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Found 12 papers, 4 papers with code
Mitigating analytical variability in fMRI results with style transfer
We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines.
The HCP multi-pipeline dataset: an opportunity to investigate analytical variability in fMRI data analysis
Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability including differences due to: the sampling of the participants, differences in acquisition protocols and material but also due to different analytical choices in the processing of the fMRI data.
Uncovering communities of pipelines in the task-fMRI analytical space
Analytical workflows in functional magnetic resonance imaging are highly flexible with limited best practices as to how to choose a pipeline.
On the benefits of self-taught learning for brain decoding
We study the benefits of using a large public neuroimaging database composed of fMRI statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks.
UniRank: Unimodal Bandit Algorithm for Online Ranking
The semi-bandit version, where a full matching is sampled at each iteration, has been addressed by \cite{ADMA}, creating an algorithm with an expected regret matching $O(\frac{L\log(L)}{\Delta}\log(T))$ with $2L$ players, $T$ iterations and a minimum reward gap $\Delta$.
Discovering Useful Compact Sets of Sequential Rules in a Long Sequence
We are interested in understanding the underlying generation process for long sequences of symbolic events.
Guided Attentive Feature Fusion for Multispectral Pedestrian Detection
Multispectral image pairs can provide complementary visual information, making pedestrian detection systems more robust and reliable.
Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection
Most deep learning object detectors are based on the anchor mechanism and resort to the Intersection over Union (IoU) between predefined anchor boxes and ground truth boxes to evaluate the matching quality between anchors and objects.
Ranked #9 on
Object Detection
on PASCAL VOC 2007
Position-Based Multiple-Play Bandits with Thompson Sampling
Multiple-play bandits aim at displaying relevant items at relevant positions on a web page.
Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks
Multispectral images (e. g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e. g. day/night outdoor scenes).
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation.
Residual Conv-Deconv Grid Network for Semantic Segmentation
However, for semantic image segmentation, where the task consists in providing a semantic class to each pixel of an image, feature maps reduction is harmful because it leads to a resolution loss in the output prediction.
