108 citations · 415 across the 23 of their papers we have counts for
11 papers · 1 filter
Contravariance Theory: Strong Alignment for Minimal Solutions to Hard Tasks
Dan Yamins, Aran Nayebi
A series of results from the NeuroAI over the past fifteen years have raised core questions both about how to compare Deep Neural Network (DNN) models to the brain, and about how m…
Developmental Curiosity and Social Interaction in Virtual Agents
Chris Doyle, Sarah Shader, Michelle Lau +3
Infants explore their complex physical and social environment in an organized way. To gain insight into what intrinsic motivations may help structure this exploration, we create a…
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics
Daniel Kunin, Javier Sagastuy-Brena, Surya Ganguli +2
Understanding the dynamics of neural network parameters during training is one of the key challenges in building a theoretical foundation for deep learning. A central obstacle is t…
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…
Active World Model Learning with Progress Curiosity
Kuno Kim, Megumi Sano, Julian De Freitas +2
World models are self-supervised predictive models of how the world evolves. Humans learn world models by curiously exploring their environment, in the process acquiring compact ab…
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…
Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins +1
Pruning the parameters of deep neural networks has generated intense interest due to potential savings in time, memory and energy both during training and at test time. Recent work…
Visual Grounding of Learned Physical Models
Yunzhu Li, Toru Lin, Kexin Yi +5
Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. The abilities to perform physical…
Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation
Nick Haber, Damian Mrowca, Li Fei-Fei +1
Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to rep…
Modular Continual Learning in a Unified Visual Environment
Kevin T. Feigelis, Blue Sheffer, Daniel L. K. Yamins
A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradig…
A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment
Kevin T. Feigelis, Daniel L. K. Yamins
Animals (especially humans) have an amazing ability to learn new tasks quickly, and switch between them flexibly. How brains support this ability is largely unknown, both neuroscie…