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Found 7 papers, 2 papers with code
Enhancing Real-World Complex Network Representations with Hyperedge Augmentation
These methods cannot fully address the complexities of real-world large-scale networks that often involve higher-order node relations beyond only being pairwise.
DiscDiff: Latent Diffusion Model for DNA Sequence Generation
This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA sequences, and Absorb-Escape, a post-training algorithm designed to refine these sequences.
Latent Diffusion Model for DNA Sequence Generation
On the other hand, Diffusion Models are a promising new class of generative models that are not burdened with these problems, enabling them to reach the state-of-the-art in domains such as image generation.
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer
This work presents Genomic Interpreter: a novel architecture for genomic assay prediction.
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets.
Distributed Reconstruction of Nonlinear Networks: An ADMM Approach
In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks.
Toggling a Genetic Switch Using Reinforcement Learning
In this paper, we consider the problem of optimal exogenous control of gene regulatory networks.
