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Found 10 papers, 1 papers with code
PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networks
However, existing methods deal solely with single-polarization images and cannot handle the multi-polarization images captured by modern satellites.
Data Transfer Approaches to Improve Seq-to-Seq Retrosynthesis
Especially, the pre-training plus fine-tuning approach boosts the accuracy scores of the baseline, achieving the new state-of-the-art.
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning.
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
A graph neural network (GNN) is a good choice for predicting the chemical properties of molecules.
Graph Residual Flow for Molecular Graph Generation
Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics.
GraphNVP: an Invertible Flow-based Model for Generating Molecular Graphs
In contrast, our model is the first invertible model for the whole graph components: both of dequantized node attributes and adjacency tensor are converted into latent vectors through two novel invertible flows.
Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
Graph Neural Network (GNN) is a popular architecture for the analysis of chemical molecules, and it has numerous applications in material and medicinal science.
Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey
We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with Hyper Suprime-Cam (HSC) on the Subaru telescope.
Instrumentation and Methods for Astrophysics
Collapsed Variational Bayes Inference of Infinite Relational Model
The Infinite Relational Model (IRM) is a probabilistic model for relational data clustering that partitions objects into clusters based on observed relationships.
Dynamic Infinite Relational Model for Time-varying Relational Data Analysis
We propose a new probabilistic model for analyzing dynamic evolutions of relational data, such as additions, deletions and split & merge, of relation clusters like communities in social networks.
