Adding metadata abruptly changes network inference outcomes.
problem Understanding the impact of metadata on network inference.
method Investigated the effect of metadata on network inference problems.
result Metadata causes abrupt transitions in inference outcomes.
Enhances Infomap algorithm to prioritize metadata in community detection.
problem Balancing metadata importance in community detection.
method Introduces a tuning parameter to the Infomap algorithm.
result Improves mutual information with metadata at the cost of structural detectability.
Method predicts missing nodes and metadata from network data.
problem Validation of community detection methods is incomplete.
method Joint generative model for data and metadata, nonparametric Bayesian inference.
result Metadata improves prediction of missing nodes and edges.
New method detects communities in networks using metadata.
problem Community detection in networks with available metadata.
method Combines network connections and metadata for more accurate community detection.
result Method learns and uses correlations between metadata and communities.
New research shows treating metadata as ground truth in network analysis leads to significant problems.
problem The use of metadata as ground truth in community detection leads to theoretical and practical issues.
method Theoretical analysis and statistical techniques to quantify the relationship between metadata and community structure.
result No algorithm can uniquely solve community detection, and treating metadata as ground truth is problematic.
MONET debiases graph embeddings by training on metadata-orthogonal dimensions.
problem Graph embeddings can be biased by node attributes, affecting fairness and interpretability.
method MONET trains embeddings on a hyperplane orthogonal to node metadata.
result MONET effectively removes bias from node embeddings, improving fairness and interpretability.
We introduce the nonparametric metadata dependent relational (NMDR) model, a Bayesian nonparametric stochastic block model for network data. The NMDR allows the entities associated with each node to have mixed membership in an unbounded collection of latent communities. Learned regression models allow these memberships…
Neural model incorporates metadata for better text analysis.
problem Lack of metadata in standard text modeling.
method General neural framework based on topic models.
result Achieves strong performance with metadata.
The study uses supervised learning to classify research data by discipline.
problem Automatically categorizing research data by discipline for scientometric analysis.
method Used a large dataset of 609,524 records for training and evaluation, employing tree-based models and neural networks.
result Multi-layer perceptron models outperformed Long Short-Term Memory models in multi-label classification tasks.
Model predicts ventricular tachyarrhythmias with high accuracy.
problem Predicting ventricular tachyarrhythmias for patient care.
method Multi-task neural network architecture with patient metadata.
result 74.02% prediction accuracy 60 seconds in advance.
Effectively modelling hidden structures in a network is very practical but theoretically challenging. Existing relational models only involve very limited information, namely the binary directional link data, embedded in a network to learn hidden networking structures. There is other rich and meaningful information (e.…
Paper proposes a method to improve graph clustering by integrating node textual metadata with node signals in GGMs.
problem Graph learning in Gaussian Graphical Models with auxiliary node metadata.
method Laplacian-constrained Gaussian Graphical Models with majorization-minimization algorithm.
result The proposed method outperforms state-of-the-art approaches that use either signals or metadata alone.
FLAMECHE solves the CFL trilemma by enabling encryption-compatible metadata-based clustering.
problem The CFL trilemma: improving two dimensions of privacy, communication, and computation comes at the expense of the third.
method FLAMECHE reformulates metadata-based CFL as a distributed EM procedure, allowing compatibility with secure FL schemes.
result FLAMECHE improves the effectiveness of client models and enables encryption-compatible clustering.
Enhances topic-metadata relationship modeling using Bayesian methods.
problem Estimating relationships between latent topics and metadata in topic modeling.
method Proposes modifications to the method of composition, using Beta regression and a fully Bayesian approach.
result Improves quantification of uncertainty in topic-metadata relationships.
DiffQue estimates relative difficulty of questions in CQA services.
problem Estimating relative difficulty of questions in community Q&A services.
method Network-aided edge directionality prediction.
result DiffQue outperforms state-of-the-art methods by significant margins.
Paper presents LLM-enhanced contract metadata extraction.
problem Automatic detection and annotation of legal clauses in contracts.
method Integration of publicly available and proprietary datasets with advanced LLM methodologies.
result Substantial improvements in clause identification accuracy and efficiency.
