A dynamic ontology for a dynamic reference work. - Mathias Niepert; Cameron Buckner; Colin Allen (JCDL, 2007)

Answer Set Programming on Expert Feedback to Populate and Extend Dynamic Ontologies. - Mathias Niepert; Cameron Buckner; Colin Allen (FLAIRS, 2008)

On the Conditional Independence Implication Problem: A Lattice-Theoretic Approach. - Mathias Niepert; Dirk Van Gucht; Marc Gyssens (UAI, 2008)

The World is Not Flat: Expertise and InPhO. - Colin Allen; Cameron Buckner; Mathias Niepert (First Monday, 2008)

Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence. - Mathias Niepert (UAI, 2009)

InPhO: A System for Collaboratively Populating and Extending a Dynamic Ontology. - Mathias Niepert; Cameron Buckner; Jaimie Murdock; Colin Allen (Bull. IEEE Tech. Comm. Digit. Libr., 2009)

A Probabilistic-Logical Framework for Ontology Matching. - Mathias Niepert; Christian Meilicke; Heiner Stuckenschmidt (AAAI, 2010)

Leveraging Terminological Structure for Object Reconciliation. - Jan Noessner; Mathias Niepert; Christian Meilicke; Heiner Stuckenschmidt (ESWC (2), 2010)

Thesaurus Extension Using Web Search Engines. - Robert Meusel; Mathias Niepert; Kai Eckert 001; Heiner Stuckenschmidt (ICADL, 2010)

A Statistical-Relational Activity Recognition Framework for Ambient Assisted Living Systems. - Rim Helaoui; Mathias Niepert; Heiner Stuckenschmidt (ISAmI, 2010)

Crowdsourcing the assembly of concept hierarchies. - Kai Eckert 001; Mathias Niepert; Christof Niemann; Cameron Buckner; Colin Allen; Heiner Stuckenschmidt (JCDL, 2010)

CODI: Combinatorial Optimization for Data Integration: results for OAEI 2010. - Jan Noessner; Mathias Niepert (OM, 2010)

BRAMBLE: A Web-based Framework for Interactive RDF-Graph Visualisation. - Nikolas Schmitt; Mathias Niepert; Heiner Stuckenschmidt (ISWC (Posters & Demos), 2010)

A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference in Markov Logic Networks. - Mathias Niepert (UAI, 2010)

Logical and algorithmic properties of stable conditional independence. - Mathias Niepert; Dirk Van Gucht; Marc Gyssens (Int. J. Approx. Reason., 2010)

"Towards Collaboratively Learning and Populating Ontologies for the Social-Semantic Web" by Mathias Niepert, with Jessica Rubart as coordinator. - Mathias Niepert (SIGWEB Newsl., 2010)

Statistical Schema Induction. - Johanna Völker; Mathias Niepert (ESWC (1), 2011)

Log-Linear Description Logics. - Mathias Niepert; Jan Noessner; Heiner Stuckenschmidt (IJCAI, 2011)

Fine-Grained Sentiment Analysis with Structural Features. - Cäcilia Zirn; Mathias Niepert; Heiner Stuckenschmidt; Michael Strube 001 (IJCNLP, 2011)

Recognizing interleaved and concurrent activities: A statistical-relational approach. - Rim Helaoui; Mathias Niepert; Heiner Stuckenschmidt (PerCom, 2011)

ELOG: A Probabilistic Reasoner for OWL EL. - Jan Noessner; Mathias Niepert (RR, 2011)

Probabilistic-Logical Web Data Integration. - Mathias Niepert; Jan Noessner; Christian Meilicke; Heiner Stuckenschmidt (Reasoning Web, 2011)

Reasoning under Uncertainty with Log-Linear Description Logics. - Mathias Niepert (URSW, 2011)

Coherent Top-k Ontology Alignment for OWL EL. - Jan Noessner; Mathias Niepert; Heiner Stuckenschmidt (SUM, 2011)

Recognizing interleaved and concurrent activities using qualitative and quantitative temporal relationships. - Rim Helaoui; Mathias Niepert; Heiner Stuckenschmidt (Pervasive Mob. Comput., 2011)

From encyclopedia to ontology: toward dynamic representation of the discipline of philosophy. - Cameron Buckner; Mathias Niepert; Colin Allen (Synth., 2011)

