Fetching the paper…
Reading the bibliography…
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning.
Acquiring a single new word
Carey, Susan and Bartlett, Elsa · 1978
Earlier work this paper cites.
The importance of shape in early lexical learning
Landau, Barbara, Smith, Linda B, and Jones, Susan S · 1988
Earlier work this paper cites.
Ontological categories guide young children’s inductions of word meaning: Object terms and substance terms
Soja, Nancy N, Carey, Susan, and Spelke, Elizabeth S · 1991
Earlier work this paper cites.
Efficient backprop
LeCun, Yann, Bottou, Léon, Orr, Genevieve B, and Müller, Klaus-Robert · 1998
Earlier work this paper cites.
Beyond fast mapping
Carey, Susan · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
Earlier work this paper cites.
Fast mapping and slow mapping in children’s word learning
Swingley, Daniel · 2010
Earlier work this paper cites.
Metric learning: A survey
Kulis, Brian et al · 2013
Cited alongside, same era.
Fast and accurate deep network learning by exponential linear units (elus)
Clevert, Djork-Arné, Unterthiner, Thomas, and Hochreiter, Sepp · 2015
Cited alongside, same era.
Siamese neural networks for one-shot image recognition
Koch, Gregory, Zemel, Richard, and Salakhutdinov, Ruslan · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Lake, Brenden M., Salakhutdinov, Ruslan, and Tenenbaum, Joshua B · 2015
Cited alongside, same era.
Order matters: Sequence to sequence for sets
Vinyals, Oriol, Bengio, Samy, and Kudlur, Manjunath · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, Chelsea, Abbeel, Pieter, and Levine, Sergey · 2017
Later among the works it cites.
Dynamic input structure and network assembly for few-shot learning
Hilliard, Nathan, Hodas, Nathan O, and Corley, Courtney D · 2017
Later among the works it cites.
Optimization as a model for few-shot learning
Ravi, Sachin and Larochelle, Hugo · 2017
Later among the works it cites.
Cognitive psychology for deep neural networks: A shape bias case study
Ritter, Samuel, Barrett, David GT, Santoro, Adam, and Botvinick, Matt M · 2017
Later among the works it cites.
A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G. T., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap, T · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian · 2016
Cited alongside, same era.
Matching networks for one shot learning
Vinyals, Oriol, Blundell, Charles, Lillicrap, Tim, Wierstra, Daan, et al · 2016
Cited alongside, same era.
Later among the works it cites.
Prototypical networks for few-shot learning
Snell, Jake, Swersky, Kevin, and Zemel, Richard · 2017
Later among the works it cites.