109,272 research outputs found
Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs
The human reasoning process is seldom a one-way process from an input leading
to an output. Instead, it often involves a systematic deduction by ruling out
other possible outcomes as a self-checking mechanism. In this paper, we
describe the design of a hybrid neural network for logical learning that is
similar to the human reasoning through the introduction of an auxiliary input,
namely the indicators, that act as the hints to suggest logical outcomes. We
generate these indicators by digging into the hidden information buried
underneath the original training data for direct or indirect suggestions. We
used the MNIST data to demonstrate the design and use of these indicators in a
convolutional neural network. We trained a series of such hybrid neural
networks with variations of the indicators. Our results show that these hybrid
neural networks are very robust in generating logical outcomes with inherently
higher prediction accuracy than the direct use of the original input and output
in apparent models. Such improved predictability with reassured logical
confidence is obtained through the exhaustion of all possible indicators to
rule out all illogical outcomes, which is not available in the apparent models.
Our logical learning process can effectively cope with the unknown unknowns
using a full exploitation of all existing knowledge available for learning. The
design and implementation of the hints, namely the indicators, become an
essential part of artificial intelligence for logical learning. We also
introduce an ongoing application setup for this hybrid neural network in an
autonomous grasping robot, namely as_DeepClaw, aiming at learning an optimized
grasping pose through logical learning.Comment: 11 pages, 9 figures, 4 table
Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data
The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations
Bayesian Neural Tree Models for Nonparametric Regression
Frequentist and Bayesian methods differ in many aspects, but share some basic
optimal properties. In real-life classification and regression problems,
situations exist in which a model based on one of the methods is preferable
based on some subjective criterion. Nonparametric classification and regression
techniques, such as decision trees and neural networks, have frequentist
(classification and regression trees (CART) and artificial neural networks) as
well as Bayesian (Bayesian CART and Bayesian neural networks) approaches to
learning from data. In this work, we present two hybrid models combining the
Bayesian and frequentist versions of CART and neural networks, which we call
the Bayesian neural tree (BNT) models. Both models exploit the architecture of
decision trees and have lesser number of parameters to tune than advanced
neural networks. Such models can simultaneously perform feature selection and
prediction, are highly flexible, and generalize well in settings with a limited
number of training observations. We study the consistency of the proposed
models, and derive the optimal value of an important model parameter. We also
provide illustrative examples using a wide variety of real-life regression data
sets
Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction
Physical motion models offer interpretable predictions for the motion of
vehicles. However, some model parameters, such as those related to aero- and
hydrodynamics, are expensive to measure and are often only roughly approximated
reducing prediction accuracy. Recurrent neural networks achieve high prediction
accuracy at low cost, as they can use cheap measurements collected during
routine operation of the vehicle, but their results are hard to interpret. To
precisely predict vehicle states without expensive measurements of physical
parameters, we propose a hybrid approach combining deep learning and physical
motion models including a novel two-phase training procedure. We achieve
interpretability by restricting the output range of the deep neural network as
part of the hybrid model, which limits the uncertainty introduced by the neural
network to a known quantity. We have evaluated our approach for the use case of
ship and quadcopter motion. The results show that our hybrid model can improve
model interpretability with no decrease in accuracy compared to existing deep
learning approaches
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction
We propose Gibbs-Duhem-informed neural networks for the prediction of binary
activity coefficients at varying compositions. That is, we include the
Gibbs-Duhem equation explicitly in the loss function for training neural
networks, which is straightforward in standard machine learning (ML) frameworks
enabling automatic differentiation. In contrast to recent hybrid ML approaches,
our approach does not rely on embedding a specific thermodynamic model inside
the neural network and corresponding prediction limitations. Rather,
Gibbs-Duhem consistency serves as regularization, with the flexibility of ML
models being preserved. Our results show increased thermodynamic consistency
and generalization capabilities for activity coefficient predictions by
Gibbs-Duhem-informed graph neural networks and matrix completion methods. We
also find that the model architecture, particularly the activation function,
can have a strong influence on the prediction quality. The approach can be
easily extended to account for other thermodynamic consistency conditions
A Comparison Between a Long Short-Term Memory Network Hybrid Model and an ARIMA Hybrid Model for Stock Return Predictability
This thesis explores the applicability of neural networks in stock return forecasts by designing a hybrid LSTM (long short-term memory) network and compares its forecasting ability with both a static LSTM network and an ARIMA hybrid model. The S&P100 stock set is employed as the prediction sample. The hybrid models use the neural network approach and frequentist method respectively to estimate Fama-French risk factors, then predict stock returns based on factor estimations that benefit from the prediction ability and computational power of the LSTM network and the ARIMA model as well as the Fama-French model’s explanatory power of returns. Better factor predictions are made by the LSTM network with a 31% reduction of Mean Squared Error (MSE) and broader ranges of estimation than the ARIMA model. Hybrid models demonstrate a better fit, resulting in more accurate predictions compared to the static LSTM network by an average of 4.6% (LSTM-FF) and 3.1% (ARIMA-FF). However, I find that the slight outperformance of the LSTM-FF hybrid model over the ARIMA-FF hybrid model is not statistically significant
Protein Inter-Residue Distance Prediction Using Residual and Capsule Networks
The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein\u27s inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms used in the state-of-the-art methods. Using a standard dataset of 3420 training proteins and an independent dataset of 150 test proteins, we show that our method can predict distances 51.06% more accurately than a standard residual network method, when accuracy of all long-range distances are evaluated using mean absolute error. To further validate our results, we demonstrate that three-dimensional models built using the distances predicted by our method are more accurate than models built using the distances predicted by residual networks. Overall, our results, for the first time, highlight the potential of capsule-residual hybrid networks for solving the protein inter-residue distance prediction problem
A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery
The field of drug discovery hinges on the accurate prediction of binding
affinity between prospective drug molecules and target proteins, especially
when such proteins directly influence disease progression. However, estimating
binding affinity demands significant financial and computational resources.
While state-of-the-art methodologies employ classical machine learning (ML)
techniques, emerging hybrid quantum machine learning (QML) models have shown
promise for enhanced performance, owing to their inherent parallelism and
capacity to manage exponential increases in data dimensionality. Despite these
advances, existing models encounter issues related to convergence stability and
prediction accuracy. This paper introduces a novel hybrid quantum-classical
deep learning model tailored for binding affinity prediction in drug discovery.
Specifically, the proposed model synergistically integrates 3D and spatial
graph convolutional neural networks within an optimized quantum architecture.
Simulation results demonstrate a 6% improvement in prediction accuracy relative
to existing classical models, as well as a significantly more stable
convergence performance compared to previous classical approaches.Comment: 5 pages, 3 figure
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