312 citations · 694 across the 17 of their papers we have counts for
6 papers · 1 filter
Algorithmic progress in computer vision
Ege Erdil, Tamay Besiroglu
We investigate algorithmic progress in image classification on ImageNet, perhaps the most well-known test bed for computer vision. We estimate a model, informed by work on neural s…
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…
Aggregating human judgment probabilistic predictions of COVID-19 transmission, burden, and preventative measures
Allison Codi, Damon Luk, David Braun +6
Aggregated human judgment forecasts for COVID-19 targets of public health importance are accurate, often outperforming computational models. Our work shows aggregated human judgmen…
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…
Chimeric forecasting: combining probabilistic predictions from computational models and human judgment
Thomas McAndrew, Allison Codi, Juan Cambeiro +5
Forecasts of the trajectory of an infectious agent can help guide public health decision making. A traditional approach to forecasting fits a computational model to structured data…