Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques | Paper Citation Record

Observation 493f9d5d-61bd-4d7c-af87-d2d76b64cf89 · outbound

This paper cites Document-Level Machine Translation with Large Language Models,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Document-Level Machine Translation with Large Language Models,

Reference 1

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Observation 3f604a5c-8fa9-4d15-862a-9eae3d5f724c · outbound

This paper cites Optimizing Statistical Machine Translation for Text Simplification,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Optimizing Statistical Machine Translation for Text Simplification,

Reference 2

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

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Neural machine translation by jointly learning to align and translate,

Reference 3

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Observation 4100f471-6dac-408a-a2c5-d43b3f95558d · outbound

This paper cites Guided source separation meets a strong ASR backend: Hitachi/Paderborn university joint investigation for dinner party ASR,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Guided source separation meets a strong ASR backend: Hitachi/Paderborn university joint investigation for dinner party ASR,

Reference 4

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Observation ed0c5df4-e019-4e1e-9017-aa83fd6a6368 · outbound

This paper cites An Enhanced Human Speech Emotion Recognition Using Hybrid of PRNN and KNN,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques An Enhanced Human Speech Emotion Recognition Using Hybrid of PRNN and KNN,

Reference 5

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Observation ce40d383-c3e6-4215-ba8b-4f6bbb44ace3 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Neural Machine Translation by Jointly Learning to Align and Translate

Reference 6

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Observation 8c462adb-6c91-4dca-9d83-57d6bd02ee8c · outbound

This paper cites Low-cost ultrasonic based object detection and collision avoidance method for autonomous robots,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Low-cost ultrasonic based object detection and collision avoidance method for autonomous robots,

Reference 7

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Observation bae6f356-8662-4635-a7b3-ceabd43f17d6 · outbound

This paper cites A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity,

Reference 8

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Observation 7b7273a8-fd42-44f7-9ca8-daf70d2c2184 · outbound

This paper cites Few-shot object detection via feature reweighting,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Few-shot object detection via feature reweighting,

Reference 9

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Observation 2a4da6ee-78e6-4ee4-b511-c042d6722e21 · outbound

This paper cites Deep learning,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Deep learning,

Reference 10

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Observation d4411203-f053-40e0-9ca7-8c2b684e6db2 · outbound

This paper cites Neural GPUs learn algorithms,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Neural GPUs learn algorithms,

Reference 11

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Observation 6149c48d-8ca9-4758-aa80-2785a8b75bbc · outbound

This paper cites A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

Reference 12

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Observation 48d0d54e-bdc0-4173-9934-b3354d1e17dc · outbound

This paper cites Incorporating Visual Information in Audio Based Self-Supervised Speaker Recognition,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Incorporating Visual Information in Audio Based Self-Supervised Speaker Recognition,

Reference 13

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Observation 526e9762-e8bb-4ef2-8c9d-ffe072c235fd · outbound

This paper cites Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization under Privacy Constraints,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization under Privacy Constraints,

Reference 14

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Observation 5ea27801-1a0b-4c27-b25c-7c233b2d5baf · outbound

This paper cites IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures,

Reference 15

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Observation e740cc8e-1fd6-41aa-8d84-de1bec0c8cf2 · outbound

This paper cites Neural GPUs Learn Algorithms.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Neural GPUs Learn Algorithms

Reference 16

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Observation 484dfc70-0344-4cae-8fda-19b2ce93e4c7 · outbound

This paper cites Pre-trained models for natural language processing: A survey,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Pre-trained models for natural language processing: A survey,

Reference 17

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Observation b2111f65-dfae-40b1-902b-068daf8a78c8 · outbound

This paper cites Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

Reference 18

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Observation 5e2d771a-d6f0-4c75-9645-0507e3c1c043 · outbound

This paper cites Quantization networks,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Quantization networks,

Reference 19

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Observation ba125e50-7933-481b-ad02-2812f04ad564 · outbound

This paper cites 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs,

Reference 20

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Observation ace356d5-e458-4b19-946c-bb45146ac00b · outbound

This paper cites This reduction is accomplished through the use of randomized rounding, which stochastically assigns gradient values to a set of discrete quantisation levels.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques This reduction is accomplished through the use of randomized rounding, which stochastically assigns gradient values to a set of discrete quantisation levels

Reference 21

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Observation d291b844-9670-45de-98b4-b3941debcbca · outbound

This paper cites An Incentive Mechanism Design for Efficient Edge Learning by Deep Reinforcement Learning Approach,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques An Incentive Mechanism Design for Efficient Edge Learning by Deep Reinforcement Learning Approach,

Reference 22

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Observation f6fbedf9-5e2c-446a-86df-237edbf6aeb3 · outbound

This paper cites Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training,

Reference 23

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Observation 0ee40370-2341-41e9-ac46-c6bf961e2dff · outbound

This paper cites Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review,

Reference 24

Resolution: verified exact; doi, observed 2026-08-11T20:27:44.722395Z

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Observation 5616a656-6253-4b29-a192-5e0bbb7678ae · outbound

This paper cites Rethinking the Inception Architecture for Computer Vision,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Rethinking the Inception Architecture for Computer Vision,

Reference 25

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Observation ebffb303-7c37-4e9c-b684-b0392ffdfc29 · outbound

This paper cites QSGD: Communication-efficient SGD via gradient quantization and encoding,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques QSGD: Communication-efficient SGD via gradient quantization and encoding,

Reference 26

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Observation f95f761c-2a75-4323-919b-1b0c53faca62 · outbound

This paper cites Multiagent systems: a survey from a machine learning perspective,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Multiagent systems: a survey from a machine learning perspective,

Reference 27

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Observation 7f9e6dc2-07b4-447c-aa8a-b1dd01bfc956 · outbound

This paper cites A survey on deep learning and its applications,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques A survey on deep learning and its applications,

Reference 28

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Observation e55c9032-6182-4c4b-bf8b-2f5a05d7ea87 · outbound

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

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques PyTorch Distributed: Experiences on Accelerating Data Parallel Training,

Reference 29

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This paper cites Beyond Data and Model Parallelism for Deep Neural Networks,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Beyond Data and Model Parallelism for Deep Neural Networks,

Reference 30

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This paper cites A comaparative study of GPU programming models and architectures using neural networks,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques A comaparative study of GPU programming models and architectures using neural networks,

Reference 31

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This paper cites Models and Languages for Parallel Computation,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Models and Languages for Parallel Computation,

Reference 32

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Observation 43532b44-7a96-4bed-9f69-9baffb1b433e · outbound

This paper cites TicTac: Accelerating Distributed Deep Learning with Communication Scheduling,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques TicTac: Accelerating Distributed Deep Learning with Communication Scheduling,

Reference 33

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Observation f31736aa-8d4c-41fc-89a5-e40169a03133 · outbound

This paper cites Gradient sparsification for communication-efficient distributed optimization,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Gradient sparsification for communication-efficient distributed optimization,

Reference 34

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Observation 0676aae4-d8e1-4056-9bdb-1c4c6b814320 · outbound

This paper cites Beyond Data and Model Parallelism for Deep Neural Networks.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Beyond Data and Model Parallelism for Deep Neural Networks

Reference 35

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This paper cites Centralized, Distributed, and Everything in between: Reviewing Access Control Solutions for the IoT,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Centralized, Distributed, and Everything in between: Reviewing Access Control Solutions for the IoT,

Reference 36

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Observation e3843f20-c150-49a8-9370-9223dce0178b · outbound

This paper cites Synchronization in distributed systems,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Synchronization in distributed systems,

Reference 37

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Observation 8087a9cd-2df9-49f5-ab25-7bd718b04b15 · outbound

This paper cites Sparsified SGD with memory,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Sparsified SGD with memory,

Reference 38

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Observation 89bcaf38-ae4f-4c8b-abd4-d88712ff39be · outbound

This paper cites AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training,

Reference 39

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Observation d81c9b46-ccad-41a1-8094-b714272a65b7 · outbound

This paper cites Adaptive quantization for deep neural network,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Adaptive quantization for deep neural network,

Reference 40

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Observation 2edbabfa-0693-45ca-9dbd-70a950da6a69 · outbound

This paper cites Characterisation of a split gradient coil design induced systemic imaging artefact on 0.35 T MR-linac systems,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Characterisation of a split gradient coil design induced systemic imaging artefact on 0.35 T MR-linac systems,

Reference 41

Resolution: verified exact; doi, observed 2026-08-11T20:27:44.630423Z

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Observation fb621819-b61a-4fdd-9879-aca8ba19e681 · outbound

This paper cites Sparse online learning via truncated gradient,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Sparse online learning via truncated gradient,

Reference 42

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Observation 6efe77f9-2fec-494f-bfe6-85456f2704e8 · outbound

This paper cites Long short-term memory,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Long short-term memory,

Reference 43

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Observation 52f78d5c-1cb1-4e90-808c-4cae973770d0 · outbound

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

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques TernGrad: Ternary gradients to reduce communication in distributed deep learning,

Reference 44

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Observation c2001f63-7254-430a-99a6-2f99e01fb892 · outbound

This paper cites Communication-Efficient Data Parallel Distributed Deep Learning: A Comprehensive Survey,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Communication-Efficient Data Parallel Distributed Deep Learning: A Comprehensive Survey,

Reference 45

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Observation 31c81f4d-418b-440d-b3f8-70675a1831a5 · outbound

This paper cites From Text to Transformation: A Comprehensive Review of Large Language Models’ Versatility,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques From Text to Transformation: A Comprehensive Review of Large Language Models’ Versatility,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:27:45.987706Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3287e550-6094-42ae-9921-6c40f03c2623 · outbound

This paper cites From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility

Reference 47

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Observation ae624c76-7cc2-4b5d-966b-f0e592601331 · outbound

This paper cites The resurgence of structure in deep neural networks,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques The resurgence of structure in deep neural networks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:27:45.974922Z

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Observation 46ea5458-fd7b-4e91-93b9-fd8a4599af01 · outbound

This paper cites Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:27:45.915440Z

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Observation 190810a0-7d34-469c-891b-5f1fdfce67c4 · outbound

This paper cites Building a large annotated corpus of English: the Penn Treebank (1993),.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Building a large annotated corpus of English: the Penn Treebank (1993),

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:27:45.902272Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bb50e076-3e27-42b6-8bf0-ae6618654156 · outbound

This paper cites Accumulated gradient normalization,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Accumulated gradient normalization,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:27:45.888395Z

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Observation 9cb6cb03-c573-4ca2-83cf-4c9043becb93 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:27:45.929377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f60e801e-ba99-4f55-bf98-354de4a493ea · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T20:27:44.445751Z

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Observation 385e5844-e6a7-4920-a258-d9bd962f174e · outbound

This paper cites Scalable distributed DNN training using commodity GPU cloud computing,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Scalable distributed DNN training using commodity GPU cloud computing,

Reference 54

Resolution: verified exact; doi, observed 2026-08-11T20:27:44.578339Z

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Observation 39179931-32ba-421a-9499-e77142ca8cf3 · outbound

This paper cites Communication Quantization for Data-Parallel Training of Deep Neural Networks,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Communication Quantization for Data-Parallel Training of Deep Neural Networks,

Reference 55

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Observation 292ec962-3d27-4f55-a12c-804a57224bfc · outbound

This paper cites Sparse communication for distributed gradient descent,.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Sparse communication for distributed gradient descent,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T20:27:44.465664Z

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Observation 674bc383-05c4-48f5-a75b-1acdefa18ce0 · outbound

This paper cites Available: https://ieeexplore.ieee.org/abstract/document/6795963/.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Available: https://ieeexplore.ieee.org/abstract/document/6795963/

Reference 2022

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Observation 531488fa-2f05-4acd-a85d-27b561b45c2f · outbound

This paper cites Document-Level Machine Translation with Large Language Models.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Document-Level Machine Translation with Large Language Models

Reference 2023

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Observation ddd575dc-c3f1-4931-9dbc-870bd0949c09 · outbound

This paper cites Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training.

Efficient Distributed Training through Gradient Compression with Sparsification and Quantization Techniques Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Reference 2024

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