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pro vyhledávání: '"Lee, Leo"'
Autor:
Atanackovic, Lazar, Zhang, Xi, Amos, Brandon, Blanchette, Mathieu, Lee, Leo J., Bengio, Yoshua, Tong, Alexander, Neklyudov, Kirill
Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical particles. Learning the dynamics of such systems is essential for predi
Externí odkaz:
http://arxiv.org/abs/2408.14608
In the face of rapidly accumulating genomic data, our understanding of the RNA regulatory code remains incomplete. Recent self-supervised methods in other domains have demonstrated the ability to learn rules underlying the data-generating process suc
Externí odkaz:
http://arxiv.org/abs/2310.08738
One of the grand challenges of cell biology is inferring the gene regulatory network (GRN) which describes interactions between genes and their products that control gene expression and cellular function. We can treat this as a causal discovery probl
Externí odkaz:
http://arxiv.org/abs/2302.04178
Publikováno v:
In Molecular Therapy - Nucleic Acids 11 June 2024 35(2)
Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can jointly learn node representations and coarsen the graph. We also int
Externí odkaz:
http://arxiv.org/abs/2002.09518
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