Kati8/ICLR_2023_2025 huggingface dataset:
authors: Puli Wang
authors: Yu Qi
authors: Yueming Wang
authors: Gang Pan
reviews: 
Comments: The paper created a hierarchical architecture for the alignment of the dynamical latent features. GAN is used for for the raw signal to match the distribution of the the source signal, semantic latent has been decomposed. The representation has been disentangled to get the domain-invariant features. The authors did experiments to show the decoding accuracy and ablation study on the each loss terms.
Strengths: The paper uses simple networks to retrieve the neural representations and latents and fair experiments to compare with the existing models. The experiment results are clearly explained. The formulas are well notations. The frame of the paper is well presented.
Weaknesses: The paper seems combining several things together, but kind of redundant work. First, GAN is used for matching the distribution, then a VAE is used for retrieving the latent. There is no need to use GAN since GAN and VAE do the same thing to approach the distribution, and for the latent decomposition in the same level, that does not change anything. The whole algorithm training process, need to alternately train several networks which seems not very efficient.
Recommendation: 5
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 3
reviews: 
Comments: Summary: In this paper, the authors propose a method named HDA for the domain adaptation of high-dimensional neural observation data by performing the alignment in both the raw neural space and the latent space. It also links the connection to the Lyapunov exponent theory. In the emprical evalution part, the decoding results of HDA outperforms other baseline methods and manifest robustness.
Strengths: 1. How to resolve the domain turnover issue across days is a crucial task in neuroscience, which the paper is working on.
2. The paper as a whole is solid and all the components i.e., hierarchical alignment, causal architecture, used are sounded helpful for the stabilization task.
3. The experiments part are abundant and have a wide comparison across baselines and datasets.
Weaknesses: 1. The combination of the adversarial training and the hierarchical adaptation sounds tedious and a bit meaningless to me. If the proposed hierarchical domain adaptation is powerful enough, why do we still need the adversarial learning component. Thus, the contribution of the hierarchical adaptation is a bit weakened.
2. The theoretical verification is not strong enough since Lyapunov theory is actually a basic concept in control systems. Do you have more detailed and in-depth theory analysis on this part?
3. As shown in Line 277, the constraints added to the algorithm seems too many and no ablation study in the experiemtal part is provided.
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 2