yusuke1997/dblp-id-title huggingface dataset:
Approximate Message Passing with Rigorous Guarantees for Pooled Data and Quantitative Group Testing
Operator Shifting for General Noisy Matrix Systems
Nonbacktracking Eigenvalues under Node Removal: X-Centrality and Targeted Immunization
SCOTT: Shape-Location Combined Tracking with Optimal Transport
Multilinear Compressive Sensing and an Application to Convolutional Linear Networks
Asymptotics of the Sketched Pseudoinverse
Spectral Triadic Decompositions of Real-World Networks
Optimal Dorfman Group Testing for Symmetric Distributions
Graph-Based Regularization for Regression Problems with Alignment and Highly Correlated Designs
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data
Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black-Scholes Partial Differential Equations
Data-Driven Mirror Descent with Input-Convex Neural Networks
Communication-Efficient Distributed Eigenspace Estimation
Supermodular Rank: Set Function Decomposition and Optimization
The Gap between Theory and Practice in Function Approximation with Deep Neural Networks
Network Interpolation
Efficient Identification of Butterfly Sparse Matrix Factorizations
Analysis of Asymptotic Escape of Strict Saddle Sets in Manifold Optimization
Moment Estimation for Nonparametric Mixture Models through Implicit Tensor Decomposition
Low-Rank Tucker Approximation of a Tensor from Streaming Data
Target Network and Truncation Overcome the Deadly Triad in \(\boldsymbol{Q}\)-Learning
Causal Structural Learning via Local Graphs
Sensitivity-Informed Provable Pruning of Neural Networks
$k$-Variance: A Clustered Notion of Variance
A Simple and Optimal Algorithm for Strict Circular Seriation
Biwhitening Reveals the Rank of a Count Matrix
Safe Rules for the Identification of Zeros in the Solutions of the SLOPE Problem
Three-Operator Splitting for Learning to Predict Equilibria in Convex Games
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations
The GenCol Algorithm for High-Dimensional Optimal Transport: General Formulation and Application to Barycenters and Wasserstein Splines
MR-GAN: Manifold Regularized Generative Adversarial Networks for Scientific Data
Kernel-Based Regularized Learning with Random Projections: Beyond Least Squares
First-Order Conditions for Optimization in the Wasserstein Space
Distributed Learning with Sparse Communications by Identification
GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery
Implicit Deep Learning
Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise
Scalable Tensor Methods for Nonuniform Hypergraphs
Robust Classification Under $\ell_0$ Attack for the Gaussian Mixture Model
The Geometric Median and Applications to Robust Mean Estimation
A Generalized CUR Decomposition for Matrix Pairs
Multikernel Regression with Sparsity Constraint
Two Models of Double Descent for Weak Features
Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry
A Bayesian Framework for Persistent Homology
A Maximum Principle Argument for the Uniform Convergence of Graph Laplacian Regressors
Stochastic Gradient MCMC for State Space Models
Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms
A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors
Block Bregman Majorization Minimization with Extrapolation
Resolving the Mixing Time of the Langevin Algorithm to Its Stationary Distribution for Log-Concave Sampling
Nonbacktracking Spectral Clustering of Nonuniform Hypergraphs
Stop Memorizing: A Data-Dependent Regularization Framework for Intrinsic Pattern Learning
Numerical Considerations and a new implementation for invariant coordinate selection
EnResNet: ResNets Ensemble via the Feynman-Kac Formalism for Adversarial Defense and Beyond
Inverse Evolution Layers: Physics-Informed Regularizers for Image Segmentation
Learning Finite-Dimensional Coding Schemes with Nonlinear Reconstruction Maps
Lipschitz-Regularized Gradient Flows and Generative Particle Algorithms for High-Dimensional Scarce Data
Gaussian Process Landmarking on Manifolds
On Neural Network Approximation of Ideal Adversarial Attack and Convergence of Adversarial Training
Introduction to SIAM Journal on Mathematics of Data Science (SIMODS)
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition
On the Effectiveness of Richardson Extrapolation in Data Science
Some Limit Properties of Markov Chains Induced by Recursive Stochastic Algorithms
Efficiency of ETA Prediction
FANOK: Knockoffs in Linear Time
Approximation of Lipschitz Functions Using Deep Spline Neural Networks
Max-Affine Regression via First-Order Methods
The Pontryagin Maximum Principle for Training Convolutional Neural Networks
Structural Balance and Random Walks on Complex Networks with Complex Weights
Adaptive Joint Distribution Learning
Fast Cluster Detection in Networks by First Order Optimization
Near-Optimal Sampling Strategies for Multivariate Function Approximation on General Domains
The Rankability of Data
High-Dimensional Analysis of Double Descent for Linear Regression with Random Projections
The Convex Mixture Distribution: Granger Causality for Categorical Time Series
Simplex-Structured Matrix Factorization: Sparsity-Based Identifiability and Provably Correct Algorithms
Recovering Hidden Components in Multimodal Data with Composite Diffusion Operators
Greed Works: An Improved Analysis of Sampling Kaczmarz-Motzkin
Mean-Field Controls with Q-Learning for Cooperative MARL: Convergence and Complexity Analysis
Stochastic Optimal Transport in Banach Spaces for Regularized Estimation of Multivariate Quantiles
Toward a Mathematical Theory of the Crystallographic Phase Retrieval Problem
Analysis of the Neighborhood Pattern Similarity Measure for the Role Extraction Problem
Spectral Discovery of Jointly Smooth Features for Multimodal Data
Moving Up the Cluster Tree with the Gradient Flow
Sequential Construction and Dimension Reduction of Gaussian Processes Under Inequality Constraints
Matching Component Analysis for Transfer Learning
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
Computing Wasserstein Barycenters via Operator Splitting: The Method of Averaged Marginals
Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms
Sequential Sampling for Optimal Weighted Least Squares Approximations in Hierarchical Spaces
Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Training
A Variational Formulation of Accelerated Optimization on Riemannian Manifolds
Subgradient Langevin Methods for Sampling from Nonsmooth Potentials
Statistical Methods for Minimax Estimation in Linear Models with Unknown Design Over Finite Alphabets
ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness
An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs
Accelerated Gossip in Networks of Given Dimension Using Jacobi Polynomial Iterations
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning
Deep Neural Networks, Generic Universal Interpolation, and Controlled ODEs
Select without Fear: Almost All Minibatch Schedules Generalize Optimally
Time-Inhomogeneous Diffusion Geometry and Topology
Memory Capacity of Two Layer Neural Networks with Smooth Activations
Post-training Quantization for Neural Networks with Provable Guarantees
On the Exact Computation of Linear Frequency Principle Dynamics and Its Generalization
Benefit of Interpolation in Nearest Neighbor Algorithms
Normal-Bundle Bootstrap
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation
Deep Neural Networks and PIDE Discretizations
Simple Alternating Minimization Provably Solves Complete Dictionary Learning
Generative Modeling of Lévy Area for High Order SDE Simulation
Satisficing Paths and Independent Multiagent Reinforcement Learning in Stochastic Games
Two Steps at a Time - Taking GAN Training in Stride with Tseng's Method
Energy-Based Sequential Sampling for Low-Rank PSD-Matrix Approximation
Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data
Adversarial Robustness of Sparse Local Lipschitz Predictors
Exponential-Wrapped Distributions on Symmetric Spaces
Empirical Regularized Optimal Transport: Statistical Theory and Applications
The Sample Complexity of Sparse Multireference Alignment and Single-Particle Cryo-Electron Microscopy
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression
Bi-Invariant Dissimilarity Measures for Sample Distributions in Lie Groups
Equivariant Neural Networks for Indirect Measurements
Randomized Low-Rank Approximations beyond Gaussian Random Matrices
Stability of Sequential Lateration and of Stress Minimization in the Presence of Noise
Decoding from Pooled Data: Sharp Information-Theoretic Bounds
Faster Rates for Compressed Federated Learning with Client-Variance Reduction
Global Minima of Overparameterized Neural Networks
Matrix Denoising for Weighted Loss Functions and Heterogeneous Signals
The Sample Complexity of Multireference Alignment
Ergodicity Coefficients for Higher-Order Stochastic Processes
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
Tukey Depths and Hamilton-Jacobi Differential Equations
An Adaptively Inexact First-Order Method for Bilevel Optimization with Application to Hyperparameter Learning
Multi-Layer Sparse Coding: The Holistic Way
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks
Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients
Overparameterization and Generalization Error: Weighted Trigonometric Interpolation
Online Machine Teaching under Learner Uncertainty: Gradient Descent Learners of a Quadratic Loss
Intrinsic Dimension Adaptive Partitioning for Kernel Methods
Persistent Laplacians: Properties, Algorithms and Implications
Sharp Estimates on Random Hyperplane Tessellations
When Big Data Actually Are Low-Rank, or Entrywise Approximation of Certain Function-Generated Matrices
Compressive Sensing for Cut Improvement and Local Clustering
Identifiability of Complete Dictionary Learning
Singularity Structures and Impacts on Parameter Estimation in Finite Mixtures of Distributions
Taming Neural Networks with TUSLA: Nonconvex Learning via Adaptive Stochastic Gradient Langevin Algorithms
On a Minimum Distance Procedure for Threshold Selection in Tail Analysis
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing
Three Hypergraph Eigenvector Centralities
Nonasymptotic Bounds for Adversarial Excess Risk under Misspecified Models
Transport Model for Feature Extraction
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Maximum Entropy Methods for Texture Synthesis: Theory and Practice
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning
Admissibility of Solution Estimators for Stochastic Optimization
Wasserstein Barycenters Are NP-Hard to Compute
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
