no code implementations • 9 Mar 2025 • Fei Tang, Yongliang Shen, Hang Zhang, Siqi Chen, Guiyang Hou, Wenqi Zhang, Wenqiao Zhang, Kaitao Song, Weiming Lu, Yueting Zhuang
This structured decomposition enables systematic understanding of both interface layouts and visual relationships.
1 code implementation • 15 Oct 2024 • Fei Tang, Yongliang Shen, Hang Zhang, Zeqi Tan, Wenqi Zhang, Zhibiao Huang, Kaitao Song, Weiming Lu, Yueting Zhuang
GaVaMoE introduces two key components: (1) a rating reconstruction module that employs Variational Autoencoder (VAE) with a Gaussian Mixture Model (GMM) to capture complex user-item collaborative preferences, serving as a pre-trained multi-gating mechanism; and (2) a set of fine-grained expert models coupled with the multi-gating mechanism for generating highly personalized explanations.
1 code implementation • 20 Jun 2024 • Zhengxin Yang, Wanling Gao, Luzhou Peng, Yunyou Huang, Fei Tang, Jianfeng Zhan
Designing and optimizing neural network architectures typically requires extensive expertise, starting with handcrafted designs and then manual or automated refinement.
2 code implementations • 24 Mar 2024 • kaiko. ai, Nanne Aben, Edwin D. de Jong, Ioannis Gatopoulos, Nicolas Känzig, Mikhail Karasikov, Axel Lagré, Roman Moser, Joost van Doorn, Fei Tang
Driven by the recent advances in deep learning methods and, in particular, by the development of modern self-supervised learning algorithms, increased interest and efforts have been devoted to build foundation models (FMs) for medical images.
no code implementations • 5 Sep 2023 • Fei Tang, Wanling Gao, Luzhou Peng, Jianfeng Zhan
Instead of a collection of blended questions, AGIBench focuses on three typical ability branches and adopts a four-tuple <ability branch, knowledge, difficulty, modal> to label the attributes of each question.
1 code implementation • 14 Mar 2023 • Moritz Neun, Christian Eichenberger, Henry Martin, Markus Spanring, Rahul Siripurapu, Daniel Springer, Leyan Deng, Chenwang Wu, Defu Lian, Min Zhou, Martin Lumiste, Andrei Ilie, Xinhua Wu, Cheng Lyu, Qing-Long Lu, Vishal Mahajan, Yichao Lu, Jiezhang Li, Junjun Li, Yue-Jiao Gong, Florian Grötschla, Joël Mathys, Ye Wei, He Haitao, Hui Fang, Kevin Malm, Fei Tang, Michael Kopp, David Kreil, Sepp Hochreiter
We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task.
no code implementations • 25 Dec 2022 • Zhengxin Yang, Wanling Gao, Chunjie Luo, Lei Wang, Fei Tang, Xu Wen, Jianfeng Zhan
The study unveils a counterintuitive revelation: deep learning inference quality exhibits fluctuations due to inference time.
1 code implementation • 31 Mar 2022 • Christian Eichenberger, Moritz Neun, Henry Martin, Pedro Herruzo, Markus Spanring, Yichao Lu, Sungbin Choi, Vsevolod Konyakhin, Nina Lukashina, Aleksei Shpilman, Nina Wiedemann, Martin Raubal, Bo wang, Hai L. Vu, Reza Mohajerpoor, Chen Cai, Inhi Kim, Luca Hermes, Andrew Melnik, Riza Velioglu, Markus Vieth, Malte Schilling, Alabi Bojesomo, Hasan Al Marzouqi, Panos Liatsis, Jay Santokhi, Dylan Hillier, Yiming Yang, Joned Sarwar, Anna Jordan, Emil Hewage, David Jonietz, Fei Tang, Aleksandra Gruca, Michael Kopp, David Kreil, Sepp Hochreiter
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
1 code implementation • 21 Oct 2021 • Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner, Viet Tran, Fei Tang, Hubert Ramsauer, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, Sepp Hochreiter
We suggest to use modern Hopfield networks to tackle the problem of explaining away.
no code implementations • 31 May 2021 • Fei Tang, Michael Kopp
In their recent paper titled "Large Associative Memory Problem in Neurobiology and Machine Learning" [arXiv:2008. 06996] the authors gave a biologically plausible microscopic theory from which one can recover many dense associative memory models discussed in the literature.
no code implementations • 25 Feb 2021 • Zihan Jiang, Wanling Gao, Fei Tang, Xingwang Xiong, Lei Wang, Chuanxin Lan, Chunjie Luo, Hongxiao Li, Jianfeng Zhan
Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality.
Image Classification
Performance
no code implementations • 6 May 2020 • Wanling Gao, Fei Tang, Jianfeng Zhan, Xu Wen, Lei Wang, Zheng Cao, Chuanxin Lan, Chunjie Luo, Xiaoli Liu, Zihan Jiang
We formalize a real-world application scenario as a Directed Acyclic Graph-based model and propose the rules to distill it into a permutation of essential AI and non-AI tasks, which we call a scenario benchmark.
no code implementations • 30 Apr 2020 • Fei Tang, Wanling Gao, Jianfeng Zhan, Chuanxin Lan, Xu Wen, Lei Wang, Chunjie Luo, Jiahui Dai, Zheng Cao, Xingwang Xiong, Zihan Jiang, Tianshu Hao, Fanda Fan, Fan Zhang, Yunyou Huang, Jianan Chen, Mengjia Du, Rui Ren, Chen Zheng, Daoyi Zheng, Haoning Tang, Kunlin Zhan, Biao Wang, Defei Kong, Minghe Yu, Chongkang Tan, Huan Li, Xinhui Tian, Yatao Li, Junchao Shao, Zhenyu Wang, Xiaoyu Wang, Hainan Ye
We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent.
no code implementations • 17 Feb 2020 • Wanling Gao, Fei Tang, Jianfeng Zhan, Chuanxin Lan, Chunjie Luo, Lei Wang, Jiahui Dai, Zheng Cao, Xiongwang Xiong, Zihan Jiang, Tianshu Hao, Fanda Fan, Xu Wen, Fan Zhang, Yunyou Huang, Jianan Chen, Mengjia Du, Rui Ren, Chen Zheng, Daoyi Zheng, Haoning Tang, Kunlin Zhan, Biao Wang, Defei Kong, Minghe Yu, Chongkang Tan, Huan Li, Xinhui Tian, Yatao Li, Gang Lu, Junchao Shao, Zhenyu Wang, Xiaoyu Wang, Hainan Ye
An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application.
no code implementations • 13 Aug 2019 • Wanling Gao, Fei Tang, Lei Wang, Jianfeng Zhan, Chunxin Lan, Chunjie Luo, Yunyou Huang, Chen Zheng, Jiahui Dai, Zheng Cao, Daoyi Zheng, Haoning Tang, Kunlin Zhan, Biao Wang, Defei Kong, Tong Wu, Minghe Yu, Chongkang Tan, Huan Li, Xinhui Tian, Yatao Li, Junchao Shao, Zhenyu Wang, Xiaoyu Wang, Hainan Ye
On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.
no code implementations • 19 Jan 2017 • Fei Tang, Hemant Ishwaran
Using a large, diverse collection of data sets, performance of various RF algorithms was assessed under different missing data mechanisms.