Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ | Paper Citation Record

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This paper cites an unresolved cited work.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Unresolved cited work

Reference 1

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This paper cites On the Equivalence between Herding and Conditional Gradient Algorithms.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ On the Equivalence between Herding and Conditional Gradient Algorithms

Reference 2

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This paper cites Neural machine translation by jointly learning to align and translate.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Neural machine translation by jointly learning to align and translate

Reference 3

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This paper cites Riemannian adaptive optimization methods.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Riemannian adaptive optimization methods

Reference 4

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Observation c92de2d9-204f-4c9b-bdf5-603499d74d77 · outbound

This paper cites Positive definite singular kernels on two-point homogeneous spaces.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Positive definite singular kernels on two-point homogeneous spaces

Reference 5

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Observation 7bfc9445-c62a-4b60-a795-4a91c06abb3e · outbound

This paper cites Spherical S liced- W asserstein.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Spherical S liced- W asserstein

Reference 6

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Observation b29d56ce-0158-4c7d-9373-0ff53e9a4d2d · outbound

This paper cites Stochastic gradient descent on R iemannian manifolds.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Stochastic gradient descent on R iemannian manifolds

Reference 7

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Observation a8cfe7cd-38f1-4eb4-b532-f93a80600451 · outbound

This paper cites Convergence properties of the KMeans algorithm.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Convergence properties of the KMeans algorithm

Reference 8

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Observation 83b96450-26e1-48fc-afdb-be9491b4ae67 · outbound

This paper cites A Test of Relative Similarity For Model Selection in Generative Models.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ A Test of Relative Similarity For Model Selection in Generative Models

Reference 9

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Observation eef202c7-83a0-4a94-b13a-bf637a7599f2 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ A simple framework for contrastive learning of visual representations

Reference 10

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Observation bddc84eb-3716-46e4-884f-997e77a1d3a6 · outbound

This paper cites Big self-supervised models are strong semi-supervised learners.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Big self-supervised models are strong semi-supervised learners

Reference 11

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Observation 8b003327-be4d-44e0-8384-3784db2c854e · outbound

This paper cites Table of spherical codes.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Table of spherical codes

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.985999Z

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Observation e709204e-7ed0-40b7-8400-88a70854b6b2 · outbound

This paper cites Sphere-packings, lattices, and groups.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Sphere-packings, lattices, and groups

Reference 13

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Observation 826d2829-2fc0-44ba-8aba-d71bbe4d11f0 · outbound

This paper cites Finite point-sets on S^2 with minimum distance as large as possible.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Finite point-sets on S^2 with minimum distance as large as possible

Reference 14

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This paper cites The power spherical distrbution.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ The power spherical distrbution

Reference 15

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Observation 71b606db-95be-4574-9f73-e230b7cf968f · outbound

This paper cites Uber eine Absch\.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Uber eine Absch\

Reference 16

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Observation b01950ea-c8c4-4506-b1ba-6e41396f61c2 · outbound

This paper cites Geodesic exponential kernels: When curvature and linearity conflict.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Geodesic exponential kernels: When curvature and linearity conflict

Reference 17

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Observation 89e56b62-eeb9-4e88-bd70-75a21b382776 · outbound

This paper cites Principal geodesic analysis for the study of nonlinear statistics of shape.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Principal geodesic analysis for the study of nonlinear statistics of shape

Reference 18

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Observation 0436b8dc-3646-4bb9-b2b3-9855ca852650 · outbound

This paper cites Minimizing a differentiable function over a differential manifold.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Minimizing a differentiable function over a differential manifold

Reference 19

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Observation 739cf760-388d-4ee9-8479-1dc29b06aeda · outbound

This paper cites Representation degeneration problem in training natural language generation models.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Representation degeneration problem in training natural language generation models

Reference 20

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Observation c11c35ae-e7bd-4ce5-b53d-2c53ecdb39c1 · outbound

This paper cites A novel approach to the spherical codes problem.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ A novel approach to the spherical codes problem

Reference 21

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Observation c8445b52-32e4-4402-ae2c-09ee5de27660 · outbound

