Zobrazeno 1 - 10
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pro vyhledávání: '"Ba, A."'
Diffusion models (DMs) have achieved state-of-the-art performance on various generative tasks such as image synthesis, text-to-image, and text-guided image-to-image generation. However, the more powerful the DMs, the more harmful they potentially are
Externí odkaz:
http://arxiv.org/abs/2408.03400
We study the competitive facility location problem, where a firm aims to establish new facilities in a market already occupied by competitors. In this problem, customer behavior is crucial for making optimal location decisions. We explore a general c
Externí odkaz:
http://arxiv.org/abs/2408.02925
Autor:
Cheng, Peng, Wang, Yuwei, Huang, Peng, Ba, Zhongjie, Lin, Xiaodong, Lin, Feng, Lu, Li, Ren, Kui
Extensive research has revealed that adversarial examples (AE) pose a significant threat to voice-controllable smart devices. Recent studies have proposed black-box adversarial attacks that require only the final transcription from an automatic speec
Externí odkaz:
http://arxiv.org/abs/2408.01808
Autor:
Jaipersaud, Brandon, Zhang, Paul, Ba, Jimmy, Petersen, Andrew, Zhang, Lisa, Zhang, Michael R.
Publikováno v:
In: Artificial Intelligence in Education. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham
We propose and evaluate a question-answering system that uses decomposed prompting to classify and answer student questions on a course discussion board. Our system uses a large language model (LLM) to classify questions into one of four types: conce
Externí odkaz:
http://arxiv.org/abs/2407.21170
Autor:
Xu, Huiyu, Zhang, Wenhui, Wang, Zhibo, Xiao, Feng, Zheng, Rui, Feng, Yunhe, Ba, Zhongjie, Ren, Kui
Recently, advanced Large Language Models (LLMs) such as GPT-4 have been integrated into many real-world applications like Code Copilot. These applications have significantly expanded the attack surface of LLMs, exposing them to a variety of threats.
Externí odkaz:
http://arxiv.org/abs/2407.16667
Autor:
Khanh, Trinh Le Ba, Nguyen, Huy-Hung, Pham, Long Hoang, Tran, Duong Nguyen-Ngoc, Jeon, Jae Wook
In object detection, unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, UDA's reliance on labeled source data restricts its adaptability in privacy-related scenarios. T
Externí odkaz:
http://arxiv.org/abs/2407.16497
Autor:
Le-Duc, Khai, Zhang, Ryan, Nguyen, Ngoc Son, Pham, Tan-Hanh, Dao, Anh, Ngo, Ba Hung, Nguyen, Anh Totti, Hy, Truong-Son
Vision-language models have been extensively explored across a wide range of tasks, achieving satisfactory performance; however, their application in medical imaging remains underexplored. In this work, we propose a unified framework - LiteGPT - for
Externí odkaz:
http://arxiv.org/abs/2407.12064
In the dynamic landscape of the Web, we are witnessing the emergence of the Web3 paradigm, which dictates that platforms should rely on blockchain technology and cryptocurrencies to sustain themselves and their profitability. Cryptocurrencies are cha
Externí odkaz:
http://arxiv.org/abs/2407.10614
Publikováno v:
Physica B: Condensed Matter 2024
In this study, based on the quantum kinetic equation approach, we systematically present the radio-electric effect in asymmetric semi-parabolic quantum wells under the influence of a laser radiation field taking into account the electron-longitudinal
Externí odkaz:
http://arxiv.org/abs/2407.09938
Autor:
Ma, Binhao, Zheng, Tianhang, Hu, Hongsheng, Wang, Di, Wang, Shuo, Ba, Zhongjie, Qin, Zhan, Ren, Kui
Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. However, this utility comes with increasing concerns about privacy, as the training data may include se
Externí odkaz:
http://arxiv.org/abs/2407.05112