312 citations · 434 across the 6 of their papers we have counts for
6 papers
Flexible inference in heterogeneous and attributed multilayer networks
Martina Contisciani, Marius Hobbhahn, Eleanor A. Power +2
Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle…
Will we run out of data? Limits of LLM scaling based on human-generated data
Pablo Villalobos, Anson Ho, Jaime Sevilla +3
We investigate the potential constraints on LLM scaling posed by the availability of public human-generated text data. We forecast the growing demand for training data based on cur…
Machine Learning Model Sizes and the Parameter Gap
Pablo Villalobos, Jaime Sevilla, Tamay Besiroglu +3
We study trends in model size of notable machine learning systems over time using a curated dataset. From 1950 to 2018, model size in language models increased steadily by seven or…
Compute Trends Across Three Eras of Machine Learning
Jaime Sevilla, Lennart Heim, Anson Ho +3
Compute, data, and algorithmic advances are the three fundamental factors that guide the progress of modern Machine Learning (ML). In this paper we study trends in the most readily…
Laplace Matching for fast Approximate Inference in Latent Gaussian Models
Marius Hobbhahn, Philipp Hennig
Bayesian inference on non-Gaussian data is often non-analytic and requires computationally expensive approximations such as sampling or variational inference. We propose an approxi…
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks
Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig
In Bayesian Deep Learning, distributions over the output of classification neural networks are often approximated by first constructing a Gaussian distribution over the weights, th…