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Found 8 papers, 3 papers with code
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition
The second task required to transfer models trained on the subjects of one or more source motor imagery datasets to perform inference on two target datasets, providing a small set of personalized calibration data for multiple test subjects.
Road Extraction from Overhead Images with Graph Neural Networks
By contrast, we propose a method that directly infers the final road graph in a single pass.
Binary Graph Neural Networks
In this paper, we present and evaluate different strategies for the binarization of graph neural networks.
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template.
Geometrically Principled Connections in Graph Neural Networks
Graph convolution operators bring the advantages of deep learning to a variety of graph and mesh processing tasks previously deemed out of reach.
Robust Kronecker Component Analysis
Dictionary learning and component analysis models are fundamental for learning compact representations that are relevant to a given task (feature extraction, dimensionality reduction, denoising, etc.).
Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling
In this paper, we introduce a new robust decomposition of images by combining ideas from sparse dictionary learning and PCP.
