1 code implementation • 15 Jan 2025 • Waqwoya Abebe, Sadegh Jafari, Sixing Yu, Akash Dutta, Jan Strube, Nathan R. Tallent, Luanzheng Guo, Pablo Munoz, Ali Jannesari
Neural Architecture Search (NAS) is a powerful approach of automating the design of efficient neural architectures.
no code implementations • 21 Oct 2024 • Nathan Tallent, Jan Strube, Luanzheng Guo, Hyungro Lee, Jesun Firoz, Sayan Ghosh, Bo Fang, Oceane Bel, Steven Spurgeon, Sarah Akers, Christina Doty, Erol Cromwell
Pacific Northwest National Laboratory's LDRD "Cloud, High-Performance Computing (HPC), and Edge for Science and Security" (CHESS) has developed a set of interrelated capabilities for enabling distributed scientific workflows and curating datasets.
no code implementations • 15 Aug 2024 • Chengyu Gong, Gefei Shen, Luanzheng Guo, Nathan Tallent, Dongfang Zhao
In order to develop such an OPDR method, our central hypothesis is that by analyzing the intrinsic relationship among key parameters during the dimension-reduction map, a quantitative function may be constructed to reveal the correlation between the target (lower) dimensionality and other variables.
1 code implementation • 9 Apr 2024 • Waqwoya Abebe, Jan Strube, Luanzheng Guo, Nathan R. Tallent, Oceane Bel, Steven Spurgeon, Christina Doty, Ali Jannesari
To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations.
1 code implementation • 25 Jun 2023 • Shuai Lu, Jun Chu, Luanzheng Guo, Xu T. Liu
Convolution is the most time-consuming operation in deep neural network operations, so its performance is critical to the overall performance of the neural network.
no code implementations • 30 Apr 2017 • Luanzheng Guo, Jun Chu
First, the line segments are refined by four consecutive operations, i. e., reclassifying, connecting, fitting, and voting.