Bertrand Charpentier | DeepAI

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∙ 06/27/2023

Adversarial Training for Graph Neural Networks

Despite its success in the image domain, adversarial training does not (...

0 Lukas Gosch, et al. ∙

∙ 06/20/2023

Uncertainty Estimation for Molecules: Desiderata and Methods

Graph Neural Networks (GNNs) are promising surrogates for quantum mechan...

0 Tom Wollschläger, et al. ∙

∙ 05/17/2023

Edge Directionality Improves Learning on Heterophilic Graphs

Graph Neural Networks (GNNs) have become the de-facto standard tool for ...

0 Emanuele Rossi, et al. ∙

∙ 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...

0 Johannes Getzner, et al. ∙

∙ 03/10/2023

Training, Architecture, and Prior for Deterministic Uncertainty Methods

Accurate and efficient uncertainty estimation is crucial to build reliab...

0 Bertrand Charpentier, et al. ∙

∙ 07/09/2022

On the Robustness and Anomaly Detection of Sparse Neural Networks

The robustness and anomaly detection capability of neural networks are c...

0 Morgane Ayle, et al. ∙

∙ 06/21/2022

Winning the Lottery Ahead of Time: Efficient Early Network Pruning

Pruning, the task of sparsifying deep neural networks, received increasi...

0 John Rachwan, et al. ∙

∙ 06/03/2022

Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning

Characterizing aleatoric and epistemic uncertainty on the predicted rewa...

0 Bertrand Charpentier, et al. ∙

∙ 03/16/2022

Differentiable DAG Sampling

We propose a new differentiable probabilistic model over DAGs (DP-DAG). ...

0 Bertrand Charpentier, et al. ∙

∙ 10/26/2021

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

The interdependence between nodes in graphs is key to improve class pred...

0 Maximilian Stadler, et al. ∙

∙ 05/10/2021

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions

Uncertainty awareness is crucial to develop reliable machine learning mo...

0 Bertrand Charpentier, et al. ∙

∙ 10/28/2020

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?

Robustness to adversarial perturbations and accurate uncertainty estimat...

11 Anna-Kathrin Kopetzki, et al. ∙

∙ 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...

11 Bertrand Charpentier, et al. ∙

∙ 11/13/2019

Uncertainty on Asynchronous Time Event Prediction

Asynchronous event sequences are the basis of many applications througho...

23 Marin Biloš, et al. ∙

∙ 07/13/2018

Learning Graph Representations by Dendrograms

Hierarchical graph clustering is a common technique to reveal the multi-...

0 Thomas Bonald, et al. ∙

∙ 06/05/2018

Hierarchical Graph Clustering using Node Pair Sampling

We present a novel hierarchical graph clustering algorithm inspired by m...

0 Thomas Bonald, et al. ∙

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