Reviews: One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

I think that the finding that LT can generalise (I use the word "can" because it does not seem that this is true consistently) is an interesting one, and with some changes, this paper would deserve publication at a top venue like NeurIPS. However, I think we still see things differently on two points. Firstly, I do not believe that comparison to existing algorithms is orthogonal to the topic of this paper. You claim that "... we may be able to generate new initialization schemes which can substantially improve training of neural networks from scratch" and I agree, but the point I am making is that there are other ways of obtaining a better initialisation (e.g., unsupervised pretraining and/or layer-wise pretraining) which are known to improve performance and speed up converge, some of them using less computation than is required to generate a lottery ticket. I view your algorithm as yet another way of generating a good init using some data which yields good performance, potentially with other benefits like compression, after some amount of fine-tuning (the fact that LT is trained from scratch and thus require more fine-tuning than using trained weights seems like a drawback, not advantage, from this viewpoint).