Search Results for author: Fei Tang

Found 16 papers, 6 papers with code

Think Twice, Click Once: Enhancing GUI Grounding via Fast and Slow Systems

no code implementations9 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.

GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation

1 code implementation15 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.

Explainable Recommendation Language Modelling +1

Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture

1 code implementation20 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.

Towards Large-Scale Training of Pathology Foundation Models

2 code implementations24 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.

Nuclear Segmentation Self-Supervised Learning +1

AGIBench: A Multi-granularity, Multimodal, Human-referenced, Auto-scoring Benchmark for Large Language Models

no code implementations5 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.

Benchmarking Zero-Shot Learning

Quality at the Tail of Machine Learning Inference

no code implementations25 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.

Autonomous Driving Benchmarking +2

A remark on a paper of Krotov and Hopfield [arXiv:2008.06996]

no code implementations31 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.

BIG-bench Machine Learning

HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking

no code implementations25 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

AIBench Scenario: Scenario-distilling AI Benchmarking

no code implementations6 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.

Benchmarking Diversity

AIBench: An Industry Standard Internet Service AI Benchmark Suite

no code implementations13 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.

Benchmarking Learning-To-Rank

Random Forest Missing Data Algorithms

no code implementations19 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.

Imputation

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