Efficient Lifelong Learning with A-GEM
In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost. Towards this end, we first introduce a new and a more realistic evaluation protocol, whereby learners observe each example only once and hyper-parameter selection is done on a small and disjoint set of tasks, which is not used for the actual learning experience and evaluation. Second, we introduce a new metric measuring how quickly a learner acquires a new skill. Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017), dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance as GEM, while being almost as computationally and memory efficient as EWC (Kirkpatrick et al., 2016) and other regularization-based methods. Finally, we show that all algorithms including A-GEM can learn even more quickly if they are provided with task descriptors specifying the classification tasks under consideration. Our experiments on several standard lifelong learning benchmarks demonstrate that A-GEM has the best trade-off between accuracy and efficiency
 This paper proposes a variant of GEM called A-GEM that substantially improves the computational characteristics of GEM while achieving quite similar performance. To me the most interesting insight of this work is the proof that an inner product between gradients can suffice instead of needing to solve the quadratic program in GEM – which I have found to be a major limitation of the original algorithm. The additional experiments using task descriptors to enable zero shot learning are also interesting. Moreover, the discussion of the new evaluation protocol and metrics make sense with further clarification from the authors. Overall, I agree with the other reviewers that this paper makes a clear and practical contribution worthy of acceptance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Efficient Lifelong Learning with A-GEM
In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task. In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost. Towards this end, we first introduce a new and a more realistic evaluation protocol, whereby learners observe each example only once and hyper-parameter selection is done on a small and disjoint set of tasks, which is not used for the actual learning experience and evaluation. Second, we introduce a new metric measuring how quickly a learner acquires a new skill. Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017), dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance as GEM, while being almost as computationally and memory efficient as EWC (Kirkpatrick et al., 2016) and other regularization-based methods. Finally, we show that all algorithms including A-GEM can learn even more quickly if they are provided with task descriptors specifying the classification tasks under consideration. Our experiments on several standard lifelong learning benchmarks demonstrate that A-GEM has the best trade-off between accuracy and efficiency
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 variant of GEM called A-GEM that substantially improves the computational characteristics of GEM while achieving quite similar performance. To me the most interesting insight of this work is the proof that an inner product between gradients can suffice instead of needing to solve the quadratic program in GEM – which I have found to be a major limitation of the original algorithm. The additional experiments using task descriptors to enable zero shot learning are also interesting. Moreover, the discussion of the new evaluation protocol and metrics make sense with further clarification from the authors. Overall, I agree with the other reviewers that this paper makes a clear and practical contribution worthy of acceptance.
assistant
 This paper proposes a variant of GEM called A-GEM that substantially improves the computational characteristics of GEM while achieving quite similar performance. To me the most interesting insight of this work is the proof that an inner product between gradients can suffice instead of needing to solve the quadratic program in GEM – which I have found to be a major limitation of the original algorithm. The additional experiments using task descriptors to enable zero shot learning are also interesting. Moreover, the discussion of the new evaluation protocol and metrics make sense with further clarification from the authors. Overall, I agree with the other reviewers that this paper makes a clear and practical contribution worthy of acceptance.