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#dimensionality — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #dimensionality, aggregated by home.social.

  1. Make It Go Faster Dept: The curse of the #Asymptotic regimes awaits if you manage to run the gauntlet of the curse of #Dimensionality. It's great fun but outside of #Science no one knows what the hell your talking about.
    Your wielding #Thor 's hammer...but Thor has been reincarnated...as a turtle. It's a grind. Slightly infuriating. The polarity change is there, but unimpressive.You can explain that it's not supposed to be there at all but by then they'll have fallen into a deep sleep...

  2. Make It Go Faster Dept: The curse of the #Asymptotic regimes awaits if you manage to run the gauntlet of the curse of #Dimensionality. It's great fun but outside of #Science no one knows what the hell your talking about.
    Your wielding #Thor 's hammer...but Thor has been reincarnated...as a turtle. It's a grind. Slightly infuriating. The polarity change is there, but unimpressive.You can explain that it's not supposed to be there at all but by then they'll have fallen into a deep sleep...

  3. Make It Go Faster Dept: The curse of the #Asymptotic regimes awaits if you manage to run the gauntlet of the curse of #Dimensionality. It's great fun but outside of #Science no one knows what the hell your talking about.
    Your wielding #Thor 's hammer...but Thor has been reincarnated...as a turtle. It's a grind. Slightly infuriating. The polarity change is there, but unimpressive.You can explain that it's not supposed to be there at all but by then they'll have fallen into a deep sleep...

  4. Make It Go Faster Dept: The curse of the #Asymptotic regimes awaits if you manage to run the gauntlet of the curse of #Dimensionality. It's great fun but outside of #Science no one knows what the hell your talking about.
    Your wielding #Thor 's hammer...but Thor has been reincarnated...as a turtle. It's a grind. Slightly infuriating. The polarity change is there, but unimpressive.You can explain that it's not supposed to be there at all but by then they'll have fallen into a deep sleep...

  5. Make It Go Faster Dept: The curse of the #Asymptotic regimes awaits if you manage to run the gauntlet of the curse of #Dimensionality. It's great fun but outside of #Science no one knows what the hell your talking about.
    Your wielding #Thor 's hammer...but Thor has been reincarnated...as a turtle. It's a grind. Slightly infuriating. The polarity change is there, but unimpressive.You can explain that it's not supposed to be there at all but by then they'll have fallen into a deep sleep...

  6. 🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess:

    🌍 doi.org/10.1103/2jt7-c8cq

    #CompNeuro #NeuralDynamics #Connectome

  7. 🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess:

    🌍 doi.org/10.1103/2jt7-c8cq

    #CompNeuro #NeuralDynamics #Connectome

  8. 🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess:

    🌍 doi.org/10.1103/2jt7-c8cq

    #CompNeuro #NeuralDynamics #Connectome

  9. 🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess:

    🌍 doi.org/10.1103/2jt7-c8cq

    #CompNeuro #NeuralDynamics #Connectome

  10. 🧠 New paper by Clark et al. (2025) shows that the #dimensionality of #PopulationActivity in #RNN can be explained by just two #connectivity parameters: effective #CouplingStrength and effective #rank. Uses networks with rapidly decaying singular value spectra and structured overlaps between left and right singular vectors. Could be useful for interpreting large scale population recordings and connectome data I guess:

    🌍 doi.org/10.1103/2jt7-c8cq

    #CompNeuro #NeuralDynamics #Connectome

  11. 😆 Wow, hold the phone! We're now measuring #embeddings like they're the new waist size competition for #AI models. 📏 Apparently, someone missed the memo that size matters, but only if you're overcompensating for something else. 🧠 Who knew #dimensionality was the new #vanity metric!
    vickiboykis.com/2025/09/01/how #Metrics #Metrics #HackerNews #ngated

  12. 😆 Wow, hold the phone! We're now measuring #embeddings like they're the new waist size competition for #AI models. 📏 Apparently, someone missed the memo that size matters, but only if you're overcompensating for something else. 🧠 Who knew #dimensionality was the new #vanity metric!
    vickiboykis.com/2025/09/01/how #Metrics #Metrics #HackerNews #ngated

  13. 😆 Wow, hold the phone! We're now measuring #embeddings like they're the new waist size competition for #AI models. 📏 Apparently, someone missed the memo that size matters, but only if you're overcompensating for something else. 🧠 Who knew #dimensionality was the new #vanity metric!
    vickiboykis.com/2025/09/01/how #Metrics #Metrics #HackerNews #ngated

