2 papers
cs.LG2025
Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks
Gianluca Bencomo, Max Gupta, Ioana Marinescu +2
Artificial neural networks can acquire many aspects of human knowledge from data, making them promising as models of human learning. But what those networks can learn depends upon…
cs.LG2025
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity
Raja Marjieh, Sreejan Kumar, Declan Campbell +4
Humans rely on effective representations to learn from few examples and abstract useful information from sensory data. Inducing such representations in machine learning models has…