Kati8/ICLR_2023_2025 huggingface dataset:
authors: Yifan Wang
authors: Yifei Liu
authors: Yingdong Shi
authors: Changming Li
authors: Anqi Pang
authors: Sibei Yang
authors: Jingyi Yu
authors: Kan Ren
reviews: 
Comments: To discover the most influential neuron path, this paper propose a joint influence measure to assess the contribution of a set of neurons to the model outcome and a layer-progressive neuron locating approach. In the experiments section, this paper first conducted a quantitative comparison with two baseline methods: Activation & Influence Pattern; And then perform a  class-level analysis and pruning validation.
Strengths: 1. This paper is well-written and easily understood, especially for the `Introduction` and `Related Work` sections. 

2. What's more, the authors performed clear and comprehensive experiments (such as pruning for potential application) and demonstration.

3. The method proposed in this paper is simple yet effective.
Weaknesses: Apart from the given analysis in this paper, what interests me most is the different patterns between the supervised ViTs and self-supervised MAE, as demonstrated in `Fig.2` & `Tab.1`. At beginning, I personally attributed it to the lack of finetuning in self-supervised MAE, while the author pointed that this was also considered in `Line 218/328`. Although this almost **inverted distribution pattern** mentioned in `Sec 3.1's Preliminary Study` does not seem to have been resolved in `Tab.1`, the neuron path in MAE plays a much greater role than in supervised ViTs with lager *Accuracy Deviation*.

Perhaps this issue is beyond the scope of this paper, but this phenomenon worth a targeted analysis or discussion (maybe in future work).
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 4
Clarity: 4
reviews: 
Comments: This study focuses on uncovering the inner workings of vision Transformer models by examining critical neurons that influence model inference. They introduce the Neuron Path approach, which uses a new joint neuron attribution measure to identify key neurons in information flow across layers. The analytical experiments highlight the significance of these neuron paths for model inference and provide insights into the internal operations of vision Transformers, enhancing the field's understanding of visual information processing.
Strengths: This paper is relatively well-motivated as understanding vision transformer is a crucial issue. I also find the evaluations thorough. The strengths are as follows:

1,The target issues of the paper are meaningful and worth exploring.
2. The motivation is clear.
3.The paper is easy to follow.
Weaknesses: 1. In section 4.4, neuron path method is used in model compression. It would be better if this method is compared to the the pruning method.
2. This method aims at vision transformer. Can this method be utilized in NLP tasks?
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3