no code implementations • 12 Mar 2024 • Yu Yang, Siddhartha Mishra, Jeffrey N Chiang, Baharan Mirzasoleiman
In clinical text summarization on the MIMIC-III dataset (Johnson et al., 2016), S2L again outperforms training on the full dataset using only 50% of the data.
no code implementations • 19 Feb 2024 • Chengyi Ju, Jiannong Cao, Yu Yang, Zhen-Qun Yang, Ho Man Lee
In response, we propose HFRec, a heterogeneity-aware hybrid federated recommender system designed for cross-school elective course recommendations.
no code implementations • 16 Jan 2024 • Qixin Zhang, Zongqi Wan, Zengde Deng, Zaiyi Chen, Xiaoming Sun, Jialin Zhang, Yu Yang
The fundamental idea of our boosting technique is to exploit non-oblivious search to derive a novel auxiliary function $F$, whose stationary points are excellent approximations to the global maximum of the original DR-submodular objective $f$.
no code implementations • 11 Jan 2024 • Yicong Li, Xiangguo Sun, Hongxu Chen, Sixiao Zhang, Yu Yang, Guandong Xu
Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability.
no code implementations • 18 Dec 2023 • Zilin Wang, Haolin Zhuang, Lu Li, Yinmin Zhang, Junjie Zhong, Jun Chen, Yu Yang, Boshi Tang, Zhiyong Wu
This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models.
1 code implementation • 10 Dec 2023 • Jianbiao Mei, Yu Yang, Mengmeng Wang, Junyu Zhu, Xiangrui Zhao, Jongwon Ra, Laijian Li, Yong liu
Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for autonomous driving.
no code implementations • 5 Dec 2023 • Yu Yang, Aaditya K. Singh, Mostafa Elhoushi, Anas Mahmoud, Kushal Tirumala, Fabian Gloeckle, Baptiste Rozière, Carole-Jean Wu, Ari S. Morcos, Newsha Ardalani
Armed with this knowledge, we devise novel pruning metrics that operate in embedding space to identify and remove low-quality entries in the Stack dataset.
no code implementations • 14 Nov 2023 • Yu Yang, Qihong Yang, Yangtao Deng, Qiaolin He
In this work, we propose an end-to-end adaptive sampling neural network (MMPDE-Net) based on the moving mesh method, which can adaptively generate new sampling points by solving the moving mesh PDE.
no code implementations • 12 Nov 2023 • Chao Xu, Yu Yang, Rongzhao Wang, Guan Wang, Bojia Lin
Multi-Stage Classifier (MSC) - several classifiers working sequentially in an arranged order and classification decision is partially made at each step - is widely used in industrial applications for various resource limitation reasons.
no code implementations • 1 Nov 2023 • Jiangnan Xia, Yu Yang, Senzhang Wang, Hongzhi Yin, Jiannong Cao, Philip S. Yu
To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.
1 code implementation • 1 Nov 2023 • Yu Yang, Xiaotong Shen
This paper presents FlowSUM, a normalizing flows-based variational encoder-decoder framework for Transformer-based summarization.
no code implementations • 21 Oct 2023 • Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, Pan Xu
Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i. e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero.
no code implementations • 10 Oct 2023 • Xuxi Chen, Yu Yang, Zhangyang Wang, Baharan Mirzasoleiman
Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar generalization performance to that of the full dataset.
1 code implementation • 4 Oct 2023 • Yinan Huang, William Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li
Despite many attempts to address non-uniqueness, most methods overlook stability, leading to poor generalization on unseen graph structures.
Molecular Property Prediction Out-of-Distribution Generalization +1
1 code implementation • 3 Oct 2023 • Anas Mahmoud, Mostafa Elhoushi, Amro Abbas, Yu Yang, Newsha Ardalani, Hugh Leather, Ari Morcos
We propose a pruning signal, Sieve, that employs synthetic captions generated by image-captioning models pretrained on small, diverse, and well-aligned image-text pairs to evaluate the alignment of noisy image-text pairs.
no code implementations • 24 Sep 2023 • Enping Lin, Ze Fang, Yuqing Huang, Yu Yang, Zhong Chen
Symmetrical NMR spectroscopy constitutes a vital branch of multidimensional NMR spectroscopy, providing a powerful tool for the structural elucidation of biological macromolecules.
