arXiv Machine Learning
Jul 7
Hierarchical Scaffolding Enables Human-Like Cognitive Selectivity under Data Scarcity
arXiv:2607. 04709v1 Announce Type: new Abstract: Modern machine learning systems demand extensive datasets for visual recognition.
Unsupervised Cognition
arXiv:2409. 18624v4 Announce Type: replace Abstract: Unsupervised learning methods have a soft inspiration in cognition models.
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arXiv Machine Learning
Aug 21
DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
arXiv:2608. 20258v1 Announce Type: new Abstract: Decision tree-based models are widely used in machine learning due to their interpretability and strong empirical performance.
arXiv Machine Learning
Aug 26
How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning
The paper examines whether Deep Embedded Clustering (DEC) truly overcomes the fundamental limitations of k‑means clustering, such as handling clusters of arbitrary shapes, varied sizes, and densities. Through analysis, it finds that DEC does not exploit the underlying data distribution and therefore fails to address these limitations. Instead, a non‑deep learning approach that leverages distributional information of clusters can achieve the intended goals of deep clustering.
arXiv Machine Learning
Jul 17
Cross-Cluster Weighted Forests
arXiv:2105. 07610v5 Announce Type: replace-cross Abstract: Building trustworthy machine learning algorithms for biological applications requires adapting to data heterogeneity from different sources, batches, distributions, or studies.
arXiv AI
Jun 4
ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
arXiv:2509. 25289v4 Announce Type: replace-cross Abstract: Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue.
Hugging Face Trending Papers
Aug 20
DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
The paper introduces DICS, a clustering-based framework that uses data-informed priors to construct a compact set of candidate splits for decision tree classifiers. By incorporating class-aware structure, DICS reduces the split search space, preserving predictive performance while cutting training time. The authors provide theoretical analysis and experimental results showing comparable accuracy to exhaustive search across synthetic and benchmark datasets.
arXiv Machine Learning
Aug 4
Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data
arXiv:2608. 00346v1 Announce Type: new Abstract: Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy.
arXiv AI
Jun 9
A Geometric Unification of Concept Learning with Concept Cones
arXiv:2512. 07355v2 Announce Type: replace Abstract: Two traditions of interpretability have evolved side by side but seldom spoken to each other: Concept Bottleneck Models (CBMs), which prescribe what a concept should be, and Sparse Autoencoders (SAEs), which discover what concepts emerge.
arXiv Machine Learning
Aug 24
Interpretable clustering via optimal multi-way decision trees
The paper introduces ICOMT, a framework for interpretable clustering using optimal multi-way decision trees. It proposes a new discretization technique based on one-dimensional K‑means, formulates a binary linear optimization problem to ensure tree optimality, and demonstrates superior clustering accuracy and shallow tree structures on four public datasets.
arXiv Computer Vision
Aug 25
Exploring learning environments for label\-efficient cancer diagnosis
arXiv:2408.07988v3 Announce Type: replace Abstract: Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial fo...
arXiv AI
Jul 1
Creating Intelligence: A Computational Foundation for AGI
arXiv:2606. 31819v1 Announce Type: new Abstract: This work introduces a new computational theory of mind grounded in set theory and hyperdimensional computing.
arXiv AI
Jul 7
HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy
arXiv:2607. 04353v1 Announce Type: cross Abstract: Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups.