no code implementations • 7 May 2024 • Alireza Koochali, Ensiye Tahaei, Andreas Dengel, Sheraz Ahmed
This paper presents VAEneu, an innovative autoregressive method for multistep ahead univariate probabilistic time series forecasting.
no code implementations • 30 Apr 2024 • Pervaiz Iqbal Khan, Andreas Dengel, Sheraz Ahmed
Detecting diseases from social media has diverse applications, such as public health monitoring and disease spread detection.
no code implementations • 16 Apr 2024 • Payal Varshney, Adriano Lucieri, Christoph Balada, Andreas Dengel, Sheraz Ahmed
In the first step, CDCT uses a Latent Diffusion Model (LDM) to generate a counterfactual trajectory dataset.
no code implementations • 20 Dec 2023 • Hamidreza Gholamrezaei, Alireza Koochali, Andreas Dengel, Sheraz Ahmed
This paper introduces Structured Noise Space GAN (SNS-GAN), a novel approach in the field of generative modeling specifically tailored for class-conditional generation in both image and time series data.
no code implementations • 31 Oct 2023 • Pervaiz Iqbal Khan, Andreas Dengel, Sheraz Ahmed
This paper proposes a training strategy Medi-CAT to overcome the underfitting and overfitting phenomena in medical imaging datasets.
no code implementations • 29 Oct 2023 • Pervaiz Iqbal Khan, Muhammad Nabeel Asim, Andreas Dengel, Sheraz Ahmed
Following the need for an optimal language model competent in extracting useful patterns from social media text, the key goal of this paper is to train language models in such a way that they learn to derive generalized patterns.
no code implementations • 5 Oct 2023 • Saifullah Saifullah, Stefan Agne, Andreas Dengel, Sheraz Ahmed
We conduct a comprehensive evaluation of the algorithm across various client and privacy settings, and demonstrate its capability to achieve comparable performance and privacy guarantees to standalone DP, even when accommodating an increasing number of participating clients.
1 code implementation • bioRxiv 2023 • Anwai Archit, Sushmita Nair, Nabeel Khalid, Paul Hilt, Vikas Rajashekar, Marei Freitag, Sagnik Gupta, Andreas Dengel, Sheraz Ahmed, Constantin Pape
We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data.
no code implementations • 28 Mar 2023 • Dominique Mercier, Andreas Dengel, Sheraz Ahmed
In this work, two very prominent GAN-based architectures were evaluated in the context of private time series classification.
no code implementations • 8 Nov 2022 • Saifullah Saifullah, Dominique Mercier, Adriano Lucieri, Andreas Dengel, Sheraz Ahmed
This work is the first to investigate the impact of private learning techniques on generated explanations for DL-based models.
Explainable Artificial Intelligence (XAI) Privacy Preserving +1
no code implementations • 14 Oct 2022 • Alireza Koochali, Maria Walch, Sankrutyayan Thota, Peter Schichtel, Andreas Dengel, Sheraz Ahmed
Generative models are designed to address the data scarcity problem.
no code implementations • 23 Sep 2022 • Christoph Balada, Max Bondorf, Sheraz Ahmed, Andreas Dengela, Markus Zdrallek
By publishing the first large-scale real-world dataset, we aim to shed light on the previously largely unrecognized potential of PLC data and emphasize machine-learning-based research in low-voltage distribution networks by presenting a variety of different use cases.
1 code implementation • 13 Jun 2022 • Adriano Lucieri, Fabian Schmeisser, Christoph Peter Balada, Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed
Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation.
no code implementations • 13 Apr 2022 • Christoph Balada, Sheraz Ahmed, Andreas Dengel, Max Bondorf, Nikolai Hopfer, Markus Zdrallek
To overcome this, power line communication (PLC) has emerged as a potential solution for reliable monitoring of the low-voltage grid.
no code implementations • 13 Apr 2022 • Pervaiz Iqbal Khan, Imran Razzak, Andreas Dengel, Sheraz Ahmed
Moreover, our analysis shows that adding noise at earlier layers improves models' performance whereas adding noise at intermediate layers deteriorates models' performance.
1 code implementation • TechArXiv 2022 • Saifullah, Stefan Agne, Andreas Dengel, Sheraz Ahmed
Our approach achieves a new peak performance in image-based classification on two popular document datasets, namely RVL-CDIP and Tobacco3482, with a top-1 classification accuracy of 94. 17% and 95. 57% on the two datasets, respectively.
