17 papers
Data augmentation as a framework for modeling hippocampal contributions to generalization
Tyler Bonnen, Andrew Kyle Lampinen
The hippocampus plays a critical role in generalization, enabling us to flexibly repurpose prior experiences to perform novel tasks. Here we suggest that data augmentation---a mach…
Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention
Jing Huang, Daniel Wurgaft, Rachit Bansal +6
Larger models learn tasks smaller models do not. What drives this phenomenon? We develop a simple phenomenological argument that power-law scaling already suggests that a larger mo…
Context Sensitivity Improves Human-Machine Visual Alignment
Frieda Born, Tom Neuhäuser, Lukas Muttenthaler +6
Modern machine learning models typically represent inputs as fixed points in a high-dimensional embedding space. While this approach has been proven powerful for a wide range of do…
Linear representations in language models can change dramatically over a conversation
Andrew Kyle Lampinen, Yuxuan Li, Eghbal Hosseini +2
Language model representations often contain linear directions that correspond to high-level concepts. Here, we study the dynamics of these representations: how representations evo…
Context Structure Reshapes the Representational Geometry of Language Models
Eghbal A. Hosseini, Yuxuan Li, Yasaman Bahri +2
Large Language Models (LLMs) have been shown to organize the representations of input sequences into straighter neural trajectories in their deep layers, which has been hypothesize…
Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences
Andrew Kyle Lampinen, Martin Engelcke, Yuxuan Li +2
When do machine learning systems fail to generalize, and what mechanisms could improve their generalization? Here, we draw inspiration from cognitive science to argue that one weak…