mlsanityData to 7 Oct 2026

Data-Centric AI Requires Rethinking Data Notion

arXiv 2110.02491 ↗First posted 6 Oct 2021cs.LG cs.NE math.CTcited by 8

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Abstract (arXiv)

The transition towards data-centric AI requires revisiting data notions from mathematical and implementational standpoints to obtain unified data-centric machine learning packages. Towards this end, this work proposes unifying principles offered by categorical and cochain notions of data, and discusses the importance of these principles in data-centric AI transition. In the categorical notion, data is viewed as a mathematical structure that we act upon via morphisms to preserve this structure. As for cochain notion, data can be viewed as a function defined in a discrete domain of interest and acted upon via operators. While these notions are almost orthogonal, they provide a unifying definition to view data, ultimately impacting the way machine learning packages are developed, implemented, and utilized by practitioners.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

14 of 32 references.

References · 14

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  3. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  4. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
  5. 2015Learning with a Wasserstein Losscited by 194
  6. 2019Topological Autoencoderscited by 124
  7. 2020Simplicial Neural Networkscited by 105
  8. 2018Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplaciancited by 84
  9. 2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 81
  10. 2021Principled Simplicial Neural Networks for Trajectory Predictioncited by 77
  11. 2020Cell Complex Neural Networkscited by 66
  12. 2021What are higher-order networks?cited by 66
2 more
  1. 2019HodgeNet: Graph Neural Networks for Edge Datacited by 39
  2. 2021Simplicial Complex Representation Learningcited by 17

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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 4; 2024: 2.

8 papers.

  1. 2023Teaching Arithmetic to Small Transformerscited by 121
  2. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  3. 2024Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimationcited by 53
  4. 2021BScNets: Block Simplicial Complex Neural Networkscited by 21
  5. 2023AlpaGasus: Training A Better Alpaca with Fewer Datacited by 19
  6. 2021DataCLUE: A Benchmark Suite for Data-centric NLPcited by 6
  7. 2023Learning to Select Pivotal Samples for Meta Re-weightingcited by 3
  8. 2023Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithmcited by 3