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Found 9 papers, 4 papers with code
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers
Traffic forecasting is an important element of mobility management, an important key that drives the logistics industry.
Traffic flow prediction using Deep Sedenion Networks
The sedenion output of the network is used to represent the multimodal traffic predictions.
Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching
Since the coronavirus disease (COVID-19) outbreak in December 2019, studies have been addressing diverse aspects in relation to COVID-19 and Variant of Concern 202012/01 (VOC 202012/01) such as potential symptoms and predictive tools.
A Novel Transformer Network with Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting
In this work, we tackle the challenge of weather forecasting using a video transformer network.
Self-Validated Physics-Embedding Network: A General Framework for Inverse Modelling
Physics-based inverse modeling techniques are typically restricted to particular research fields, whereas popular machine-learning-based ones are too data-dependent to guarantee the physical compatibility of the solution.
Flame-state monitoring based on very low number of visible or infrared images via few-shot learning
We analyzed the training process, test performance and inference speed of two algorithms on both image formats, and also used t-SNE to visualize learned features.
Spatially-resolved Thermometry from Line-of-Sight Emission Spectroscopy via Machine Learning
The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data.
EEE, Remediating the failure of machine learning models via a network-based optimization patch
To improve optimization efficiency and convergence, the most important metrics in the context of this research, we follow a three-faceted approach based on the error from the validation process.