  14. 😆 Wow, hold the phone! We're now measuring #embeddings like they're the new waist size competition for #AI models. 📏 Apparently, someone missed the memo that size matters, but only if you're overcompensating for something else. 🧠 Who knew #dimensionality was the new #vanity metric!
    vickiboykis.com/2025/09/01/how #Metrics #Metrics #HackerNews #ngated

  15. Гравитация – это иллюзия? Скрытые силы в действии

    Гравитация традиционно считается одной из основных сил, определяющих динамику тел во Вселенной. Но что если её влияние можно уравновесить без использования тяговых двигателей и энергозатрат? Однако гипотеза о "гравитационно-угловом балансе" предлагает новый взгляд на это взаимодействие. В этой статье мы разберёмся с гипотезой гравитационно-углового баланса – концепцией, основанной на базовых законах физики, которая позволяет найти точки нулевого гравитационного воздействия и по-новому взглянуть на динамику тел в космосе, где гравитация фактически исчезает Читать

    habr.com/ru/articles/881582/

    #Gravitation #balance #Dimensionality #quantum #illusion

  16. Гравитация – это иллюзия? Скрытые силы в действии

    Гравитация традиционно считается одной из основных сил, определяющих динамику тел во Вселенной. Но что если её влияние можно уравновесить без использования тяговых двигателей и энергозатрат? Однако гипотеза о "гравитационно-угловом балансе" предлагает новый взгляд на это взаимодействие. В этой статье мы разберёмся с гипотезой гравитационно-углового баланса – концепцией, основанной на базовых законах физики, которая позволяет найти точки нулевого гравитационного воздействия и по-новому взглянуть на динамику тел в космосе, где гравитация фактически исчезает Читать

    habr.com/ru/articles/881582/

    #Gravitation #balance #Dimensionality #quantum #illusion

  17. Гравитация – это иллюзия? Скрытые силы в действии

    Гравитация традиционно считается одной из основных сил, определяющих динамику тел во Вселенной. Но что если её влияние можно уравновесить без использования тяговых двигателей и энергозатрат? Однако гипотеза о "гравитационно-угловом балансе" предлагает новый взгляд на это взаимодействие. В этой статье мы разберёмся с гипотезой гравитационно-углового баланса – концепцией, основанной на базовых законах физики, которая позволяет найти точки нулевого гравитационного воздействия и по-новому взглянуть на динамику тел в космосе, где гравитация фактически исчезает Читать

    habr.com/ru/articles/881582/

    #Gravitation #balance #Dimensionality #quantum #illusion

  18. Гравитация – это иллюзия? Скрытые силы в действии

    Гравитация традиционно считается одной из основных сил, определяющих динамику тел во Вселенной. Но что если её влияние можно уравновесить без использования тяговых двигателей и энергозатрат? Однако гипотеза о "гравитационно-угловом балансе" предлагает новый взгляд на это взаимодействие. В этой статье мы разберёмся с гипотезой гравитационно-углового баланса – концепцией, основанной на базовых законах физики, которая позволяет найти точки нулевого гравитационного воздействия и по-новому взглянуть на динамику тел в космосе, где гравитация фактически исчезает Читать

    habr.com/ru/articles/881582/

    #Gravitation #balance #Dimensionality #quantum #illusion

  19. The Dimensions of dimensionality
    cell.com/trends/cognitive-scie

    Globally interpretable feature dimensions are one property, among others like prediction performance and compactness, that researchers can prioritize when inferring latent representations.

    Multidimensional representational spaces can capture complex structures such as hierarchical relationships, lexical entailment, and compositional features.

    Superficially different representations, such as graphs and multidimensional data, can capture the same structural relationships under appropriate settings.

    The dimensionality of a representation conveys relatively little information on its own.

    #representation #dimensionality #cogsci #ml #datascience

  20. The Dimensions of dimensionality
    cell.com/trends/cognitive-scie

    Globally interpretable feature dimensions are one property, among others like prediction performance and compactness, that researchers can prioritize when inferring latent representations.

    Multidimensional representational spaces can capture complex structures such as hierarchical relationships, lexical entailment, and compositional features.

    Superficially different representations, such as graphs and multidimensional data, can capture the same structural relationships under appropriate settings.

    The dimensionality of a representation conveys relatively little information on its own.

    #representation #dimensionality #cogsci #ml #datascience

  21. The Dimensions of dimensionality
    cell.com/trends/cognitive-scie

    Globally interpretable feature dimensions are one property, among others like prediction performance and compactness, that researchers can prioritize when inferring latent representations.

    Multidimensional representational spaces can capture complex structures such as hierarchical relationships, lexical entailment, and compositional features.

    Superficially different representations, such as graphs and multidimensional data, can capture the same structural relationships under appropriate settings.

    The dimensionality of a representation conveys relatively little information on its own.

