no code implementations • 20 Mar 2024 • Matt White, Ibrahim Haddad, Cailean Osborne, Xiao-Yang, Liu, Ahmed Abdelmonsef, Sachin Varghese
Generative AI (GAI) offers unprecedented possibilities but its commercialization has raised concerns about transparency, reproducibility, bias, and safety.
2 code implementations • 3 Nov 2023 • Huang-Chou Lin, Kuang-Hao, Liu
Extensive simulations are conducted to evaluate the performance of the proposed AOBA in comparison with several existing beam alignment schemes.
1 code implementation • Proceedings of the AAAI Conference on Artificial Intelligence 2023 • Zeng, D., Liu, Chen, W., Zhou, L., Zhang, M., & Qu, H
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL. Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.
Ranked #7 on Graph Regression on ZINC-500k
1 code implementation • 2 Jun 2023 • Sin-Yu Huang, Kuang-Hao, Liu
A dual-hop status update system aided by energy harvesting (EH) relays with finite data and energy buffers is studied in this work.
no code implementations • 11 Mar 2022 • Yi Cheng, Xiaoyan, Liu
At the same time, it proves that the accuracy of the model obtained by BCSVM algorithm is higher than that of CSVM.
1 code implementation • CVPR 2022 • Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti, Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
no code implementations • 16 Jul 2020 • Zhiyuan Liu, Huazheng Wang, Bo Waggoner, Youjian, Liu, Lijun Chen
We investigate the sparse linear contextual bandit problem where the parameter $\theta$ is sparse.
no code implementations • 13 Feb 2019 • Jorge, Davila-Chacon, Jindong, Liu, Stefan, Wermter
The approach is verified by measuring the impact of SSL with a humanoid robot head on the performance of an ASR system.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2