activity
20162024
most citedTAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network

111 citations · 354 across the 26 of their papers we have counts for

collaborators

37 papers

cs.LG2024

VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting

Alireza Koochali, Ensiye Tahaei, Andreas Dengel +1

This paper presents VAEneu, an innovative autoregressive method for multistep ahead univariate probabilistic time series forecasting. We employ the conditional VAE framework and op…

cs.CL2024

Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning

Pervaiz Iqbal Khan, Andreas Dengel, Sheraz Ahmed

Detecting diseases from social media has diverse applications, such as public health monitoring and disease spread detection. While language models (LMs) have shown promising perfo…

cs.CL20221 cited

A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets

Pervaiz Iqbal Khan, Imran Razzak, Andreas Dengel +1

Health mention classification deals with the disease detection in a given text containing disease words. However, non-health and figurative use of disease words adds challenges to…

cs.AI20222 cited

Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training

Pervaiz Iqbal Khan, Shoaib Ahmed Siddiqui, Imran Razzak +2

Health mentioning classification (HMC) classifies an input text as health mention or not. Figurative and non-health mention of disease words makes the classification task challengi…

cs.IR2022

Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysis

Dominique Mercier, Syed Tahseen Raza Rizvi, Vikas Rajashekar +2

Citations are generally analyzed using only quantitative measures while excluding qualitative aspects such as sentiment and intent. However, qualitative aspects provide deeper insi…

cs.LG2022

KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting

Muhammad Ali Chattha, Ludger van Elst, Muhammad Imran Malik +2

End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-w…