yusuke1997/dblp-id-title huggingface dataset:
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks
A Simple Way to Learn Metrics Between Attributed Graphs
Cycle Invariant Positional Encoding for Graph Representation Learning
Knowledge Graph Preference Contrastive Learning for Recommendation
Faster Optimization on Sparse Graphs via Neural Reparametrization
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
Transferable Hypergraph Neural Networks via Spectral Similarity
HEAL: Unlocking the Potential of Learning on Hypergraphs Enriched With Attributes and Layers
United We Stand, Divided We Fall: Networks to Graph (N2G) Abstraction for Robust Graph Classification Under Graph Label Corruption
A Survey on Deep Graph Generation: Methods and Applications
Cayley Graph Propagation
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models
Revisiting Graph Homophily Measures
Gradual Weisfeiler-Leman: Slow and Steady Wins the Race
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
De Bruijn Goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs
Mitigating Over-Smoothing and Over-Squashing Using Augmentations of Forman-Ricci Curvature
Expander Graph Propagation
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Do Neural Scaling Laws Exist on Graph Self-Supervised Learning?
Flashlight: Scalable Link Prediction With Effective Decoders
Rethinking Higher-Order Representation Learning With Graph Neural Networks
Motif-Aware Attribute Masking for Molecular Graph Pre-Training
Learning on Graphs Conference, 27-30 November 2023, Virtual Event
HOT: Higher-Order Dynamic Graph Representation Learning With Efficient Transformers
Representing Edge Flows on Graphs via Sparse Cell Complexes
Hyperbolic Kernel Convolution: A Generic Framework
Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations
Well-Conditioned Spectral Transforms for Dynamic Graph Representation
Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks
A Pure Transformer Pretraining Framework on Text-Attributed Graphs
Neural Graph Modelling of Whole Slide Images for Survival Ranking
Sub-Graph Based Diffusion Model for Link Prediction
Graph Neural Network With Local Frame for Molecular Potential Energy Surface
Learnable Commutative Monoids for Graph Neural Networks
Asynchronous Algorithmic Alignment With Cocycles
TopoImb: Toward Topology-Level Imbalance in Learning From Graphs
BeMap: Balanced Message Passing for Fair Graph Neural Network
TRIX: A More Expressive Model for Zero-Shot Domain Transfer in Knowledge Graphs
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks
Asymptotic Generalization Error of a Single-Layer Graph Convolutional Network
Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems
Graph-Time Convolutional Autoencoders
Generalized Reasoning With Graph Neural Networks by Relational Bayesian Network Encodings
Simple GNNs With Low Rank Non-Parametric Aggregators
A Spectral Framework for Tracking Communities in Evolving Networks
Stochastic Experience-Replay for Graph Continual Learning
Learning Graph Search Heuristics
Neighborhood-Aware Scalable Temporal Network Representation Learning
Spectral Subgraph Localization
Metric Based Few-Shot Graph Classification
FakeEdge: Alleviate Dataset Shift in Link Prediction
PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations
Efficient Representation Learning for Higher-Order Data With Simplicial Complexes
MUDiff: Unified Diffusion for Complete Molecule Generation
Neural Algorithmic Reasoning for Combinatorial Optimisation
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
Neural Graph Databases
Lifted Model Construction Without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs
Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies
Dynamic Representations of Global Crises: A Temporal Knowledge Graph for Conflicts, Trade and Value Networks
A Systematic Evaluation of Node Embedding Robustness
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling
Matrix Completion With Hypergraphs: Sharp Thresholds and Efficient Algorithms
KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation
Towards a General Recipe for Combinatorial Optimization With Multi-Filter GNNs
Interaction Models and Generalized Score Matching for Compositional Data
Piecewise-Velocity Model for Learning Continuous-Time Dynamic Node Representations
Edge Directionality Improves Learning on Heterophilic Graphs
Optimal Performance of Graph Convolutional Networks on the Contextual Stochastic Block Model
The Second Learning on Graphs Conference: Preface
EMP: Effective Multidimensional Persistence for Graph Representation Learning
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning
GEFL: Extended Filtration Learning for Graph Classification
Jointly Modelling Uncertainty and Diversity for Active Molecular Property Prediction
Label-Wise Graph Convolutional Network for Heterophilic Graphs
Reasoning-Modulated Representations
CEP3: Community Event Prediction With Neural Point Process on Graph
Do We Really Need Complicated Graph Learning Models? - A Simple but Effective Baseline
On the Expressivity of Persistent Homology in Graph Learning
Leveraging Temporal Graph Networks Using Module Decoupling
Enhancing Topological Dependencies in Spatio-Temporal Graphs With Cycle Message Passing Blocks
Recursive Algorithmic Reasoning
DIGRAC: Digraph Clustering Based on Flow Imbalance
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Generative Modeling of Labeled Graphs Under Data Scarcity
Understanding Feature/Structure Interplay in Graph Neural Networks
Towards Training GNNs Using Explanation Directed Message Passing
GraTeD-MLP: Efficient Node Classification via Graph Transformer Distillation to MLP
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
Meta-Path Learning for Multi-Relational Graph Neural Networks
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics
GSCAN: Graph Stability Clustering for Applications With Noise Using Edge-Aware Excess-of-Mass
CliquePH: Higher-Order Information for Graph Neural Networks Through Persistent Homology on Clique Graphs
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs
Ising on the Graph: Task-Specific Graph Subsampling via the Ising Model
Towards Efficient and Expressive GNNs for Graph Classification via Subgraph-Aware Weisfeiler-Lehman
The First Learning on Graphs Conference: Preface
Pruning Edges and Gradients to Learn Hypergraphs From Larger Sets
Transfer Learning Using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes
Continuous Neural Algorithmic Planners
On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features
DiffWire: Inductive Graph Rewiring via the Lovász Bound
Multicoated and Folded Graph Neural Networks With Strong Lottery Tickets
NP-NDS: A Nature-Powered Nonlinear Dynamical System for Power Grid Forecasting
Oversquashing in Hypergraph Neural Networks
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
Smoothed Graph Contrastive Learning via Seamless Proximity Integration
UnRavL: A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs
Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction
Shortest Path Networks for Graph Property Prediction
T-Gae: Transferable Graph Autoencoder for Network Alignment
Similarity-Based Link Prediction From Modular Compression of Network Flows
Combining Graph and Recurrent Networks for Efficient and Effective Segment Tagging
Latent Space Representations of Neural Algorithmic Reasoners
Flexible Diffusion Scopes With Parameterized Laplacian for Heterophilic Graph Learning
Effective Higher-Order Link Prediction and Reconstruction From Simplicial Complex Embeddings
A Latent Diffusion Model for Protein Structure Generation
Artificial Reasoning Toward Goal-Oriented Adaptive Arrays of Sensors
Boosting Sparse Point Cloud Object Detection via Image Fusion
Sense-making and knowledge construction via constructivist learning paradigm
Towards Generating Consumer Labels for Machine Learning Models
Human vs. Artificial Intelligence
Optimizing Human-Robot Collaboration in Industry 5.0: A Comparative Study of Communication Mediums and Their Impact on Worker Well-being and Productivity
Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media
Evolving Cognitive Models: A Novel Approach to Verbal Learning
Real-Time Data Quality Analysis
Benchmarking Contemporary Deep Learning Hardware and Frameworks: A Survey of Qualitative Metrics
Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources
Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery
Multimodal Models for Contextual Affect Assessment in Real-Time
Automatic bird sound detection in long range field recordings using Wavelets & Mel filter bank features
A Graph-based Approach to Human Activity Recognition
Unseen Filler Generalization In Attention-based Natural Language Reasoning Models
Causal Intervention and Semantic Knowledge for Object Relationships
Enhancing Solar Flare Prediction with Innovative Data-Driven Labels
Trust and Collaboration Testing in Controlled Human-Robot Environments
Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision
ATARAXIS: A Deep Learning Approach for Hardwareless In-Vehicle Presence Detection