MetaDVFS uses device and application metadata to improve DVFS efficiency.
problem Improving energy efficiency in mobile platforms with diverse applications and hardware.
method Formulates DVFS as a multi-task reinforcement learning problem and introduces MetaDVFS, leveraging metadata for knowledge transfer.
result MetaDVFS achieves up to 26% improvement in Quality of Experience and up to 17% improvement in Performance-Power Ratio.
The paper examines how NFT valuations correlate with market data and social trends.
problem Predicting NFT valuations based on market data and social trends.
method Utilizes public market data, NFT metadata, and social trends data; employs linear regression and recurrent neural networks.
result Identifies correlations between NFT valuations and various features.
New method uses multi-hop assortativity to classify network functionalities.
problem Classifying network functionalities based on structure and metadata.
method Introduces multi-hop assortativity to capture node similarity in paths.
result Multi-hop assortativity features outperform state-of-the-art methods in network classification.
Deep neural network predicts cardiac shape from MRI images and patient data.
problem Automatic 3D cardiac shape analysis for large-scale studies.
method Uses deep neural networks combining MRI images and patient metadata.
result Significant agreement with reference shapes in cardiac parameters.
LLMs learn to recommend models and hyperparameters from dataset metadata.
problem Model and hyperparameter selection in machine learning is challenging and resource-intensive.
method Converted datasets into metadata and prompted LLMs to recommend models and hyperparameters.
result LLMs can recommend competitive models and hyperparameters without search.
Improved 3D MRI classification using contrastive learning with continuous proxy metadata.
problem Insufficient labelled data for 3D medical image classification.
method Proposed a new loss function (y-Aware InfoNCE) to leverage continuous proxy metadata in contrastive learning.
result 3D CNN model pre-trained on 10^4 multi-site healthy brain MRI scans outperforms fully-supervised methods.
SEMASIA provides a large dataset of latent representations for model comparison.
problem Difficulty in comparing semantic structures across different neural network models.
method Collection of latent representations from 1700 pretrained models across various benchmarks.
result Consistent semantic organization across models and datasets.
A novel multilayer network approach for text analysis.
problem Clustering documents and finding topics in large collections with metadata and hyperlinks.
method Multilayer Networks and Stochastic Block Models applied to multiple data types.
result Taking into account multiple types of information improves topic and document clustering.
Study improves ECG analysis accuracy using state space models, self-supervised learning, and patient metadata.
problem Improving quantitative accuracy of ECG analysis using deep learning.
method Explored state space models, self-supervised learning, and patient metadata integration.
result Improved ECG analysis accuracy through these components, no significant advantage from higher sampling rates or longer input sizes.
The paper evaluates machine learning for song similarity based on metadata and user tags.
problem Determining song similarity using metadata and user tags.
method Machine learning algorithms including tf-idf, Word2Vec, k-NN, and SVM were evaluated.
result tf-idf outperformed Word2Vec in modeling song metadata, and k-NN outperformed SVM and Linear Regression.
Deep DPP model uses neural networks to learn kernel matrices for DPPs.
problem Limitations of DPPs in capturing nonlinear interactions and incorporating item metadata.
method Integrates a deep feed-forward neural network to learn the kernel matrix of DPPs.
result Deep DPP model improves predictive performance and outperforms baselines.
GASC models semantic change in Ancient Greek texts using genre metadata.
problem Associating correct meanings in historical Ancient Greek texts.
method Develops a dynamic semantic change model leveraging genre metadata.
result Improves predictive performance on semantic change in Ancient Greek texts.
AutoML uses dataset and algorithm descriptions to improve performance.
problem Improving automated machine learning performance.
method Uses language embeddings to augment AutoML recommendations.
result Zero-shot AutoML system provides good solutions in under a second.
Meta-learning improves model performance by optimizing data acquisition.
problem Lack of operationally realistic data limits model performance.
method Gaussian process surrogate fit to metadata-driven training data variations.
result Meta-learning enhances model performance compared to random data acquisition.
Two Python frameworks, Blocks and Fuel, for deep learning.
problem Training complex neural networks on large datasets.
method Blocks is based on Theano, providing parametrized operations and utilities. Fuel provides a standard dataset format.
result Facilitates efficient training and manipulation of large datasets.
