Paper Citation Record · LEDGER

Mixing Configurations for Downstream Prediction

As of 7 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2510.19248.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2510.19248 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:46:04.982077Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation aea872a4-3b84-44c1-a513-8b7856af2233 · outbound

This paper cites Some Methods for Classification and Analysis of Multivariate Observations.

Mixing Configurations for Downstream Prediction Some Methods for Classification and Analysis of Multivariate Observations

Reference 1

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Observation f8b91db4-2008-421f-a8e6-bff26406fd13 · outbound

This paper cites Normalized Cuts and Image Segmentation.

Mixing Configurations for Downstream Prediction Normalized Cuts and Image Segmentation

Reference 2

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Observation ffbb36aa-4565-4017-82a7-0f13f0bfa33e · outbound

This paper cites On Spectral Clustering: Analysis and an Algorithm.

Mixing Configurations for Downstream Prediction On Spectral Clustering: Analysis and an Algorithm

Reference 3

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Observation 85fc6450-474f-421a-a510-7f994254748f · outbound

This paper cites Perceptual Cues That Permit Categorical Differentiation of Animal Species by Infants.

Mixing Configurations for Downstream Prediction Perceptual Cues That Permit Categorical Differentiation of Animal Species by Infants

Reference 4

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Observation 16ec7979-49e5-4fb3-8a9a-b4202920f534 · outbound

This paper cites Infant Object Categorization Transcends Diverse Object- Context Relations.

Mixing Configurations for Downstream Prediction Infant Object Categorization Transcends Diverse Object- Context Relations

Reference 5

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Observation efd2b51d-98d5-4775-8ff0-7c1be9d37dce · outbound

This paper cites Lessons from Infant Learning for Unsupervised Machine Learning.

Mixing Configurations for Downstream Prediction Lessons from Infant Learning for Unsupervised Machine Learning

Reference 6

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Observation 22a5a20c-7e1f-47b3-a533-eb2c0ec3c6c9 · outbound

This paper cites Aligning Machine and Human Visual Representations across Abstraction Levels.

Mixing Configurations for Downstream Prediction Aligning Machine and Human Visual Representations across Abstraction Levels

Reference 7

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Observation bcde908d-4593-4249-b799-53042c38d284 · outbound

This paper cites Parallel Clustering with Resolution Variation.

Mixing Configurations for Downstream Prediction Parallel Clustering with Resolution Variation

Reference 8

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Observation 759b14d0-6544-4a82-814c-f661f5cf5c35 · outbound

This paper cites Krizhevsky.Learning Multiple Layers of Features from Tiny Images.

Mixing Configurations for Downstream Prediction Krizhevsky.Learning Multiple Layers of Features from Tiny Images

Reference 9

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Observation f74fa977-674e-46fe-87d4-00934d9c042f · outbound

This paper cites 16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils, and Pitfalls.

Mixing Configurations for Downstream Prediction 16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils, and Pitfalls

Reference 10

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Observation 85fa370f-22c6-44af-b90e-b91f67efd5f3 · outbound

This paper cites Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy.

Mixing Configurations for Downstream Prediction Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy

Reference 11

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Observation a3af702f-7e68-4c43-9541-34f18c83df82 · outbound

This paper cites The Active Microbial Community More Accurately Reflects the Anaerobic Digestion Process: 16S rRNA (Gene) Sequencing as a Predictive Tool.

Mixing Configurations for Downstream Prediction The Active Microbial Community More Accurately Reflects the Anaerobic Digestion Process: 16S rRNA (Gene) Sequencing as a Predictive Tool

Reference 12

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Observation ad9b3b43-b289-4383-9cd2-018e77f00415 · outbound

This paper cites Evaluation of 16S rRNA Gene Sequencing for Species and Strain-Level Microbiome Analysis.

Mixing Configurations for Downstream Prediction Evaluation of 16S rRNA Gene Sequencing for Species and Strain-Level Microbiome Analysis

Reference 13

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Observation 94dfe11d-0d97-446a-a5bb-8980226ed3d8 · outbound

This paper cites Deep Clustering for Unsupervised Learning of Visual Features.

Mixing Configurations for Downstream Prediction Deep Clustering for Unsupervised Learning of Visual Features

Reference 14

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Observation f7b49d47-e3ad-4b03-8a91-16680ca9298f · outbound

This paper cites Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering.

Mixing Configurations for Downstream Prediction Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering

Reference 15

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Observation cad761b9-5fde-4123-ae43-9e55ac3f92c0 · outbound

This paper cites Vision Transformers Need Registers.

Mixing Configurations for Downstream Prediction Vision Transformers Need Registers

Reference 16

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Observation b3e94c9e-1578-49fd-9b46-cdbcebf5caae · outbound

This paper cites Object-Centric Learning with Slot Attention.

Mixing Configurations for Downstream Prediction Object-Centric Learning with Slot Attention

Reference 17

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Observation e663be7a-0ab7-443e-9d0d-ab74c70fcb51 · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Mixing Configurations for Downstream Prediction Emerging Properties in Self-Supervised Vision Transformers

Reference 18

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Observation 5ad78eb8-4afa-4707-a208-22b63a4b0a21 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Mixing Configurations for Downstream Prediction DINOv2: Learning Robust Visual Features without Supervision

Reference 19

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Observation 7b4b6fe7-f32a-47a8-bffe-627fa8bacd08 · outbound

This paper cites Localizing Objects with Self-Supervised Transformers and no Labels.

