Reinforcement Learning Neural Turing Machines - Revised

By Wojciech Zaremba, Ilya Sutskever. 2015

164 citations recorded by the source corpus.

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In Reinforcement Learning

Citations in the corpus

Learning Through Dialogue Interactions (2016, methodology): “We refer the readers to (Ranzato et al., 2015; Zaremba & Sutskever, 2015) for more details.”
Deep Reinforcement Learning for Dialogue Generation (2016, methodology): “We refer the readers to Zaremba and Sutskever (2015) and Williams (1992) for details.”
Neural Programmer-Interpreters (2015, background): “Contemporary to this work, several papers have also studied program induction with variants of recurrent neural networks (Zaremba & Sutskever, 2015; Zaremba et al., 2015; Kaiser & Sutskever, 2015; Kurach et al., 2015; Neelakantan et al., 2015).”
Dialogue Learning With Human-In-The-Loop (2016, neutral): “We refer the readers to (Ranzato et al., 2015; Zaremba & Sutskever, 2015) for more details.”
Multi-Reward Reinforced Summarization with Saliency and Entailment (2018, methodology): “We use the RE-INFORCE algorithm (Williams, 1992; Zaremba and Sutskever, 2015) to learn a policy p θ de-ﬁned by the model parameters θ to predict the next action (word) and update its internal (LSTM) states.”
Neural Random Access Machines (2015, methodology): “A related model is the Reinforcement Learning Neural Turing Machine (Zaremba & Sutskever, 2015), which attempted to use reinforcement learning techniques to train a discrete-continuous hybrid model.”
MuProp: Unbiased Backpropagation for Stochastic Neural Networks (2015, background): “…& Tao, 2001; Peters & Schaal, 2006), probabilistic latent variable models for structured prediction, unsupervised learning of generative models (Tang & Salakhutdinov, 2013; Kingma & Welling, 2014), and most recently, attention and memory networks (Mnih et al., 2014; Zaremba & Sutskever, 2015).”
Variational Inference for Monte Carlo Objectives (2016, methodology): “…the potential pitfalls described above, estimators involving sampling from the prior have been used successfully for training models for structured output prediction (Tang & Salakhutdinov, 2013; Dauphin & Grangier, 2016) and models with hard attention (Mnih et al., 2014; Zaremba & Sutskever, 2015).”
Control of Memory, Active Perception, and Action in Minecraft (2016, background): “Zaremba & Sutskever (2015) proposed RL-NTM that has a non-differentiable memory to scale up the addressing mechanism of NTM and applied policy gradient to train the architecture.”
Learning Simple Algorithms from Examples (2015, methodology): “A notable exception is the Reinforcement Learning Neural Turing Machine (RLNTM) [Zaremba & Sutskever (2015)] which is a version of the NTM with discrete interfaces.”
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning (2017, methodology): “Since instructions should be executed sequentially, we use a location-based memory addressing mechanism (Zaremba and Sutskever, 2015; Graves et al., 2014) to manage the instruction pointer.”