1 code implementation • 9 Aug 2023 • Chang Meng, Chenhao Zhai, Yu Yang, Hengyu Zhang, Xiu Li
In the fusion step, advanced neural networks are used to model the hierarchical correlations between user behaviors.
1 code implementation • 27 Jun 2023 • Jianbiao Mei, Yu Yang, Mengmeng Wang, Xiaojun Hou, Laijian Li, Yong liu
Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with the ``sampling-shifting-grouping" scheme to directly group thing points into instances from the raw point cloud efficiently.
1 code implementation • 27 Jun 2023 • Jianbiao Mei, Yu Yang, Mengmeng Wang, Tianxin Huang, Xuemeng Yang, Yong liu
However, how to effectively exploit the relationships between the semantic context in semantic segmentation and geometric structure in scene completion remains under exploration.
1 code implementation • 21 Jun 2023 • Siddharth Joshi, Yu Yang, Yihao Xue, Wenhan Yang, Baharan Mirzasoleiman
Deep neural networks often exploit non-predictive features that are spuriously correlated with class labels, leading to poor performance on groups of examples without such features.
1 code implementation • 2 Jun 2023 • Yu Yang, Hao Kang, Baharan Mirzasoleiman
To improve the efficiency and sustainability of learning deep models, we propose CREST, the first scalable framework with rigorous theoretical guarantees to identify the most valuable examples for training non-convex models, particularly deep networks.
no code implementations • 30 May 2023 • Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman
In this work, we provide the first theoretical analysis of the effect of simplicity bias on learning spurious correlations.
no code implementations • 23 May 2023 • Yihao Xue, Ali Payani, Yu Yang, Baharan Mirzasoleiman
Pretrained machine learning models need to be adapted to distribution shifts when deployed in new target environments.
no code implementations • 8 Apr 2023 • Yu Yang, Besmira Nushi, Hamid Palangi, Baharan Mirzasoleiman
Spurious correlations that degrade model generalization or lead the model to be right for the wrong reasons are one of the main robustness concerns for real-world deployments.
no code implementations • 15 Mar 2023 • Yu Yang, Helin Gong, Qihong Yang, Yangtao Deng, Qiaolin He, Shiquan Zhang
In practical engineering experiments, the data obtained through detectors are inevitably noisy.
1 code implementation • ICCV 2023 • Hritik Bansal, Nishad Singhi, Yu Yang, Fan Yin, Aditya Grover, Kai-Wei Chang
Multimodal contrastive pretraining has been used to train multimodal representation models, such as CLIP, on large amounts of paired image-text data.
no code implementations • 28 Feb 2023 • Xinjiang Chen, Yu Yang, Jianxiao Wang, Jie Song, Guannan He
Battery swapping as a business model for battery energy storage (BES) has great potential in future integrated low-carbon energy and transportation systems.
no code implementations • 20 Dec 2022 • Liyi Luo, Xintong Jiang, Yu Yang, Eugene Roy Antony Samy, Mark Lefsrud, Valerio Hoyos-Villegas, Shangpeng Sun
Second, a weakly-supervised deep learning method was proposed for plant organ segmentation.
no code implementations • 27 Nov 2022 • Hari Prasanna Das, Yu-Wen Lin, Utkarsha Agwan, Lucas Spangher, Alex Devonport, Yu Yang, Jan Drgona, Adrian Chong, Stefano Schiavon, Costas J. Spanos
In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient.
1 code implementation • 25 Nov 2022 • Qiran Zou, Yu Yang, Wing Yin Cheung, Chang Liu, Xiangyang Ji
Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise.
1 code implementation • ICLR 2022 • Yu Yang, Xiaotian Cheng, Hakan Bilen, Xiangyang Ji
The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate.
1 code implementation • 6 Nov 2022 • Yu Yang, Xiaotian Cheng, Chang Liu, Hakan Bilen, Xiangyang Ji
In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains.
no code implementations • 5 Nov 2022 • Yu Yang, Wing Yin Cheung, Chang Liu, Xiangyang Ji
Multiview self-supervised representation learning roots in exploring semantic consistency across data of complex intra-class variation.
2 code implementations • 18 Oct 2022 • Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman
Data poisoning causes misclassification of test time target examples by injecting maliciously crafted samples in the training data.
1 code implementation • 22 Sep 2022 • Qihong Yang, Yangtao Deng, Yu Yang, Qiaolin He, Shiquan Zhang
In this article, we propose two kinds of neural networks inspired by power method and inverse power method to solve linear eigenvalue problems.
1 code implementation • 19 Sep 2022 • Longtao Tang, Ying Zhou, Yu Yang
We present GRU2Set, which is an instance of our sequence-to-set method and employs the famous GRU model as the sequence generative model.
1 code implementation • IEEE Open Journal of Instrumentation and Measurement (Volume: 1) 2022 • Jie Lin, Song Chen, Enping Lin, Yu Yang
Deep learning neural network serves as a powerful tool for visual anomaly detection (AD) and fault diagnosis, attributed to its strong abstractive interpretation ability in the representation domain.
Ranked #50 on Anomaly Detection on MVTec AD
no code implementations • 18 Aug 2022 • Qixin Zhang, Zengde Deng, Xiangru Jian, Zaiyi Chen, Haoyuan Hu, Yu Yang
Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics.
no code implementations • 16 Aug 2022 • Qixin Zhang, Zengde Deng, Zaiyi Chen, Kuangqi Zhou, Haoyuan Hu, Yu Yang
In this paper, we revisit the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, which finds wide real-world applications in the domain of machine learning, economics, and operations research.
1 code implementation • 14 Aug 2022 • Tian Yu Liu, Yu Yang, Baharan Mirzasoleiman
We make the key observation that attacks introduce local sharp regions of high training loss, which when minimized, results in learning the adversarial perturbations and makes the attack successful.
1 code implementation • 15 Jul 2022 • Chenghui Yu, Mingkang Tang, ShengGe Yang, Mingqing Wang, Zhe Xu, Jiangpeng Yan, HanMo Chen, Yu Yang, Xiao-jun Zeng, Xiu Li
Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic early-stage melanoma diagnosis.
no code implementations • 1 Jul 2022 • Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen, Quoc Viet Hung Nguyen, Xiaofang Zhou, Lei Chen
Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information.
no code implementations • 22 Apr 2022 • Hongbin Zhang, Yu Yang, Feng Wu, Qixin Zhang
Optimizing the assortment of products to display to customers is a key to increasing revenue for both offline and online retailers.
1 code implementation • CVPR 2022 • Yu Yang, Seungbae Kim, Jungseock Joo
We also demonstrate a novel application of our method for unsupervised dataset bias analysis which allows us to automatically discover hidden biases in datasets or compare different subsets without using additional labels.
no code implementations • 13 Feb 2022 • Yu Yang, Jia Mao, Richard Nguyen, Annas Tohmeh, Hen-Geul Yeh
A machine learning framework for 1-hour ahead solar power prediction is developed in this paper based on the historical data.
no code implementations • 8 Feb 2022 • Zhuolin Wang, Rongping Zhu, Jian-Ya Ding, Yu Yang, Keyou You
The package shipment problem requires to optimally co-design paths for both packages and a heterogeneous fleet in a transit center network (TCN).
1 code implementation • 24 Jan 2022 • Bo Li, Qiulin Wang, JiQuan Pei, Yu Yang, Xiangyang Ji
First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e. g., face parsers and face landmark detectors.
no code implementations • 3 Jan 2022 • Qixin Zhang, Zengde Deng, Zaiyi Chen, Haoyuan Hu, Yu Yang
In the online setting, for the first time we consider the adversarial delays for stochastic gradient feedback, under which we propose a boosting online gradient algorithm with the same non-oblivious function $F$.
no code implementations • 28 Dec 2021 • Yue Chen, Yu Yang, Xiaoyuan Xu
The development of distributed energy resources, such as rooftop photovoltaic (PV) panels, batteries, and electric vehicles (EVs), has decentralized our power system operation, where transactive energy markets empower local energy exchanges.
1 code implementation • 21 Dec 2021 • Jiafei Lyu, Yu Yang, Jiangpeng Yan, Xiu Li
It is vital to accurately estimate the value function in Deep Reinforcement Learning (DRL) such that the agent could execute proper actions instead of suboptimal ones.
