4,058 research outputs found
Searching for A Robust Neural Architecture in Four GPU Hours
Conventional neural architecture search (NAS) approaches are based on
reinforcement learning or evolutionary strategy, which take more than 3000 GPU
hours to find a good model on CIFAR-10. We propose an efficient NAS approach
learning to search by gradient descent. Our approach represents the search
space as a directed acyclic graph (DAG). This DAG contains billions of
sub-graphs, each of which indicates a kind of neural architecture. To avoid
traversing all the possibilities of the sub-graphs, we develop a differentiable
sampler over the DAG. This sampler is learnable and optimized by the validation
loss after training the sampled architecture. In this way, our approach can be
trained in an end-to-end fashion by gradient descent, named Gradient-based
search using Differentiable Architecture Sampler (GDAS). In experiments, we can
finish one searching procedure in four GPU hours on CIFAR-10, and the
discovered model obtains a test error of 2.82\% with only 2.5M parameters,
which is on par with the state-of-the-art. Code is publicly available on
GitHub: https://github.com/D-X-Y/NAS-Projects.Comment: Minor modifications to the CVPR 2019 camera-ready version (add code
link
Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
Localization in challenging, natural environments such as forests or
woodlands is an important capability for many applications from guiding a robot
navigating along a forest trail to monitoring vegetation growth with handheld
sensors. In this work we explore laser-based localization in both urban and
natural environments, which is suitable for online applications. We propose a
deep learning approach capable of learning meaningful descriptors directly from
3D point clouds by comparing triplets (anchor, positive and negative examples).
The approach learns a feature space representation for a set of segmented point
clouds that are matched between a current and previous observations. Our
learning method is tailored towards loop closure detection resulting in a small
model which can be deployed using only a CPU. The proposed learning method
would allow the full pipeline to run on robots with limited computational
payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info:
https://ori.ox.ac.uk/esm-localizatio
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