Search Results for author: Fang Yang

Found 7 papers, 3 papers with code

Single-Collision Model for Non-Line-of-Sight UV Communication Channel With Obstacle

no code implementations8 Nov 2024 Tianfeng Wu, Fang Yang, Renzhi Yuan, Tian Cao, Ling Cheng, Jian Song, Julian Cheng, Zhu Han

Existing research on non-line-of-sight (NLoS) ultraviolet (UV) channel modeling mainly focuses on scenarios where the signal propagation process is not affected by any obstacle and the radiation intensity (RI) of the light source is uniformly distributed.

MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training

no code implementations16 Jul 2024 Pinxue Zhao, Hailin Zhang, Fangcheng Fu, Xiaonan Nie, Qibin Liu, Fang Yang, Yuanbo Peng, Dian Jiao, Shuaipeng Li, Jinbao Xue, Yangyu Tao, Bin Cui

By leveraging fine-grained activation memory management, MEMO facilitates efficient training of 7B LLM with 1 million sequence length on just 8 A800 GPUs, achieving an MFU of 52. 30%.

Management

Optical Integrated Sensing and Communication: Architectures, Potentials and Challenges

no code implementations21 Dec 2023 Yunfeng Wen, Fang Yang, Jian Song, Zhu Han

Integrated sensing and communication (ISAC) is viewed as a crucial component of future mobile networks and has gained much interest in both academia and industry.

Free Space Optical Communication for Inter-Satellite Link: Architecture, Potentials and Trends

no code implementations26 Oct 2023 Guanhua Wang, Fang Yang, Jian Song, Zhu Han

The sixth-generation (6G) network is expected to achieve global coverage based on the space-air-ground integrated network, and the latest satellite network will play an important role in it.

Scheduling

Improving Sequential Recommendation Models with an Enhanced Loss Function

1 code implementation3 Jan 2023 Fangyu Li, Shenbao Yu, Feng Zeng, Fang Yang

We conduct extensive experiments on two influential open-source libraries, and the results demonstrate that our improved loss function significantly enhances the performance of GRU4Rec, SASRec, SR-GNN, and S3Rec models, improving their benchmarks significantly.

Benchmarking Sequential Recommendation

Graph-based prediction of Protein-protein interactions with attributed signed graph embedding

1 code implementation BMC Bioinformatics 2020 Fang Yang, Kunjie Fan, Dandan song, Huakang Lin

Moreover, our study takes advantage of a representation learning model and employs a graph-based deep learning method for PPI prediction, which shows superiority over existing sequence-based methods.

Graph Embedding Graph Representation Learning

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