Search Results for author:
Found 6 papers, 1 papers with code
ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport
We present a new extension for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regularisation on conjugate potentials.
Align Your Intents: Offline Imitation Learning via Optimal Transport
We report that AILOT outperforms state-of-the art offline imitation learning algorithms on D4RL benchmarks and improves the performance of other offline RL algorithms in the sparse-reward tasks.
Explain My Surprise: Learning Efficient Long-Term Memory by Predicting Uncertain Outcomes
This requires to store prohibitively large intermediate data if a sequence consists of thousands or even millions elements, and as a result, makes learning of very long-term dependencies infeasible.
Data Augmentation with Manifold Barycenters
Oftentimes, these methods either fail to produce enough new data or expand the dataset beyond the original knowledge domain.
BRULÈ: Barycenter-Regularized Unsupervised Landmark Extraction
Unsupervised retrieval of image features is vital for many computer vision tasks where the annotation is missing or scarce.
Unsupervised non-parametric change point detection in quasi-periodic signals
We propose a new unsupervised and non-parametric method to detect change points in intricate quasi-periodic signals.
