Concepedia

Few-shot Regression

Few-Shot Regression is a meta-learning framework that enables the rapid adaptation of predictive models to new regression tasks from limited labeled data by leveraging prior experience across related tasks.

31

Publications

8.7K

Citations

105

Authors

36

Institutions

Publications per year

2017–2026

31

Parents

Publications

31

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Chelsea Finn, Pieter Abbeel, Sergey Levine · arXiv (Cornell University) · 2017 · 5.8K citations · Full text

, , +14

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Zhenguo Li, Fengwei Zhou, Fei Chen et al. · arXiv (Cornell University) · 2017 · 845 citations · Full text

, , +12

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Chelsea Finn, Pieter Abbeel, Sergey Levine · arXiv (Cornell University) · 2017 · 799 citations · Full text

, , +14

Learning to Model the Tail

Yu-Xiong Wang, Deva Ramanan, Martial Hebert · Neural Information Processing Systems · 2017 · 267 citations

, , +16

Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning

Sungyong Baik, Janghoon Choi, Heewon Kim et al. · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2021 · 141 citations

, , +13

1–5 of 31

Authors

105

  1. HY
    Huaxiu Yao

    Pennsylvania State University, United States

    3 publications · 153 citations

    3

    h-index

  2. SL
    Sergey Levine

    University of California, Berkeley, United States

    2 publications · 6.6K citations

    2

    h-index

  3. ZL
    Zhenhui Li

    Kunming Medical University, China

    2 publications · 131 citations

    2

    h-index

  4. JH
    Junzhou Huang

    The University of Texas at Arlington, United States

    2 publications · 131 citations

    2

    h-index

  5. CF
    Chelsea Finn

    University of California, Berkeley, United States

    2 publications · 6.6K citations

    2

    h-index

1–5 of 105

Institutions

36

  1. Pennsylvania State University

    State College, United States

    3 h-index

    3 publications

    153 citations

  2. OpenAI (United States)

    San Francisco, United States

    2 h-index

    2 publications

    6.6K citations

  3. University of California, Berkeley

    Berkeley, United States

    2 h-index

    6 publications

    19.8K citations

  4. Peng Cheng Laboratory

    Shenzhen, China

    1 h-index

    1 publications

    29 citations

  5. Chinese Academy of Sciences

    Beijing, China

    1 h-index

    2 publications

    46 citations

  6. The University of Tokyo

    Tokyo, Japan

    1 h-index

    3 publications

    66 citations

  7. Robert Bosch (India)

    Bengaluru, India

    1 h-index

    3 publications

    42 citations

  8. Carnegie Mellon University

    Pittsburgh, United States

    1 h-index

    3 publications

    801 citations

1–8 of 36

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 7.8K citations

    13

    h-index

  2. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 2 publications · 151 citations

    2

    h-index

  3. 2

    h-index

  4. Reliability Engineering & System Safety

    1 publications · 29 citations

    1

    h-index

  5. International Conference on Machine Learning

    Conference · 1 publications · 22 citations

    1

    h-index

1–5