Multi-Domain Learning with Global Expert Mapping | Paper Citation Record

Observation 6ad25bad-63b5-4d56-a9d1-588a8cdd0164 · outbound

This paper cites Zero-shot sparse mixture of low-rank experts construction from pre- trained foundation models.

Multi-Domain Learning with Global Expert Mapping Zero-shot sparse mixture of low-rank experts construction from pre- trained foundation models

Reference 1

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Observation a32d70cd-121f-4b82-957b-cb418204a810 · outbound

This paper cites Large-scale object detection in the wild with imbalanced data distribution, and multi-labels.

Multi-Domain Learning with Global Expert Mapping Large-scale object detection in the wild with imbalanced data distribution, and multi-labels

Reference 2

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Observation db227370-aff5-4437-bf5c-390e9323ce8c · outbound

This paper cites Learning heterogeneous mixture of scene experts for large-scale neural radiance fields.

Multi-Domain Learning with Global Expert Mapping Learning heterogeneous mixture of scene experts for large-scale neural radiance fields

Reference 3

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Observation 99f14c2b-ae96-4aa7-88bd-5b09471850f4 · outbound

This paper cites Sparse mixture-of-experts are domain generalizable learners.

Multi-Domain Learning with Global Expert Mapping Sparse mixture-of-experts are domain generalizable learners

Reference 4

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Observation 6ec88ae9-31f7-4a87-b050-3e1c8451d68e · outbound

This paper cites Setformer is what you need for vision and language.

Multi-Domain Learning with Global Expert Mapping Setformer is what you need for vision and language

Reference 5

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Observation b9ee69eb-8ab5-41a2-93cb-e6317eba06ab · outbound

This paper cites Learning general and specific embedding with transformer for few-shot object detection.

Multi-Domain Learning with Global Expert Mapping Learning general and specific embedding with transformer for few-shot object detection

Reference 6

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Observation cbe823b0-2fe7-4c25-992b-8135a03545ff · outbound

This paper cites Microsoft coco: Common objects in context.

Multi-Domain Learning with Global Expert Mapping Microsoft coco: Common objects in context

Reference 7

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Observation 325786aa-de03-4b2a-a188-3b21eb42f7b6 · outbound

This paper cites Cross-domain weakly-supervised object detection through progressive domain adapta- tion.

Multi-Domain Learning with Global Expert Mapping Cross-domain weakly-supervised object detection through progressive domain adapta- tion

Reference 8

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Observation ce1e2c1c-5706-448c-bee7-dd7050330476 · outbound

This paper cites Efficient parametrization of multi-domain deep neural networks.

Multi-Domain Learning with Global Expert Mapping Efficient parametrization of multi-domain deep neural networks

Reference 9

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Observation 721cf479-7680-4728-b7c4-4a8345cd25e4 · outbound

This paper cites Damex: Dataset-aware mixture- of-experts for visual understanding of mixture-of-datasets.

Multi-Domain Learning with Global Expert Mapping Damex: Dataset-aware mixture- of-experts for visual understanding of mixture-of-datasets

Reference 10

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Observation 3721a61a-8f91-43c7-b2ed-785acfe82592 · outbound

This paper cites Simple multi-dataset detection.

Multi-Domain Learning with Global Expert Mapping Simple multi-dataset detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.781872Z

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Observation aa1f1ab3-2ae8-47f3-84b5-161588190a40 · outbound

This paper cites Towards universal object detection by domain attention.

Multi-Domain Learning with Global Expert Mapping Towards universal object detection by domain attention

Reference 12

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Observation 257d7eeb-df60-49c6-9edf-71373554fa83 · outbound

This paper cites Detection hub: Unifying object detection datasets via query adaptation on language embedding.

Multi-Domain Learning with Global Expert Mapping Detection hub: Unifying object detection datasets via query adaptation on language embedding

Reference 13

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Observation 77256ac4-8359-42a2-ab22-4108cadc2ec6 · outbound

This paper cites Multi-dataset, multitask learning of egocentric vision tasks.

Multi-Domain Learning with Global Expert Mapping Multi-dataset, multitask learning of egocentric vision tasks

Reference 14

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Observation f3513801-bb1f-467a-b1bd-9d418e67dd06 · outbound

This paper cites Plain-det: A plain multi-dataset object detector.

