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Found 10 papers, 4 papers with code
Modelling and Kron reduction of power flow networks in directed graphs
Electrical grids are large-sized complex systems that require strong computing power for monitoring and analysis.
Robust Meta-Representation Learning via Global Label Inference and Classification
Empirically, MeLa outperforms existing methods across a diverse range of benchmarks, in particular under a more challenging setting where the number of training tasks is limited and labels are task-specific.
Schedule-Robust Online Continual Learning
A continual learning (CL) algorithm learns from a non-stationary data stream.
The Role of Global Labels in Few-Shot Classification and How to Infer Them
Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data.
Structured Prediction for Conditional Meta-Learning
We derive task-adaptive structured meta-learning (TASML), a principled framework that yields task-specific objective functions by weighing meta-training data on target tasks.
Support-weighted Adversarial Imitation Learning
To address the challenges, we propose Support-weighted Adversarial Imitation Learning (SAIL), a general framework that extends a given AIL algorithm with information derived from support estimation of the expert policies.
Support-guided Adversarial Imitation Learning
We propose Support-guided Adversarial Imitation Learning (SAIL), a generic imitation learning framework that unifies support estimation of the expert policy with the family of Adversarial Imitation Learning (AIL) algorithms.
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals.
Real-Time Workload Classification during Driving using HyperNetworks
Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics.
MAGAN: Margin Adaptation for Generative Adversarial Networks
We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs) algorithm, a novel training procedure for GANs to improve stability and performance by using an adaptive hinge loss function.
