Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The paper presents a neural architecture that models a dynamical system by building representations for both individual and collective population dynamics separately. The first transformer operates only on individual time series and the second transformer acts as an interaction network to come up with the collective representation of the models. The models are trained to classify or regress the target values. Authors then test this model on a toy datasets and neural activity modelling.
strengths_and_weaknesses: **Strengths**
- The paper is well written and easy to follow.
- The paper demonstrates the clear benefits of decomposing dynamics modelling into  individual dynamics and collective dynamic for better transfer learning.
- The application to neural activity datasets is interesting.

**Weaknesses:**
- The method is limited in terms of novelty as the individual modules for capturing individual dynamics and collective dynamics are commonly used transformer architectures.
- The method is only applicable to the data where the existence and number of separate channels in the input data is known. The limits it applicability to models where the number of interacting agents in the systems are not known.
questions: - The method doesn't seem to infer or decompose entities from raw observational data. It requires the knowledge of separate channels and feed them separately to individual transformer which a simpler problem than a cocktail party problem. Doesn't it restrict its applicability for e.g. in many cases we don't know the number of interacting agents.
- How do you infer the components/objects/neurons whose dynamics needs to be modelled individually?
- In the works of [1], [2], [3], [4] the models first try to infer individual entities from the observed frames in videos and then try to model dynamics of each entity individually. Moreover, an interaction module is used to explicitly model objects interaction and combined influence on their dynamics. [3] even uses transformer for modelling dynamics. It would be interesting to position this paper w.r.t them.
- Can interaction transformer introduce some kind of bias towards the order of channels?
- Since it’s not a generative model, it’s hard to quantitatively see how individual channels are independent. Could there be a test to measure how independent individual dynamics are? Can authors try permuting the channels and see if the results on the test set remain the same?
- [Related to above question]: Did you try dropping one or a few channel to see how models behave?
- How does interaction network $g$ capture complex interaction without any specific inductive bias for capturing such interactions for e.g. as shown in [5]. - Could there be any specific interaction module to account for the interactions between individual agents? Would it work for physics based interactions between agents e.g. collision between objects or if agents exhibits some magnetic push/pull?

[1] https://arxiv.org/abs/1806.04166
[2] https://arxiv.org/abs/1806.01794
[3] https://arxiv.org/abs/2107.09240
[4] https://arxiv.org/abs/2205.14065
[5] https://arxiv.org/abs/1612.00222

limitations: The model applicability seems to be limited to the problems where the exact number of agents contributing to overall population dynamics need to be known beforehand.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper presents a transformer architecture-based time-varying data learning method. It consists of a temporal-spatial module and a projection module to learn the descriptions of both the individuals and collective population and then leverages the interactions to predict the target behavior. The experimental evaluation is performed on synthetic many-body systems and real neural response datasets.

strengths_and_weaknesses: --- Strong points:

This paper proposes a disentangled representation learning which models individual channel time series and their interactions. 


--- Weak points:

The proposed method is built on top of existing ideas and lacks novelty and insights. Meanwhile, some design choices are not well justified theatrically or experimentally. 

(a) The problem formulation is confusing. As is mentioned in Sec. 3.1, the input observation and target sequence are both in the time range from 1 to T. Depending on the conditioned observations on which the prediction of the target at a certain timestamp is, it can lead to different types of problem setups, e.g., forecasting, filtering or smoothing. These problems have different directions of solutions and the author is expected to explicitly specify the problem type. 

(b) The optimization problem (3) is ill-defined in that the input into g() is of the shape N*T, while the function g() is defined as a mapping from R^N to R^N. Meanwhile, this definition is different from the loss function (4), thereby giving the impression that the optimization problem seems not clearly defined in the first place and the subsequently proposed method can hardly be solid enough. 

(c) The temporal-spatial attention and then slicing along different dimensions of the output representations have been extensively studied in the architectures like RNN and CNN [1, 3]. It is also not novel to explore disentangled individual representations of multi-channel time series [2, 3]. The Transformer is applied in this paper as an off-the-shelf encoder. Overall, the paper seems to bear marginal technical contributions regarding model architectures. 