This paper cites Frage: Frequency-agnostic word representation.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Frage: Frequency-agnostic word representation

Reference 22

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Observation 3efc5af2-2612-49fd-8787-53f0ecf8d0e1 · outbound

This paper cites Borgwardt, Malte J.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Borgwardt, Malte J

Reference 23

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Observation 6fc27e87-b1d8-4fce-bf77-42903e529fe6 · outbound

This paper cites Inequalities.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Inequalities

Reference 24

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Observation 13394abc-afae-4ffa-9d87-bbaa07c512d1 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Momentum contrast for unsupervised visual representation learning

Reference 25

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Observation 4c1fe0c2-da46-4a0f-b9e6-784f8f0898da · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Learning deep representations by mutual information estimation and maximization

Reference 26

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Observation 9d69372b-e9d9-4f25-b300-98fd2e703a7b · outbound

This paper cites Probability inequalities for sums of bounded random variables.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Probability inequalities for sums of bounded random variables

Reference 27

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Observation 3f0946d7-eb57-4bc0-afbb-a0af136f0181 · outbound

This paper cites Topics in circular statistics.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Topics in circular statistics

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.763844Z

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Observation 03f4543c-3022-4e13-ad53-cef29a0a3f11 · outbound

This paper cites S entence P iece: A simple and language independent subword tokenizer and detokenizer for neural text processing.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ S entence P iece: A simple and language independent subword tokenizer and detokenizer for neural text processing

Reference 29

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Observation d10d4d8b-b146-48da-b7a2-b2d3fe141598 · outbound

This paper cites Determinantal point processes for machine learning.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Determinantal point processes for machine learning

Reference 30

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Observation a5155dc4-ddd3-4894-9e9a-fe05085eaecf · outbound

This paper cites von Mises-Fisher loss for training sequence to sequence models with continuous outputs.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ von Mises-Fisher loss for training sequence to sequence models with continuous outputs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.744363Z

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Observation 15ff6b72-09de-4b11-ac90-27c7fdc0d2b8 · outbound

This paper cites Determinantal point process models and statistical inference.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Determinantal point process models and statistical inference

Reference 32

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Unresolved cited work

Reference 33

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Observation b0923c39-d224-46cb-838c-4560d4b9d259 · outbound

This paper cites Quantization and clustering on riemannian manifolds with an application to air traffic analysis.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Quantization and clustering on riemannian manifolds with an application to air traffic analysis

Reference 34

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Observation b334b0e7-b085-4952-8f63-0bcd3027adf3 · outbound

This paper cites Sample estimate of the entropy of a random vector.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Sample estimate of the entropy of a random vector

Reference 35

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Observation bc01c7b8-d4cd-40dc-9ee4-efad461b4767 · outbound

This paper cites Minimisation des fonctionnelles d \'e finies sur une vari \'e t \'e par la methode du gradi \"e nt conjugu \'e.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Minimisation des fonctionnelles d \'e finies sur une vari \'e t \'e par la methode du gradi \"e nt conjugu \'e

Reference 36

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Observation fb6a51b4-0ab7-4635-969e-349e8f8bd28c · outbound

This paper cites Riemannian stein variational gradient descent for bayesian inference.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Riemannian stein variational gradient descent for bayesian inference

Reference 37

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Observation d79f61ad-6c00-4190-b9fe-eb5deb15835e · outbound

This paper cites Stein variational gradient descent: A general purpose bayesian inference algorithm.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Stein variational gradient descent: A general purpose bayesian inference algorithm

Reference 38

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Observation 8f431ad3-dd96-472d-933b-7e7f59464b8d · outbound

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Sphereface: Deep hypersphere embedding for face recognition

Reference 39

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This paper cites Liu, Lixin Liu, Zhiding Yu, Bo Dai, and Le Song.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Liu, Lixin Liu, Zhiding Yu, Bo Dai, and Le Song

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.636797Z

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Observation 1c0a6eab-215b-4ddf-bd03-5ab8d78b464e · outbound

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Learning with hyperspherical uniformity

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.620324Z

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Observation 35d3745f-c00e-4a7a-bfb5-bd3386824b23 · outbound

This paper cites Multilingual denoising pre-training for neural machine translation.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Multilingual denoising pre-training for neural machine translation

Reference 42

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Observation f0c2cbcf-f661-474c-bbb6-f1d82bf22eb0 · outbound

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Least squares quantization in PCM

Reference 43

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Observation 7888512a-142d-45be-aeb4-b1a80de8edc8 · outbound

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Luenberger

Reference 44

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Observation f4088dde-1359-4de7-88f5-a1ff1df58bc7 · outbound

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Reliable measures of spread in high dimensional latent spaces

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.605935Z

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Observation e37ed280-4ae5-45f6-b894-99c5589d9dc3 · outbound

This paper cites Mardia and P.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Mardia and P

Reference 46

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Observation 08410f47-2dbe-4e3d-a50b-1f2e8ca89e15 · outbound

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Statistics of directional data

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.590071Z

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Observation 9b587b1f-e104-435f-aebf-a31a31586a92 · outbound

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Spherical text embedding

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.574209Z

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Observation 94b160e4-b7a1-47f4-a2bd-244913df4029 · outbound

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Hyperspherical prototype networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.557441Z

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Observation 60881bc6-0510-40fb-8674-0a75973681ac · outbound

This paper cites Determinantal point process models on the sphere.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Determinantal point process models on the sphere

Reference 50

Resolution: verified exact; raw_fallback, observed 2026-08-08T06:03:12.827508Z

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Observation 42920733-a974-450f-9b64-c65017cd9dfd · outbound

This paper cites Musin and Alexey S.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Musin and Alexey S

Reference 51

Resolution: verified exact; doi, observed 2026-08-08T06:02:12.352382Z

Source-reported events for the cited work

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Observation bf0b7df5-00d7-486c-b0a8-a45f51f0d88d · outbound

This paper cites Musin and Alexey S.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Musin and Alexey S

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T06:02:12.113396Z

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Observation 1201fabd-4ebc-42cf-8db9-9d0b5e998795 · outbound

This paper cites Bastiaan Kleijn.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Bastiaan Kleijn

Reference 53

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T06:02:42.614192Z

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Observation 66faff56-2802-467d-bc1c-f380d728b003 · outbound

This paper cites fairseq: A fast, extensible toolkit for sequence modeling.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ fairseq: A fast, extensible toolkit for sequence modeling

Reference 54

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This paper cites B leu: a method for automatic evaluation of machine translation.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ B leu: a method for automatic evaluation of machine translation

Reference 55

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Observation acce373e-95d5-437a-bfac-05a5916a4751 · outbound

This paper cites Intrinsic statistics on R iemannian manifolds: Basic tools for geometric measurements.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Intrinsic statistics on R iemannian manifolds: Basic tools for geometric measurements

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T06:02:12.130875Z

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Observation e0c2450a-ac1b-4da8-ab4a-b7cfbb4f5d4d · outbound

This paper cites Computational optimal transport: With applications to data science.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Computational optimal transport: With applications to data science

Reference 57

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This paper cites A call for clarity in reporting BLEU scores.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ A call for clarity in reporting BLEU scores

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T06:02:12.140079Z

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This paper cites Wasserstein barycenter and its application to texture mixing.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Wasserstein barycenter and its application to texture mixing

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.509357Z

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This paper cites COMET : A neural framework for MT evaluation.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ COMET : A neural framework for MT evaluation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T06:02:12.149071Z

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Arrangement of 24 points on a sphere

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.492491Z

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This paper cites Orthogonal estimation of wasserstein distances.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Orthogonal estimation of wasserstein distances

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.477613Z

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Spreading vectors for similarity search

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.461574Z

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This paper cites Soft-max and Soft-argmax , 2024.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Soft-max and Soft-argmax , 2024

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.445751Z

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This paper cites Web-scale K -means clustering.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Web-scale K -means clustering

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.430681Z

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This paper cites Approximation theorems of mathematical statistics.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Approximation theorems of mathematical statistics

