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09/11/2023
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss
Active learning (AL) reduces the amount of labeled data needed to train ...
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08/16/2023
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
We perform the first adversarial robustness study into Graph Neural Netw...
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08/09/2023
AI-Enabled Software and System Architecture Frameworks: Focusing on smart Cyber-Physical Systems (CPS)
Several architecture frameworks for software, systems, and enterprises h...
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07/10/2023
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
The ability to detect learned objects regardless of their appearance is ...
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07/03/2023
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP...
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06/27/2023
Adversarial Training for Graph Neural Networks
Despite its success in the image domain, adversarial training does not (...
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06/20/2023
Uncertainty Estimation for Molecules: Desiderata and Methods
Graph Neural Networks (GNNs) are promising surrogates for quantum mechan...
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05/30/2023
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
Recent advances in machine learning for molecules exhibit great potentia...
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05/17/2023
Edge Directionality Improves Learning on Heterophilic Graphs
Graph Neural Networks (GNNs) have become the de-facto standard tool for ...
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05/01/2023
Revisiting Robustness in Graph Machine Learning
Many works show that node-level predictions of Graph Neural Networks (GN...
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04/30/2023
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness
Emerging quantum computing technologies, such as Noisy Intermediate-Scal...
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04/06/2023
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Bayesian inference in deep neural networks is challenging due to the hig...
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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...
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03/10/2023
Training, Architecture, and Prior for Deterministic Uncertainty Methods
Accurate and efficient uncertainty estimation is crucial to build reliab...
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03/08/2023
Ewald-based Long-Range Message Passing for Molecular Graphs
Neural architectures that learn potential energy surfaces from molecular...
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02/08/2023
Generalizing Neural Wave Functions
Recent neural network-based wave functions have achieved state-of-the-ar...
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02/06/2023
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
In tasks like node classification, image segmentation, and named-entity ...
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01/31/2023
Transformers Meet Directed Graphs
Transformers were originally proposed as a sequence-to-sequence model fo...
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01/31/2023
Are Defenses for Graph Neural Networks Robust?
A cursory reading of the literature suggests that we have made a lot of ...
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01/05/2023
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
Randomized smoothing is one of the most promising frameworks for certify...
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01/02/2023
Training Differentially Private Graph Neural Networks with Random Walk Sampling
Deep learning models are known to put the privacy of their training data...
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12/18/2022
Influence-Based Mini-Batching for Graph Neural Networks
Using graph neural networks for large graphs is challenging since there ...
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11/25/2022
Invariance-Aware Randomized Smoothing Certificates
Building models that comply with the invariances inherent to different d...
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11/04/2022
Modeling Temporal Data as Continuous Functions with Process Diffusion
Temporal data like time series are often observed at irregular intervals...
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10/28/2022
Localized Randomized Smoothing for Collective Robustness Certification
Models for image segmentation, node classification and many other tasks ...
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10/22/2022
torchode: A Parallel ODE Solver for PyTorch
We introduce an ODE solver for the PyTorch ecosystem that can solve mult...
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10/19/2022
Irregularly-Sampled Time Series Modeling with Spline Networks
Observations made in continuous time are often irregular and contain the...
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10/15/2022
Unveiling the Sampling Density in Non-Uniform Geometric Graphs
A powerful framework for studying graphs is to consider them as geometri...
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09/16/2022
A Systematic Evaluation of Node Embedding Robustness
Node embedding methods map network nodes to low dimensional vectors that...
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09/15/2022
MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna ML-Quadrat
In this paper, we propose to adopt the MDE paradigm for the development ...
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07/17/2022
United States Politicians' Tone Became More Negative with 2016 Primary Campaigns
There is a widespread belief that the tone of US political language has ...
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07/09/2022
On the Robustness and Anomaly Detection of Sparse Neural Networks
The robustness and anomaly detection capability of neural networks are c...
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06/21/2022
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
Pruning, the task of sparsifying deep neural networks, received increasi...
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06/03/2022
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
Characterizing aleatoric and epistemic uncertainty on the predicted rewa...
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05/30/2022
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
Obtaining the energy of molecular systems typically requires solving the...
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04/28/2022
Predicting single-cell perturbation responses for unseen drugs
Single-cell transcriptomics enabled the study of cellular heterogeneity ...
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04/06/2022
How Do Graph Networks Generalize to Large and Diverse Molecular Systems?
The predominant method of demonstrating progress of atomic graph neural ...
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03/16/2022
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks
We propose a new method for spatio-temporal forecasting on arbitrarily d...
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03/16/2022
Is it all a cluster game? – Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space
It is essential for safety-critical applications of deep neural networks...
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03/16/2022
Differentiable DAG Sampling
We propose a new differentiable probabilistic model over DAGs (DP-DAG). ...
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03/06/2022
Enabling Automated Machine Learning for Model-Driven AI Engineering
Developing smart software services requires both Software Engineering an...
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02/17/2022
Graph Data Augmentation for Graph Machine Learning: A Survey
Data augmentation has recently seen increased interest in graph machine ...
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02/17/2022
Multi-Objective Model Selection for Time Series Forecasting
Research on time series forecasting has predominantly focused on develop...
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11/08/2021
Directional Message Passing on Molecular Graphs via Synthetic Coordinates
Graph neural networks that leverage coordinates via directional message ...
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10/26/2021
Robustness of Graph Neural Networks at Scale
Graph Neural Networks (GNNs) are increasingly important given their popu...
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10/26/2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
The interdependence between nodes in graphs is key to improve class pred...
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10/25/2021
Neural Flows: Efficient Alternative to Neural ODEs
Neural ordinary differential equations describe how values change in tim...
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10/21/2021
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
End-to-end (geometric) deep learning has seen first successes in approxi...
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10/11/2021
Intriguing Properties of Input-dependent Randomized Smoothing
Randomized smoothing is currently considered the state-of-the-art method...
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10/11/2021