Search Results for author:
Found 117 papers, 62 papers with code
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss
We exploited the temporal properties for such image streams in our work and proposed the novel temporal predicted loss (TPL) method.
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
We perform the first adversarial robustness study into Graph Neural Networks (GNNs) that are provably more powerful than traditional Message Passing Neural Networks (MPNNs).
AI-Enabled Software and System Architecture Frameworks: Focusing on smart Cyber-Physical Systems (CPS)
Therefore, they failed to address the architecture viewpoints and views responsive to the concerns of the data science community.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications.
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i. e. whether they are feasible or not, to circumvent potential efficiency degradation.
Adversarial Training for Graph Neural Networks
Including these contributions, we demonstrate that adversarial training is a state-of-the-art defense against adversarial structure perturbations.
Uncertainty Estimation for Molecules: Desiderata and Methods
Graph Neural Networks (GNNs) are promising surrogates for quantum mechanical calculations as they establish unprecedented low errors on collections of molecular dynamics (MD) trajectories.
MAGNet: Motif-Agnostic Generation of Molecules from Shapes
To this end, we introduce a novel factorisation of the molecules' data distribution that accounts for the molecules' global context and facilitates learning adequate assignments of atoms and bonds onto shapes.
Generative Diffusion for 3D Turbulent Flows
Turbulent flows are well known to be chaotic and hard to predict; however, their dynamics differ between two and three dimensions.
Edge Directionality Improves Learning on Heterophilic Graphs
Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data.
Ranked #1 on
Node Classification
on snap-patents
Revisiting Robustness in Graph Machine Learning
Many works show that node-level predictions of Graph Neural Networks (GNNs) are unrobust to small, often termed adversarial, changes to the graph structure.
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness
We conduct a detailed analysis for the decomposition of MILP with Benders and Dantzig-Wolfe methods.
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape.
The power of motifs as inductive bias for learning molecular distributions
Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules.
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 amounts of energy, thus incurring large carbon footprints (Strubell et al., 2019).
Training, Architecture, and Prior for Deterministic Uncertainty Methods
Accurate and efficient uncertainty estimation is crucial to build reliable Machine Learning (ML) models capable to provide calibrated uncertainty estimates, generalize and detect Out-Of-Distribution (OOD) datasets.
Ewald-based Long-Range Message Passing for Molecular Graphs
Neural architectures that learn potential energy surfaces from molecular data have undergone fast improvement in recent years.
Generalizing Neural Wave Functions
To overcome this limitation, we present Graph-learned orbital embeddings (Globe), a neural network-based reparametrization method that can adapt neural wave functions to different molecules.
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a vector of labels) based on a single input, i. e. a single graph, image, or document respectively.
Are Defenses for Graph Neural Networks Robust?
A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs).
Transformers Meet Directed Graphs
Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected graphs.
Ranked #1 on
Graph Property Prediction
on ogbg-code2
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
To remedy this, we propose novel gray-box certificates that exploit the message-passing principle of GNNs: We randomly intercept messages and carefully analyze the probability that messages from adversarially controlled nodes reach their target nodes.
Training Differentially Private Graph Neural Networks with Random Walk Sampling
We propose to solve this issue by training graph neural networks on disjoint subgraphs of a given training graph.
Influence-Based Mini-Batching for Graph Neural Networks
Using graph neural networks for large graphs is challenging since there is no clear way of constructing mini-batches.
Invariance-Aware Randomized Smoothing Certificates
Building models that comply with the invariances inherent to different domains, such as invariance under translation or rotation, is a key aspect of applying machine learning to real world problems like molecular property prediction, medical imaging, protein folding or LiDAR classification.
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion
Temporal data such as time series can be viewed as discretized measurements of the underlying function.
Localized Randomized Smoothing for Collective Robustness Certification
We further show that this approach is beneficial for the larger class of softly local models, where each output is dependent on the entire input but assigns different levels of importance to different input regions (e. g. based on their proximity in the image).
torchode: A Parallel ODE Solver for PyTorch
We introduce an ODE solver for the PyTorch ecosystem that can solve multiple ODEs in parallel independently from each other while achieving significant performance gains.
Irregularly-Sampled Time Series Modeling with Spline Networks
Observations made in continuous time are often irregular and contain the missing values across different channels.
Unveiling the Sampling Density in Non-Uniform Geometric Graphs
A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if their distance is less than a specified neighborhood radius.
A Systematic Evaluation of Node Embedding Robustness
We find that node classification results are impacted more than network reconstruction ones, that degree-based and label-based attacks are on average the most damaging and that label heterophily can strongly influence attack performance.
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 of Machine Learning (ML)-enabled software systems with a focus on the Internet of Things (IoT) domain.
United States Politicians' Tone Became More Negative with 2016 Primary Campaigns
There is a widespread belief that the tone of US political language has become more negative recently, in particular when Donald Trump entered politics.
On the Robustness and Anomaly Detection of Sparse Neural Networks
The robustness and anomaly detection capability of neural networks are crucial topics for their safe adoption in the real-world.
