Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts | Paper Citation Record

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This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 1

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This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 2

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This paper cites Scaling Laws for Neural Language Models.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Scaling Laws for Neural Language Models

Reference 3

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This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 4

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This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 5

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This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022

Reference 6

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Observation 07fe1b71-d834-4af7-9164-0fc1304e57e5 · outbound

This paper cites Adaptive mixtures of local experts.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Adaptive mixtures of local experts

Reference 7

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This paper cites What Does BERT Look At? An Analysis of BERT's Attention.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts What Does BERT Look At? An Analysis of BERT's Attention

Reference 8

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This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 9

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This paper cites Bert: Pre-training of deep bidirectional transform- ers for language understanding.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Bert: Pre-training of deep bidirectional transform- ers for language understanding

Reference 10

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Observation 380e7d19-998e-4c41-86d2-dc1cbe62254f · outbound

This paper cites Chatgpt: Optimizing language models for dialogue, Nov 2022.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Chatgpt: Optimizing language models for dialogue, Nov 2022

Reference 11

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Observation bc83fb04-e1a6-4ac1-905c-270fe3c952d3 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts LLaMA: Open and Efficient Foundation Language Models

Reference 12

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Observation e0d0b965-b4b4-4de5-b6ca-918a8a3aaf06 · outbound

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

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Glam: Efficient scaling of language models with mixture-of-experts

Reference 13

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Observation 81c2cb9a-e169-4e83-92dc-b58f18f78bbf · outbound

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

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Glam: Efficient scaling of language models with mixture-of-experts

Reference 14

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Observation 722a6d10-20c9-480f-9b15-5bc66e284559 · outbound

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

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 15

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Observation bcdbf71e-4747-4e51-ab4e-fb3743e3a3ea · outbound

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

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Mixture-of-Experts with Expert Choice Routing

Reference 16

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This paper cites Palm: Scaling language modeling with pathways.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Palm: Scaling language modeling with pathways

Reference 17

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This paper cites The Efficiency Misnomer.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts The Efficiency Misnomer

Reference 18

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This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 19

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This paper cites Big bird: Transformers for longer sequences.Advances in neural information processing systems, 33:17283–17297, 2020.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Big bird: Transformers for longer sequences.Advances in neural information processing systems, 33:17283–17297, 2020

Reference 20

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This paper cites Long Range Arena: A Benchmark for Efficient Transformers.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Long Range Arena: A Benchmark for Efficient Transformers

Reference 21

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

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Base layers: Simplifying training of large, sparse models

Reference 22

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This paper cites Sparse is enough in scaling transformers.Advances in Neural Information Processing Systems, 34:9895–9907, 2021.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Sparse is enough in scaling transformers.Advances in Neural Information Processing Systems, 34:9895–9907, 2021

Reference 23

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This paper cites Hash layers for large sparse models.advances in neural information processing systems, 34:17555–17566, 2021.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Hash layers for large sparse models.advances in neural information processing systems, 34:17555–17566, 2021

Reference 24

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This paper cites Synthesizer: Rethinking self-attention for transformer models.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Synthesizer: Rethinking self-attention for transformer models

Reference 25

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This paper cites Random Feature Attention.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Random Feature Attention

Reference 26

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This paper cites Skyformer: Remodel self-attention with gaussian kernel and nystr\" om method.Advances in Neural Information Processing Systems, 34:2122–2135, 2021.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Skyformer: Remodel self-attention with gaussian kernel and nystr\" om method.Advances in Neural Information Processing Systems, 34:2122–2135, 2021

Reference 27

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

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Fastermoe: modeling and optimizing training of large-scale dynamic pre-trained models

Reference 28

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This paper cites MegaBlocks: Efficient Sparse Training with Mixture-of-Experts.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts MegaBlocks: Efficient Sparse Training with Mixture-of-Experts

Reference 29

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This paper cites Flex- moe: Scaling large-scale sparse pre-trained model training via dynamic device placement.Proceedings of the ACM on Management of Data, 1(1):1–19, 2023.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Flex- moe: Scaling large-scale sparse pre-trained model training via dynamic device placement.Proceedings of the ACM on Management of Data, 1(1):1–19, 2023

Reference 30

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This paper cites Go wider instead of deeper.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Go wider instead of deeper

Reference 31

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Observation 14c12484-f515-47ff-be29-bd1b956b6f9f · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 32

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This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 33

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 34

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This paper cites Dolan and Chris Brockett.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Dolan and Chris Brockett

Reference 35

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This paper cites Efficient Large Scale Language Modeling with Mixtures of Experts.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Efficient Large Scale Language Modeling with Mixtures of Experts

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

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 37

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Observation 6f302862-a9a5-4880-bb6a-98b23e3ba248 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Linformer: Self-Attention with Linear Complexity

