Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation | Paper Citation Record

Observation 0a280264-2777-44ee-86ec-191dfdd495f2 · outbound

This paper cites Attention is all you need,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Attention is all you need,

Reference 1

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

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This paper cites Language models are unsupervised multitask learners,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Language models are unsupervised multitask learners,

Reference 3

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Observation 9301b913-d786-4804-a656-d5c6c6488c5d · outbound

This paper cites Scaling Laws for Neural Language Models.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Scaling Laws for Neural Language Models

Reference 4

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Observation f5496b08-8a18-4e12-88dc-df73a3efa4fe · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 5

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Observation 4e0bebad-8f71-4e95-9af0-e5d4172f6b1c · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 6

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Observation 40f0ac79-2cb9-4f8b-991a-9287f134d4d2 · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Deepspeed-moe: Advancing mixture-of- experts inference and training to power next-generation ai scale,

Reference 7

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Observation 32743ec6-8d9a-4074-b121-3a21e783519c · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Tutel: Adaptive mixture-of-experts at scale,

Reference 8

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Observation 47cd25e9-1ff7-4056-876b-015c3601e47c · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Flexmoe: Scaling large-scale sparse pre-trained model training via dynamic device placement,

Reference 9

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Observation d67466c7-272d-4c1e-a165-0ac5b345c237 · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Janus: A unified distributed training framework for sparse mixture-of-experts models,

Reference 10

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Observation af105974-1e52-46b4-b291-0f139b6f2d39 · outbound

This paper cites Accelerating distributed {MoE} training and inference with lina,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Accelerating distributed {MoE} training and inference with lina,

Reference 11

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Observation 90e63e08-7b37-4d80-a0b2-5e79652addb8 · outbound

This paper cites Gating dropout: Communication-efficient regularization for sparsely activated transform- ers,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Gating dropout: Communication-efficient regularization for sparsely activated transform- ers,

Reference 12

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Observation 3c50de7e-42a4-428e-9e76-6fda5d12953d · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Faster- moe: modeling and optimizing training of large-scale dynamic pre- trained models,

Reference 13

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Observation 473b3e24-55b5-4b80-b7e8-0f6c585f1f46 · outbound

This paper cites Dota: detect and omit weak attentions for scalable transformer acceleration,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Dota: detect and omit weak attentions for scalable transformer acceleration,

Reference 14

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Observation 494eb4b3-c4e4-4195-8db4-5ab5e9f58cd3 · outbound

This paper cites Vitcod: Vision transformer acceleration via dedicated algorithm and accelerator co-design,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Vitcod: Vision transformer acceleration via dedicated algorithm and accelerator co-design,

Reference 15

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Observation c83d3174-2cf8-45c8-b86b-d079ce6cf3c1 · outbound

This paper cites Scaling vision with sparse mixture of experts,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Scaling vision with sparse mixture of experts,

Reference 16

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Observation cda643f5-befb-4273-8c2b-16b4640fd7cd · outbound

This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 17

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Observation df9ea7e2-6dc4-4fc8-ae7f-9b8ba5f3c86b · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale,

Reference 18

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Observation 9d67b0c2-877f-446f-af4d-5dc95e558616 · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Bagualu: targeting brain scale pretrained models with over 37 million cores,

Reference 19

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Observation 718c5dc3-3874-4f11-91c0-c0f7d46d9915 · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation FastMoE: A Fast Mixture-of-Expert Training System

Reference 20

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Observation 8456a3e5-5214-4706-a9f4-ec1966c179e8 · outbound

This paper cites {SmartMoE}: Efficiently training {Sparsely-Activated} models through combining offline and online parallelization,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation {SmartMoE}: Efficiently training {Sparsely-Activated} models through combining offline and online parallelization,

Reference 21

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Observation d08a7bd0-15ac-456c-b82b-9fa41790159e · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 22

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This paper cites Mixtral of Experts.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Mixtral of Experts

Reference 23

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This paper cites Evaluating the stability of embedding- based word similarities,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Evaluating the stability of embedding- based word similarities,

Reference 24

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This paper cites Sentiment classification using docu- ment embeddings trained with cosine similarity,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Sentiment classification using docu- ment embeddings trained with cosine similarity,

