{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/mixture-of-experts/papers/4","list_of":"/task/mixture-of-experts","task":"Mixture-of-Experts","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":14,"rows_per_page":100,"rows":[301,400],"of":1312,"counts":{"archive_papers_tagged":1312,"with_a_code_link":516,"where_syntology_ran_a_sample":216,"not_listed_spam_title":0,"listed":1312,"listed_where_code_ran":216,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":184,"every_run_a_failure_of_syntologys_instrument":32,"listed_with_a_run_with_no_instrument_failure":184,"listed_every_run_a_failure_of_syntologys_instrument":32,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/mixture-of-experts","prev":"/task/mixture-of-experts/papers/3","next":"/task/mixture-of-experts/papers/5","papers":[{"url":"/paper/decomposing-the-neurons-activation-sparsity","slug":"decomposing-the-neurons-activation-sparsity","title":"Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation","date":"2024-05-26","arxiv_id":"2405.16486","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":1,"n_no_contract":8,"n_pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 1 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/decomposing-the-neurons-activation-sparsity#ran","syntology_url":"https://syntology.ai/paper/2405.16486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16486"}},"official":{"repos":["royzry98/moase-pytorch"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/moeut-mixture-of-experts-universal","slug":"moeut-mixture-of-experts-universal","title":"MoEUT: Mixture-of-Experts Universal Transformers","date":"2024-05-25","arxiv_id":"2405.16039","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/moeut-mixture-of-experts-universal#ran","syntology_url":"https://syntology.ai/paper/2405.16039","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16039"}},"official":{"repos":["robertcsordas/moeut"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-mixture-of-experts-an-auto-tuning","slug":"dynamic-mixture-of-experts-an-auto-tuning","title":"Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models","date":"2024-05-23","arxiv_id":"2405.14297","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamic-mixture-of-experts-an-auto-tuning#ran","syntology_url":"https://syntology.ai/paper/2405.14297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14297"}},"official":{"repos":["lins-lab/dynmoe"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-sparsification-via-mixture-of-graphs","slug":"graph-sparsification-via-mixture-of-graphs","title":"Graph Sparsification via Mixture of Graphs","date":"2024-05-23","arxiv_id":"2405.14260","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-sparsification-via-mixture-of-graphs#ran","syntology_url":"https://syntology.ai/paper/2405.14260","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14260"}},"official":{"repos":["yanweiyue/mog"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mixture-of-experts-meets-prompt-based","slug":"mixture-of-experts-meets-prompt-based","title":"Mixture of Experts Meets Prompt-Based Continual Learning","date":"2024-05-23","arxiv_id":"2405.14124","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 2 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mixture-of-experts-meets-prompt-based#ran","syntology_url":"https://syntology.ai/paper/2405.14124","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14124"}},"official":{"repos":["minhchuyentoancbn/moe_promptcl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-moe-and-dense-speed-accuracy","slug":"revisiting-moe-and-dense-speed-accuracy","title":"Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training","date":"2024-05-23","arxiv_id":"2405.15052","repositories_listed":1,"syntology":null},{"url":"/paper/unchosen-experts-can-contribute-too","slug":"unchosen-experts-can-contribute-too","title":"Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast","date":"2024-05-23","arxiv_id":"2405.14507","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unchosen-experts-can-contribute-too#ran","syntology_url":"https://syntology.ai/paper/2405.14507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14507"}},"official":{"repos":["davidfanzz/scmoe"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/directmultistep-direct-route-generation-for","slug":"directmultistep-direct-route-generation-for","title":"DirectMultiStep: Direct Route Generation for Multi-Step Retrosynthesis","date":"2024-05-22","arxiv_id":"2405.13983","repositories_listed":1,"syntology":null},{"url":"/paper/xrag-extreme-context-compression-for","slug":"xrag-extreme-context-compression-for","title":"xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token","date":"2024-05-22","arxiv_id":"2405.13792","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/xrag-extreme-context-compression-for#ran","syntology_url":"https://syntology.ai/paper/2405.13792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.13792"}},"official":{"repos":["Hannibal046/xRAG"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ensemble-and-mixture-of-experts-deeponets-for","slug":"ensemble-and-mixture-of-experts-deeponets-for","title":"Ensemble and Mixture-of-Experts DeepONets For Operator