Meta-Learning: Learning to Learn. Extensive innovation for machine... | by Wie Kiang H. | Towards Data Science

In order to successfully understand and study new concepts or views, people generally use one example in their commonplace.
They learn new skills much quicker and in more productive ways than machines; action, imagination, and explanation.
For instance, children who have seen butterflies and ants a few times can promptly recognize them. In the same way, teens who grasp how to handle a bike are likely to discover how to ride a motorcycle with a limited demonstration. However, hundreds of models to label and train are required by machine learning algorithms to deliver similar accuracy.
In computer science, meta-learning is a member of machine learning, which can be defined as learning to learn. It is an automatic algorithm that applies to metadata to recognize how learning can become flexible in interpreting difficulties, thus enhancing the performance of an existent algorithm or learning the algorithm itself (Thrun & Pratt, 19981; Andrychowicz et al., 20162)
This research studies the use of evolutionary algorithms and neural networks in terms of meta-learning process. Begin with an explanation of the evolutionary algorithm process to avoid overfitting, followed by the concept of neural networks. Finally, a conclusion of the relationship between evolutionary algorithms and neural networks due to the meta-learning technique.
Meta-learning algorithms generally make Artificial Intelligence (AI) systems learn effectively, adapt to shifts in their conditions in a more robust way, and generalize to more tasks. They can be used to optimize a model’s architecture, parameters, and some combination of them. This research focuses on a particular meta-learning technique called neuro-evolution, as well it is called an evolutionary algorithm to learn neural architectures.
Charles Darwin, an evolutionary biologist, visited the Galapagos Islands decades ago and noticed that some birds appeared to have evolved from a single genetic flock. They shared standard features characterized by their distinct beak figures, which sprung from their unique DNA (Steinheimer, 20043), as represented in Figure 1.
Steinheimer (2004) asserts that DNA is a blueprint that controls the replication of cells. The hypothesis was the withdrawal of each species into a popular theory of natural selection. This process is algorithmic and can be simulated by creating evolutionary algorithms.
Additionally, the evolutionary algorithms are mutating some members randomly to attempt to find even more qualified applicants. This process repeats countless iterations as necessary. Actually, in this context, they can be represented as generations. These steps are all stimulated by the Darwin theory of natural selection. They are all a part of the broader class of algorithms called evolutionary computation.
Currently, neural networks can perform tasks that are likely challenging for humans if they present large amounts of training data. The optimal architectures for these networks are non-trivial and take a lot of trial and error. In fact, researchers worked hard on improving architectures that are progressively delivered to newer levels over the years.
Researchers discovered that neuro-evolution approaches are being used by a famous tech company, Uber, who started adopting Evolution Strategies (ES) to help enhance the performance of their services by concatenating with the Atari games.
“even a very basic decades-old evolution strategy provides comparable results; thus, more modern evolution strategies should be considered as a potentially competitive approach to modern deep Reinforcement Learning (RL) algorithms” (Such et al., 20174; Chrabaszcz, Loshchilov & Hutter, 20185).
In Figure 2, Uber AI Labs conducted a test with several Atari games. This line chart depicts the comparison between standard evolution strategies with an enhanced exploration of the algorithms. It seems that various tweaking in the hyperparameters gives significant rewards value. The number of reward amounts rises significantly, from 0 to further than 30.
In the first 200 generation numbers, rewards value in both strategies is racing up to 5, and keeping steadily to more than 300 generation numbers. It then dramatically grows from 320 generation numbers until the maximum 800 generation numbers.
In contemporary work, appearances in deep learning patterns have been associated mainly with the number and diversity of data collected. Practically speaking, data augmentation recognizes a significantly enhanced uniqueness of available data to train the models, except, discovering the most current data existence. Data augmentation has several routines, which are cropping, padding, and horizontal flipping (Figure 3).
These techniques are frequently applied to train deep neural networks (Shorten & Khoshgoftaar, 20196). Indeed, most of the neural networks’ training time strategy is using the basic types of data augmentation.
“the best classification accuracy obtained by our models is not in the top 10 approaches using the CIFAR-10 dataset. This makes the proposed architecture ideal for embedded systems unlike the best performing approaches” (Çalik & Demirci, 20187)
Despite this, the Canadian Institute for Advanced Research (CIFAR-10) successfully applied the image classification problems with Convolutional Neural Networks (CNN). Furthermore, Cubuk, Zoph, Mane, Vasudevan, and Le (2019)8 explain that the most recent accuracy on datasets, such as CIFAR-10 with AutoAugment is an innovative automated data augmentation method.