Layer-Parallel Training of Deep Residual Neural Networks
Estimating a Potential Without the Agony of the Partition Function
Online MCMC Thinning with Kernelized Stein Discrepancy
Local Versions of Sum-of-Norms Clustering
Convergence of Recursive Stochastic Algorithms Using Wasserstein Divergence
Topological Fingerprints for Audio Identification
Variational Representations and Neural Network Estimation of Rényi Divergences
Metric-Constrained Optimization for Graph Clustering Algorithms
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization
Convergence of a Constrained Vector Extrapolation Scheme
Entropic Optimal Transport on Random Graphs
Optimal Estimation of Smooth Transport Maps with Kernel SoS
Simplicial \({\boldsymbol{q}}\) -Connectivity of Directed Graphs with Applications to Network Analysis
Optimization on Spheres: Models and Proximal Algorithms with Computational Performance Comparisons
On the Rates of Convergence for Learning with Convolutional Neural Networks
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks
Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format
Group-Invariant Tensor Train Networks for Supervised Learning
Approximation Properties of Ridge Functions and Extreme Learning Machines
A Rigorous Theory of Conditional Mean Embeddings
Persistent Cohomology for Data With Multicomponent Heterogeneous Information
What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory
Relating Modularity Maximization and Stochastic Block Models in Multilayer Networks
Error Bounds for Dynamical Spectral Estimation
Matrix Rigidity and the Ill-Posedness of Robust PCA and Matrix Completion
Optimal Approximation with Sparsely Connected Deep Neural Networks
Stability of Deep Neural Networks via Discrete Rough Paths
Reconstruction of a Riemannian Manifold from Noisy Intrinsic Distances
Determinantal Point Processes Implicitly Regularize Semiparametric Regression Problems
Approximate Q Learning for Controlled Diffusion Processes and Its Near Optimality
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing
Binary Component Decomposition Part I: The Positive-Semidefinite Case
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection
ReLU Neural Networks with Linear Layers Are Biased towards Single- and Multi-index Models
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks
Supervised Gromov-Wasserstein Optimal Transport with Metric-Preserving Constraints
Accelerated Bregman Primal-Dual Methods Applied to Optimal Transport and Wasserstein Barycenter Problems
Spectral Neighbor Joining for Reconstruction of Latent Tree Models
Recovering Trees with Convex Clustering
Memory-Efficient Structured Convex Optimization via Extreme Point Sampling
\({O({k})}\)-Equivariant Dimensionality Reduction on Stiefel Manifolds
Extending the Step-Size Restriction for Gradient Descent to Avoid Strict Saddle Points
On Stochastic Gradient Langevin Dynamics with Dependent Data Streams: The Fully Nonconvex Case
Multilayer Modularity Belief Propagation to Assess Detectability of Community Structure
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Estimating Gaussian Mixtures Using Sparse Polynomial Moment Systems
An Optimal Algorithm for Strict Circular Seriation
Generalization Error of Minimum Weighted Norm and Kernel Interpolation
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise]
Finite-Time Analysis of Natural Actor-Critic for POMDPs
Speedy Categorical Distributional Reinforcement Learning and Complexity Analysis
Analysis of Spatial and Spatiotemporal Anomalies Using Persistent Homology: Case Studies with COVID-19 Data
Approximation Bounds for Sparse Programs
Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees
Random Projection Neural Networks of Best Approximation: Convergence Theory and Practical Applications
Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations
Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization
Blind Identification of Stochastic Block Models from Dynamical Observations
Clustering with t-SNE, Provably
Multi-Reference Alignment in High Dimensions: Sample Complexity and Phase Transition
Spectral Barron Space for Deep Neural Network Approximation
Probabilistic Registration for Gaussian Process Three-Dimensional Shape Modelling in the Presence of Extensive Missing Data
A Linear-Time Algorithm and Analysis of Graph Relative Hausdorff Distance
Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling
Variational Limits of k-NN Graph-Based Functionals on Data Clouds
Diffeomorphic Measure Matching with Kernels for Generative Modeling
Core-Periphery Detection in Hypergraphs
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization
Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods
Rank 2r Iterative Least Squares: Efficient Recovery of Ill-Conditioned Low Rank Matrices from Few Entries
Semi-supervised Learning for Aggregated Multilayer Graphs Using Diffuse Interface Methods and Fast Matrix-Vector Products
Applications of No-Collision Transportation Maps in Manifold Learning
Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting
The Trimmed Lasso: Sparse Recovery Guarantees and Practical Optimization by the Generalized Soft-Min Penalty
Balancing Geometry and Density: Path Distances on High-Dimensional Data
GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models
Why Are Big Data Matrices Approximately Low Rank?
Joint Community Detection and Rotational Synchronization via Semidefinite Programming
Accelerated and Instance-Optimal Policy Evaluation with Linear Function Approximation
Ensemble Linear Interpolators: The Role of Ensembling
Expertensysteme in der Krise
Agent-Supported Information Brokering
VISATEC: Vision-based Integrated Systems Adaptive to Task and Environment with Cognitive abilities
International Conference on Machine Learning (ICML-99)
Dynamic Programming on Tree Decompositions with D-FLAT
KogWis'08
crowd: A Visual Tool for Involving Stakeholders into Ontology Engineering Tasks
Spatial Cognition 2008 - Call for Papers
Einsatz eines Neuro-Fuzzy-Klassifikators beim Glaukom-Monitoring
LindEvol: Artificial Models for Natural Plant Evolution
Assessing the Attitude Towards Artificial Intelligence: Introduction of a Short Measure in German, Chinese, and English Language
KI und Roboter
Data Mining of Travel Surveys Using Bayesian Network Learning
Teleteaching - Serviceteil
Suchanfragen als Informationsquelle für Benutzermodelle
Design, Implementierung und Evaluierung einer virtuellen Maschine für Oz
LWA 2006
Rewriting Approaches for Ontology-Mediated Query Answering
Stream Reasoning with LARS
A Quality Evaluation of Combined Search on a Knowledge Base and Text
Cognitive Computer Vision - Service
Learning to Discover Political Activism in the Twitterverse
Anfragesprachen neu erdacht
Künstliche Intelligenz (KI-2001)
German Conference on Artificial Intelligence
Remember to Correct the Bias When Using Deep Learning for Regression!
Von Artificial Intelligence zur Artificial Life und wieder zurück
Data, Knowledge, and Computation
Agent-based Knowledge Management
Space Robotics Means Business
Die besonderen Eigenschaften Neuronaler Netze bei der Approximation von Funktionen
Hybride Sprachgenerierung in einem natürlichsprachlichen Dialogsystem
Hierarchical Hybrid Planning for Mobile Robots
Towards Efficiently Implementing Dodgson's Formally Intractable Voting Rule
Intelligent Mobility - Autonomous Outdoor Robotics at the DFKI
PuK 2004
Intelligent Tutoring Systems from Birth to Now
Neuronale Netzwerke als Qualiätssicherung in der Halbleiterherstellung
Special Issue on General Game Playing
Gasteditorial
Stellungnahme der Arbeitsgemeinschaft der deutschen KI-Intitute (AKI) zur KI-Evaluierungsstudie von Arthur D. Little
Zeitrepräsentation der industriellen Produktionsplanung und -steuerung
Kooperierende Roboter: Gemeinsame Erledigung einer Reinigungsaufgabe
Generierung grafischer Wissenserwerbssysteme für starke Problemlösungsmethoden
Die Ausdruckstärke von Planungsformalismen: Eine formale Charakterisierung
Towards Automation of Simulation Studies - Artificial Intelligence Methodologies for the Control and Analysis of Simulation Experiments
LILOG - Konzepte und Methoden zum Verstehen deutscher Texte
Visuelle Aufmerksamkeitssteuerung zur Unterstützung gestikbasierter Mensch-Maschine Interaktion
Unsicherheit und Vagheit: Begriffe, Methoden, Forschungsthemen
Können Computer logisch denken? Reflektionen über die IJCAI-93 in Chambéry
Konnektionistische/Hybride Verarbeitung Natürlicher Sprache, Zusammenfassung von der AAAI92, ECAI92, INSP92 und dem Workshop über "Neural Networks and a New AI"
Obituary for Jeff Rickel, a Pioneer in the Field of Pedagogical Agents
Active and Incremental Learning with Weak Supervision
