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Found 3 papers, 1 papers with code
Improving Top-K Decoding for Non-Autoregressive Semantic Parsing via Intent Conditioning
As the top-level intent largely governs the syntax and semantics of a parse, the intent conditioning allows the model to better control beam search and improves the quality and diversity of top-k outputs.
CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving
The task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving.
HCNAF: Hyper-Conditioned Neural Autoregressive Flow and its Application for Probabilistic Occupancy Map Forecasting
We introduce Hyper-Conditioned Neural Autoregressive Flow (HCNAF); a powerful universal distribution approximator designed to model arbitrarily complex conditional probability density functions.
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