Mathias Niepert
61 publications
17 venues
H Index 23
Affiliation
University of Stuttgart, GermanyNEC Laboratories Europe, Heidelberg, Germany
Links
| Name | Venue | Year | citations |
|---|---|---|---|
On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation.
|
ICML | 2025 | 5 |
| Tractable Transformers for Flexible Conditional Generation.
|
ICML | 2025 | 0 |
| Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching.
|
ICML | 2025 | 0 |
| Physics-Informed Weakly Supervised Learning For Interatomic Potentials.
|
ICML | 2025 | 0 |
| Active Learning for Neural PDE Solvers.
|
ICLR | 2025 | 0 |
| Learning to Discretize Denoising Diffusion ODEs.
|
ICLR | 2025 | 0 |
| Discrete Copula Diffusion.
|
ICLR | 2025 | 0 |
| Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks.
|
ICML | 2024 | 8 |
| Probabilistic Graph Rewiring via Virtual Nodes.
|
NIPS/NeurIPS | 2024 | 14 |
| Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model.
|
ACML | 2024 | 7 |
| Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations.
|
ICML | 2024 | 10 |
| Accelerating Transformers with Spectrum-Preserving Token Merging.
|
NIPS/NeurIPS | 2024 | 29 |
| Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing.
|
NIPS/NeurIPS | 2024 | 14 |
| Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks.
|
ICLR | 2024 | 0 |
| Probabilistically Rewired Message-Passing Neural Networks.
|
ICLR | 2024 | 0 |
| Image Inpainting via Tractable Steering of Diffusion Models.
|
ICLR | 2024 | 0 |
| L2XGNN: learning to explain graph neural networks.
|
MLJ | 2024 | 0 |
| Learning Disentangled Discrete Representations.
|
ECML/PKDD | 2023 | 2 |
| LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching.
|
NIPS/NeurIPS | 2023 | 84 |
| Learning Neural PDE Solvers with Parameter-Guided Channel Attention.
|
ICML | 2023 | 38 |
| Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models.
|
AAAI | 2023 | 0 |
| SIMPLE: A Gradient Estimator for k-Subset Sampling.
|
ICLR | 2023 | 0 |
| State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions.
|
TPAMI | 2023 | 0 |
| PDEBench: An Extensive Benchmark for Scientific Machine Learning.
|
NIPS/NeurIPS | 2022 | 362 |
| Ordered Subgraph Aggregation Networks.
|
NIPS/NeurIPS | 2022 | 74 |
| MILIE: Modular & Iterative Multilingual Open Information Extraction.
|
ACL | 2022 | 0 |
| BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation.
|
ACL | 2022 | 0 |
| Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions.
|
NIPS/NeurIPS | 2021 | 110 |
| Interpreting Node Embedding with Text-labeled Graphs.
|
IJCNN | 2021 | 1 |
| Learning Sparsity of Representations with Discrete Latent Variables.
|
IJCNN | 2021 | 0 |
| Explaining Neural Matrix Factorization with Gradient Rollback.
|
AAAI | 2021 | 0 |
| Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders.
|
AAAI | 2021 | 0 |
| Efficient Learning of Discrete-Continuous Computation Graphs.
|
NIPS/NeurIPS | 2021 | 0 |
| Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs.
|
ICLR | 2021 | 0 |
| TransRev: Modeling Reviews as Translations from Users to Items.
|
ECIR | 2020 | 0 |
| State-Regularized Recurrent Neural Networks.
|
ICML | 2019 | 44 |
| MMKG: Multi-modal Knowledge Graphs.
|
ESWC | 2019 | 297 |
| Learning Discrete Structures for Graph Neural Networks.
|
ICML | 2019 | 478 |
| A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning.
|
IJCAI | 2019 | 0 |
| Learning Sequence Encoders for Temporal Knowledge Graph Completion.
|
EMNLP | 2018 | 513 |
| LRMM: Learning to Recommend with Missing Modalities.
|
EMNLP | 2018 | 33 |
| KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features.
|
UAI | 2018 | 0 |
| Learning Graph Representations with Embedding Propagation.
|
NIPS/NeurIPS | 2017 | 175 |
| Discriminative Gaifman Models.
|
NIPS/NeurIPS | 2016 | 43 |
| Learning Convolutional Neural Networks for Graphs.
|
ICML | 2016 | 2263 |
| Learning and Inference in Tractable Probabilistic Knowledge Bases.
|
UAI | 2015 | 12 |
| Lifted Probabilistic Inference for Asymmetric Graphical Models.
|
AAAI | 2015 | 0 |
| Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference.
|
AAAI | 2014 | 80 |
| Exchangeable Variable Models.
|
ICML | 2014 | 20 |
| Symmetry-Aware Marginal Density Estimation.
|
AAAI | 2013 | 21 |
| RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models.
|
AAAI | 2013 | 105 |
| On the conditional independence implication problem: A lattice-theoretic approach.
|
Artificial Intelligence | 2013 | 0 |
| Markov Chains on Orbits of Permutation Groups.
|
UAI | 2012 | 64 |
| Statistical Schema Induction.
|
ESWC | 2011 | 225 |
| Fine-Grained Sentiment Analysis with Structural Features.
|
IJCNLP | 2011 | 156 |
| Log-Linear Description Logics.
|
IJCAI | 2011 | 71 |
| Leveraging Terminological Structure for Object Reconciliation.
|
ESWC | 2010 | 99 |
| A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks.
|
UAI | 2010 | 17 |
| A Probabilistic-Logical Framework for Ontology Matching.
|
AAAI | 2010 | 90 |
| Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence.
|
UAI | 2009 | 17 |
| On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach.
|
UAI | 2008 | 45 |