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 propose to add noise to the gradients computed while optimizing deep neural networks with stochastic gradient based methods. They show results multiple data sets which indicate that the method can counteract bad parameter initialization and that it can be especially beneficial for training more complicated architectures. The method is tested on a multitude of different tasks and architectures. The results would be more convincing if they would be accompanied by confidence intervals but I understand that some of the experiments must have taken very long to run. I like that the results include both situations in which the gradient noise helps a lot and situations in which it doesn’t seem to add much to the other optimization or initialization tools employed. The quantity of the experiments and the variety of the models provide quite convincing evidence that the effect of the gradient noise generalizes to many settings. The results were not always that convincing. In Section 4.2, the method only helped significantly when a sub-optimal training scheme was used, for example. The results on MNIST are not very good compared to the state-of-the-art. Since the method is so simple, I was hoping to see more theoretical arguments for its usefulness. That said, the experimental investigations into the importance of the annealing procedure, the comparison with the effect of gradient stochasticity and the comparison with weight noise, provide some additional insight. The paper is well written and cites relevant prior work. The proposed method is described clearly and concisely, which is to be expected given its simplicity. The proposed idea is not very original. As the authors acknowledge, very similar algorithms have been used for training and it is pretty much identical to simulating Langevin dynamics but with the goal of finding a single optimum in mind rather than approximating an expected value. The work is the evaluation of an old tool in a new era where models have become bigger and more complex. Despite the lack of novelty of the method, I do think that the results are valuable. The method is so easy to implement and seems to be so useful for complicated model which are hard to initialize, that it is important for others in the field to know about it. I suspect many people will at least try the method. The variety of the architectures and tasks for which the method was useful suggests that many people may also add it to their repertoire of optimization tricks. Pros: * The idea is easy to implement. * The method is evaluated on a variety of tasks and for very different models. * Some interesting experiments which compare the method with similar approaches and investigate the importance of the annealing scheme. * The paper is well-written. Cons: * The idea is not very original. * There is no clear theoretical motivation of analysis. * Not all the results are convincing.
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 propose to add noise to the gradients computed while optimizing deep neural networks with stochastic gradient based methods. They show results multiple data sets which indicate that the method can counteract bad parameter initialization and that it can be especially beneficial for training more complicated architectures. The method is tested on a multitude of different tasks and architectures. The results would be more convincing if they would be accompanied by confidence intervals but I understand that some of the experiments must have taken very long to run. I like that the results include both situations in which the gradient noise helps a lot and situations in which it doesn’t seem to add much to the other optimization or initialization tools employed. The quantity of the experiments and the variety of the models provide quite convincing evidence that the effect of the gradient noise generalizes to many settings. The results were not always that convincing. In Section 4.2, the method only helped significantly when a sub-optimal training scheme was used, for example. The results on MNIST are not very good compared to the state-of-the-art. Since the method is so simple, I was hoping to see more theoretical arguments for its usefulness. That said, the experimental investigations into the importance of the annealing procedure, the comparison with the effect of gradient stochasticity and the comparison with weight noise, provide some additional insight. The paper is well written and cites relevant prior work. The proposed method is described clearly and concisely, which is to be expected given its simplicity. The proposed idea is not very original. As the authors acknowledge, very similar algorithms have been used for training and it is pretty much identical to simulating Langevin dynamics but with the goal of finding a single optimum in mind rather than approximating an expected value. The work is the evaluation of an old tool in a new era where models have become bigger and more complex. Despite the lack of novelty of the method, I do think that the results are valuable. The method is so easy to implement and seems to be so useful for complicated model which are hard to initialize, that it is important for others in the field to know about it. I suspect many people will at least try the method. The variety of the architectures and tasks for which the method was useful suggests that many people may also add it to their repertoire of optimization tricks. Pros: * The idea is easy to implement. * The method is evaluated on a variety of tasks and for very different models. * Some interesting experiments which compare the method with similar approaches and investigate the importance of the annealing scheme. * The paper is well-written. Cons: * The idea is not very original. * There is no clear theoretical motivation of analysis. * Not all the results are convincing.
assistant
The authors propose to add noise to the gradients computed while optimizing deep neural networks with stochastic gradient based methods. They show results multiple data sets which indicate that the method can counteract bad parameter initialization and that it can be especially beneficial for training more complicated architectures. The method is tested on a multitude of different tasks and architectures. The results would be more convincing if they would be accompanied by confidence intervals but I understand that some of the experiments must have taken very long to run. I like that the results include both situations in which the gradient noise helps a lot and situations in which it doesn’t seem to add much to the other optimization or initialization tools employed. The quantity of the experiments and the variety of the models provide quite convincing evidence that the effect of the gradient noise generalizes to many settings. The results were not always that convincing. In Section 4.2, the method only helped significantly when a sub-optimal training scheme was used, for example. The results on MNIST are not very good compared to the state-of-the-art. Since the method is so simple, I was hoping to see more theoretical arguments for its usefulness. That said, the experimental investigations into the importance of the annealing procedure, the comparison with the effect of gradient stochasticity and the comparison with weight noise, provide some additional insight. The paper is well written and cites relevant prior work. The proposed method is described clearly and concisely, which is to be expected given its simplicity. The proposed idea is not very original. As the authors acknowledge, very similar algorithms have been used for training and it is pretty much identical to simulating Langevin dynamics but with the goal of finding a single optimum in mind rather than approximating an expected value. The work is the evaluation of an old tool in a new era where models have become bigger and more complex. Despite the lack of novelty of the method, I do think that the results are valuable. The method is so easy to implement and seems to be so useful for complicated model which are hard to initialize, that it is important for others in the field to know about it. I suspect many people will at least try the method. The variety of the architectures and tasks for which the method was useful suggests that many people may also add it to their repertoire of optimization tricks. Pros: * The idea is easy to implement. * The method is evaluated on a variety of tasks and for very different models. * Some interesting experiments which compare the method with similar approaches and investigate the importance of the annealing scheme. * The paper is well-written. Cons: * The idea is not very original. * There is no clear theoretical motivation of analysis. * Not all the results are convincing.