Fetching the paper…
Reading the bibliography…
In this work we study generalization of neural networks in gradient-based meta-learning by analyzing various properties of the objective landscapes.
Flat minima
Hochreiter, S. and Schmidhuber, J. (1997) · 1997
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
Earlier work this paper cites.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Dauphin, Y., Pascanu, R., Gülçehre, Ç., Cho, K., Ganguli, S., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
On the saddle point problem for non-convex optimization
Pascanu, R., Dauphin, Y. N., Ganguli, S., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
Topology and Geometry of Half-Rectified Network Optimization
Freeman, C. D. and Bruna, J. (2016) · 2016
Earlier work this paper cites.
On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P. (2016) · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T. P., Kavukcuoglu, K., and Wierstra, D. (2016) · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S. (2017) · 2017
Cited alongside, same era.
Three factors influencing minima in SGD
Jastrzebski, S., Kenton, Z., Arpit, D., Ballas, N., Fischer, A., Bengio, Y., and Storkey, A. J. (2017) · 2017
Cited alongside, same era.
Visualizing the loss landscape of neural nets
Li, H., Xu, Z., Taylor, G., and Goldstein, T. (2017) · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H. (2017) · 2017
Cited alongside, same era.
A bayesian perspective on generalization and stochastic gradient descent
Smith, S. L. and Le, Q. V. (2017) · 2017
A Walk with SGD
Xing, C., Arpit, D., Tsirigotis, C., and Bengio, Y. (2018) · 2017
Later among the works it cites.
Measuring and regularizing networks in function space
Benjamin, A. S., Rolnick, D., and Körding, K. P. (2018) · 2018
Later among the works it cites.
Essentially No Barriers in Neural Network Energy Landscape
Draxler, F., Veschgini, K., Salmhofer, M., and Hamprecht, F. A. (2018) · 2018
Later among the works it cites.
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., and Wilson, A. G. (2018) · 2018
Later among the works it cites.
TADAM: task dependent adaptive metric for improved few-shot learning
Oreshkin, B. N., López, P. R., and Lacoste, A. (2018) · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R. S. (2017) · 2017
Cited alongside, same era.
Rothfuss, J., Lee, D., Clavera, I., Asfour, T., and Abbeel, P. (2018) · 2018
Later among the works it cites.