- As far as I see, there is no mention of limitations of this work, let alone a Limitations section. No work is perfect, and every work should include a Limitations section so that, only two reasons given here for concision, (1) readers are quickly aware of cases in which this work applies and in which it doesn't and (2) readers have confidence that the paper is at least somewhat cognizant of (1). I'm unsure whether this is in the Appendix or Supplementary Material.
- Very limited Related Works section. A large section of related works that is relevant is "sparsity in neural networks," and this could be broken down into multiple relevant subsections, such as "sparsity over training progress", "sparsity with respect to {eigenvalues, spectral norms, Hessian properties [1], etc.}"
- Limited rigor in original (at least original as far as I know, such as the categorization of salient features) concepts.
- What quantitative rigor justifies the categorization of a feature into one of the 5 mentioned categories?
- Is there some sort of goodness of fit test or statistical hypothesis test or principled approach for assigning a feature to a category?
- What if the training epochs were extended and the utility trended in a way that changed categorization?
- What was the stopping criteria for training?
- Was any analysis done for the reliability of assigning features to categories?
- Unclear in several aspects. Some include
- Why use only one layer for each of the DNNs? How was this layer selected? How would results changing using a different intermediate layer?
- Why use the threshold values for rank, approximation error for salient feature count, the number of training epochs used, among others?
- Are the results in Figure 5a, 5b, and 5c each for one "sample", "sentence", and "image" in the single DNN model and single dataset listed?
- Do Figures X and Y show results for randomly sampled images? Since it's impossible to confirm whether this was actually the case, are there examples that do not align with these results, or even contradict these results? Is there analysis as to why?
- The novelty of using PCA to reduce interaction count seems incremental and the significance of the paper results is unclear to me. Using PCA to reduce the interaction count seems intuitive, as PCA aims to retain the maximum information in the data with the reduced dimensionality chosen, assuming certain assumptions are met. How well are the assumptions met?
[1] Dombrowski, Ann-Kathrin, Christopher J. Anders, Klaus-Robert Müller, and Pan Kessel. "Towards robust explanations for deep neural networks." Pattern Recognition 121 (2022): 108194.
- The novelty of using PCA to reduce interaction count seems incremental and the significance of the paper results is unclear to me. Using PCA to reduce the interaction count seems intuitive, as PCA aims to retain the maximum information in the data with the reduced dimensionality chosen, assuming certain assumptions are met. How well are the assumptions met? [1] Dombrowski, Ann-Kathrin, Christopher J. Anders, Klaus-Robert Müller, and Pan Kessel. "Towards robust explanations for deep neural networks." Pattern Recognition 121 (2022): 108194.