What are the core learning algorithms in brains? Nativists propose that intelligence emerges
from innate domain-specific knowledge systems, whereas empiricists propose that …

Abstract Recently, Orhan and Lake demonstrated the computational plausibility that children
can acquire sophisticated visual representations from natural input data without inherent …

While high-performing language models are typically trained on hundreds of billions of
words, human children become fluent language users with a much smaller amount of data …

Our algorithmic understanding of vision has been revolutionized by a reverse engineering
paradigm that involves building artificial systems that perform the same tasks as biological …

Masked Image Modeling (MIM) has emerged as a powerful self-supervised learning
paradigm for visual representation learning, enabling models to acquire rich visual …

Recent self-supervised learning (SSL) models trained on human-like egocentric visual
inputs substantially underperform on image recognition tasks compared to humans. These …

Human children far exceed modern machine learning algorithms in their sample efficiency,
achieving high performance in key domains with much less data than current models …

Infants develop complex visual understanding rapidly, even preceding of the acquisition of
linguistic inputs. As computer vision seeks to replicate the human vision system …

Due to significant variations in the projection of the same object from different viewpoints,
machine learning algorithms struggle to recognize the same object across various …

Humans judge the similarity of two objects not just based on their visual appearance but
also based on their semantic relatedness. However, it remains unclear how humans learn …