Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper proposes a convolutional binary neural network to learn binary classification rule for sequence data. The learned rule is able to learn global/local pattern for better explainability. Furthermore, the authors propose a training strategy with latent weights and regularization term penalizing the complexity of the rule, and dynamically enforces the sparsity using sparsity-during-training method. Experiment results on 4 synthetic datasets and one real dataset are shown to exhibit the effectiveness of the proposed method.
strength_and_weaknesses: Strength
1. The paper improves on the base rule model by operating on sequence data and could learn global/local pattern
2. The training strategy on penalizing the complexity of the rule and sparsify-during-training is useful and could benefit other rule learning algorithm  

Weaknesses
1. The proposed method does not work for unbalanced data for learning an empty rule. I am wondering if up/down-sample the positive/negative class helps
2. The paper could be polished in terms of clarity. For example, the authors should explain the notations for grammar in equations 3-5 for better presentation. Equation 9 is also not explained with the motivation on using such form for the rate.
3. There is no experiment results on explainability on real dataset. The paper will be stronger and better motivated with such a demo.
clarity,_quality,_novelty_and_reproducibility: Clarity
In general the paper is easy to follow. However, the paper could be polished in terms of clarity. For example, the authors should explain the notations for grammar in equations 3-5 for better presentation. Equation 9 is also not explained with the motivation on using such form for the rate.

Quality, Novelty And Reproducibility
The paper is novel for proposing rule learning model for sequence data. There is no code provided and detailed experiment setting so I doubt with reproducibility, which downgrades the quality of the paper. The paper could also be improved by more explanations/motivations on the techniques used and providing experiment result on explainability.
summary_of_the_review: Overall I think the paper contributes a novel method for learning rule with sequence data, and recommend for acceptance.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: Learning rules is difficult and often lead to low performances. From the computational point of view, it poses a problem of differentiability. This article propose to learn both the discriminant pattern and explicit decision rules.
The authors first insist on the cabablities associated with a 3 layer rule network. Then, they describe the convolution layer which applies rules on the discrete signal. Section 3 explains how the architecture is learnt. Then, the authors conduct experiments on synthetic and real discrete signals.
strength_and_weaknesses: **positive**

Architecture with potential for xAI

A very interesting way to learn rules with gradient descent

**negative**

n section 3, the authors should define the notation and matrix dimensions: I don't understand (6) and (7).
I can't understand the regularization procedure.

I am not familiar with logical networks, but I don't get how the author make the architecture differentiable



clarity,_quality,_novelty_and_reproducibility: The model is not described with enough details to enable reproducibility
summary_of_the_review: The idea of the article is really interesting but the whole model description (section 3), in particular the learning process, is impossible to understand (at least for a reviewer that is not familiar with rule neural networks). For this reason, I can't accept this article.


**AFTER REBUTAL**

The paper has been significantly improved by the authors. The procedure is much more detailed than in the original version and an anonymous repository has been made available. This encourage me to change my ratings.

I am not familiar enough with the previous contribution in the field to assess reliably the novelty of the proposal.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper proposes a method based on a binary neural network to learn classification rules from sequential data. The model contains a stackedOR layer for categorical variables, a logical AND layer and an OR layer. The training strategy includes dynamically-induced sparsity.  

strength_and_weaknesses: Strengths. The paper introduces a method that can be useful for real applications. The paper is well-written. 

Weaknesses. Comparison to state-of-the-art methods are missing. 
The local and global patters are not so easy to understand. In general, in my opinion, Section 2.2 is not easy to follow. 

Two window values (for sequences), namely, 3 and 6 were tested. It is difficult to make strong conclusions from results based on these two values only. It is also mentioned that it is a good idea to take a window size which equals to the maximum length. First, I wonder whether if such a big window is taken, the model would suffer from overfitting. Second, it is indeed interesting to take quite a big window but also include into the model some shorter windows (similarly to n-gram models). Third, windows of various lengths (performing data segmentation) would be an optimal choice.  

Speaking about interpretability: what kind of rules were learnt? 
I did not really see that the interpretable aspect of the method was validated, i.e., that some rules were extracted and considered/validated by human experts. 

Other remarks. In Section 3, "the model is trained via automatic differentiation", and it is also mentioned that the losses are differentiable. Why do you prefer automatic differentiation if the first derivative can be computed?   

In Section 3, the paragraph "Loss function" is not very easy to understand: why there is product of three \Pi matrices? Is the multiplication coordinate-wise, if yes, do they have the same dimensions? What is \Pi_{stack} and why the sum is taken over \Pi_{stack} matrices?

It is mentioned that both balanced and unbalanced data sets are considered. How the data sets are balanced? I did not find any information on whether some undersampling or weighted loss was used. 

In Table 2: what the column "Penalty" contains?
clarity,_quality,_novelty_and_reproducibility: The paper is clearly written and some ideas are novel. 
summary_of_the_review: The method is sound but some points should be clarified.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The paper presents a new method for interpretable classification of sequential data using a binary CNN with an interpretable filter, trained using dynamically-enforced sparsity. The method has several novel aspects - the global/local pattern learning and using sparsity during training and rule learning with gradient descent. The main weakness of the paper was its clarity. However, this aspect was improved considerably following the rebuttal stages. Reviewer r7kZ has correctly pointed out that the paper does not include comparisons with existing work, however, given that most other work in XAI does not perform rule learning, an appropriate baseline is not immediately evident. Because this paper essentially comes up with a model that other deep XAI methods do not, and because it has several novel aspects, I conclude that it would be a useful paper to include in the ICLR proceedings.
justification_for_why_not_higher_score: To warrant a higher score, the experimental evaluation should have been more extensive.
justification_for_why_not_lower_score: The approach has several novel aspects.
Decision: Accept: poster