Zobrazeno 1 - 10
of 391
pro vyhledávání: '"Liao, Xiangke"'
The high communication costs impede scalability in distributed systems. Multimodal models like Sora exacerbate this issue by requiring more resources than current networks can support. However, existing network architectures fail to address this gap.
Externí odkaz:
http://arxiv.org/abs/2405.17870
Autor:
Ma, Yingwei, Yu, Yue, Li, Shanshan, Jiang, Yu, Guo, Yong, Zhang, Yuanliang, Xie, Yutao, Liao, Xiangke
Large language models (LLMs) have showcased remarkable prowess in code generation. However, automated code generation is still challenging since it requires a high-level semantic mapping between natural language requirements and codes. Most existing
Externí odkaz:
http://arxiv.org/abs/2310.10698
Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the branch-and-bound. We extract the graph features, global features and h
Externí odkaz:
http://arxiv.org/abs/2307.01434
Autor:
Geng, Mingyang, Wang, Shangwen, Dong, Dezun, Wang, Haotian, Li, Ge, Jin, Zhi, Mao, Xiaoguang, Liao, Xiangke
Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code s
Externí odkaz:
http://arxiv.org/abs/2304.11384
As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefits a range of tasks and models.
Externí odkaz:
http://arxiv.org/abs/2303.15822
Autor:
Du, Jiangsu, Li, Dongsheng, Wen, Yingpeng, Jiang, Jiazhi, Huang, Dan, Liao, Xiangke, Lu, Yutong
Novel artificial intelligence (AI) technology has expedited various scientific research, e.g., cosmology, physics and bioinformatics, inevitably becoming a significant category of workload on high performance computing (HPC) systems. Existing AI benc
Externí odkaz:
http://arxiv.org/abs/2212.03410
With the great success of pre-trained models, the pretrain-then-finetune paradigm has been widely adopted on downstream tasks for source code understanding. However, compared to costly training a large-scale model from scratch, how to effectively ada
Externí odkaz:
http://arxiv.org/abs/2112.02268
Communication overhead is the key challenge for distributed training. Gradient compression is a widely used approach to reduce communication traffic. When combining with parallel communication mechanism method like pipeline, gradient compression tech
Externí odkaz:
http://arxiv.org/abs/2106.10796
Autor:
Zeng, Chen, Yu, Yue, Li, Shanshan, Xia, Xin, Wang, Zhiming, Geng, Mingyang, Xiao, Bailin, Dong, Wei, Liao, Xiangke
With the rapid increase in the amount of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language. Despite existing deep learning based approaches(e.g., DeepCS and MMAN) have provided th
Externí odkaz:
http://arxiv.org/abs/2103.13020