Zobrazeno 1 - 10
of 7 681
pro vyhledávání: '"An, Le Thanh"'
Autor:
Le, Thanh-Dung, Ha, Vu Nguyen, Nguyen, Ti Ti, Eappen, Geoffrey, Thiruvasagam, Prabhu, Chou, Hong-fu, Tran, Duc-Dung, Garces-Socarras, Luis M., Gonzalez-Rios, Jorge L., Merlano-Duncan, Juan Carlos, Chatzinotas, Symeon
This study presents an innovative dynamic weighting knowledge distillation (KD) framework tailored for efficient Earth observation (EO) image classification (IC) in resource-constrained settings. Utilizing EfficientViT and MobileViT as teacher models
Externí odkaz:
http://arxiv.org/abs/2411.00209
Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. However, most FL approaches assume that clients possess labeled data, which is often not the case
Externí odkaz:
http://arxiv.org/abs/2410.23227
Autor:
Chou, Hong-fu, Ha, Vu Nguyen, Thiruvasagam, Prabhu, Le, Thanh-Dung, Eappen, Geoffrey, Nguyen, Ti Ti, Tran, Duc Dung, Garces-Socarras, Luis M., Merlano-Duncan, Juan Carlos, Chatzinotas, Symeon
Earth observation (EO) systems are essential for mapping, catastrophe monitoring, and resource management, but they have trouble processing and sending large amounts of EO data efficiently, especially for specialized applications like agriculture and
Externí odkaz:
http://arxiv.org/abs/2410.21916
Continual Event Detection (CED) poses a formidable challenge due to the catastrophic forgetting phenomenon, where learning new tasks (with new coming event types) hampers performance on previous ones. In this paper, we introduce a novel approach, Lif
Externí odkaz:
http://arxiv.org/abs/2410.08905
Autor:
Chou, Hong-fu, Ha, Vu Nguyen, Thiruvasagam, Prabhu, Le, Thanh-Dung, Eappen, Geoffrey, Nguyen, Ti Ti, Garces-Socarras, Luis M., Gonzalez-Rios, Jorge L., Merlano-Duncan, Juan Carlos, Chatzinotas, Symeon
Earth Observation (EO) systems are crucial for cartography, disaster surveillance, and resource administration. Nonetheless, they encounter considerable obstacles in the processing and transmission of extensive data, especially in specialized domains
Externí odkaz:
http://arxiv.org/abs/2409.15246
Autor:
Le, Thanh-Dung, Ha, Vu Nguyen, Nguyen, Ti Ti, Eappen, Geoffrey, Thiruvasagam, Prabhu, Garces-Socarras, Luis M., Chou, Hong-fu, Gonzalez-Rios, Jorge L., Merlano-Duncan, Juan Carlos, Chatzinotas, Symeon
This study focuses on identifying the most effective pre-trained model for land use classification in onboard satellite processing, emphasizing achieving high accuracy, computational efficiency, and robustness against noisy data conditions commonly e
Externí odkaz:
http://arxiv.org/abs/2409.03901
Autor:
Nguyên, Lê Thành Dũng
You might know that the name "tree transducers" refers to various kinds of automata that compute functions on ranked trees, i.e. terms over a first-order signature. But have you ever wondered about how to remember what a macro tree transducer does? O
Externí odkaz:
http://arxiv.org/abs/2409.03169
In this paper, we aim to enhance the performance of SwiftBrush, a prominent one-step text-to-image diffusion model, to be competitive with its multi-step Stable Diffusion counterpart. Initially, we explore the quality-diversity trade-off between Swif
Externí odkaz:
http://arxiv.org/abs/2408.14176
Fine-tuning Large Language Models (LLMs) for clinical Natural Language Processing (NLP) poses significant challenges due to the domain gap and limited data availability. This study investigates the effectiveness of various adapter techniques, equival
Externí odkaz:
http://arxiv.org/abs/2407.19299
Remote patient monitoring has emerged as a prominent non-invasive method, using digital technologies and computer vision (CV) to replace traditional invasive monitoring. While neonatal and pediatric departments embrace this approach, Pediatric Intens
Externí odkaz:
http://arxiv.org/abs/2407.13341