1 code implementation • 13 Jul 2021 • Tianhao Wang, Yu Yang, Ruoxi Jia
The Shapley value (SV) and Least core (LC) are classic methods in cooperative game theory for cost/profit sharing problems.
no code implementations • 30 Jun 2021 • Xu Geng, Yilun Jin, Zhengfei Zheng, Yu Yang, Yexin Li, Han Tian, Peibo Duan, Leye Wang, Jiannong Cao, Hai Yang, Qiang Yang, Kai Chen
Data-driven approaches have been applied to many problems in urban computing.
no code implementations • NAACL 2021 • Qianlan Ying, Payal Bajaj, Budhaditya Deb, Yu Yang, Wei Wang, Bojia Lin, Milad Shokouhi, Xia Song, Yang Yang, Daxin Jiang
Faced with increased compute requirements and low resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system.
no code implementations • International Conference on Digital Home 2021 • Fei Wang, Yu Yang, Baoquan Zhao, Dazhi Jiang, Siwei Chen, Jianqiang Sheng
It creates the transformation representation of a sketch by extracting the shape features of an input sketch and transformation template samples.
no code implementations • 22 Apr 2021 • Hongcheng Liu, Yu Yang
This paper concerns a convex, stochastic zeroth-order optimization (S-ZOO) problem.
no code implementations • 1 Apr 2021 • Yu Yang, Hakan Bilen, Qiran Zou, Wing Yin Cheung, Xiangyang Ji
Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task.
no code implementations • 25 Feb 2021 • Rui Yang, Jiafei Lyu, Yu Yang, Jiangpeng Yan, Feng Luo, Dijun Luo, Lanqing Li, Xiu Li
Two main challenges in multi-goal reinforcement learning are sparse rewards and sample inefficiency.
no code implementations • 1 Nov 2020 • Zicun Cong, Lingyang Chu, Yu Yang, Jian Pei
One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test.
no code implementations • 29 Oct 2020 • Yu Yang, Guoqiang Hu, Costas J. Spanos
Further, we demonstrate both the building-wise and community-wise economic benefits are enhanced with the ES sharing model over the individual ES (IES) model.
Fairness Computer Science and Game Theory
no code implementations • 6 Jun 2020 • Yu Yang, Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Hongzhi Yin, Xiaofang Zhou
We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline learning behaviors.
no code implementations • 7 May 2019 • Lior Deutsch, Erik Nijkamp, Yu Yang
Recent work on mode connectivity in the loss landscape of deep neural networks has demonstrated that the locus of (sub-)optimal weight vectors lies on continuous paths.
no code implementations • 21 Jan 2019 • Quanshi Zhang, Yu Yang, Qian Yu, Ying Nian Wu
This paper focuses on a new task, i. e., transplanting a category-and-task-specific neural network to a generic, modular network without strong supervision.
no code implementations • 21 Jan 2019 • Quanshi Zhang, Yu Yang, Ying Nian Wu
This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i. e., the explainer uses interpretable visual concepts to explain features in middle conv-layers of a CNN.
no code implementations • 8 Jan 2019 • Zenan Ling, Haotian Ma, Yu Yang, Robert C. Qiu, Song-Chun Zhu, Quanshi Zhang
In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network.
no code implementations • 18 May 2018 • Quanshi Zhang, Yu Yang, Yuchen Liu, Ying Nian Wu, Song-Chun Zhu
Given feature maps of a certain conv-layer of the CNN, the explainer performs like an auto-encoder, which first disentangles the feature maps into object-part features and then inverts object-part features back to features of higher conv-layers of the CNN.
no code implementations • 26 Apr 2018 • Quanshi Zhang, Yu Yang, Qian Yu, Ying Nian Wu
This paper focuses on a new task, i. e., transplanting a category-and-task-specific neural network to a generic, modular network without strong supervision.
no code implementations • ECCV 2018 • Jialin Wu, Dai Li, Yu Yang, Chandrajit Bajaj, Xiangyang Ji
We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbor regions.
no code implementations • 14 Mar 2018 • Zhan Yusen, Da Qing, Xiao Fei, Zeng An-xiang, Yu Yang
Solving the problem by reinforcement learning, we propose the RankCFS, which has been assessed in an off-line environment as well as a real-world on-line environment (Taobao. com).
no code implementations • CVPR 2019 • Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu
We propose to learn a decision tree, which clarifies the specific reason for each prediction made by the CNN at the semantic level.
no code implementations • 23 Sep 2017 • Lingyang Chu, Zhefeng Wang, Jian Pei, Yanyan Zhang, Yu Yang, Enhong Chen
Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities.
no code implementations • ICCV 2015 • Xi Peng, Shaoting Zhang, Yu Yang, Dimitris N. Metaxas
Face alignment, especially on real-time or large-scale sequential images, is a challenging task with broad applications.