Learning","date":"2024-05-20","arxiv_id":"2405.11907","repositories_listed":1,"syntology":null},{"url":"/paper/meteora-multiple-tasks-embedded-lora-for","slug":"meteora-multiple-tasks-embedded-lora-for","title":"MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models","date":"2024-05-19","arxiv_id":"2405.13053","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":2,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/meteora-multiple-tasks-embedded-lora-for#ran","syntology_url":"https://syntology.ai/paper/2405.13053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.13053"}},"official":{"repos":["paragonlight/meteor-of-lora"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/uni-moe-scaling-unified-multimodal-llms-with","slug":"uni-moe-scaling-unified-multimodal-llms-with","title":"Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts","date":"2024-05-18","arxiv_id":"2405.11273","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/uni-moe-scaling-unified-multimodal-llms-with#ran","syntology_url":"https://syntology.ai/paper/2405.11273","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.11273"}},"official":{"repos":["hitsz-tmg/umoe-scaling-unified-multimodal-llms"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/m-4-oe-a-foundation-model-for-medical","slug":"m-4-oe-a-foundation-model-for-medical","title":"M$^4$oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Experts","date":"2024-05-15","arxiv_id":"2405.09446","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/m-4-oe-a-foundation-model-for-medical#ran","syntology_url":"https://syntology.ai/paper/2405.09446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.09446"}},"official":{"repos":["jefferyjiang-yf/m4oe"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-mixture-of-experts-approach-to-3d-human","slug":"a-mixture-of-experts-approach-to-3d-human","title":"A Mixture of Experts Approach to 3D Human Motion Prediction","date":"2024-05-09","arxiv_id":"2405.06088","repositories_listed":1,"syntology":null},{"url":"/paper/cumo-scaling-multimodal-llm-with-co-upcycled","slug":"cumo-scaling-multimodal-llm-with-co-upcycled","title":"CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts","date":"2024-05-09","arxiv_id":"2405.05949","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cumo-scaling-multimodal-llm-with-co-upcycled#ran","syntology_url":"https://syntology.ai/paper/2405.05949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.05949"}},"official":{"repos":["shi-labs/cumo"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ewmoe-an-effective-model-for-global-weather","slug":"ewmoe-an-effective-model-for-global-weather","title":"EWMoE: An effective model for global weather forecasting with mixture-of-experts","date":"2024-05-09","arxiv_id":"2405.06004","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-rgbt-tracking-benchmarks-from-the","slug":"revisiting-rgbt-tracking-benchmarks-from-the","title":"Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and Method","date":"2024-04-30","arxiv_id":"2405.00168","repositories_listed":1,"syntology":null},{"url":"/paper/m3oe-multi-domain-multi-task-mixture-of","slug":"m3oe-multi-domain-multi-task-mixture-of","title":"M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework","date":"2024-04-29","arxiv_id":"2404.18465","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/m3oe-multi-domain-multi-task-mixture-of#ran","syntology_url":"https://syntology.ai/paper/2404.18465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.18465"}},"official":{"repos":["applied-machine-learning-lab/m3oe"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/large-multi-modality-model-assisted-ai","slug":"large-multi-modality-model-assisted-ai","title":"Large Multi-modality Model Assisted AI-Generated Image Quality Assessment","date":"2024-04-27","arxiv_id":"2404.17762","repositories_listed":1,"syntology":null},{"url":"/paper/multi-head-mixture-of-experts","slug":"multi-head-mixture-of-experts","title":"Multi-Head Mixture-of-Experts","date":"2024-04-23","arxiv_id":"2404.15045","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-head-mixture-of-experts#ran","syntology_url":"https://syntology.ai/paper/2404.15045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.15045"}},"official":{"repos":["yushuiwx/mh-moe"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/xft-unlocking-the-power-of-code-instruction","slug":"xft-unlocking-the-power-of-code-instruction","title":"XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts","date":"2024-04-23","arxiv_id":"2404.15247","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/xft-unlocking-the-power-of-code-instruction#ran","syntology_url":"https://syntology.ai/paper/2404.15247","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.15247"}},"official":{"repos":["ise-uiuc/xft"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/countering-mainstream-bias-via-end-to-end","slug":"countering-mainstream-bias-via-end-to-end","title":"Countering Mainstream Bias via End-to-End Adaptive Local