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提出了一个将持久同调整合进DMAE骨干网络的拓扑感知表示学习框架，用于编码三周期极小曲面(TPMS)机械超材料的全局结构连通性，在机械属性无监督聚类上提升最高达51.5%，回归R²达0.993。
The paper introduces Ψ-Resilience, a model-free feature importance method that derives explanations from 1D topological signals using 0-dimensional persistence of class-disagreement landscapes, achieving competitive Spearman correlations with SHAP and mutual information on synthetic and real datasets.
The paper proposes Skeletal Prototypes on Iterative Nerve Expansions (SPINE), a new prototype reduction method that models classes as embedded 1-complexes, achieving superior accuracy and efficiency on benchmark datasets compared to existing techniques.
Topological Steering is a new framework for controlling large language model behavior using topological data analysis to capture global structures in activation spaces, enabling more robust behavioral control.
This paper presents TGO-IV, a topological framework using persistent homology to analyze how transformer representations evolve across layers, complementing prior spectral and geometric observatories.
The paper studies the topology of learned representations in predictive coding networks using persistent homology, finding that smaller models simplify topology earlier than larger ones and that earlier simplification correlates with worse reconstruction performance.
TAGTorch is an open-source PyTorch library that unifies tools for topology, algebra, and geometry-aware machine learning, covering preprocessing, architectures, training techniques, and model analysis.
A tweet promoting the book 'The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R' by Colleen M. Farrelly and Yaé Ulrich Gaba, highlighting its practical approach to using geometry and topology in data science.
This paper presents an exploratory benchmark for detecting sparse-ring fraud in dynamic transaction graphs using quantum-inspired Contextual Machine Learning (CML) compared to a GRU baseline, finding that hybrid graph features combining identity-preserving and topological summaries yield the best results.
This paper applies topological data analysis to flood detection by extracting topological features from satellite imagery and incorporating them into neural networks, demonstrating improved robustness and interpretability over conventional methods.
This paper investigates the latent structure of multimodal embeddings from a masked autoencoder for pediatric sleep analysis. It shows that augmenting embeddings with geometric, topological, and clinical features improves prediction and calibration for sleep-related events.
HodgeCover uses higher-order topological coverage to compress sparse Mixture-of-Experts layers by addressing irreducible mergeability barriers that pairwise signals miss, matching state-of-the-art baselines on expert reduction and leading on aggressive compression.