no code implementations • 15 Nov 2017 • Aaqib Saeed, Stojan Trajanovski
Stress can be seen as a physiological response to everyday emotional, mental and physical challenges.
no code implementations • 27 Aug 2018 • Aaqib Saeed, Tanir Ozcelebi, Stojan Trajanovski, Johan Lukkien
In this paper, we propose a multi-stream temporal convolutional network to address the problem of multi-label behavioral context recognition.
no code implementations • 27 Jul 2019 • Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien
We learn a multi-task temporal convolutional network to recognize transformations applied on an input signal.
no code implementations • 25 Jul 2020 • Aaqib Saeed, Flora D. Salim, Tanir Ozcelebi, Johan Lukkien
Federated learning provides a compelling framework for learning models from decentralized data, but conventionally, it assumes the availability of labeled samples, whereas on-device data are generally either unlabeled or cannot be annotated readily through user interaction.
no code implementations • 28 Sep 2020 • Aaqib Saeed, Victor Ungureanu, Beat Gfeller
Likewise, the learned representations with self-supervision are found to be highly transferable between related datasets, even when few labeled instances are available from the target domains.
2 code implementations • 21 Oct 2020 • Aaqib Saeed, David Grangier, Neil Zeghidour
We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio.
Ranked #4 on Spoken Command Recognition on Speech Command v2
no code implementations • 21 Oct 2020 • Aaqib Saeed, David Grangier, Olivier Pietquin, Neil Zeghidour
We propose CHARM, a method for training a single neural network across inconsistent input channels.
no code implementations • 7 Feb 2021 • Zaharah A. Bukhsh, Nils Jansen, Aaqib Saeed
We, therefore, evaluate a combination of in-domain and cross-domain transfer learning strategies for damage detection in bridges.
1 code implementation • 1 Apr 2021 • Zaharah A. Bukhsh, Aaqib Saeed, Remco M. Dijkman
Nevertheless, designing a deep neural architecture that performs competitively across various tasks is challenging as existing methods fail to capture long-range dependencies in the input sequences and perform poorly for lengthy process traces.
1 code implementation • 14 Jul 2021 • Vasileios Tsouvalas, Aaqib Saeed, Tanir Ozcelebi
Notably, we show that with as little as 3% labeled data available, FedSTAR on average can improve the recognition rate by 13. 28% compared to the fully supervised federated model.
no code implementations • 27 Sep 2021 • Aaqib Saeed
Deep neural networks have become larger over the years with increasing demand of computational resources for inference; incurring exacerbate costs and leaving little room for deployment on devices with limited battery and other resources for real-time applications.
1 code implementation • 1 Dec 2021 • Yuki M. Asano, Aaqib Saeed
What can neural networks learn about the visual world when provided with only a single image as input?
no code implementations • 6 Jun 2022 • Shohreh Deldari, Hao Xue, Aaqib Saeed, Jiayuan He, Daniel V. Smith, Flora D. Salim
Unlike existing reviews of SSRL that have pre-dominately focused upon methods in the fields of CV or NLP for a single modality, we aim to provide the first comprehensive review of multimodal self-supervised learning methods for temporal data.
no code implementations • 17 Jun 2022 • Aaqib Saeed
Deep neural networks have significantly improved performance on a range of tasks with the increasing demand for computational resources, leaving deployment on low-resource devices (with limited memory and battery power) infeasible.
1 code implementation • 30 Jun 2022 • Harlin Lee, Aaqib Saeed
But pediatric sleep is severely under-researched compared to adult sleep in the context of machine learning for health, and sleep scoring algorithms developed for adults usually perform poorly on infants.
1 code implementation • 12 Jul 2022 • Harlin Lee, Aaqib Saeed
This work introduces BRILLsson, a novel binary neural network-based representation learning model for a broad range of non-semantic speech tasks.
1 code implementation • 31 Jul 2022 • Shohreh Deldari, Hao Xue, Aaqib Saeed, Daniel V. Smith, Flora D. Salim
Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data.
1 code implementation • 19 Aug 2022 • Vasileios Tsouvalas, Aaqib Saeed, Tanir Ozcelebi, Nirvana Meratnia
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the clients.
1 code implementation • 27 Oct 2022 • Zaharah Bukhsh, Aaqib Saeed
Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +7
1 code implementation • 3 May 2023 • Aaqib Saeed, Vasileios Tsouvalas
As technology advances and digital devices become prevalent, seamless human-machine communication is increasingly gaining significance.
no code implementations • 29 Nov 2023 • Vasileios Tsouvalas, Yuki Asano, Aaqib Saeed
Foundation Models (FMs) have revolutionized machine learning with their adaptability and high performance across tasks; yet, their integration into Federated Learning (FL) is challenging due to substantial communication overhead from their extensive parameterization.
1 code implementation • 25 Jan 2024 • Vasileios Tsouvalas, Aaqib Saeed, Tanir Ozcelebi, Nirvana Meratnia
Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy.
no code implementations • 25 Jan 2024 • Aaqib Saeed, Dimitris Spathis, JungWoo Oh, Edward Choi, Ali Etemad
We show that FHLR achieves significantly better performance when learning from noisy labels and achieves state-of-the-art by a large margin, with up to 19% accuracy improvement under symmetric and asymmetric noise.
no code implementations • 14 Mar 2024 • Dimitris Spathis, Aaqib Saeed, Ali Etemad, Sana Tonekaboni, Stefanos Laskaridis, Shohreh Deldari, Chi Ian Tang, Patrick Schwab, Shyam Tailor
This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion.