Volker Tresp
130 publications
34 venues
H Index 40
Affiliation
Ludwig Maximilian University of Munich, GermanyLinks
| Name | Venue | Year | citations |
|---|---|---|---|
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning.
|
AAAI | 2024 | 0 |
| Provably Better Explanations with Optimized Aggregation of Feature Attributions.
|
ICML | 2024 | 0 |
| LookupViT: Compressing Visual Information to a Limited Number of Tokens.
|
ECCV | 2024 | 0 |
| Why long model-based rollouts are no reason for bad Q-value estimates.
|
ESANN | 2024 | 0 |
| Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance.
|
KDD | 2024 | 0 |
| Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation.
|
CVPR | 2024 | 0 |
| InstanceFormer: An Online Video Instance Segmentation Framework.
|
AAAI | 2023 | 0 |
| Adaptive Multi-Resolution Attention with Linear Complexity.
|
IJCNN | 2023 | 0 |
| Learning Meta-Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction.
|
IJCNN | 2023 | 0 |
| ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs.
|
ISWC | 2023 | 0 |
| Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs Using Confidence-Augmented Reinforcement Learning.
|
ECML/PKDD | 2023 | 0 |
| Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models.
|
NIPS/NeurIPS | 2023 | 0 |
| Multi-event Video-Text Retrieval.
|
ICCV | 2023 | 0 |
| FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation.
|
ICCV | 2023 | 0 |
| Do DALL-E and Flamingo Understand Each Other?
|
ICCV | 2023 | 0 |
| SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness.
|
ECCV | 2022 | 2 |
| Relationformer: A Unified Framework for Image-to-Graph Generation.
|
ECCV | 2022 | 7 |
| On Calibration of Graph Neural Networks for Node Classification.
|
IJCNN | 2022 | 0 |
| Improving Scene Graph Classification by Exploiting Knowledge from Texts.
|
AAAI | 2022 | 0 |
| TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs.
|
AAAI | 2022 | 0 |
| Are Vision Transformers Robust to Patch Perturbations?
|
ECCV | 2022 | 0 |
| Towards Data-Free Domain Generalization.
|
ACML | 2022 | 0 |
| Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract).
|
IJCAI | 2022 | 0 |
| Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework.
|
TPAMI | 2022 | 0 |
| Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs.
|
EMNLP | 2021 | 17 |
| Mutual Information State Intrinsic Control.
|
ICLR | 2021 | 16 |
| Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework.
|
EMNLP | 2021 | 7 |
| Capsule Network Is Not More Robust Than Convolutional Network.
|
CVPR | 2021 | 8 |
| Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering.
|
ISWC | 2021 | 10 |
| Effective and Efficient Vote Attack on Capsule Networks.
|
ICLR | 2021 | 10 |
| Semantics for Global and Local Interpretation of Deep Convolutional Neural Networks.
|
IJCNN | 2021 | 0 |
| Improving Inductive Link Prediction Using Hyper-relational Facts.
|
ISWC | 2021 | 10 |
| Neural Multi-hop Reasoning with Logical Rules on Biomedical Knowledge Graphs.
|
ESWC | 2021 | 19 |
| Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs.
|
ICLR | 2021 | 44 |
| Few-Shot One-Class Classification via Meta-Learning.
|
AAAI | 2021 | 0 |
| Classification by Attention: Scene Graph Classification with Prior Knowledge.
|
AAAI | 2021 | 0 |
| Active Learning for Entity Alignment.
|
ECIR | 2021 | 0 |
| Causal Inference under Networked Interference and Intervention Policy Enhancement.
|
AISTATS | 2021 | 0 |
| PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings.
|
JMLR | 2021 | 0 |
| DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion.
|
EMNLP | 2020 | 28 |
| Reasoning on Knowledge Graphs with Debate Dynamics.
|
AAAI | 2020 | 24 |
| Introspective Learning by Distilling Knowledge from Online Self-explanation.
|
ACCV | 2020 | 1 |
| CSSA'20: Workshop on Combining Symbolic and Sub-Symbolic Methods and their Applications.
|
CIKM | 2020 | 2 |
| Controllable Multi-Character Psychology-Oriented Story Generation.
|
CIKM | 2020 | 5 |
| Search for Better Students to Learn Distilled Knowledge.
|
ECAI | 2020 | 13 |
| Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned.
|
ECIR | 2020 | 0 |
| Improving the Robustness of Capsule Networks to Image Affine Transformations.
