Search Results for author: Fei Tang

Found 12 papers, 3 papers with code

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

no code implementations25 Dec 2022 Zhengxin Yang, Wanling Gao, Chunjie Luo, Lei Wang, Fei Tang, Xu Wen, Jianfeng Zhan

Benchmarking and evaluating deep learning models and systems necessitate a meticulous approach to ensure comprehensive assessment.


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.


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.


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