11 citations · 19 across the 8 of their papers we have counts for
3 papers · 1 filter
Empirical influence functions to understand the logic of fine-tuning
Jordan K. Matelsky, Lyle Ungar, Konrad P. Kording
Understanding the process of learning in neural networks is crucial for improving their performance and interpreting their behavior. This can be approximately understood by asking…
Neural Networks as Paths through the Space of Representations
Richard D. Lange, Devin Kwok, Jordan Matelsky +3
Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy to understand, but the resulting overall computation is generally difficult to…
Scatterbrained: A flexible and expandable pattern for decentralized machine learning
Miller Wilt, Jordan K. Matelsky, Andrew S. Gearhart
Federated machine learning is a technique for training a model across multiple devices without exchanging data between them. Because data remains local to each compute node, federa…