While most bounds [29, 3, 11, 31, 27, 32] apply to the original network, they are neither numerically small for realistic dataset sizes, nor exhibit the desired width/depth dependencies (in fact, these The first RL work to make the front page was the original DeepMind paper on learning to play Atari games using the ALE environment. The accuracy of deep learning, i.e., deep neural networks, can be characterized by dividing the total error into three main types: approximation error, optimization error, and generalization error. /Annots [ 150 0 R 151 0 R 152 0 R 153 0 R 154 0 R 155 0 R 156 0 R ] This paper provides non-vacuous and numerically-tight generalization guarantees for deep learning, as well as theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, responding to an open question in the literature. >> /Author (Behnam Neyshabur\054 Srinadh Bhojanapalli\054 David Mcallester\054 Nati Srebro) When learning to play some game, we might like our policy to learn to avoid enemies, jump over obstacles and grab the treasure. In what follows, I am going to focus on the first type. Several other papers suggest that RL policies can be brittle to very minor mismatches between the environment they learn on and the one they are expected to be deployed in, making adoption of RL in the real world very difficult. /Parent 1 0 R /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R 13 0 R ] In this paper, we discuss these challenging issues in the context of wide neural networks at large depths where we will see that the situation simplifies considerably. Make learning your daily ritual. Because the cardinality of F is typically (uncountably) infinite, a direct use of the union bound over all elements inF yields a vacuous bound, leading to the need to consider different quantities to characterizeF; e.g., We present a benchmark for studying generalization in deep reinforcementlearning (RL). /MediaBox [ 0 0 612 792 ] /Annots [ 104 0 R 105 0 R 106 0 R 107 0 R 108 0 R ] /Annots [ 219 0 R 220 0 R 221 0 R 222 0 R 223 0 R 224 0 R ] %PDF-1.3 This is usually referred to as Generalization, or the ability to learn something that is useful beyond the specifics of the training environment. Coming back to deep … Interestingly, they are not so common in the deep RL literature, as it is not always obvious that they help. << Results were significantly better using the larger model. 1 0 obj 5 0 obj Bousquet, O., U. von Luxburg and G. Ratsch, Springer, Heidelberg, Germany (2004) Bousquet, O. and A. Elisseef (2002), Stability and Generalization, Journal of Machine Learning Research, 499-526. 8 0 obj x�}Zϗ� ��_��ތ*R�{iw�I�m�n�ӗ�d��++K�$g2����(K����$"A >��6ϛh��H��~�p�6j��b�GEX�tS�1.�M�E������?޼{x�o�x��P�4�<. In what way can these MDPs differ from each other? A common challenge in machine learning is avoiding Overfitting, which is a condition in which our model fits “too well” to the specifics and nuances of the training data, in a way that is detrimental to its performance on the test data. They suggest to add a convolutional layer just between the input image and the neural network policy, that transforms the … Regularization: the most common set of techniques used in supervised learning to improve generalization are things like L2 regularization, Dropout and Batch Normalization. endobj /Created (2017) << have observed that neural networks can easily overfit randomly-generated labels. Divide a data set into a training set and a test set. in this paper the authors examined the effect of several variables on the generalization capability of the learned policy: Size of training set: the authors have shown that increasing the number of training MDPs increases the generalization capability, this can be seen: as is the case in supervised learning, we can see that increasing the amount of training “data” makes it more difficult for the policy to succeed on the training set, but increases its ability to generalize to unseen instances. They suggest to add a convolutional layer just between the input image and the neural network policy, that transforms the input image. /Published (2017) /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) S. Arora, R. Ge, B. Neyshabur, Y. Zhang, Stronger generalization bounds for deep nets via a compression approach, in Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, 10–15 July 2018, pp. /Type (Conference Proceedings) An example of this some robotic manipulation tasks, in which various physical parameters such as friction coefficients and mass might change, but we would like our policy to be able to adapt to these changes, or otherwise be robust to them if possible. >> endobj /Resources 399 0 R Unfortunately, domain randomization is known to suffer from high sample complexity and high variance in policy performance. Analyzing Optimization and Generalization in Deep Learning via Trajectories of Gradient Descent - Duration: 46:28. I see three key possible differences: 1. generalization in deep learning), there is no known bound that meets all of them simultaneously. An example of such an environment is CoinRun, introduced by OpenAI in the paper “Quantifying Generalization in Reinforcement Learning”. Machine learning is a discipline in which given some training data\environment, we would like to find a model that optimizes some