Aaron Courville

Recurrent Batch Normalization

2016 · arXiv (Cornell University)

We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is …

End-to-end optimization of goal-driven and visually grounded dialogue systems

2017 · arXiv (Cornell University)

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning …

Data-Efficient Reinforcement Learning with Self-Predictive Representations

2020 · arXiv (Cornell University)

L'efficacité des données reste un défi majeur dans l'apprentissage par renforcement profond. Bien que les techniques modernes soient capables d'atteindre des performances élevées dans des tâches extrêmement complexes, y compris les jeux de stratégie comme …

Unsupervised Learning of Dense Visual Representations

2020 · arXiv (Cornell University)

Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, these methods learn global (image-level) representations that are invariant to different views (i.e., compositions of data augmentation) of the …

Meta-Value Learning: a General Framework for Learning with Learning Awareness

2023 · arXiv (Cornell University)

Gradient-based learning in multi-agent systems is difficult because the gradient derives from a first-order model which does not account for the interaction between agents' learning processes. LOLA (arXiv:1709.04326) accounts for this by differentiating through one …

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

2016 · PolyPublie (École Polytechnique de Montréal)

We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain their previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. …

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

We introduce a new class of models called multiresolution recurrent neural networks, which explicitly model natural language generation at multiple levels of abstraction. The models extend the sequence-to-sequence framework to generate two parallel stochastic processes: …

An Actor-Critic Algorithm for Sequence Prediction

2016 · arXiv (Cornell University)

We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Current log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models …

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, …