MATES at Net.ObjectDays
Enabling Autonomous Robots to Perform Complex Tasks
Sorten für das automatische Beweisen höherer Stufe
Case-Based Reasoning - Service
KI 2008
firstCS - New Aspects on Combining Constraint Programming with Object-Orientation in Java
Wissensbasierte Konfigurierung: Das Projekt PROKON im Überblick
Wissensbasiertes Projektmanagement großer DV-Vorhaben
MAAMAW '91
Towards Higher-order OWL
PISA/XP - Ein Expertensystem zur Unterstützung des Datenbank-Entwurfs
Die Konzipierung, Entwicklung und Einführung eines XPS bei VW
Browsing-Assistenten, Tour Guides und adaptive WWW-Server
Aus den Turnschuhen
Theatre, Perception, Symbol
Gasteditorial Erklärungen
Learning Inference Rules from Data
Semantics of Analogies from a Logical Perspective
Systematische Wiederverwendung im Produktlinienumfeld - Ein Entscheidungsproblem
Integration of Similarity-based and Deductive Reasoning for Knowledge Management
Modell und Gegenstand - untrennbar miteinander verbunden
A Framework for Learning Event Sequences and Explaining Detected Anomalies in a Smart Home Environment
MATES @ Net.ObjectDays
Premiere von KI
Automatic Generation of Computer Animation: Using AI for Movie Animation
Rule-Based modelling of Intelligent Environment Behaviour
TL Users Workshop
The CoRg Project: Cognitive Reasoning
Sensor-Based Elicitation of Manual Assembly Processes
Societal Implications of Big Data
Integration von Programmieren und Lernen in eine Steuerungssprache für autonome Roboter
Strategy Generation and Evaluation for Meta-Game Playing
KI-Systeme zu Lande, zu Wasser und in der Luft
Panen mit Planungsgraphen in IPP
Kontext-adaptive Fahrerinformationssysteme am Beispiel eines Navigationssystems
AAMAS 2007
Neuronale Netzwerke als Werkzeuge zur Qualitätssicherung in der Halbleiterherstellung
Strengthening AI Investments in Germany
The World of PUK - Service
Konstruktgitter-Verfahren (Repertory Grid Method)
Interview with Jim Hendler on Social Media and Collective Intelligence
AI: Back to the Roots?
Neuronale Netze zur Steuerung der Laufmaschine LAURON
Logic-Based Question Answering
Pleased to Meet You!
Science Fiction Meets Reality
Towards Entity Status
Special Issue on Ontologies and Data Management: Part II
FABEL
Bericht über den Workshop on Formal Aspects of Semantic Networks
HyperLMNtal: An Extension of a Hierarchical Graph Rewriting Model
SEPAL: Schema Enhanced Programming for Linked Data
Multiagenten-Systeme
Planen und Konfigurieren
Data Mining Serviceteil
KI-Forschung bei Daimler-Benz AG
Die zukünftige Ausrichtung und Positionierung des Deutschen Forschungszentrums für Künstliche Intelligenz
The Transregional Collaborative Research Center
Volvo Group Collaborative Robot Systems Laboratory: A Collaborative Way for Academia and Industry to be at the Forefront of Artificial Intelligence
Das Verbundprojekt "Veränderungen der Wissensproduktion und -verteilung durch Expertensysteme"
SeSAm: Programmieren von Multiagentensimulationen auf Spezifikationsebene
Special Issue on Smart Production
Ein Roboter spielt Domino
Induktion von Entscheidungsbäumen zur qualitativen Analyse kontinuierlicher Funktionen
EUMAS 2005 - Third European Workshop on Multi-Agent Systems
Konfigurieren von industriellen Rührwerken
Quo Vadis PuK?
Quantitative Variants of Language Equations and their Applications to Description Logics