11 citations · 19 across the 8 of their papers we have counts for
8 papers
Zero-Ablation Overstates Register Content Dependence in DINO Vision Transformers
Felipe Parodi, Jordan Matelsky, Melanie Segado
Zero-ablation -- replacing token activations with zero vectors -- is widely used to probe token function in vision transformers. Register zeroing in DINOv2+registers and DINOv3 pro…
Grounding Intelligence in Movement
Melanie Segado, Felipe Parodi, Jordan K. Matelsky +3
Recent advances in machine learning have dramatically improved our ability to model language, vision, and other high-dimensional data, yet they continue to struggle with one of the…
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…
Vision-language models for decoding provider attention during neonatal resuscitation
Felipe Parodi, Jordan Matelsky, Alejandra Regla-Vargas +6
Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously. Gaze strongly influences decis…
The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions
Gal Haspel, Ben Baker, Isabel Beets +34
Just like electrical engineers understand how microprocessors execute programs in terms of how transistor currents are affected by their inputs, neuroscientists want to understand…
A large language model-assisted education tool to provide feedback on open-ended responses
Jordan K. Matelsky, Felipe Parodi, Tony Liu +2
Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material. Providing feedback for such r…
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…