Zobrazeno 1 - 10
of 40 608
pro vyhledávání: '"Aamir ."'
Modern applications, such as autonomous vehicles, require deploying deep learning algorithms on resource-constrained edge devices for real-time image and video processing. However, there is limited understanding of the efficiency and performance of v
Externí odkaz:
http://arxiv.org/abs/2409.16808
Autor:
Xu, Lang, Anthony, Quentin, Zhou, Qinghua, Alnaasan, Nawras, Gulhane, Radha R., Shafi, Aamir, Subramoni, Hari, Panda, Dhabaleswar K.
Data Parallelism (DP), Tensor Parallelism (TP), and Pipeline Parallelism (PP) are the three strategies widely adopted to enable fast and efficient Large Language Model (LLM) training. However, these approaches rely on data-intensive communication rou
Externí odkaz:
http://arxiv.org/abs/2409.02423
Autor:
Yao, Jinghan, Jacobs, Sam Ade, Tanaka, Masahiro, Ruwase, Olatunji, Shafi, Aamir, Subramoni, Hari, Panda, Dhabaleswar K.
Large Language Models (LLMs) with long context capabilities are integral to complex tasks in natural language processing and computational biology, such as text generation and protein sequence analysis. However, training LLMs directly on extremely lo
Externí odkaz:
http://arxiv.org/abs/2408.16978
Autor:
Khodakhah, Farnaz, Mahmood, Aamir, Stefanović, Čedomir, Farag, Hossam, Österberg, Patrik, Gidlund, Mikael
Through the lens of average and peak age-of-information (AoI), this paper takes a fresh look into the uplink medium access solutions for mission-critical (MC) communication coexisting with enhanced mobile broadband (eMBB) service. Considering the sto
Externí odkaz:
http://arxiv.org/abs/2408.12926
The Euclidean Shortest Path Problem (ESPP), which involves finding the shortest path in a Euclidean plane with polygonal obstacles, is a classic problem with numerous real-world applications. The current state-of-the-art solution, Euclidean Hub Label
Externí odkaz:
http://arxiv.org/abs/2408.11341
Autor:
Bonetto, Elia, Ahmad, Aamir
Synthetic data is increasingly being used to address the lack of labeled images in uncommon domains for deep learning tasks. A prominent example is 2D pose estimation of animals, particularly wild species like zebras, for which collecting real-world
Externí odkaz:
http://arxiv.org/abs/2408.10831
Autor:
Anthony, Quentin, Michalowicz, Benjamin, Hatef, Jacob, Xu, Lang, Abduljabbar, Mustafa, Shafi, Aamir, Subramoni, Hari, Panda, Dhabaleswar
Deep learning (DL) models based on the transformer architecture have revolutionized many DL applications such as large language models (LLMs), vision transformers, audio generation, and time series prediction. Much of this progress has been fueled by
Externí odkaz:
http://arxiv.org/abs/2408.10197
BM25, a widely-used lexical search algorithm, remains crucial in information retrieval despite the rise of pre-trained and large language models (PLMs/LLMs). However, it neglects query-document similarity and lacks semantic understanding, limiting it
Externí odkaz:
http://arxiv.org/abs/2408.06643
Biomedical image segmentation plays a vital role in diagnosis of diseases across various organs. Deep learning-based object detection methods are commonly used for such segmentation. There exists an extensive research in this topic. However, there is
Externí odkaz:
http://arxiv.org/abs/2408.03393
Quantum machine learning through variational quantum algorithms (VQAs) has gained substantial attention in recent years. VQAs employ parameterized quantum circuits, which are typically optimized using gradient-based methods. However, these methods of
Externí odkaz:
http://arxiv.org/abs/2407.13858