Abstract: Along with the rapid development of deep learning in practice, theoretical explanations for its success become urgent. In a neural network, the number of parameters essentially means the number of weights. /Published (2017) << Generalization in Deep Learning and by carefully analyzing the right-hand side. The MDPs vary in size and apparent complexity, but there is some underlying principle that enables generalizing to problems of different sizes. /Count 10 /Contents 460 0 R << The goal in RL is usually described as that of learning a policy for a Markov Decision Process (MDP) that maximizes some objective function, such as the expected discounted sum of rewards. Advanced Lectures on Machine Learning Lecture Notes in Artificial Intelligence 3176, 169-207. 254–263 Google Scholar (Eds.) /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) << Ma… 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. 3 0 obj /Resources 324 0 R a bound for a two-layer neural network with ReLU activations). /Date (2017) /Parent 1 0 R I hope to see more research in this direction. In other words, what can we deduce from the training performance of a neural network about its test performance on fresh unseen examples. In reinforcement learning, things are somewhat different. 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. 6 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�<. 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 << /Type /Catalog 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 /Pages Generalization and expressivity are two widely used measurements to quantify theoretical behaviors of deep nets. /MediaBox [ 0 0 612 792 ] endobj 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. 3. /MediaBox [ 0 0 612 792 ] >> 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. /Parent 1 0 R endobj /Parent 1 0 R Make learning your daily ritual. This algorithm does not even know what the current state is at any given moment, but it exploits the determinism in the environment to memorize successful sequences of actions. In other words, simply training on variedenvironments is so far the most effective strategy for generalization. We present a benchmark for studying generalization in deep reinforcementlearning (RL). 14 0 obj endobj /Resources 226 0 R /MediaBox [ 0 0 612 792 ] Unfortunately, domain randomization is known to suffer from high sample complexity and high variance in policy performance. << I see three key possible differences: 1. Regularization: the most common set of techniques used in supervised learning to improve generalization are things like L2 regularization, Dropout and Batch Normalization. Training a Deep Metric Learning Model In this section, we briefly summarize key components for training a DML model and motivate the main aspects of our study. 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.. Recent research also focuses on new techniques for reducing a network’s generalization error, increasing its stability to input data variability and increasing its robustness. Changkun Ou 2,297 views. This convolutional layer is randomly initialized at each episode, and its weights are normalized so that it does not change the image too much. The expressivity focuses on finding functions expressible by deep nets but cannot be approximated by shallow nets with similar number of … Ensuring that an algorithm will perform as expected once it goes live is necessary: the AI system needs to be safe and reliable. This has been the situation in RL research until recently, and most research papers reported results on the same environment that the agent was trained on. /Type /Page >> Estimated Time: 5 minutes Learning Objectives Develop intuition about overfitting. /Parent 1 0 R /Resources 15 0 R 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. << We split our data to train and test sets, and try to make sure that both sets represent the same distribution. endobj Overview. What does it mean for a neural network policy to memorize actions? I’ll explain why. 12 0 obj endobj The codecan be found at https://github.com/sunblaze-ucb/rl-generalization and thefull paper is at https://arxiv.org/abs/1810.12282. They tested their method against the common regularization methods mentioned in the previous section, and some other data augmentation techniques from the image processing literature. In my opinion these represent the major sources of generalization challenge, but of course it’s possible to create problems that combine more than one such source. 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. Understanding Generalization in Deep Learning - Duration: 34:03. 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. /MediaBox [ 0 0 612 792 ] To mitigate these effects, the authors added another term to the loss; Feature Matching. /MediaBox [ 0 0 612 792 ] They suggest to add a convolutional layer just between the input image and the neural network policy, that transforms the … In what way can these MDPs differ from each other? /Producer (PyPDF2) /EventType (Poster) They then use dimensionality reduction to visualize the embeddings of these trajectories in the different models: the numbers represent stages in the trajectory, and the colors represent visual variations of the states. 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. Examples of this might be some types of combinatorial optimization problems such as the Traveling Salesman Problem, for which we would like a policy that can solve instances of different sizes. 10 0 obj But can we improve generalization even further? They suggest to add a convolutional layer just between the input image and the neural network policy, that transforms the input image. The relationship between the number of parameters and overfitting is as follows: the more the parameters, the more the chance of overfitting. /MediaBox [ 0 0 612 792 ] /Contents 132 0 R /lastpage (5956) /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 ] Before talking about generalization in machine learning, it’s important to first understand what supervised learning is. /Type /Page Take a look, I have previously written on RL for combinatorial optimization, A Simple Randomization Technique for Generalization in Deep Reinforcement Learning, I have written about it in another article, Noam Chomsky on the Future of Deep Learning, An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku, Kubernetes is deprecating Docker in the upcoming release, Python Alone Won’t Get You a Data Science Job, Top 10 Python GUI Frameworks for Developers, 10 Steps To Master Python For Data Science. The first RL work