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Recurrent Neural Networks Design And Applications

With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.

Recurrent Neural Networks Design And Applications

The aim of this chapter is to provide a series of tricks and recipes for neural state estimation, particularly for real world applications of reinforcement learning. We use various topologies of recurrent neural networks as they allow to identify the continuous valued, possibly high dimensional state space of complex dynamical systems. Recurrent neural networks explicitly offer possibilities to account for time and memory, in principle they are able to model any type of dynamical system. Because of these capabilities recurrent neural networks are a suitable tool to approximate a Markovian state space of dynamical systems. In a second step, reinforcement learning methods can be applied to solve a defined control problem. Besides the trick of using a recurrent neural network for state estimation, various issues regarding real world problems such as, large sets of observables and long-term dependencies are addressed.

In the development of advanced nanoporous materials, one clear and unavoidable challenge in hand is the sheer size (in principle, infinite) of the materials space to be explored. While high-throughput screening techniques allow us to narrow down the enormous-scale database of nanoporous materials, there are still practical limitations stemming from a costly molecular simulation in estimating a material's performance and the necessity of a sophisticated descriptor identifying materials. With an attempt to transition away from the screening-based approaches, this paper presents a computational approach combining the Monte Carlo tree search and recurrent neural networks for the tailor-made design of metal-organic frameworks toward the desired target applications. In the demonstration cases for methane-storage and carbon-capture applications, our approach showed significant efficiency in designing promising and novel metal-organic frameworks. We expect that this approach would easily be extended to other applications by simply adjusting the reward function according to the target performance property.

Artificial intelligence (AI) has experienced a tremendous surge in recent years, resulting in high demand for a wide array of implementations of algorithms in the field. With the rise of Internet-of-Things devices, the need for artificial intelligence algorithms implemented in hardware with tight design restrictions has become even more prevalent. In terms of low power and area, ASIC implementations have the best case. However, these implementations suffer from high non-recurring engineering costs, long time-to-market, and a complete lack of flexibility, which significantly hurts their appeal in an environment where time-to-market is so critical. The time-to-market gap can be shortened through the use of reconfigurable solutions, such as FPGAs, but these come with high cost per unit and significant power and area deficiencies over their ASIC counterparts. To bridge these gaps, this dissertation work develops two methodologies to improve the usability of ASIC implementations of neural networks in these applications.

A special type of recurrent neural network that overcomes this issue is the long short-term memory (LSTM) network. LSTM networks use additional gates to control what information in the hidden cell makes it to the output and the next hidden state. This allows the network to more effectively learn long-term relationships in the data. LSTMs are a commonly implemented type of RNN.

MATLAB has a full set of features and functionality to train and implement LSTM networks with text, image, signal, and time series data. The next sections will explore the applications of RNNs and some examples using MATLAB.

The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on the Lyapunov approach, to establish its properties. The book contains two sections: the first focuses on the analyses of control techniques; the second is dedicated to illustrating results of real-time applications. It also provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme.

Each time a neural network is trained, can result in a differentsolution due to different initial weight and bias values and differentdivisions of data into training, validation, and test sets. As aresult, different neural networks trained on the same problem cangive different outputs for the same input. To ensure that a neuralnetwork of good accuracy has been found, retrain several times.

Typically, these reviews consider RNNs that are artificial neural networks (aRNN) useful in technological applications. To complement these contributions, the present summary focuses on biological recurrent neural networks (bRNN) that are found in the brain. Since feedback is ubiquitous in the brain, this task, in full generality, could include most of the brain's dynamics. The current review divides bRNNS into those in which feedback signals occur in neurons within a single processing layer, which occurs in networks for such diverse functional roles as storing spatial patterns in short-term memory, winner-take-all decision making, contrast enhancement and normalization, hill climbing, oscillations of multiple types (synchronous, traveling waves, chaotic), storing temporal sequences of events in working memory, and serial learning of lists; and those in which feedback signals occur between multiple processing layers, such as occurs when bottom-up adaptive filters activate learned recognition categories and top-down learned expectations focus attention on expected patterns of critical features and thereby modulate both types of learning.

where \(\textsgn(w) = +1\) if \(w > 0\), \(0\) if \(w = 0\), and \(-1\) if \(w

Another classical tradition arose from the analysis of how the excitable membrane of a single neuron can generate electrical spikes capable of rapidly and non-decrementally traversing the axon, or pathway, from one neuron's cell body to a neuron to which it is sending signals. This experimental and modeling work on the squid giant axon by Hodgkin and Huxley (1952) also led to the award of a Nobel prize. Since this work focused on individual neurons rather than neural networks, it will not be further discussed herein except to note that it provides a foundation for the Shunting Model described below.

Another source of continuous-nonlinear RNNs arose through a study of adaptive behavior in real time, which led to the derivation of neural networks that form the foundation of most current biological neural network research (Grossberg, 1967, 1968b, 1968c). These laws were discovered in 1957-58 when Grossberg, then a college Freshman, introduced the paradigm of using nonlinear systems of differential equations to model how brain mechanisms can control behavioral functions. The laws were derived from an analysis of how psychological data about human and animal learning can arise in an individual learner adapting autonomously in real time. Apart from the Rockefeller Institute student monograph Grossberg (1964), it took a decade to get them published.

Early applications of the Additive Model included computational analyses of vision, learning, recognition, reinforcement learning, and learning of temporal order in speech, language, and sensory-motor control (Grossberg, 1969b, 1969c, 1969d, 1970a, 1970b, 1971a, 1971b, 1972a, 1972b, 1974, 1975; Grossberg and Pepe, 1970, 1971). The Additive Model has continued to be a cornerstone of neural network research to the present time; e.g., in decision-making (Usher and McClelland, 2001). Physicists and engineers unfamiliar with the classical status of the Additive Model in neural networks called it the Hopfield model after the first application of this equation in Hopfield (1984). Grossberg (1988) summarizes historical factors that contributed to their unfamiliarity with the neural network literature. The Additive Model in (7) may be generalized in many ways, including the effects of delays and other factors. In the limit of infinitely many cells, an abstraction which does not exist in the brain, the discrete sum in (7) may be replaced by an integral (see Neural fields).

Grossberg (1964, 1968b, 1969b) also derived an STM equation for neural networks that more closely model the shunting dynamics of individual neurons (Hodgkin, 1964). In such a shunting equation, each STM trace is bounded within an interval \([-D,B]\). Automatic gain control, instantiated by multiplicative shunting, or mass action, terms, interacts with balanced positive and negative signals and inputs to maintain the sensitivity of each STM trace within its interval (see The Noise-Saturation Dilemma):

The brain is designed to process patterned information that is distributed across networks of neurons. For example, a picture is meaningless as a collection of independent pixels. In order to understand 2D pictures and 3D scenes, the brain processes the spatial pattern of inputs that is received from them by the photosensitive retinas. Within the context of a spatial pattern, the information from each pixel can acquire meaning. The same is true during temporal processing. For example, individual speech sounds heard out of context may sound like meaningless chirps. They sound like speech and language when they are part of a characteristic temporal pattern of signals. The STM, MTM, and LTM equations enable the brain to effectively process and learn from both spatial and temporal patterns of information. 041b061a72


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