output
20132025
most citedZoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

167 citations

Showing 2018Show all

5 papers · 1 filter

cs.LG2018★ 9 cited

On the Learning Dynamics of Deep Neural Networks

Remi Tachet, Mohammad Pezeshki, Samira Shabanian +2

While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely misunderstood. In this work, we study t…

cs.LG2018★ 16 cited

Negative Momentum for Improved Game Dynamics

Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki +4

Games generalize the single-objective optimization paradigm by introducing different objective functions for different players. Differentiable games often proceed by simultaneous o…

stat.ML2018★ 89 cited

Quaternion Recurrent Neural Networks

Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid +4

Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements o…

cs.LG2018★ 156 cited

Bayesian Model-Agnostic Meta-Learning

Taesup Kim, Jaesik Yoon, Ousmane Dia +3

Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper,…

cs.CL2018★ 23 cited

Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph

Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra +2

While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While…