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The goal of this paper is to design image classification systems that, after an initial multi-task training phase, can automatically adapt to new tasks encountered at test time.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 1903
Earlier work this paper cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 1903
Earlier work this paper cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 1903
Earlier work this paper cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 1903
Earlier work this paper cites.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 1903
Earlier work this paper cites.
On the prediction of observables: a selective update
Seymour Geisser · 1983
Earlier work this paper cites.
On the prediction of observables: a selective update
Seymour Geisser · 1983
Earlier work this paper cites.
On the prediction of observables: a selective update
Seymour Geisser · 1983
Earlier work this paper cites.
On the prediction of observables: a selective update
Seymour Geisser · 1983
Earlier work this paper cites.
On the prediction of observables: a selective update
Seymour Geisser · 1983
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Jürgen Schmidhuber · 1987
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Jürgen Schmidhuber · 1987
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Jürgen Schmidhuber · 1987
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Jürgen Schmidhuber · 1987
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Jürgen Schmidhuber · 1987
Earlier work this paper cites.
Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
Earlier work this paper cites.
Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
Earlier work this paper cites.
Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
Earlier work this paper cites.
Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
Earlier work this paper cites.
Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
Earlier work this paper cites.
Child: A first step towards continual learning
Mark B Ring · 1997
Earlier work this paper cites.
Child: A first step towards continual learning
Mark B Ring · 1997
Earlier work this paper cites.
Child: A first step towards continual learning
Mark B Ring · 1997
Earlier work this paper cites.
Child: A first step towards continual learning
Mark B Ring · 1997
Earlier work this paper cites.
Child: A first step towards continual learning
Mark B Ring · 1997
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 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.
One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 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.
One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 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.
One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 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.
One shot learning of simple visual concepts
Brenden Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua Tenenbaum · 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.
Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
Earlier work this paper cites.
Active learning
Burr Settles · 2012
Earlier work this paper cites.
Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
Earlier work this paper cites.
Active learning
Burr Settles · 2012
Earlier work this paper cites.
Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
Earlier work this paper cites.
Active learning
Burr Settles · 2012
Earlier work this paper cites.
Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
Earlier work this paper cites.
Active learning
Burr Settles · 2012
Earlier work this paper cites.
Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
Earlier work this paper cites.
Active learning
Burr Settles · 2012
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Detection of traffic signs in real-world images: The german traffic sign detection benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Detection of traffic signs in real-world images: The german traffic sign detection benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 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.
Detection of traffic signs in real-world images: The german traffic sign detection benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 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.
Detection of traffic signs in real-world images: The german traffic sign detection benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 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.
Detection of traffic signs in real-world images: The german traffic sign detection benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 2013
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Cited alongside, same era.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
Cited alongside, same era.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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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
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Discriminative k-shot learning using probabilistic models
Matthias Bauer, Mateo Rojas-Carulla, Jakub Bartłomiej Świątkowski, Bernhard Schölkopf, and Richard E Turner · 2017
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A neural representation of sketch drawings
David Ha and Douglas Eck · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2017
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Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Cited alongside, same era.
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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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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Few-shot image recognition by predicting parameters from activations
Siyuan Qiao, Chenxi Liu, Wei Shen, and Alan Yuille · 2017
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Predictive inference
Seymour Geisser · 2017
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Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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A meta-learning perspective on cold-start recommendations for items
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle · 2017
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Optimization as a model for few-shot learning
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Discriminative k-shot learning using probabilistic models
Matthias Bauer, Mateo Rojas-Carulla, Jakub Bartłomiej Świątkowski, Bernhard Schölkopf, and Richard E Turner · 2017
Later among the works it cites.
A neural representation of sketch drawings
David Ha and Douglas Eck · 2017
Later among the works it cites.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Later among the works it cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Reptile: a scalable metalearning algorithm
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FiLM: Visual reasoning with a general conditioning layer
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Efficient parametrization of multi-domain deep neural networks
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Meta-learning with latent embedding optimization
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CAML: Fast context adaptation via meta-learning
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TADAM: Task dependent adaptive metric for improved few-shot learning
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
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Bayesian model-agnostic meta-learning
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Inference suboptimality in variational autoencoders
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Reptile: a scalable metalearning algorithm
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FiLM: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
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Efficient parametrization of multi-domain deep neural networks
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Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2018
Later among the works it cites.
