1 code implementation • 20 Feb 2024 • Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, Can Wang
DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support.
no code implementations • 12 Feb 2024 • Yangxinyu Xie, Tanwi Mallick, Joshua David Bergerson, John K. Hutchison, Duane R. Verner, Jordan Branham, M. Ross Alexander, Robert B. Ross, Yan Feng, Leslie-Anne Levy, Weijie Su
The recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence (AI) and machine learning (ML).
1 code implementation • 11 Jan 2024 • Wujie Sun, Defang Chen, Jiawei Chen, Yan Feng, Chun Chen, Can Wang
Deep learning has witnessed significant advancements in recent years at the cost of increasing training, inference, and model storage overhead.
1 code implementation • 13 Aug 2023 • Zijie Song, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data.
no code implementations • 7 Aug 2023 • Yan Feng, Panchamy Krishnakumari
This paper demonstrates the potential of applying a machine learning algorithm to study pedestrian route choice behavior in complex indoor buildings.
1 code implementation • NeurIPS 2023 • Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Yan Feng, Chun Chen, Can Wang
Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption.
1 code implementation • 1 Jan 2023 • Fei Yin, Yong Zhang, Baoyuan Wu, Yan Feng, Jingyi Zhang, Yanbo Fan, Yujiu Yang
In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget.
no code implementations • 28 Dec 2022 • Qihao Shi, Bingyang Fu, Can Wang, Jiawei Chen, Sheng Zhou, Yan Feng, Chun Chen
The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology.
1 code implementation • 30 Nov 2022 • Daniel Getter, Julie Bessac, Johann Rudi, Yan Feng
For each downscaling factor, we consider three CNN configurations that generate super-resolved predictions of fine-scale wind speed, which take between 1 to 3 input fields: coarse wind speed, fine-scale topography, and diurnal cycle.
1 code implementation • 22 Nov 2022 • Wujie Sun, Defang Chen, Can Wang, Deshi Ye, Yan Feng, Chun Chen
Instead of aligning output images, we distill teacher's sharpened feature distribution into the student with a dataset-independent classifier, making the student focus on those important features to improve performance.
no code implementations • 26 Aug 2022 • Yi-chong Xia, Bin Chen, Yan Feng, Tian-shuo Ge
As a probabilistic modeling technique, the flow-based model has demonstrated remarkable potential in the field of lossless compression \cite{idf, idf++, lbb, ivpf, iflow},.
no code implementations • 21 Jun 2022 • Yan Feng, Tao Xiong, Ruofan Wu, LingJuan Lv, Leilei Shi
In addition, with fixed privacy and communication level, the performance of sqSGD significantly dominates that of various baseline algorithms.
no code implementations • 7 Jun 2022 • Yichen Liu, Jiawei Chen, Defang Chen, Zhehui Zhou, Yan Feng, Can Wang
Knowledge Graph Embedding (KGE), which projects entities and relations into continuous vector spaces, have garnered significant attention.
1 code implementation • CVPR 2022 • Defang Chen, Jian-Ping Mei, Hailin Zhang, Can Wang, Yan Feng, Chun Chen
Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance.
Ranked #3 on Knowledge Distillation on CIFAR-100
no code implementations • 28 Dec 2021 • Can Wang, Zhe Wang, Defang Chen, Sheng Zhou, Yan Feng, Chun Chen
However, its effect on graph neural networks is less than satisfactory since the graph topology and node attributes are likely to change in a dynamic way and in this case a static teacher model is insufficient in guiding student training.
no code implementations • 19 Oct 2021 • Ge Zhang, Shaohui Mei, Mingyang Ma, Yan Feng, Qian Du
Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis.
no code implementations • 14 Sep 2021 • Defang Chen, Can Wang, Yan Feng, Chun Chen
Knowledge distillation is a generalized logits matching technique for model compression.
no code implementations • 1 Jan 2021 • Yan Feng, Tao Xiong, Ruofan Wu, Yuan Qi
We also initialize a discussion about the role of quantization and perturbation in FL algorithm design with privacy and communication constraints.
2 code implementations • 6 Dec 2020 • Defang Chen, Jian-Ping Mei, Yuan Zhang, Can Wang, Yan Feng, Chun Chen
Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model.
1 code implementation • 16 Nov 2020 • Can Wang, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen
However, the social network information may not be available in many recommender systems, which hinders application of SamWalker.
no code implementations • 16 Nov 2020 • Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
1 code implementation • CVPR 2022 • Yan Feng, Baoyuan Wu, Yanbo Fan, Li Liu, Zhifeng Li, Shutao Xia
This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while other information such as model parameters or the training datasets are unknown.
1 code implementation • 16 Mar 2020 • Yiming Li, Baoyuan Wu, Yan Feng, Yanbo Fan, Yong Jiang, Zhifeng Li, Shu-Tao Xia
In this work, we propose a novel defense method, the robust training (RT), by jointly minimizing two separated risks ($R_{stand}$ and $R_{rob}$), which is with respect to the benign example and its neighborhoods respectively.
no code implementations • 4 Mar 2020 • Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen
A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence.
no code implementations • 26 Feb 2020 • Yan Feng, Bin Chen, Tao Dai, Shu-Tao Xia
Deep product quantization network (DPQN) has recently received much attention in fast image retrieval tasks due to its efficiency of encoding high-dimensional visual features especially when dealing with large-scale datasets.
1 code implementation • 23 Feb 2020 • Xue Yang, Yan Feng, Weijun Fang, Jun Shao, Xiaohu Tang, Shu-Tao Xia, Rongxing Lu
However, the strong defence ability and high learning accuracy of these schemes cannot be ensured at the same time, which will impede the wide application of FL in practice (especially for medical or financial institutions that require both high accuracy and strong privacy guarantee).
2 code implementations • 1 Dec 2019 • Defang Chen, Jian-Ping Mei, Can Wang, Yan Feng, Chun Chen
The second-level distillation is performed to transfer the knowledge in the ensemble of auxiliary peers further to the group leader, i. e., the model used for inference.