Sparse Data

Sparse data, characterized by limited or missing observations, presents a significant challenge across numerous scientific and engineering domains. Current research focuses on developing robust methods to handle this scarcity, employing techniques like generative adversarial networks (GANs), probabilistic frameworks, and novel neural network architectures (e.g., UNet modifications, Transformers) tailored for sparse data imputation, reconstruction, and prediction. These advancements are crucial for improving the accuracy and reliability of models in various applications, ranging from climate modeling and personalized medicine to traffic flow prediction and intelligent tutoring systems.

Papers

December 1, 2023

Spatiotemporal Transformer for Imputing Sparse Data: A Deep Learning Approach
Kehui Yao, Jingyi Huang, Jun Zhu
Deep Learning Approach Sparse Data Soil Moisture Spatiotemporal Transformer Spatiotemporal Datasets Spatiotemporal Imputation

November 27, 2023

Experimental Analysis of Large-scale Learnable Vector Storage Compression
Hailin Zhang, Penghao Zhao, Xupeng Miao, Yingxia Shao, Zirui Liu, Tong Yang, Bin Cui
Jina Embeddings Linear Compression High Dimension Sparse Data Retrieval Task Experimental Analysis

November 3, 2023

SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data
Jose Luis Ponton, Haoran Yun, Andreas Aristidou, Carlos Andujar, Nuria Pelechano
Motion Capture Sparse Data Motion Reconstruction Full Body Avatar

September 13, 2023

Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets
Amy K. Strong, Ethan J. LoCicero, Leila J. Bridgeman
Imitation Learning Sparse Data Discrete Environment Adversarial Imitation Learning Learned Controller Output Feedback

September 4, 2023

Classic algorithms are fair learners: Classification Analysis of natural weather and wildfire occurrences
Senthilkumar Gopal
Supervised Learning Decision Tree Sparse Data Extreme Event Classification Problem Classical Algorithm Fair Learning Forest Wildfire Observation

August 22, 2023

Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables
Anirban Mukherjee, Hannah H. Chang
Causal Model Causal Effect Estimation Reproducing Kernel Hilbert Space Sparse Data Categorical Data

August 6, 2023

Visualization of Extremely Sparse Contingency Table by Taxicab Correspondence Analysis: A Case Study of Textual Data
V. Choulakian, J. Allard
Case Study Interactive Visualization Dimension Reduction Sparse Data Correspondence Based Method

July 10, 2023

Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis
Mingyuan Liu, Lu Xu, Jicong Zhang
Sparse Data Local Descriptor Intra Class Class Separability Disease Detection Model Margin Loss Sparse Embeddings

May 26, 2023

Feature Adaptation for Sparse Linear Regression
Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
Sparse Data Sparse Linear Regression Multivariate Gaussian Feature Adaptation Localized Sparse Incomplete

April 8, 2023

Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data
Elena Camuffo, Simone Milani
Continual LEArning LiDAR Point Cloud Coarse to Fine Sparse Data Lidar Semantic Segmentation Class Incremental Continual Learning Continual Learning Paradigm

March 14, 2023

Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin, Markus Spanring, Rahul Siripurapu, Daniel Springer, Leyan Deng, Chenwang Wu, Defu Lian, Min Zhou, Martin Lumiste, Andrei Ilie, Xinhua Wu, Cheng Lyu, Qing-Long Lu, Vishal Mahajan, Yichao Lu, Jiezhang Li, Junjun Li, Yue-Jiao Gong, Florian Grötschla, Joël Mathys, Ye Wei, He Haitao, Hui Fang, Kevin Malm, Fei Tang, Michael Kopp, David Kreil, Sepp Hochreiter
Sparse Data Traffic Pattern Urban Traffic Dynamic Prediction Traffic4cast Competition Future Traffic Graph Edge Road Network Data

February 10, 2023

Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions
Roland Oruche, Fearghal O'Donncha
Domain Adaptation Sparse Data Streamflow Prediction

January 12, 2023

December 26, 2022

Artificial Intelligence to Enhance Mission Science Output for In-situ Observations: Dealing with the Sparse Data Challenge
M. I. Sitnov, G. K. Stephens, V. G. Merkin, C. -P. Wang, D. Turner, K. Genestreti, M. Argall, T. Y. Chen, A. Y. Ukhorskiy, S. Wing, Y. -H. Liu
Artificial Intelligence Data Assimilation Sparse Data Mission Specification Situ Data Geomagnetic Storm

December 9, 2022

A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data
Lena Podina, Brydon Eastman, Mohammad Kohandel
Physic Informed Neural Network Scientific Discovery Sparse Data PINN Method Solution Operator Differential Operator

September 8, 2022

Stochastic gradient descent with gradient estimator for categorical features
Paul Peseux, Maxime Berar, Thierry Paquet, Victor Nicollet
Stochastic Gradient Descent Sparse Data Categorical Data Gradient Estimator Categorical Feature

September 7, 2022

Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter
Alexander Schperberg, Yusuke Tanaka, Feng Xu, Marcel Menner, Dennis Hong
Predictive Control Robotic System Trajectory Optimization Trajectory Planning Model Based Real Robot Sparse Data Unscented Kalman Filter Real to Sim Residual Error

August 13, 2022

Modeling Network-level Traffic Flow Transitions on Sparse Data
Xiaoliang Lei, Hao Mei, Bin Shi, Hua Wei
Sparse Data Traffic Management System Future Traffic

August 5, 2022

Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
Hao Xu, Junsheng Zeng, Dongxiao Zhang
Partial Differential Equation Scientific Discovery Sparse Data Information Criterion

Sparse Data

Papers

Spatiotemporal Transformer for Imputing Sparse Data: A Deep Learning Approach

Experimental Analysis of Large-scale Learnable Vector Storage Compression

SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data

Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets

Classic algorithms are fair learners: Classification Analysis of natural weather and wildfire occurrences

Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variables

Visualization of Extremely Sparse Contingency Table by Taxicab Correspondence Analysis: A Case Study of Textual Data

Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis

Feature Adaptation for Sparse Linear Regression

Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data

Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions

HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments

A Novel Framework for Handling Sparse Data in Traffic Forecast

Artificial Intelligence to Enhance Mission Science Output for In-situ Observations: Dealing with the Sparse Data Challenge

A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data

Stochastic gradient descent with gradient estimator for categorical features

Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter

Modeling Network-level Traffic Flow Transitions on Sparse Data

Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion