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Found 8 papers, 4 papers with code
Improving Antibody Humanness Prediction using Patent Data
We investigate the potential of patent data for improving the antibody humanness prediction using a multi-stage, multi-loss training process.
NESS: Node Embeddings from Static SubGraphs
We present a framework for learning Node Embeddings from Static Subgraphs (NESS) using a graph autoencoder (GAE) in a transductive setting.
Ranked #1 on
Link Prediction
on Cora
Parameter Averaging for Feature Ranking
We conduct extensive experiments on a variety of synthetic and real-world data, demonstrating that the XTab can be used to obtain the global feature importance that is not sensitive to sub-optimal model initialisation.
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
Self-supervised learning has been shown to be very effective in learning useful representations, and yet much of the success is achieved in data types such as images, audio, and text.
Ranked #4 on
Unsupervised MNIST
on MNIST
Bayesian Relational Generative Model for Scalable Multi-modal Learning
First, they are not stochastic processes, leading to poor uncertainty estimations over their predictions.
One-Shot Learning for Language Modelling
Humans can infer a great deal about the meaning of a word, using the syntax and semantics of surrounding words even if it is their first time reading or hearing it.
Inverse Graphics: Unsupervised Learning of 3D Shapes from Single Images
Our approach is to use a generative model that produces 2-D images as projections of a latent 3D voxel grid, which we train either as a variational auto-encoder or using adversarial methods.
Bridging the ELBO and MMD
The $\mu$-VAE is less prone to posterior collapse, and can generate reconstructions and new samples in good quality.
