Pith. sign in

Paper Citation Record · LEDGER

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM

As of 18 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.00982.

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

pith.paper-citation-record.v1
2505.00982 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:35:58.060389Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

  • verified exact3
  • verified fuzzy23
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f82cd0a1-e4d5-4e35-8825-0cd7993c8243 · outbound

This paper cites Deep residual learning for image recognition,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Deep residual learning for image recognition,

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:57.901929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:57.901929Z digest=sha256:e29797f5416b8b8b7eb31a882484b19cf562958502303edd4786286401a91da5

Observation 04a91772-b86d-478f-ae62-8355fd89f511 · outbound

This paper cites Resfusion: Denoising diffusion probabilistic models for image restoration based on prior residual noise,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Resfusion: Denoising diffusion probabilistic models for image restoration based on prior residual noise,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.562852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.906604Z digest=sha256:2f8bf6859135f9adcc668e6d9d63b710eb6d7b891a1e0b98ce44595cd768a2ff

Observation c8ff4062-ada3-4d97-9ac9-0af5e5305c3f · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM LLaMA: Open and Efficient Foundation Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:57.910941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:57.910941Z digest=sha256:ad2e0cdaef399153f0fb01a6b23d5f4f947a220bf58fb66bfd1f9b6e5ae9fe9f

Observation 32c6d930-f6db-45be-8a33-96c28abab184 · outbound

This paper cites Imagenet training in minutes,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Imagenet training in minutes,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:57.915627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:57.915627Z digest=sha256:23999de239bc205ed9e045434b5b12e7ac9654403eed688e95bc7e3285d04080

Observation 4136fc88-43b9-46b7-aee7-0241748ab2a4 · outbound

This paper cites Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.542700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.920364Z digest=sha256:cbbbfe21aee2195f3cfa59ba3d59a3ae7e907e2c772e2b45139708681950d89c

Observation 098f20e4-80b2-44f9-91f7-eba0d8dfadcd · outbound

This paper cites Towards federated customized neural architecture search for remote sensing scene classification,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Towards federated customized neural architecture search for remote sensing scene classification,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.530513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.926356Z digest=sha256:ef187df866588c8ddc19a8b592319a75c055112a7170cc58b565c406266a46c5

Observation 4216fc77-4461-4a96-98d5-ce37573aaa6b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Adam: A Method for Stochastic Optimization

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:57.931169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:57.931169Z digest=sha256:d9b0f74872389d9693a448faa7e583d024a61aa753eb0807cde94aae58fcb1b8

Observation 73470cca-58f9-4dca-9d98-7af45bd1e587 · outbound

This paper cites Neural networks for machine learning lecture 6a overview of mini-batch gradient descent,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Neural networks for machine learning lecture 6a overview of mini-batch gradient descent,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.517618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.935450Z digest=sha256:7411421b33a8a5b3f416465bdc7105f6580e76533ad26665289731081b0c5905

Observation e8184d7b-5624-4ad9-b192-607cb8b27a7b · outbound

This paper cites Optimization methods for large- scale machine learning,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Optimization methods for large- scale machine learning,

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:57.939127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:57.939127Z digest=sha256:886872ab5cd1fa3b5a5fcc600f19cd5c9bb6d5fa27df65283bc494fe646fa7cd

Observation 939d16d9-97e9-40fb-9487-c1e4766c4c45 · outbound

This paper cites Large- batch training for lstm and beyond,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Large- batch training for lstm and beyond,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.496855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.942683Z digest=sha256:f12a82ba448b4561c3dc9af1d39186154fecdbf82cc3b62db7784db2778524f2

Observation 967bdf35-0b9e-408c-bc4e-79740397e8f2 · outbound

This paper cites Deep neural network training with distributed k-fac,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Deep neural network training with distributed k-fac,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.482325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.946345Z digest=sha256:661076b359c83421c932517ae7238e20a0509942a112bd5b5a9a6d66449d678c

Observation 9b2fc8e5-cbe0-4a3f-9627-e9adc83f1ef1 · outbound

This paper cites A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:57.949886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:57.949886Z digest=sha256:ef7a159d4636fa09b9f15b23b878d1cd670b82584332535b5cfc14201432466a

Observation d355ce0a-11ba-4f53-987b-82e93c31f8ff · outbound

This paper cites Optimizing neural networks with kronecker- factored approximate curvature,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Optimizing neural networks with kronecker- factored approximate curvature,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.470009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.954154Z digest=sha256:254239402634ed2bb03848f53941adf6a5fdaac86212e0e6d0f3d2e892ee6bc8

