Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Feng, Aosong"'
Autor:
Yang, Rui, Yang, Boming, Ouyang, Sixun, She, Tianwei, Feng, Aosong, Jiang, Yuang, Lecue, Freddy, Lu, Jinghui, Li, Irene
Knowledge graphs (KGs) are crucial in the field of artificial intelligence and are widely applied in downstream tasks, such as enhancing Question Answering (QA) systems. The construction of KGs typically requires significant effort from domain expert
Externí odkaz:
http://arxiv.org/abs/2407.10794
Autor:
Zhang, Jiasheng, Chen, Jialin, Yang, Menglin, Feng, Aosong, Liang, Shuang, Shao, Jie, Ying, Rex
Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios, where each node and edge are associated with text descriptions, and both the graph structure and text descriptions evolve over time. Despite their broad applicabil
Externí odkaz:
http://arxiv.org/abs/2406.12072
Autor:
Chen, Jialin, Lenssen, Jan Eric, Feng, Aosong, Hu, Weihua, Fey, Matthias, Tassiulas, Leandros, Leskovec, Jure, Ying, Rex
Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting performance by treating different channels individually, while it leads
Externí odkaz:
http://arxiv.org/abs/2404.01340
Solving image inverse problems (e.g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image). By using the input image as guidance, we can leverage a pret
Externí odkaz:
http://arxiv.org/abs/2403.10585
Autor:
Feng, Aosong, Chen, Jialin, Garza, Juan, Berry, Brooklyn, Salazar, Francisco, Gao, Yifeng, Ying, Rex, Tassiulas, Leandros
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effectively, it is imperative that the state-of-the-art attention model is s
Externí odkaz:
http://arxiv.org/abs/2403.04882
Autor:
Feng, Aosong, Qiu, Weikang, Bai, Jinbin, Zhang, Xiao, Dong, Zhen, Zhou, Kaicheng, Ying, Rex, Tassiulas, Leandros
Building on the success of text-to-image diffusion models (DPMs), image editing is an important application to enable human interaction with AI-generated content. Among various editing methods, editing within the prompt space gains more attention due
Externí odkaz:
http://arxiv.org/abs/2403.04880
Autor:
Yang, Rui, Yang, Boming, Ouyang, Sixun, She, Tianwei, Feng, Aosong, Jiang, Yuang, Lecue, Freddy, Lu, Jinghui, Li, Irene
In the domain of Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated promise in text-generation tasks. However, their educational applications, particularly for domain-specific queries, remain underexplored. This study i
Externí odkaz:
http://arxiv.org/abs/2402.14293
In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge. This paper introduces a pioneering framework that integrates viewpoint information to enhance the control of image editi
Externí odkaz:
http://arxiv.org/abs/2310.16002
Recently, diffusion models have excelled in image generation tasks and have also been applied to neural language processing (NLP) for controllable text generation. However, the application of diffusion models in a cross-lingual setting is less unexpl
Externí odkaz:
http://arxiv.org/abs/2307.13560
Publikováno v:
ACL 2023 main proceedings
Encoding long sequences in Natural Language Processing (NLP) is a challenging problem. Though recent pretraining language models achieve satisfying performances in many NLP tasks, they are still restricted by a pre-defined maximum length, making them
Externí odkaz:
http://arxiv.org/abs/2305.03319