Hierarchical Machine Learning
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ArrowFlow: Hierarchical Machine Learning in the Space of Permutations
Announce Type: replace Abstract: We introduce ArrowFlow, a machine learning architecture that operates entirely in the space of permutations. Its computational units are ranking filters, learned orderings that compare inputs via Spearman's footrule distance and update through permutation-matrix accumulation, a non-gradient rule rooted in displacement evidence. Layers compose hierarchically: each layer's output ranking becomes the next layer's input, enabling deep ordinal representation...
$p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences
arXiv:2606.06117v1 Announce Type: cross Abstract: We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis. Each DNA sequence is encoded along two complementary axes: a $p$-adic distance on $k$-mer prefixes, which captures hierarchical positional structure, and a compositional $L_1$ distance on $k$-mer frequencies, which captures local sequence content. The two distances jointly...
MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery
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TaxoFormer: Hierarchical Transformer for Predicting the Full Taxonomic Lineage of Protein Sequences
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Deep learning four decades of human migration
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Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
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Conveyance: A Versatile Framework for Learning in Structured Class Spaces
arXiv:2605.28420v2 Announce Type: replace Abstract: While machine learning (ML) architectures have evolved rapidly to account for complex data, loss functions like cross-entropy remain mostly structure-agnostic in many real-world applications. However, the "class-symmetric" nature of these standard losses fundamentally limits the ability of ML models to exploit structural relationships between classes, particularly when facing structured noise. We propose Conveyance, a new classification...
Deep networks learn to parse uniform-depth context-free languages from local statistics
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Rethinking Sales Lead Scoring with LLM-based Hierarchical Preference Ranking
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VirtualMLE: A Virtual ML Engineer that Optimizes Sequential Recommenders
arXiv:2606.03221v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning, reflection, and tool utilization, unlocking new paradigms for automating complex engineering workflows. However, in the domain of sequential recommendation (SR), tuning models on new datasets still relies heavily on the manual trial-and-error of experienced machine learning engineers. To bridge this gap, we propose \textbf{VirtualMLE}, an...