Zobrazeno 1 - 10
of 486
pro vyhledávání: '"Yang, Jiacheng"'
Autor:
Meng, Chunlei, Yang, Jiacheng, Lin, Wei, Liu, Bowen, Zhang, Hongda, ouyang, chun, Gan, Zhongxue
Convolutional neural networks (CNNs) and vision transformers (ViTs) have become essential in computer vision for local and global feature extraction. However, aggregating these architectures in existing methods often results in inefficiencies. To add
Externí odkaz:
http://arxiv.org/abs/2410.11428
Autor:
Giannoula, Christina, Yang, Peiming, Fernandez, Ivan, Yang, Jiacheng, Durvasula, Sankeerth, Li, Yu Xin, Sadrosadati, Mohammad, Luna, Juan Gomez, Mutlu, Onur, Pekhimenko, Gennady
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenec
Externí odkaz:
http://arxiv.org/abs/2402.16731
Modern workloads are demanding increasingly larger memory capacity. Compute Express Link (CXL)-based memory tiering has emerged as a promising solution for addressing this trend by utilizing traditional DRAM alongside slow-tier CXL-memory devices in
Externí odkaz:
http://arxiv.org/abs/2312.04789
Autor:
Yang, Jiacheng, Giannoula, Christina, Wu, Jun, Elhoushi, Mostafa, Gleeson, James, Pekhimenko, Gennady
Sparse Convolution (SC) is widely used for processing 3D point clouds that are inherently sparse. Different from dense convolution, SC preserves the sparsity of the input point cloud by only allowing outputs to specific locations. To efficiently comp
Externí odkaz:
http://arxiv.org/abs/2401.06145
Autor:
Yang, Jiacheng
Publikováno v:
In Alexandria Engineering Journal February 2025 113:294-305
Autor:
Deng, Shumin, Yang, Jiacheng, Ye, Hongbin, Tan, Chuanqi, Chen, Mosha, Huang, Songfang, Huang, Fei, Chen, Huajun, Zhang, Ningyu
Natural language generation from structured data mainly focuses on surface-level descriptions, suffering from uncontrollable content selection and low fidelity. Previous works leverage logical forms to facilitate logical knowledge-conditioned text ge
Externí odkaz:
http://arxiv.org/abs/2112.01404
Autor:
Liang, Huan, Ning, Guobao, Mao, Kang, Zhu, Hongmin, Ma, Ding, Zhou, Fengqi, Li, Jing, Huang, Yufan, Yang, Jiacheng, Zhao, Hui, Li, Can-Peng
Publikováno v:
In Microchemical Journal December 2024 207
Publikováno v:
In Neural Networks December 2024 180
Publikováno v:
In Journal of the Neurological Sciences 15 August 2024 463
Publikováno v:
In Journal of Engineering Research June 2024 12(2):266-274