research
∙
06/27/2023
Adversarial Training for Graph Neural Networks
Despite its success in the image domain, adversarial training does not (...
share
research
∙
06/20/2023
Uncertainty Estimation for Molecules: Desiderata and Methods
Graph Neural Networks (GNNs) are promising surrogates for quantum mechan...
research
∙
05/17/2023
Edge Directionality Improves Learning on Heterophilic Graphs
Graph Neural Networks (GNNs) have become the de-facto standard tool for ...
research
∙
04/03/2023
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models
Modern machine learning models have started to consume incredible amount...
research
∙
03/10/2023
Training, Architecture, and Prior for Deterministic Uncertainty Methods
Accurate and efficient uncertainty estimation is crucial to build reliab...
research
∙
07/09/2022
On the Robustness and Anomaly Detection of Sparse Neural Networks
The robustness and anomaly detection capability of neural networks are c...
research
∙
06/21/2022
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
Pruning, the task of sparsifying deep neural networks, received increasi...
research
∙
06/03/2022
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
Characterizing aleatoric and epistemic uncertainty on the predicted rewa...
research
∙
03/16/2022
Differentiable DAG Sampling
We propose a new differentiable probabilistic model over DAGs (DP-DAG). ...
research
∙
10/26/2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
The interdependence between nodes in graphs is key to improve class pred...
research
∙
05/10/2021
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Uncertainty awareness is crucial to develop reliable machine learning mo...
research
∙
10/28/2020
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Robustness to adversarial perturbations and accurate uncertainty estimat...
research
∙
06/16/2020
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Accurate estimation of aleatoric and epistemic uncertainty is crucial to...
research
∙
11/13/2019
Uncertainty on Asynchronous Time Event Prediction
Asynchronous event sequences are the basis of many applications througho...
research
∙
07/13/2018
Learning Graph Representations by Dendrograms
Hierarchical graph clustering is a common technique to reveal the multi-...
research
∙
06/05/2018