Ranked #1 on Document Image Classification on Tobacco-3482
no code implementations • 3 Mar 2022 • Pervaiz Iqbal Khan, Shoaib Ahmed Siddiqui, Imran Razzak, Andreas Dengel, Sheraz Ahmed
The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results.
no code implementations • 22 Feb 2022 • Dominique Mercier, Syed Tahseen Raza Rizvi, Vikas Rajashekar, Sheraz Ahmed, Andreas Dengel
However, qualitative aspects provide deeper insights into the impact of a scientific research artifact and make it possible to focus on relevant literature free from bias associated with quantitative aspects.
no code implementations • 16 Feb 2022 • Dominique Mercier, Andreas Dengel, Sheraz Ahmed
Deep neural networks are one of the most successful classifiers across different domains.
no code implementations • 8 Feb 2022 • Muhammad Ali Chattha, Ludger van Elst, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
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-world applications.
no code implementations • 8 Feb 2022 • Dominique Mercier, Jwalin Bhatt, Andreas Dengel, Sheraz Ahmed
However, due to the lack of transparency the use of these networks is hampered in the areas with safety critical areas.
no code implementations • 21 Jan 2022 • Alireza Koochali, Peter Schichtel, Andreas Dengel, Sheraz Ahmed
The recent developments in the machine learning domain have enabled the development of complex multivariate probabilistic forecasting models.
no code implementations • 4 Jan 2022 • Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
This work presents ExAID (Explainable AI for Dermatology), a novel framework for biomedical image analysis, providing multi-modal concept-based explanations consisting of easy-to-understand textual explanations supplemented by visual maps justifying the predictions.
1 code implementation • 29 Nov 2021 • Dominique Mercier, Adriano Lucieri, Mohsin Munir, Andreas Dengel, Sheraz Ahmed
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.
1 code implementation • Nature Methods 2021 • Christoffer Edlund, Timothy R. Jackson, Nabeel Khalid, Nicola Bevan, Timothy Dale, Andreas Dengel, Sheraz Ahmed, Johan Trygg, Rickard Sjögren
Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena.
Ranked #1 on Cell Segmentation on LIVECell
Cell Segmentation Cultural Vocal Bursts Intensity Prediction +4
no code implementations • IJCNN 2021 • Nabeel Khalid, Mohsin Munir, Christoffer Edlund, Timothy R Jackson, Johan Trygg, Rickard Sjögren, Andreas Dengel, Sheraz Ahmed
To address the aforementioned challenges, DeepCeNS is proposed in this paper to detect and segment cells and nucleus in microscopic images.
Ranked #1 on Cell Segmentation on EVICAN
no code implementations • 26 May 2021 • Pervaiz Iqbal Khan, Imran Razzak, Andreas Dengel, Sheraz Ahmed
These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread.
no code implementations • 14 May 2021 • Sebastian Palacio, Adriano Lucieri, Mohsin Munir, Jörn Hees, Sheraz Ahmed, Andreas Dengel
The field of explainable AI (XAI) has quickly become a thriving and prolific community.
no code implementations • 2 Mar 2021 • Adriano Lucieri, Andreas Dengel, Sheraz Ahmed
Moreover, the possibility to intervene and guide models in case of misbehaviour is identified as a major step towards successful deployment of AI as DL-based DSS and beyond.
no code implementations • 11 Feb 2021 • Dominique Mercier, Andreas Dengel, Sheraz Ahmed
The classification of time-series data is pivotal for streaming data and comes with many challenges.
1 code implementation • 3 Dec 2020 • Vinu Joseph, Shoaib Ahmed Siddiqui, Aditya Bhaskara, Ganesh Gopalakrishnan, Saurav Muralidharan, Michael Garland, Sheraz Ahmed, Andreas Dengel
With the rise in edge-computing devices, there has been an increasing demand to deploy energy and resource-efficient models.
no code implementations • 26 Nov 2020 • Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel, Sheraz Ahmed
Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable.
no code implementations • 30 Aug 2020 • Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed
This paves the way for future research in the direction of adversarial attacks and defenses, particularly for time-series data.
2 code implementations • 28 May 2020 • Muhammad Naseer Bajwa, Gur Amrit Pal Singh, Wolfgang Neumeier, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD).
no code implementations • 28 May 2020 • Muhammad Naseer Bajwa, Yoshinobu Taniguchi, Muhammad Imran Malik, Wolfgang Neumeier, Andreas Dengel, Sheraz Ahmed
Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina.
no code implementations • 28 May 2020 • Muhammad Naseer Bajwa, Muhammad Imran Malik, Shoaib Ahmed Siddiqui, Andreas Dengel, Faisal Shafait, Wolfgang Neumeier, Sheraz Ahmed
For glaucoma classification we achieved AUC equal to 0. 874 which is 2. 7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA.
1 code implementation • 5 May 2020 • Dominique Mercier, Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed
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 well as dataset debugging.
1 code implementation • 5 May 2020 • Dominique Mercier, Syed Tahseen Raza Rizvi, Vikas Rajashekar, Andreas Dengel, Sheraz Ahmed
Therefore, citation impact analysis (which includes sentiment and intent classification) enables us to quantify the quality of the citations which can eventually assist us in the estimation of ranking and impact.
Ranked #1 on Citation Intent Classification on SciCite (using extra training data)
no code implementations • 5 May 2020 • 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, segmentation, and other tasks.
no code implementations • 5 May 2020 • Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists.
1 code implementation • 4 May 2020 • Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel, Sheraz Ahmed
We evaluated our proposed method on SCDB as well as a real-world dataset called CelebA.
1 code implementation • 3 May 2020 • Alireza Koochali, Andreas Dengel, Sheraz Ahmed
The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN's component carefully and efficiently.