Learning","date":"2024-04-13","arxiv_id":"2404.08887","repositories_listed":1,"syntology":null},{"url":"/paper/moe-ffd-mixture-of-experts-for-generalized","slug":"moe-ffd-mixture-of-experts-for-generalized","title":"MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection","date":"2024-04-12","arxiv_id":"2404.08452","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/moe-ffd-mixture-of-experts-for-generalized#ran","syntology_url":"https://syntology.ai/paper/2404.08452","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.08452"}},"official":{"repos":["lovesiamesecat/moe-ffd"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/two-heads-are-better-than-one-nested-poe-for","slug":"two-heads-are-better-than-one-nested-poe-for","title":"Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors","date":"2024-04-02","arxiv_id":"2404.02356","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/two-heads-are-better-than-one-nested-poe-for#ran","syntology_url":"https://syntology.ai/paper/2404.02356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02356"}},"official":{"repos":["victoriagraf/nested_poe"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/lite-modeling-environmental-ecosystems-with","slug":"lite-modeling-environmental-ecosystems-with","title":"LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models","date":"2024-04-01","arxiv_id":"2404.01165","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/lite-modeling-environmental-ecosystems-with#ran","syntology_url":"https://syntology.ai/paper/2404.01165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01165"}},"official":{"repos":["hrlics/lite"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/prompt-prompted-mixture-of-experts-for","slug":"prompt-prompted-mixture-of-experts-for","title":"Prompt-prompted Adaptive Structured Pruning for Efficient LLM Generation","date":"2024-04-01","arxiv_id":"2404.01365","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/prompt-prompted-mixture-of-experts-for#ran","syntology_url":"https://syntology.ai/paper/2404.01365","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.01365"}},"official":{"repos":["hdong920/griffin"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-task-dense-prediction-via-mixture-of","slug":"multi-task-dense-prediction-via-mixture-of","title":"Multi-Task Dense Prediction via Mixture of Low-Rank Experts","date":"2024-03-26","arxiv_id":"2403.17749","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multi-task-dense-prediction-via-mixture-of#ran","syntology_url":"https://syntology.ai/paper/2403.17749","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.17749"}},"official":{"repos":["yuqiyang213/mlore"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/desire-me-domain-enhanced-supervised","slug":"desire-me-domain-enhanced-supervised","title":"DESIRE-ME: Domain-Enhanced Supervised Information REtrieval using Mixture-of-Experts","date":"2024-03-20","arxiv_id":"2403.13468","repositories_listed":1,"syntology":null},{"url":"/paper/task-customized-mixture-of-adapters-for","slug":"task-customized-mixture-of-adapters-for","title":"Task-Customized Mixture of Adapters for General Image Fusion","date":"2024-03-19","arxiv_id":"2403.12494","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/task-customized-mixture-of-adapters-for#ran","syntology_url":"https://syntology.ai/paper/2403.12494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12494"}},"official":{"repos":["yangsun22/tc-moa"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/dynamic-tuning-towards-parameter-and","slug":"dynamic-tuning-towards-parameter-and","title":"Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation","date":"2024-03-18","arxiv_id":"2403.11808","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dynamic-tuning-towards-parameter-and#ran","syntology_url":"https://syntology.ai/paper/2403.11808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11808"}},"official":{"repos":["nus-hpc-ai-lab/dynamic-tuning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/skeleton-based-human-action-recognition-with-1","slug":"skeleton-based-human-action-recognition-with-1","title":"Skeleton-Based Human Action Recognition with Noisy Labels","date":"2024-03-15","arxiv_id":"2403.09975","repositories_listed":1,"syntology":null},{"url":"/paper/unleashing-the-power-of-meta-tuning-for-few","slug":"unleashing-the-power-of-meta-tuning-for-few","title":"Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts","date":"2024-03-13","arxiv_id":"2403.08477","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unleashing-the-power-of-meta-tuning-for-few#ran","syntology_url":"https://syntology.ai/paper/2403.08477","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.08477"}},"official":{"repos":["szc12153/sparse_meta_tuning"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/branch-train-mix-mixing-expert-llms-into-a","slug":"branch-train-mix-mixing-expert-llms-into-a","title":"Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM","date":"2024-03-12","arxiv_id":"2403.07816","repositories_listed":1,"syntology":null},{"url":"/paper/equipping-computational-pathology-systems","slug":"equipping-computational-pathology-systems","title":"Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs","date":"2024-03-12","arxiv_id":"2403.07743","repositories_listed":1,"syntology":null},{"url":"/paper/harder-tasks-need-more-experts-dynamic","slug":"harder-tasks-need-more-experts-dynamic","title":"Harder Tasks Need More Experts: Dynamic Routing in MoE