Functional Perceptron using Multi-dimensional Activation Functions
Emerging Technologies: Connecting Millennials and Manufacturing
6th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2024, Washington, DC, USA, October 28-31, 2024
Classification of Space Objects Using Machine Learning Methods
4th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2022, Atlanta, GA, USA, December 14-17, 2022
Third IEEE International Conference on Cognitive Machine Intelligence, CogMI 2021, Atlanta, GA, USA, December 13-15, 2021
Conference Keynote Synthesizing Interpretable Behavior for Human-Aware AI Systems
Impact Patterns of Combining Model Pruning and Continual Learning on Model Performance
Sensory Audio Focusing Detection Using Brain-Computer Interface Archetype
Applying Software Design Metrics to Developer Story: A Supervised Machine Learning Analysis
Self-Adaptive Tuning for Speech Enhancement Algorithm Based on Evolutionary Approach
On the Design of Medical Data Ecosystem for Improving Healthcare Research and Commercial Incentive
Decoding Linguistic Nuances in Mental Health Text Classification Using Expressive Narrative Stories
PSLotto: A Privacy-Enhanced COVID Lottery System
Promoting High Diversity Ensemble Learning with EnsembleBench
CLIMAX: An Exploration of Classifier-Based Contrastive Explanations
A Methodology for Energy Usage Prediction in Long-Lasting Abnormal Events
FairShades: Fairness Auditing via Explainability in Abusive Language Detection Systems
Valence/Arousal Estimation of Occluded Faces from VR Headsets
Cross-Domain Sentiment Classification with Attention-Assisted GAN
TRIPLEx: Triple Extraction for Explanation
Cortically-Coupled Generative Adversarial Network for Target Image Retrieval in Rapid Image Search
New Heuristic Methods for Protein Model Quality Assessment via Two-Stage Machine Learning and Hierarchical Ensemble
Enabling Hyperparameter Tuning of Machine Learning Classifiers in Production
Cognitive Chunks, Neural Engrams and Natural Concepts: Bridging the Gap between Connectionism and Symbolism
Modeling Misinformation Diffusion in Social Media: Beyond Network Properties
Enhancing Federated Learning Security: Combating Clients' Data Poisoning with Classifier Ensembles
Causal Inductive Biases for Cognitive Machine Learning
Interpretable Next Basket Prediction Boosted with Representative Recipes
Annete: An Intelligent Tutoring Companion Embedded into the Eclipse IDE
Development of New Aerial Image Datasets and Deep Learning Methods for Waterfowl Detection and Classification
Optimized Machine Learning Based Multimodal UAV Detection Using Ensemble Stacking
EMDA-Net: Earth Mover's Distance (EMD) influenced Attention-aided Neural Network for Medical Image Classification
Neural Bezier Interpolation with Manifold Learning for Reliable Vessel Trajectory Prediction
Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking in Multi-Camera Networks
Discovery of Action Rules for Continuously Valued Data
Automating Deep Neural Network Model Selection for Edge Inference
Forecasting the Spread of Toxicity on Twitter
Parallel Detection for Efficient Video Analytics at the Edge
No Classifier Left Behind: An In-depth Study of the RBF SVM Classifier's Vulnerability to Image Extraction Attacks via Confidence Information Exploitation
The Intersection of Usability Evaluation and Machine Learning in Software Systems
Amplifying Object Tracking Performance on Edge Devices
Adversarial Promotion for Video based Recommender Systems
Evaluating the Privacy Exposure of Interpretable Global Explainers
Advancing Mental Health Pre-Screening: A New Custom GPT for Psychological Distress Assessment
Building Explainable Predictive Analytics for Location-Dependent Time-Series Data
Towards a trustworthy, secure and reliable enclave for machine learning in a hospital setting: The Essen Medical Computing Platform (EMCP)
5th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2023, Atlanta, GA, USA, November 1-4, 2023
Performance Evaluation of Text Augmentation Methods with BERT on Small-sized, Imbalanced Datasets
Hierarchical Deep Reinforcement Learning for Multi-robot Cooperation in Partially Observable Environment
A Serverless Computing Fabric for Edge & Cloud
Improving Time-Series Classification Accuracy Based on Temporal Feature Representation Learning Using CRU-LSTM Autoencoder
Quantum N-Gram Language Models for Tweet Classification
Low-shot Learning in Natural Language Processing
Inference for Trustworthy Machine Intelligence: Challenges and Solutions
