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pro vyhledávání: '"Son IN"'
Transformer-based large-scale pre-trained models achieve great success, and fine-tuning, which tunes a pre-trained model on a task-specific dataset, is the standard practice to utilize these models for downstream tasks. Recent work has developed adap
Externí odkaz:
http://arxiv.org/abs/2412.03587
Autor:
Nguyen, Viet Anh, Nguyen, Linh Thi Dieu, Do, Thi Thu Ha, Wu, Ye, Sergeev, Aleksandr A., Zhu, Ding, Valuckas, Vytautas, Pham, Duong, Bui, Hai Xuan Son, Hoang, Duy Mai, Bui, Son Tung, Bui, Xuan Khuyen, Nguyen, Binh Thanh, Nguyen, Hai Son, Vu, Lam Dinh, Rogach, Andrey, Ha, Son Tung, Le-Van, Quynh
Publikováno v:
J Phys Chem Lett J Phys Chem Lett . 2024 Nov 14;15(45):11291-11299
Enhancing light emission from perovskite nanocrystal (NC) films is essential in light-emitting devices, as their conventional stacks often restrict the escape of emitted light. This work addresses this challenge by employing a TiO$_2$ grating to enha
Externí odkaz:
http://arxiv.org/abs/2411.12463
Efficiently preparing approximate ground-states of large, strongly correlated systems on quantum hardware is challenging and yet nature is innately adept at this. This has motivated the study of thermodynamically inspired approaches to ground-state p
Externí odkaz:
http://arxiv.org/abs/2412.04554
Autor:
Cho, Sung Woong, Son, Hwijae
Inverse problems involving partial differential equations (PDEs) can be seen as discovering a mapping from measurement data to unknown quantities, often framed within an operator learning approach. However, existing methods typically rely on large am
Externí odkaz:
http://arxiv.org/abs/2412.03161
Contrastive learning has significantly improved representation quality, enhancing knowledge transfer across tasks in continual learning (CL). However, catastrophic forgetting remains a key challenge, as contrastive based methods primarily focus on "s
Externí odkaz:
http://arxiv.org/abs/2412.02865
Autor:
Nguyen, Trung-Hieu, Vuong, Truong-Giang, Duong, Hong-Nam, Nguyen, Son, Vo, Hieu Dinh, Aoki, Toshiaki, Nguyen, Thu-Trang
Autonomous vehicles (AVs) have demonstrated significant potential in revolutionizing transportation, yet ensuring their safety and reliability remains a critical challenge, especially when exposed to dynamic and unpredictable environments. Real-world
Externí odkaz:
http://arxiv.org/abs/2412.02574
In the field of legal information retrieval, effective embedding-based models are essential for accurate question-answering systems. However, the scarcity of large annotated datasets poses a significant challenge, particularly for Vietnamese legal te
Externí odkaz:
http://arxiv.org/abs/2412.00657
In-context learning refers to the emerging ability of large language models (LLMs) to perform a target task without additional training, utilizing demonstrations of the task. Recent studies aim to enhance in-context learning performance by selecting
Externí odkaz:
http://arxiv.org/abs/2411.19581
In this paper, we introduce V2SFlow, a novel Video-to-Speech (V2S) framework designed to generate natural and intelligible speech directly from silent talking face videos. While recent V2S systems have shown promising results on constrained datasets
Externí odkaz:
http://arxiv.org/abs/2411.19486
We study the phase transitions in the simplicial Ising model on hypergraphs, in which the energy within each hyperedge (group) is lowered only when all the member spins are unanimously aligned. The Hamiltonian of the model is equivalent to a weighted
Externí odkaz:
http://arxiv.org/abs/2411.19080