no code implementations • 17 Jan 2024 • Yao Lu, Song Bian, Lequn Chen, Yongjun He, Yulong Hui, Matthew Lentz, Beibin Li, Fei Liu, Jialin Li, Qi Liu, Rui Liu, Xiaoxuan Liu, Lin Ma, Kexin Rong, Jianguo Wang, Yingjun Wu, Yongji Wu, Huanchen Zhang, Minjia Zhang, Qizhen Zhang, Tianyi Zhou, Danyang Zhuo
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
no code implementations • 27 May 2023 • Song Bian, Zhao Song, Junze Yin
Many convex optimization problems with important applications in machine learning are formulated as empirical risk minimization (ERM).
no code implementations • 8 Mar 2023 • Song Bian, Xiating Ouyang, Zhiwei Fan, Paraschos Koutris
We present (i) a linear time algorithm in the number of entries in the dataset that decides whether a test point is certifiably robust for NBC, (ii) an algorithm that counts for each label, the number of cleaned datasets on which the NBC can be trained to predict that label, and (iii) an efficient optimal algorithm that poisons a clean dataset by inserting the minimum number of missing values such that a test point is not certifiably robust for NBC.
no code implementations • 6 Jan 2023 • Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman
Finally, we provide insights for future development of model parallelism compression algorithms.
1 code implementation • 19 Oct 2020 • Kotaro Matsuoka, Ryotaro Banno, Naoki Matsumoto, Takashi Sato, Song Bian
Our experiments show that both the pipelined architecture and the CMUX Memory technique are effective in improving the performance of the proposed processor.
Cryptography and Security
no code implementations • 11 Aug 2020 • Kenta Nagura, Song Bian, Takashi Sato
In this work, we find out that averaging models from different clients significantly diminishes the norm of the update vectors, resulting in slow learning rate and low prediction accuracy.
no code implementations • 14 Jul 2020 • Song Bian, Xiaowei Xu, Weiwen Jiang, Yiyu Shi, Takashi Sato
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data.
no code implementations • NeurIPS 2020 • Qian Lou, Song Bian, Lei Jiang
Prior HPPNNs over-pessimistically select huge HE parameters to maintain large noise budgets, since they use the same set of HE parameters for an entire network and ignore the error tolerance capability of a network.
no code implementations • 26 Apr 2020 • Keyu Yang, Yunjun Gao, Lei Liang, Song Bian, Lu Chen, Baihua Zheng
We propose Crowd-based neural networks for Text Sentiment Classification (CrowdTSC for short).
no code implementations • CVPR 2020 • Song Bian, Tianchen Wang, Masayuki Hiromoto, Yiyu Shi, Takashi Sato
In this work, we propose ENSEI, a secure inference (SI) framework based on the frequency-domain secure convolution (FDSC) protocol for the efficient execution of privacy-preserving visual recognition.
no code implementations • 30 Jan 2020 • Song Bian, Weiwen Jiang, Qing Lu, Yiyu Shi, Takashi Sato
Due to increasing privacy concerns, neural network (NN) based secure inference (SI) schemes that simultaneously hide the client inputs and server models attract major research interests.
1 code implementation • 11 May 2019 • Ting Chen, Song Bian, Yizhou Sun
In this work, we propose a dissection of GNNs on graph classification into two parts: 1) the graph filtering, where graph-based neighbor aggregations are performed, and 2) the set function, where a set of hidden node features are composed for prediction.
Ranked #1 on Graph Classification on RE-M12K
2 code implementations • WSDM '19 Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining 2019 • Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, Wei Wang
Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs.
Ranked #1 on Graph Similarity on IMDb