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Found 8 papers, 3 papers with code
Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells
The discovery of new functional and stable materials is a big challenge due to its complexity.
False membership rate control in mixture models
In this paper the approach is revisited in an unsupervised mixture model framework and the purpose is to develop a method that comes with the guarantee that the false membership rate (FMR) does not exceed a pre-defined nominal level $\alpha$.
Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the $σ-$phase as an example
Machine learning (ML) methods are becoming integral to scientific inquiry in numerous disciplines, such as material sciences.
Latent Instrumental Variables as Priors in Causal Inference based on Independence of Cause and Mechanism
The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be inferred for two observations.
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network
In this work, we present a novel and simple convolutional neural network architecture for supervised learning on graphs that is provably invariant to node permutation.
Ranked #1 on
Graph Classification
on HYDRIDES
CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications.
Disease Classification in Metagenomics with 2D Embeddings and Deep Learning
Generally, when the sample size ($N$) is much bigger than the number of features ($d$), DL often outperforms other machine learning (ML) techniques, often through the use of Convolutional Neural Networks (CNNs).
Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks
However, in many bioinformatics ML tasks, we encounter the opposite situation where d is greater than N. In these situations, applying DL techniques (such as feed-forward networks) would lead to severe overfitting.
