
Amortized learning of neural causal representations
Causal models can compactly and efficiently encode the datagenerating p...
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Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations a...
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Learning Neural Causal Models from Unknown Interventions
Metalearning over a set of distributions can be interpreted as learning...
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Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future
In modelbased reinforcement learning, the agent interleaves between mod...
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hdetach: Modifying the LSTM Gradient Towards Better Optimization
Recurrent neural networks are known for their notorious exploding and va...
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Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding
Learning longterm dependencies in extended temporal sequences requires ...
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Focused Hierarchical RNNs for Conditional Sequence Processing
Recurrent Neural Networks (RNNs) with attention mechanisms have obtained...
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A Deep Reinforcement Learning Chatbot (Short Version)
We present MILABOT: a deep reinforcement learning chatbot developed by t...
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Ethical Challenges in DataDriven Dialogue Systems
The use of dialogue systems as a medium for humanmachine interaction is...
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ZForcing: Training Stochastic Recurrent Networks
Many efforts have been devoted to training generative latent variable mo...
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ACtuAL: ActorCritic Under Adversarial Learning
Generative Adversarial Networks (GANs) are a powerful framework for deep...
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Sparse Attentive Backtracking: LongRange Credit Assignment in Recurrent Networks
A major drawback of backpropagation through time (BPTT) is the difficult...
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Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
We propose a novel method to directly learn a stochastic transition oper...
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A Deep Reinforcement Learning Chatbot
We present MILABOT: a deep reinforcement learning chatbot developed by t...
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Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
We propose zoneout, a novel method for regularizing RNNs. At each timest...
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Cascading Bandits for LargeScale Recommendation Problems
Most recommender systems recommend a list of items. The user examines th...
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Transferring Knowledge from a RNN to a DNN
Deep Neural Network (DNN) acoustic models have yielded many stateofthe...
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Nan Rosemary Ke
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PHD Student at MILA, Montreal institute of Learning Algorithms