Probabilistic Optimization of Semantic Process Model Matching. - Henrik Leopold; Mathias Niepert; Matthias Weidlich 001; Jan Mendling; Remco M. Dijkman; Heiner Stuckenschmidt (BPM, 2012)

Towards Distributed MCMC Inference in Probabilistic Knowledge Bases. - Mathias Niepert; Christian Meilicke; Heiner Stuckenschmidt (AKBC-WEKEX@NAACL-HLT, 2012)

Lifted Probabilistic Inference: An MCMC Perspective. - Mathias Niepert (StarAI@UAI, 2012)

Markov Chains on Orbits of Permutation Groups. - Mathias Niepert (UAI, 2012)

Symmetry-Aware Marginal Density Estimation. - Mathias Niepert (AAAI, 2013)

RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models. - Jan Noessner; Mathias Niepert; Heiner Stuckenschmidt (StarAI@AAAI, 2013)

Computing Incoherence Explanations for Learned Ontologies. - Daniel Fleischhacker; Christian Meilicke; Johanna Völker; Mathias Niepert (RR, 2013)

Statistical Relational Data Integration for Information Extraction. - Mathias Niepert (Reasoning Web, 2013)

Integrating Open and Closed Information Extraction: Challenges and First Steps. - Arnab Dutta 001; Christian Meilicke; Mathias Niepert; Simone Paolo Ponzetto (NLP-DBPEDIA@ISWC, 2013)

On the conditional independence implication problem: A lattice-theoretic approach. - Mathias Niepert; Marc Gyssens; Bassem Sayrafi; Dirk Van Gucht (Artif. Intell., 2013)

Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference. - Mathias Niepert; Guy Van den Broeck (AAAI, 2014)

Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond. - Mathias Niepert; Pedro M. Domingos (StarAI@AAAI, 2014)

Exchangeable Variable Models. - Mathias Niepert; Pedro M. Domingos (ICML, 2014)

LODE: Linking digital humanities content to the web of data. - Timo Sztyler; Jakob Huber; Jan Noessner; Jaimie Murdock; Colin Allen; Mathias Niepert (JCDL, 2014)

Completeness and optimality in ontology alignment debugging. - Jan Noessner; Heiner Stuckenschmidt; Christian Meilicke; Mathias Niepert (OM, 2014)

On the completeness of the semigraphoid axioms for deriving arbitrary from saturated conditional independence statements. - Marc Gyssens; Mathias Niepert; Dirk Van Gucht (Inf. Process. Lett., 2014)

Lifted Probabilistic Inference for Asymmetric Graphical Models. - Guy Van den Broeck; Mathias Niepert (AAAI, 2015)

Learning and Inference in Tractable Probabilistic Knowledge Bases. - Mathias Niepert; Pedro M. Domingos (UAI, 2015)

Learning Convolutional Neural Networks for Graphs. - Mathias Niepert; Mohamed Ahmed 001; Konstantin Kutzkov (ICML, 2016)

Discriminative Gaifman Models. - Mathias Niepert (NIPS, 2016)

Learning Graph Representations with Embedding Propagation. - Alberto García-Durán; Mathias Niepert (NIPS, 2017)

Network Data Monetization Using Net2Vec. - Roberto Gonzalez; Alberto García-Durán; Filipe Manco; Mathias Niepert; Pelayo Vallina (SIGCOMM Posters and Demos, 2017)

Net2Vec: Deep Learning for the Network. - Roberto Gonzalez; Filipe Manco; Alberto García-Durán; Jose Mendes; Felipe Huici; Saverio Niccolini; Mathias Niepert (Big-DAMA@SIGCOMM, 2017)

Representation Learning for Visual-Relational Knowledge Graphs. - Daniel Oñoro-Rubio; Mathias Niepert; Alberto García-Durán; Roberto Gonzalez; Roberto Javier López-Sastre (CoRR, 2017)

An infrastructure for probabilistic reasoning with web ontologies. - Jakob Huber; Mathias Niepert; Jan Noessner; Joerg Schoenfisch; Christian Meilicke; Heiner Stuckenschmidt (Semantic Web, 2017)

Learning Short-Cut Connections for Object Counting. - Daniel Oñoro-Rubio; Roberto Javier López-Sastre; Mathias Niepert (BMVC, 2018)

Knowledge Graph Completion to Predict Polypharmacy Side Effects. - Brandon M. Malone; Alberto García-Durán; Mathias Niepert (DILS, 2018)

Towards a Spectrum of Graph Convolutional Networks. - Mathias Niepert; Alberto García-Durán (DSW, 2018)

Learning Sequence Encoders for Temporal Knowledge Graph Completion. - Alberto García-Durán; Sebastijan Dumancic; Mathias Niepert (EMNLP, 2018)

LRMM: Learning to Recommend with Missing Modalities. - Cheng Wang 002; Mathias Niepert; Hui Li 057 (EMNLP, 2018)

KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features. - Alberto García-Durán; Mathias Niepert (UAI, 2018)

Representation Learning for Resource Usage Prediction. - Florian Schmidt 002; Mathias Niepert; Felipe Huici (CoRR, 2018)

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism. - Nicolas Weber; Florian Schmidt 002; Mathias Niepert; Felipe Huici (CoRR, 2018)

Contextual Hourglass Networks for Segmentation and Density Estimation. - Daniel Oñoro-Rubio; Mathias Niepert (CoRR, 2018)

On embeddings as an alternative paradigm for relational learning. - Sebastijan Dumancic; Alberto García-Durán; Mathias Niepert (CoRR, 2018)

Learning Representations of Missing Data for Predicting Patient Outcomes. - Brandon M. Malone; Alberto García-Durán; Mathias Niepert (CoRR, 2018)

Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs. - Daniel Oñoro-Rubio; Mathias Niepert; Alberto García-Durán; Roberto Gonzalez-Sanchez; Roberto Javier López-Sastre (AKBC, 2019)

Early Detection of Infection Chains & Outbreaks: Use Case Infection Control. - A. Sargeant; Tatiana von Landesberger; Claas Baier; F. Bange; Alexander Dalpke; Tim Eckmanns; Stephan Glöckner; Martin Kaase; Gérard Krause; Michael Marschollek; Brandon M. Malone; Mathias Niepert; et al. (EFMI-STC, 2019)

Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas. - Kosuke Akimoto; Takuya Hiraoka; Kunihiko Sadamasa; Mathias Niepert (EMNLP/IJCNLP (1), 2019)

Attending to Future Tokens for Bidirectional Sequence Generation. - Carolin Lawrence; Bhushan Kotnis; Mathias Niepert (EMNLP/IJCNLP (1), 2019)

MMKG: Multi-modal Knowledge Graphs. - Ye Liu 002; Hui Li 057; Alberto García-Durán; Mathias Niepert; Daniel Oñoro-Rubio; David S. Rosenblum (ESWC, 2019)

Learning Discrete Structures for Graph Neural Networks. - Luca Franceschi 001; Mathias Niepert; Massimiliano Pontil; Xiao He (ICML, 2019)

State-Regularized Recurrent Neural Networks. - Cheng Wang 002; Mathias Niepert (ICML, 2019)

A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning. - Sebastijan Dumancic; Alberto García-Durán; Mathias Niepert (IJCAI, 2019)

TransRev: Modeling Reviews as Translations from Users to Items. - Alberto García-Durán; Roberto Gonzalez; Daniel Oñoro-Rubio; Mathias Niepert; Hui Li 057 (ECIR (1), 2020)

RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems. - Cheng Wang 002; Mathias Niepert; Hui Li 057 (IEEE Trans. Neural Networks Learn. Syst., 2020)

Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders. - Bhushan Kotnis; Carolin Lawrence; Mathias Niepert (AAAI, 2021)

Explaining Neural Matrix Factorization with Gradient Rollback. - Carolin Lawrence; Timo Sztyler; Mathias Niepert (AAAI, 2021)

Behavioral Testing of Knowledge Graph Embedding Models for Link Prediction. - Wiem Ben Rim; Carolin Lawrence; Kiril Gashteovski; Mathias Niepert; Naoaki Okazaki (AKBC, 2021)

Wayfinder: towards automatically deriving optimal OS configurations. - Alexander Jung; Hugo Lefeuvre; Charalampos Rotsos; Pierre Olivier; Daniel Oñoro-Rubio; Felipe Huici; Mathias Niepert (APSys, 2021)

User profiling by network observers. - Roberto Gonzalez; Claudio Soriente; Juan Miguel Carrascosa; Alberto García-Durán; Costas Iordanou; Mathias Niepert (CoNEXT, 2021)

Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs. - Cheng Wang 002; Carolin Lawrence; Mathias Niepert (ICLR, 2021)

Interpreting Node Embedding with Text-labeled Graphs. - Giuseppe Serra 002; Zhao Xu 001; Mathias Niepert; Carolin Lawrence; Peter Tiño; Xin Yao 001 (IJCNN, 2021)

Learning Sparsity of Representations with Discrete Latent Variables. - Zhao Xu 001; Daniel Oñoro-Rubio; Giuseppe Serra 002; Mathias Niepert (IJCNN, 2021)

Backpropagating through Markov Logic Networks. - Patrick Betz; Mathias Niepert; Pasquale Minervini; Heiner Stuckenschmidt (NeSy, 2021)

Efficient Learning of Discrete-Continuous Computation Graphs. - David Friede; Mathias Niepert (NeurIPS, 2021)

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions. - Mathias Niepert; Pasquale Minervini; Luca Franceschi 001 (NeurIPS, 2021)

VEGN: Variant Effect Prediction with Graph Neural Networks. - Jun Cheng 007; Carolin Lawrence; Mathias Niepert (CoRR, 2021)

BenchIE: Open Information Extraction Evaluation Based on Facts, Not Tokens. - Kiril Gashteovski; Mingying Yu; Bhushan Kotnis; Carolin Lawrence; Goran Glavas; Mathias Niepert (CoRR, 2021)

Integrating diverse extraction pathways using iterative predictions for Multilingual Open Information Extraction. - Bhushan Kotnis; Kiril Gashteovski; Carolin Lawrence; Daniel Oñoro-Rubio; Vanesa Rodriguez-Tembras; Makoto Takamoto; Mathias Niepert (CoRR, 2021)

AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark. - Niklas Friedrich; Kiril Gashteovski; Mingying Yu; Bhushan Kotnis; Carolin Lawrence; Mathias Niepert; Goran Glavas (ACL (demo), 2022)

BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation. - Kiril Gashteovski; Mingying Yu; Bhushan Kotnis; Carolin Lawrence; Mathias Niepert; Goran Glavas (ACL (1), 2022)

MILIE: Modular & Iterative Multilingual Open Information Extraction. - Bhushan Kotnis; Kiril Gashteovski; Daniel Oñoro-Rubio; Ammar Shaker; Vanesa Rodriguez-Tembras; Makoto Takamoto; Mathias Niepert; Carolin Lawrence (ACL (1), 2022)

Joint Multilingual Knowledge Graph Completion and Alignment. - Vinh Tong; Dat Quoc Nguyen; Trung Thanh Huynh; Tam Thanh Nguyen; Quoc Viet Hung Nguyen; Mathias Niepert (EMNLP (Findings), 2022)

Ordered Subgraph Aggregation Networks. - Chendi Qian; Gaurav Rattan; Floris Geerts; Mathias Niepert; Christopher Morris 001 (NeurIPS, 2022)

PDEBench: An Extensive Benchmark for Scientific Machine Learning. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (NeurIPS, 2022)

Learning to Explain Graph Neural Networks. - Giuseppe Serra 002; Mathias Niepert (CoRR, 2022)

Joint Multilingual Knowledge Graph Completion and Alignment. - Vinh Tong; Dat Quoc Nguyen; Thanh Trung Huynh; Thanh Tam Nguyen; Nguyen Quoc Viet Hung; Mathias Niepert (CoRR, 2022)

PDEBench Pretrained Models. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2022)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2022)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2022)

Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models. - Pasquale Minervini; Luca Franceschi 001; Mathias Niepert (AAAI, 2023)

SIMPLE: A Gradient Estimator for k-Subset Sampling. - Kareem Ahmed; Zhe Zeng; Mathias Niepert; Guy Van den Broeck (ICLR, 2023)

Learning Neural PDE Solvers with Parameter-Guided Channel Attention. - Makoto Takamoto; Francesco Alesiani; Mathias Niepert (ICML, 2023)

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. - Duy M. H. Nguyen; Hoang Nguyen; Nghiem Tuong Diep; Tan Ngoc Pham; Tri Cao; Binh T. Nguyen 001; Paul Swoboda; Nhat Ho; Shadi Albarqouni; Pengtao Xie; Daniel Sonntag; Mathias Niepert (NeurIPS, 2023)

Learning Disentangled Discrete Representations. - David Friede; Christian Reimers; Heiner Stuckenschmidt; Mathias Niepert (ECML/PKDD (4), 2023)

Approximate Answering of Graph Queries. - Michael Cochez; Dimitrios Alivanistos; Erik Arakelyan; Max Berrendorf; Daniel Daza; Mikhail Galkin 001; Pasquale Minervini; Mathias Niepert; Hongyu Ren (CoRR, 2023)

Continual Invariant Risk Minimization. - Francesco Alesiani; Shujian Yu; Mathias Niepert (CoRR, 2023)

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation. - Duy Minh Ho Nguyen; Tan Ngoc Pham; Nghiem Tuong Diep; Nghi Quoc Phan; Quang Pham; Vinh Tong; Binh T. Nguyen 001; Ngan Hoang Le; Nhat Ho; Pengtao Xie; Daniel Sonntag; Mathias Niepert (CoRR, 2023)

Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching. - Federico Errica; Henrik Christiansen; Viktor Zaverkin; Takashi Maruyama; Mathias Niepert; Francesco Alesiani (CoRR, 2023)

Approximate Answering of Graph Queries. - Michael Cochez; Dimitrios Alivanistos; Erik Arakelyan; Max Berrendorf; Daniel Daza; Mikhail Galkin 001; Pasquale Minervini; Mathias Niepert; Hongyu Ren (Compendium of Neurosymbolic Artificial Intelligence, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions. - Cheng Wang 002; Carolin Lawrence; Mathias Niepert (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Pretrained Models. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2023)

Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model. - Duy Minh Ho Nguyen; An Thai Le 001; Trung Quoc Nguyen; Nghiem Tuong Diep; Tai Nguyen 008; Duy Duong-Tran; Jan Peters 001; Li Shen 001; Mathias Niepert; Daniel Sonntag (ACML, 2024)

Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks. - Federico Errica; Mathias Niepert (ICLR, 2024)

Image Inpainting via Tractable Steering of Diffusion Models. - Anji Liu; Mathias Niepert; Guy Van den Broeck (ICLR, 2024)

Probabilistically Rewired Message-Passing Neural Networks. - Chendi Qian; Andrei Manolache; Kareem Ahmed; Zhe Zeng; Guy Van den Broeck; Mathias Niepert; Christopher Morris 001 (ICLR, 2024)

Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations. - Jan Hagnberger; Marimuthu Kalimuthu; Daniel Musekamp; Mathias Niepert (ICML, 2024)

Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. - Duy Minh Ho Nguyen; Nina Lukashina; Tai Nguyen 008; An T. Le 001; TrungTin Nguyen; Nhat Ho; Jan Peters 001; Daniel Sonntag; Viktor Zaverkin; Mathias Niepert (ICML, 2024)

Probabilistic Graph Rewiring via Virtual Nodes. - Chendi Qian; Andrei Manolache; Christopher Morris 001; Mathias Niepert (NeurIPS, 2024)

Accelerating Transformers with Spectrum-Preserving Token Merging. - Chau Tran; Duy M. H. Nguyen; Manh-Duy Nguyen; TrungTin Nguyen; Ngan Le; Pengtao Xie; Daniel Sonntag; James Y. Zou; Binh Nguyen; Mathias Niepert (NeurIPS, 2024)

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. - Viktor Zaverkin; Francesco Alesiani; Takashi Maruyama; Federico Errica; Henrik Christiansen; Makoto Takamoto; Nicolas Weber; Mathias Niepert (NeurIPS, 2024)

Learning to Discretize Denoising Diffusion ODEs. - Vinh Tong; Anji Liu; Trung-Dung Hoang; Guy Van den Broeck; Mathias Niepert (CoRR, 2024)

