research
∙
10/07/2022
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps
Learning a new task from a handful of examples remains an open challenge...
share
research
∙
08/03/2022
Quantum-Inspired Tensor Neural Networks for Partial Differential Equations
Partial Differential Equations (PDEs) are used to model a variety of dyn...
research
∙
07/06/2022
Don't overfit the history – Recursive time series data augmentation
Time series observations can be seen as realizations of an underlying dy...
research
∙
04/26/2022
Meta-free few-shot learning via representation learning with weight averaging
Recent studies on few-shot classification using transfer learning pose c...
research
∙
02/10/2021
Attentive Gaussian processes for probabilistic time-series generation
The transduction of sequence has been mostly done by recurrent networks,...
research
∙
07/02/2020
ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning
Resolving the exploration-exploitation trade-off remains a fundamental p...
research
∙
06/10/2020
Bayesian Experience Reuse for Learning from Multiple Demonstrators
Learning from demonstrations (LfD) improves the exploration efficiency o...
research
∙
02/29/2020