Search Results for author:
Found 15 papers, 5 papers with code
Gyroscope-Assisted Motion Deblurring Network
Yet, their practical usage in real-world deblurring, especially motion blur, remains limited due to the lack of pixel-aligned training triplets (background, blurred image, and blur heat map) and restricted information inherent in blurred images.
Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks
However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards.
PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences
PADME uses a federated approach where the model is implemented and deployed by all parties and visits each data location incrementally for training.
Deep Author Name Disambiguation using DBLP Data
This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research.
Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective
Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning.
Whois? Deep Author Name Disambiguation using Bibliographic Data
As the number of authors is increasing exponentially over years, the number of authors sharing the same names is increasing proportionally.
Ensemble and Multimodal Approach for Forecasting Cryptocurrency Price
Since the birth of Bitcoin in 2009, cryptocurrencies have emerged to become a global phenomenon and an important decentralized financial asset.
COIN: Counterfactual Image Generation for VQA Interpretation
Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer.
Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims
In this paper, we propose to leverage commonsense knowledge for the tasks of false news classification and check-worthy claim detection.
Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data
In this paper, we propose a novel approach to link author names to their real-world entities by relying on their co-authorship pattern and area of research.
BiblioDAP: The 1st Workshop on Bibliographic Data Analysis and Processing
Automatic processing of bibliographic data becomes very important in digital libraries, data science and machine learning due to its importance in keeping pace with the significant increase of published papers every year from one side and to the inherent challenges from the other side.
MexPub: Deep Transfer Learning for Metadata Extraction from German Publications
This variety makes traditional NLP methods fail to accurately extract metadata from these publications.
Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction
Physical motion models offer interpretable predictions for the motion of vehicles.
ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information
Social media platforms are vulnerable to fake news dissemination, which causes negative consequences such as panic and wrong medication in the healthcare domain.
LaHAR: Latent Human Activity Recognition using LDA
Processing sequential multi-sensor data becomes important in many tasks due to the dramatic increase in the availability of sensors that can acquire sequential data over time.