Observation a14b3053-b592-4867-964a-a0dd62e4a85d · outbound

This paper cites Shampoo: Preconditioned stochastic tensor optimization,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Shampoo: Preconditioned stochastic tensor optimization,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.457417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.957855Z digest=sha256:f08b222443f880a26159c639ceb5be63ce5e35727f2c2e1d5b991d1f04de731c

Observation c67222e1-ff86-444e-8ffb-feaf77080358 · outbound

This paper cites Automon: Automatic distributed monitoring for arbitrary multivariate functions,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Automon: Automatic distributed monitoring for arbitrary multivariate functions,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.444636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.961570Z digest=sha256:ab3b3929dcf410969983f2cbe23f38332cd40f6eb22607f69176d1006e37afce

Observation fbab591e-eda7-4647-8308-64eceef0ee2f · outbound

This paper cites Fosi: Hybrid first and second order optimization,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Fosi: Hybrid first and second order optimization,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.432171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.965195Z digest=sha256:24366bd8aa6c579c60a9fd736df0edeb2ed0114858949d230b9da392b22cd91a

Observation f74826c6-a80e-409d-99d9-7c39a29c2284 · outbound

This paper cites On the limited memory bfgs method for large scale optimization,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM On the limited memory bfgs method for large scale optimization,

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:57.968920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:57.968920Z digest=sha256:cc225e72d2a3e99fef77bb08bb6fa49c122f9fefc610fd159c69c18bdeb34a62

Observation 5e6b71ba-4433-4fc9-9ab6-712b34b1295d · outbound

This paper cites Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information Analysis.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information Analysis

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-08-16T04:35:58.186612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.972902Z digest=sha256:85b5381d59c4fae586bc04935fef1b44e14e90fe23b27b13246ee6f905e0b359

Observation ff637452-467f-4e81-b79c-7f4b066536d4 · outbound

This paper cites Nesterov momentum based optimization algorithm for deep learning,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Nesterov momentum based optimization algorithm for deep learning,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.411208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.977152Z digest=sha256:329db998639a6333c394b6a43e4d8d89585329259ff019dd932e3c4174aca6b8

Observation 4564236c-6a76-4563-9dcc-2beb845977f3 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:57.980873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:57.980873Z digest=sha256:bd00a10f05b547db51a18286226005e69a07d340fbb36e822e55324378e996e4

Observation 0622c8ea-5aea-4ccb-9995-ae27003a58df · outbound

This paper cites An iteration method for the solution of the eigenvalue problem of linear differential and integral operators,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM An iteration method for the solution of the eigenvalue problem of linear differential and integral operators,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.398969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.985345Z digest=sha256:8db0e9976d2baa748001d805b9444c33eaf1d79d94e3a86c892d8b2977cb7219

Observation d3bbcfd1-2677-42c1-bdeb-0cc5260033b1 · outbound

This paper cites Federated learning via inexact admm,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Federated learning via inexact admm,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.386530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.989251Z digest=sha256:0794acd61185ca2eac5e235b043f477fb9548727f8d17c9dd0ae41baf4678282

Observation 93f1d6d4-3966-4159-ba03-7bf21f1ed08f · outbound

This paper cites An inexact admm for separable nonconvex and nonsmooth optimization,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM An inexact admm for separable nonconvex and nonsmooth optimization,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.373656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.993209Z digest=sha256:1106c9cd0793a0be6590869c1c150b9124d7f739efd71920814b25b8b6fa5d04

Observation 3195e879-76da-4430-8f80-49e97f8e19f3 · outbound

This paper cites Newton raphson method,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Newton raphson method,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.360743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:57.997220Z digest=sha256:e5644e7187eea1eea2def53d319b46197ffd7256d4f49a2b88b59ec5ba0c50ee

Observation 1df4aa1b-8d06-44c6-b565-41476c66799d · outbound

This paper cites SOAP: Improving and stabilizing shampoo using adam for language modeling,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM SOAP: Improving and stabilizing shampoo using adam for language modeling,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.346558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.000926Z digest=sha256:d4635e0c49267f2c393856b09fe0b14e652e03f2c2c842fcf23d95ff739eabe1

Observation e2d6d9d9-14ef-4ea5-85ad-aab5ca1c48e3 · outbound

This paper cites 4-bit shampoo for memory- efficient network training,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM 4-bit shampoo for memory- efficient network training,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.332616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.005034Z digest=sha256:6325eaacf78ea527fc6eca8f4f0e7c7c07823b04a013148ec2562620344da9f2

Observation 161519ff-73d8-4423-8ff3-24c5d3325839 · outbound

This paper cites Preconditioned stochastic gradient descent,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Preconditioned stochastic gradient descent,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.318587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.009061Z digest=sha256:e8803586e36ceb5b9f2ffcd839c0f2788e5c6a96c316536e100f4b40f71425b9