[1] Qin, Yao, et al. "A dual-stage attention-based recurrent neural network for time series prediction." Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017.

[2] Guo, Tian, Tao Lin, and Nino Antulov-Fantulin. "Exploring interpretable LSTM neural networks over multi-variable data." International conference on machine learning. PMLR, 2019.

[3] Assaf, Roy, et al. "Mtex-CNN: Multivariate time series explanations for predictions with convolutional neural networks." 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019.

(d) The motivation for using the Transformer on individual time series is weak. The Transformer is intended to capture pair-wise relations between tokens/steps in a multi-dimensional sequence. As far as I know, recent works mostly applied the Transformer or variants to multi-dimension time series [4, 5]. However, applying Transformer on univariate time series riks overcomplicating the model and overfitting as well. It would be more convincing to see the comparison between conventional sequence models and Transformer in the proposed framework.

[4] Tang, Binh, and David S. Matteson. "Probabilistic transformer for time series analysis." Advances in Neural Information Processing Systems 34 (2021): 23592-23608.

[5] Zhou, Tian, et al. "FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting." arXiv preprint arXiv:2201.12740 (2022).

(d) A projection module and two different project operations are described in Sec. 3.2. It is necessary to clarify their difference and connection. Meanwhile, the projection module consists of some plain operations, e.g., dense transformation and concatenation. Technically, this module bears marginal novelty or new insights. 

(e) In the synthetic data experiment, there is no reference for the baseline method and thus it is difficult to evaluate the significance of the proposed method.   

(f) The hyper-parameter search process is missing for baselines. It is important to describe the process, since it significantly affects the performance and the validity of the comparison. 


questions: Apart from the presentation issues mentioned in the above weak point summary, comparing the Transformer and conventional sequence models on univariate time series in the proposed framework would be useful for assessing the significance of the proposed method.  
limitations: No potential negative societal impact is found.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: The authors present a novel approach for inference over collective systems, through a separable transformer-based neural network architecture. The novelty lies in learning both the individual components of the system (the trees) as well as the population-level dynamics (the forest). The authors correctly identify that separating individual from system level dynamics has previously been investigated (e.g., in the GroupFormer archetecture [Li, Shuaicheng, et al]) but set themselves apart from previous work by noting that they learn representations at both scales in a quasi-independent manner or, in the authors words, they 'restrain the direct computation' between the two learned maps. To move between the two maps, population and individual, a projection module is learned simultaneously which provides access to both representations. 

Crucially, by disentangling learning over the collective into two independent representations, the authors demonstrate that they can use embeddings of individual-scale dynamics for transfer learning on new datasets. This can be applied to new datasets of arbitrary size and/or ordering due to the permutation-invariance property (a consequence of separating individual and population learning tasks). 

The authors test their approach on two datasets: a synthetic many-body simulation (of two/three masses in orbit) and neuronal behavioural data. They find strong performance both on the synthetic dataset and on the neuronal data. Importantly, the authors find that they can use a model trained on one dataset and apply the invariant portion of this model to a new unseen dataset. This retrained model outperforms many others which learn from scratch. 

Li, Shuaicheng, et al. "Groupformer: Group activity recognition with clustered spatial-temporal transformer." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.

strengths_and_weaknesses: This paper is well structured and written, provides a novel approach to an interesting problem and is considered in its discussion of its own strengths and weaknesses. I think the work will be seen as significant, especially given its framework with respect to challenges in neuroscience. 

Specifically, by focussing on learning representations of individual dynamics distinct from population dynamics, the authors rightly point to the added benefit of transfer learning by levering permutation-invariance of the separated system. I see this form of transfer learning as the central result of the approach and the authors did well to emphasis this. 