Li, Zhou, Chen, and Li (2017)9 study that research in meta-learning has commonly concentrated on data and model designs, with exemptions such as meta-learning optimizers, Stochastic Gradient Descent (SGD) for instance, which appears to still fall under the model optimization. Data augmentation is the most easily recognized in the context of image data. These image augmentations typically involve horizontal flips and small magnitudes of rotations or translations.
There is a combined interaction in two intertwined processes, inter-life learning, and intra-life learning. Think of inter-life learning as a process of evolution through natural selection. In contrast, intra-life learning relates to how an animal learns throughout its existence, for example, identifying an object, learning to communicate, and walking.
Furthermore, evolutionary algorithms can be reflected as inter-life learning, whereas neural networks can be thought of as intra-life learning. Evolutionary algorithms and neural networks are likely the main factor to accomplish an optimized algorithm in deep reinforcement learning techniques.
Bingham, Macke, and Miikkulainen (2020)10 emphasize there are four main features associated with evolutionary algorithms, which are, numbers of network layers, numbers of neurons in each layer, activation function, and optimization algorithm.
These start with the initialization of multiple neural networks with random weights to generate a population. Next is to train the weights of each network using an image dataset, then benchmark how strongly it performs at classifying test data. Moreover, another feature is applying the classification precision on the test set and implementing the fitness function to decide the top-scoring networks to be a member of the next generation.
On the other hand, evolutionary algorithms filter out the lowest-performing networks and eliminate them. Finally, they feature selecting a few of the lower-scoring networks, which might be conceivably valuable in a local maximum when optimizing the networks. These techniques are an evolutionary way of preventing overfitting.
Neural Networks are an attempt to simulate the information processing abilities of the biological nervous scheme, reflected in Figure 4. The human body consists of trillions of cell bodies, and the nervous system cells called nucleus are trained to transfer messages or information through an electrochemical means.
In simple terms, neural networks are a collection of connected input and output units, in which each connection has an associated weight (Figure 5). Neural networks generate a new network by blending a random collection of parameters from their parent networks. An instance could have an equal number of layers as one origin, and the rest of its parameters are from a different parent. This process reflects how biology works in real life and helps algorithms converge on an optimized network.
As the parameter complexity of the network rises, evolutionary algorithms produce exponential speedups. Krizhevsky, Sutskever, and Hinton (2017)11 mentioned that this process requires stacks of data and computing power and uses hundreds of GPUs and TPUs for days. It is initialized as 1000 identical convolutional neural networks with no hidden layers. Then through the evolutionary process, networks with higher accuracies are elected as parents, copied and mutated to generate children, while the rest are eliminated.
In later practice, neural networks utilized a fixed stack of repeated modules called cells. The number of cells stayed the same, but the architecture of each cell mutated over time. They also decided to use a specific form of regularisation to improve network accuracy.
Instead of eliminating the lowest-scoring networks, they exclude the oldest ones regardless of how well they scored, and it ended up enhancing the accuracy, and training from scratch. This technique elects for neural networks that remain accurate when they are retrained along with the evolution algorithms.
Meta-learning is the process of learning to learn. Moreover, Artificial Intelligence (AI) optimizes one or numerous other AIs.
This research has concluded that evolutionary algorithms use notions from the evolutionary means and mimic the Charles Darwin hypothesis, for instance, mutation and natural selection, to interpret complicated puzzles.
In contrast, a meta-learning technique called neuro-evolution might utilize evolutionary algorithms to optimize neural networks individually. Although meta-learning is currently extremely fashionable, utilizing these algorithms on real-life problems remains particularly challenging.
However, with the continually advancing computational power, dedicated machine learning hardware, and advancements in meta-learning algorithms, these will likely become more reliable and trustworthy.
References#1 Learning to Learn#2 Learning to learn by gradient descent by gradient descent#3 Charles Darwin’s bird collection and ornithological knowledge during the voyage of H.M.S. “Beagle”, 1831–1836#4 Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning#5 Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari#6 A survey on Image Data Augmentation for Deep Learning#7 Cifar-10 Image Classification with Convolutional Neural Networks for Embedded Systems#8 AutoAugment: Learning Augmentation Strategies From Data#9 Meta-SGD: Learning to Learn Quickly for Few-Shot Learning#10 Evolutionary Optimization of Deep Learning Activation Functions#11 ImageNet classification with deep convolutional neural networks