Kristian Kersting

is this you? claim profile

∙ 0 followers
Technische Universität Darmstadt ∙ TU Dortmund

Professor in Computer Science Department and Centre for Cognitive Science at TU Darmstadt

Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation

We present ObSuRF, a method which turns a single image of a scene into a...
04/02/2021 ∙ by Karl Stelzner, et al. ∙ 11 ∙ share

read it
Language Models have a Moral Dimension

Artificial writing is permeating our lives due to recent advances in lar...
03/08/2021 ∙ by Patrick Schramowski, et al. ∙ 0 ∙ share

read it
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models

While probabilistic models are an important tool for studying causality,...
02/20/2021 ∙ by Matej Zečević, et al. ∙ 16 ∙ share

read it
Recurrent Rational Networks

Latest insights from biology show that intelligence does not only emerge...
02/18/2021 ∙ by Quentin Delfosse, et al. ∙ 15 ∙ share

read it
Monte-Carlo Graph Search for AlphaZero

The AlphaZero algorithm has been successfully applied in a range of disc...
12/20/2020 ∙ by Johannes Czech, et al. ∙ 121 ∙ share

read it
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation

Most deep neural networks are considered to be black boxes, meaning thei...
12/15/2020 ∙ by Sophie Burkhardt, et al. ∙ 21 ∙ share

read it
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations

Most explanation methods in deep learning map importance estimates for a...
11/25/2020 ∙ by Wolfgang Stammer, et al. ∙ 17 ∙ share

read it
Alfie: An Interactive Robot with a Moral Compass

This work introduces Alfie, an interactive robot that is capable of answ...
09/11/2020 ∙ by Cigdem Turan, et al. ∙ 0 ∙ share

read it
TUDataset: A collection of benchmark datasets for learning with graphs

Recently, there has been an increasing interest in (supervised) learning...
07/16/2020 ∙ by Christopher Morris, et al. ∙ 25 ∙ share

read it
Fitted Q-Learning for Relational Domains

We consider the problem of Approximate Dynamic Programming in relational...
06/10/2020 ∙ by Srijita Das, et al. ∙ 0 ∙ share

read it
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
04/13/2020 ∙ by Robert Peharz, et al. ∙ 10 ∙ share

read it
CryptoSPN: Privacy-preserving Sum-Product Network Inference

AI algorithms, and machine learning (ML) techniques in particular, are i...
02/03/2020 ∙ by Amos Treiber, et al. ∙ 4 ∙ share

read it
Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations

Deep neural networks have shown excellent performances in many real-worl...
01/15/2020 ∙ by Patrick Schramowski, et al. ∙ 24 ∙ share

read it
BERT has a Moral Compass: Improvements of ethical and moral values of machines

Allowing machines to choose whether to kill humans would be devastating ...
12/11/2019 ∙ by Patrick Schramowski, et al. ∙ 0 ∙ share

read it
Structured Object-Aware Physics Prediction for Video Modeling and Planning

When humans observe a physical system, they can easily locate objects, u...
10/06/2019 ∙ by Jannik Kossen, et al. ∙ 0 ∙ share

read it
DeepDB: Learn from Data, not from Queries!

The typical approach for learned DBMS components is to capture the behav...
09/02/2019 ∙ by Benjamin Hilprecht, et al. ∙ 0 ∙ share

read it
Neural Networks for Relational Data

While deep networks have been enormously successful over the last decade...
08/28/2019 ∙ by Navdeep Kaur, et al. ∙ 0 ∙ share

read it
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...
08/19/2019 ∙ by Johannes Czech, et al. ∙ 7 ∙ share

read it
Random Sum-Product Forests with Residual Links

Tractable yet expressive density estimators are a key building block of ...
08/08/2019 ∙ by Fabrizio Ventola, et al. ∙ 0 ∙ share

read it
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks

The performance of deep network learning strongly depends on the choice ...
07/15/2019 ∙ by Alejandro Molina, et al. ∙ 8 ∙ share

read it
Declarative Learning-Based Programming as an Interface to AI Systems

Data-driven approaches are becoming more common as problem-solving techn...
06/18/2019 ∙ by Parisa Kordjamshidi, et al. ∙ 2 ∙ share

read it
Neural-Symbolic Argumentation Mining: an Argument in Favour of Deep Learning and Reasoning

Deep learning is bringing remarkable contributions to the field of argum...
05/22/2019 ∙ by Andrea Galassi, et al. ∙ 0 ∙ share

read it
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures

Bayesian networks are a central tool in machine learning and artificial ...
05/21/2019 ∙ by Xiaoting Shao, et al. ∙ 0 ∙ share

read it
Was ist eine Professur fuer Kuenstliche Intelligenz?

