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Found 15 papers, 4 papers with code
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
This paper proposes a semi-supervised methodology for training physics-informed machine learning methods.
Characteristics-Informed Neural Networks for Forward and Inverse Hyperbolic Problems
This paper focuses on linear transport phenomena, in which case it is shown that, if the characteristic ODEs can be solved exactly, then the output of a CINN is an exact solution of the PDE, even at initialization, preventing the occurrence of non-physical solutions.
Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning
Additionally, height of plastic bags had significant effect (p < 0. 0001) on overall detection accuracy.
Assessing The Performance of YOLOv5 Algorithm for Detecting Volunteer Cotton Plants in Corn Fields at Three Different Growth Stages
The boll weevil (Anthonomus grandis L.) is a serious pest that primarily feeds on cotton plants.
Computer Vision for Volunteer Cotton Detection in a Corn Field with UAS Remote Sensing Imagery and Spot Spray Applications
To control boll weevil (Anthonomus grandis L.) pest re-infestation in cotton fields, the current practices of volunteer cotton (VC) (Gossypium hirsutum L.) plant detection in fields of rotation crops like corn (Zea mays L.) and sorghum (Sorghum bicolor L.) involve manual field scouting at the edges of fields.
Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery
The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level.
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation.
PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization
In this paper, we propose the use of a particle swarm optimization (PSO) approach to train PINNs.
Generalized Resubstitution for Classification Error Estimation
In the two-class case, we show that a generalized resubstitution estimator is consistent and asymptotically unbiased, regardless of the distribution of the features and label, if the corresponding generalized empirical measure converges uniformly to the standard empirical measure and the classification rule has a finite VC dimension.
A stochastic metapopulation state-space approach to modeling and estimating Covid-19 spread
Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination.
Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI
Our results show that classification algorithms for SS models display large variance in performance.
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs).
A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes
To address this, we introduce genetic Generative Adversarial Networks (gGAN), a semi-supervised approach based on an innovative GAN architecture to create large synthetic genetic data sets starting with a small amount of labeled data and a large amount of unlabeled data.
Deep Multimodal Transfer-Learned Regression in Data-Poor Domains
In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc.
Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs
Reinforcement learning then is used to learn the cost function from the available gene expression data.