Mixing Configurations for Downstream Prediction Localizing Objects with Self-Supervised Transformers and no Labels

Reference 20

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Observation 23b02479-1896-47b6-bd7a-5172393fc784 · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

Mixing Configurations for Downstream Prediction DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 21

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Observation 4beaff46-7abe-498f-abdd-6c2e75252028 · outbound

This paper cites Attention Is All You Need.

Mixing Configurations for Downstream Prediction Attention Is All You Need

Reference 22

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Observation ae1e63df-96d9-4e9c-9126-bee3d7b1eb40 · outbound

This paper cites Digraph Clustering by the BlueRed Method.

Mixing Configurations for Downstream Prediction Digraph Clustering by the BlueRed Method

Reference 23

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Observation d9845ad0-9b6d-48f8-913e-970e2c329f4d · outbound

This paper cites A Global Geometric Framework for Nonlinear Dimensionality Reduction.

Mixing Configurations for Downstream Prediction A Global Geometric Framework for Nonlinear Dimensionality Reduction

Reference 24

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Observation 99b5dc25-3d24-4159-b09a-c3147b8d7711 · outbound

This paper cites Spaceland Embedding of Sparse Stochastic Graphs.

Mixing Configurations for Downstream Prediction Spaceland Embedding of Sparse Stochastic Graphs

Reference 25

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Observation 90f5772e-9af6-46b4-8a56-d23df928aea0 · outbound

This paper cites Visualizing Data Using T-SNE.

Mixing Configurations for Downstream Prediction Visualizing Data Using T-SNE

Reference 26

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Observation 0ca15649-1a29-4e8b-8229-813a6c84b8b3 · outbound

This paper cites From Louvain to Leiden: Guaranteeing Well-Connected Communi- ties.

Mixing Configurations for Downstream Prediction From Louvain to Leiden: Guaranteeing Well-Connected Communi- ties

Reference 27

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Observation 7b819041-a2ef-40f4-a6f3-8a99a8fd2b5c · outbound

This paper cites Automated Selection of Results in Hierarchical Segmentations of Remotely Sensed Hyperspectral Images.

Mixing Configurations for Downstream Prediction Automated Selection of Results in Hierarchical Segmentations of Remotely Sensed Hyperspectral Images

Reference 28

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Observation 76787728-b85b-4b6d-96dc-5f0c34bab79c · outbound

This paper cites Comparing Partitions.

Mixing Configurations for Downstream Prediction Comparing Partitions

Reference 29

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Observation ca0db2d3-83f3-42b3-a252-96b58c3c32d7 · outbound

This paper cites The Hungarian Method for the Assignment Problem.

Mixing Configurations for Downstream Prediction The Hungarian Method for the Assignment Problem

Reference 30

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Observation c18fbed8-da6a-44f6-a22b-c5792639af4e · outbound

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Mixing Configurations for Downstream Prediction A Shortest Augmenting Path Algorithm for Dense and Sparse Linear Assignment Problems

Reference 31

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Observation 4c6b44e1-cdf1-42b9-9c31-012f02c55cd8 · outbound

This paper cites Auction Algorithms for Network Flow Problems: A Tutorial Introduction.

Mixing Configurations for Downstream Prediction Auction Algorithms for Network Flow Problems: A Tutorial Introduction

Reference 32

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Observation 6fdc8064-db4c-472b-9899-ae6015e9c4e3 · outbound

This paper cites Algebraic Connectivity of Graphs.

Mixing Configurations for Downstream Prediction Algebraic Connectivity of Graphs

Reference 33

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This paper cites Algebraic Vertex Ordering of a Sparse Graph for Adjacency Access Locality and Graph Compression.

Mixing Configurations for Downstream Prediction Algebraic Vertex Ordering of a Sparse Graph for Adjacency Access Locality and Graph Compression

Reference 34

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Mixing Configurations for Downstream Prediction Adam: A Method for Stochastic Optimization

Reference 35

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Mixing Configurations for Downstream Prediction Unresolved cited work

Reference 36

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Mixing Configurations for Downstream Prediction National Institutes of Health (NIH).URL: https://www.nih.gov/ (visited on 02/04/2025)

Reference 37

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This paper cites Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning.

Mixing Configurations for Downstream Prediction Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning

Reference 38

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Mixing Configurations for Downstream Prediction com/articles/nbt.2023(visited on 02/04/2025)

Reference 40

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Mixing Configurations for Downstream Prediction Quantum Chemistry Structures and Properties of 134 Kilo Molecules

Reference 41

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Mixing Configurations for Downstream Prediction Hedonic Housing Prices and the Demand for Clean Air

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Mixing Configurations for Downstream Prediction Gradient-Based Learning Applied to Document Recognition

Reference 43

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Mixing Configurations for Downstream Prediction Random Forests

Reference 44

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Mixing Configurations for Downstream Prediction XGBoost: A Scalable Tree Boosting System

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Mixing Configurations for Downstream Prediction CatBoost: Unbiased Boosting with Categorical Features

Reference 46

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Mixing Configurations for Downstream Prediction TabNet: Attentive Interpretable Tabular Learning

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Mixing Configurations for Downstream Prediction TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Reference 48

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Mixing Configurations for Downstream Prediction Revisiting Deep Learning Models for Tabular Data

Reference 49

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Mixing Configurations for Downstream Prediction Predicting the Optimal Growth Temperatures of Prokaryotes Using Only Genome Derived Features

Reference 50

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Mixing Configurations for Downstream Prediction Geometry-Enhanced Molecular Representation Learning for Property Prediction

Reference 51

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Mixing Configurations for Downstream Prediction UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 53

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Mixing Configurations for Downstream Prediction 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005

Reference 2005

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Pith citing papers

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