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Found 9 papers, 3 papers with code
A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density
The imminent need to interpret the output of a Machine Learning model with counterfactual (CF) explanations - via small perturbations to the input - has been notable in the research community.
HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments
Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level.
A Novel Framework for Handling Sparse Data in Traffic Forecast
In this paper we present a deep learning framework that encodes the sparse recent traffic information and forecasts the future traffic condition.
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC.
Particle Cloud Generation with Message Passing Generative Adversarial Networks
We propose JetNet as a novel point-cloud-style dataset for the ML community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models.
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC).
Infant Mortality Prediction using Birth Certificate Data
The Infant Mortality Rate (IMR) is the number of infants per 1000 that do not survive until their first birthday.
First Story Detection using Entities and Relations
News portals, such as Yahoo News or Google News, collect large amounts of documents from a variety of sources on a daily basis.
A Burstiness-aware Approach for Document Dating
A large number of mainstream applications, like temporal search, event detection, and trend identification, assume knowledge of the timestamp of every document in a given textual collection.
Ranked #3 on
Document Dating
on APW
