Multiagent System for Layer Free Network
We propose a multiagent system that have feedforward networks as its subset while free from layer structure with matrix-vector scheme. Deep networks are often compared to the brain neocortex or visual perception system. One of the largest difference from human brain is the use of matrix-vector multiplication based on layer architecture. It would help understanding the way human brain works if we manage to develop good deep network model without the layer architecture while preserving their performance. The brain neocortex works as an aggregation of the local level interactions between neurons, which is rather similar to multiagent system consists of autonomous partially observing agents than units aligned in column vectors and manipulated by global level algorithm. Therefore we suppose that it is an effective approach for developing more biologically plausible model while preserving compatibility with deep networks to alternate units with multiple agents. Our method also has advantage in scalability and memory efficiency. We reimplemented Stacked Denoising Autoencoder(SDAE) as a concrete instance with our multiagent system and verified its equivalence with the standard SDAE from both theoritical and empirical perspectives. Additionary, we also proposed a variant of our multiagent SDAE named *Sparse Connect SDAE*, and showed its computational advantage with the MNIST dataset.
The paper reframes feed forward neural networks as a multi-agent system. It seems to start from the wrong premise that multi-layer neural networks were created expressed as full matrix multiplications. This ignores the decades-long history of development of artificial neural networks, inspired by biological neurons, which thus started from units with arbitrarily sparse connectivity envisioned as computing in parallel. The matrix formulation is primarily a notational convenience; note also that when working with sparse matrix operations (or convolutions) zeros are neither stored not multiplied by. Besides the change in terminology, essentially renaming neurons agents, I find the paper brings nothing new and interesting to the table. Pulling in useful insights from a different communitiy such as multi-agent systems would be most welcome. But for this to be compelling, it would have to be largely unheard-of elements in neural net research, with clear supporting empirical evidence that they significantly improve accuracy or efficiency. This is not achieved in the present paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Multiagent System for Layer Free Network
We propose a multiagent system that have feedforward networks as its subset while free from layer structure with matrix-vector scheme. Deep networks are often compared to the brain neocortex or visual perception system. One of the largest difference from human brain is the use of matrix-vector multiplication based on layer architecture. It would help understanding the way human brain works if we manage to develop good deep network model without the layer architecture while preserving their performance. The brain neocortex works as an aggregation of the local level interactions between neurons, which is rather similar to multiagent system consists of autonomous partially observing agents than units aligned in column vectors and manipulated by global level algorithm. Therefore we suppose that it is an effective approach for developing more biologically plausible model while preserving compatibility with deep networks to alternate units with multiple agents. Our method also has advantage in scalability and memory efficiency. We reimplemented Stacked Denoising Autoencoder(SDAE) as a concrete instance with our multiagent system and verified its equivalence with the standard SDAE from both theoritical and empirical perspectives. Additionary, we also proposed a variant of our multiagent SDAE named *Sparse Connect SDAE*, and showed its computational advantage with the MNIST dataset.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper reframes feed forward neural networks as a multi-agent system. It seems to start from the wrong premise that multi-layer neural networks were created expressed as full matrix multiplications. This ignores the decades-long history of development of artificial neural networks, inspired by biological neurons, which thus started from units with arbitrarily sparse connectivity envisioned as computing in parallel. The matrix formulation is primarily a notational convenience; note also that when working with sparse matrix operations (or convolutions) zeros are neither stored not multiplied by. Besides the change in terminology, essentially renaming neurons agents, I find the paper brings nothing new and interesting to the table. Pulling in useful insights from a different communitiy such as multi-agent systems would be most welcome. But for this to be compelling, it would have to be largely unheard-of elements in neural net research, with clear supporting empirical evidence that they significantly improve accuracy or efficiency. This is not achieved in the present paper.
assistant
The paper reframes feed forward neural networks as a multi-agent system. It seems to start from the wrong premise that multi-layer neural networks were created expressed as full matrix multiplications. This ignores the decades-long history of development of artificial neural networks, inspired by biological neurons, which thus started from units with arbitrarily sparse connectivity envisioned as computing in parallel. The matrix formulation is primarily a notational convenience; note also that when working with sparse matrix operations (or convolutions) zeros are neither stored not multiplied by. Besides the change in terminology, essentially renaming neurons agents, I find the paper brings nothing new and interesting to the table. Pulling in useful insights from a different communitiy such as multi-agent systems would be most welcome. But for this to be compelling, it would have to be largely unheard-of elements in neural net research, with clear supporting empirical evidence that they significantly improve accuracy or efficiency. This is not achieved in the present paper.