43 citations · 75 across the 7 of their papers we have counts for
4 papers · 1 filter
ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
David Thulke, Yingbo Gao, Petrus Pelser +23
This paper introduces ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change. We trained two 7B models fro…
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling
Kirill Fedyanin, Evgenii Tsymbalov, Maxim Panov
Uncertainty estimation for machine learning models is of high importance in many scenarios such as constructing the confidence intervals for model predictions and detection of out-…
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning
Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev +1
Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation. Such an approach requires eithe…
Dropout-based Active Learning for Regression
Evgenii Tsymbalov, Maxim Panov, Alexander Shapeev
Active learning is relevant and challenging for high-dimensional regression models when the annotation of the samples is expensive. Yet most of the existing sampling methods cannot…