|
CVPR | 2020 | 0 |
| An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing.
|
EMNLP | 2020 | 0 |
| A Recommender System for Complex Real-World Applications with Nonlinear Dependencies and Knowledge Graph Context.
|
ESWC | 2019 | 12 |
| Maximum Entropy-Regularized Multi-Goal Reinforcement Learning.
|
ICML | 2019 | 57 |
| Understanding Individual Decisions of CNNs via Contrastive Backpropagation.
|
ACCV | 2018 | 63 |
| Holistic Representations for Memorization and Inference.
|
UAI | 2018 | 16 |
| Energy-Based Hindsight Experience Prioritization.
|
CoRL | 2018 | 51 |
| Improving Information Extraction from Images with Learned Semantic Models.
|
IJCAI | 2018 | 8 |
| Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems.
|
ECML/PKDD | 2018 | 11 |
| Attention-based Information Fusion using Multi-Encoder-Decoder Recurrent Neural Networks.
|
ESANN | 2017 | 4 |
| Embedding Learning for Declarative Memories.
|
ESWC | 2017 | 20 |
| Improving Visual Relationship Detection Using Semantic Modeling of Scene Descriptions.
|
ISWC | 2017 | 48 |
| Tensor-Train Recurrent Neural Networks for Video Classification.
|
ICML | 2017 | 164 |
| Embedding Mapping Approaches for Tensor Factorization and Knowledge Graph Modelling.
|
ESWC | 2016 | 4 |
| Probabilistic Hoeffding Trees - Sped-Up Convergence and Adaption of Online Trees on Changing Data Streams.
|
ICDM | 2015 | 1 |
| Type-Constrained Representation Learning in Knowledge Graphs.
|
ISWC | 2015 | 179 |
| Querying Factorized Probabilistic Triple Databases.
|
ISWC | 2014 | 31 |
| Reducing the Rank in Relational Factorization Models by Including Observable Patterns.
|
NIPS/NeurIPS | 2014 | 72 |
| An Analysis of Tensor Models for Learning on Structured Data.
|
ECML/PKDD | 2013 | 22 |
| Tensor Factorization for Multi-relational Learning.
|
ECML/PKDD | 2013 | 54 |
| Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction.
|
ESWC | 2012 | 14 |
| Scalable Relation Prediction Exploiting Both Intrarelational Correlation and Contextual Information.
|
ECML/PKDD | 2012 | 3 |
| Factorizing YAGO: scalable machine learning for linked data.
|
WWW | 2012 | 407 |
| Mining the Semantic Web - Statistical learning for next generation knowledge bases.
|
DMKD | 2012 | 0 |
| Statistical relational learning of trust.
|
MLJ | 2011 | 37 |
| A Three-Way Model for Collective Learning on Multi-Relational Data.
|
ICML | 2011 | 1674 |
| Multivariate Prediction for Learning on the Semantic Web.
|
ILP | 2010 | 36 |
| Digging for knowledge with information extraction: a case study on human gene-disease associations.
|
CIKM | 2010 | 6 |
| Multi-Relational Learning with Gaussian Processes.
|
IJCAI | 2009 | 58 |
| Statistical Relational Learning with Formal Ontologies.
|
ECML/PKDD | 2009 | 46 |
| Hierarchical Bayesian Models for Collaborative Tagging Systems.
|
ICDM | 2009 | 51 |
| Tutorial summary: Learning with dependencies between several response variables.
|
ICML | 2009 | 7 |
| A statistical relational model for trust learning.
|
AAMAS | 2008 | 33 |
| Structure Learning with Nonparametric Decomposable Models.
|
ICANN | 2007 | 6 |
| Robust multi-task learning with
|
ICML | 2007 | 0 |
| Collaborative ordinal regression.
|
ICML | 2006 | 59 |
| Supervised probabilistic principal component analysis.
|
KDD | 2006 | 165 |
| Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures.
|
ECML/PKDD | 2006 | 5 |
| Stochastic Relational Models for Discriminative Link Prediction.
|
NIPS/NeurIPS | 2006 | 171 |
| Infinite Hidden Relational Models.
|
UAI | 2006 | 191 |
| Active learning via transductive experimental design.
|
ICML | 2006 | 339 |
| Hierarchy-Regularized Latent Semantic Indexing.
|
ICDM | 2005 | 1 |
| Learning Gaussian processes from multiple tasks.
|
ICML | 2005 | 420 |
| Dirichlet Enhanced Latent Semantic Analysis.
|
AISTATS | 2005 | 39 |
| Multi-Output Regularized Projection.
|
CVPR | 2005 | 1 |
| Soft Clustering on Graphs.
|
NIPS/NeurIPS | 2005 | 121 |
| A Probabilistic Clustering-Projection Model for Discrete Data.
|
ECML/PKDD | 2005 | 8 |
| Multi-label informed latent semantic indexing.
|
SIGIR | 2005 | 246 |
| Dirichlet enhanced relational learning.
|
ICML | 2005 | 23 |
| Learning Gaussian Process Kernels via Hierarchical Bayes.
|
NIPS/NeurIPS | 2004 | 193 |
| A nonparametric hierarchical bayesian framework for information filtering.
|
SIGIR | 2004 | 72 |
| Representative Sampling for Text Classification Using Support Vector Machines.
|
ECIR | 2003 | 245 |
| GPPS: A Gaussian Process Positioning System for Cellular Networks.
|
NIPS/NeurIPS | 2003 | 157 |
| A Hybrid Relevance-Feedback Approach to Text Retrieval.
|
ECIR | 2003 | 19 |
| Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes.
|
UAI | 2003 | 0 |
| The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging.
|
NIPS/NeurIPS | 2002 | 8 |
| Removing redundancy and inconsistency in memory-based collaborative filtering.
|
CIKM | 2002 | 5 |
| Transductive and Inductive Methods for Approximate Gaussian Process Regression.
|
NIPS/NeurIPS | 2002 | 89 |
| The Bayesian Committee Support Vector Machine.
|
ICANN | 2001 | 27 |
| Scalable Kernel Systems.
|
ICANN | 2001 | 7 |
| Scaling Kernel-Based Systems to Large Data Sets.
|
DMKD | 2001 | 35 |
| Mixtures of Gaussian Processes.
|
NIPS/NeurIPS | 2000 | 226 |
| The generalized Bayesian committee machine.
|
KDD | 2000 | 26 |
| Neural-network models for the blood glucose metabolism of a diabetic.
|
IEEE Trans. Neural Networks | 1999 | 86 |
| Mean field inference in a general probabilistic setting.
|
AISTATS | 1999 | 2 |
| Robust Neural Network Regression for Offline and Online Learning.
|
NIPS/NeurIPS | 1999 | 15 |
| Mixture Approximations to Bayesian Networks.
|
UAI | 1999 | 5 |
| Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models.
|
NIPS/NeurIPS | 1998 | 31 |
| Averaging, maximum penalized likelihood and Bayesian estimation for improving Gaussian mixture probability density estimates.
|
IEEE Trans. Neural Networks | 1998 | 125 |
| Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model.
|
NIPS/NeurIPS | 1998 | 73 |
| Nonlinear Markov Networks for Continuous Variables.
|
NIPS/NeurIPS | 1997 | 36 |
| Combining Regularized Neural Networks.
|
ICANN | 1997 | 10 |
| A Solution for Missing Data in Recurrent Neural Networks with an Application to Blood Glucose Prediction.
|
NIPS/NeurIPS | 1997 | 43 |
| Representing Probabilistic Rules with Networks of Gaussian Basis Functions.
|
MLJ | 1997 | 14 |
| Early Brain Damage.
|
NIPS/NeurIPS | 1996 | 40 |
| Discovering Structure in Continuous Variables Using Bayesian Networks.
|
NIPS/NeurIPS | 1995 | 74 |
| Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging.
|
NIPS/NeurIPS | 1995 | 87 |
| Efficient Methods for Dealing with Missing Data in Supervised Learning.
|
NIPS/NeurIPS | 1994 | 96 |
| Combining Estimators Using Non-Constant Weighting Functions.
|
NIPS/NeurIPS | 1994 | 154 |
| Training Neural Networks with Deficient Data.
|
NIPS/NeurIPS | 1993 | 126 |
| Network Structuring and Training Using Rule-Based Knowledge.
|
NIPS/NeurIPS | 1992 | 118 |
| Some Solutions to the Missing Feature Problem in Vision.
|
NIPS/NeurIPS | 1992 | 71 |
| Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency.
|
NIPS/NeurIPS | 1991 | 35 |
| A Neural Network Approach for Three-Dimensional Object Recognition.
|
NIPS/NeurIPS | 1990 | 5 |