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Found 8 papers, 4 papers with code
ADRMX: Additive Disentanglement of Domain Features with Remix Loss
To this end, most of existing studies focus on extracting domain invariant features across the available source domains in order to mitigate the effects of inter-domain distributional changes.
Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
This paper presents a source free domain adaptation method for steady-state visually evoked potentials (SSVEP) based brain-computer interface (BCI) spellers.
DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image
To that end, we propose a novel GAR technique for volleyball videos, DECOMPL, which consists of two complementary branches.
Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training
We transfer this ensemble of fine-tuned DNNs to the new user instance, determine the k most representative DNNs according to the participants' statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions.
A Deep Neural Network for SSVEP-based Brain-Computer Interfaces
Objective: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell.
A Neural Network Approach for Online Nonlinear Neyman-Pearson Classification
Our algorithm is appropriate for large scale data applications and provides a decent false positive rate controllability with real time processing since it only has O(N) computational and O(1) space complexity (N: number of data instances).
An Asymptotically Optimal Contextual Bandit Algorithm Using Hierarchical Structures
Therefore, we design our algorithms based on the optimal adaptive combination and asymptotically achieve the performance of the best mapping as well as the best arm selection policy.
Data Imputation through the Identification of Local Anomalies
We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e. g., an occluder in the case of a visual recording.