The Federal Government of Germany aims to boost the research in the fiel...
02/17/2019 ∙ by Kristian Kersting, et al. ∙ 0 ∙ share

read it
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks

We introduce SPFlow, an open-source Python library providing a simple in...
01/11/2019 ∙ by Alejandro Molina, et al. ∙ 0 ∙ share

read it
Model-based Approximate Query Processing

Interactive visualizations are arguably the most important tool to explo...
11/15/2018 ∙ by Moritz Kulessa, et al. ∙ 0 ∙ share

read it
Structure Learning for Relational Logistic Regression: An Ensemble Approach

We consider the problem of learning Relational Logistic Regression (RLR)...
08/06/2018 ∙ by Nandini Ramanan, et al. ∙ 14 ∙ share

read it
Automatic Bayesian Density Analysis

Making sense of a dataset in an automatic and unsupervised fashion is a ...
07/24/2018 ∙ by Antonio Vergari, et al. ∙ 0 ∙ share

read it
Probabilistic Deep Learning using Random Sum-Product Networks

Probabilistic deep learning currently receives an increased interest, as...
06/05/2018 ∙ by Robert Peharz, et al. ∙ 0 ∙ share

read it
"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...
05/22/2018 ∙ by Stefano Teso, et al. ∙ 0 ∙ share

read it
Neural Conditional Gradients

The move from hand-designed to learned optimizers in machine learning ha...
03/12/2018 ∙ by Patrick Schramowski, et al. ∙ 0 ∙ share

read it
Lifted Filtering via Exchangeable Decomposition

We present a model for recursive Bayesian filtering based on lifted mult...
01/31/2018 ∙ by Stefan Lüdtke, et al. ∙ 0 ∙ share

read it
Sum-Product Networks for Hybrid Domains

While all kinds of mixed data -from personal data, over panel and scient...
10/09/2017 ∙ by Alejandro Molina, et al. ∙ 0 ∙ share

read it
Coresets for Dependency Networks

Many applications infer the structure of a probabilistic graphical model...
10/09/2017 ∙ by Alejandro Molina, et al. ∙ 0 ∙ share

read it
Global Weisfeiler-Lehman Graph Kernels

Most state-of-the-art graph kernels only take local graph properties int...
03/07/2017 ∙ by Christopher Morris, et al. ∙ 0 ∙ share

read it
A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels

Non-linear kernel methods can be approximated by fast linear ones using ...
03/02/2017 ∙ by Nils M. Kriege, et al. ∙ 0 ∙ share

read it
Faster Kernels for Graphs with Continuous Attributes via Hashing

While state-of-the-art kernels for graphs with discrete labels scale wel...
10/01/2016 ∙ by Christopher Morris, et al. ∙ 0 ∙ share

read it
Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage

Migration crisis, climate change or tax havens: Global challenges need g...
06/16/2016 ∙ by Elena Erdmann, et al. ∙ 0 ∙ share

read it
Lifted Convex Quadratic Programming

Symmetry is the essential element of lifted inference that has recently ...
06/14/2016 ∙ by Martin Mladenov, et al. ∙ 0 ∙ share

read it
How is a data-driven approach better than random choice in label space division for multi-label classification?

We propose using five data-driven community detection approaches from so...
06/07/2016 ∙ by Piotr Szymański, et al. ∙ 0 ∙ share

read it
The Symbolic Interior Point Method

A recent trend in probabilistic inference emphasizes the codification of...
05/26/2016 ∙ by Martin Mladenov, et al. ∙ 0 ∙ share

read it
Propagation Kernels

We introduce propagation kernels, a general graph-kernel framework for e...
10/13/2014 ∙ by Marion Neumann, et al. ∙ 0 ∙ share

read it
Relational Linear Programs

We propose relational linear programming, a simple framework for combing...
10/12/2014 ∙ by Kristian Kersting, et al. ∙ 0 ∙ share

read it
Mind the Nuisance: Gaussian Process Classification using Privileged Noise

The learning with privileged information setting has recently attracted ...
07/01/2014 ∙ by Daniel Hernández-Lobato, et al. ∙ 0 ∙ share

read it
Efficient Information Theoretic Clustering on Discrete Lattices

We consider the problem of clustering data that reside on discrete, low ...
10/26/2013 ∙ by Christian Bauckhage, et al. ∙ 0 ∙ share

read it
Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants

Understanding the adaptation process of plants to drought stress is esse...
10/16/2012 ∙ by Mirwaes Wahabzada, et al. ∙ 0 ∙ share

read it
'Say EM' for Selecting Probabilistic Models for Logical Sequences

Many real world sequences such as protein secondary structures or shell ...
07/04/2012 ∙ by Kristian Kersting, et al. ∙ 0 ∙ share

read it
Counting Belief Propagation

A major benefit of graphical models is that most knowledge is captured i...
05/09/2012 ∙ by Kristian Kersting, et al. ∙ 0 ∙ share

read it

Kristian Kersting

Featured Co-authors