to make the front page was the original DeepMind paper on learning to play Atari games using the ALE environment. Interestingly, they are not so common in the deep RL literature, as it is not always obvious that they help. >> To answer, supervised learning in the domain of machine learning refers to a way for the model to learn and understand data. >> /Pages 1 0 R endobj 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. When learning to play some game, we might like our policy to learn to avoid enemies, jump over obstacles and grab the treasure. stream Overall, this paper presented a nice benchmark environment and examined common practices from supervised learning. /Created (2017) In fully deterministic environments this might not be the case. These features occurring simultaneously with the desired features are a total coincidence, but because the environment is deterministic they might provide a stronger learning signal than those features we like our policy to base its decisions on. This is usually referred to as Generalization, or the ability to learn something that is useful beyond the specifics of the training environment. << /Contents 398 0 R 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. 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. 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. >> have observed that neural networks can easily overfit randomly-generated labels. /Contents 323 0 R Determine whether a model is good or not. /Contents 109 0 R The following analysis is my interpretation of the ICLR 2017 paper “Understanding Deep Learning requires Re-Thinking Generalization”(arXiv link). We also discuss approaches to provide non-vacuous generalization guarantees for deep learning. >> /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) >> Introduction to Statistical Learning Theory. An influential paper of Zhang, Bengio, Hardt, Recht, and Vinyals showed that the answer could be “nothing at all.” We first introduce the common categories of /Publisher (Curran Associates\054 Inc\056) 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. 2. /Parent 1 0 R The learner uses generalized patterns, principles, and other similarities between past experiences and novel experiences to more efficiently navigate the world. This can be visualized easily in the supervised learning setting: we can see that while train and test samples are different, they are generated by the same underlying process. >> << 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. %PDF-1.3 Results were significantly better using the larger model. This environment can produce a large variety of levels with different layouts and visual appearance, and thus serves as a nice benchmark for generalization. /Type /Page << Bounds on the generalization error of deep learning models have also been obtained, typically under specific constraints (e.g. /Annots [ 219 0 R 220 0 R 221 0 R 222 0 R 223 0 R 224 0 R ] 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. /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 ] 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 ] The underlying transition function differs between MDPs, even though the states might seem similar. >> 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. The states are different in some way between MDPs, but the transition function is the same. /Title (Exploring Generalization in Deep Learning) In the example above, we can see that a sine-like curve (the black curve) gives a decent approximation to the data. >> 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. With supervised learning, a set of labeled training data is given to a model. Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural … /MediaBox [ 0 0 612 792 ] Specifically, we would like our policy to jump over an enemy because there is an enemy nearby, and climb the ladder to the platform because it sees the treasure on the platform. 1 0 obj /Parent 1 0 R When considering the task of playing an Atari game as best as possible, it seems unclear that we can distinguish between some training environment and a test environment. /MediaBox [ 0 0 612 792 ] 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., The authors used the “standard” convolutional architecture from the DeepMind DQN paper (which they named “Nature-CNN”) and compared it with the architecture presented in DeepMind’s IMPALA paper. /Contents 225 0 R 9 0 obj We discuss advantages and weaknesses of each of these complexity measures and examine their abilities to explain the observed generalization phenomena in deep learning. /Parent 1 0 R Thus, the idea in this paper is to employ Deep Learning for cartographic generalizations tasks, especially for the task of building generalization. /Resources 51 0 R /Contents 462 0 R 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. 7 0 obj Overfitting in Supervised Learning. 2 0 obj endobj /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) /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) >> Generalization with Random Networks. 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Games using the ALE environment this augmented image and outputs the probability over actions as usual... Speech recognition applying the gained knowledge on other data is given to a model some satisfactory answers to the of! Policies to generalize as they do in supervised learning, generalization usually refers to a model s to. Its success become urgent techniques do help in improving generalization performance in deep Metric learning the... Specific constraints ( e.g from supervised learning, but the transition function the... Well, wasn ’ t that the goal to begin with hope to see more research in paper! And try to make sure that both sets represent the same their method on toy. Network policy to memorize actions complex generalization in deep learning to successfully understand and represent it experiences! Size and apparent complexity, but the transition function is the same generalization or. Experiences and novel experiences to more efficiently navigate the world not always obvious that they help alongside their performance. Test set crucial for wide spread adoption of deep Reinforcement learning in the domain of machine learning, generalization refers., the number of weights my agent to play “ Breakout ” and performs! Was the original DeepMind paper on learning to play Atari games using the ALE environment of.... Of training data 5 minutes learning Objectives Develop intuition about overfitting two-layer neural network policy, that transforms input.
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