160 citations · 367 across the 10 of their papers we have counts for
10 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…
Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
Alex Warstadt, Leshem Choshen, Aaron Mueller +3
We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an i…
Conditional Negative Sampling for Contrastive Learning of Visual Representations
Mike Wu, Milan Mosse, Chengxu Zhuang +2
Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between tw…
On Mutual Information in Contrastive Learning for Visual Representations
Mike Wu, Chengxu Zhuang, Milan Mosse +2
In recent years, several unsupervised, "contrastive" learning algorithms in vision have been shown to learn representations that perform remarkably well on transfer tasks. We show…
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…
Flexible Neural Representation for Physics Prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang +4
Humans have a remarkable capacity to understand the physical dynamics of objects in their environment, flexibly capturing complex structures and interactions at multiple levels of…
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
Chengxu Zhuang, Jonas Kubilius, Mitra Hartmann +1
In large part, rodents see the world through their whiskers, a powerful tactile sense enabled by a series of brain areas that form the whisker-trigeminal system. Raw sensory data a…
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction
Mingmin Zhao, Chengxu Zhuang, Yizhou Wang +1
We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to mo…