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Found 5 papers, 1 papers with code
Flexible Variational Information Bottleneck: Achieving Diverse Compression with a Single Training
We theoretically demonstrate that across all values of reasonable $\beta$, FVIB can simultaneously maximize an approximation of the objective function for Variational Information Bottleneck (VIB), the conventional IB method.
Pre-training of Molecular GNNs via Conditional Boltzmann Generator
We show that our model has a better prediction performance for molecular properties than existing pre-training methods using molecular graphs and three-dimensional molecular structures.
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model
To overcome this limitation, we propose a novel molecular deep generative model that incorporates a hierarchical structure into the probabilistic latent vectors.
Automated Sleep Staging via Parallel Frequency-Cut Attention
This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance.
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data
In this paper, we propose to investigate automatic subtyping from an unsupervised learning perspective by directly constructing the underlying data distribution itself, hence sufficient data can be generated to alleviate the issue of overfitting.
