1 citations · 1 across the 2 of their papers we have counts for
4 papers
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
Alexander Popov, Alperen Degirmenci, David Wehr +9
We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a neural network capable of predicting an…
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders
Yitong Jiang, Hongjun Wang, Collin McCarthy +15
Vision foundation models are bottlenecked by the quadratic cost of self-attention, which limits usable resolution and increases the cost of large-scale pretraining. Subquadratic al…
GSPN-2: Efficient Parallel Sequence Modeling
Hongjun Wang, Yitong Jiang, Collin McCarthy +12
Efficient vision transformer remains a bottleneck for high-resolution images and long-video related real-world applications. Generalized Spatial Propagation Network (GSPN) addresse…