Classi-Fly uses machine learning to infer aircraft categories from open data.
problem Lack of metadata for aircraft in open data sources.
method Machine learning approach based on aircraft movement patterns.
result Correct aircraft category inference with over 88% accuracy.
Recovering core nodes from graph data with missing fringe interactions.
problem Recovering the core set from graph data with missing fringe interactions.
method Developed a theoretical framework and algorithms based on fixed-parameter tractability.
result Our algorithms outperform existing methods on various real-world datasets.
Improved malware detection by adding auxiliary loss terms to a neural network.
problem Malware detection accuracy with a single label.
method Fit deep neural networks to multiple auxiliary prediction targets derived from metadata.
result Significant improvement in detection performance, reducing false negatives by 42.6% at a low false positive rate.
Improved speaker verification with condition-aware backend.
problem Speaker verification calibration issues under unknown conditions.
method Discriminative PLDA model with joint training and condition integration.
result Out-of-the-box excellent calibration performance.
We create a large dataset for fact checking claims and improve prediction accuracy.
problem Fact checking claims from multiple sources is challenging.
method We created a comprehensive dataset and developed a novel method for automatic veracity prediction.
result Our model achieves a Macro F1 of 49.2%, showing significant performance improvements.
New feature mapping approach improves recommendation accuracy and explainability.
problem Balancing recommendation accuracy and explainability using metadata.
method Maps uninterpretable features to interpretable aspect features, minimizing both prediction and interpretation losses.
result Strong performance in recommendation and explainability, eliminating metadata need.
Study presents a dataset and methods to handle noisy labels in sound event classification.
problem Label noise in sound event classification datasets.
method Developed a dataset with noisy labels and evaluated CNN baseline systems.
result Training with large amounts of noisy data can outperform training with carefully-labeled data.
Survey classifies Clustered Federated Learning into three types of approaches.
problem Non-independent and identically distributed (non-IID) data in Federated Learning.
method Systematic review of CFL literature, principled taxonomy.
result Core CFL and Metadata-based approaches have distinct focuses.
FairGround offers a diverse dataset corpus for fair ML research.
problem Lack of diverse, well-annotated datasets in fair ML research.
method Unified framework and Python package for reproducible fair ML research.
result Advances reproducibility and generalizability of fair ML research.
This research predicts the popularity of new video contents using a hybrid machine learning approach.
problem Predicting the popularity of new video contents before they are published.
method Hybrid machine learning approach combining XGBoosting and deep neural nets, using metadata and categorical embedding techniques.
result Achieved better performance than standalone methods, validated on a dataset from a top streaming service.
New method uses LLMs to extract financial insights from Q&A sections of reports.
problem Scalability and accuracy issues in extracting valuable insights from financial report Q&A sections.
method Combines retrieval-augmented generation technique with metadata.
result Empirically demonstrates superior performance of the proposed method.
New dissimilarity measures enhance affinity propagation for complex network clustering.
problem Improving community detection in complex networks using affinity propagation.
method Leverage network latent geometry to design dissimilarity matrices.
result Affinity propagation outperforms state-of-the-art methods in community detection.
This paper detects anomalies in cellular network traffic using hybrid methods.
problem Detecting anomalies in network traffic for security and analysis.
method Hybrid method combining GARCH, K-means, and Neural Network.
result Anomaly detection in cellular network traffic successfully achieved.
Zero-shot understanding of accidents from surveillance videos using vision-language models
problem Accident understanding from surveillance videos
method Three-stage pipeline with vision-language similarity, metadata-driven multi-prompt reasoning, and entropy-gated pairwise adjudicator
result Substantial improvement in harmonic-mean score over baseline
Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent search, statistical characterization, and meaningful classification. Latent Dirichlet…
A novel deep learning method predicts Twitter users' locations using multiple data types.
problem Predicting Twitter users' locations on large social networks.
method Combines content-based and network-based approaches using a multi-entry neural network architecture (MENET).
result MENET outperforms state-of-the-art methods by a large margin on three benchmark datasets.
TorchIO simplifies medical image processing for deep learning.
problem Challenges in processing medical images like MRI and CT.
method Efficient loading, preprocessing, augmentation, and patch-based sampling.
result Enables researchers to focus on deep learning experiments.