What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data

11 June 2021

Papers citing "What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data"

9 / 9 papers shown

Title
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning Shuangjia Zheng Sijie Mai Ya Sun Haifeng Hu Yuedong Yang 30 21 0 26 Jul 2021
Pre-trained Models for Natural Language Processing: A Survey Xipeng Qiu Tianxiang Sun Yige Xu Yunfan Shao Ning Dai Xuanjing Huang LM&MA VLM 241 1,444 0 18 Mar 2020
TaskNorm: Rethinking Batch Normalization for Meta-Learning J. Bronskill Jonathan Gordon James Requeima Sebastian Nowozin Richard E. Turner 54 89 0 06 Mar 2020
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification Tianyu Gao Xu Han Hao Zhu Zhiyuan Liu Peng Li Maosong Sun Jie Zhou 203 244 0 16 Oct 2019
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML Aniruddh Raghu M. Raghu Samy Bengio Oriol Vinyals 172 639 0 19 Sep 2019
Probabilistic Model-Agnostic Meta-Learning Chelsea Finn Kelvin Xu Sergey Levine BDL 165 666 0 07 Jun 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,659 0 09 Mar 2017
Learning Deep Representations of Fine-grained Visual Descriptions Scott E. Reed Zeynep Akata Bernt Schiele Honglak Lee OCL VLM 160 841 0 17 May 2016
Efficient Estimation of Word Representations in Vector Space Tomáš Mikolov Kai Chen G. Corrado J. Dean 3DV 228 31,150 0 16 Jan 2013