Multi-Domain Learning with Global Expert Mapping Plain-det: A plain multi-dataset object detector

Reference 15

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Observation f2c52820-da6b-41e1-9bb2-01a10a6de8e0 · outbound

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

Multi-Domain Learning with Global Expert Mapping Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.731853Z

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Observation 4948a7f9-c4e0-453f-9e1b-6ccddbeae36d · outbound

This paper cites Remoe: Fully differentiable mixture-of- experts with relu routing.

Multi-Domain Learning with Global Expert Mapping Remoe: Fully differentiable mixture-of- experts with relu routing

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.702952Z

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Observation b011d169-0d30-40cb-baf1-191fa6b508e1 · outbound

This paper cites Mergeme: Model merging techniques for homogeneous and heterogeneous moes.

Multi-Domain Learning with Global Expert Mapping Mergeme: Model merging techniques for homogeneous and heterogeneous moes

Reference 18

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Observation 1f10ebe5-c5a0-4d97-be39-29d82718be30 · outbound

This paper cites Mocae: Mixture of calibrated experts significantly improves object detection.

Multi-Domain Learning with Global Expert Mapping Mocae: Mixture of calibrated experts significantly improves object detection

Reference 19

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Observation 3a822ced-d81d-4058-99ec-aab9f7939638 · outbound

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

Multi-Domain Learning with Global Expert Mapping Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.685768Z

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Observation 303aaa31-492f-4875-b291-100ad221fc7b · outbound

This paper cites Deepseekmoe: Towards ultimate expert specialization in mixture-of-experts language models.

Multi-Domain Learning with Global Expert Mapping Deepseekmoe: Towards ultimate expert specialization in mixture-of-experts language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.759957Z

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Observation 2451a8db-9f46-4d6b-8b98-26527a126e6c · outbound

This paper cites Multilinear mixture of experts: Scalable expert specialization through factorization.

Multi-Domain Learning with Global Expert Mapping Multilinear mixture of experts: Scalable expert specialization through factorization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.635747Z

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Observation b5f95fa8-e254-4d92-93b1-d65e5b6965f2 · outbound

This paper cites Load balancing mixture of experts with similarity preserving routers.

Multi-Domain Learning with Global Expert Mapping Load balancing mixture of experts with similarity preserving routers

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:50:21.266871Z

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Observation 2914106e-8134-437e-a933-ca2c6983b8a5 · outbound

This paper cites Uni-moe: Scaling unified multimodal llms with mixture of experts.

Multi-Domain Learning with Global Expert Mapping Uni-moe: Scaling unified multimodal llms with mixture of experts

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.768959Z

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Observation 74f20259-c46d-4be0-a1a2-8db234cc451b · outbound

This paper cites Buffer overflow in mixture of experts.

Multi-Domain Learning with Global Expert Mapping Buffer overflow in mixture of experts

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.756825Z

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Observation d2a84be3-e972-403f-890a-5b5b51157df1 · outbound

This paper cites Harder tasks need more experts: Dynamic routing in moe models.

Multi-Domain Learning with Global Expert Mapping Harder tasks need more experts: Dynamic routing in moe models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.680248Z

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Observation ed5d8151-9b50-4fed-9f6a-a03b79149f7d · outbound

This paper cites Machine learning in compiler optimization.

Multi-Domain Learning with Global Expert Mapping Machine learning in compiler optimization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.716499Z

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Observation 701cd51e-8f5f-4563-8e37-87431d4285ef · outbound

This paper cites Machine-learning-based self-optimizing compiler heuristics.

Multi-Domain Learning with Global Expert Mapping Machine-learning-based self-optimizing compiler heuristics

Reference 28

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Observation da53f31c-c77a-4b92-b42c-880706f8473d · outbound

This paper cites Improved deterministic distributed matching via rounding.

Multi-Domain Learning with Global Expert Mapping Improved deterministic distributed matching via rounding

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.765821Z

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Observation 4e7e68a3-965f-447a-9919-f9207ceeabaa · outbound

This paper cites Hierarchical clustering via spreading metrics.

Multi-Domain Learning with Global Expert Mapping Hierarchical clustering via spreading metrics

Reference 30

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Observation 3d4b1485-5f82-4480-aba3-4d20ad9c9990 · outbound

This paper cites Dynamic algorithms for packing-covering lps via multiplicative weight updates.

