Fetching the paper…
Reading the bibliography…
Meta learning has attracted much attention recently in machine learning community.
Reminiscence and rote learning
Lewis B Ward · 1937
Earlier work this paper cites.
A new look at the statistical model identification
Hirotugu Akaike · 1974
Earlier work this paper cites.
Cross-validatory choice and assessment of statistical predictions
Mervyn Stone · 1974
Earlier work this paper cites.
Estimating the dimension of a model
Gideon Schwarz et al · 1978
Earlier work this paper cites.
A theory of meta-learning and principles of facilitation: An organismic perspective
Donald B Maudsley · 1980
Earlier work this paper cites.
Learning how to learn: The significance and current status of learning set formation
Allan M Schrier · 1984
Earlier work this paper cites.
The role of metalearning in study processes
John B Biggs · 1985
Earlier work this paper cites.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber · 1987
Earlier work this paper cites.
Learning a synaptic learning rule
Yoshua Bengio, Samy Bengio, and Jocelyn Cloutier · 1990
Earlier work this paper cites.
Minimum complexity density estimation
Andrew R Barron and Thomas M Cover · 1991
Earlier work this paper cites.
Structural risk minimization over data-dependent hierarchies
John Shawe-Taylor, Peter L Bartlett, Robert C Williamson, and Martin Anthony · 1998
Earlier work this paper cites.
An overview of statistical learning theory
Vladimir N Vapnik · 1999
Earlier work this paper cites.
A model of inductive bias learning
Jonathan Baxter · 2000
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
Learning adaptive loss for robust learning with noisy labels
Jun Shu, Qian Zhao, Keyu Chen, Zongben Xu, and Deyu Meng · 2002
Earlier work this paper cites.
A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
Earlier work this paper cites.
Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
Earlier work this paper cites.
Algorithmic stability and meta-learning
Andreas Maurer and Tommi Jaakkola · 2005
Earlier work this paper cites.
Prediction, learning, and games
Nicolo Cesa-Bianchi and Gábor Lugosi · 2006
Earlier work this paper cites.
An experimental study on pedestrian classification
Stefan Munder and Dariu M Gavrila · 2006
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
Meta feature modulator for long-tailed recognition
Renzhen Wang, Kaiqin Hu, Yanwen Zhu, Jun Shu, Qian Zhao, and Deyu Meng · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
Earlier work this paper cites.
A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Online learning and online convex optimization
Shai Shalev-Shwartz et al · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
Earlier work this paper cites.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
Earlier work this paper cites.
Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
Earlier work this paper cites.
Probability in Banach Spaces: isoperimetry and processes
Michel Ledoux and Michel Talagrand · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
Earlier work this paper cites.
The nature of statistical learning theory
Vladimir Vapnik · 2013
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
Earlier work this paper cites.
A pac-bayesian bound for lifelong learning
Anastasia Pentina and Christoph Lampert · 2014
Earlier work this paper cites.
Machine learning: Trends, perspectives, and prospects
Michael I Jordan and Tom M Mitchell · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Earlier work this paper cites.
Metalearning: a survey of trends and technologies
Christiane Lemke, Marcin Budka, and Bogdan Gabrys · 2015
Earlier work this paper cites.
Lifelong learning with non-iid tasks
Anastasia Pentina and Christoph H Lampert · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
Earlier work this paper cites.
Dynamic image networks for action recognition
Hakan Bilen, Basura Fernando, Efstratios Gavves, Andrea Vedaldi, and Stephen Gould · 2016
Earlier work this paper cites.
Deep learning , volume 1
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
A chain rule for the expected suprema of gaussian processes
Andreas Maurer · 2016
Earlier work this paper cites.
The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
Earlier work this paper cites.
A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
Earlier work this paper cites.
Spectrally-normalized margin bounds for neural networks
Peter L Bartlett, Dylan J Foster, and Matus Telgarsky · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Learning active learning from data
Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua · 2017
Cited alongside, same era.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
Cited alongside, same era.
An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright · 2019
Later among the works it cites.
Tafe-net: Task-aware feature embeddings for low shot learning
Xin Wang, Fisher Yu, Ruth Wang, Trevor Darrell, and Joseph E Gonzalez · 2019
Later among the works it cites.
