3 citations · 3 across the 2 of their papers we have counts for
3 papers
Soup to go: mitigating forgetting during continual learning with model averaging
Anat Kleiman, Gintare Karolina Dziugaite, Jonathan Frankle +2
In continual learning, where task data arrives in a sequence, fine-tuning on later tasks will often lead to performance degradation on earlier tasks. This is especially pronounced…
Transcendence: Generative Models Can Outperform The Experts That Train Them
Edwin Zhang, Vincent Zhu, Naomi Saphra +5
Generative models are trained with the simple objective of imitating the conditional probability distribution induced by the data they are trained on. Therefore, when trained on da…
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning
Chi Zhang, Ryan Marcus, Anat Kleiman +1
In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance. We introd…