Reference 25

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This paper cites Problems with cosine as a measure of embedding similarity for high frequency words,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Problems with cosine as a measure of embedding similarity for high frequency words,

Reference 26

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Observation 776d376f-a324-46e6-ba43-979288ca7ad6 · outbound

This paper cites PanGu-$\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation PanGu-$\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation

Reference 27

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This paper cites Turbotransformers: an efficient gpu serving system for transformer models,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Turbotransformers: an efficient gpu serving system for transformer models,

Reference 28

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This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Flashattention: Fast and memory-efficient exact attention with io-awareness,

Reference 29

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This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Pytorch: An imperative style, high-performance deep learning library,

Reference 30

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This paper cites Deep graph library: Towards efficient and scalable deep learning on graphs,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Deep graph library: Towards efficient and scalable deep learning on graphs,

Reference 31

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This paper cites Pointer sentinel mix- ture models,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Pointer sentinel mix- ture models,

Reference 32

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This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 33

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This paper cites Samsum corpus: A human-annotated dialogue dataset for abstractive summarization,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Samsum corpus: A human-annotated dialogue dataset for abstractive summarization,

Reference 34

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Observation 7434d240-26bc-4341-9dce-c9dfaa2304a0 · outbound

This paper cites Language models with image descriptors are strong few-shot video-language learners,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Language models with image descriptors are strong few-shot video-language learners,

Reference 35

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Observation 6816a34e-bba2-4804-90d8-13fc59af7298 · outbound

This paper cites Multitask mixture of sequential experts for user activity streams,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Multitask mixture of sequential experts for user activity streams,

Reference 36

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Observation 1b998076-e7ee-4d05-b0e6-200ffa91e923 · outbound

This paper cites Taming Sparsely Activated Transformer with Stochastic Experts.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Taming Sparsely Activated Transformer with Stochastic Experts

Reference 37

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This paper cites Generalizable person re- identification with relevance-aware mixture of experts,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Generalizable person re- identification with relevance-aware mixture of experts,

Reference 38

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Observation f692a302-b56d-4130-912b-043a87f3d6e0 · outbound

This paper cites Vlmo: Unified vision-language pre-training with mixture-of-modality-experts,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Vlmo: Unified vision-language pre-training with mixture-of-modality-experts,

Reference 39

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Observation 326748dc-4b49-4e69-b1b5-d9002782496a · outbound

This paper cites Palm: Scal- ing language modeling with pathways,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Palm: Scal- ing language modeling with pathways,

Reference 40

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This paper cites Glam: Efficient scaling of language models with mixture-of-experts,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Glam: Efficient scaling of language models with mixture-of-experts,

Reference 41

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This paper cites Llama-moe: Building mixture-of-experts from llama with continual pre-training,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Llama-moe: Building mixture-of-experts from llama with continual pre-training,

Reference 42

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Observation a9bce051-d954-492c-8611-0506c5d3fdf8 · outbound

This paper cites OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Reference 43

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Observation ef3facce-caa7-4c67-8bb6-456edfd66297 · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 44

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This paper cites GSPMD: General and Scalable Parallelization for ML Computation Graphs.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation GSPMD: General and Scalable Parallelization for ML Computation Graphs

Reference 45

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Base layers: Simplifying training of large, sparse models,

Reference 46

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This paper cites fairseq: A Fast, Extensible Toolkit for Sequence Modeling.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Reference 47

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This paper cites Pipemoe: Accelerating mixture- of-experts through adaptive pipelining,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Pipemoe: Accelerating mixture- of-experts through adaptive pipelining,

Reference 48

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This paper cites Mpipemoe: Memory efficient moe for pre-trained models with adaptive pipeline parallelism,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Mpipemoe: Memory efficient moe for pre-trained models with adaptive pipeline parallelism,

Reference 49

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This paper cites MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services

Reference 50

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This paper cites Alpa: Automating inter-and {Intra- Operator} parallelism for distributed deep learning,.

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Alpa: Automating inter-and {Intra- Operator} parallelism for distributed deep learning,

Reference 51

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Observation a263021b-5801-415c-ab26-56f647fab923 · outbound

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

Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation Schemoe: An extensible mixture-of-experts distributed training system with tasks scheduling,

Reference 52

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