Paper: PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction (ICLR-2024)

Reviewer comment (Experiments):
Lack of comparison to SOTA. In Tab. 1 and Tab. 2, the only previous works on few-shot MMP included are ADKF-IFT and PAR. The authors should compare the proposed methods with more existing SOTAs such as [a].

Author response:
*Reply*. We compare PACIA with ADKF-IFT and PAR, which are recent baselines obtaining the best performance on MoleculeNet and FS-Mol respectively. We also mention MHNfs (Schimunek et al., 2023), which is not included as it uses additional reference molecules from external datasets. 
For reference [a], could you please provide the paper title?

Reviewer comment (Novelty):
Lack of novelty. The core component is HyperNet. Based on HyperNet, the proposed PACIA makes no significant technical contribution.

Author response:
*Reply*. Hypernetworks are a general concept involving neural networks that are trained to generate parameters for a main network. 
Designing how a hypernetwork modulates the main network is a problem-specific and non-trivial challenge.
One cannot directly apply hypernetworks designed for other problems to a new problem. 
Considering the few-shot MPP problem, existing works can be summarized within an encoder-predictor framework, where GNNs perform effectively, acting as both encoders and predictors. 
Correspondingly, we have designed a unified GNN adapter that generates a few adaptive parameters. These parameters modulate the message passing process of the GNN in two key aspects: node embedding and propagation depth.
As a result, the encoder adapts at the property level, while the predictor adapts at the molecule level. 
Our proposed PACIA effectively addresses the few-shot MPP problem, achieving state-of-the-art performance on benchmark datasets.
In conclusion, PACIA contributes significantly to both the technological and empirical advancements in the field of few-shot MPP.

Reviewer comment (Writing):
Poor writing. Many sentences are grammatically wrong. Some examples are:  
* [In Sec. 1] First is that ... difference.  
* [In Sec. 1] The chemical space is enormous that ... range.  
* [In Sec. 1] The molecule-level difference ... molecules.  
* [In Sec. 1] While others ... accurately.  
Note that four of the first 6 sentences of this paragraph are grammatically incorrect. There are many errors in addition to the above examples. The authors are suggested to carefully proofread the paper to correct the errors.

Author response:
*Reply*. We sincerely apologize for the grammatical issues. We have now carefully proofread the manuscript and marked the grammar corrections in the revised version.

Reviewer comment (Theory):
The design in modulating propagation depth seems not fully reasonable. According to Eqn. (8), $[p]_l$ measures the probability of the event that "the $l$-th layer is in the model". However, from Eqn. (7), the only constraint on $[p]_l$ is $\sum_l [p]_l = 1$. So it is likely that for some $1<i<j<L$, $[p]_i < [p]_j$. That is to say, a hidden layer (i.e., layer $j$) is more likely to be in the model than a layer before it (i.e., layer $i$), which is unreasonable.

Author response:
*Reply*. This is a misunderstanding: the relative magnitude between $[p]_i$ and $[p]_j$ is independent of the order between i and j.

For any input sample $\mathcal{X}_{\tau,q}$, it will always go through a complete message passing procedure in a $L$-layer GNN. Afterwards, for each layer $l\in1,2\cdots,L$, hypernetwork takes the node embeddings after $l$-th layer (i.e., {$\textbf{h}^l$}) as input, and generates a scalar $[p]_l$ representing how likely it can get good performance by using {$\textbf{h}^l$} as the final node embeddings.  
During meta-training, $\{[p]_l\}$ act as differentiable combination weights in Equation (8). In this way, the hypernetwork is trained to learn the optimal mapping from {$\textbf{h}^l$} to $[p]_l$, through gradient flow among Equation (12)-(3)-(5)-(8)-(10) or Equation (12)-(3)-(5)-(8)-(11). 
During meta-testing, we directly select one optimal layer $l'$ by Equation (13). Then, only {$\textbf{h}^{l'}$} of $l'$th layer are fed to next module.

Reviewer comment (Writing):
The exposition on the methodology appears somewhat nebulous, which hampers a clear comprehension of the distinct contributions of each module. Specifically, the average representation at the l-th GNN layer, as depicted in Equation (9), seems disconnected from subsequent steps. The property adaptation and molecule adaptation are not clear in the algorithm.

Author response:
*Reply*. We apologize for the confusion. 
We have revised Generating Adaptive Parameters by Hypernetwork paragraph in Section 4.1 to make it clearer. 
Property-level adaptation is generated by Equation (9) and Equation (10), and molecule-level adaptation is generated by Equation (9) and Equation (11).
In Equation (9), we obtain class prototypes $\textbf{r}\_{{\tau},+}^l$ and $\textbf{r}\_{{\tau},-}^l$ by mean-pooling over the embeddings of samples in active class (+) and inactive class (-) from $\mathcal{S}\_{\tau}$. Consequently, the generated adaptive parameter are permutation-invariant to the order of input samples in $\mathcal{S}_{\tau}$.

Reviewer comment (Presentation):
The presentation of results in Tables 1 and 2 would benefit from a consistent format, ensuring ease of interpretation for readers.

Author response:
*Reply*. Thanks for the advice. We now use a consistent format for Table 1 and Table 2 in the revised manuscript.