Zobrazeno 1 - 10
of 13 428
pro vyhledávání: '"Xu, Qiang"'
Circuit representation learning is increasingly pivotal in Electronic Design Automation (EDA), serving various downstream tasks with enhanced model efficiency and accuracy. One notable work, DeepSeq, has pioneered sequential circuit learning by encod
Externí odkaz:
http://arxiv.org/abs/2411.00530
Continual learning requires to overcome catastrophic forgetting when training a single model on a sequence of tasks. Recent top-performing approaches are prompt-based methods that utilize a set of learnable parameters (i.e., prompts) to encode task k
Externí odkaz:
http://arxiv.org/abs/2410.20444
Autor:
Xu, Qiang, Hurtut, Thomas
In the digital landscape, the ubiquity of data visualizations in media underscores the necessity for accessibility to ensure inclusivity for all users, including those with visual impairments. Current visual content often fails to cater to the needs
Externí odkaz:
http://arxiv.org/abs/2409.17494
Whole-body multimodal motion generation, controlled by text, speech, or music, has numerous applications including video generation and character animation. However, employing a unified model to achieve various generation tasks with different conditi
Externí odkaz:
http://arxiv.org/abs/2407.21136
Autor:
Cui, Fan, Yin, Chenyang, Zhou, Kexing, Xiao, Youwei, Sun, Guangyu, Xu, Qiang, Guo, Qipeng, Song, Demin, Lin, Dahua, Zhang, Xingcheng, Yun, Liang
Recent studies have demonstrated the significant potential of Large Language Models (LLMs) in generating Register Transfer Level (RTL) code, with notable advancements showcased by commercial models such as GPT-4 and Claude3-Opus. However, these propr
Externí odkaz:
http://arxiv.org/abs/2407.16237
This is the technique report for the winning solution of the CVPR2024 GenAI Media Generation Challenge Workshop's Instruction-guided Image Editing track. Instruction-guided image editing has been largely studied in recent years. The most advanced met
Externí odkaz:
http://arxiv.org/abs/2407.13139
Circuit representation learning has shown promising results in advancing the field of Electronic Design Automation (EDA). Existing models, such as DeepGate Family, primarily utilize Graph Neural Networks (GNNs) to encode circuit netlists into gate-le
Externí odkaz:
http://arxiv.org/abs/2407.11095
Autor:
Ju, Xuan, Gao, Yiming, Zhang, Zhaoyang, Yuan, Ziyang, Wang, Xintao, Zeng, Ailing, Xiong, Yu, Xu, Qiang, Shan, Ying
Sora's high-motion intensity and long consistent videos have significantly impacted the field of video generation, attracting unprecedented attention. However, existing publicly available datasets are inadequate for generating Sora-like videos, as th
Externí odkaz:
http://arxiv.org/abs/2407.06358
Concerned with elliptic operators with stationary random coefficients governed by linear or nonlinear mixing conditions and bounded (or unbounded) $C^1$ domains, this paper mainly studies (weighted) annealed Calder\'on-Zygmund estimates, some of whic
Externí odkaz:
http://arxiv.org/abs/2405.19102
In quantitative investment, constructing characteristic-sorted portfolios is a crucial strategy for asset allocation. Traditional methods transform raw stock data of varying frequencies into predictive characteristic factors for asset sorting, often
Externí odkaz:
http://arxiv.org/abs/2405.15833