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
Pruning, the task of sparsifying deep neural networks, received increasing attention recently.
Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model
Climate change is perhaps the biggest single threat to humankind and the environment, as it severely impacts our terrestrial surface, home to most of the living species.
Ranked #2 on
Earth Surface Forecasting
on EarthNet2021 OOD Track
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
Characterizing aleatoric and epistemic uncertainty can be used to speed up learning in a training environment, improve generalization to similar testing environments, and flag unfamiliar behavior in anomalous testing environments.
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
In this work, we address the inference shortcomings by proposing the Potential learning from ab-initio Networks (PlaNet) framework, in which we simultaneously train a surrogate model in addition to the neural wave function.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
This work investigates this question by first developing the GemNet-OC model based on the large Open Catalyst 2020 (OC20) dataset.
Ranked #1 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
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 to determine when new inputs are significantly different from the training distribution.
Differentiable DAG Sampling
To this end, DP-DAG samples a DAG by successively (1) sampling a linear ordering of the node and (2) sampling edges consistent with the sampled linear ordering.
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks
We propose a new method for spatio-temporal forecasting on arbitrarily distributed points.
Interpretability Techniques for Deep Learning
Interpretable Machine Learning
+2
Enabling Automated Machine Learning for Model-Driven AI Engineering
Developing smart software services requires both Software Engineering and Artificial Intelligence (AI) skills.
Multi-Objective Model Selection for Time Series Forecasting
By learning a mapping from forecasting models to performance metrics, we show that our method PARETOSELECT is able to accurately select models from the Pareto front -- alleviating the need to train or evaluate many forecasting models for model selection.
Graph Data Augmentation for Graph Machine Learning: A Survey
Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain.
Directional Message Passing on Molecular Graphs via Synthetic Coordinates
We furthermore set the state of the art on ZINC and coordinate-free QM9 by incorporating synthetic coordinates in the SMP and DimeNet++ models.
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
GPN outperforms existing approaches for uncertainty estimation in the experiments.
Robustness of Graph Neural Networks at Scale
Graph Neural Networks (GNNs) are increasingly important given their popularity and the diversity of applications.
Neural Flows: Efficient Alternative to Neural ODEs
Neural ordinary differential equations describe how values change in time.
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
Specifically, most datasets only capture a simpler subproblem and likely suffer from spurious features.
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Solving the Schr\"odinger equation is key to many quantum mechanical properties.
Intriguing Properties of Input-dependent Randomized Smoothing
We show that in general, the input-dependent smoothing suffers from the curse of dimensionality, forcing the variance function to have low semi-elasticity.
3D Infomax improves GNNs for Molecular Property Prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
Provably Robust Transfer
Our experiments show how adversarial training on the source domain affects robustness on source and target domain, and we propose the first provably robust transfer learning models.
Locality-Based Mini Batching for Graph Neural Networks
Training graph neural networks on large graphs is challenging since there is no clear way of how to extract mini batches from connected data.
3D Pre-training improves GNNs for Molecular Property Prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
End-to-End Learning of Probabilistic Hierarchies on Graphs
We propose a novel probabilistic model over hierarchies on graphs obtained by continuous relaxation of tree-based hierarchies.
A Study of Joint Graph Inference and Forecasting
We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series.
On Second-order Optimization Methods for Federated Learning
We consider federated learning (FL), where the training data is distributed across a large number of clients.
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.
OODformer: Out-Of-Distribution Detection Transformer
A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model training, but performs much worse for out-of-distribution (OOD) samples.
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
The current best practice for computing optimal transport (OT) is via entropy regularization and Sinkhorn iterations.
MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence
Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems.
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering
We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs.
ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services
In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven Software Engineering (MDSE) for smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT).
A Model-Driven Approach to Machine Learning and Software Modeling for the IoT
In particular, we implement the proposed approach, called ML-Quadrat, based on ThingML, and validate it using a case study from the IoT domain, as well as through an empirical user evaluation.
Supporting AI Engineering on the IoT Edge through Model-Driven TinyML
We focus on a sub-discipline of AI, namely Machine Learning (ML) and propose the delegation of data analytics and ML to the IoT edge.
On Out-of-distribution Detection with Energy-based Models
Several density estimation methods have shown to fail to detect out-of-distribution (OOD) samples by assigning higher likelihoods to anomalous data.
Detecting Anomalous Event Sequences with Temporal Point Processes
Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security.
GemNet: Universal Directional Graph Neural Networks for Molecules
Effectively predicting molecular interactions has the potential to accelerate molecular dynamics by multiple orders of magnitude and thus revolutionize chemical simulations.
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions
Uncertainty awareness is crucial to develop reliable machine learning models.
Neural Temporal Point Processes: A Review
Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences.
Language-Agnostic Representation Learning of Source Code from Structure and Context
Source code (Context) and its parsed abstract syntax tree (AST; Structure) are two complementary representations of the same computer program.
Warpspeed Computation of Optimal Transport, Graph Distances, and Embedding Alignment
Optimal transport (OT) is a cornerstone of many machine learning tasks.