Models","date":"2024-03-12","arxiv_id":"2403.07652","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/harder-tasks-need-more-experts-dynamic#ran","syntology_url":"https://syntology.ai/paper/2403.07652","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07652"}},"official":{"repos":["zhenweian/dynamic_moe"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/moai-mixture-of-all-intelligence-for-large","slug":"moai-mixture-of-all-intelligence-for-large","title":"MoAI: Mixture of All Intelligence for Large Language and Vision Models","date":"2024-03-12","arxiv_id":"2403.07508","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/moai-mixture-of-all-intelligence-for-large#ran","syntology_url":"https://syntology.ai/paper/2403.07508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07508"}},"official":{"repos":["ByungKwanLee/MoAI"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mixture-of-loras-an-efficient-multitask","slug":"mixture-of-loras-an-efficient-multitask","title":"Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models","date":"2024-03-06","arxiv_id":"2403.03432","repositories_listed":1,"syntology":null},{"url":"/paper/video-relationship-detection-using-mixture-of-1","slug":"video-relationship-detection-using-mixture-of-1","title":"Video Relationship Detection Using Mixture of Experts","date":"2024-03-06","arxiv_id":"2403.03994","repositories_listed":1,"syntology":null},{"url":"/paper/testam-a-time-enhanced-spatio-temporal","slug":"testam-a-time-enhanced-spatio-temporal","title":"TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts","date":"2024-03-05","arxiv_id":"2403.02600","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/testam-a-time-enhanced-spatio-temporal#ran","syntology_url":"https://syntology.ai/paper/2403.02600","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.02600"}},"official":{"repos":["hyunwookl/testam"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dmoerm-recipes-of-mixture-of-experts-for","slug":"dmoerm-recipes-of-mixture-of-experts-for","title":"DMoERM: Recipes of Mixture-of-Experts for Effective Reward Modeling","date":"2024-03-02","arxiv_id":"2403.01197","repositories_listed":1,"syntology":null},{"url":"/paper/sequence-level-semantic-representation-fusion","slug":"sequence-level-semantic-representation-fusion","title":"Sequence-level Semantic Representation Fusion for Recommender Systems","date":"2024-02-28","arxiv_id":"2402.18166","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-efficiency-in-sparse-models-with","slug":"enhancing-efficiency-in-sparse-models-with","title":"XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection","date":"2024-02-27","arxiv_id":"2403.18926","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhancing-efficiency-in-sparse-models-with#ran","syntology_url":"https://syntology.ai/paper/2403.18926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18926"}},"official":{"repos":["ysngki/xmoe"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/m2mkd-module-to-module-knowledge-distillation","slug":"m2mkd-module-to-module-knowledge-distillation","title":"m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers","date":"2024-02-26","arxiv_id":"2402.16918","repositories_listed":1,"syntology":null},{"url":"/paper/asem-enhancing-empathy-in-chatbot-through","slug":"asem-enhancing-empathy-in-chatbot-through","title":"ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling","date":"2024-02-25","arxiv_id":"2402.16194","repositories_listed":1,"syntology":null},{"url":"/paper/llmbind-a-unified-modality-task-integration","slug":"llmbind-a-unified-modality-task-integration","title":"LLMBind: A Unified Modality-Task Integration Framework","date":"2024-02-22","arxiv_id":"2402.14891","repositories_listed":1,"syntology":null},{"url":"/paper/not-all-experts-are-equal-efficient-expert","slug":"not-all-experts-are-equal-efficient-expert","title":"Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models","date":"2024-02-22","arxiv_id":"2402.14800","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/not-all-experts-are-equal-efficient-expert#ran","syntology_url":"https://syntology.ai/paper/2402.14800","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.14800"}},"official":{"repos":["lucky-lance/expert_sparsity"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/bimedix-bilingual-medical-mixture-of-experts","slug":"bimedix-bilingual-medical-mixture-of-experts","title":"BiMediX: Bilingual Medical Mixture of Experts LLM","date":"2024-02-20","arxiv_id":"2402.13253","repositories_listed":1,"syntology":null},{"url":"/paper/hypermoe-towards-better-mixture-of-experts","slug":"hypermoe-towards-better-mixture-of-experts","title":"HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts","date":"2024-02-20","arxiv_id":"2402.12656","repositories_listed":1,"syntology":{"n":18,"n_ran":14,"n_constructed":2,"n_ran_checked":13,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":7,"phrase":"14 ran (of which 2 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/hypermoe-towards-better-mixture-of-experts#ran","syntology_url":"https://syntology.ai/paper/2402.12656","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12656"}},"official":{"repos":["bumble666/hypermoe_early_version"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/moelora-contrastive-learning-guided-mixture","slug":"moelora-contrastive-learning-guided-mixture","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","date":"2024-02-20","arxiv_id":"2402.12851","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-physics-informed-hard-constraints","slug":"scaling-physics-informed-hard-constraints","title":"Scaling