    #representation #dimensionality #cogsci #ml #datascience

  22. The Dimensions of dimensionality
    cell.com/trends/cognitive-scie

    Globally interpretable feature dimensions are one property, among others like prediction performance and compactness, that researchers can prioritize when inferring latent representations.

    Multidimensional representational spaces can capture complex structures such as hierarchical relationships, lexical entailment, and compositional features.

    Superficially different representations, such as graphs and multidimensional data, can capture the same structural relationships under appropriate settings.

    The dimensionality of a representation conveys relatively little information on its own.

    #representation #dimensionality #cogsci #ml #datascience

  23. The Dimensions of dimensionality
    cell.com/trends/cognitive-scie

    Globally interpretable feature dimensions are one property, among others like prediction performance and compactness, that researchers can prioritize when inferring latent representations.

    Multidimensional representational spaces can capture complex structures such as hierarchical relationships, lexical entailment, and compositional features.

    Superficially different representations, such as graphs and multidimensional data, can capture the same structural relationships under appropriate settings.

    The dimensionality of a representation conveys relatively little information on its own.

    #representation #dimensionality #cogsci #ml #datascience

  24. 'Spherical Rotation Dimension Reduction with Geometric Loss Functions', by Hengrui Luo, Jeremy E. Purvis, Didong Li.

    jmlr.org/papers/v25/23-0547.ht

    #spherical #rotation #dimensionality

  25. 'Spherical Rotation Dimension Reduction with Geometric Loss Functions', by Hengrui Luo, Jeremy E. Purvis, Didong Li.

    jmlr.org/papers/v25/23-0547.ht

    #spherical #rotation #dimensionality

  26. 'Spherical Rotation Dimension Reduction with Geometric Loss Functions', by Hengrui Luo, Jeremy E. Purvis, Didong Li.

    jmlr.org/papers/v25/23-0547.ht

    #spherical #rotation #dimensionality

  27. 'Spherical Rotation Dimension Reduction with Geometric Loss Functions', by Hengrui Luo, Jeremy E. Purvis, Didong Li.

    jmlr.org/papers/v25/23-0547.ht

    #spherical #rotation #dimensionality

  28. 'Spherical Rotation Dimension Reduction with Geometric Loss Functions', by Hengrui Luo, Jeremy E. Purvis, Didong Li.

    jmlr.org/papers/v25/23-0547.ht

    #spherical #rotation #dimensionality

  29. Literally Didn't see either video Dept: But this thumbnail flashed by indicating a potential #SuperDeterminism food fight. Not jumping in but SD is interesting because if you don't specify #Dimensionality you might not be super. Some might insist that local determinism has nothing to do with Super...but if causality is acting strangely (say hello to most open problems in the SM)your often forced to consider some topological problem with extra dimensions...So you might have Super in N dimensions-

  30. Literally Didn't see either video Dept: But this thumbnail flashed by indicating a potential #SuperDeterminism food fight. Not jumping in but SD is interesting because if you don't specify #Dimensionality you might not be super. Some might insist that local determinism has nothing to do with Super...but if causality is acting strangely (say hello to most open problems in the SM)your often forced to consider some topological problem with extra dimensions...So you might have Super in N dimensions-

  31. 'Operator learning with PCA-Net: upper and lower complexity bounds', by Samuel Lanthaler.

    jmlr.org/papers/v24/23-0478.ht

    #pca #complexity #dimensionality

  32. 'Operator learning with PCA-Net: upper and lower complexity bounds', by Samuel Lanthaler.

    jmlr.org/papers/v24/23-0478.ht

    #pca #complexity #dimensionality

  33. 'Operator learning with PCA-Net: upper and lower complexity bounds', by Samuel Lanthaler.

    jmlr.org/papers/v24/23-0478.ht

    #pca #complexity #dimensionality

  34. 'Operator learning with PCA-Net: upper and lower complexity bounds', by Samuel Lanthaler.

    jmlr.org/papers/v24/23-0478.ht

    #pca #complexity #dimensionality

  35. 'Operator learning with PCA-Net: upper and lower complexity bounds', by Samuel Lanthaler.

    jmlr.org/papers/v24/23-0478.ht

    #pca #complexity #dimensionality

  36. 'Two Sample Testing in High Dimension via Maximum Mean Discrepancy', by Hanjia Gao, Xiaofeng Shao.

    jmlr.org/papers/v24/22-1136.ht

    #dimensionality #gaussian #statistics

  37. 'Two Sample Testing in High Dimension via Maximum Mean Discrepancy', by Hanjia Gao, Xiaofeng Shao.

    jmlr.org/papers/v24/22-1136.ht

    #dimensionality #gaussian #statistics