Despite the success of deep learning in domains such as image, voice, and graphs, there has been little progress in deep representation learning for domains without a known structure between features. For instance, a tabular dataset of different demographic and clinical factors where the feature interactions are not given as a prior. In this paper, we propose Group-Connected Multilayer Perceptron (GMLP) networks to enable deep representation learning in these domains. GMLP is based on the idea of learning expressive feature combinations (groups) and exploiting them to reduce the network complexity by defining local group-wise operations. During the training phase, GMLP learns a sparse feature grouping matrix using temperature annealing softmax with an added entropy loss term to encourage the sparsity. Furthermore, an architecture is suggested which resembles binary trees, where group-wise operations are followed by pooling operations to combine information; reducing the number of groups as the network grows in depth. To evaluate the proposed method, we conducted experiments on five different real-world datasets covering various application areas. Additionally, we provide visualizations on MNIST and synthesized data. According to the results, GMLP is able to successfully learn and exploit expressive feature combinations and achieve state-of-the-art classification performance on different datasets.
This paper addresses the problem of learning expressive feature combinations in order to improve learning for domains where there is no known structure between features. These settings would normally lead to the use of fully-connected MLP networks, which unfortunately have problems with efficient training and generalization after a few layers of depth. The main idea is to use grouping at first, in combination with smaller fully-connected layers for each group, as well as pooling pairs of groups together as the layers go on. Results are shown as comparisons on 5 real-world datasets, and intuitive visualizations on two other datasets. Related work covered MLPs, regularization techniques, sparse networks, random forest models, and other feature grouping. The paper is well written and easy to read. This work did a good job with giving implementation details as well as performing hyperparameter searches and giving the baselines a good effort. 
My current decision is a weak reject, for a well-written paper, but some concerns as follows:
-The results do not show much improvement (i.e., < 0.3% improvement for 3 of the datasets, and < 1% for another one), aside from CIFAR-10. Considering that the premise of the paper is that MLP’s are not good enough when dealing with data in which the relationships between features are unknown, it seems like these are definitely not good datasets on which to demonstrate this notion of “there has been little progress in deep reinforcement learning for domains without a known structure between features.”
-The MNIST visualization of group-select felt informative, but the XOR example for grouping visualizations seemed too easy. It would’ve been good to see visualizations or intuitions regarding grouping for harder datasets, in order to be convinced of the need for more expressive feature representations than standard MLP’s.
-I’m not an expert on causality, but it seems like citations from that area are required for this problem statement of dealing with features where the connections between them are unknown but potentially very important. 
Less major:
-It would have been nice to include related work on other ways to encourage inter-feature interactions, such as perhaps taking the outer product of the input with itself. 
-It seems like different sizes per group would be a more realistic expectation, and that perhaps this should be worked into the algorithm. Similarly, pooling only 2 groups together (from pre-specified positions) seems like it would be limiting as well. It also seems like the algorithm should account for being able to use a high-level feature from one layer as part of multiple groups in the future (i.e. reuse). Even if any of these options don’t make a difference, it would be good to check/evaluate.
Minor:
-Equation 8 did not fully make sense to me.
-Why were “random horizontal flips” used as preprocessing for the permutation-invariant CIFAR-10 dataset? This shouldn’t make a difference at all if the pixels become randomly shuffled anyway.