919 citations · 1.4k across the 13 of their papers we have counts for
13 papers
The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences
Bria Long, Robert Z. Sparks, Violet Xiang +9
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. This ''d…
The Limiting Dynamics of SGD: Modified Loss, Phase Space Oscillations, and Anomalous Diffusion
Daniel Kunin, Javier Sagastuy-Brena, Lauren Gillespie +4
In this work we explore the limiting dynamics of deep neural networks trained with stochastic gradient descent (SGD). As observed previously, long after performance has converged,…
Physion: Evaluating Physical Prediction from Vision in Humans and Machines
Daniel M. Bear, Elias Wang, Damian Mrowca +12
While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion,…
Local Label Propagation for Large-Scale Semi-Supervised Learning
Chengxu Zhuang, Xuehao Ding, Divyanshu Murli +1
A significant issue in training deep neural networks to solve supervised learning tasks is the need for large numbers of labelled datapoints. The goal of semi-supervised learning i…
Unsupervised Learning from Video with Deep Neural Embeddings
Chengxu Zhuang, Tianwei She, Alex Andonian +2
Because of the rich dynamical structure of videos and their ubiquity in everyday life, it is a natural idea that video data could serve as a powerful unsupervised learning signal f…
Local Aggregation for Unsupervised Learning of Visual Embeddings
Chengxu Zhuang, Alex Lin Zhai, Daniel Yamins
Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks w…