7.4k citations · 24.2k across the 136 of their papers we have counts for
19 papers · 1 filter
Deep Generative Modeling of LiDAR Data
Lucas Caccia, Herke van Hoof, Aaron Courville +1
Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has be…
Planning in Dynamic Environments with Conditional Autoregressive Models
Johanna Hansen, Kyle Kastner, Aaron Courville +1
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oord et al., 2017b) for forward planning w…
Representation Mixing for TTS Synthesis
Kyle Kastner, João Felipe Santos, Yoshua Bengio +1
Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. However, the choice between character or p…
Harmonic Recomposition using Conditional Autoregressive Modeling
Kyle Kastner, Rithesh Kumar, Tim Cooijmans +1
We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(2017). Recomposition (Casal & Casey, 2010)…
Blindfold Baselines for Embodied QA
Ankesh Anand, Eugene Belilovsky, Kyle Kastner +2
We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question by intelligently navigating in a simulate…
Improving Explorability in Variational Inference with Annealed Variational Objectives
Chin-Wei Huang, Shawn Tan, Alexandre Lacoste +1
Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can still limit the density that is ultimatel…