physics-informed hard constraints with mixture-of-experts","date":"2024-02-20","arxiv_id":"2402.13412","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/scaling-physics-informed-hard-constraints#ran","syntology_url":"https://syntology.ai/paper/2402.13412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13412"}},"official":{"repos":["ask-berkeley/physics-nns-hard-constraints"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/higher-layers-need-more-lora-experts","slug":"higher-layers-need-more-lora-experts","title":"Higher Layers Need More LoRA Experts","date":"2024-02-13","arxiv_id":"2402.08562","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/higher-layers-need-more-lora-experts#ran","syntology_url":"https://syntology.ai/paper/2402.08562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.08562"}},"official":{"repos":["gcyzsl/mola"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mixture-of-link-predictors","slug":"mixture-of-link-predictors","title":"Mixture of Link Predictors on Graphs","date":"2024-02-13","arxiv_id":"2402.08583","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-laws-for-fine-grained-mixture-of","slug":"scaling-laws-for-fine-grained-mixture-of","title":"Scaling Laws for Fine-Grained Mixture of Experts","date":"2024-02-12","arxiv_id":"2402.07871","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/scaling-laws-for-fine-grained-mixture-of#ran","syntology_url":"https://syntology.ai/paper/2402.07871","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07871"}},"official":{"repos":["llm-random/llm-random"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fiddler-cpu-gpu-orchestration-for-fast","slug":"fiddler-cpu-gpu-orchestration-for-fast","title":"Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models","date":"2024-02-10","arxiv_id":"2402.07033","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-clinical-trial-outcome-prediction","slug":"multimodal-clinical-trial-outcome-prediction","title":"Multimodal Clinical Trial Outcome Prediction with Large Language Models","date":"2024-02-09","arxiv_id":"2402.06512","repositories_listed":1,"syntology":null},{"url":"/paper/interpretcc-conditional-computation-for","slug":"interpretcc-conditional-computation-for","title":"Intrinsic User-Centric Interpretability through Global Mixture of Experts","date":"2024-02-05","arxiv_id":"2402.02933","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/interpretcc-conditional-computation-for#ran","syntology_url":"https://syntology.ai/paper/2402.02933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02933"}},"official":{"repos":["epfl-ml4ed/interpretcc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/competesmoe-effective-training-of-sparse","slug":"competesmoe-effective-training-of-sparse","title":"CompeteSMoE - Effective Training of Sparse Mixture of Experts via Competition","date":"2024-02-04","arxiv_id":"2402.02526","repositories_listed":1,"syntology":null},{"url":"/paper/fedmoe-data-level-personalization-with","slug":"fedmoe-data-level-personalization-with","title":"pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning","date":"2024-02-02","arxiv_id":"2402.01350","repositories_listed":1,"syntology":null},{"url":"/paper/blackmamba-mixture-of-experts-for-state-space","slug":"blackmamba-mixture-of-experts-for-state-space","title":"BlackMamba: Mixture of Experts for State-Space Models","date":"2024-02-01","arxiv_id":"2402.01771","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-fine-tuning-of-audio-spectrogram","slug":"efficient-fine-tuning-of-audio-spectrogram","title":"Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of Adapters","date":"2024-02-01","arxiv_id":"2402.00828","repositories_listed":1,"syntology":null},{"url":"/paper/merging-multi-task-models-via-weight","slug":"merging-multi-task-models-via-weight","title":"Merging Multi-Task Models via Weight-Ensembling Mixture of Experts","date":"2024-02-01","arxiv_id":"2402.00433","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/merging-multi-task-models-via-weight#ran","syntology_url":"https://syntology.ai/paper/2402.00433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.00433"}},"official":{"repos":["tanganke/weight-ensembling_moe"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/checkmating-one-by-using-many-combining","slug":"checkmating-one-by-using-many-combining","title":"Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess","date":"2024-01-30","arxiv_id":"2401.16852","repositories_listed":1,"syntology":null},{"url":"/paper/openmoe-an-early-effort-on-open-mixture-of","slug":"openmoe-an-early-effort-on-open-mixture-of","title":"OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models","date":"2024-01-29","arxiv_id":"2402.01739","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-and-mixture-of-experts","slug":"contrastive-learning-and-mixture-of-experts","title":"Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings","date":"2024-01-28","arxiv_id":"2401.15713","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-inter-layer-expert-affinity-for","slug":"exploiting-inter-layer-expert-affinity-for","title":"Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference","date":"2024-01-16","arxiv_id":"2401.08383","repositories_listed":1,"syntology":null},{"url":"/paper/moe-mamba-efficient-selective-state-space","slug":"moe-mamba-efficient-selective-state-space","title":"MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts","date":"2024-01-08","arxiv_id":"2401.04081","repositories_listed":1,"syntology":null},{"url":"/paper/subjective-and-objective-analysis-of-indian","slug":"subjective-and-objective-analysis-of-indian","title":"Subjective