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Found 4 papers, 2 papers with code
Compositional Data and Task Augmentation for Instruction Following
Executing natural language instructions in a physically grounded domain requires a model that understands both spatial concepts such as “left of” and “above”, and the compositional language used to identify landmarks and articulate instructions relative to them.
ForkGAN: Seeing into the Rainy Night
We present a ForkGAN for task-agnostic image translation that can boost multiple vision tasks in adverse weather conditions.
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces
Bayesian optimization (BO) is a popular black-box function optimization method, which makes sequential decisions based on a Bayesian model, typically a Gaussian process (GP), of the function.
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes
However, those prior learning methods typically assume that the input domains are the same for all tasks, weakening their ability to use observations on functions with different domains or generalize the learned priors to BO on different search spaces.