My main concern is that the synthetic dataset is a relatively simple example when compared against the application of neuronal data. The two-body system will not have particularly complex dynamics and I did not find it particularly compelling. Whereas I appreciate the three-body system will have chaotic dynamics under some parameter values, it wasn't clear to me what proportion of the orbits were expected to be chaotic. Additionally, predicting the trajectories and underlying parameters for a three-body system seems an easy problem when compared to the neural dataset. I suggest the authors introduce a third example of intermediate difficulty, either with significantly more individuals/channels, time-varying interactions, or a typical dataset from previous work on neural relational inference (see, for example, [Kipf, Thomas, et al.] and [Graber, Colin, and Alexander Schwing]). 

There are some minor languages points. I would consider rewording sentence on lines 86-87. Additionally, the second challenge in section "Learning across recordings or individuals" is not especially clear. I think the authors are referring here to heterogeneity in labels, both within (e.g. a brain), across a single species/system, and across different species/systems. I think the reference to number of neurons (on line 98) is referencing number of channels/individuals. 

Kipf, Thomas, et al. "Neural relational inference for interacting systems." International Conference on Machine Learning. PMLR, 2018.

Graber, Colin, and Alexander Schwing. "Dynamic neural relational inference for forecasting trajectories." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020.
questions: The authors mention in their conclusion that by treating individual systems separately, the individuals have limited dimensionality. I would have also expected that approaches that decouple learned representations of individual from the population have poorer performance on population-scale prediction tasks. My reasoning is that population-based learning can be improved by leveraging/constraining learned representations on accurate relational/individual representations. Do the authors have any thoughts on this? 

I believe that decoupling the learning of individual from population allows more efficient scaling to large datasets. Is this correct? 
limitations: The authors highlight two limitations of their work: reliance on deterministic trajectories at the individual-scale and lost information due to treating systems independently. I have asked for clarification on whether population-based predictions also suffer due to decoupling of learning on individuals from population, and whether this is an additional limitation of the approach.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: Authors introduce a method to decompose the representations of a population of neurons of the individual dynamics from the population dynamics which allows decoding generalization to unseen populations of neurons.

The results are presented on a behavioral set of benchmark datasets where the author's method outperforms (by a meaningful margin) on 6/10 tasks.

Overall, the work is impactful in that generalization to unseen populations of neurons is a big unblock for the neuroscience community.
strengths_and_weaknesses: Strengths:
- motivation is very strong
- the approach is well explained by breaking up the dynamic into f, g which enable the decomposition desired and a general approach where anything can be used for f or g
- formulation is simple which is a huge plus.

Weaknesses:
- would have liked to see more benchmarks, but understand that there is limited availability of this.
questions: - there wasn't a lot on related work on the comp neuro side. are there related papers that attempt something similar?
limitations: Have not addressed society impacts directly... however, this is HUGELY (positively) impactful for applications like prosthesis where the prosthesis design needs to generalize to different neural populations for each individual.
review_title: null

Meta-review: metareview: This paper introduces the multi-stage Embedded Interaction Transformer which models individual channels of systems with multiple interacting elements and then models their interactions.  The approach is applied to simulated systems to recover known interaction-dynamics as well as neural datasets, revealing transferability of some model parameters across animals.  Essentially the model is structured to solve a supervised regression/classification problem where the input corresponds to many (possibly a variable number of) timeseries and the timeseries channels reflect observations of interacting elements.

Reviewers generally found the paper clear, proposing a simple innovation, and applied to an interesting problem class involving neural data.  Two of the reviewers expressed some concerns about the degree of novelty in the proposed approach.  The authors responded to this point directly and while they didn't totally satisfy reviewer 6wnv, this reviewer still updated their rating from a 4 to a 5.  

In my own assessment, aligned with the less enthusiastic reviewers, I found the technical contribution somewhat incremental, but clearly enough described.  And I found the evaluation somewhat limited (consistent with reviewer 7m6L), given the moderate magnitudes of improvement, but adequate.  That the EIT generalizes better than other approaches does increase its potential impact and may inspire future follow-up work.  Given the distribution of reviewer ratings, I'm willing to endorse the paper.
confidence: Less certain
Decision: Accept