Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Si, Nianwen"'
Large language models are known for encoding a vast amount of factual knowledge, but they often becomes outdated due to the ever-changing nature of external information. A promising solution to this challenge is the utilization of model editing metho
Externí odkaz:
http://arxiv.org/abs/2401.03190
In recent years, large language models (LLMs) have spurred a new research paradigm in natural language processing. Despite their excellent capability in knowledge-based question answering and reasoning, their potential to retain faulty or even harmfu
Externí odkaz:
http://arxiv.org/abs/2311.15766
With the emergence of large language models (LLMs), multimodal models based on LLMs have demonstrated significant potential. Models such as LLaSM, X-LLM, and SpeechGPT exhibit an impressive ability to comprehend and generate human instructions. Howev
Externí odkaz:
http://arxiv.org/abs/2310.02050
Publikováno v:
IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 31, 2023
The end-to-end speech translation (E2E-ST) model has gradually become a mainstream paradigm due to its low latency and less error propagation. However, it is non-trivial to train such a model well due to the task complexity and data scarcity. The spe
Externí odkaz:
http://arxiv.org/abs/2304.10309
Existing techniques often attempt to make knowledge transfer from a powerful machine translation (MT) to speech translation (ST) model with some elaborate techniques, which often requires transcription as extra input during training. However, transcr
Externí odkaz:
http://arxiv.org/abs/2304.10295
Publikováno v:
In Computers & Security September 2024 144
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Journal of Ambient Intelligence and Humanized Computing; 20240101, Issue: Preprints p1-13, 13p
Publikováno v:
Multimedia Tools and Applications. 81:2733-2756
Deep convolution neural networks have been widely studied and applied in many computer vision tasks. However, they are commonly treated as black-boxes and plagued by the inexplicability. In this paper, we propose a novel method to visually interpret