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Found 8 papers, 4 papers with code
Algorithmic Identification of Essential Exogenous Nodes for Causal Sufficiency in Brain Networks
In the investigation of any causal mechanisms, such as the brain's causal networks, the assumption of causal sufficiency plays a critical role.
PW-Self: Patch-Wise Self-Supervised Visual Representation Learning
To this end, we present a simple yet effective patch-matching algorithm that can find the corresponding patches across the augmented views.
Discovering Dynamic Effective Connectome of Brain with Bayesian Dynamic DAG Learning
However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data.
Brain Effective Connectome based on fMRI and DTI Data: Bayesian Causal Learning and Assessment
By leveraging the DTI data as prior knowledge, we introduce two Bayesian causal discovery frameworks -- the Bayesian GOLEM (BGOLEM) and Bayesian FGES (BFGES) methods -- that offer significantly more accurate and reliable ECs and address the shortcomings of the existing causal discovery methods in discovering ECs based on only fMRI data.
Stochastic First-Order Learning for Large-Scale Flexibly Tied Gaussian Mixture Model
Gaussian Mixture Models (GMMs) are one of the most potent parametric density models used extensively in many applications.
Contour Integration using Graph-Cut and Non-Classical Receptive Field
Many edge and contour detection algorithms give a soft-value as an output and the final binary map is commonly obtained by applying an optimal threshold.
Attention-based Assisted Excitation for Salient Object Detection
In this paper, object-based attention in human visual cortex inspires us to introduce a mechanism for modification of activations in feature maps of CNNs.
Incremental learning of high-level concepts by imitation
In ILoCI, observed multimodal spatio-temporal demonstrations are incrementally abstracted and generalized based on both their perceptual and functional similarities during the imitation.
