Search Results for author: Dimitrios Gunopulos

Found 9 papers, 3 papers with code

A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density

no code implementations20 Apr 2024 Kleopatra Markou, Dimitrios Tomaras, Vana Kalogeraki, Dimitrios Gunopulos

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

no code implementations12 Jan 2023 Nikolaos Zygouras, Nikolaos Panagiotou, Yang Li, Dimitrios Gunopulos, Leonidas Guibas

Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level.

Travel Time Estimation

A Novel Framework for Handling Sparse Data in Traffic Forecast

no code implementations12 Jan 2023 Nikolaos Zygouras, Dimitrios Gunopulos

In this paper we present a deep learning framework that encodes the sparse recent traffic information and forecasts the future traffic condition.

Decoder Time Series +1

Particle Cloud Generation with Message Passing Generative Adversarial Networks

2 code implementations NeurIPS 2021 Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopulos

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.

First Story Detection using Entities and Relations

1 code implementation COLING 2016 Nikolaos Panagiotou, Cem Akkaya, Kostas Tsioutsiouliklis, Vana Kalogeraki, Dimitrios Gunopulos

News portals, such as Yahoo News or Google News, collect large amounts of documents from a variety of sources on a daily basis.

Relation Extraction

A Burstiness-aware Approach for Document Dating

no code implementations1 Jul 2014 Dimitrios Kotsakos, Theodoros Lappas, Dimitrios Kotzias, Dimitrios Gunopulos, Nattiya Kanhabua, Kjetil Nørvåg

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.

Document Dating Event Detection

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