Discrete-Valued Neural Networks Using Variational Inference
The increasing demand for neural networks (NNs) being employed on embedded devices has led to plenty of research investigating methods for training low precision NNs. While most methods involve a quantization step, we propose a principled Bayesian approach where we first infer a distribution over a discrete weight space from which we subsequently derive hardware-friendly low precision NNs. To this end, we introduce a probabilistic forward pass to approximate the intractable variational objective that allows us to optimize over discrete-valued weight distributions for NNs with sign activation functions. In our experiments, we show that our model achieves state of the art performance on several real world data sets. In addition, the resulting models exhibit a substantial amount of sparsity that can be utilized to further reduce the computational costs for inference.
 The authors consider the problem of ultra-low precision neural networks motivated by limited computation and bandwidth. Their approach first posits a Bayesian neural network a discrete prior on the weights followed by central limit approximations to efficiently approximate the likelihood. The authors propose several tricks like normalization and cost rescaling to help performance. They compare their results on several versions of MNIST. The paper is promising, but I have several questions: 1) One major concern is that the experimental results are only on MNIST. It*s important to have another (larger) dataset to understand how sensitive the approach is to characteristics of the data. It seems plausible that a more difficulty problem may require more precision. 2) Likelihood weighting is related to annealing and variational tempering 3) The structure of the paper could be improved: - The introduction contains way too many details about the method and related work without a clear boundary. - I would add the model up front at the start of section 2 - Section 2.1 could be reversed or equations 2-5 could be broken with text explaining each choice 4) What does training time look like? Is the Bayesian optimization necessary?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Discrete-Valued Neural Networks Using Variational Inference
The increasing demand for neural networks (NNs) being employed on embedded devices has led to plenty of research investigating methods for training low precision NNs. While most methods involve a quantization step, we propose a principled Bayesian approach where we first infer a distribution over a discrete weight space from which we subsequently derive hardware-friendly low precision NNs. To this end, we introduce a probabilistic forward pass to approximate the intractable variational objective that allows us to optimize over discrete-valued weight distributions for NNs with sign activation functions. In our experiments, we show that our model achieves state of the art performance on several real world data sets. In addition, the resulting models exhibit a substantial amount of sparsity that can be utilized to further reduce the computational costs for inference.
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 authors consider the problem of ultra-low precision neural networks motivated by limited computation and bandwidth. Their approach first posits a Bayesian neural network a discrete prior on the weights followed by central limit approximations to efficiently approximate the likelihood. The authors propose several tricks like normalization and cost rescaling to help performance. They compare their results on several versions of MNIST. The paper is promising, but I have several questions: 1) One major concern is that the experimental results are only on MNIST. It*s important to have another (larger) dataset to understand how sensitive the approach is to characteristics of the data. It seems plausible that a more difficulty problem may require more precision. 2) Likelihood weighting is related to annealing and variational tempering 3) The structure of the paper could be improved: - The introduction contains way too many details about the method and related work without a clear boundary. - I would add the model up front at the start of section 2 - Section 2.1 could be reversed or equations 2-5 could be broken with text explaining each choice 4) What does training time look like? Is the Bayesian optimization necessary?
assistant
 The authors consider the problem of ultra-low precision neural networks motivated by limited computation and bandwidth. Their approach first posits a Bayesian neural network a discrete prior on the weights followed by central limit approximations to efficiently approximate the likelihood. The authors propose several tricks like normalization and cost rescaling to help performance. They compare their results on several versions of MNIST. The paper is promising, but I have several questions: 1) One major concern is that the experimental results are only on MNIST. It*s important to have another (larger) dataset to understand how sensitive the approach is to characteristics of the data. It seems plausible that a more difficulty problem may require more precision. 2) Likelihood weighting is related to annealing and variational tempering 3) The structure of the paper could be improved: - The introduction contains way too many details about the method and related work without a clear boundary. - I would add the model up front at the start of section 2 - Section 2.1 could be reversed or equations 2-5 could be broken with text explaining each choice 4) What does training time look like? Is the Bayesian optimization necessary?