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Found 4 papers, 0 papers with code
BTranspose: Bottleneck Transformers for Human Pose Estimation with Self-Supervised Pre-Training
In this paper, we consider the recently proposed Bottleneck Transformers [2], which combine CNN and multi-head self attention (MHSA) layers effectively, and we integrate it with a Transformer encoder and apply it to the task of 2D human pose estimation.
Stochastic Adversarial Koopman Model for Dynamical Systems
Specifically, the latent encoding of the system is modeled as a Gaussian, and is advanced in time by using an auxiliary neural network that outputs two Koopman matrices $K_{\mu}$ and $K_{\sigma}$.
An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation
Training robots to navigate diverse environments is a challenging problem as it involves the confluence of several different perception tasks such as mapping and localization, followed by optimal path-planning and control.
Deep Adversarial Koopman Model for Reaction-Diffusion systems
Reaction-diffusion systems are ubiquitous in nature and in engineering applications, and are often modeled using a non-linear system of governing equations.
