Fetching the paper…
Reading the bibliography…
We describe a mechanism by which artificial neural networks can learn rapid adaptation - the ability to adapt on the fly, with little data, to new tasks - that we call conditionally shifted neurons.
Using fast weights to deblur old memories
Hinton, Geoffrey E and Plaut, David C · 1987
Earlier work this paper cites.
Evolutionary principles in self-referential learning
Schmidhuber, Jürgen · 1987
Earlier work this paper cites.
Learning a synaptic learning rule
Bengio, Yoshua, Bengio, Samy, and Cloutier, Jocelyn · 1990
Earlier work this paper cites.
Explanation-based neural network learning for robot control
Mitchell, Tom M, Thrun, Sebastian B, et al · 1993
Earlier work this paper cites.
A ‘self-referential’ weight matrix
Schmidhuber, Jürgen · 1993
Earlier work this paper cites.
Long short-term memory
Hochreiter, Sepp and Schmidhuber, Jürgen · 1997
Earlier work this paper cites.
Learning to learn using gradient descent
Hochreiter, Sepp, Younger, A Steven, and Conwell, Peter R · 2001
Earlier work this paper cites.
A perspective view and survey of meta-learning
Vilalta, Ricardo and Drissi, Youssef · 2002
Earlier work this paper cites.
Task switching
Monsell, Stephen · 2003
Earlier work this paper cites.
Task set and prefrontal cortex
Sakai, Katsuyuki · 2008
Earlier work this paper cites.
Compete to compute
Srivastava, Rupesh K, Masci, Jonathan, Kazerounian, Sohrob, Gomez, Faustino, and Schmidhuber, Jürgen · 2013
Earlier work this paper cites.
Dynamic coding for cognitive control in prefrontal cortex
Stokes, Mark G, Kusunoki, Makoto, Sigala, Natasha, Nili, Hamed, Gaffan, David, and Duncan, John · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob · 2013
Earlier work this paper cites.
An empirical investigation of catastrophic forgetting in gradient-based neural networks
Goodfellow, Ian J, Mirza, Mehdi, Xiao, Da, Courville, Aaron, and Bengio, Yoshua · 2014
Cited alongside, same era.
Towards biologically plausible deep learning
Bengio, Yoshua, Lee, Dong-Hyun, Bornschein, Jorg, Mesnard, Thomas, and Lin, Zhouhan · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, Sergey and Szegedy, Christian · 2015
Cited alongside, same era.
Siamese neural networks for one-shot image recognition
Koch, Gregory · 2015
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, James, Pascanu, Razvan, Rabinowitz, Neil, Veness, Joel, Desjardins, Guillaume, Rusu, Andrei A, Milan, Kieran, Quan, John, Ramalho, Tiago, Grabska-Barwinska, Agnieszka, et al · 2016
Later among the works it cites.
Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, Timothy P, Cownden, Daniel, Tweed, Douglas B, and Akerman, Colin J · 2016
Later among the works it cites.
Direct feedback alignment provides learning in deep neural networks
Nøkland, Arild · 2016
Later among the works it cites.
Meta-learning with memory-augmented neural networks
Santoro, Adam, Bartunov, Sergey, Botvinick, Matthew, Wierstra, Daan, and Lillicrap, Timothy · 2016
Later among the works it cites.
Matching networks for one shot learning
Vinyals, Oriol, Blundell, Charles, Lillicrap, Tim, Wierstra, Daan, et al · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Human-level concept learning through probabilistic program induction
Lake, Brenden M, Salakhutdinov, Ruslan, and Tenenbaum, Joshua B · 2015
Cited alongside, same era.
Predicting deep zero-shot convolutional neural networks using textual descriptions
Lei Ba, Jimmy, Swersky, Kevin, Fidler, Sanja, et al · 2015
Cited alongside, same era.
Working memory capacity: Limits on the bandwidth of cognition
Miller, Earl K and Buschman, Timothy J · 2015
Cited alongside, same era.
Cortical information flow during flexible sensorimotor decisions
Siegel, Markus, Buschman, Timothy J, and Miller, Earl K · 2015
Cited alongside, same era.
Chainer: a next-generation open source framework for deep learning
Tokui, Seiya, Oono, Kenta, Hido, Shohei, and Clayton, Justin · 2015
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
Andrychowicz, Marcin, Denil, Misha, Gomez, Sergio, Hoffman, Matthew W, Pfau, David, Schaul, Tom, and de Freitas, Nando · 2016
Cited alongside, same era.
Prefrontal cortex networks shift from external to internal modes during learning
Brincat, Scott L and Miller, Earl K · 2016
Cited alongside, same era.
Learning algorithms for active learning
Bachman, Philip, Sordoni, Alessandro, and Trischler, Adam · 2017
Closest in time.
Modulating early visual processing by language
De Vries, Harm, Strub, Florian, Mary, Jérémie, Larochelle, Hugo, Pietquin, Olivier, and Courville, Aaron C · 2017
Closest in time.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, Chelsea, Abbeel, Pieter, and Levine, Sergey · 2017
Closest in time.
Meta-learning with temporal convolutions
Mishra, Nikhil, Rohaninejad, Mostafa, Chen, Xi, and Abbeel, Pieter · 2017
Closest in time.
Meta networks
Munkhdalai, Tsendsuren and Yu, Hong · 2017
Closest in time.
Film: Visual reasoning with a general conditioning layer
Perez, Ethan, Strub, Florian, De Vries, Harm, Dumoulin, Vincent, and Courville, Aaron · 2017
Closest in time.
Optimization as a model for few-shot learning
Ravi, Sachin and Larochelle, Hugo · 2017
Closest in time.