and Objective Analysis of Indian Social Media Video Quality","date":"2024-01-05","arxiv_id":"2401.02794","repositories_listed":1,"syntology":null},{"url":"/paper/frequency-adaptive-pan-sharpening-with","slug":"frequency-adaptive-pan-sharpening-with","title":"Frequency-Adaptive Pan-Sharpening with Mixture of Experts","date":"2024-01-04","arxiv_id":"2401.02151","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":10,"n_ran_checked":11,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":17,"phrase":"12 ran (of which 10 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/frequency-adaptive-pan-sharpening-with#ran","syntology_url":"https://syntology.ai/paper/2401.02151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02151"}},"official":{"repos":["alexhe101/fame-net"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":10,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/k-winners-take-all-ensemble-neural-network","slug":"k-winners-take-all-ensemble-neural-network","title":"k-Winners-Take-All Ensemble Neural Network","date":"2024-01-04","arxiv_id":"2401.02092","repositories_listed":1,"syntology":null},{"url":"/paper/fast-inference-of-mixture-of-experts-language","slug":"fast-inference-of-mixture-of-experts-language","title":"Fast Inference of Mixture-of-Experts Language Models with Offloading","date":"2023-12-28","arxiv_id":"2312.17238","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fast-inference-of-mixture-of-experts-language#ran","syntology_url":"https://syntology.ai/paper/2312.17238","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17238"}},"official":{"repos":["dvmazur/mixtral-offloading"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/aurora-activating-chinese-chat-capability-for","slug":"aurora-activating-chinese-chat-capability-for","title":"Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-Tuning","date":"2023-12-22","arxiv_id":"2312.14557","repositories_listed":1,"syntology":null},{"url":"/paper/finemogen-fine-grained-spatio-temporal-motion-1","slug":"finemogen-fine-grained-spatio-temporal-motion-1","title":"FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing","date":"2023-12-22","arxiv_id":"2312.15004","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/finemogen-fine-grained-spatio-temporal-motion-1#ran","syntology_url":"https://syntology.ai/paper/2312.15004","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.15004"}},"official":{"repos":["mingyuan-zhang/FineMoGen"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/when-parameter-efficient-tuning-meets-general","slug":"when-parameter-efficient-tuning-meets-general","title":"When Parameter-efficient Tuning Meets General-purpose Vision-language Models","date":"2023-12-16","arxiv_id":"2312.12458","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/when-parameter-efficient-tuning-meets-general#ran","syntology_url":"https://syntology.ai/paper/2312.12458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12458"}},"official":{"repos":["melonking32/petal"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/the-art-of-balancing-revolutionizing-mixture","slug":"the-art-of-balancing-revolutionizing-mixture","title":"LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin","date":"2023-12-15","arxiv_id":"2312.09979","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":14,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/the-art-of-balancing-revolutionizing-mixture#ran","syntology_url":"https://syntology.ai/paper/2312.09979","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.09979"}},"official":{"repos":["ablustrund/loramoe"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/online-action-recognition-for-human-risk","slug":"online-action-recognition-for-human-risk","title":"Online Action Recognition for Human Risk Prediction with Anticipated Haptic Alert via Wearables","date":"2023-12-14","arxiv_id":"2401.05365","repositories_listed":1,"syntology":null},{"url":"/paper/adaptir-parameter-efficient-multi-task","slug":"adaptir-parameter-efficient-multi-task","title":"Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts","date":"2023-12-12","arxiv_id":"2312.08881","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptir-parameter-efficient-multi-task#ran","syntology_url":"https://syntology.ai/paper/2312.08881","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08881"}},"official":{"repos":["csguoh/adaptir"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperrouter-towards-efficient-training-and","slug":"hyperrouter-towards-efficient-training-and","title":"HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts","date":"2023-12-12","arxiv_id":"2312.07035","repositories_listed":1,"syntology":null},{"url":"/paper/mixture-of-linear-experts-for-long-term-time","slug":"mixture-of-linear-experts-for-long-term-time","title":"Mixture-of-Linear-Experts for Long-term Time