Adding Gradient Noise Improves Learning for Very Deep Networks
Deep feedforward and recurrent networks have achieved impressive results in many perception and language processing applications. Recently, more complex architectures such as Neural Turing Machines and Memory Networks have been proposed for tasks including question answering and general computation, creating a new set of optimization challenges. In this paper, we explore the low-overhead and easy-to-implement optimization technique of adding annealed Gaussian noise to the gradient, which we find surprisingly effective when training these very deep architectures. Unlike classical weight noise, gradient noise injection is complementary to advanced stochastic optimization algorithms such as Adam and AdaGrad. The technique not only helps to avoid overfitting, but also can result in lower training loss. We see consistent improvements in performance across an array of complex models, including state-of-the-art deep networks for question answering and algorithm learning. We observe that this optimization strategy allows a fully-connected 20-layer deep network to escape a bad initialization with standard stochastic gradient descent. We encourage further application of this technique to additional modern neural architectures.
The authors consider a simple optimization technique consisting of adding gradient noise with a specific schedule. They test their method on a number of recently proposed neural networks for simulating computer logic (end-to-end memory network, neural programmer, neural random access machines). On these networks, the question of optimization has so far not been studied as extensively as for more standard networks. A study specific to this class of models is therefore welcome. Results consistently show better optimization properties from adding noise in the training procedure. One issue with the paper is that it is not clear whether the proposed optimization strategy permits to learn actually good models, or simply better than those that do not use noise. A comparison to results obtained in the literature would be desirable. For example, in the MNIST experiments of Section 4.1, the optimization procedure reaches in the most favorable scenario an average accuracy level of approximately 92%, which is still far from having actually learned an interesting problem representation (a linear model would probably reach similar accuracy). I understand that the architecture is specially designed to be difficult to optimize (20 layers of 50 HUs), but it would have been more interesting to consider a scenario where depth is actually beneficial for solving the problem.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Adding Gradient Noise Improves Learning for Very Deep Networks
Deep feedforward and recurrent networks have achieved impressive results in many perception and language processing applications. Recently, more complex architectures such as Neural Turing Machines and Memory Networks have been proposed for tasks including question answering and general computation, creating a new set of optimization challenges. In this paper, we explore the low-overhead and easy-to-implement optimization technique of adding annealed Gaussian noise to the gradient, which we find surprisingly effective when training these very deep architectures. Unlike classical weight noise, gradient noise injection is complementary to advanced stochastic optimization algorithms such as Adam and AdaGrad. The technique not only helps to avoid overfitting, but also can result in lower training loss. We see consistent improvements in performance across an array of complex models, including state-of-the-art deep networks for question answering and algorithm learning. We observe that this optimization strategy allows a fully-connected 20-layer deep network to escape a bad initialization with standard stochastic gradient descent. We encourage further application of this technique to additional modern neural architectures.
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 a simple optimization technique consisting of adding gradient noise with a specific schedule. They test their method on a number of recently proposed neural networks for simulating computer logic (end-to-end memory network, neural programmer, neural random access machines). On these networks, the question of optimization has so far not been studied as extensively as for more standard networks. A study specific to this class of models is therefore welcome. Results consistently show better optimization properties from adding noise in the training procedure. One issue with the paper is that it is not clear whether the proposed optimization strategy permits to learn actually good models, or simply better than those that do not use noise. A comparison to results obtained in the literature would be desirable. For example, in the MNIST experiments of Section 4.1, the optimization procedure reaches in the most favorable scenario an average accuracy level of approximately 92%, which is still far from having actually learned an interesting problem representation (a linear model would probably reach similar accuracy). I understand that the architecture is specially designed to be difficult to optimize (20 layers of 50 HUs), but it would have been more interesting to consider a scenario where depth is actually beneficial for solving the problem.
assistant
The authors consider a simple optimization technique consisting of adding gradient noise with a specific schedule. They test their method on a number of recently proposed neural networks for simulating computer logic (end-to-end memory network, neural programmer, neural random access machines). On these networks, the question of optimization has so far not been studied as extensively as for more standard networks. A study specific to this class of models is therefore welcome. Results consistently show better optimization properties from adding noise in the training procedure. One issue with the paper is that it is not clear whether the proposed optimization strategy permits to learn actually good models, or simply better than those that do not use noise. A comparison to results obtained in the literature would be desirable. For example, in the MNIST experiments of Section 4.1, the optimization procedure reaches in the most favorable scenario an average accuracy level of approximately 92%, which is still far from having actually learned an interesting problem representation (a linear model would probably reach similar accuracy). I understand that the architecture is specially designed to be difficult to optimize (20 layers of 50 HUs), but it would have been more interesting to consider a scenario where depth is actually beneficial for solving the problem.