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Found 7 papers, 2 papers with code
Understanding deep neural networks through the lens of their non-linearity
The remarkable success of deep neural networks (DNN) is often attributed to their high expressive power and their ability to approximate functions of arbitrary complexity.
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle.
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation
Domain adaptation is a common problem in robotics, with applications such as transferring policies from simulation to real world and lifelong learning.
Autoencoding Slow Representations for Semi-supervised Data Efficient Regression
We find that slow representations lead to equal or better downstream task performance and data efficiency in different experiment domains when compared to representations without slowness regularization.
MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations
Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics.
ViTa-SLAM: A Bio-inspired Visuo-Tactile SLAM for Navigation while Interacting with Aliased Environments
RatSLAM is a rat hippocampus-inspired visual Simultaneous Localization and Mapping (SLAM) framework capable of generating semi-metric topological representations of indoor and outdoor environments.
Robotics
LeagueAI: Improving object detector performance and flexibility through automatically generated training data and domain randomization
In an experiment I compared a model trained on synthetic data to a model trained on hand labeled data and a model trained on a combined dataset.
