arXiv AI
Joint Embedding with Dynamic Integration network (JEDI-net)
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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
2d ago
TopoMap: A Feature-based Discriminator of the Topographical Regions in the Test Input Space
arXiv:2509.03242v3 Announce Type: replace Abstract: Testing Deep Learning (DL)-based systems is an open challenge. Although it is relatively easy to find inputs that cause a DL model to misbehave, th...
arXiv AI
Sep 21
Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Data
The paper introduces FedDCN, a federated deep clustering network that jointly optimizes reconstruction and clustering losses for high‑dimensional, heterogeneous data. It addresses challenges of non‑IID client data by generating synthetic augmentations and applying geometric regularization to align latent spaces. Experiments show the method’s effectiveness under both IID and non‑IID settings, and the authors outline future research directions.
arXiv Machine Learning
Aug 28
Aitchison Embeddings for Learning Compositional Graph Representations
The paper introduces a compositional graph embedding framework based on Aitchison geometry, where nodes are represented as simplex-valued mixtures over latent archetypal factors. By embedding these mixtures using isometric log-ratio coordinates, the method preserves Aitchison distances while allowing unconstrained optimization in Euclidean space, yielding intrinsically interpretable embeddings. The approach achieves competitive performance on node classification and link prediction tasks and enables principled component restriction through subcompositional coherence, allowing analysis of how archetype groups influence representations and predictions.
arXiv Machine Learning
Jul 7
WeightCLIP: Aligning Datasets and Models for Weight Space Learning
arXiv:2607. 03551v1 Announce Type: new Abstract: Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks.
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.