1 code implementation • 21 Mar 2024 • Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao, Jie Jiang
In this paper, we uncover a new challenge associated with BCE loss in scenarios with sparse positive feedback, such as CTR prediction: the gradient vanishing for negative samples.
no code implementations • 22 Feb 2024 • Junwei Pan, Wei Xue, Ximei Wang, Haibin Yu, Xun Liu, Shijie Quan, Xueming Qiu, Dapeng Liu, Lei Xiao, Jie Jiang
In this paper, we present an industry ad recommendation system, paying attention to the challenges and practices of learning appropriate representations.
no code implementations • 6 Oct 2023 • Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, Mingsheng Long
Recent advances in deep foundation models have led to a promising trend of developing large recommendation models to leverage vast amounts of available data.
no code implementations • 19 Sep 2023 • Ximei Wang, Junwei Pan, Xingzhuo Guo, Dapeng Liu, Jie Jiang
Multi-domain learning (MDL) aims to train a model with minimal average risk across multiple overlapping but non-identical domains.
1 code implementation • 16 Aug 2023 • Liangcai Su, Junwei Pan, Ximei Wang, Xi Xiao, Shijie Quan, Xihua Chen, Jie Jiang
Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks.
1 code implementation • 2 Feb 2023 • Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long
This paper aims at generalizing CLIP to out-of-distribution test data on downstream tasks.
1 code implementation • NeurIPS 2023 • Junguang Jiang, Baixu Chen, Junwei Pan, Ximei Wang, Liu Dapeng, Jie Jiang, Mingsheng Long
Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks.
no code implementations • 28 Nov 2022 • Enneng Yang, Junwei Pan, Ximei Wang, Haibin Yu, Li Shen, Xihua Chen, Lei Xiao, Jie Jiang, Guibing Guo
In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter.
1 code implementation • 15 Feb 2022 • Baixu Chen, Junguang Jiang, Ximei Wang, Pengfei Wan, Jianmin Wang, Mingsheng Long
Yet these datasets are time-consuming and labor-exhaustive to obtain on realistic tasks.
no code implementations • ICLR 2022 • Ximei Wang, Xinyang Chen, Jianmin Wang, Mingsheng Long
To take the power of both worlds, we propose a novel X-model by simultaneously encouraging the invariance to {data stochasticity} and {model stochasticity}.
2 code implementations • CVPR 2021 • Junguang Jiang, Yifei Ji, Ximei Wang, Yufeng Liu, Jianmin Wang, Mingsheng Long
First, based on our observation that the probability density of the output space is sparse, we introduce a spatial probability distribution to describe this sparsity and then use it to guide the learning of the adversarial regressor.
2 code implementations • 25 Feb 2021 • Ximei Wang, Jinghan Gao, Mingsheng Long, Jianmin Wang
Deep learning has made revolutionary advances to diverse applications in the presence of large-scale labeled datasets.
no code implementations • 12 Nov 2020 • Jincheng Zhong, Ximei Wang, Zhi Kou, Jianmin Wang, Mingsheng Long
It is common within the deep learning community to first pre-train a deep neural network from a large-scale dataset and then fine-tune the pre-trained model to a specific downstream task.
no code implementations • NeurIPS 2020 • Ximei Wang, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan
In this paper, we delve into the open problem of Calibration in DA, which is extremely challenging due to the coexistence of domain shift and the lack of target labels.
3 code implementations • ECCV 2020 • Ying Jin, Ximei Wang, Mingsheng Long, Jian-Min Wang
It can be characterized as (1) a non-adversarial DA method without explicitly deploying domain alignment, enjoying faster convergence speed; (2) a versatile approach that can handle four existing scenarios: Closed-Set, Partial-Set, Multi-Source, and Multi-Target DA, outperforming the state-of-the-art methods in these scenarios, especially on one of the largest and hardest datasets to date (7. 3% on DomainNet).
Ranked #3 on Multi-target Domain Adaptation on DomainNet
1 code implementation • NeurIPS 2019 • Ximei Wang, Ying Jin, Mingsheng Long, Jian-Min Wang, Michael. I. Jordan
Deep neural networks (DNNs) excel at learning representations when trained on large-scale datasets.
2 code implementations • International Conference on Machine Learning 2019 • Kaichao You, Ximei Wang, Mingsheng Long, Michael Jordan
Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain.