Paper Citation Record · LEDGER

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2410.15155.

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

pith.paper-citation-record.v1
2410.15155 v3

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T19:15:54.807005Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

34 of 34 outbound references displayed

verified exact8
verified fuzzy25
unresolved0
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malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6045e214-2b4f-4278-bc91-981e313a5732 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-23T19:18:20.788962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7d13b050-6441-41ce-8094-bc92d7a8d74c · outbound

This paper cites Neural network accelerator design with resistive crossbars: Opportunities and challenges.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Neural network accelerator design with resistive crossbars: Opportunities and challenges

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.621629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a5e0a058-9922-4814-9da5-acedb7b910a4 · outbound

This paper cites PyTorch Distributed: Experiences on Accelerating Data Parallel Training.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-23T19:18:20.784211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b8fbd6f6-ac21-4c13-960d-547e05b61b19 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-23T19:18:20.799974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5975fada-e53c-4790-a040-d13cbc96361f · outbound

This paper cites Large batch optimization for deep learning: Training BERT in 76 minutes.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Large batch optimization for deep learning: Training BERT in 76 minutes

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.610987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 941109df-58c9-4c3c-8346-f8e6a1ecda96 · outbound

This paper cites Parallelizing DNN training on GPUs: Chal- lenges and opportunities.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Parallelizing DNN training on GPUs: Chal- lenges and opportunities

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.618109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 223c50c8-23e8-47a0-9942-9d710a3d7620 · outbound

This paper cites Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses) using phase-change memory as the synaptic weight element.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses) using phase-change memory as the synaptic weight element

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.607870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 10860706-e6ec-4917-a804-f339076a67d0 · outbound

This paper cites Acceleration of deep neural network training with resistive cross-point devices: Design considerations.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Acceleration of deep neural network training with resistive cross-point devices: Design considerations

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.597844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9640c866-ee75-48f9-9ca9-9794c79ce3f0 · outbound

This paper cites Algorithm for training neural networks on resistive device arrays.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Algorithm for training neural networks on resistive device arrays

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.594680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f5f50697-2ec0-4dcb-b605-202ea61d9634 · outbound

This paper cites Towards exact gradient-based training on analog in-memory computing.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Towards exact gradient-based training on analog in-memory computing

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.604470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 59643314-7d92-46fa-8e00-22636e4e34cd · outbound

This paper cites Enabling training of neural networks on noisy hardware.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Enabling training of neural networks on noisy hardware

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.614092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3323db27-56b3-48dc-918b-5802471e0f62 · outbound

This paper cites Fast offset corrected in-memory training.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Fast offset corrected in-memory training

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:18:20.774412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0d9332f8-b6a4-4608-8a9e-d5e842ade84f · outbound

This paper cites Neural network training with asymmetric crosspoint elements.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Neural network training with asymmetric crosspoint elements

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.662106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 565946c0-295f-4835-b669-8f4d700b9e0d · outbound

This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.666028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6d41002f-63ff-4917-bd41-0c90d622bc63 · outbound

This paper cites torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:18:20.810027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e8639a0c-122b-490e-a135-b73af85ed4a3 · outbound

This paper cites Zero-offload: Democratizing billion-scale model training.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Zero-offload: Democratizing billion-scale model training

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.669486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 572ed9ce-d888-4211-a562-39aa1b12c217 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-23T19:18:20.805241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fb5c6eb2-2b62-45cf-ac0f-ad96dadad573 · outbound

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

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-23T19:18:20.779225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5903b594-6c87-4e1f-9c98-8e52d5b85b78 · outbound

This paper cites Colossal-AI: A unified deep learning system for large-scale parallel training.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Colossal-AI: A unified deep learning system for large-scale parallel training

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.646721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7e631d7e-cb7e-42fd-b0e6-086ed8ed09bc · outbound

This paper cites Pipelined backpropagation at scale: training large models without batches.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Pipelined backpropagation at scale: training large models without batches

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.649876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 38f749c8-302b-44d1-b225-0bc55faae608 · outbound

This paper cites Pipedream: generalized pipeline parallelism for DNN training.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Pipedream: generalized pipeline parallelism for DNN training

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.652815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2879146b-6b76-4803-ae72-70c2e8065d60 · outbound

This paper cites SApipe: Staleness-aware pipeline for data parallel DNN training.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training SApipe: Staleness-aware pipeline for data parallel DNN training

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.655768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ad27c476-f59e-44c5-941c-60d084951117 · outbound

This paper cites Pipe-SGD: A decentralized pipelined SGD framework for distributed deep net training.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Pipe-SGD: A decentralized pipelined SGD framework for distributed deep net training

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.601252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9359de13-5cca-452d-b78f-2744fb707d27 · outbound

This paper cites ISAAC: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training ISAAC: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.637162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 490c0a34-d585-4716-a926-29402a436eb3 · outbound

This paper cites Pipelayer: A pipelined ReRAM-based accelerator for deep learning.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Pipelayer: A pipelined ReRAM-based accelerator for deep learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.640552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4dd9077b-7fce-4add-82bb-e8324d65c813 · outbound

This paper cites Decoupled parallel backpropagation with conver- gence guarantee.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Decoupled parallel backpropagation with conver- gence guarantee

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.631128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7e4e0d4c-b70b-4099-9374-c0482c293db7 · outbound

This paper cites Overparameterized nonlinear learning: Gradient descent takes the shortest path? In International Conference on Machine Learning , pages 4951–4960.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Overparameterized nonlinear learning: Gradient descent takes the shortest path? In International Conference on Machine Learning , pages 4951–4960

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.673049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9bd67c44-bbe8-418e-bb17-e76752999d52 · outbound

This paper cites Loss landscapes and optimization in over- parameterized non-linear systems and neural networks.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Loss landscapes and optimization in over- parameterized non-linear systems and neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.628062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f0a170ba-3332-4899-9bb1-07aefffbc260 · outbound

This paper cites An improved analysis of training over-parameterized deep neural networks.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training An improved analysis of training over-parameterized deep neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.634072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ebf828d5-26af-4d1c-9a65-2913968664fb · outbound

This paper cites Optimization Methods for Large-Scale Machine Learning.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Optimization Methods for Large-Scale Machine Learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.643547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9cd4d463-c74f-4aa8-9d85-823a70829289 · outbound

This paper cites A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.658974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 60fb2632-2a5f-41e8-829d-7b2227bbfbcf · outbound

This paper cites Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:18:20.794826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 50f33d41-b458-486b-a9b1-ed3e6b5500da · outbound

This paper cites Training deep convolutional neural networks with resistive cross-point devices.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training Training deep convolutional neural networks with resistive cross-point devices

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T19:18:21.624595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1b2a879f-ca77-4c78-8e17-65f77560074b · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training AutoAugment: Learning Augmentation Policies from Data

Reference 34

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T19:18:20.769518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.