FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models | Paper Citation Record

Observation 6eca6e43-5a8a-4632-be01-fc69e718477d · outbound

This paper cites https://developer.nvidia.com/blog/doubling-all2all- performance-with-nvidia-collective-communication-library-2-12/.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models https://developer.nvidia.com/blog/doubling-all2all- performance-with-nvidia-collective-communication-library-2-12/

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.906955Z

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 7ae7939d-1a8c-450f-ae8a-9912a56d556d · outbound

This paper cites Deepspeed-inference: enabling efficient infer- ence of transformer models at unprecedented scale.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Deepspeed-inference: enabling efficient infer- ence of transformer models at unprecedented scale

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.895967Z

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 a8bf19b0-5037-4016-a15e-e11f7b484f37 · outbound

This paper cites Language models are few-shot learners.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Language models are few-shot learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.121971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dfe1503-fe16-4ecd-9e8c-9c6b07ef8dd9 · outbound

This paper cites FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.126143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07bbda01-9b0a-45de-a31f-80cd7be94982 · outbound

This paper cites Centauri: Enabling efficient sched- uling for communication-computation overlap in large model train- ing via communication partitioning.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Centauri: Enabling efficient sched- uling for communication-computation overlap in large model train- ing via communication partitioning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.877549Z

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 2c3471e6-22bb-4817-a501-5d26b5371634 · outbound

This paper cites On the representation collapse of sparse mixture of experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models On the representation collapse of sparse mixture of experts

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.866473Z

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 91b8be49-ef8e-47b9-87da-b812022e2b72 · outbound

This paper cites Palm: Scaling language modeling with pathways.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Palm: Scaling language modeling with pathways

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.138161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96093689-98ff-4c7c-82c8-acc315e8ae4f · outbound

This paper cites Stablemoe: Stable routing strategy for mixture of experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Stablemoe: Stable routing strategy for mixture of experts

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.849207Z

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 a9ed7357-2285-4a41-83fc-f6ff33610287 · outbound

This paper cites Large scale distributed deep networks.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Large scale distributed deep networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.145012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27b22620-c9b6-4b58-bc38-3cf0ad4ac1fd · outbound

This paper cites Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.148417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b705c846-265e-4b57-8a8f-d58dbca2b99f · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models PaLM-E: An Embodied Multimodal Language Model

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.151848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f150b62a-8c17-4517-afe3-92c62b7d4306 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.155744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e984d50e-df31-4780-aa12-a2adcb3a7427 · outbound

This paper cites FastMoE: A Fast Mixture-of-Expert Training System.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models FastMoE: A Fast Mixture-of-Expert Training System

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.159063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f4daa76-9012-4571-9e41-e1ff91bc8ae1 · outbound

This paper cites FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models FasterMoE: modeling and optimizing training of large-scale dynamic pre-trained models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.815293Z

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 18eda69a-b686-4d42-ba83-a3c27739cd61 · outbound

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

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.166393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78f4a3b7-a753-4059-98a5-2809dbed33f5 · outbound

This paper cites Experts Weights Averaging: A New General Training Scheme for Vision Transformers.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Experts Weights Averaging: A New General Training Scheme for Vision Transformers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.169824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 634bc3ae-50d3-473e-a721-10f5ad0a058d · outbound

This paper cites Tutel: Adaptive mixture-of-experts at scale.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Tutel: Adaptive mixture-of-experts at scale

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.797394Z

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 83246816-fbbf-4c31-8cf4-f1b71791528f · outbound

This paper cites Breaking the computation and communication abstraction barrier in distributed machine learning workloads.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Breaking the computation and communication abstraction barrier in distributed machine learning workloads

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.786294Z

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 7088d145-0197-4f83-ab97-0eee12afdced · outbound

This paper cites Highly scalable deep learning training system with mixed-precision: Training ImageNet in four minutes.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Highly scalable deep learning training system with mixed-precision: Training ImageNet in four minutes

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.775383Z

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 e201b5e5-da61-4f10-9622-d1c5cf2c216a · outbound

This paper cites Mixtral of Experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Mixtral of Experts

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.184623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd46062d-05f2-4e73-b91b-3ebd3e54bb9d · outbound

This paper cites Lancet: Accelerating mixture-of-experts training by over- lapping weight gradient computation and all-to-all communication.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Lancet: Accelerating mixture-of-experts training by over- lapping weight gradient computation and all-to-all communication

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.763414Z

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 339ab4a9-001b-4c33-a978-aa7fe1876c84 · outbound

This paper cites Gshard: Scaling giant models with conditional compu- tation and automatic sharding.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Gshard: Scaling giant models with conditional compu- tation and automatic sharding

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.752070Z

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 b95e5bc8-9bad-4eae-b600-eddb1a94b54b · outbound

This paper cites BASE layers: Simplifying training of large, sparse models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models BASE layers: Simplifying training of large, sparse models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.741240Z

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 8fb7b99b-61d0-48b7-882b-b28145780031 · outbound

This paper cites Acceler- ating distributed{MoE} training and inference with lina.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Acceler- ating distributed{MoE} training and inference with lina

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.730249Z

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 71b22f36-7a78-4d02-b6df-3910757c8aae · outbound

This paper cites Janus: A unified dis- tributed training framework for sparse mixture-of-experts models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Janus: A unified dis- tributed training framework for sparse mixture-of-experts models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.717844Z

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 36092380-1151-4b27-8fc5-ccf28957205b · outbound

This paper cites Gating dropout: Communication-efficient regularization for sparsely activated transformers.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Gating dropout: Communication-efficient regularization for sparsely activated transformers

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.704357Z

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 a3148a43-8b3e-42db-a45f-4a910214841e · outbound

This paper cites Modeling task relationships in multi-task learning with multi- gate mixture-of-experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Modeling task relationships in multi-task learning with multi- gate mixture-of-experts

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.691636Z

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 ee9cd83a-4fed-4246-ad23-f565fc339481 · outbound

This paper cites Bagualu: targeting brain scale pretrained models with over 37 million cores.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Bagualu: targeting brain scale pretrained models with over 37 million cores

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.678285Z

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 f4051df7-4d54-4b72-a120-664d48e2a369 · outbound

This paper cites Efficient large- scale language model training on GPU clusters using Megatron-LM.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Efficient large- scale language model training on GPU clusters using Megatron-LM

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.664058Z

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 df6aacf6-b731-4fa8-a8e7-6483b5eca643 · outbound

This paper cites Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.645363Z

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 7a7c8003-d358-44a5-8c97-ee152cf73e38 · outbound

This paper cites HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.225891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e0c7257-5456-482f-b6bc-294482416b5e · outbound

This paper cites Springer, 1999.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Springer, 1999

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.229870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b9e643d-5a85-4770-8283-463e0c0ee288 · outbound

This paper cites Parm: Efficient training of large sparsely-activated models with dedicated schedules.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Parm: Efficient training of large sparsely-activated models with dedicated schedules

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.623764Z

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 637c6623-802f-4d7d-94e8-908e7294bf6b · outbound

This paper cites Sinclair.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Sinclair

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.610245Z

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 dcf067a7-1b92-4851-b2f1-48df2593038a · outbound

This paper cites Differential evolution.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Differential evolution

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.597154Z

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 97fbd66a-aa5f-4700-a6e9-6a3ee8637a87 · outbound

This paper cites From Sparse to Soft Mixtures of Experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models From Sparse to Soft Mixtures of Experts

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.244443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9527e6fe-90cd-4b97-be65-8a0bddcc8609 · outbound

This paper cites Beckmann.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Beckmann

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.583586Z

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 63a85fec-f237-48cf-a2aa-62c79a08a610 · outbound

This paper cites Language models are unsupervised multitask learners.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Language models are unsupervised multitask learners

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.252114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 936a2d2d-802f-491f-bae0-7c05ef6573c6 · outbound

This paper cites Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.560292Z

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 bb688046-91ee-43d3-96bf-356c9dabf994 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.546293Z

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 8829b396-86f2-4407-b2a7-edefdf954dd6 · outbound

This paper cites Exploiting simultaneous communications to accelerate data parallel distributed deep learning.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Exploiting simultaneous communications to accelerate data parallel distributed deep learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.533093Z

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 6cb210e4-3597-4aea-b8ef-2c3ed4f88006 · outbound

This paper cites PipeMoE: Ac- celerating mixture-of-experts through adaptive pipelining.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models PipeMoE: Ac- celerating mixture-of-experts through adaptive pipelining

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.518217Z

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 5ade87aa-a439-476f-97d5-c54b1ed5f19d · outbound

This paper cites Schemoe: An ex- tensible mixture-of-experts distributed training system with tasks scheduling.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Schemoe: An ex- tensible mixture-of-experts distributed training system with tasks scheduling

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.504495Z

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 3fa568d8-b289-4def-bee2-4f77473dc4e9 · outbound

This paper cites A hybrid tensor-expert- data parallelism approach to optimize mixture-of-experts training.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models A hybrid tensor-expert- data parallelism approach to optimize mixture-of-experts training

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.485399Z

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 e9e45f2c-f3cd-4126-b2c9-30360a6bdf08 · outbound

This paper cites Attention is all you need.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Attention is all you need

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.276366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b2112e4-8c85-4dc1-85ab-63011375483b · outbound

This paper cites Overlap communication with dependent compu- tation via decomposition in large deep learning models.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Overlap communication with dependent compu- tation via decomposition in large deep learning models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.468097Z

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 d8952115-49a6-4eb3-8d5f-a75eb3d24060 · outbound

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

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Large batch optimization for deep learning: Training BERT in 76 minutes

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.457249Z

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 3c0f0013-3795-4faa-acdd-7e56b87e99e7 · outbound

This paper cites SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.285944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c5b9b8b-b453-497a-906e-b1a03ab170a3 · outbound

This paper cites SmartMoE: Efficiently training Sparsely-Activated mod- els through combining offline and online parallelization.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models SmartMoE: Efficiently training Sparsely-Activated mod- els through combining offline and online parallelization

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.446707Z

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 d9c12ac6-24df-4160-81c0-2d54e0dd9b73 · outbound

This paper cites Pit: Optimization of dynamic sparse deep learning models via permutation invariant transformation.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Pit: Optimization of dynamic sparse deep learning models via permutation invariant transformation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.435346Z

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 487fa7b9-974a-45bc-9f95-830f85be3280 · outbound

This paper cites Mixture-of-Experts with Expert Choice Routing.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Mixture-of-Experts with Expert Choice Routing

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T19:05:55.296446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4b27ede-16fc-4098-bbbe-a828271fcf63 · outbound

This paper cites Taming sparsely activated transformer with stochastic experts.

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models Taming sparsely activated transformer with stochastic experts

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:05:55.423266Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers