Mathias Niepert

research

∙ 08/12/2023

Approximate Answering of Graph Queries

Knowledge graphs (KGs) are inherently incomplete because of incomplete w...

0 Michael Cochez, et al. ∙

research

∙ 07/26/2023

Efficient Learning of Discrete-Continuous Computation Graphs

Numerous models for supervised and reinforcement learning benefit from c...

0 David Friede, et al. ∙

research

∙ 07/26/2023

Learning Disentangled Discrete Representations

Recent successes in image generation, model-based reinforcement learning...

0 David Friede, et al. ∙

research

∙ 06/20/2023

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Obtaining large pre-trained models that can be fine-tuned to new tasks w...

8 Duy M. H. Nguyen, et al. ∙

research

∙ 05/17/2023

Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks

We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabili...

0 Federico Errica, et al. ∙

research

∙ 04/27/2023

Learning Neural PDE Solvers with Parameter-Guided Channel Attention

Scientific Machine Learning (SciML) is concerned with the development of...

0 Makoto Takamoto, et al. ∙

research

∙ 04/03/2023

Learning Sparsity of Representations with Discrete Latent Variables

Deep latent generative models have attracted increasing attention due to...

6 Zhao Xu, et al. ∙

research

∙ 12/10/2022

State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions

Recurrent neural networks are a widely used class of neural architecture...

0 Cheng Wang, et al. ∙

research

∙ 10/17/2022

Joint Multilingual Knowledge Graph Completion and Alignment

Knowledge graph (KG) alignment and completion are usually treated as two...

0 Vinh Tong, et al. ∙

research

∙ 10/13/2022

PDEBENCH: An Extensive Benchmark for Scientific Machine Learning

Machine learning-based modeling of physical systems has experienced incr...

0 Makoto Takamoto, et al. ∙

research

∙ 10/04/2022

SIMPLE: A Gradient Estimator for k-Subset Sampling

k-subset sampling is ubiquitous in machine learning, enabling regulariza...

0 Kareem Ahmed, et al. ∙

research

∙ 09/28/2022

Learning to Explain Graph Neural Networks

Graph Neural Networks (GNNs) are a popular class of machine learning mod...

0 Giuseppe Serra, et al. ∙

research

∙ 09/11/2022

Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models

The integration of discrete algorithmic components in deep learning arch...

0 Pasquale Minervini, et al. ∙

research

∙ 06/22/2022

Ordered Subgraph Aggregation Networks

Numerous subgraph-enhanced graph neural networks (GNNs) have emerged rec...

8 Chendi Qian, et al. ∙

research

∙ 10/15/2021

Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction

In this paper we investigate a simple hypothesis for the Open Informatio...

0 Bhushan Kotnis, et al. ∙

research

∙ 09/15/2021

AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark

Open Information Extraction (OIE) is the task of extracting facts from s...

0 Niklas Friedrich, et al. ∙

research

∙ 09/14/2021

BenchIE: Open Information Extraction Evaluation Based on Facts, Not Tokens

Intrinsic evaluations of OIE systems are carried out either manually – w...

0 Kiril Gashteovski, et al. ∙

research

∙ 06/25/2021

VEGN: Variant Effect Prediction with Graph Neural Networks

Genetic mutations can cause disease by disrupting normal gene function. ...

6 Jun Cheng, et al. ∙

research

∙ 06/03/2021

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

Integrating discrete probability distributions and combinatorial optimiz...

0 Mathias Niepert, et al. ∙

research

∙ 11/24/2020

Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs

Uncertainty quantification is crucial for building reliable and trustabl...

2 Cheng Wang, et al. ∙

research

∙ 10/12/2020

Explaining Neural Matrix Factorization with Gradient Rollback

Explaining the predictions of neural black-box models is an important pr...

0 Carolin Lawrence, et al. ∙

research

∙ 04/06/2020

Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders

Representation learning for knowledge graphs (KGs) has focused on the pr...

0 Bhushan Kotnis, et al. ∙

research

∙ 08/16/2019

Attending to Future Tokens For Bidirectional Sequence Generation

Neural sequence generation is typically performed token-by-token and lef...

3 Carolin Lawrence, et al. ∙

research

∙ 03/28/2019

Learning Discrete Structures for Graph Neural Networks

Graph neural networks (GNNs) are a popular class of machine learning mod...

0 Luca Franceschi, et al. ∙

research

∙ 03/26/2019

RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems

Data sparsity and data imbalance are practical and challenging issues in...

0 Cheng Wang, et al. ∙

research

∙ 03/13/2019

MMKG: Multi-Modal Knowledge Graphs

We present MMKG, a collection of three knowledge graphs that contain bot...