Efficient meta learning via minibatch proximal update
Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, and Jiashi Feng · 2019
Later among the works it cites.
Provable representation learning for imitation learning via bi-level optimization
Sanjeev Arora, Simon Du, Sham Kakade, Yuping Luo, and Nikunj Saunshi · 2020
Later among the works it cites.
Learning to forget for meta-learning
Sungyong Baik, Seokil Hong, and Kyoung Mu Lee · 2020
Later among the works it cites.
Tasknorm: Rethinking batch normalization for meta-learning
John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, and Richard Turner · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning to learn: Meta-critic networks for sample efficient learning
Flood Sung, Li Zhang, Tao Xiang, Timothy Hospedales, and Yongxin Yang · 2017
Cited alongside, same era.
Learned optimizers that scale and generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando Freitas, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
Cited alongside, same era.
Meta-learning by adjusting priors based on extended pac-bayes theory
Ron Amit and Ron Meir · 2018
Cited alongside, same era.
Metareg: Towards domain generalization using meta-regularization
Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa · 2018
Cited alongside, same era.
Learning to learn around a common mean
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, and Massimiliano Pontil · 2018
Cited alongside, same era.
Later among the works it cites.
A closer look at the training strategy for modern meta-learning
Jiaxin Chen, Xiao-Ming Wu, Yanke Li, Qimai Li, Li-Ming Zhan, and Fu-lai Chung · 2020
Later among the works it cites.
The advantage of conditional meta-learning for biased regularization and fine tuning
Giulia Denevi, Massimiliano Pontil, and Carlo Ciliberto · 2020
Later among the works it cites.
Meta-q-learning
Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, and Alexander J Smola · 2020
Later among the works it cites.
On the convergence theory of gradient-based model-agnostic meta-learning algorithms
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
Later among the works it cites.
Meta-learning with warped gradient descent
Sebastian Flennerhag, Andrei A Rusu, Razvan Pascanu, Francesco Visin, Hujun Yin, and Raia Hadsell · 2020
Later among the works it cites.
Improved training speed, accuracy, and data utilization through loss function optimization
Santiago Gonzalez and Risto Miikkulainen · 2020
Later among the works it cites.
Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
Later among the works it cites.
Meta dropout: Learning to perturb latent features for generalization
Hae Beom Lee, Taewook Nam, Eunho Yang, and Sung Ju Hwang · 2020
Later among the works it cites.
Towards fast adaptation of neural architectures with meta learning
Dongze Lian, Yin Zheng, Yintao Xu, Yanxiong Lu, Leyu Lin, Peilin Zhao, Junzhou Huang, and Shenghua Gao · 2020
Later among the works it cites.
Learning to generate noise for robustness against multiple perturbations
Divyam Madaan, Jinwoo Shin, and Sung Ju Hwang · 2020
Later among the works it cites.
Metaperturb: Transferable regularizer for heterogeneous tasks and architectures
Jeong Un Ryu, JaeWoong Shin, Hae Beom Lee, and Sung Ju Hwang · 2020
Later among the works it cites.
Es-maml: Simple hessian-free meta learning
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, and Yunhao Tang · 2020
Later among the works it cites.
On the theory of transfer learning: The importance of task diversity
Nilesh Tripuraneni, Michael Jordan, and Chi Jin · 2020
Later among the works it cites.
Meta-learning without memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, and Chelsea Finn · 2020
Later among the works it cites.
A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
Later among the works it cites.
How important is the train-validation split in meta-learning?
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason Lee, Sham Kakade, Huan Wang, and Caiming Xiong · 2021
Closest in time.
Meta-learning with negative learning rates
Alberto Bernacchia · 2021
Closest in time.
Generalization bounds for meta-learning: An information-theoretic analysis
Qi Chen, Changjian Shui, and Mario Marchand · 2021
Closest in time.
How fine-tuning allows for effective meta-learning
Kurtland Chua, Qi Lei, and Jason D Lee · 2021
Closest in time.
Bridging the gap between practice and pac-bayes theory in few-shot meta-learning
Nan Ding, Xi Chen, Tomer Levinboim, Sebastian Goodman, and Radu Soricut · 2021
Closest in time.
Few-shot learning via learning the representation, provably
Simon S Du, Wei Hu, Sham M Kakade, Jason D Lee, and Qi Lei · 2021
Closest in time.
