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Found 6 papers, 5 papers with code
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?
Mutual information maximization provides an appealing formalism for learning representations of data.
MELD: Meta-Reinforcement Learning from Images via Latent State Models
Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks.
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience.
Few-Shot Segmentation Propagation with Guided Networks
Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors.
Clockwork Convnets for Video Semantic Segmentation
Recent years have seen tremendous progress in still-image segmentation; however the na\"ive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video.
A Century of Portraits: A Visual Historical Record of American High School Yearbooks
4) A new method for discovering and displaying the visual elements used by the CNN-based date-prediction model to date portraits, finding that they correspond to the tell-tale fashions of each era.