Series Forecasting","date":"2023-12-11","arxiv_id":"2312.06786","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/mixture-of-linear-experts-for-long-term-time#ran","syntology_url":"https://syntology.ai/paper/2312.06786","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.06786"}},"official":{"repos":["rogerni/mole"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/graphmetro-mitigating-complex-distribution","slug":"graphmetro-mitigating-complex-distribution","title":"GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts","date":"2023-12-07","arxiv_id":"2312.04693","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graphmetro-mitigating-complex-distribution#ran","syntology_url":"https://syntology.ai/paper/2312.04693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04693"}},"official":{"repos":["wuyxin/graphmetro"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-task-reinforcement-learning-with-1","slug":"multi-task-reinforcement-learning-with-1","title":"Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts","date":"2023-11-19","arxiv_id":"2311.11385","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-task-reinforcement-learning-with-1#ran","syntology_url":"https://syntology.ai/paper/2311.11385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.11385"}},"official":{"repos":["AhmedMagdyHendawy/MOORE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/damex-dataset-aware-mixture-of-experts-for-1","slug":"damex-dataset-aware-mixture-of-experts-for-1","title":"DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets","date":"2023-11-08","arxiv_id":"2311.04894","repositories_listed":1,"syntology":null},{"url":"/paper/sida-sparsity-inspired-data-aware-serving-for","slug":"sida-sparsity-inspired-data-aware-serving-for","title":"SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models","date":"2023-10-29","arxiv_id":"2310.18859","repositories_listed":1,"syntology":null},{"url":"/paper/qmoe-practical-sub-1-bit-compression-of","slug":"qmoe-practical-sub-1-bit-compression-of","title":"QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models","date":"2023-10-25","arxiv_id":"2310.16795","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/qmoe-practical-sub-1-bit-compression-of#ran","syntology_url":"https://syntology.ai/paper/2310.16795","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16795"}},"official":{"repos":["ist-daslab/qmoe"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mixture-of-tokens-efficient-llms-through","slug":"mixture-of-tokens-efficient-llms-through","title":"Mixture of Tokens: Continuous MoE through Cross-Example Aggregation","date":"2023-10-24","arxiv_id":"2310.15961","repositories_listed":1,"syntology":null},{"url":"/paper/stelocoder-a-decoder-only-llm-for-multi","slug":"stelocoder-a-decoder-only-llm-for-multi","title":"SteloCoder: a Decoder-Only LLM for Multi-Language to Python Code Translation","date":"2023-10-24","arxiv_id":"2310.15539","repositories_listed":1,"syntology":null},{"url":"/paper/manifold-preserving-transformers-are","slug":"manifold-preserving-transformers-are","title":"Manifold-Preserving Transformers are Effective for Short-Long Range Encoding","date":"2023-10-22","arxiv_id":"2310.14206","repositories_listed":1,"syntology":null},{"url":"/paper/image-super-resolution-via-latent-diffusion-a","slug":"image-super-resolution-via-latent-diffusion-a","title":"Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach","date":"2023-10-18","arxiv_id":"2310.12004","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/image-super-resolution-via-latent-diffusion-a#ran","syntology_url":"https://syntology.ai/paper/2310.12004","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.12004"}},"official":{"repos":["tencent-ailab/frequency_aug_vae_moesr"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/multi-view-contrastive-learning-for-entity","slug":"multi-view-contrastive-learning-for-entity","title":"Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs","date":"2023-10-18","arxiv_id":"2310.12008","repositories_listed":1,"syntology":null},{"url":"/paper/merging-experts-into-one-improving","slug":"merging-experts-into-one-improving","title":"Merging Experts into One: Improving Computational Efficiency of Mixture of Experts","date":"2023-10-15","arxiv_id":"2310.09832","repositories_listed":1,"syntology":{"n":21,"n_ran":18,"n_constructed":2,"n_ran_checked":15,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":21,"phrase":"18 ran (of which 2 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/merging-experts-into-one-improving#ran","syntology_url":"https://syntology.ai/paper/2310.09832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09832"}},"official":{"repos":["shwai-he/meo"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":2,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ft-shield-a-watermark-against-unauthorized","slug":"ft-shield-a-watermark-against-unauthorized","title":"FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models","date":"2023-10-03","arxiv_id":"2310.02401","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ft-shield-a-watermark-against-unauthorized#ran","syntology_url":"https://syntology.ai/paper/2310.02401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02401"}},"official":null}},{"url":"/paper/merge-then-compress-demystify-efficient-smoe","slug":"merge-then-compress-demystify-efficient-smoe","title":"Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy","date":"2023-10-02","arxiv_id":"2310.01334","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":1,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":4,"n_pointer_only":0,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 