Observation 755bd746-16ee-4e27-90f9-d90e7b1aa43e · outbound

This paper cites Fast exact multiplication by the hessian,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Fast exact multiplication by the hessian,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.305117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.012739Z digest=sha256:e834ac84bd289ba412085c5aba186922b65aa086e65490d40b521ada5d6329f6

Observation 5430bcff-5ce3-4f35-bf34-202ebca4af90 · outbound

This paper cites AA-DLADMM: An Accelerated ADMM-based Framework for Training Deep Neural Networks.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM AA-DLADMM: An Accelerated ADMM-based Framework for Training Deep Neural Networks

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-08-16T04:35:58.156628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.017123Z digest=sha256:b74f32b6fa3b94b15be95a547e6d600fc40d1d35254a1dab574dd4cca52e3470

Observation 550cf32f-8c52-4fbd-a9e4-eaf6c74ea897 · outbound

This paper cites Preconditioned Inexact Stochastic ADMM for Deep Model.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Preconditioned Inexact Stochastic ADMM for Deep Model

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-08-16T04:35:58.138389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.021347Z digest=sha256:8b673d8f47091b87f98a83ef9e07d23cf01896ff8e1380ed5abee6437d2d8279

Observation 75813759-f3f2-4614-b9ba-8bdcc41c67f8 · outbound

This paper cites A dual algorithm for the solution of nonlinear variational problems via finite element approximation,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM A dual algorithm for the solution of nonlinear variational problems via finite element approximation,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.291834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.026067Z digest=sha256:8de5aab73ea496b04ff32859a2579e5da32c1c84680b223cffac241b07193409

Observation 8c4d2f35-cd0d-4c00-87b0-b032d5c52658 · outbound

This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Distributed optimization and statistical learning via the alternating direction method of multipliers,

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:58.029972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:58.029972Z digest=sha256:fa3d4766b40304ef6a1ef5cbb38b90402f93083660eb4e34054f7ed27706fda6

Observation b8a08be1-08b2-407a-93d8-059e32fde87d · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:58.034077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:58.034077Z digest=sha256:ba2ff0afc5910372954f0b469a4d7c29d57b40276dde480016730c2980108eec

Observation c26ddbe2-91cc-4d20-8f61-9023338ba0a5 · outbound

This paper cites Learning multiple layers of features from tiny images,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Learning multiple layers of features from tiny images,

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:58.038169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:58.038169Z digest=sha256:99f88c49529e13d24260c44a25f7f8cbfed2fcf4e77f9ebd69f646923fe84119

Observation 38927258-dbf9-4cbf-b18f-e2f4e26db4eb · outbound

This paper cites Tiny imagenet,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Tiny imagenet,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.263297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.042093Z digest=sha256:6cfbabc13984303c36298adc543f792b9aac98cb4775d2c15789aa0f31b703fb

Observation bea35495-e3ae-4fdd-9ac1-ff3173c710b8 · outbound

This paper cites Dct-snn: Using dct to distribute spatial information over time for low-latency spiking neural networks,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Dct-snn: Using dct to distribute spatial information over time for low-latency spiking neural networks,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.250710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.046002Z digest=sha256:a325f4329dfac25daf793149c0ec594b6a485d1dc495c2db93bac4cad52f6c2d

Observation b8f37a35-6c00-416b-9794-466f5b8e0db5 · outbound

This paper cites Fedqclip: Accelerating federated learning via quantized clipped sgd,.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Fedqclip: Accelerating federated learning via quantized clipped sgd,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:35:58.237427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-16T04:35:58.050937Z digest=sha256:7bf5094ab76dbbd152f8fffb310274a0e2bece4fbf6a84e6d0fa1114f27cfbe3

Observation 89423169-2734-4721-96e9-c1c93db93e50 · outbound

This paper cites Decoupled Weight Decay Regularization.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM Decoupled Weight Decay Regularization

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:58.055534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:58.055534Z digest=sha256:92f30634bfdc602d0dec1853ef84502a14d1a6fe4507667202b355f8afe1028b

Observation 57277992-8131-4ed0-870c-bea3122d3f30 · outbound

This paper cites One weird trick for parallelizing convolutional neural networks.

DHO$_2$: Accelerating Distributed Hybrid Order Optimization via Model Parallelism and ADMM One weird trick for parallelizing convolutional neural networks

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T04:35:58.060389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:35:58.060389Z digest=sha256:2e53fd31416048c920ce2f89b61de5a8f357de0da7d367cab86aa014a36f0d5f

Pith citing papers

No inbound Pith citation observations are available.