Atomic Compression Networks
Compressed forms of deep neural networks are essential in deploying large-scale computational models on resource-constrained devices. Contrary to analogous domains where large-scale systems are build as a hierarchical repetition of small- scale units, the current practice in Machine Learning largely relies on models with non-repetitive components. In the spirit of molecular composition with repeating atoms, we advance the state-of-the-art in model compression by proposing Atomic Compression Networks (ACNs), a novel architecture that is constructed by recursive repetition of a small set of neurons. In other words, the same neurons with the same weights are stochastically re-positioned in subsequent layers of the network. Empirical evidence suggests that ACNs achieve compression rates of up to three orders of magnitudes compared to fine-tuned fully-connected neural networks (88× to 1116× reduction) with only a fractional deterioration of classification accuracy (0.15% to 5.33%). Moreover our method can yield sub-linear model complexities and permits learning deep ACNs with less parameters than a logistic regression with no decline in classification accuracy.
This paper explores the use of replicating neurons across and within layers to compress fully connected neural networks. The idea is simple, and is evaluated on a number of datasets and compared with fully connected, single layer, and several compression schemes. Strengths: a lot of nice experiments with clearly advantageous results are given. Weaknesses: One obvious baseline missing is sparse compression, which can be achieved using either l1 regularization, or hard thresholding + fine tuning, both of which are easy to implement and appear in several works, e.g. Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization (Yang, Ruozzi, Gogate) Training skinny deep neural networks with iterative hard thresholding methods (Yin, Yuan, Feng, Yan) ... many others just via googling ... Also, I think this work should be compared with compression schemes that work via kronecker product, which seem very similar to this scheme (but where the kronecker matrix is binary to produce replication) Compression of Fully-Connected Layer in Neural Network by Kronecker Product (Zhou, Wu) (more via google) One obvious advantage of replication over kronecker product is lower complexity, but nonetheless, the methods belong in a similar family. Otherwise, I think the work makes sense, the idea is nice, and the results show promise! After rebuttal: I have read the rebuttal and the authors have basically addressed all my concerns. It is a bit disappointing that simple L1 regularization can give competitive results, but the fact that the authors are willing to do the experiment and incorporate the results convinces me that there*s nothing being hidden here, and the reader can make a fair and informed conclusion, so I have no more complaints.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Atomic Compression Networks
Compressed forms of deep neural networks are essential in deploying large-scale computational models on resource-constrained devices. Contrary to analogous domains where large-scale systems are build as a hierarchical repetition of small- scale units, the current practice in Machine Learning largely relies on models with non-repetitive components. In the spirit of molecular composition with repeating atoms, we advance the state-of-the-art in model compression by proposing Atomic Compression Networks (ACNs), a novel architecture that is constructed by recursive repetition of a small set of neurons. In other words, the same neurons with the same weights are stochastically re-positioned in subsequent layers of the network. Empirical evidence suggests that ACNs achieve compression rates of up to three orders of magnitudes compared to fine-tuned fully-connected neural networks (88× to 1116× reduction) with only a fractional deterioration of classification accuracy (0.15% to 5.33%). Moreover our method can yield sub-linear model complexities and permits learning deep ACNs with less parameters than a logistic regression with no decline in classification accuracy.
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>



This paper explores the use of replicating neurons across and within layers to compress fully connected neural networks. The idea is simple, and is evaluated on a number of datasets and compared with fully connected, single layer, and several compression schemes. Strengths: a lot of nice experiments with clearly advantageous results are given. Weaknesses: One obvious baseline missing is sparse compression, which can be achieved using either l1 regularization, or hard thresholding + fine tuning, both of which are easy to implement and appear in several works, e.g. Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization (Yang, Ruozzi, Gogate) Training skinny deep neural networks with iterative hard thresholding methods (Yin, Yuan, Feng, Yan) ... many others just via googling ... Also, I think this work should be compared with compression schemes that work via kronecker product, which seem very similar to this scheme (but where the kronecker matrix is binary to produce replication) Compression of Fully-Connected Layer in Neural Network by Kronecker Product (Zhou, Wu) (more via google) One obvious advantage of replication over kronecker product is lower complexity, but nonetheless, the methods belong in a similar family. Otherwise, I think the work makes sense, the idea is nice, and the results show promise! After rebuttal: I have read the rebuttal and the authors have basically addressed all my concerns. It is a bit disappointing that simple L1 regularization can give competitive results, but the fact that the authors are willing to do the experiment and incorporate the results convinces me that there*s nothing being hidden here, and the reader can make a fair and informed conclusion, so I have no more complaints.
assistant
This paper explores the use of replicating neurons across and within layers to compress fully connected neural networks. The idea is simple, and is evaluated on a number of datasets and compared with fully connected, single layer, and several compression schemes. Strengths: a lot of nice experiments with clearly advantageous results are given. Weaknesses: One obvious baseline missing is sparse compression, which can be achieved using either l1 regularization, or hard thresholding + fine tuning, both of which are easy to implement and appear in several works, e.g. Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization (Yang, Ruozzi, Gogate) Training skinny deep neural networks with iterative hard thresholding methods (Yin, Yuan, Feng, Yan) ... many others just via googling ... Also, I think this work should be compared with compression schemes that work via kronecker product, which seem very similar to this scheme (but where the kronecker matrix is binary to produce replication) Compression of Fully-Connected Layer in Neural Network by Kronecker Product (Zhou, Wu) (more via google) One obvious advantage of replication over kronecker product is lower complexity, but nonetheless, the methods belong in a similar family. Otherwise, I think the work makes sense, the idea is nice, and the results show promise! After rebuttal: I have read the rebuttal and the authors have basically addressed all my concerns. It is a bit disappointing that simple L1 regularization can give competitive results, but the fact that the authors are willing to do the experiment and incorporate the results convinces me that there*s nothing being hidden here, and the reader can make a fair and informed conclusion, so I have no more complaints.