Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: This blog post provides a high-level introduction to the paper "Contrastive Learning of Structured World Models" (C-SWM; Kipf et al., ICLR 2020) and describes it in the context of a larger trend in the machine learning community to develop neural network models that use (and manipulate) symbol-like representations. Beyond this, the main contribution of this blog post is to introduce new terminology ("token parser" and "token manipulator") for architectural components of existing architectures in this research area, at the example of the C-SWM model. This blog post further references and briefly describes several related works in the area of (unsupervised) object-centric learning and draws parallels to supervised transformer architectures, some of which share similar architectural principles (e.g. DETR; Carion et al., ECCV 2020).

The article does a good job at creating enthusiasm/excitement for this line of research. It mentions several important related works and follow-up works, and draws some connections to otherwise recent related works (e.g. Vision Transformers and DETR).

Overall, however, I think that this article has a range of clarity and quality issues, in addition to lacking a broader discussion of related works. While the blog post format should encourage a more light-weight discussion of related works, I think this particular article has stepped too far and only mentions some related work in passing without providing sufficient context (e.g. for the connection to Vision Transformers and DETR). Other important related work is missing, most notably some early work in object-centric world models such as OP3 [1] and Relational NEM [2].

I am also unsure whether it is worth introducing new terminology ("token parser" and "token manipulator") for systems with existing terminology, e.g. "abstraction" or "symbol binding" (instead of "token parsing"). I would recommend adopting some of the terminology introduced in Greff et al., "On the binding problem in artificial neural networks" (from which Figure 1 in the article is taken).

In terms of quality and clarity issues: this blog post only provides an extremely superficial treatment of this topic and most arguments brought forward are "hand-wavy", i.e. lack substance (not backed up by references or a clear explanation).

All the figures in the post are copied from referenced papers, likely without the permission of the respective authors (at least it is unclear whether this permission was granted for some figures). The edits made to Figure 2 are of poor quality (inconsistent font sizes, alignment and cut-off borders). Figure 3 is a low-quality screenshot from the C-SWM paper.

Issues in terms of clarity / problematic statements:
* "this object abstraction will emerges as a byproduct of optimisation [Burgess, 2019]" -> I would not only ascribe the emergence of objects in MONet to the optimization process, but in a large part to their particular choice of architecture.
* "A world model refers to a compressed representation": this is not very precise, since learning a compressed representation and learning a world model are not necessarily connected. One can learn a world model (i.e. an action-conditioned model of an environment) without necessarily learning a (heavily) compressed representation of the input. 
* "A structured world model implies a world model that explicitly captures the structures of the world in its representation" -> this statement is also not very clear. Usually, a structured (or sometimes called "factored" or "object-centric") world-model refers to a model that uses a factored latent space representation (e.g. in the form of a set of latent vectors) to represent the state of the environment.
* "[...] learned end to end from raw observation only, as all world models should. After all, God does not give us the answer to the ultimate question of life, universe and everything else either." -> I guess this argument is meant as a joke, but I found it difficult to follow/understand what the author had in mind here. I recommend removing this and instead providing an actual argument for what benefits unsupervised models might (or might not) have over supervised counterparts.
* A very large number of grammatical issues throughout the article, e.g. "Among many approach" -> "Among many approaches"; "While there are still a lot of work" -> "While there is still a lot of work" (in the first paragraph alone). I recommend a careful round of copy editing.

Overall, I think this article, in its current form, is not suitable for publication in the ICLR blog post track and needs more work to 1) improve its argumentation, 2) provide a more thorough coverage/treatment of related work and follow-up work to C-SWM, and 3) improve overall quality of presentation and clarity of writing.

[1] Veerapaneni et al., Entity Abstraction in Visual Model-Based Reinforcement Learning (2019)
[2] Steenkiste et al., Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions (2018)
review_title: Blog post creates excitement but lacks substance

Decision: Reject