Every paper, ordered by impact or potential impact. About ICML 2013
| ID | Paper | Authors | Source | Date |
|---|---|---|---|---|
| 1 | On The Difficulty Of Training Recurrent Neural Networks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper we attempt to improve the understanding of the underlying issues by exploring these problems from an analytical, a geometric and a dynamical systems perspective. | Razvan Pascanu; Tomas Mikolov; Yoshua Bengio; | icml | 2013-06-17 |
| 2 | On The Importance Of Initialization And Momentum In Deep Learning IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. | Ilya Sutskever; James Martens; George Dahl et al. | icml | 2013-06-17 |
| 3 | Regularization Of Neural Networks Using DropConnect IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We introduce DropConnect, a generalization of DropOut, for regularizing large fully-connected layers within neural networks. | Li Wan; Matthew Zeiler; Sixin Zhang et al. | icml | 2013-06-17 |
| 4 | Making A Science Of Model Search: Hyperparameter Optimization In Hundreds Of Dimensions For Vision Architectures IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a meta-modeling approach to support automated hyperparameter optimization, with the goal of providing practical tools that replace hand-tuning with a reproducible and unbiased optimization process. | James Bergstra; Daniel Yamins; David Cox; | icml | 2013-06-17 |
| 5 | Learning Fair Representations IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly). | Rich Zemel; Yu Wu; Kevin Swersky et al. | icml | 2013-06-17 |
This page lists the papers of International Conference on Machine Learning (ICML 2013), ordered by impact or potential impact.