Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

Mallick, Tanwi; Balaprakash, Prasanna; Rask, Eric; Macfarlane, Jane

doi:10.1109/icpr48806.2021.9413270

Cited by 36 publications

(20 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the potential further advancements in TL is the application of the diffused model with TL. 63 The spatial and temporal dependency of data is captured with the diffusion of convolutional neural networks and recurrent neural network to upgrade the model predicting behavior.…”

Section: Resultsmentioning

confidence: 99%

“…This can be observed from the scatter plot of Figure , where the predicted capacitance values slightly more deviate from their measured values, but the R 2 score of 0.8753 is still satisfactory. One of the potential further advancements in TL is the application of the diffused model with TL . The spatial and temporal dependency of data is captured with the diffusion of convolutional neural networks and recurrent neural network to upgrade the model predicting behavior.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning

et al. 2022

View full text Add to dashboard Cite

It is always highly desired to have a well-defined relationship between the chemistry in semiconductor processing and the device characteristics. With the shrinkage of technology nodes in the semiconductors roadmap, it becomes more complicated to understand the relation between the device electrical characteristics and the process parameters such as oxidation and wafer cleaning procedures. In this work, we use a novel machine learning approach, i.e., physics-assisted multitask and transfer learning, to construct a relationship between the process conditions and the device capacitance voltage curves. While conventional semiconductor processes and device modeling are based on a physical model, recently, the machine learning-based approach or hybrid approaches have drawn significant attention. In general, a huge amount of data is required to train a machine learning model. Since producing data in the semiconductor industry is not an easy task, physics-assisted artificial intelligence has become an obvious choice to resolve these issues. The predicted C – V uses the hybridization of physics, and machine learning provides improvement while the coefficient of determination ( R 2 ) is 0.9442 for semisupervised multitask learning (SS-MTL) and 0.9253 for transfer learning (TL), referenced to 0.6108 in the pure machine learning model using multilayer perceptrons. The machine learning architecture used in this work is capable of handling data sparsity and promotes the usage of advanced algorithms to model the relationship between complex chemical reactions in semiconductor manufacturing and actual device characteristics. The code is available at .

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The results showed that the finetuned model performed much better and needed only a small sample of extra data to improve performance by a margin of 37%. Another approach tries to predict traffic congestion of a network with a small amount of historical data by training a recurrent neural network on a traffic network with a lot of historic data [21]. Zero-shot learning is less popular in the network setting, but few approaches that exist closely follow the paradigms of zero-shot learning [37].…”

Section: Transfer Learningmentioning

confidence: 99%

Transfer Learning for Node Regression Applied to Spreading Prediction

Mežnar¹,

Lavrač²,

Škrlj³

2021

Preprint

View full text Add to dashboard Cite

Understanding how information propagates in real-life complex networks yields a better understanding of dynamic processes such as misinformation or epidemic spreading. The recently introduced branch of machine learning methods for learning node representations offers many novel applications, one of them being the task of spreading prediction addressed in this paper. We explore the utility of the state-ofthe-art node representation learners when used to assess the effects of spreading from a given node, estimated via extensive simulations. Further, as many real-life networks are topologically similar, we systematically investigate whether the learned models generalize to previously unseen networks, showing that in some cases very good model transfer can be obtained. This work is one of the first to explore transferability of the learned representations for the task of node regression; we show there exist pairs of networks with similar structure between which the trained models can be transferred (zero-shot), and demonstrate their competitive performance. To our knowledge, this is one of the first attempts to evaluate the utility of zero-shot transfer for the task of node regression.

show abstract

“…Graph neural networks (GNNs) have received considerable attention in recent years due to their remarkable performance on a variety of graph learning tasks, including social analysis [27,21,33], drug discovery [43,16,28], traffic forecasting [22,3,7], recommendation system [36,40] and computer vision [42,5].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited

He¹,

Wei²,

Wen³

2022

Preprint

View full text Add to dashboard Cite

Designing spectral convolutional networks is a challenging problem in graph learning. ChebNet, one of the early attempts, approximates the spectral convolution using Chebyshev polynomials. GCN simplifies ChebNet by utilizing only the first two Chebyshev polynomials while still outperforming it on real-world datasets. GPR-GNN and BernNet demonstrate that the Monomial and Bernstein bases also outperform the Chebyshev basis in terms of learning the spectral convolution. Such conclusions are counter-intuitive in the field of approximation theory, where it is established that the Chebyshev polynomial achieves the optimum convergent rate for approximating a function. In this paper, we revisit the problem of approximating the spectral convolution with Chebyshev polynomials. We show that ChebNet's inferior performance is primarily due to illegal coefficients learnt by ChebNet approximating analytic filter functions, which leads to over-fitting. We then propose ChebNetII, a new GNN model based on Chebyshev interpolation, which enhances the original Chebyshev polynomial approximation while reducing the Runge phenomena. We conducted an extensive experimental study to demonstrate that ChebNetII can learn arbitrary graph spectrum filters and achieve superior performance in both full-and semi-supervised node classification tasks. 1 IntroductionGraph neural networks (GNNs) have received considerable attention in recent years due to their remarkable performance on a variety of graph learning tasks, including social analysis [27,21,33], drug discovery [43,16,28], traffic forecasting [22,3,7], recommendation system [36,40] and computer vision [42,5].Spatial-based and spectral-based graph neural networks (GNNs) are the two primary categories of GNNs. To learn node representations, spatial-based GNNs [18,13,34] often rely on a message propagation and aggregation mechanism between neighboring nodes. Spectral-based methods [8] create spectrum graph convolutions or, equivalently, spectral graph filters, in the spectral domain of the Laplacian matrix. We can further divide spectral-based GNNs into two categories based on whether or not their graph convolutions can be learned.• Predetermined graph convolutions: GCN [18] uses a simplified first-order Chebyshev polynomial as graph convolution, which is proven to be a low-pass filter [1,35,37,44]. APPNP [19] utilizes Personalized PageRank (PPR) to set the graph convolution and achieves a low-pass filter as well [20,44]. GNN-LF/HF [44] designs graph convolutions from the perspective of graph optimization functions, which can simulate high-and low-pass filters. • Learnable graph convolutions: ChebNet [8] approximates the graph convolution with Chebyshev polynomials and learns the convolutional filters via trainable weights of the Chebyshev basis. GPR-GNN [6] uses the Monomial basis to approximate graph convolutions, which can derive high-or low-pass filters. ARMA [2] learns a rational convolutional filter via the family of Auto-Regressive Moving Average filters [24]. BernNet [15] utilizes th...

show abstract

Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

Cited by 36 publications

References 11 publications

Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning

Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning

Transfer Learning for Node Regression Applied to Spreading Prediction

Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited

Contact Info

Product

Resources

About