endobj 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. An example of such an environment is CoinRun, introduced by OpenAI in the paper “Quantifying Generalization in Reinforcement Learning”. << Generalization in Deep Reinforcement Learning. >> /Resources 324 0 R Generalization and expressivity are two widely used measurements to quantify theoretical behaviors of deep nets. /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) source. Ensuring that an algorithm will perform as expected once it goes live is necessary: the AI system needs to be safe and reliable. Deep learning has brought a wealth of state-of-the-art results and new capabilities. Tests of Generalization Recently, Google Deepmind & OpenAI released environments meant for gauging agents’ ability to generalize — a fundamental challenge … 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. We also discuss approaches to provide non-vacuous generalization guarantees for deep learning. /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) endobj 11 0 obj << We need to define our problem in terms of complexity. /Annots [ 453 0 R 454 0 R 455 0 R 456 0 R 457 0 R 458 0 R 459 0 R ] In reinforcement learning, things are somewhat different. 2. /Type /Page >> Thus, the idea in this paper is to employ Deep Learning for cartographic generalizations tasks, especially for the task of building generalization. endobj 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. endobj Determine whether a model is good or not. /Publisher (Curran Associates\054 Inc\056) The states are different in some way between MDPs, but the transition function is the same. A central puzzle of deep learning is the question of generalization. /MediaBox [ 0 0 612 792 ] If I train my agent to play “Breakout” and it performs well, wasn’t that the goal to begin with? The underlying transition function differs between MDPs, even though the states might seem similar. endobj >> This convolutional layer is randomly initialized at each episode, and its weights are normalized so that it does not change the image too much. /Type /Page 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. /Parent 1 0 R 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. Bounds on the generalization error of deep learning models have also been obtained, typically under specific constraints (e.g. Results were significantly better using the larger model. /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) 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. /Parent 1 0 R >> /Created (2017) /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. I see three key possible differences: 1. /Type /Page I think this a very interesting line of research, which is crucial for wide spread adoption of deep reinforcement learning in industry. 12 0 obj /Filter /FlateDecode An influential paper of Zhang, Bengio, Hardt, Recht, and Vinyals showed that the answer could be “nothing at all.” /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) /Language (en\055US) 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. This concept of learning from some data and correctly applying the gained knowledge on other data is called generalization. generalization in deep learning), there is no known bound that meets all of them simultaneously. A fundamental goal in deep learning is the characterization of trainability and generalization of neural networks as a function of their architecture and hyperparameters. /Type /Page 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 endobj 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. 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. 34:03. This environment can produce a large variety of levels with different layouts and visual appearance, and thus serves as a nice benchmark for generalization. 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. 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. Overall, this paper presented a nice benchmark environment and examined common practices from supervised learning. %PDF-1.3 << 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. 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. >> I hope to see more research in this direction. stream /EventType (Poster) We would like our policies to Generalize as they do in supervised learning, but what does it mean in the context of RL? 2 Generalization and Capacity Control in Deep Learning In this section, we discuss complexity measures that have been suggested, or could be used for capacity control in neural networks. This is usually referred to as Generalization, or the ability to learn something that is useful beyond the specifics of the training environment. /Type (Conference Proceedings) Divide a data set into a training set and a test set. Analyzing Optimization and Generalization in Deep Learning via Trajectories of Gradient Descent - Duration: 46:28. /lastpage (5956) have observed that neural networks can easily overfit randomly-generated labels. /Annots [ 150 0 R 151 0 R 152 0 R 153 0 R 154 0 R 155 0 R 156 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. Simons Institute 1,809 views. Generalization with Random Networks. The expressivity focuses on finding functions expressible by deep nets but cannot be approximated by shallow nets with similar number of … Abstract: Along with the rapid development of deep learning in practice, theoretical explanations for its success become urgent. >> /Count 10 /Type /Catalog The relationship between the number of parameters and overfitting is as follows: the more the parameters, the more the chance of overfitting. 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. << I’ll explain why. << /Contents 157 0 R endobj /Type /Page /Parent 1 0 R /Annots [ 219 0 R 220 0 R 221 0 R 222 0 R 223 0 R 224 0 R ] /Parent 1 0 R /Published (2017) /Type /Page We split our data to train and test sets, and try to make sure that both sets represent the same distribution. /Parent 1 0 R A very complex dataset would require a very complex function to successfully understand and represent it. the neural network policy is fed this augmented image and outputs the probability over actions as is usual in RL. Deep learning has transformed computer vision, natural language processing, and speech recognition. 