Element Level Information

Element-level information focuses on extracting and utilizing the individual components within complex data structures, aiming to improve the accuracy and efficiency of analysis and prediction. Current research emphasizes the development of advanced machine learning models, including deep learning frameworks (e.g., leveraging graph convolutional networks and attention mechanisms) and novel quantization techniques, to effectively represent and process this granular information. This work has significant implications across diverse fields, from materials science and geomechanics (improving multiscale modeling) to natural language processing (enhancing language model understanding and text analysis) and computer vision (advancing 3D visual grounding and image compression). The ability to effectively leverage element-level information is crucial for unlocking insights from increasingly complex datasets in various scientific and engineering domains.

Papers

August 11, 2022

Searching for chromate replacements using natural language processing and machine learning algorithms
Shujing Zhao, Nick Birbilis
Machine Learning Natural Language Processing BERT Model Machine Learning Algorithm Element Level Information Word2Vec Model

February 2, 2022

Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties
Andrij Vasylenko, Dmytro Antypov, Vladimir Gusev, Michael W. Gaultois, Matthew S. Dyer, Matthew J. Rosseinsky
Machine Learning Many Property Element Level Information Chemical Discovery Phase Field

November 15, 2021

A Comparative Study on Basic Elements of Deep Learning Models for Spatial-Temporal Traffic Forecasting
Yuyol Shin, Yoonjin Yoon
Deep Learning Model Comparative Study Graph Attention Traffic Forecasting Attention Based Model Temporal Traffic Element Level Information RNN Based Model

November 8, 2021

Learning Context-Aware Representations of Subtrees
Cedric Cook
LeArning Abstract Contextual Representation Better Representation Efficient Representation Element Level Information Tree LSTMs

Element Level Information

Papers

Searching for chromate replacements using natural language processing and machine learning algorithms

Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties

A Comparative Study on Basic Elements of Deep Learning Models for Spatial-Temporal Traffic Forecasting

Learning Context-Aware Representations of Subtrees