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20232026
most citedTowards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence

7 citations · 25 across the 37 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CV2024

Navigation World Models

Amir Bar, Gaoyue Zhou, Danny Tran +2

Navigation is a fundamental skill of agents with visual-motor capabilities. We introduce a Navigation World Model (NWM), a controllable video generation model that predicts future…

cs.RO2024★ 5 cited

DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Gaoyue Zhou, Hengkai Pan, Yann LeCun +1

The ability to predict future outcomes given control actions is fundamental for physical reasoning. However, such predictive models, often called world models, remains challenging…

cs.CV2024

-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs

Vlad Sobal, Mark Ibrahim, Randall Balestriero +5

Learning good representations involves capturing the diverse ways in which data samples relate. Contrastive loss - an objective matching related samples - underlies methods from se…

cs.SE2024★ 7 cited

Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence

Adrien Basdevant, Camille François, Victor Storchan +13

Over the past year, there has been a robust debate about the benefits and risks of open sourcing foundation models. However, this discussion has often taken place at a high level o…

cs.LG2024★ 1 cited

Hierarchical World Models as Visual Whole-Body Humanoid Controllers

Nicklas Hansen, Jyothir S, Vlad Sobal +3

Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visu…