Multi-Domain Learning with Global Expert Mapping Dynamic algorithms for packing-covering lps via multiplicative weight updates

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.747365Z

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Observation bda63494-4191-49bd-8870-3f169142f57c · outbound

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

Multi-Domain Learning with Global Expert Mapping Base layers: Simplifying training of large, sparse models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.710004Z

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Observation bb425d88-10a6-49a2-bb01-83448b1b6780 · outbound

This paper cites Unified scaling laws for routed language models.

Multi-Domain Learning with Global Expert Mapping Unified scaling laws for routed language models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.743960Z

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Observation 18a46503-c115-43a1-92a1-c54b4f0f8857 · outbound

This paper cites Sparsity-constrained optimal transport.

Multi-Domain Learning with Global Expert Mapping Sparsity-constrained optimal transport

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.749311Z

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Observation 521342f2-7a92-4d71-9245-6102703766b5 · outbound

This paper cites Dino: Detr with improved denoising anchor boxes for end-to-end object detection.

Multi-Domain Learning with Global Expert Mapping Dino: Detr with improved denoising anchor boxes for end-to-end object detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.659896Z

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Observation abd5aed1-04ca-430e-a810-41e9b9badd99 · outbound

This paper cites From sparse to soft mixtures of experts.

Multi-Domain Learning with Global Expert Mapping From sparse to soft mixtures of experts

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.759111Z

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Observation 91cfecda-41d6-4dc0-a7e8-e93b84152123 · outbound

This paper cites Moe++: Accelerating mixture- of-experts methods with zero-computation experts.

Multi-Domain Learning with Global Expert Mapping Moe++: Accelerating mixture- of-experts methods with zero-computation experts

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.666632Z

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Observation 92a3a9d2-4a78-4cab-94c5-7b0e19a06b1a · outbound

This paper cites Scaling vision with sparse mixture of experts.

Multi-Domain Learning with Global Expert Mapping Scaling vision with sparse mixture of experts

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.699609Z

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Observation 0836ffa5-6306-489b-a697-87228cb06005 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

Multi-Domain Learning with Global Expert Mapping Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.740758Z

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Observation d8a95417-118f-49c9-8c90-7ccb6aadb80b · outbound

This paper cites Multi-source and multi- target domain adaptation based on dynamic generator with attention.

Multi-Domain Learning with Global Expert Mapping Multi-source and multi- target domain adaptation based on dynamic generator with attention

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.753500Z

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Observation 4c458e31-b133-4687-9d25-13d5d8c942db · outbound

This paper cites Generalizing to new tasks via one-shot compositional subgoals.

Multi-Domain Learning with Global Expert Mapping Generalizing to new tasks via one-shot compositional subgoals

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.727772Z

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Observation 84992ce6-18dd-4389-b3ce-53fa65bf5a64 · outbound

This paper cites Omnivore: A single model for many visual modalities.

Multi-Domain Learning with Global Expert Mapping Omnivore: A single model for many visual modalities

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.636033Z

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Observation 4c1eb527-5a85-4d64-ac2f-a0f4b414267e · outbound

This paper cites Detecting 11k classes: Large scale object detection without fine-grained bounding boxes.

Multi-Domain Learning with Global Expert Mapping Detecting 11k classes: Large scale object detection without fine-grained bounding boxes

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.706475Z

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Observation 18247942-7fc5-4529-a539-112dd1e264a6 · outbound

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

Multi-Domain Learning with Global Expert Mapping Glam: Efficient scaling of language models with mixture-of-experts

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.696160Z

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Observation 573ce47c-a557-4355-999b-e3a6d67e73c6 · outbound

This paper cites Mixture-of-experts with expert choice routing.

Multi-Domain Learning with Global Expert Mapping Mixture-of-experts with expert choice routing

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.726768Z

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Observation 6a7de3d9-6e04-427f-9597-b2b27d7cc1f3 · outbound

This paper cites Scaling laws for fine-grained mixture of experts.

Multi-Domain Learning with Global Expert Mapping Scaling laws for fine-grained mixture of experts

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.762873Z

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Observation 3f373fe5-e3f0-42a2-9651-7b7bc14bc17b · outbound

This paper cites Mixtral of Experts.

Multi-Domain Learning with Global Expert Mapping Mixtral of Experts

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-05-10T11:50:21.263231Z

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Observation b34ffed2-5fda-47cf-86f0-3b2a7972f008 · outbound

This paper cites Continuous action reinforcement learning from a mixture of interpretable experts.

Multi-Domain Learning with Global Expert Mapping Continuous action reinforcement learning from a mixture of interpretable experts

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.713362Z

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Observation b2d6bd86-68ea-4897-a97a-6ece0a90e420 · outbound

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

Multi-Domain Learning with Global Expert Mapping On the representation collapse of sparse mixture of experts

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.668340Z

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Observation 8e73edd7-80e4-4b8c-9899-a28c7fcb2470 · outbound

This paper cites Regularized box- simplex games and dynamic decremental bipartite matching.

Multi-Domain Learning with Global Expert Mapping Regularized box- simplex games and dynamic decremental bipartite matching

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.692430Z

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Observation 5324c6b8-b666-4183-9f27-5a335fc8a7a3 · outbound

This paper cites An approximation algorithm for the generalized assignment problem.

Multi-Domain Learning with Global Expert Mapping An approximation algorithm for the generalized assignment problem

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.765605Z

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Observation c92bb1c5-a43d-450e-8a3f-934988892b70 · outbound

This paper cites Deterministic decre- mental reachability, scc, and shortest paths via directed expanders and congestion balancing.

Multi-Domain Learning with Global Expert Mapping Deterministic decre- mental reachability, scc, and shortest paths via directed expanders and congestion balancing

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.742772Z

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Observation e6ac0391-af58-4e20-bf72-f61b223b7af5 · outbound

This paper cites Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow.

Multi-Domain Learning with Global Expert Mapping Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.720035Z

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Observation 5bd65bd6-0d04-4561-8ce7-dd1743654350 · outbound

This paper cites Entropy regularization and faster decremental matching in general graphs.

Multi-Domain Learning with Global Expert Mapping Entropy regularization and faster decremental matching in general graphs

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.750533Z

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Observation 27a3c306-a5ab-4d44-a343-f77a8acb8985 · outbound

This paper cites Lvis: A dataset for large vocabu- lary instance segmentation.

Multi-Domain Learning with Global Expert Mapping Lvis: A dataset for large vocabu- lary instance segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.652835Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9425d425-1150-4c48-9789-4859d4d4c566 · outbound

This paper cites The pascal visual object classes challenge: A retrospec- tive.

Multi-Domain Learning with Global Expert Mapping The pascal visual object classes challenge: A retrospec- tive

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.713907Z

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Observation e4de1739-af3c-4698-8b15-7aec4ca9ad78 · outbound

This paper cites Wider face: A face detection benchmark.

Multi-Domain Learning with Global Expert Mapping Wider face: A face detection benchmark

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.762422Z

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Observation fc2e4088-6b09-4ece-a545-c40776e334a5 · outbound

This paper cites Deep lesion graphs in the wild: relationship learning and organization of significant radiology image findings in a diverse large- scale lesion database.

Multi-Domain Learning with Global Expert Mapping Deep lesion graphs in the wild: relationship learning and organization of significant radiology image findings in a diverse large- scale lesion database

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.622142Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 93d89964-59ae-4a73-9a2f-32370f8a9ffb · outbound

This paper cites kitchen-object-detection dataset.

Multi-Domain Learning with Global Expert Mapping kitchen-object-detection dataset

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.735378Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7fd47a91-25bf-49d7-9aa1-c96cb19c0621 · outbound

This paper cites Dota: A large-scale dataset for object detection in aerial images.

Multi-Domain Learning with Global Expert Mapping Dota: A large-scale dataset for object detection in aerial images

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.730502Z

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Observation f6b43479-f2d5-41ff-a4d7-ca89dd0e44d2 · outbound

This paper cites Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey.

Multi-Domain Learning with Global Expert Mapping Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.738983Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 70f53ca1-d3dd-48ae-8066-af481b0cc53a · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite.

Multi-Domain Learning with Global Expert Mapping Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.756218Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 16f18b7e-1ccc-42fa-a8c9-daf3ccda6b87 · outbound

This paper cites k-means++: The advantages of careful seeding.

Multi-Domain Learning with Global Expert Mapping k-means++: The advantages of careful seeding

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.654090Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d30effad-c830-483d-b053-349b14cb571b · outbound

This paper cites Objects365: A large-scale, high-quality dataset for object detection.

Multi-Domain Learning with Global Expert Mapping Objects365: A large-scale, high-quality dataset for object detection

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.689327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cc7c4e16-89d8-45ea-be3e-07030cd891e2 · outbound

This paper cites Unbiased scene graph generation from biased training.

Multi-Domain Learning with Global Expert Mapping Unbiased scene graph generation from biased training

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T02:34:32.737409Z

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