Search Results for author:
Found 9 papers, 2 papers with code
Transferable End-to-end Room Layout Estimation via Implicit Encoding
Here we propose an end-to-end method that directly predicts parametric layouts from an input panorama image.
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning
We present an approach to contrastive representation learning for semantic segmentation.
Jointly Learning Identification and Control for Few-Shot Policy Adaptation
We evaluate our approach on several complex systems and tasks, and experimentally analyze the advantages over model-free and model-based methods in terms of performance and sample efficiency.
Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search
Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware.
High-dimensional Convolutional Networks for Geometric Pattern Recognition
Many problems in science and engineering can be formulated in terms of geometric patterns in high-dimensional spaces.
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS
Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware.
Deep Layers as Stochastic Solvers
In particular, we show that a forward pass through a standard dropout layer followed by a linear layer and a non-linear activation is equivalent to optimizing a convex optimization objective with a single iteration of a $\tau$-nice Proximal Stochastic Gradient method.
Deep Fundamental Matrix Estimation
We present an approach to robust estimation of fundamental matrices from noisy data contaminated by outliers.
Dense Monocular Depth Estimation in Complex Dynamic Scenes
We present an approach to dense depth estimation from a single monocular camera that is moving through a dynamic scene.