Reference 38

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This paper cites Reformer: The Efficient Transformer.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Reformer: The Efficient Transformer

Reference 39

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Observation 923ac743-556d-46f9-a7ea-c4b6c7c65b90 · outbound

This paper cites Rethinking Attention with Performers.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Rethinking Attention with Performers

Reference 40

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

Reference 42

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Observation 39b74526-d5c2-4629-bf7c-b2179ccb8030 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 43

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Observation fea4b75b-7025-4fd8-b160-97d4ce5e80af · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

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Observation 67704a54-058a-4ec7-8875-311ae2f4964d · outbound

This paper cites Superglue: A stickier benchmark for general-purpose language understanding systems.Advances in neural information processing systems, 32, 2019.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Superglue: A stickier benchmark for general-purpose language understanding systems.Advances in neural information processing systems, 32, 2019

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Observation eb2e7070-68de-4329-a7f9-3679667f5e83 · outbound

This paper cites Measuring massive multitask language understanding.Proceedings of the International Conference on Learning Representations, 2021.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Measuring massive multitask language understanding.Proceedings of the International Conference on Learning Representations, 2021

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Observation 7767dbbf-f17c-4873-bfe0-d3410182ba68 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts CMMLU: Measuring massive multitask language understanding in Chinese

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Observation 4ccf242e-1ea1-4eeb-9979-d6badaa23c22 · outbound

This paper cites C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models

Reference 48

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Observation 20cff156-4cbb-4f87-9db4-a7e43b3891fa · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 49

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Observation ef53fdfa-93b6-4e3a-9271-161471d16a54 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Training Verifiers to Solve Math Word Problems

Reference 50

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This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Measuring Mathematical Problem Solving With the MATH Dataset

Reference 51

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This paper cites Program Synthesis with Large Language Models.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Program Synthesis with Large Language Models

Reference 52

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Observation f5338c63-003a-49a5-83da-7e989e0e3965 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Evaluating Large Language Models Trained on Code

Reference 53

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Observation d36177ae-036b-41b6-8caa-36cf8829469b · outbound

This paper cites Qwen3 technical report, 2025.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Qwen3 technical report, 2025

Reference 54

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Observation 23e9b094-a44d-403a-a3ec-2ea2b7caa112 · outbound

This paper cites Chatglm: A family of large language models from glm-130b to glm-4 all tools, 2024.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Chatglm: A family of large language models from glm-130b to glm-4 all tools, 2024

Reference 55

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Observation 4aec8be8-f622-4277-958d-21d921fe120e · outbound

This paper cites Gemma 3 technical report, 2025.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Gemma 3 technical report, 2025

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:32:04.798914Z

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This paper cites The Llama 3 Herd of Models, 2024.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts The Llama 3 Herd of Models, 2024

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:32:04.777934Z

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This paper cites Hewett, Mojan Javaheripi, Piero Kauffmann, James R.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Hewett, Mojan Javaheripi, Piero Kauffmann, James R

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:32:04.754191Z

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Observation 8f61c776-484d-4836-ab5f-6d27171eb856 · outbound

This paper cites Multi-Scale Dense Networks for Resource Efficient Image Classification.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Multi-Scale Dense Networks for Resource Efficient Image Classification

Reference 59

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Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Manning, Andrew Ng, and Christopher Potts

Reference 60

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Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Unresolved cited work

Reference 61

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Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Unresolved cited work

Reference 62

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Observation a72df6b2-87ca-4b4b-a0f8-eb6ad8848330 · outbound

This paper cites Squad: 100,000+ questions for machine com- prehension of text.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Squad: 100,000+ questions for machine com- prehension of text

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:32:04.681840Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f607e9a1-5cd4-4e2f-84d4-3f398bb577c9 · outbound

This paper cites The pascal recognising textual entailment challenge.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts The pascal recognising textual entailment challenge

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:32:04.662745Z

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Observation 8f91b630-358f-4061-8801-cb20e554f439 · outbound

This paper cites PaLM 2 Technical Report.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts PaLM 2 Technical Report

Reference 65

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Observation 8a9d747b-9a42-4583-9d1b-02b07b4e6363 · outbound

This paper cites Linear attention is (maybe) all you need (to understand transformer optimization).

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Linear attention is (maybe) all you need (to understand transformer optimization)

Reference 66

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Observation fc30f7b7-3e5a-4e4a-80ed-637a98deb910 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T16:32:03.714918Z

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Observation 440c65a9-df59-4eaa-99f9-ad67e76f2045 · outbound

This paper cites FNet: Mixing Tokens with Fourier Transforms.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts FNet: Mixing Tokens with Fourier Transforms

Reference 68

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Observation 598c47b0-3365-4852-a613-acafa17a55f4 · outbound

This paper cites Densely Connected Convolutional Networks.

Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts Densely Connected Convolutional Networks

Reference 69

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Outbound references

Pith citing papers