1 citations · 2 across the 4 of their papers we have counts for
7 papers
3D Point World Models: Point Completion Enables More Accurate Dynamics Learning
Skand Peri, Hung Nguyen, Chanho Kim +2
Learning predictive models of the world enables robotic control through planning, potentially allowing robots to improvise solutions on new tasks. However, large video-based dynami…
Humanoid Hanoi: Investigating Shared Whole-Body Control for Skill-Based Box Rearrangement
Minku Kim, Kuan-Chia Chen, Aayam Shrestha +3
We investigate a skill-based framework for humanoid box rearrangement that enables long-horizon execution by sequencing reusable skills at the task level. In our architecture, all…
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions
Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi +2
Despite extensive research on training generative adversarial networks (GANs) with limited training data, learning to generate images from long-tailed training distributions remain…
Cycle-Consistent Counterfactuals by Latent Transformations
Saeed Khorram, Li Fuxin
CounterFactual (CF) visual explanations try to find images similar to the query image that change the decision of a vision system to a specified outcome. Existing methods either re…
Stochastic Block-ADMM for Training Deep Networks
Saeed Khorram, Xiao Fu, Mohamad H. Danesh +2
In this paper, we propose Stochastic Block-ADMM as an approach to train deep neural networks in batch and online settings. Our method works by splitting neural networks into an arb…
Deep Convolution for Irregularly Sampled Temporal Point Clouds
Erich Merrill, Stefan Lee, Li Fuxin +2
We consider the problem of modeling the dynamics of continuous spatial-temporal processes represented by irregular samples through both space and time. Such processes occur in sens…