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Found 11 papers, 3 papers with code
Hypergraph Analysis Toolbox for Chromosome Conformation
Recent advances in biological technologies, such as multi-way chromosome conformation capture (3C), require development of methods for analysis of multi-way interactions.
ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense
K-Nearest Neighbor (kNN)-based deep learning methods have been applied to many applications due to their simplicity and geometric interpretability.
RAILS: A Robust Adversarial Immune-inspired Learning System
Initializing a population of exemplars that is balanced across classes, RAILS starts from a uniform label distribution that encourages diversity and uses an evolutionary optimization process to adaptively adjust the predictive label distribution in a manner that emulates the way the natural immune system recognizes novel pathogens.
Immuno-mimetic Deep Neural Networks (Immuno-Net)
This immuno-mimetic model leads to a new computational biology framework for robustification of deep neural networks against adversarial attacks.
Hypergraph Dissimilarity Measures
In this paper, we propose two novel approaches for hypergraph comparison.
RAILS: A Robust Adversarial Immune-inspired Learning System
RAILS incorporates an Adaptive Immune System Emulation (AISE), which emulates in silico the biological mechanisms that are used to defend the host against attacks by pathogens.
Understanding Memory B Cell Selection
However, memory B cells appear to be purposely selected earlier in the affinity maturation process and have lower affinity.
Controllability of Hypergraphs
In this paper, we develop a notion of controllability for hypergraphs via tensor algebra and polynomial control theory.
Tensor Entropy for Uniform Hypergraphs
In this paper, we develop the notion of entropy for uniform hypergraphs via tensor theory.
Latent heterogeneous multilayer community detection
We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks.
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence.