2 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/merge-then-compress-demystify-efficient-smoe#ran","syntology_url":"https://syntology.ai/paper/2310.01334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01334"}},"official":{"repos":["unites-lab/mc-smoe"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":1,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mocae-mixture-of-calibrated-experts","slug":"mocae-mixture-of-calibrated-experts","title":"MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection","date":"2023-09-26","arxiv_id":"2309.14976","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mocae-mixture-of-calibrated-experts#ran","syntology_url":"https://syntology.ai/paper/2309.14976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14976"}},"official":{"repos":["fiveai/MoCaE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/llmcarbon-modeling-the-end-to-end-carbon","slug":"llmcarbon-modeling-the-end-to-end-carbon","title":"LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models","date":"2023-09-25","arxiv_id":"2309.14393","repositories_listed":1,"syntology":null},{"url":"/paper/pushing-mixture-of-experts-to-the-limit","slug":"pushing-mixture-of-experts-to-the-limit","title":"Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning","date":"2023-09-11","arxiv_id":"2309.05444","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pushing-mixture-of-experts-to-the-limit#ran","syntology_url":"https://syntology.ai/paper/2309.05444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05444"}},"official":{"repos":["for-ai/parameter-efficient-moe"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-sparse-moe-in-gans-for-text","slug":"exploring-sparse-moe-in-gans-for-text","title":"Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis","date":"2023-09-07","arxiv_id":"2309.03904","repositories_listed":1,"syntology":{"n":24,"n_ran":19,"n_constructed":0,"n_ran_checked":18,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":24,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/exploring-sparse-moe-in-gans-for-text#ran","syntology_url":"https://syntology.ai/paper/2309.03904","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.03904"}},"official":{"repos":["zhujiapeng/aurora"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":18,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-multi-modal-generative-models-with","slug":"learning-multi-modal-generative-models-with","title":"Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives","date":"2023-09-01","arxiv_id":"2309.00380","repositories_listed":1,"syntology":null},{"url":"/paper/motion-in-betweening-with-phase-manifolds","slug":"motion-in-betweening-with-phase-manifolds","title":"Motion In-Betweening with Phase Manifolds","date":"2023-08-24","arxiv_id":"2308.12751","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/motion-in-betweening-with-phase-manifolds#ran","syntology_url":"https://syntology.ai/paper/2308.12751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12751"}},"official":{"repos":["pauzii/phasebetweener"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pre-gated-moe-an-algorithm-system-co-design","slug":"pre-gated-moe-an-algorithm-system-co-design","title":"Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference","date":"2023-08-23","arxiv_id":"2308.12066","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pre-gated-moe-an-algorithm-system-co-design#ran","syntology_url":"https://syntology.ai/paper/2308.12066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12066"}},"official":{"repos":["ranggihwang/pregated_moe"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-nerf-akin-to-enhancing-llms","slug":"enhancing-nerf-akin-to-enhancing-llms","title":"Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts","date":"2023-08-22","arxiv_id":"2308.11793","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":4,"n_instrument":7,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":12,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 7 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enhancing-nerf-akin-to-enhancing-llms#ran","syntology_url":"https://syntology.ai/paper/2308.11793","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11793"}},"official":{"repos":["vita-group/gnt-move"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-sharing-conflict-aware-multivariate","slug":"beyond-sharing-conflict-aware-multivariate","title":"Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection","date":"2023-08-17","arxiv_id":"2308.08915","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/beyond-sharing-conflict-aware-multivariate#ran","syntology_url":"https://syntology.ai/paper/2308.08915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.08915"}},"official":{"repos":["dawnvince/mts_cad"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"8a799c9c1a05bcabe5fd32ab37f49145a4edc6e91f080764d1ea7dd1864a2594","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}