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Found 3 papers, 1 papers with code
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.
GFlowOut: Dropout with Generative Flow Networks
These methods face two important challenges: (a) the posterior distribution over masks can be highly multi-modal which can be difficult to approximate with standard variational inference and (b) it is not trivial to fully utilize sample-dependent information and correlation among dropout masks to improve posterior estimation.
Approximate Bayesian Optimisation for Neural Networks
A body of work has been done to automate machine learning algorithm to highlight the importance of model choice.
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