- Learning Dynamics and Heterogeneity of Spatial|Temporal Graph ...🔍
- Traffic Forecasting of Back Servers Based on ARIMA|LSTM|CF ...🔍
- Tabular Learning|Based Traffic Event Prediction for Intelligent Social ...🔍
- forecasting🔍
- Factors influencing transfer learning of traffic forecasting models🔍
- Machine Learning Glossary🔍
- Attribute|Augmented Spatiotemporal Graph Convolutional Network ...🔍
- AI for Time Series 🔍
A Fundamental Model with Stable Interpretability for Traffic Forecasting
Learning Dynamics and Heterogeneity of Spatial-Temporal Graph ...
analysis models for the traffic forecasting problem in the ear- lier days ... Traffic prediction is a fundamental problem in Intelligent.
Traffic Forecasting of Back Servers Based on ARIMA-LSTM-CF ...
Traditional prediction models ignore the unique data characteristics of server traffic that can be used to optimize the prediction model, so ...
Tabular Learning-Based Traffic Event Prediction for Intelligent Social ...
A framework to integrate the social traffic data and use the TabNet model to facilitate the representation learning task in traffic event prediction is ...
forecasting - ML4ITS - Machine Learning for Irregular Time Series
Abstract Accurately forecasting traffic in telecommunication networks is essential for operators to efficiently allocate resources, provide better services, ...
Factors influencing transfer learning of traffic forecasting models
Their proposed method outperforms GRU and CNN models in accuracy and stability. Ren et al. (2021) proposed a study on traffic flow prediction by ...
Machine Learning Glossary - Google for Developers
During the forward pass, the system processes a batch of examples to yield prediction(s). The system compares each prediction to each label ...
Attribute-Augmented Spatiotemporal Graph Convolutional Network ...
INDEX TERMS Traffic forecasting, graph convolutional network, external factors, spatiotemporal models. I. INTRODUCTION. As one of the essential ...
AI for Time Series (AI4TS) Papers, Tutorials, and Surveys - GitHub
Time Series Forecasting (Traffic) · Frigate: Frugal Spatio-temporal Forecasting on Road Networks [paper] [official code] · Transferable Graph Structure Learning ...
Delft University of Technology Uncertainty Quantification and ...
tions in a deep-learning-based macroscopic traffic forecasting model. ... First, we improve the interpretability of deep-learning-based traffic ...
Short‐Term Traffic Flow Prediction: A Method of Combined Deep ...
The results validate that the model can obtain better prediction performance compared with other representative forecast models. Tian et al. [48] ...
Counterfactual Explanations for Deep Learning-Based Traffic ...
Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, the black-box ...
Explainable AI: A Review of Machine Learning Interpretability Methods
First introduced in [45], the local interpretable model-agnostic explanations (LIME) method is one of the most popular interpretability methods for black-box ...
How would you describe the trade-off between model interpretability ...
Here is a very good post discussing ideas on how to interpret machine learning models. Industries, while being very cautious, are slowly ...
IPAD: Stable Interpretable Forecasting with Knockoffs Inference
Interpretability and stability are two important features that are desired in many contemporary big data applications arising in statistics, economics, and ...
Artificial intelligence-based traffic flow prediction: a comprehensive ...
Also, the Auto-Regressive Integrated Moving Average (ARIMA) model is a well-known and standard framework for predicting short-term traffic flow ...
Adaptive Seasonal Time Series Models for Forecasting Short-Term ...
Conventionally, most traffic forecasting models have been applied in a static framework in which new observations are not used to update model parameters ...
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models · Diffusion Tuning: Transferring Diffusion Models via ...
TensorFlow 2 quickstart for beginners
While this can make the model output more directly interpretable ... stable loss calculation for all models when using a softmax output.
more rewards to predicting traffic at tougher locations and times. Interpretable traffic prediction Many machine learning models are used for traffic prediction ...
Opening the Language Model Pipeline: A Tutorial on Data Preparation, Model Training, and Adaptation ... Stable Diffusion · A two-scale Complexity Measure for ...