Collective Robustness Certificates
In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a vector of labels) based on a single input, i. e. a single graph, image, or document respectively.
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
We propose a Monte Carlo objective that leverages the conditional linearity by computing the corresponding conditional expectations in closed-form and a suitable proposal distribution that is factorised similarly to the optimal proposal distribution.
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
Many important tasks in chemistry revolve around molecules during reactions.
Reliable Graph Neural Networks via Robust Aggregation
Perturbations targeting the graph structure have proven to be extremely effective in reducing the performance of Graph Neural Networks (GNNs), and traditional defenses such as adversarial training do not seem to be able to improve robustness.
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Dirichlet-based uncertainty (DBU) models are a recent and promising class of uncertainty-aware models.
Scalable Normalizing Flows for Permutation Invariant Densities
Modeling sets is an important problem in machine learning since this type of data can be found in many domains.
Equivariant Normalizing Flows for Point Processes and Sets
To model this behavior, it is enough to transform the samples from the uniform process with a sufficiently complex equivariant function.
ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning
In this paper, we present the current position of the research project ML-Quadrat, which aims to extend the methodology, modeling language and tool support of ThingML - an open source modeling tool for IoT/CPS - to address Machine Learning needs for the IoT applications.
From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)
In this paper, we illustrate how to enhance an existing state-of-the-art modeling language and tool for the Internet of Things (IoT), called ThingML, to support machine learning on the modeling level.
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Existing techniques for certifying the robustness of models for discrete data either work only for a small class of models or are general at the expense of efficiency or tightness.
Reachable Sets of Classifiers and Regression Models: (Non-)Robustness Analysis and Robust Training
This principle is highly versatile, as we show.
Deep Representation Learning and Clustering of Traffic Scenarios
Determining the traffic scenario space is a major challenge for the homologation and coverage assessment of automated driving functions.
Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks.
Scene Graph Reasoning for Visual Question Answering
We propose a novel method that approaches the task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene.
Fast and Flexible Temporal Point Processes with Triangular Maps
Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data.
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
The posterior distributions learned by PostNet accurately reflect uncertainty for in- and out-of-distribution data -- without requiring access to OOD data at training time.
Continual Learning with Bayesian Neural Networks for Non-Stationary Data
We represent the posterior approximation of the network weights by a diagonal Gaussian distribution and a complementary memory of raw data.
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs
The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types.
Directional Message Passing for Molecular Graphs
Each message is associated with a direction in coordinate space.
Ranked #2 on
Drug Discovery
on QM9
Oktoberfest Food Dataset
We release a realistic, diverse, and challenging dataset for object detection on images.
Uncertainty on Asynchronous Time Event Prediction
Asynchronous event sequences are the basis of many applications throughout different industries.
Certifiable Robustness to Graph Perturbations
Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness.
Diffusion Improves Graph Learning
In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC).
Ranked #3 on
Node Classification
on AMZ Comp
Overlapping Community Detection with Graph Neural Networks
Community detection is a fundamental problem in machine learning.
Intensity-Free Learning of Temporal Point Processes
The standard way of learning in such models is by estimating the conditional intensity function.
Certifiable Robustness and Robust Training for Graph Convolutional Networks
Recent works show that Graph Neural Networks (GNNs) are highly non-robust with respect to adversarial attacks on both the graph structure and the node attributes, making their outcomes unreliable.
Ranked #20 on
Node Classification
on Pubmed
Adversarial Attacks on Node Embeddings
The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks.
Adversarial Attacks on Graph Neural Networks via Meta Learning
Deep learning models for graphs have advanced the state of the art on many tasks.
Pitfalls of Graph Neural Network Evaluation
We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models.
Multi-Source Neural Variational Inference
Learning from multiple sources of information is an important problem in machine-learning research.
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings.
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP.
Ranked #1 on
Node Classification
on MS ACADEMIC
General Classification
Node Classification on Non-Homophilic (Heterophilic) Graphs
Mining Contrasting Quasi-Clique Patterns
Mining dense quasi-cliques is a well-known clustering task with applications ranging from social networks over collaboration graphs to document analysis.
Adversarial Attacks on Node Embeddings via Graph Poisoning
The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks.
Dual-Primal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs.
Adversarial Attacks on Neural Networks for Graph Data
Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given.
NetGAN: Generating Graphs via Random Walks
NetGAN is able to produce graphs that exhibit well-known network patterns without explicitly specifying them in the model definition.
GraphGAN: Generating Graphs via Random Walks
Moreover, GraphGAN learns a semantic mapping from the latent input space to the generated graph's properties.
Introduction to Tensor Decompositions and their Applications in Machine Learning
Tensors are multidimensional arrays of numerical values and therefore generalize matrices to multiple dimensions.
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification.
Linearized and Single-Pass Belief Propagation
Often, we can answer such questions and label nodes in a network based on the labels of their neighbors and appropriate assumptions of homophily ("birds of a feather flock together") or heterophily ("opposites attract").
