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Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to related tasks.
Codimension-one foliations of spheres
H Blaine Lawson · 1971
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Accessible sets, orbits, and foliations with singularities
Peter Stefan · 1974
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On k-nearest neighbor voronoi diagrams in the plane
Der-Tsai Lee · 1982
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Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
David Marr · 1982
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On the geometry and dynamics of diffeomorphisms of surfaces
William P Thurston et al · 1988
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Introduction to topology
Bert Mendelson · 1990
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Equivalence, invariants and symmetry
Peter J Olver · 1995
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Lifelong robot learning
Sebastian Thrun and Tom M Mitchell · 1995
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Is learning the n-th thing any easier than learning the first?
Sebastian Thrun · 1996
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Multitask learning
Rich Caruana · 1997
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Elements of the theory of functions and functional analysis
Sergeĭ Vasil’evich Fomin et al · 1999
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Introduction to smooth manifolds
John M Lee · 2001
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Exploiting task relatedness for multiple task learning
Shai Ben-David and Reba Schuller · 2003
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A notion of task relatedness yielding provable multiple-task learning guarantees
Shai Ben-David and Reba Schuller Borbely · 2008
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Initialization for k-means clustering using voronoi diagram
Damodar Reddy, Prasanta K Jana, and IEEE Senior Member · 2012
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Mathematical methods of classical mechanics
Vladimir Igorevich Arnol’d · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Geometric theory of foliations
César Camacho and Alcides Lins Neto · 2013
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
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Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 2018
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Learning an embedding space for transferable robot skills
Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, and Martin Riedmiller · 2018
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Meta reinforcement learning with latent variable gaussian processes
Steindór Sæmundsson, Katja Hofmann, and Marc P. Deisenroth · 2018
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An Introduction to Systems Biology: Design Principles of Biological Circuits
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Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
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What makes imagenet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Lifelong learning of human actions with deep neural network self-organization
German I Parisi, Jun Tani, Cornelius Weber, and Stefan Wermter · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
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Uri Alon · 2019
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Disentangled skill embeddings for reinforcement learning
Janith C. Petangoda, Sergio Pascual-Diaz, Vincent Adam, Peter Vrancx, and Jordi Grau-Moya · 2019
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Meta-learning in neural networks: A survey
Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 2020
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Meta-learning symmetries by reparameterization
Allan Zhou, Tom Knowles, and Chelsea Finn · 2020
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