NeurIPS 2020

H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

Review 1

Summary and Contributions: The paper discusses a neural hetero-associative memory system that incorporates fast Hebbian learning. The method is tested on a question answering task and a new image pairing task, and it outperforms LSTMs in these tasks.

Strengths: - The model is elegant. - The writing is clear. - The demonstration with the image task illustrated the capabilities of the model very nicely. - The model does well with small training sets.

Weaknesses: - The method is only compared to prior models with long-term memory on the question answering task, and doesn't perform as well as the prior MemN2N or EntNet models on that task.

Correctness: I believe the claims and methods are correct.

Clarity: The paper is very clearly written.

Relation to Prior Work: Relationships with past work are discussed. Unfortunately I am not familiar enough with this line of work to evaluate this discussion, but light searching didn't turn up any glaring omissions.

Reproducibility: Yes

Additional Feedback: - Is it essential that the key-value matrix is Hebbian? The rest of the network is trained with backpropagation and Adam. Would it also work just as well to train the key-value matrix in the same way but with a higher rate? Is it important that it's Hebbian? - How and why do the query and storage keys differ? - This likely reflects my own ignorance more than anything else, but isn't it possible to achieve good performance on the tasks in the paper with simple non-neural methods (e.g. storing key-value pairs of embeddings directly and finding the most similar key at lookup time)? Because I'm not clear on this, the practical value isn't obvious to me (in contrast e.g. to various vision problems that can't be done well by classical methods). Of course a model with limited practical value could still be important. For example, it might shed light on physiological function. However the physiological realism of this model wasn't argued (in fact the authors disclaimed it somewhat in the second paragraph of the discussion). For readers like me, it would be helpful to explain the practicality or physiological relevance in more detail. - UPDATE AFTER REBUTTAL: The authors raised convincing points and I have raised my score.

Review 2

Summary and Contributions: In this article the authors propose a novel neural network architecture, similar to other memory-augmented networks, but with the addition of an associative memory module. The introduction of this module allows the network to perform tasks that require maintenance and association over long time periods, with greater accuracy than LSTMs and many other memory-augmented networks. This appears to be one of the first attempts to augment networks with an associative memory, and may open the field to further investigation.

Strengths: The authors introduced and succinctly described the importance of an associative module, and how it interacts with the two controllers required for the network to perform the task. Because most neural networks do not contain this explicit dichotomy of associative versus supervised modules, this is a novel contribution. The authors justify their choice to use an associative module based on previous research.

Weaknesses: While the paper is overall strong and the results are exciting to those interested in biologically inspired networks, the current framing may be too narrow to interest many others in the NeurIPS community. Perhaps the authors could describe what modifications of the associative module (including non-biological inspiration) might further improve performance, or how the task-control vs memory dichotomy could be more widely used in neural network research. Following the author's rebuttal, they seem inclined to include more details regarding the biological inspiration and other details that should increase interest of the wider audience.

Correctness: The empirical methodology appears standard, and is reported in sufficient detail to recreate the results. The resulting claims are justified by the performance of the network.

Clarity: The writing is clear and succinct.

Relation to Prior Work: The authors reference prior work throughout the paper, while also emphasizing how the associative module makes the current implementation different from those approaches. One very recent paper that the authors may want to address is Le, Tran, Venkatesh, Self-attentive Associative Memory, ArXiV 2000, where a similar controller+associative memory approach is taken.

Reproducibility: Yes

Additional Feedback: The section of the discussion addressing biological plausibility and spiking neural network applications seems disjointed from the majority of the paper. Dropping this section would allow the authors to instead discuss what modifications of the associative memory module might be of interest in future research. [Following author responses]: The authors intend to address the various concerns of the reviewers, and I stand by my original score.

Review 3

Summary and Contributions: This work proposes a new architecture of memory network based on the Hebbian theory called Hebbian Memory Network (H-Mem). On an image paring task, the model showed much better results than LSTM. On most of the bAbI tasks, the model showed comparable performance to existing models.

Strengths: 1. The idea is novel. With memory module (Wassoc), the architecture innovatively combines the Hebbian rule and backpropagation. 2. The proposed model is quite simple but performed well on two tasks, which is surprising.

Weaknesses: 1. The motivation of the model is unclear. In other words, why can this model work on the two tasks? We cannot simply say it uses Hebbian rule which agrees with biological system then it should work. A reason, or intuition, from the perspective of machine learning should be provided. I want to see explanations on both tasks in the rebuttal. 2. Some important technical details are missing. First, are the image pairs (a natural image+a digital image) presented as a whole to the store branch, or separately (i.e., natural image features presented to W_key and digital image features presented to W_val)? In the latter case, it is easy to understand why the model works on this task. But from the description of the method, the former is used, which makes me confusing. In Fig 2, three pairs of images are dipicted in the storage stage. Is this the setting for real implementation or just an illustration and in practice there are M>>3 pairs of images in the storage stage? How large is M? Second, it is said 100 epochs were trained for the first task. What's the defintion of a epoch? In other words, how many samples were in one epoch and what's the definition of one "sample"? Does one "sample" refers to a natural-digital image pair or a sequence of such pairs? Third, in the first task, during the presence of the first 3 pairs of images, is there a teacher signal for the output? Since there is a delay between sample presentations, is there teacher signals during the delay? If yes, what are the teacher signals? Finally, it is said that W^assoc is not optimized, but how is (1) implemented during the training process. Note that (1) describes an iterative process while the training process is also an iterative process. How do the two process interact? These problems prevents me giving higher score to the paper.

Correctness: Maybe

Clarity: Some important technical details are missing.

Relation to Prior Work: Yes

Reproducibility: No

Additional Feedback: All of my questions have been clarified. Thanks. But an important technical detail is still unclear to me: In the first experiment, it is said that the teaching signal is given only after the query (the last image in a sequence of images), then this signal can influence the weights W_key^q by using BP algorithm. But how does this signal influence the weights W_val^S and W_key^S?

Review 4

Summary and Contributions: The paper presents a hetero-associate memory model inspired by Hebbian synaptic plasticity and applies it to a single-shot association task and question answering task.

Strengths: The paper is well positioned using relevant benchmark problems and comparisons to applicable alternatives. The review of previous work is very strong. Throughout the discussion the authors show a strong familiarity with the relevant literature. Experimental work is carefully done and the results are convincing.

Weaknesses: I would like to see a brief conclusion section. The papers seems to just trail off. Other than that, it seems like a strong contribution.

Correctness: Looks good to me.

Clarity: Few, minor grammatical errors. One more careful proof read would be good.

Relation to Prior Work: Good discussion of both similarities and differences to previous works. This is a strong foundation that makes everything else very convincing.

Reproducibility: Yes

Additional Feedback: I originally thought that this paper should be accepted and the authors' rebuttal did not sway me from that opinion. ;)