Recent advances in deep learning for traffic probabilistic prediction

Probabilistic Deep Learning with TensorFlow 2 - Coursera

This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets. This is a ...

Seminar on Advances in Probabilistic Machine Learning

Abstract: While Bayesian deep learning has been a popular field of research in recent years, most of the work has focused on improving inference methods for ...

An Introduction to Probabilistic Deep Learning Explained in Simple ...

Deep learning is nothing else than probability. There are two principles involved in it, one is the maximum likelihood and the other one is Bayes.

Recent advances in data-driven prediction for wind power - Frontiers

As AI technology advances, it has led to the increased adoption of deep learning models in WPP. These models, such as deep belief network (DBN), auto-encoder ( ...

Probabilistic spatio-temporal graph convolutional network for traffic ...

Recent advances in deep learning for traffic probabilistic prediction · Authors · Source Information.

Probabilistic prediction of anaerobic reactor performance using ...

The data pre-processing and optimisation of the neural network model are reported. The findings also indicate using a dropout probability beyond 40% adversely ...

Graph Neural Networks for Traffic Pattern Recognition: An Overview

GNNs do not only offer new and exciting applications and generalization potential for deep learning models, but also can significantly improve the performance ...

Models for forecasting the traffic flow within the city of Ljubljana

The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, ...

Classification in Machine Learning: A Guide for Beginners - DataCamp

Researchers can use machine learning models to predict new diseases that are more likely to emerge in the future. Education. Education is one of the domains ...

Discrete Residual Flow for Probabilistic Pedestrian Behavior ...

Motion prediction of traffic actors for autonomous driving using deep convolutional networks. arXiv preprint arXiv:1808.05819, 2018. [21] X. Shi, Z. Chen, H ...

Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction

Recent advances have significantly boosted the research of traffic flow prediction with vari- ous deep learning techniques, e.g., convolutional neural net-.

Probabilistic Time Series Forecasting - Papers With Code

We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.

AutoBNN: Probabilistic time series forecasting with compositional ...

To that end, we introduce AutoBNN, a new open-source package written in JAX. AutoBNN automates the discovery of interpretable time series ...

Artificial Intelligence for Traffic Prediction and Estimation in ...

Deep learning models are trained on this real-world traffic information to detect better and forecast the probability of crashes. This work aims ...

Master's in Artificial Intelligence | Computer & Data Science Online

Advances in Deep Learning. This course provides an ... learning to major recent applications in housing market analysis and transportation.

A DeepAR based hybrid probabilistic prediction model for ... - OUCI

Arora, Probabilistic wind power forecasting using optimized deep auto-regressive recurrent neural networks, IEEE Transactions on Industrial Informatics ...

[PDF] PredictionNet: Real-Time Joint Probabilistic Traffic Prediction ...

Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving. To this end, we present PredictionNet, a deep neural ...

Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction

The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such ...

An Introductory Review of Deep Learning for Prediction Models With ...

Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis ...

A survey on modern deep neural network for traffic prediction

Recently, researchers have started to focus on machine learning models because of their power and flexibility. As theoretical and technological advances emerge, ...