35 citations · 38 across the 3 of their papers we have counts for
3 papers · 1 filter
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Kashif Rasul, Arjun Ashok, Andrew Robert Williams +15
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. However,…
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu, Moksh Jain, Bonaventure Dossou +10
Bayesian Inference offers principled tools to tackle many critical problems with modern neural networks such as poor calibration and generalization, and data inefficiency. However,…
Approximate Bayesian Optimisation for Neural Networks
Nadhir Hassen, Irina Rish
A body of work has been done to automate machine learning algorithm to highlight the importance of model choice. Automating the process of choosing the best forecasting model and i…