101 citations · 410 across the 25 of their papers we have counts for
4 papers · 1 filter
Learning to Learn with Generative Models of Neural Network Checkpoints
William Peebles, Ilija Radosavovic, Tim Brooks +2
We explore a data-driven approach for learning to optimize neural networks. We construct a dataset of neural network checkpoints and train a generative model on the parameters. In…
Distribution-Free, Risk-Controlling Prediction Sets
Stephen Bates, Anastasios Angelopoulos, Lihua Lei +2
While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying learning systems in…
Better Knowledge Retention through Metric Learning
Ke Li, Shichong Peng, Kailas Vodrahalli +1
In continual learning, new categories may be introduced over time, and an ideal learning system should perform well on both the original categories and the new categories. While de…
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
Jeffrey O Zhang, Alexander Sax, Amir Zamir +2
When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights. Adaptation can be useful in…