200 citations · 255 across the 4 of their papers we have counts for
5 papers
Edge-labeling Graph Neural Network for Few-shot Learning
Jongmin Kim, Taesup Kim, Sungwoong Kim +1
In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous grap…
A Deep Reinforcement Learning Chatbot (Short Version)
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…
A Deep Reinforcement Learning Chatbot
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…
Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition
Taesup Kim, Inchul Song, Yoshua Bengio
Layer normalization is a recently introduced technique for normalizing the activities of neurons in deep neural networks to improve the training speed and stability. In this paper,…
Deep Directed Generative Models with Energy-Based Probability Estimation
Taesup Kim, Yoshua Bengio
Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distributio…