Especially, more task conditioning methods (e.g., MMAML) are considered in this paper. However, my major concern has not been addressed. The authors still ignore the discussion with multi-task learning. From my perspective, the goal for meta-learning is to generalize knowledge from previous tasks, which further benefits the training of a new task. The setting in this paper allows a new meta-testing task to access all meta-training tasks.

The reviewers agreed that this paper brings an important and relevant contribution to the NeurIPS community, and presents comprehensive experiments to validate the proposed approach. The authors are strongly encouraged to revise the submitted paper according to the feedback in the reviews, including a discussion of multi-task learning, adding the requested clarifications, and fixing typos.

Originality: 7 Quality:8 Clarity: 4 Significance:7 Mixmatch combined a lot of classical extraordinary methods that used for semi-supervised learning and achieved state-of-the-art results by a large margin across many datasets and labeled data amounts. Compared to previous method, this method is not only a simple combination of different data augmentation methods and other methods, such as exponential model average (EMA), it also explores a path to fully combine the advantages of different methods. In short, this method is of course a big step for semi-supervised learning on image classification. However, the experiments on this paper still needs to be modified to be perfect and a fair comparison with previous paper, such as Mean-Teacher. Also, some small problems need to be fixed to be finally published.

The reviewers are in consensus that this is well-written paper, which combine a number of well-studied SSL methods. The results include good performance over a number of datasets. Thus the recommendation to accept this paper.

Self-supervised learning (SSL) aims to find meaningful representations from unlabeled data by encoding semantic similarities through data augmentations. Despite its current popularity, theoretical insights about SSL are still scarce. For example, it is not yet known whether commonly used SSL loss functions can be related to a statistical model, much in the same as OLS, generalized linear models or PCA naturally emerge as maximum likelihood estimates of an underlying generative process. In this short paper, we consider a latent variable statistical model for SSL that exhibits an interesting property: Depending on the informativeness of the data augmentations, the MLE of the model either reduces to PCA, or approaches a simple non-contrastive loss. We analyze the model and also empirically illustrate our findings.

The work does not include original ideas. It is exclusively a collection of previous ideas combined together in a rather classical way. Major remarks: Equation (6) makes loss non-smooth and non-differentiable. The authors do not discuss how they handle this. I assume they use the typical approach by getting the right'case' in the forward step and then doing back-prop on the fixed smooth function.

The paper presents a new approach to tracking and pixel level correspondence using self-supervised learning in video. It goes in the direction of multi-task learning. As well results are solid. The reviewers at the beginning gave a score of 5,6 and 7, than after rebuttal also the more skeptic reviewer was convinced to improve its rate. .

The model is learned by breaking the structure into parts and performing kernel ridge regression on the parts. They show elaborate convergence rate analysis in the estimation. The theoretical analysis is the strong part of this paper. In a lot of computer vision and NLP applications the latest research is about capturing long range dependencies. The correlation in Figure 1 is highly concentrated at the central patch because it's the average of many different images, but on individual images the correlation patten can be very different.

The authors propose a general theoretical framework for structured prediction that deals with cases where the data exhibits a local structure, so that the inputs and outputs can be decomposed into parts. The reviewers deemed the theoretical contributions to be of original and of a high quality. The author response addressed the perceived weaknesses, in particular in the empirical evaluation, in a satisfcatory way.

Strengths: * The paper is concerned with the derivation of k-argmax function's continuous approximation as a generic projection of a score vector onto the (n, 1)-simplex or the (n, k)-simplex (for predicting top-k relevant labels) based on a strongly convex function g . The first interesting contribution shows how to obtain such approximation and derives the general solution of this problem provided some properties of g (it is separable, 1-strongly convex). Relevant g s are quadratic function, negative entropy. Specifically, Euclidean projection with adapted Lipschitz constant \alpha of the projection to the training instance is devised as the Rankmax operator. The key element is that \alpha can be computed such that the sample's labels occurs in the top-k.