Accelerating Transformers with Spectrum-Preserving Token Merging. - Hoai-Chau Tran; Duy M. H. Nguyen; Duy M. Nguyen; TrungTin Nguyen; Ngan Le; Pengtao Xie; Daniel Sonntag; James Y. Zou; Binh T. Nguyen 001; Mathias Niepert (CoRR, 2024)

Physics-Informed Weakly Supervised Learning for Interatomic Potentials. - Makoto Takamoto; Viktor Zaverkin; Mathias Niepert (CoRR, 2024)

A Looming Replication Crisis in Evaluating Behavior in Language Models? Evidence and Solutions. - Laurène Vaugrante; Mathias Niepert; Thilo Hagendorff (CoRR, 2024)

LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model. - Duy M. H. Nguyen; Nghiem T. Diep; Trung Q. Nguyen; Hoang-Bao Le; Tai Nguyen 008; Tien Nguyen; TrungTin Nguyen; Nhat Ho; Pengtao Xie; Roger Wattenhofer; James Zhou; Daniel Sonntag; et al. (CoRR, 2024)

MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning. - Andrei Manolache; Dragos Tantaru; Mathias Niepert (CoRR, 2024)

PDEBench Datasets. - Makoto Takamoto; Timothy Praditia; Raphael Leiteritz; Daniel MacKinlay; Francesco Alesiani; Dirk Pflüger; Mathias Niepert (DaRUS, 2024)

L2XGNN: learning to explain graph neural networks. - Giuseppe Serra 002; Mathias Niepert (Mach. Learn., 2024)

Discrete Copula Diffusion. - Anji Liu; Oliver Broadrick; Mathias Niepert; Guy Van den Broeck (ICLR, 2025)

Active Learning for Neural PDE Solvers. - Daniel Musekamp; Marimuthu Kalimuthu; David Holzmüller; Makoto Takamoto; Mathias Niepert (ICLR, 2025)

Learning to Discretize Denoising Diffusion ODEs. - Vinh Tong; Dung-Trung Hoang; Anji Liu; Guy Van den Broeck; Mathias Niepert (ICLR, 2025)

Adaptive Physics-informed Neural Networks: A Survey. - Edgar Torres; Mathias Niepert (Trans. Mach. Learn. Res., 2025)

Adaptive Width Neural Networks. - Federico Errica; Henrik Christiansen; Viktor Zaverkin; Mathias Niepert; Francesco Alesiani (CoRR, 2025)

On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation. - Nghiem T. Diep; Huy Nguyen; Chau Nguyen; Minh Le; Duy M. H. Nguyen; Daniel Sonntag; Mathias Niepert; Nhat Ho (CoRR, 2025)

Tractable Transformers for Flexible Conditional Generation. - Anji Liu; Xuejie Liu; Dayuan Zhao; Mathias Niepert; Yitao Liang; Guy Van den Broeck (CoRR, 2025)

Symmetry-Preserving Diffusion Models via Target Symmetrization. - Vinh Tong; Yun Ye; Trung-Dung Hoang; Anji Liu; Guy Van den Broeck; Mathias Niepert (CoRR, 2025)

Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. - Maya Bechler-Speicher; Ben Finkelshtein; Fabrizio Frasca; Luis Müller; Jan Tönshoff; Antoine Siraudin; Viktor Zaverkin; Michael M. Bronstein; Mathias Niepert; Bryan Perozzi; Mikhail Galkin 001; Christopher Morris 001 (CoRR, 2025)

Preference-Based Gradient Estimation for ML-Based Approximate Combinatorial Optimization. - Arman Mielke; Uwe Bauknecht; Thilo Strauss; Mathias Niepert (CoRR, 2025)

Adaptive Physics-informed Neural Networks: A Survey. - Edgar Torres; Jonathan Schiefer; Mathias Niepert (CoRR, 2025)

LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators. - Marimuthu Kalimuthu; David Holzmüller; Mathias Niepert (CoRR, 2025)

CALM-PDE: Continuous and Adaptive Convolutions for Latent Space Modeling of Time-dependent PDEs. - Jan Hagnberger; Daniel Musekamp; Mathias Niepert (CoRR, 2025)

Learning (Approximately) Equivariant Networks via Constrained Optimization. - Andrei Manolache; Luiz F. O. Chamon; Mathias Niepert (CoRR, 2025)