Search Results for author:
Found 8 papers, 4 papers with code
CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data
To tackle this problem, we introduce the CityRefer dataset for city-level visual grounding.
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
To achieve this, we propose a new building block for geometric deep learning, which we denote SPD domain-specific momentum batch normalization (SPDDSMBN).
ScanQA: 3D Question Answering for Spatial Scene Understanding
We propose a new 3D spatial understanding task of 3D Question Answering (3D-QA).
On the interpretation of linear Riemannian tangent space model parameters in M/EEG
Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development.
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling
We present a novel probabilistic framework for a hierarchical extension of independent component analysis (ICA), with a particular motivation in neuroscientific data analysis and modeling.
Robust Spatial Filtering with Beta Divergence
The efficiency of Brain-Computer Interfaces (BCI) largely depends upon a reliable extraction of informative features from the high-dimensional EEG signal.
How to Explain Individual Classification Decisions
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data.
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation
In this paper, we propose a direct importance estimation method that does not require the input density estimates.
