Search Results for author:
Found 7 papers, 4 papers with code
Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs
Deep learning methods for perception are the cornerstone of many robotic systems.
Convolutional Occupancy Models for Dense Packing of Complex, Novel Objects
Dense packing in pick-and-place systems is an important feature in many warehouse and logistics applications.
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN
Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous scenes, and as such can cause critical errors in high-performance applications.
Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction
3D bounding boxes are a widespread intermediate representation in many computer vision applications.
PixelSNAIL: An Improved Autoregressive Generative Model
Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio.
A Simple Neural Attentive Meta-Learner
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task.
Prediction and Control with Temporal Segment Models
We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions.
