
Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation
We present ObSuRF, a method which turns a single image of a scene into a...
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Language Models have a Moral Dimension
Artificial writing is permeating our lives due to recent advances in lar...
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Interventional SumProduct Networks: Causal Inference with Tractable Probabilistic Models
While probabilistic models are an important tool for studying causality,...
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Recurrent Rational Networks
Latest insights from biology show that intelligence does not only emerge...
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MonteCarlo Graph Search for AlphaZero
The AlphaZero algorithm has been successfully applied in a range of disc...
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Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation
Most deep neural networks are considered to be black boxes, meaning thei...
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Right for the Right Concept: Revising NeuroSymbolic Concepts by Interacting with their Explanations
Most explanation methods in deep learning map importance estimates for a...
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Alfie: An Interactive Robot with a Moral Compass
This work introduces Alfie, an interactive robot that is capable of answ...
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TUDataset: A collection of benchmark datasets for learning with graphs
Recently, there has been an increasing interest in (supervised) learning...
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Fitted QLearning for Relational Domains
We consider the problem of Approximate Dynamic Programming in relational...
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Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
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CryptoSPN: Privacypreserving SumProduct Network Inference
AI algorithms, and machine learning (ML) techniques in particular, are i...
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Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations
Deep neural networks have shown excellent performances in many realworl...
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BERT has a Moral Compass: Improvements of ethical and moral values of machines
Allowing machines to choose whether to kill humans would be devastating ...
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Structured ObjectAware Physics Prediction for Video Modeling and Planning
When humans observe a physical system, they can easily locate objects, u...
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DeepDB: Learn from Data, not from Queries!
The typical approach for learned DBMS components is to capture the behav...
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Neural Networks for Relational Data
While deep networks have been enormously successful over the last decade...
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Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data
Deep neural networks have been successfully applied in learning the boar...
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Random SumProduct Forests with Residual Links
Tractable yet expressive density estimators are a key building block of ...
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Padé Activation Units: Endtoend Learning of Flexible Activation Functions in Deep Networks
The performance of deep network learning strongly depends on the choice ...
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Declarative LearningBased Programming as an Interface to AI Systems
Datadriven approaches are becoming more common as problemsolving techn...
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NeuralSymbolic Argumentation Mining: an Argument in Favour of Deep Learning and Reasoning
Deep learning is bringing remarkable contributions to the field of argum...
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Conditional SumProduct Networks: Imposing Structure on Deep Probabilistic Architectures
Bayesian networks are a central tool in machine learning and artificial ...
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Was ist eine Professur fuer Kuenstliche Intelligenz?
The Federal Government of Germany aims to boost the research in the fiel...
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SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using SumProduct Networks
We introduce SPFlow, an opensource Python library providing a simple in...
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Modelbased Approximate Query Processing
Interactive visualizations are arguably the most important tool to explo...
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Structure Learning for Relational Logistic Regression: An Ensemble Approach
We consider the problem of learning Relational Logistic Regression (RLR)...
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Automatic Bayesian Density Analysis
Making sense of a dataset in an automatic and unsupervised fashion is a ...
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Probabilistic Deep Learning using Random SumProduct Networks
Probabilistic deep learning currently receives an increased interest, as...
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"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users
Although interactive learning puts the user into the loop, the learner r...
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Neural Conditional Gradients
The move from handdesigned to learned optimizers in machine learning ha...
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Lifted Filtering via Exchangeable Decomposition
We present a model for recursive Bayesian filtering based on lifted mult...
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SumProduct Networks for Hybrid Domains
While all kinds of mixed data from personal data, over panel and scient...
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Coresets for Dependency Networks
Many applications infer the structure of a probabilistic graphical model...
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Global WeisfeilerLehman Graph Kernels
Most stateoftheart graph kernels only take local graph properties int...
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A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels
Nonlinear kernel methods can be approximated by fast linear ones using ...
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Faster Kernels for Graphs with Continuous Attributes via Hashing
While stateoftheart kernels for graphs with discrete labels scale wel...
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Machine Learning meets DataDriven Journalism: Boosting International Understanding and Transparency in News Coverage
Migration crisis, climate change or tax havens: Global challenges need g...
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Lifted Convex Quadratic Programming
Symmetry is the essential element of lifted inference that has recently ...
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How is a datadriven approach better than random choice in label space division for multilabel classification?
We propose using five datadriven community detection approaches from so...
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The Symbolic Interior Point Method
A recent trend in probabilistic inference emphasizes the codification of...
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Propagation Kernels
We introduce propagation kernels, a general graphkernel framework for e...
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Relational Linear Programs
We propose relational linear programming, a simple framework for combing...
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Mind the Nuisance: Gaussian Process Classification using Privileged Noise
The learning with privileged information setting has recently attracted ...
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Efficient Information Theoretic Clustering on Discrete Lattices
We consider the problem of clustering data that reside on discrete, low ...
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Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants
Understanding the adaptation process of plants to drought stress is esse...
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'Say EM' for Selecting Probabilistic Models for Logical Sequences
Many real world sequences such as protein secondary structures or shell ...
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Counting Belief Propagation
A major benefit of graphical models is that most knowledge is captured i...
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Kristian Kersting
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Professor in Computer Science Department and Centre for Cognitive Science at TU Darmstadt