Reducing neural network training time by using smaller, representative datasets.
problem Long training times for neural networks.
method Topology-based representative datasets, measured by persistence diagrams.
result Neural network accuracy on representative datasets is similar to original datasets for perceptrons and mean squared error.
With the growing interest on Network Analysis, Relational Data Mining is becoming an emphasized domain of Data Mining. This paper addresses the problem of extracting representative elements from a relational dataset. After defining the notion of degree of representativeness, computed using the Borda aggregation procedu…
Dependent MMD coresets help compare multiple related datasets.
problem Comparing multiple related datasets for insights into model generalization.
method Dependent MMD coresets for collections of datasets.
result Dependent MMD coresets facilitate comparison and understanding of multiple related datasets.
This paper reviews and introduces measures for data representativity in AI systems.
problem Ensuring appropriate inference from data in AI systems.
method Defined and evaluated three measurable concepts of representativity.
result Contrasts between coverage and distribution representativity are crucial for AI system building.
This article reviews datasets for COVID-19 detection using ML.
problem Lack of accessible datasets for COVID-19 detection research.
method Analyzed 96 papers on COVID-19 detection from January 2020 to June 2020.
result Identified and summarized datasets used in COVID-19 detection studies.
The paper tackles sampling biases by ensuring minority groups are adequately represented in training data.
problem Sampling biases in training data lead to algorithmic biases in machine learning systems.
method The paper presents adaptive sampling methods to determine if it's possible to assemble a representative dataset from given data sources.
result The methods presented can determine with high confidence if a representative dataset can be assembled from given data sources.
Develops metrics to identify outliers in machine learning datasets.
problem Identifying outliers in machine learning datasets.
method Five methods to score examples based on well-representedness.
result All methods correlate highly, suggesting a quantifiable notion of well-representedness.
Random shuffle method boosts HF dataset size 10-21 times.
problem Narrow dataset cardinality in HF clinical studies.
method Random shuffle method to enhance dataset size.
result Enhanced HF dataset size 10-21 times.
Method generates prototypes from small datasets for efficient learning.
problem Efficiently learning from small datasets with soft labels.
method Modular method for generating soft-label prototypical lines and Hierarchical Soft-Label Prototype k-Nearest Neighbor algorithm.
result High classification accuracy with significantly fewer prototypes than classes.
Study reveals AI skin cancer classifiers underperform for darker skin phototypes, advocating for fairness auditing.
problem AI bias in dermatology, particularly for darker skin phototypes.
method Predictive Representativity (PR) framework, evaluating classifiers on HAM10000 and BOSQUE Test sets.
result Substantial performance disparities by skin phototype, highlighting AI bias.
VillageNet clusters high-dimensional datasets without needing cluster count, using K-Means and WLCF.
problem Clustering large, high-dimensional datasets with diverse variables.
method VillageNet operates in two phases: K-Means for village division and WLCF for network community detection.
result VillageNet autonomously determines optimal number of clusters and outperforms other methods in NMI.
Proposes a method to predict cluster number and cluster representatives using cluster stability analysis.
problem Determining the number of clusters in a dataset.
method Analyzes cluster stability using Monte-Carlo simulation to predict cluster number and find cluster representatives.
result Significant improvement in predicting cluster numbers and cluster composition in large datasets.
Contrastive ICA identifies features in experimental groups relative to controls.
problem Jointly analyzing experimental and control datasets to identify salient features.
method Developed contrastive ICA (cICA) using tensor decomposition.
result cICA identifies patterns and visualizes data effectively, outperforming existing methods.
Labeling each instance in a large dataset is extremely labor- and time- consuming . One way to alleviate this problem is active learning, which aims to which discover the most valuable instances for labeling to construct a powerful classifier. Considering both informativeness and representativeness provides a promising…
Quantum annealing speeds up extreme clustering.
problem Efficiently grouping large datasets into many representative clusters.
method Distributed quantum annealing method.
result Optimal clustering assignments achieved under separability assumption.
Optimizes sample weights for representative data averages.
problem Achieving sample averages close to prescribed values.
method Formulates as an optimization problem, often convex and efficiently solvable.
result Heuristic methods based on convex optimization perform well.