Generalization of model-agnostic meta-learning algorithms: Recurring and unseen tasks
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2021
Closest in time.
Generalization bounds for meta-learning via pac-bayes and uniform stability
Alec Farid and Anirudha Majumdar · 2021
Closest in time.
Distance-based regularisation of deep networks for fine-tuning
Henry Gouk, Hospedales Subhransu, and Massimiliano Pontil · 2021
Closest in time.
Bilevel optimization: Convergence analysis and enhanced design
Kaiyi Ji, Junjie Yang, and Yingbin Liang · 2021
Closest in time.
Information-theoretic generalization bounds for meta-learning and applications
Sharu Theresa Jose and Osvaldo Simeone · 2021
Closest in time.
Transfer meta-learning: Information-theoretic bounds and information meta-risk minimization
Sharu Theresa Jose, Osvaldo Simeone, and Giuseppe Durisi · 2021
Closest in time.
Meta attention networks: Meta-learning attention to modulate information between recurrent independent mechanisms
Madan Kanika, Ke Nan Rosemary, Goyal Anirudh, Schölkopf Bernhard, and Bengio Yoshua · 2021
Closest in time.
Learning a minimax optimizer: A pilot study
Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, and Zhangyang Wang · 2021
Closest in time.
Towards sample-efficient overparameterized meta-learning
Yue Sun, Adhyyan Narang, Ibrahim Gulluk, Samet Oymak, and Maryam Fazel · 2021
Closest in time.
Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael Jordan · 2021
Closest in time.
Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation
Haoxiang Wang, Han Zhao, and Bo Li · 2021
Closest in time.
Learning to purify noisy labels via meta soft label corrector
Yichen Wu, Jun Shu, Qi Xie, Qian Zhao, and Deyu Meng · 2021
Closest in time.
Representation learning beyond linear prediction functions
Ziping Xu and Ambuj Tewari · 2021
Closest in time.
Improving generalization in meta-learning via task augmentation
Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, et al · 2021
Closest in time.
Metanorm: Learning to normalize few-shot batches across domains
Du Yingjun, Zhen Xiantong, Shao Ling, and Snoek Cees G. M · 2021
Closest in time.
Meta label correction for noisy label learning
Guoqing Zheng, Ahmed Hassan Awadallah, and Susan Dumais · 2021
Closest in time.
Understanding benign overfitting in gradient-based meta learning
Lisha Chen, Songtao Lu, and Tianyi Chen · 2022
Closest in time.
Maml and anil provably learn representations
Liam Collins, Aryan Mokhtari, Sewoong Oh, and Sanjay Shakkottai · 2022
Closest in time.
Conditional meta-learning of linear representations
Giulia Denevi, Carlo Ciliberto, et al · 2022
Closest in time.
Evaluated cmi bounds for meta learning: Tightness and expressiveness
Fredrik Hellström and Giuseppe Durisi · 2022
Closest in time.
Provable generalization of overparameterized meta-learning trained with sgd
Yu Huang, Yingbin Liang, and Longbo Huang · 2022
Closest in time.
Theoretical convergence of multi-step model-agnostic meta-learning
Kaiyi Ji, Junjie Yang, and Yingbin Liang · 2022
Closest in time.
Maml is a noisy contrastive learner in classification
Chia-Hsiang Kao, Wei-Chen Chiu, and Pin-Yu Chen · 2022
Closest in time.
Pac-bayes meta-learning with implicit task-specific posteriors
Cuong Nguyen, Thanh-Toan Do, and Gustavo Carneiro · 2022
Closest in time.
A unified view on pac-bayes bounds for meta-learning
Arezou Rezazadeh · 2022
Closest in time.
A hyper-weight network for hyperspectral image denoising
Xiangyu Rui, Xiangyong Cao, Jun Shu, Qian Zhao, and Deyu Meng · 2022
Closest in time.
Meta-lr-schedule-net: Learned lr schedules that scale and generalize
Jun Shu, Yanwen Zhu, Qian Zhao, Deyu Meng, and Zongben Xu · 2022
Closest in time.
Improve noise tolerance of robust loss via noise-awareness
Kehui Ding, Jun Shu, Deyu Meng, and Zongben Xu · 2023
Closest in time.