0 Ye Liu, et al. ∙

research

∙ 01/25/2019

State-Regularized Recurrent Neural Networks

Recurrent neural networks are a widely used class of neural architecture...

16 Cheng Wang, et al. ∙

research

∙ 11/12/2018

Learning Representations of Missing Data for Predicting Patient Outcomes

Extracting actionable insight from Electronic Health Records (EHRs) pose...

0 Brandon Malone, et al. ∙

research

∙ 10/22/2018

Knowledge Graph Completion to Predict Polypharmacy Side Effects

The polypharmacy side effect prediction problem considers cases in which...

0 Brandon Malone, et al. ∙

research

∙ 09/10/2018

Learning Sequence Encoders for Temporal Knowledge Graph Completion

Research on link prediction in knowledge graphs has mainly focused on st...

0 Alberto García-Durán, et al. ∙

research

∙ 08/21/2018

LRMM: Learning to Recommend with Missing Modalities

Multimodal learning has shown promising performance in content-based rec...

0 Cheng Wang, et al. ∙

research

∙ 06/29/2018

On embeddings as an alternative paradigm for relational learning

Many real-world domains can be expressed as graphs and, more generally, ...

0 Sebastijan Dumančić, et al. ∙

research

∙ 06/29/2018

On embeddings as alternative paradigm for relational learning

Many real-world domains can be expressed as graphs and, more generally, ...

0 Sebastijan Dumančić, et al. ∙

research

∙ 06/08/2018

Contextual Hourglass Networks for Segmentation and Density Estimation

Hourglass networks such as the U-Net and V-Net are popular neural archit...

0 Daniel Oñoro-Rubio, et al. ∙

research

∙ 05/08/2018

Learning Short-Cut Connections for Object Counting

Object counting is an important task in computer vision due to its growi...

0 Daniel Oñoro-Rubio, et al. ∙

research

∙ 05/04/2018

Towards a Spectrum of Graph Convolutional Networks

We present our ongoing work on understanding the limitations of graph co...

0 Mathias Niepert, et al. ∙

research

∙ 04/23/2018

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

Neural network frameworks such as PyTorch and TensorFlow are the workhor...

0 Nicolas Weber, et al. ∙

research

∙ 02/02/2018

Representation Learning for Resource Usage Prediction

Creating a model of a computer system that can be used for tasks such as...

0 Florian Schmidt, et al. ∙

research

∙ 01/30/2018

TransRev: Modeling Reviews as Translations from Users to Items

The text of a review expresses the sentiment a customer has towards a pa...

0 Alberto García-Durán, et al. ∙

research

∙ 09/14/2017

KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

We present KBLRN, a novel framework for end-to-end learning of knowledge...

0 Alberto García-Durán, et al. ∙

research

∙ 09/07/2017

Representation Learning for Visual-Relational Knowledge Graphs

A visual-relational knowledge graph (KG) is a KG whose entities are asso...

0 Daniel Oñoro-Rubio, et al. ∙

research

∙ 05/17/2016

Learning Convolutional Neural Networks for Graphs

Numerous important problems can be framed as learning from graph data. W...

0 Mathias Niepert, et al. ∙

research

∙ 12/01/2014

Lifted Probabilistic Inference for Asymmetric Graphical Models

Lifted probabilistic inference algorithms have been successfully applied...

0 Guy Van den Broeck, et al. ∙

research

∙ 08/09/2014

Markov Chains on Orbits of Permutation Groups

We present a novel approach to detecting and utilizing symmetries in pro...

0 Mathias Niepert, et al. ∙

research

∙ 08/09/2014

On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach

A lattice-theoretic framework is introduced that permits the study of th...

0 Mathias Niepert, et al. ∙

research

∙ 05/02/2014

Exchangeable Variable Models

A sequence of random variables is exchangeable if its joint distribution...

0 Mathias Niepert, et al. ∙

research

∙ 01/07/2014

Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference

Exchangeability is a central notion in statistics and probability theory...

0 Mathias Niepert, et al. ∙

research

∙ 04/16/2013

RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models

RockIt is a maximum a-posteriori (MAP) query engine for statistical rela...

0 Jan Noessner, et al. ∙

research

∙ 04/09/2013

Symmetry-Aware Marginal Density Estimation

The Rao-Blackwell theorem is utilized to analyze and improve the scalabi...

0 Mathias Niepert, et al. ∙

research

∙ 05/09/2012

Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence

Logical inference algorithms for conditional independence (CI) statement...

0 Mathias Niepert, et al. ∙

Mathias Niepert

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