Search Results for author:
Found 6 papers, 3 papers with code
Re-parameterizing VAEs for stability
We propose a theoretical approach towards the training numerical stability of Variational AutoEncoders (VAE).
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining
We propose the Graph Context Encoder (GCE), a simple but efficient approach for graph representation learning based on graph feature masking and reconstruction.
Realistic molecule optimization on a learned graph manifold
Deep learning based molecular graph generation and optimization has recently been attracting attention due to its great potential for de novo drug design.
Graph convolutions that can finally model local structure
Despite quick progress in the last few years, recent studies have shown that modern graph neural networks can still fail at very simple tasks, like detecting small cycles.
Ranked #13 on
Graph Property Prediction
on ogbg-molpcba
Anomaly localization by modeling perceptual features
Although unsupervised generative modeling of an image dataset using a Variational AutoEncoder (VAE) has been used to detect anomalous images, or anomalous regions in images, recent works have shown that this method often identifies images or regions that do not concur with human perception, even questioning the usability of generative models for robust anomaly detection.
Ranked #18 on
Anomaly Detection
on VisA
Iterative energy-based projection on a normal data manifold for anomaly localization
Indeed, an autoencoder trained on normal data is expected to only be able to reconstruct normal features of the data, allowing the segmentation of anomalous pixels in an image via a simple comparison between the image and its autoencoder reconstruction.
Ranked #81 on
Anomaly Detection
on MVTec AD
(Segmentation AUROC metric)
