Zobrazeno 1 - 10
of 529
pro vyhledávání: '"LU Yutong"'
We introduce a novel framework to financial time series forecasting that leverages causality-inspired models to balance the trade-off between invariance to distributional changes and minimization of prediction errors. To the best of our knowledge, th
Externí odkaz:
http://arxiv.org/abs/2408.09960
Remote Memory Access (RMA) enables direct access to remote memory to achieve high performance for HPC applications. However, most modern parallel programming models lack schemes for the remote process to detect the completion of RMA operations. Many
Externí odkaz:
http://arxiv.org/abs/2408.07428
This article explores operator learning models that can deduce solutions to partial differential equations (PDEs) on arbitrary domains without requiring retraining. We introduce two innovative models rooted in boundary integral equations (BIEs): the
Externí odkaz:
http://arxiv.org/abs/2406.02298
Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home. Traditional deployment approaches offload the inference workloads to the remote cloud server, which would induc
Externí odkaz:
http://arxiv.org/abs/2405.17245
Most advances in medical image recognition supporting clinical auxiliary diagnosis meet challenges due to the low-resource situation in the medical field, where annotations are highly expensive and professional. This low-resource problem can be allev
Externí odkaz:
http://arxiv.org/abs/2402.03783
Autor:
Zheng, Fudan, Li, Mengfei, Wang, Ying, Yu, Weijiang, Wang, Ruixuan, Chen, Zhiguang, Xiao, Nong, Lu, Yutong
Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processing approaches to tackle this problem are limited in two aspects. First,
Externí odkaz:
http://arxiv.org/abs/2402.03754
Large language models exhibit enhanced zero-shot performance on various tasks when fine-tuned with instruction-following data. Multimodal instruction-following models extend these capabilities by integrating both text and images. However, existing mo
Externí odkaz:
http://arxiv.org/abs/2308.16463
Autor:
Gu, Liang, Liu, Yang, Chen, Pin, Huang, Haiyou, Chen, Ning, Li, Yang, Lu, Yutong, Su, Yanjing
Predicting high temperature superconductors has long been a great challenge. A major difficulty is how to predict the transition temperature Tc of superconductors. Recently, progress in material informatics has led to a number of machine learning mod
Externí odkaz:
http://arxiv.org/abs/2308.11160
Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the symmetric geo
Externí odkaz:
http://arxiv.org/abs/2309.04475
Publikováno v:
IEEE Transaction on Pattern Analysis and Machine Intelligence, 2023
Vision Transformer (ViT) has shown great potential for various visual tasks due to its ability to model long-range dependency. However, ViT requires a large amount of computing resource to compute the global self-attention. In this work, we propose a
Externí odkaz:
http://arxiv.org/abs/2304.03481