objective, but with the intent of performing well on data that has never been seen by the model during training. The underlying transition function differs between MDPs, even though the states might seem similar. Generalization in Deep Reinforcement Learning. Tests of Generalization Recently, Google Deepmind & OpenAI released environments meant for gauging agents’ ability to generalize — a fundamental challenge … the neural network policy is fed this augmented image and outputs the probability over actions as is usual in RL. /Date (2017) Simons Institute 1,809 views. Understanding Generalization in Deep Learning - Duration: 34:03. An influential paper of Zhang, Bengio, Hardt, Recht, and Vinyals showed that the answer could be “nothing at all.” /Contents 460 0 R endobj Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. /Parent 1 0 R /ModDate (D\07220180212212356\05508\04700\047) /Contents 157 0 R /Resources 324 0 R 12 0 obj Overview. /Parent 1 0 R 3. The absence of bounds on generalization performance is a serious hindrance to the reliability, explainability, and trustworthiness of neural networks, especially for tasks where a representative test set may be impossible or impractical to obtain and mathematically-guided approaches may be of benefit. The Deep Model Generalization Dataset In addition to our paper, we are introducing the Deep Model Generalization (DEMOGEN) dataset, which consists of of 756 trained deep models, along with their training and test performance on the CIFAR-10 and CIFAR-100 datasets. >> Certifying the performance of AI algorithms is necessary to drive adoption and trust Furthermore, understanding the generalization properties of algorithms is a requirement dictated by policymakers, as highlighted by the Ethics Guidelines for Trustworthy Artificial Intelligence (AI) released by the European Co… (I have previously written on RL for combinatorial optimization). >> We first introduce the common categories of /Parent 1 0 R /MediaBox [ 0 0 612 792 ] Having a variety of visually different inputs should help the model learn features that are more general and less likely to overfit to visual nuances of the environment. /Resources 226 0 R 2. Deep learning has transformed computer vision, natural language processing, and speech recognition. In other words, simply training on variedenvironments is so far the most effective strategy for generalization. Generalization refers to your model's ability to adapt properly to new, previously unseen data, drawn from the same distribution as the one used to create the model.. << This convolutional layer is randomly initialized at each episode, and its weights are normalized so that it does not change the image too much. This works somewhat like data augmentation techniques, automatically generating a larger variety of training data. /Book (Advances in Neural Information Processing Systems 30) Thus, the idea in this paper is to employ Deep Learning for cartographic generalizations tasks, especially for the task of building generalization. The results are quite interesting: In the RL setting, they tested their method against these baselines in several problems, including CoinRun, and achieved superior results in terms of generalization. /MediaBox [ 0 0 612 792 ] /Type /Catalog /MediaBox [ 0 0 612 792 ] With supervised learning, a set of labeled training data is given to a model. Our policy might learn that it needs to jump at a certain point because of some visual feature on the background wall such as a unique tile texture or a painting, and learn to climb the ladder when it sees an enemy in a certain position on some distant platform. /Parent 1 0 R 14 0 obj Recently, researchers have begun to systematically explore generalization in RL by developing novel simulated environments that enable creating a distribution of MDPs and splitting unique training and testing instances. Determine whether a model is good or not. Estimated Time: 5 minutes Learning Objectives Develop intuition about overfitting. As a proof of concept, the authors first tried their method on a toy supervised learning problem; classifying cats and dogs. /Contents 323 0 R /Contents 50 0 R These techniques constrict the neural network’s capacity to overfit by adding noise and decreasing the size of the weights, and have become a standard in supervised learning. /Annots [ 29 0 R 30 0 R 31 0 R 32 0 R 33 0 R 34 0 R 35 0 R 36 0 R 37 0 R 38 0 R 39 0 R 40 0 R 41 0 R 42 0 R 43 0 R 44 0 R 45 0 R 46 0 R 47 0 R 48 0 R 49 0 R ] /Annots [ 119 0 R 120 0 R 121 0 R 122 0 R 123 0 R 124 0 R 125 0 R 126 0 R 127 0 R 128 0 R 129 0 R 130 0 R 131 0 R ] 13 0 obj This is commonly known as Domain Randomization and is often used to help bridge the gap between simulation and reality in robot applications of RL (I have written about it in another article). /Resources 15 0 R >> It is evident that using the new technique, the states are much better clustered together in embedding space, suggesting that the model has indeed learned features that are more invariant to these distracting visual variations. /EventType (Poster) << Revisiting Training Strategies and Generalization Performance in Deep Metric Learning of the learned embedding space. Generalization in Deep Learning Kenji Kawaguchi, Leslie Pack Kaelbling, Yoshua Bengio This paper provides theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, responding to an open question in the literature. << /MediaBox [ 0 0 612 792 ] Although methods have achieved near human-level performance on many benchmarks, numerous recent studies imply that these benchmarks only weakly test their intended purpose, and that simple examples produced either by human or machine, cause systems to fail spectacularly. The expressivity focuses on finding functions expressible by deep nets but cannot be approximated by shallow nets with similar number of … endobj /Contents 398 0 R << /Contents 132 0 R Whereas there are some satisfactory answers to the problems of approximation and optimization, much less is known about the theory of generalization. Had that not been the case, the standard concept of generalization in supervised learning would not hold, and it would be difficult to justify our expectation that learning on the training set should yield good results on the test set as well. Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural … /Parent 1 0 R Deep learning has brought a wealth of state-of-the-art results and new capabilities. This means that we give our model an original image and an augmented image (using the random layer), and encourage it to have similar features for both by adding the mean squared error between them to the loss. /Description-Abstract (With a goal of understanding what drives generalization in deep networks\054 we consider several recently suggested explanations\054 including norm\055based control\054 sharpness and robustness\056 We study how these measures can ensure generalization\054 highlighting the importance of scale normalization\054 and making a connection between sharpness and PAC\055Bayes theory\056 We then investigate how well the measures explain different observed phenomena\056) stream A very complex dataset would require a very complex function to successfully understand and represent it. This raised the concern that some learned agents might be “memorizing” action sequences in some sense, and exploiting nuances of states to remember which action to take. >> In fully deterministic environments this might not be the case. 9 0 obj /Length 3663 << 2 0 obj << << (Eds.) In a neural network, the number of parameters essentially means the number of weights. To mitigate these effects, the authors added another term to the loss; Feature Matching. Generalization in Deep Learning and by carefully analyzing the right-hand side. /Title (Exploring Generalization in Deep Learning) We need to define our problem in terms of complexity. I hope to see more research in this direction. Bounds on the generalization error of deep learning models have also been obtained, typically under specific constraints (e.g. endobj An MDP is characterized by a set of states S, a set of actions A, a transition function P and a reward function R. When we discuss generalization, we can propose a different formulation, in which we wish our policy to perform well on a distribution of MDPs. Size of neural network: another finding from the paper that resonates with current practices in supervised learning, is that larger neural networks can often attain better generalization performance than smaller ones. /Parent 1 0 R /Contents 225 0 R /firstpage (5947) /Editors (I\056 Guyon and U\056V\056 Luxburg and S\056 Bengio and H\056 Wallach and R\056 Fergus and S\056 Vishwanathan and R\056 Garnett) We split our data to train and test sets, and try to make sure that both sets represent the same distribution. To answer, supervised learning in the domain of machine learning refers to a way for the model to learn and understand data. ALE is deterministic, and a 2015 paper called “The Arcade Learning Environment: An Evaluation Platform For General Agents” showed that using a naïve trajectory optimization algorithm named “Brute” can yield state of the art results on some games. We would like our policies to Generalize as they do in supervised learning, but what does it mean in the context of RL? >> /Count 10 /Producer (PyPDF2) In machine learning, generalization usually refers to the ability of an algorithm to be effective across a range of inputs and applications. /Resources 133 0 R I’ll explain why. An example of this is playing different versions of a video game in which the colors and textures might change, but the behavior of the policy should not change as a result. For this to be possible, we usually require that the training data distribution be representative of the real data distribution on which we are really interested in performing well. /Type /Page << /Type /Page Abstract: Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. /Type /Page /MediaBox [ 0 0 612 792 ] /Resources 158 0 R endobj >> 254–263 Google Scholar We also discuss approaches to provide non-vacuous generalization guarantees for deep learning. /MediaBox [ 0 0 612 792 ] endobj Systematic empirical evaluation shows that vanilla deep RLalgorithms generalize better than specialized deep RL algorithms designedspecifically for generalization. Subsequent papers have begun to explore ways to introduce stochasticity to the games, to discourage the agents from memorizing action sequences and instead learn more meaningful behaviors. 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