Collaboration, Self-Reflection, and Adaptation in Robot Communities: Using Multi-Agent Distributed Learning for Coordination Planning
Next - Location Prediction Using Federated Learning on a Blockchain
#selfharm on Instagram: Quantitative Analysis and Classification of Non-Suicidal Self-Injury
Open Human-Robot Collaboration Systems (OHRCS): A Research Perspective
Deep Learning Mechanism for Pervasive Internet Addiction Prediction
A Novel Approach to Synthesize Class Labels in Highly Imbalanced Large Data
Harnessing Deep Learning for Fault Detection in Industry 4.0: A Multimodal Approach
The Case for Micro Foundation Models to Support Robust Edge Intelligence
Enhanced Model Robustness by Integrated Local and Global Processing
Research Challenges for Combined Autonomy, AI, and Real-Time Assurance
Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning
Addressing the Limitations of AI/ML in creating Cognitive Solutions
Evaluate Effectiveness of NAO Robot to Train Children with Autism Spectrum Disorder (ASD)
Towards Effective Authorship Attribution: Integrating Class-Incremental Learning
Explaining Any Time Series Classifier
Deep Learning Methods for Tree Detection and Classification
Effective Diversity Optimizations for High Accuracy Deep Ensembles
Challenges and Directions for Ambient Intelligence: A Cyber Physical Systems Perspective
2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020, Atlanta, GA, USA, October 28-31, 2020
Improved Res2Net Model for Person re-Identification
Thoughts on Non-IID Data Impact in Healthcare with Federated Learning Medical Blockchain
Location-Adaptive Generative Graph Augmentation for Fraud Detection
Dynamic Network Analysis of Cognitive Attacks Using Meta-Network Modeling and Community Detection Algorithms
A Decentralized Approach for Determining Configurator Placement in Dynamic Edge Networks
Skills Extraction from Entities in Arabic CVs
An Easy-to-Classify Approach for the Bot-IoT Dataset
An approach to dealing with incremental concept drift in personalized learning systems
Large Foundational Model is a Blessing to Natural Language Understanding and Data Mining
Revisiting Metric Space Similarity Methods for High Performance Deep Embedding Learning
Creating Cyber Deception Games
Detecting Cybersecurity Attacks Using Different Network Features with LightGBM and XGBoost Learners
Feature Attribution Explanations for Spiking Neural Networks
An Empathetic AI Coach for Self-Attachment Therapy
ABC: Automatic Bottom-Up Construction of Configuration Knowledge Base for Multi-Vendor Networks
On Detection of Out of Distribution Inputs in Deep Neural Networks
A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual Visions
2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI), Los Angeles, CA, USA, December 12-14, 2019
Artificial Intelligence Meets Tactical Autonomy: Challenges and Perspectives
Detecting Perceived Appropriateness of a Robot's Social Positioning Behavior from Non-Verbal Cues
Online Intelligent Music Recommendation: The Opportunity and Challenge for People Well-Being Improvement
Pre-training Graph Neural Network for Cross Domain Recommendation
Random Sampling Deep Learning Mechanism for Discovering Unique Property of No Specific Local Feature Images
A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports
Community AI: Towards Community-based Federated Learning
GRAHIES: Multi-Scale Graph Representation Learning with Latent Hierarchical Structure
How We Browse: Measurement and Analysis of Browsing Behavior
Towards Distributed Edge-based Systems
Ensuring Trustworthy Neural Network Training via Blockchain
Rethinking Learning Rate Tuning in the Era of Large Language Models
Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models
Computational Scientific Discovery in Cognitive Science
Deep Heterogeneous Social Network Alignment
Causality and Uncertainty of Information for Content Understanding
Data Integrity and Artificial Reasoning
Deployment of Deep Learning Models to Mobile Devices for Spam Classification
A Cognitive Behavioral AI: Novel Conversational Memory Elements for Technical Understanding of Medical Deep Denoisers
Artificial Dendrites: an Algorithm
Approximate Deep Network Embedding for Mining Large-Scale Graphs
Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders
Social Media and Ubiquitous Technologies for Remote Worker Wellbeing and Productivity in a Post-Pandemic World
AI-Powered IoT System at the Edge
FireWarn: Fire Hazards Detection Using Deep Learning Models
Statistically-Sound Knowledge Discovery from Data: Challenges and Directions
