PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning | Paper Citation Record

Observation 1b16e1ef-9415-4003-aa77-449a7e2e1fc3 · outbound

This paper cites Lamda: Language models for dialog applications,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Lamda: Language models for dialog applications,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:13.028880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 02c42822-2011-4d44-8a9d-91aa8f60608e · outbound

This paper cites Software- defined network assimilation: bridging the last mile towards centralized network configuration management with nassim,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Software- defined network assimilation: bridging the last mile towards centralized network configuration management with nassim,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:13.019865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7394f8b4-4d7d-43d9-be79-6cd4c137d5b4 · outbound

This paper cites Netllm: Adapting large language models for networking,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Netllm: Adapting large language models for networking,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:13.009768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0ad9e34f-df9e-4de4-9261-21f8535d1703 · outbound

This paper cites Llama: Open and efficient foundation language models,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Llama: Open and efficient foundation language models,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.999753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f702503a-9e72-4ed9-8673-3232dafe8785 · outbound

This paper cites Language models are few-shot learners,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Language models are few-shot learners,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.989667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 97014f19-5034-4ac3-b7e0-dba8b33bf6f1 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.979925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 40c0a9ee-b5d8-4fe5-ab97-de1576beca71 · outbound

This paper cites You only look once: Unified, real-time object detection,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning You only look once: Unified, real-time object detection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.970717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6720b734-7f20-4baf-a23d-8a94754854b7 · outbound

This paper cites Accelerating model training in multi-cluster environments with consumer-grade gpus,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Accelerating model training in multi-cluster environments with consumer-grade gpus,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.960959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a69a6ddd-5b04-4e0d-9519-2f9418da3606 · outbound

This paper cites Crux: Gpu-efficient communication scheduling for deep learning training,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Crux: Gpu-efficient communication scheduling for deep learning training,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.951209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 15ac1d75-6a3a-45d0-928d-5680e32563e8 · outbound

This paper cites MegaScale: Scaling large language model training to more than 10,000 GPUs,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning MegaScale: Scaling large language model training to more than 10,000 GPUs,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.942249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4ec3f4ce-21e0-4688-a093-31c5d6ba4b4e · outbound

This paper cites Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:33:10.803961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 660b1518-15a4-463c-8e75-2399b1d5d1ed · outbound

This paper cites Optimal and Near-Optimal Adaptive Vector Quantization.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Optimal and Near-Optimal Adaptive Vector Quantization

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:33:10.495984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7a1b3fba-1e85-441d-83f2-76e9a546105b · outbound

This paper cites Terngrad: Ternary gradients to reduce communication in distributed deep learning,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Terngrad: Ternary gradients to reduce communication in distributed deep learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.932782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f24faac5-a4fd-4fb4-b585-e4bddcef9e40 · outbound

This paper cites THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:05.623994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9414227c-d76b-4e8e-b74f-cd54e3ebbf12 · outbound

This paper cites Sparse communication for distributed gradient descent,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Sparse communication for distributed gradient descent,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.923440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a580c70b-d1f2-424a-aa98-3f10561c8d9b · outbound

This paper cites Deep gradient compression: Reducing the communication bandwidth for distributed training,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Deep gradient compression: Reducing the communication bandwidth for distributed training,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.914298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e2d0e15c-5da9-4404-adb1-92cbbe2c74de · outbound

This paper cites PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:05.904277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7ee06a5-28c6-4166-a4c9-d63d20e7873e · outbound

This paper cites Grace: A compressed communication framework for distributed machine learning,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Grace: A compressed communication framework for distributed machine learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.903920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4709eb89-bbbb-41ec-b027-f9267ef15a97 · outbound

This paper cites Efficient sparse collective communication and its application to accelerate distributed deep learning,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Efficient sparse collective communication and its application to accelerate distributed deep learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.894496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2537ae0a-8c51-4093-aae5-d706f2c0d8b8 · outbound

This paper cites Empowering Distributed Training with Sparsity-driven Data Synchronization.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Empowering Distributed Training with Sparsity-driven Data Synchronization

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:33:10.293013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 46aa9f87-30d4-41ed-bffd-49ac97ae2787 · outbound

This paper cites Embrace: Accelerating sparse communication for distributed training of deep neural networks,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Embrace: Accelerating sparse communication for distributed training of deep neural networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.884913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 06ba9546-6f63-4b94-ae5d-eb54e8c08417 · outbound

This paper cites Hi-speed dnn training with espresso: Unleashing the full potential of gradient compression with near-optimal usage strategies,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Hi-speed dnn training with espresso: Unleashing the full potential of gradient compression with near-optimal usage strategies,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.875489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 22cd188f-e60a-4168-956b-d21feef6b59d · outbound

This paper cites Mccs: A service-based approach to collective communication for multi- tenant cloud,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Mccs: A service-based approach to collective communication for multi- tenant cloud,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.866365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef1cb3be-3835-4b1c-bb81-caca54b4bb62 · outbound

This paper cites Swing: Short- cutting rings for higher bandwidth allreduce,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Swing: Short- cutting rings for higher bandwidth allreduce,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.776047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4479651a-a866-4b78-bd8a-49317d198bbc · outbound

This paper cites Scaling distributed machine learning with the parameter server,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Scaling distributed machine learning with the parameter server,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.636800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3239e28-1721-4bf8-8d1d-0c4116ded69c · outbound

This paper cites Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.385243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ec05e3e5-aa0a-404b-a502-5423fb4fbad9 · outbound

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

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Learning multiple layers of features from tiny images,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.211594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 68352ec8-5c49-4c41-90db-1451e8fc49d6 · outbound

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

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:06.781146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29a5d79a-a9ee-418d-b885-49b8877bc7fd · outbound

This paper cites Deep Residual Learning for Image Recognition.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Deep Residual Learning for Image Recognition

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:06.832443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c16b236-6aad-4096-a90a-ff047dd730b5 · outbound

This paper cites Learning both weights and connections for efficient neural networks,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Learning both weights and connections for efficient neural networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:12.117984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8c6163a6-2948-4970-a0eb-0f8a4b423fd9 · outbound

This paper cites Pruning filters for efficient convnets,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Pruning filters for efficient convnets,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:11.974867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aa9ae124-1a0b-4850-8648-05823f84458c · outbound

This paper cites Pruning convolutional neural networks for resource efficient inference,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Pruning convolutional neural networks for resource efficient inference,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:11.835003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 178f5eae-2081-4793-af0b-8ce5660f1d23 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning The lottery ticket hypothesis: Finding sparse, trainable neural networks,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:11.725087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation edd56c52-c9cd-49b3-a35b-63d5a590ede8 · outbound

This paper cites Earlybert: Efficient bert training via early-bird lottery tickets,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Earlybert: Efficient bert training via early-bird lottery tickets,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:11.475502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c4d30ed4-1b4a-4db1-bdba-bfcab31b904a · outbound

This paper cites When to prune? a policy towards early structural pruning,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning When to prune? a policy towards early structural pruning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:11.347953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e3598b0d-dd44-4edc-87cf-65be90744cb3 · outbound

This paper cites Similarity of Neural Network Representations Revisited.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Similarity of Neural Network Representations Revisited

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:07.453865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4be9c19-7374-4467-97ce-d73aa8f2c4bd · outbound

This paper cites Model Sparsity Can Simplify Machine Unlearning.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Model Sparsity Can Simplify Machine Unlearning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:07.577411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 977b89b3-1b0d-42b5-a137-606f9053807d · outbound

This paper cites DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:07.641230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5531f0b4-2c7b-4375-a194-6241cb0d90e3 · outbound

This paper cites FedMef: Towards Memory-efficient Federated Dynamic Pruning.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning FedMef: Towards Memory-efficient Federated Dynamic Pruning

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:33:10.010083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a1e18b4a-ad6d-4010-abd0-81ec12e8042d · outbound

This paper cites ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:08.014894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8b4ffed-e768-441e-bc14-042c92e7d805 · outbound

This paper cites Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:33:09.687915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0f972ac8-43ec-42de-80a7-0cbee178e8ed · outbound

This paper cites Distributed Pruning Towards Tiny Neural Networks in Federated Learning.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Distributed Pruning Towards Tiny Neural Networks in Federated Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:08.315250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2dfc3e8-e0e1-4ed3-a4ef-7c1095a32b0f · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning ImageNet Large Scale Visual Recognition Challenge

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:08.453842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08e9e393-0a91-4574-8f6d-197804569981 · outbound

This paper cites Tiny imagenet visual recognition challenge,.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Tiny imagenet visual recognition challenge,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:11.225143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ea803949-0526-4e7c-9a97-99374dd142f1 · outbound

This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:09.091356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e51246c9-3a45-43a1-b1a9-c44c796cc09d · outbound

This paper cites Nvidia collective communications library (nccl),.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Nvidia collective communications library (nccl),

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:11.026796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ba89639-9951-4bca-a99a-a2af97db07f3 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 16664790.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Available: https://api.semanticscholar.org/CorpusID: 16664790

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:33:11.162717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dd5850b4-e66a-4141-8a24-ed4516cc1ca6 · outbound

This paper cites TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:05.545231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eccd5bca-28b6-453b-a1f2-f0907fcf9127 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning LaMDA: Language Models for Dialog Applications

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:04.403728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers