Neural networks have the ability to serve as universal function approximators, but they are not interpretable and don't generalize well outside of their training region. Both of these issues are problematic when trying to apply standard neural ordinary differential equations (neural ODEs) to dynamical systems. We introduce the polynomial neural ODE, which is a deep polynomial neural network inside of the neural ODE framework. We demonstrate the capability of polynomial neural ODEs to predict outside of the training region, as well as perform direct symbolic regression without additional tools such as SINDy.

This paper aims at proposing a new machine learning for classification problems. The classification problem has a wide range of applications, and there are many approaches such as decision trees, neural networks, and Bayesian nets. In this paper, we focus on the action of neurons in the brain, especially the EPSP/IPSP cancellation between excitatory and inhibitory synapses, and propose a Machine Learning that does not belong to any conventional method. The feature is to consider one neuron and give it a multivariable Xj (j = 1, 2,.) and its function value F(Xj) as data to the input layer. The multivariable input layer and processing neuron are linked by two lines to each variable node. One line is called an EPSP edge, and the other is called an IPSP edge, and a parameter {\Delta}j common to each edge is introduced. The processing neuron is divided back and forth into two parts, and at the front side, a pulse having a width 2{\Delta}j and a height 1 is defined around an input X . The latter half of the processing neuron defines a pulse having a width 2{\Delta}j centered on the input Xj and a height F(Xj) based on a value obtained from the input layer of F(Xj). This information is defined as belonging to group i. In other words, the group i has a width of 2{\Delta}j centered on the input Xj, is defined in a region of height F(Xj), and all outputs of xi within the variable range are F(Xi). This group is learned and stored by a few minutes of the Teaching signals, and the output of the TEST signals is predicted by which group the TEST signals belongs to. The parameter {\Delta}j is optimized so that the accuracy of the prediction is maximized. The proposed method was applied to the flower species classification problem of Iris, the rank classification problem of used cars, and the ring classification problem of abalone, and the calculation was compared with the neural networks.

You may not be able to teach an old dog new tricks, but Cornell researchers have found a way to train physical systems, ranging from computer speakers and lasers to simple electronic circuits, to perform machine-learning computations, such as identifying handwritten numbers and spoken vowel sounds. The experiment is no mere stunt or parlor trick. By turning these physical systems into the same kind of neural networks that drive services like Google Translate and online searches, the researchers have demonstrated an early but viable alternative to conventional electronic processors--one with the potential to be orders of magnitude faster and more energy efficient than the power-gobbling chips in data centers and server farms that support many artificial-intelligence applications. "Many different physical systems have enough complexity in them that they can perform a large range of computations," said Peter McMahon, assistant professor of applied and engineering physics in the College of Engineering, who led the project. "The systems we performed our demonstrations with look nothing like each other, and they seem to [be] having nothing to do with handwritten-digit recognition or vowel classification, and yet you can train them to do it."

You may not be able to teach an old dog new tricks, but Cornell researchers have found a way to train physical systems, ranging from computer speakers and lasers to simple electronic circuits, to perform machine-learning computations, such as identifying handwritten numbers and spoken vowel sounds. Cornell researchers have successfully trained (from left to right) a computer speaker, a simple electronic circuit and a laser to perform machine-learning computations. The experiment is no mere stunt or parlor trick. By turning these physical systems into the same kind of neural networks that drive services like Google Translate and online searches, the researchers have demonstrated an early but viable alternative to conventional electronic processors – one with the potential to be orders of magnitude faster and more energy efficient than the power-gobbling chips in data centers and server farms that support many artificial-intelligence applications. "Many different physical systems have enough complexity in them that they can perform a large range of computations," said Peter McMahon, assistant professor of applied and engineering physics in the College of Engineering, who led the project.

The development of technology has raised humanity to unprecedented heights. The fields of medicine, security, education, and other types of care are at their peak. But that is not all. Artificial intelligence is the next big thing in the world of technology and computer science, but to understand it, it's important to know what it is made of. It is important to know what deep learning is and what an artificial neural network is.

In a September 2020 essay in Nature Energy, three scientists posed several "grand challenges" -- one of which was to find suitable materials for thermal energy storage devices that could be used in concert with solar energy systems. Fortuitously, Mingda Li -- the Norman C. Rasmussen Assistant Professor of Nuclear Science and Engineering at MIT, who heads the department's Quantum Matter Group -- was already thinking along similar lines. In fact, Li and nine collaborators (from MIT, Lawrence Berkeley National Laboratory, and Argonne National Laboratory) were developing a new methodology, involving a novel machine-learning approach, that would make it faster and easier to identify materials with favorable properties for thermal energy storage and other uses. The results of their investigation appear this month in a paper for Advanced Science. "This is a revolutionary approach that promises to accelerate the design of new functional materials," comments physicist Jaime Fernandez-Baca, a distinguished staff member at Oak Ridge National Laboratory.

In recent years, there has been considerable innovation in the world of predictive methodologies. This is evident by the relative domination of machine learning approaches in various classification competitions. While these algorithms have excelled at multivariate problems, they have remained dormant in the realm of functional data analysis. We extend notable deep learning methodologies to the domain of functional data for the purpose of classification problems. We highlight the effectiveness of our method in a number of classification applications such as classification of spectrographic data. Moreover, we demonstrate the performance of our classifier through simulation studies in which we compare our approach to the functional linear model and other conventional classification methods.

There are two main steps in the conventional machine learning or ML pipeline: feature extraction and classification. The goal of feature extraction is to represent data in a numerical space, also called feature space. The goal of classification is to determine the group that each data point belongs to. If we can simply design a classifier to separate data into classes within the feature space, it means that feature extraction and classification work as needed. However, the story is not always as simple as this.

Radiological sciences in the last ten years have advanced in a revolutionary manner, especially when it comes about medical imaging and computerized medical image processing. These techniques help in the understanding of the disease as well as initiation and evaluation of ongoing treatment. Apart from this, the dataset of these images is used in further analysis of such diseases occurring around the world as a whole. Heather Landi, a senior editor at Fierce Healthcare, writes in an article that IBM researchers estimate that medical images, as the largest and fastest-growing data source in the healthcare industry, account for at least 90 percent of all medical data. We can use a computer to process and manipulate the multidimensional digital images of psychological structures in order to visualize hidden characteristic diagnostic features that are very difficult or perhaps impossible to see using planer imaging methods.

Recently, machine learning has prevailed in many academia and industrial applications. At the same time, quantum computing, once seen as not realizable, has been brought to markets by several tech giants. However, these machines are not fault-tolerant and can not execute very deep circuits. Therefore, it is urgent to design suitable algorithms and applications implementable on these machines. In this work, we demonstrate a novel approach which applies variational quantum circuits to deep reinforcement learning. With the proposed method, we can implement famous deep reinforcement learning algorithms such as experience replay and target network with variational quantum circuits. In this framework, with appropriate information encoding scheme, the possible quantum advantage is the number of circuit parameters with $poly(\log{} N)$ compared to $poly(N)$ in conventional neural network where $N$ is the dimension of input vectors. Such an approach can be deployed on near-term noisy intermediate-scale quantum machines.