CAML: Fast context adaptation via meta-learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2018
Later among the works it cites.
TADAM: Task dependent adaptive metric for improved few-shot learning
Boris N Oreshkin, Alexandre Lacoste, and Pau Rodriguez · 2018
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Fgvcx fungi classification challenge at fgvc5
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Bayesian model-agnostic meta-learning
Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
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Inference suboptimality in variational autoencoders
Chris Cremer, Xuechen Li, and David Duvenaud · 2018
Later among the works it cites.
Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Later among the works it cites.
FiLM: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Later among the works it cites.
Efficient parametrization of multi-domain deep neural networks
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2018
Later among the works it cites.
Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2018
Later among the works it cites.
CAML: Fast context adaptation via meta-learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2018
Later among the works it cites.
TADAM: Task dependent adaptive metric for improved few-shot learning
Boris N Oreshkin, Alexandre Lacoste, and Pau Rodriguez · 2018
Later among the works it cites.
Fgvcx fungi classification challenge at fgvc5
Brigit Schroeder and Yin Cui · 2018
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Bayesian model-agnostic meta-learning
Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
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Inference suboptimality in variational autoencoders
Chris Cremer, Xuechen Li, and David Duvenaud · 2018
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Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Later among the works it cites.
FiLM: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Later among the works it cites.
Efficient parametrization of multi-domain deep neural networks
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2018
Later among the works it cites.
Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2018
Later among the works it cites.
CAML: Fast context adaptation via meta-learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2018
Later among the works it cites.
TADAM: Task dependent adaptive metric for improved few-shot learning
Boris N Oreshkin, Alexandre Lacoste, and Pau Rodriguez · 2018
Later among the works it cites.
Fgvcx fungi classification challenge at fgvc5
Brigit Schroeder and Yin Cui · 2018
Later among the works it cites.
Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Bayesian model-agnostic meta-learning
Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
Later among the works it cites.
Inference suboptimality in variational autoencoders
Chris Cremer, Xuechen Li, and David Duvenaud · 2018
Later among the works it cites.
Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Later among the works it cites.
FiLM: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Later among the works it cites.
Efficient parametrization of multi-domain deep neural networks
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2018
Later among the works it cites.
Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2018
Later among the works it cites.
CAML: Fast context adaptation via meta-learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2018
Later among the works it cites.
TADAM: Task dependent adaptive metric for improved few-shot learning
Boris N Oreshkin, Alexandre Lacoste, and Pau Rodriguez · 2018
Later among the works it cites.
Fgvcx fungi classification challenge at fgvc5
Brigit Schroeder and Yin Cui · 2018
Later among the works it cites.
Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
Later among the works it cites.
Bayesian model-agnostic meta-learning
Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
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Inference suboptimality in variational autoencoders
Chris Cremer, Xuechen Li, and David Duvenaud · 2018
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Meta-learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner · 2019
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Siddharth Swaroop, Cuong V Nguyen, Thang D Bui, and Richard E Turner · 2019
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Attentive neural processes
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Meta-learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner · 2019
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Improving and understanding variational continual learning
Siddharth Swaroop, Cuong V Nguyen, Thang D Bui, and Richard E Turner · 2019
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Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2019
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Meta-learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner · 2019
Closest in time.
Improving and understanding variational continual learning
Siddharth Swaroop, Cuong V Nguyen, Thang D Bui, and Richard E Turner · 2019
Closest in time.
Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2019
Closest in time.
Meta-learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner · 2019
Closest in time.
Improving and understanding variational continual learning
Siddharth Swaroop, Cuong V Nguyen, Thang D Bui, and Richard E Turner · 2019
Closest in time.
Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2019
Closest in time.
Meta-learning probabilistic inference for prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, and Richard Turner · 2019
Closest in time.
Improving and understanding variational continual learning
Siddharth Swaroop, Cuong V Nguyen, Thang D Bui, and Richard E Turner · 2019
Closest in time.
Attentive neural processes
Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh · 2019
Closest in time.