AMBIENT: Accelerated Convolutional Neural Network Architecture Search for Regulatory Genomics
Zhang, Z.; Cofer, E. M.; Troyanskaya, O. G.
Show abstract
Convolutional neural networks (CNN) have become a standard approach for modeling genomic sequences. CNNs can be effectively built by Neural Architecture Search (NAS) by trading computing power for accurate neural architectures. Yet, the consumption of immense computing power is a major practical, financial, and environmental issue for deep learning. Here, we present a novel NAS framework, AMBIENT, that generates highly accurate CNN architectures for biological sequences of diverse functions, while substantially reducing the computing cost of conventional NAS.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Graph Contrastive Learning of Subcellular-resolution Spatial Transcriptomics Improves Cell Type Annotation and Reveals Critical Molecular Pathways 95%
- Kolmogorov-Arnold Networks for Genomic Tasks 94%
- Synthetic observations from deep generative models and binary omics data with limited sample size 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.