A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Nathan Shone, Trần Nguyên Ngọc, Vu Dinh Phai, et al.
Proposes a nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning and a stacked NDAE classifier for network intrusion detection, evaluated on KDD Cup '99 and NSL-KDD datasets.
Riccardo Miotto, Li Li, Brian Kidd, et al.
Unsupervised deep feature learning with stacked denoising autoencoders on EHRs of ~700k patients yields a general-purpose patient representation that significantly improves predictive modeling for 78 diseases.
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, et al.
ConvNeXt V2 enhances pure convolutional networks with a fully convolutional masked autoencoder and Global Response Normalization for superior self-supervised learning.
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An encoder-decoder architecture that reconstructs input images by masking random patches and leveraging a high proportion of masking for self-supervision.
Diederik P. Kingma, Max Welling
This paper provides a comprehensive introduction to variational autoencoders, a principled framework for learning deep latent-variable models and inference models.
Zonghan Wu, Shirui Pan, Fengwen Chen, et al.
A comprehensive survey categorizing graph neural networks into recurrent, convolutional, autoencoder, and spatial-temporal types, with applications and benchmarks.