Reference 66

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This paper cites A user's guide to sampling strategies for sliced optimal transport.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ A user's guide to sampling strategies for sliced optimal transport

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.405849Z

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This paper cites Distribution of points in a cube and approximate evaluation of integrals.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Distribution of points in a cube and approximate evaluation of integrals

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.391410Z

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This paper cites Unbiased estimators for the variance of MMD estimators.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Unbiased estimators for the variance of MMD estimators

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T06:02:12.190014Z

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This paper cites On the origin of number and arrangement of the places of exit on the surface of pollen-grains.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ On the origin of number and arrangement of the places of exit on the surface of pollen-grains

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.375672Z

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Observation 134a2893-d0ca-42c7-aa4a-b5970629a61d · outbound

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Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Unresolved cited work

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T06:02:12.199192Z

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This paper cites The unreasonable effectiveness of random target embeddings for continuous-output neural machine translation.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ The unreasonable effectiveness of random target embeddings for continuous-output neural machine translation

Reference 72

Resolution: verified exact; doi, observed 2026-08-08T06:02:12.307206Z

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This paper cites Hubs and hyperspheres: Reducing hubness and improving transductive few-shot learning with hyperspherical embeddings.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Hubs and hyperspheres: Reducing hubness and improving transductive few-shot learning with hyperspherical embeddings

Reference 73

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T06:02:12.458017Z

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Observation 3b09b6de-2ded-46a0-b4c3-6e489fbb1ade · outbound

This paper cites Auf welcher kugel haben 5, 6, 7, 8 oder 9 punkte mit mindestabstand eins platz ? Mathematische Annalen, 123: 0 96--124, 1951.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Auf welcher kugel haben 5, 6, 7, 8 oder 9 punkte mit mindestabstand eins platz ? Mathematische Annalen, 123: 0 96--124, 1951

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.359948Z

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Observation 8f75ca71-a33c-4f03-ad4e-c753b9febb43 · outbound

This paper cites Gomez, ukasz Kaiser, and Illia Polosukhin.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Gomez, ukasz Kaiser, and Illia Polosukhin

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.344182Z

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Observation 909f705b-e2d0-404a-8d4e-a16ffdc50833 · outbound

This paper cites Optimal transport: old and new, volume 338.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Optimal transport: old and new, volume 338

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.328553Z

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Observation bea249f2-d403-4f2e-ad0a-310ae82734a1 · outbound

This paper cites On convergence of projected gradient descent for minimizing a large-scale quadratic over the unit sphere.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ On convergence of projected gradient descent for minimizing a large-scale quadratic over the unit sphere

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.311947Z

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Observation 08904f17-384c-4c3a-a6ce-440de1de1ddc · outbound

This paper cites Improving neural language generation with spectrum control.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Improving neural language generation with spectrum control

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.295147Z

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Observation 8a41a669-91e2-4007-b53c-92bbdd55f722 · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Understanding contrastive representation learning through alignment and uniformity on the hypersphere

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.278784Z

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This paper cites Mma regularization: Decorrelating weights of neural networks by maximizing the minimal angles, 2021.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Mma regularization: Decorrelating weights of neural networks by maximizing the minimal angles, 2021

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.262205Z

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Observation dcc83e2a-b405-4ec9-aeab-c1246ec33eb3 · outbound

This paper cites Atan2 --- Wikipedia , the free encyclopedia.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Atan2 --- Wikipedia , the free encyclopedia

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T06:04:13.246817Z

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Observation 82ef3c7f-573f-43e4-9ae9-a76f4438646d · outbound

This paper cites Frequency-aware contrastive learning for neural machine translation.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ Frequency-aware contrastive learning for neural machine translation

Reference 82

Resolution: verified exact; doi, observed 2026-08-08T06:02:12.292023Z

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Observation e2a0c028-cfa4-4571-9551-f310bd7429f7 · outbound

This paper cites write newline.

Keep your distance: learning dispersed embeddings on $\mathbb{S}_m$ write newline

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-08T06:02:12.252510Z

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