A Modular Approach to Building Solar Energetic Particle Event Forecasting Systems
Explainable Anomaly Detection: Counterfactual driven What-If Analysis
Fractal Base Antennas Effects on Wi-Fi Harvesting Technologies
Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence
Machine Learning and Human Cognition Combined to Enhance Knowledge Discovery Fidelity
Artificial Intelligence Trends and Future Scenarios: Relations Between Statistics and Opinions
On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems
State-Of-The-Art and Challenges in Causal Inference on Graphs: Confounders and Interferences
Fine-grained, aspect-based semantic sentiment analysis within the economic and financial domains
Multi-agent Reinforcement Learning for Dynamic Dispatching in Material Handling Systems
Acute Mental Stress Measurement using Brain-IoT System
Cross-Modal Joint Embedding with Diverse Semantics
Evolution of Knowledge in Social Media and Their Relationship to an Evolving Real World
Investigating the Impact of Randomness on Reproducibility in Computer Vision: A Study on Applications in Civil Engineering and Medicine
Perennial, Permuted, and Pervasive Search in Ambient Intelligence
Diagnosis, Prevention, and Cure for Misinformation
Classification and Authentication of Mineral Water Samples using Electronic Tongue and Deep Neural Networks
A2Text-Net: A Novel Deep Neural Network for Sarcasm Detection
Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers
A Tipping Point? Heightened self-disclosure during the Coronavirus pandemic
Fair Evaluator: An Adversarial Debiasing-based Deep Learning Framework in Student Admissions
Using Automated Core and Spurious Features Detection in Scene Recognition to Explain Computer Vision Model
Artificial Reasoning in the Streetscape
A Novel Application of a Semi-Supervised Learning Approach to Identify Choroidal Tumors in Fundus Photographs
A Study of Response Time Instability of Microservices at High Resource Utilization in the Cloud
Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models
Towards a new paradigm for managing computing continuum applications
A Multilingual Virtual Guide for Self-Attachment Technique
MimicAI: An LLM based system that mimics and explains
Data-Driven Vulnerable Community Identification During Compound Disasters
Social Edge Intelligence: Integrating Human and Artificial Intelligence at the Edge
Toward Autonomy: Symbiotic Formal and Statistical Machine Reasoning
PerfML: Smart Management of Complex Performance Data and Analytics
On Urban Event Tracking from Online Media: A Social Cognition Perspective
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
A Novel Approach for Unsupervised Learning of Highly-Imbalanced Data
Global Pandemic: Business Model Impact on Enterprises reTHINK, reIMAGINE, reINVENT Businesses
Active Region-Based Flare Forecasting with Sliding Window Multivariate Time Series Forest Classifiers
Link Streams as a Generalization of Graphs and Time Series
Face Biometric Fairness Evaluation on Real vs Synthetic Cross-Spectral Images
Learning Latent Perception Graphs for Personalized Unknowns Recommendation
How Network Analysis Can Improve the Reliability of Modern Software Ecosystems
Overcoming Trust and Incentive Barriers in Decentralized AI
A Comparative Study of Model-Agnostic and Importance-Based Feature Selection Approaches
Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals
Generalized Craig Interpolation for Stochastic Satisfiability Modulo Theory Problems
Towards the Frontier between Decidability and Undecidability for Hyperbolic Cellular Automata
On Solving Word Equations Using SAT
Reachability Problems, 4th International Workshop, RP 2010, Brno, Czech Republic, August 28-29, 2010. Proceedings
Qualitative Multi-objective Reachability for Ordered Branching MDPs
Reachability Analysis of Nonlinear ODEs Using Polytopic Based Validated Runge-Kutta
Improving Reachability Analysis of Infinite State Systems by Specialization
Graph Games with Reachability Objectives - (Invited Talk)
Randomness Quality and Trade-Offs for CA Random String Generators
An Abstract Machine for Asynchronous Programs with Closures and Priority Queues
Efficient Bounded Reachability Computation for Rectangular Automata
Parametric Interrupt Timed Automata
Parameter Synthesis for Probabilistic Timed Automata Using Stochastic Game Abstractions
Reachability for Continuous and Hybrid Systems
Parameterised Verification of Publish/Subscribe Networks with Exception Handling
Formalisms for Specifying Markovian Population Models
Reachability in Succinct One-Counter Games