most citedVideo as the New Language for Real-World Decision Making

2 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2024

Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback

Hiroki Furuta, Heiga Zen, Dale Schuurmans +4

Large text-to-video models hold immense potential for a wide range of downstream applications. However, they struggle to accurately depict dynamic object interactions, often result…

cs.AI2024

VideoAgent: Self-Improving Video Generation

Achint Soni, Sreyas Venkataraman, Abhranil Chandra +4

Video generation has been used to generate visual plans for controlling robotic systems. Given an image observation and a language instruction, previous work has generated video pl…

cond-mat.mtrl-sci2024★ 1 cited

Generative Hierarchical Materials Search

Sherry Yang, Simon Batzner, Ruiqi Gao +7

Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials…

cs.DB2024★ 2 cited

UQE: A Query Engine for Unstructured Databases

Hanjun Dai, Bethany Yixin Wang, Xingchen Wan +7

Analytics on structured data is a mature field with many successful methods. However, most real world data exists in unstructured form, such as images and conversations. We investi…

cs.LG2024

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF

Shicong Cen, Jincheng Mei, Katayoon Goshvadi +6

Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of pr…

cs.CV2024★ 2 cited

Video as the New Language for Real-World Decision Making

Sherry Yang, Jacob Walker, Jack Parker-Holder +5

Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally l…