108 citations · 255 across the 9 of their papers we have counts for
15 papers
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI
Chuang Gan, Siyuan Zhou, Jeremy Schwartz +8
We introduce a visually-guided and physics-driven task-and-motion planning benchmark, which we call the ThreeDWorld Transport Challenge. In this challenge, an embodied agent equipp…
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…
Identifying Learning Rules From Neural Network Observables
Aran Nayebi, Sanjana Srivastava, Surya Ganguli +1
The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Ma…
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…
Learning Physical Graph Representations from Visual Scenes
Daniel M. Bear, Chaofei Fan, Damian Mrowca +8
Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, an…