Zobrazeno 1 - 10
of 4 548
pro vyhledávání: '"Nguyen, Tan"'
Autor:
Hai, Vu Tuan, Viet, Nguyen Tan, Urbaneja, Jesus, Linh, Nguyen Vu, Tran, Lan Nguyen, Ho, Le Bin
In quantum computing, quantum compilation involves transforming information from a target unitary into a trainable unitary represented by a quantum circuit. Traditional quantum compilation optimizes circuits for a single target. However, many quantum
Externí odkaz:
http://arxiv.org/abs/2407.01010
Autor:
Nguyen, Tan M., Nguyen, Tam, Ho, Nhat, Bertozzi, Andrea L., Baraniuk, Richard G., Osher, Stanley J.
Self-attention is key to the remarkable success of transformers in sequence modeling tasks including many applications in natural language processing and computer vision. Like neural network layers, these attention mechanisms are often developed by h
Externí odkaz:
http://arxiv.org/abs/2406.13781
Pairwise dot-product self-attention is key to the success of transformers that achieve state-of-the-art performance across a variety of applications in language and vision. This dot-product self-attention computes attention weights among the input to
Externí odkaz:
http://arxiv.org/abs/2406.13770
Autor:
Teo, Rachel S. Y., Nguyen, Tan M.
The remarkable success of transformers in sequence modeling tasks, spanning various applications in natural language processing and computer vision, is attributed to the critical role of self-attention. Similar to the development of most deep learnin
Externí odkaz:
http://arxiv.org/abs/2406.13762
Sliced Wasserstein (SW) distance in Optimal Transport (OT) is widely used in various applications thanks to its statistical effectiveness and computational efficiency. On the other hand, Tree Wassenstein (TW) and Tree-sliced Wassenstein (TSW) are ins
Externí odkaz:
http://arxiv.org/abs/2406.13725
Autor:
Nguyen, Hai-Long, Nguyen, Duc-Minh, Nguyen, Tan-Minh, Nguyen, Ha-Thanh, Vuong, Thi-Hai-Yen, Satoh, Ken
Large language models with billions of parameters, such as GPT-3.5, GPT-4, and LLaMA, are increasingly prevalent. Numerous studies have explored effective prompting techniques to harness the power of these LLMs for various research problems. Retrieva
Externí odkaz:
http://arxiv.org/abs/2403.18093
Autor:
Nguyen, Cong T., Saputra, Yuris Mulya, Van Huynh, Nguyen, Nguyen, Tan N., Hoang, Dinh Thai, Nguyen, Diep N, Pham, Van-Quan, Voznak, Miroslav, Chatzinotas, Symeon, Tran, Dinh-Hieu
Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference a
Externí odkaz:
http://arxiv.org/abs/2403.07763
In this work, we address two main shortcomings of transformer architectures: input corruption and rank collapse in their output representation. We unveil self-attention as an autonomous state-space model that inherently promotes smoothness in its sol
Externí odkaz:
http://arxiv.org/abs/2402.15989
Autor:
Tran, Minh-Phuong, Nguyen, Thanh-Nhan, Nguyen, Thai-Hung, Nguyen, Tan-Phuc, Nguyen, Tien-Khai, Nguyen, Cong-Duy-Nguyen, Huynh, Trung-Hieu
In this paper, we study a new approach related to the convergence analysis of Ishikawa-type iterative models to a common fixed point of two non-expansive mappings in Banach spaces. The main novelty of our contribution lies in the so-called \emph{opti
Externí odkaz:
http://arxiv.org/abs/2401.02093
The current paradigm of training deep neural networks for classification tasks includes minimizing the empirical risk that pushes the training loss value towards zero, even after the training error has been vanished. In this terminal phase of trainin
Externí odkaz:
http://arxiv.org/abs/2401.02058