100 citations · 191 across the 15 of their papers we have counts for
15 papers
FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
Deep neural networks are among the most successful algorithms in terms of performance and scalability across different domains. However, since these networks are black boxes, their…
From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
Deep learning has proven to be successful in various domains and for different tasks. However, when it comes to private data several restrictions are making it difficult to use dee…
Privacy Meets Explainability: A Comprehensive Impact Benchmark
Saifullah Saifullah, Dominique Mercier, Adriano Lucieri +2
Since the mid-10s, the era of Deep Learning (DL) has continued to this day, bringing forth new superlatives and innovations each year. Nevertheless, the speed with which these inno…
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…
Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods
Dominique Mercier, Jwalin Bhatt, Andreas Dengel +1
In the last decade neural network have made huge impact both in industry and research due to their ability to extract meaningful features from imprecise or complex data, and by ach…
TimeREISE: Time-series Randomized Evolving Input Sample Explanation
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
Deep neural networks are one of the most successful classifiers across different domains. However, due to their limitations concerning interpretability their use is limited in safe…
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification
Dominique Mercier, Adriano Lucieri, Mohsin Munir +2
With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders. It is pivot…
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
The classification of time-series data is pivotal for streaming data and comes with many challenges. Although the amount of publicly available datasets increases rapidly, deep neur…
Interpreting Deep Models through the Lens of Data
Dominique Mercier, Shoaib Ahmed Siddiqui, Andreas Dengel +1
Identification of input data points relevant for the classifier (i.e. serve as the support vector) has recently spurred the interest of researchers for both interpretability as wel…
ImpactCite: An XLNet-based method for Citation Impact Analysis
Dominique Mercier, Syed Tahseen Raza Rizvi, Vikas Rajashekar +2
Citations play a vital role in understanding the impact of scientific literature. Generally, citations are analyzed quantitatively whereas qualitative analysis of citations can rev…
P2ExNet: Patch-based Prototype Explanation Network
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
Deep learning methods have shown great success in several domains as they process a large amount of data efficiently, capable of solving complex classification, forecast, segmentat…
TSInsight: A local-global attribution framework for interpretability in time-series data
Shoaib Ahmed Siddiqui, Dominique Mercier, Andreas Dengel +1
With the rise in the employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regar…
SentiCite: An Approach for Publication Sentiment Analysis
Dominique Mercier, Akansha Bhardwaj, Andreas Dengel +1
With the rapid growth in the number of scientific publications, year after year, it is becoming increasingly difficult to identify quality authoritative work on a single topic. Tho…
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features
Mohsin Munir, Shoaib Ahmed Siddiqui, Ferdinand Küsters +3
Neural networks (NN) are considered as black-boxes due to the lack of explainability and transparency of their decisions. This significantly hampers their deployment in environment…
TSViz: Demystification of Deep Learning Models for Time-Series Analysis
Shoaib Ahmed Siddiqui, Dominik Mercier, Mohsin Munir +2
This paper presents a novel framework for demystification of convolutional deep learning models for time-series analysis. This is a step towards making informed/explainable decisio…