Multivariate Time Series Forecasting Probabilistic Time Series Forecasting +1
2 code implementations • 19 Apr 2020 • Mateus Dias Ribeiro, Abdul Rehman, Sheraz Ahmed, Andreas Dengel
Computational Fluid Dynamics (CFD) simulation by the numerical solution of the Navier-Stokes equations is an essential tool in a wide range of applications from engineering design to climate modeling.
no code implementations • ICLR 2020 • Shoaib Ahmed Siddiqui, Dominique Mercier, Andreas Dengel, Sheraz Ahmed
We approach the problem of interpretability in a novel way by proposing TSInsight where we attach an auto-encoder to the classifier with a sparsity-inducing norm on its output and fine-tune it based on the gradients from the classifier and a reconstruction penalty.
no code implementations • 23 Dec 2019 • Muhammad Nabeel Asima, Muhammad Imran Malik, Andreas Dengela, Sheraz Ahmed
In order to assess the effectiveness of deeper architectures for small non-coding RNA classification, we also adapted two ResNet architectures having different number of layers.
no code implementations • 16 Dec 2019 • Syed Tahseen Raza Rizvi, Andreas Dengel, Sheraz Ahmed
DeepBiRD was evaluated on two different datasets to demonstrate the generalization of this approach.
no code implementations • 7 Oct 2019 • Dominique Mercier, Akansha Bhardwaj, Andreas Dengel, Sheraz Ahmed
This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e. g., reference to a dataset, reading reference.
1 code implementation • 12 Sep 2019 • Muhammad Nabeel Asim, Muhammad Usman Ghani Khan, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7. 7% on 20 Newsgroups and 6. 6% on BBC news datasets.
1 code implementation • 26 May 2019 • Kumar Shridhar, Joonho Lee, Hideaki Hayashi, Purvanshi Mehta, Brian Kenji Iwana, Seokjun Kang, Seiichi Uchida, Sheraz Ahmed, Andreas Dengel
We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.
no code implementations • 15 May 2019 • Mohsin Munir, Shoaib Ahmed Siddiqui, Ferdinand Küsters, Dominique Mercier, Andreas Dengel, Sheraz Ahmed
This indicates a vital gap between the explainability provided by the systems and the novice user.
1 code implementation • 29 Mar 2019 • Alireza Koochali, Peter Schichtel, Sheraz Ahmed, Andreas Dengel
To investigate probabilistic forecasting of ForGAN, we create a new dataset and demonstrate our method abilities on it.
Generative Adversarial Network Probabilistic Time Series Forecasting +3
no code implementations • 15 Feb 2019 • Muhammad Ali Chattha, Shoaib Ahmed Siddiqui, Muhammad Imran Malik, Ludger van Elst, Andreas Dengel, Sheraz Ahmed
The promise of ANNs to automatically discover and extract useful features/patterns from data without dwelling on domain expertise although seems highly promising but comes at the cost of high reliance on large amount of accurately labeled data, which is often hard to acquire and formulate especially in time-series domains like anomaly detection, natural disaster management, predictive maintenance and healthcare.
3 code implementations • 19 Dec 2018 • Mohsin Munir, Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed
In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series.
no code implementations • 2 Aug 2018 • Hiroki Ohashi, Mohammad Al-Naser, Sheraz Ahmed, Katsuyuki Nakamura, Takuto Sato, Andreas Dengel
ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes.
1 code implementation • 8 Feb 2018 • Shoaib Ahmed Siddiqui, Dominik Mercier, Mohsin Munir, Andreas Dengel, Sheraz Ahmed
This is a step towards making informed/explainable decisions in the domain of time-series, powered by deep learning.
5 code implementations • 11 Apr 2017 • Muhammad Zeshan Afzal, Andreas Kölsch, Sheraz Ahmed, Marcus Liwicki
We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half.
Ranked #27 on Document Image Classification on RVL-CDIP
4 code implementations • 19 Mar 2017 • Ayushman Dash, John Cristian Borges Gamboa, Sheraz Ahmed, Marcus Liwicki, Muhammad Zeshan Afzal
In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions.
4 code implementations • 28 Oct 2016 • Brian Kenji Iwana, Syed Tahseen Raza Rizvi, Sheraz Ahmed, Andreas Dengel, Seiichi Uchida
Book covers communicate information to potential readers, but can that same information be learned by computers?
Ranked #1 on Genre classification on Book Cover Dataset
no code implementations • 3 Aug 2016 • Sebastian Baumbach, Frank Wittich, Florian Sachs, Sheraz Ahmed, Andreas Dengel
The existing approaches for site selection (commonly used by economists) are manual, subjective, and not scalable, especially to Big Data.
no code implementations • 4 May 2016 • Sheraz Ahmed, Muhammad Imran Malik, Muhammad Zeshan Afzal, Koichi Kise, Masakazu Iwamura, Andreas Dengel, Marcus Liwicki
The method is generic, language independent and can be used for generation of labeled documents datasets (both scanned and cameracaptured) in any cursive and non-cursive language, e. g., English, Russian, Arabic, Urdu, etc.