Normalization layers and activation functions are fundamental components in deep networks and typically co-locate with each other. Here we propose to design them using an automated approach. Instead of designing them separately, we unify them into a single tensor-to-tensor computation graph, and evolve its structure starting from basic mathematical functions. Examples of such mathematical functions are addition, multiplication and statistical moments. The use of low-level mathematical functions, in contrast to the use of high-level modules in mainstream NAS, leads to a highly sparse and large search space which can be challenging for search methods.

Normalization layers and activation functions are fundamental components in deep networks and typically co-locate with each other. Here we propose to design them using an automated approach. Instead of designing them separately, we unify them into a single tensor-to-tensor computation graph, and evolve its structure starting from basic mathematical functions. Examples of such mathematical functions are addition, multiplication and statistical moments. The use of low-level mathematical functions, in contrast to the use of high-level modules in mainstream NAS, leads to a highly sparse and large search space which can be challenging for search methods.

Symbolic regression (SR) is an emerging branch of machine learning focused on discovering simple and interpretable mathematical expressions from data. Although a wide-variety of SR methods have been developed, they often face challenges such as high computational cost, poor scalability with respect to the number of input dimensions, fragility to noise, and an inability to balance accuracy and complexity. This work introduces SyMANTIC, a novel SR algorithm that addresses these challenges. SyMANTIC efficiently identifies (potentially several) low-dimensional descriptors from a large set of candidates (from $\sim 10^5$ to $\sim 10^{10}$ or more) through a unique combination of mutual information-based feature selection, adaptive feature expansion, and recursively applied $\ell_0$-based sparse regression. In addition, it employs an information-theoretic measure to produce an approximate set of Pareto-optimal equations, each offering the best-found accuracy for a given complexity. Furthermore, our open-source implementation of SyMANTIC, built on the PyTorch ecosystem, facilitates easy installation and GPU acceleration. We demonstrate the effectiveness of SyMANTIC across a range of problems, including synthetic examples, scientific benchmarks, real-world material property predictions, and chaotic dynamical system identification from small datasets. Extensive comparisons show that SyMANTIC uncovers similar or more accurate models at a fraction of the cost of existing SR methods.

Normalization layers and activation functions are fundamental components in deep networks and typically co-locate with each other. Here we propose to design them using an automated approach. Instead of designing them separately, we unify them into a single tensor-to-tensor computation graph, and evolve its structure starting from basic mathematical functions. Examples of such mathematical functions are addition, multiplication and statistical moments. The use of low-level mathematical functions, in contrast to the use of high-level modules in mainstream NAS, leads to a highly sparse and large search space which can be challenging for search methods.

This paper presents the development of a distributed application that facilitates the understanding and application of swarm intelligence in solving optimization problems. The platform comprises a search space of customizable random particles, allowing users to tailor the solution to their specific needs. By leveraging the power of Ray distributed computing, the application can support multiple users simultaneously, offering a flexible and scalable solution. The primary objective of this project is to provide a user-friendly platform that enhances the understanding and practical use of swarm intelligence in problem-solving.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.

So you've learned the basics of Python and you're looking for a more powerful way to analyse data? NumPy is what you need.NumPy is a module for Python that allows you to work with multidimensional arrays and matrices. In addition, NumPy includes support for signal processing and linear algebra operations. So if you need to do any mathematical operations on your data, NumPy is probably the library for you. In this tutorial, we'll show you how to use NumPy to its full potential. You'll learn more about arrays as well as operate on them using mathematical functions. NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed. In this Python Numpy Tutorial, we will be learning about NumPy in Python, What is NumPy in Python, Data Types in NumPy, and more. NumPy in Python is a library that is used to work with arrays and was created in 2005 by Travis Oliphant.

I hope that you've come across, from algorithms achieving super-human level performance at Atari 2600 games, beating professional players at GO, Dota 2 and StarCraft II and to algorithms controlling nuclear-fusion reactors. These are the success stories of reinforcement learning algorithms combined with deep learning (Deep Reinforcement learning or DeepRL). Google's DeepMind and OpenAI heavily does research in this area and thinks that DeepRL is the future of AI. Some Researchers even think that RL might be the key to Artificial General Intelligence (AGI). Reinforcement Learning (RL) is one of the paradigms of Machine learning along with supervised and unsupervised learning.

There are so many machine learning algorithms out there, and we can find different kinds of overviews and cheat sheets. When trying to connect the dots of the multitude of machine learning algorithms, I discovered is that there are often several approaches to build/understand an algorithm. There is no better way, and each approach allows us to understand one aspect. One approach can be easier than another depending on what you already know. My aim in this article is to find a meaningful way to connect them.

In the previous article Neural Networks-Part(1): Introduction to Neuron and Single Neuron Neural Network we mentioned using activation functions. Just like Optimus prime transforms, activation functions help us to transform the data of our neurons. We haven't talked about layers in a neural network yet but just a brief for them is layers in neural networks are composed of neurons. We add these layers between inputs and outputs and when a model is prepared for production, the user will only have access to the inputs and outputs of your model and not the layers in between and hence they are called hidden layers. I explained it with an example, but it is a convention that we call those layers hidden layers.