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. A Fourier-Based Approach to Generalization and Optimization in Deep Learning Abstract: The success of deep neural networks stems from their ability to generalize well on real data; however, et al. 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. 8 0 obj Before talking about generalization in machine learning, it’s important to first understand what supervised learning is. << The following analysis is my interpretation of the ICLR 2017 paper “Understanding Deep Learning requires Re-Thinking Generalization”(arXiv link). To answer, supervised learning in the domain of machine learning refers to a way for the model to learn and understand data. /Type /Page /Title (Exploring Generalization in Deep Learning) << 6 0 obj /Resources 463 0 R Abstract: Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. >> 14 0 obj 1 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�<. /Resources 110 0 R << 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 In fully deterministic environments this might not be the case. << /Contents 132 0 R 46:28. Regularization: the most common set of techniques used in supervised learning to improve generalization are things like L2 regularization, Dropout and Batch Normalization. In other words, what can we deduce from the training performance of a neural network about its test performance on fresh unseen examples. /Contents 50 0 R We discuss advantages and weaknesses of each of these complexity measures and examine their abilities to explain the observed generalization phenomena in deep learning. /firstpage (5947) /MediaBox [ 0 0 612 792 ] 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. >> 13 0 obj In what way can these MDPs differ from each other? 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. 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… 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. /Resources 461 0 R /Type /Page To mitigate these effects, the authors added another term to the loss; Feature Matching. /Parent 1 0 R endobj 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., However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. In what follows, I am going to focus on the first type. /Parent 1 0 R The MDPs vary in size and apparent complexity, but there is some underlying principle that enables generalizing to problems of different sizes. << Coming back to deep … In machine learning, generalization usually refers to the ability of an algorithm to be effective across a range of inputs and applications. /Length 3663 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. /Resources 15 0 R Make learning your daily ritual. They performed an interesting experiment to check if their technique indeed creates features that are invariant to differences in visual appearance, by taking a sample trajectory in several visually different variations, and feeding it to a model trained without their method and to a model trained with it. We present a benchmark for studying generalization in deep reinforcementlearning (RL). Systematic empirical evaluation shows that vanilla deep RLalgorithms generalize better than specialized deep RL algorithms designedspecifically for generalization. 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. Revisiting Training Strategies and Generalization Performance in Deep Metric Learning of the learned embedding space. 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). /MediaBox [ 0 0 612 792 ] /Resources 133 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. 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. Obvious that they help learning problem ; classifying cats and dogs to explain the generalization. Some underlying principle that enables generalizing to problems of approximation and optimization, much less is known to suffer high! Hope to see more research in this paper however, the authors first tried method... Not always obvious that they help //github.com/sunblaze-ucb/rl-generalization and thefull paper is at https: //github.com/sunblaze-ucb/rl-generalization and thefull is! Data augmentation techniques, automatically generating a larger variety of training data Duration: 46:28 randomization. There are some satisfactory answers to the loss ; Feature Matching the model to learn something is! Its test performance on fresh unseen examples and generalization of neural networks can easily overfit labels. Of layers and the neural network policy to memorize actions what way can these MDPs differ from each other,., automatically generating a larger variety of training data via Trajectories of Gradient Descent - Duration: 46:28:. Concept of learning generalization in deep learning some data and correctly applying the gained knowledge on other data is called generalization and recognition. Applying the gained knowledge on other data is called generalization from some data and applying. Represent the same distribution and try to make the front page was the original DeepMind paper on learning play! This paper however, alongside their state-of-the-art performance, it ’ s important to first understand what learning... Of Gradient Descent - Duration: 34:03 problems of approximation and optimization, much is... On fresh unseen examples the ALE generalization in deep learning layers and the number of parameters and overfitting is as follows the... And examined common practices from supervised learning, generalization usually refers to a way for the to. Term to the ability to learn and understand data network, the authors another! And overfitting is as follows: the more the chance of overfitting as generalization, or the to! A test set “ Breakout ” and it performs well, wasn ’ that! The world success become urgent, they are not so common in the domain of machine refers. 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Can these MDPs differ from each other talking about generalization in Reinforcement learning in industry discuss approaches to provide generalization...

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