adaptation that partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, the context parameters are updated with one or several gradient steps on a task-specific loss that is backpropagated through the shared part of the network. Compared to approaches that adjust all parameters on a new task (e.g., MAML), our method can be scaled up to larger networks without overfitting on a single task, is easier to implement, and saves memory writes during training and network communication at test time for distributed machine learning systems. We show empirically that this approach outperforms MAML, is less sensitive to the task-specific learning rate, can capture meaningful task embeddings with the context parameters, and outperforms alternative partitionings of the parameter vectors.
 The paper talks about Meta-Learning where some of the parameters of the models adapt to the new task (context parameters) and rest of the parameters are kept fixed (shared parameters). The authors propose a more general approach and show how CAML works for supervised learning and reinforcement learning paradigms. quality - The paper is written with good mathematical notation and in general is of high quality. The references to related work and motivation of the problem is good. clarity - While the paper is clear in many parts, it can be a lot better. Specifically it is unclear why authors chose regression, classification and RL to make their point without landing either one of them fully confidently. originality - the idea is good and general enough to be applicable for many situations. While variants of this idea have been tried with fine-tuning for transferred learning I still think this work can classify as original and novel. significance of this work - The significance of meta learning is good but based on the experiments authors conducted I am worried it has little significance. pros and cons - Overall, while I am supportive of a weak accept because of the idea and it*s broad applicability I feel authors should maybe chose one of the tasks and show much more value in using the CAML framework. The three tasks they chose are all toy problems and do not instill confidence in the validity of CAML for either large scale experiments or in setups where distribution is changing but tasks remain same. It would be great to strengthen the paper with a more cleaner story on the experiments section and show CAML achieves SOA convincingly.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
CAML: Fast Context Adaptation via Meta-Learning
We propose CAML, a meta-learning method for fast adaptation that partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, the context parameters are updated with one or several gradient steps on a task-specific loss that is backpropagated through the shared part of the network. Compared to approaches that adjust all parameters on a new task (e.g., MAML), our method can be scaled up to larger networks without overfitting on a single task, is easier to implement, and saves memory writes during training and network communication at test time for distributed machine learning systems. We show empirically that this approach outperforms MAML, is less sensitive to the task-specific learning rate, can capture meaningful task embeddings with the context parameters, and outperforms alternative partitionings of the parameter vectors.
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 talks about Meta-Learning where some of the parameters of the models adapt to the new task (context parameters) and rest of the parameters are kept fixed (shared parameters). The authors propose a more general approach and show how CAML works for supervised learning and reinforcement learning paradigms. quality - The paper is written with good mathematical notation and in general is of high quality. The references to related work and motivation of the problem is good. clarity - While the paper is clear in many parts, it can be a lot better. Specifically it is unclear why authors chose regression, classification and RL to make their point without landing either one of them fully confidently. originality - the idea is good and general enough to be applicable for many situations. While variants of this idea have been tried with fine-tuning for transferred learning I still think this work can classify as original and novel. significance of this work - The significance of meta learning is good but based on the experiments authors conducted I am worried it has little significance. pros and cons - Overall, while I am supportive of a weak accept because of the idea and it*s broad applicability I feel authors should maybe chose one of the tasks and show much more value in using the CAML framework. The three tasks they chose are all toy problems and do not instill confidence in the validity of CAML for either large scale experiments or in setups where distribution is changing but tasks remain same. It would be great to strengthen the paper with a more cleaner story on the experiments section and show CAML achieves SOA convincingly.
assistant
 The paper talks about Meta-Learning where some of the parameters of the models adapt to the new task (context parameters) and rest of the parameters are kept fixed (shared parameters). The authors propose a more general approach and show how CAML works for supervised learning and reinforcement learning paradigms. quality - The paper is written with good mathematical notation and in general is of high quality. The references to related work and motivation of the problem is good. clarity - While the paper is clear in many parts, it can be a lot better. Specifically it is unclear why authors chose regression, classification and RL to make their point without landing either one of them fully confidently. originality - the idea is good and general enough to be applicable for many situations. While variants of this idea have been tried with fine-tuning for transferred learning I still think this work can classify as original and novel. significance of this work - The significance of meta learning is good but based on the experiments authors conducted I am worried it has little significance. pros and cons - Overall, while I am supportive of a weak accept because of the idea and it*s broad applicability I feel authors should maybe chose one of the tasks and show much more value in using the CAML framework. The three tasks they chose are all toy problems and do not instill confidence in the validity of CAML for either large scale experiments or in setups where distribution is changing but tasks remain same. It would be great to strengthen the paper with a more cleaner story on the experiments section and show CAML achieves SOA convincingly.