150 citations · 175 across the 3 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2020★ 150 cited
Scaling Laws for Autoregressive Generative Modeling
Tom Henighan, Jared Kaplan, Mor Katz +16
We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal imagetext models, and mathemat…
cs.LG2019★ 9 cited
An Empirical Study on Hyperparameters and their Interdependence for RL Generalization
Xingyou Song, Yilun Du, Jacob Jackson
Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains. A key…
cs.LG2019★ 16 cited
Semi-Supervised Learning by Label Gradient Alignment
Jacob Jackson, John Schulman
We present label gradient alignment, a novel algorithm for semi-supervised learning which imputes labels for the unlabeled data and trains on the imputed labels. We define a semant…