81 citations · 196 across the 8 of their papers we have counts for
8 papers · 1 filter
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Yijie Guo, Jongwook Choi, Marcin Moczulski +4
Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can b…
Contingency-Aware Exploration in Reinforcement Learning
Jongwook Choi, Yijie Guo, Marcin Moczulski +4
This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning. To investigate t…
A Robust Adaptive Stochastic Gradient Method for Deep Learning
Caglar Gulcehre, Jose Sotelo, Marcin Moczulski +1
Stochastic gradient algorithms are the main focus of large-scale optimization problems and led to important successes in the recent advancement of the deep learning algorithms. The…
Mollifying Networks
Caglar Gulcehre, Marcin Moczulski, Francesco Visin +1
The optimization of deep neural networks can be more challenging than traditional convex optimization problems due to the highly non-convex nature of the loss function, e.g. it can…
Noisy Activation Functions
Caglar Gulcehre, Marcin Moczulski, Misha Denil +1
Common nonlinear activation functions used in neural networks can cause training difficulties due to the saturation behavior of the activation function, which may hide dependencies…
A Controller-Recognizer Framework: How necessary is recognition for control?
Marcin Moczulski, Kelvin Xu, Aaron Courville +1
Recently there has been growing interest in building active visual object recognizers, as opposed to the usual passive recognizers which classifies a given static image into a pred…
ACDC: A Structured Efficient Linear Layer
Marcin Moczulski, Misha Denil, Jeremy Appleyard +1
The linear layer is one of the most pervasive modules in deep learning representations. However, it requires parameters and operations. These costs can be prohibi…
Deep Fried Convnets
Zichao Yang, Marcin Moczulski, Misha Denil +4
The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the…