Dataset2Vec learns dataset meta-features without expert knowledge.
problem Learning meta-features for datasets requires expert domain knowledge.
method Dataset2Vec combines DeepSet architecture with hierarchical sets to learn meta-features.
result Meta-features learned by Dataset2Vec outperform engineered features.
Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean fluorescence…
ENN neural network learns logical syllogisms using Euler diagrams.
problem Traditional neural networks struggle with logical reasoning, especially syllogisms.
method ENN represents logical relations as Euler diagrams, optimizing syllogism structures with a novel back-propagation algorithm.
result ENN can precisely represent and reason with all 24 syllogism structures.
SPOT uses optimal transport to select important prototypes.
problem Summarizing datasets for better understanding and decision making.
method Modeling prototype selection as a submodular optimization problem and using a greedy algorithm.
result Our approach efficiently selects prototypes with optimal transport that best represent the target dataset.
A new clustering method preserves data distribution.
problem Distorted cluster centers in k-means clustering.
method Distributional Clustering method ensuring cluster centers mimic data distribution.
result Cluster centers converge to data generating distribution.
Study shows DL models trained on healthy subjects perform worse on patients' ECG data.
problem Inefficiency of DL models on heterogeneous datasets for heart beat detection.
method Investigated and evaluated the use of Transfer Learning to adapt DL models to different datasets.
result Transfer Learning improves classification performance on small sample size datasets.
Deep learning has been widely accepted as a promising solution for medical image segmentation, given a sufficiently large representative dataset of images with corresponding annotations. With ever increasing amounts of annotated medical datasets, it is infeasible to train a learning method always with all data from scr…
Method for creating synthetic multi-fidelity data sets.
problem Lack of representative synthetic datasets for multifidelity optimisation benchmarks.
method Systematic generation of synthetic fidelities from preexisting datasets.
result Allows systematic investigation of lower fidelity proxies' influence.
Model trains passing events on a bridge using multilevel Gaussian process.
problem Represent aggregate train-passing events from a bridge monitoring system.
method Formulate a combined model with low-rank approximation hierarchical Gaussian process, incorporating domain expertise as constraints.
result Allow for simulation of previously unobserved train types.
CLIP dataset helps extract action items from hospital discharge notes.
problem Lost action items in long discharge notes hinder information sharing.
method Created CLIP dataset of annotated discharge notes, used multi-aspect extractive summarization, trained models on pre-trained language models and context.
result Best models improved by incorporating context and pre-trained language models.
Gradient flows on distributions of distributions for machine learning tasks.
problem Designing gradient flows for datasets of probability distributions.
method Representing classes as conditional distributions, modeling datasets as mixture distributions, using Wasserstein over Wasserstein (WoW) distance and gradients.
result Demonstrated gradient flows for dataset transfer and distillation tasks.
Introduces MLM dataset for multitask learning across multiple languages and modalities.
problem Training and evaluating multitask systems on diverse data.
method Develops a new benchmark dataset with semantic data and modalities.
result Demonstrates challenges in generalizing on diverse data.
Generative models help debug data issues in private, decentralized datasets.
problem Manual data inspection is impractical for private, decentralized datasets.
method Differentially private federated generative models.
result Generative models can effectively debug data issues in private datasets.
MOSAIC selects few informative exemplars from high-dimensional data with non-linear structures.
problem Representative selection from high-dimensional data with non-linear structures.
method MOSAIC uses a multi-criteria approach with a quadratic formulation to maximize global representation power, diversity, and outlier detection.
result MOSAIC maximizes data coverage in a transformed space and achieves robustness to various outlier types.
A new hierarchical clustering method selects representative points from sub-minimum-spanning-trees.
problem Selecting representative points for hierarchical clustering to improve robustness and reliability.
method Identify representative points using reciprocal nearest data points in sub-minimum-spanning-trees.
result The proposed algorithm outperforms other methods in accuracy and efficiency.
MCCA extracts shared structure from multiple tensor datasets.
problem Extracting shared structure from multiple tensor datasets.
method Multilinear common component analysis (MCCA) using Kronecker products of mode-wise covariance matrices.
result MCCA constructs a common basis that retains information from multiple tensor datasets.
Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure by learning a basis that can efficiently express the collection. Recently, "self expression", the idea of using a small subset of data vect…
This work improves metric learning models by incorporating class hierarchies.
problem Class hierarchies are often ignored in classification-based metric learning models.
method Trained softmax classifier and metric learning models with predefined class hierarchies.
result ProxyDR model performs better in hierarchical inference and hierarchy-informed performance.
This paper is based on a previous publication [29]. Our work extends exception mining and outlier detection to the case of object-relational data. Object-relational data represent a complex heterogeneous network [12], which comprises objects of different types, links among these objects, also of different types, and at…
This paper proposes new methods for ALR that consider informativeness, representativeness, and diversity.
problem Efficiently label samples for regression models with limited labeled data.
method Integrates informativeness, representativeness, and diversity in pool-based sequential active learning.
result Demonstrates effectiveness of new ALR approaches on 12 datasets.
Proposes HBGNN for better recommendation systems using graph neural networks.
problem Sparse structured data in recommendation systems lacking feature richness.
method Hierarchical BiGraph Neural Network (HBGNN) using bigraph framework.
result Competitive performance compared to current methods.
Proposes a quantum-inspired algorithm for selecting representative data subsets.
problem Selecting the most representative subset of data from a larger dataset.
method Uses a Quadratic Unconstrained Binary Optimization (QUBO) problem approach.
result Demonstrates the effectiveness of the selector algorithm in finance applications.
New methods improve brain data analysis from fMRI datasets.
problem Simplified brain models from correlational values are insufficient.
method Deep learning and geometric deep learning techniques.
result Improved predictive spatio-temporal brain data representation.
FWC creates fair synthetic samples for machine learning tasks.
problem Addressing biases in machine learning models for fair decision-making.
method FWC uses an efficient majority minimization algorithm to minimize Wasserstein distance while enforcing demographic parity.
result FWC achieves a competitive fairness-utility tradeoff and reduces biases in predictions from large language models.
Wiki-CS dataset benchmarks Graph Neural Networks using Wikipedia articles.
problem Benchmarking Graph Neural Networks on a new domain with structural differences.
method Derived from Wikipedia, nodes represent Computer Science articles, edges from hyperlinks, 10 classes for different branches, evaluated semi-supervised node classification and link prediction.
result Graph Neural Networks perform well on Wiki-CS, showing structural differences from earlier benchmarks.
A neural network with a single hidden layer can't represent certain multivariable functions.
problem Representing certain multivariable functions with a neural network having only one hidden layer.
method Developed a continuum version of a one-hidden-layer neural network with ReLU activation, and proved constraints on its parameters and second derivative.
result Existence of a smooth binary function that cannot be precisely represented by any such neural network.
Batch Normalization (BN) is a common technique used to speed-up and stabilize training. On the other hand, the learnable parameters of BN are commonly used in conditional Generative Adversarial Networks (cGANs) for representing class-specific information using conditional Batch Normalization (cBN). In this paper we pro…
iRDM selects unlabeled samples for regression without labels, improving model accuracy.
problem Selecting unlabeled samples for regression without label information.
method Iterative representativeness-diversity maximization (iRDM).
result iRDM significantly outperforms supervised ALR, especially with limited labeled samples.
Study how models represent features in naturalistic learning problems.
problem Understanding which features models use and ignore in naturalistic tasks.
method Synthetic datasets with controlled task-relevance of features, training models to recognize both easy and hard features.
result Models preferentially represent task-relevant features and suppress task-irrelevant ones over training.
New approach tackles class imbalance in long-tailed datasets using domain adaptation techniques.
problem Class imbalance in long-tailed datasets leading to poor model performance.
method Proposes a meta-learning approach to estimate differences between class-conditioned distributions.
result Validated approach on six benchmark datasets and three loss functions.
Reweighting training data to better represent new tasks.
problem Deploying machine learning models to new tasks is challenging due to training data distribution.
method Formulate an exponential tilt distribution shift model and learn train data importance weights to minimize KL divergence.
result The learned train data weights improve target performance evaluation, fine-tuning, and model selection.
An online convex matrix factorization algorithm with interpretable bases.
problem Scaling and interpretability in matrix factorization for large datasets.
method Online algorithm with representative data samples for interpretability.
result Significant computational savings compared to classical convex MF.