{"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/diversity/papers/21","list_of":"/task/diversity","task":"Diversity","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":21,"pages_in_order":91,"rows_per_page":100,"rows":[2001,2100],"of":9051,"counts":{"archive_papers_tagged":9051,"with_a_code_link":3166,"where_syntology_ran_a_sample":890,"not_listed_spam_title":0,"listed":9051,"listed_where_code_ran":890,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":736,"every_run_a_failure_of_syntologys_instrument":154,"listed_with_a_run_with_no_instrument_failure":736,"listed_every_run_a_failure_of_syntologys_instrument":154,"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/diversity","prev":"/task/diversity/papers/20","next":"/task/diversity/papers/22","papers":[{"url":"/paper/pushing-the-limits-of-self-supervised-speaker","slug":"pushing-the-limits-of-self-supervised-speaker","title":"Pushing the limits of self-supervised speaker verification using regularized distillation framework","date":"2022-11-08","arxiv_id":"2211.04168","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-image-generation-with-diffusion","slug":"few-shot-image-generation-with-diffusion","title":"Few-shot Image Generation with Diffusion Models","date":"2022-11-07","arxiv_id":"2211.03264","repositories_listed":1,"syntology":null},{"url":"/paper/using-set-covering-to-generate-databases-for","slug":"using-set-covering-to-generate-databases-for","title":"Using Set Covering to Generate Databases for Holistic Steganalysis","date":"2022-11-07","arxiv_id":"2211.03447","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-quality-diversity-algorithms-on","slug":"benchmarking-quality-diversity-algorithms-on","title":"Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning","date":"2022-11-04","arxiv_id":"2211.02193","repositories_listed":1,"syntology":null},{"url":"/paper/diversity-based-deep-reinforcement-learning","slug":"diversity-based-deep-reinforcement-learning","title":"Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI","date":"2022-11-04","arxiv_id":"2211.02759","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/diversity-based-deep-reinforcement-learning#ran","syntology_url":"https://syntology.ai/paper/2211.02759","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.02759"}},"official":{"repos":["emily-halina/brisket"],"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/rethinking-the-transfer-learning-for-fcn","slug":"rethinking-the-transfer-learning-for-fcn","title":"Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy","date":"2022-11-04","arxiv_id":"2211.02416","repositories_listed":1,"syntology":null},{"url":"/paper/dataset-factorization-for-condensation","slug":"dataset-factorization-for-condensation","title":"Dataset Factorization for Condensation","date":"2022-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/how-well-do-unsupervised-learning-algorithms","slug":"how-well-do-unsupervised-learning-algorithms","title":"How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?","date":"2022-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lila-a-unified-benchmark-for-mathematical","slug":"lila-a-unified-benchmark-for-mathematical","title":"Lila: A Unified Benchmark for Mathematical Reasoning","date":"2022-10-31","arxiv_id":"2210.17517","repositories_listed":1,"syntology":null},{"url":"/paper/tree-detection-and-diameter-estimation-based","slug":"tree-detection-and-diameter-estimation-based","title":"Tree Detection and Diameter Estimation Based on Deep Learning","date":"2022-10-31","arxiv_id":"2210.17424","repositories_listed":1,"syntology":null},{"url":"/paper/space-time-design-for-deep-joint-source","slug":"space-time-design-for-deep-joint-source","title":"Space-time design for deep joint source channel coding of images Over MIMO channels","date":"2022-10-30","arxiv_id":"2210.16985","repositories_listed":1,"syntology":null},{"url":"/paper/towards-attribute-entangled-controllable-text","slug":"towards-attribute-entangled-controllable-text","title":"Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation","date":"2022-10-29","arxiv_id":"2210.16557","repositories_listed":1,"syntology":null},{"url":"/paper/latent-space-is-feature-space-regularization","slug":"latent-space-is-feature-space-regularization","title":"Latent Space is Feature Space: Regularization Term for GANs Training on Limited Dataset","date":"2022-10-28","arxiv_id":"2210.16251","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-image-generation-via-masked","slug":"few-shot-image-generation-via-masked","title":"Few-shot Image Generation via Masked Discrimination","date":"2022-10-27","arxiv_id":"2210.15194","repositories_listed":1,"syntology":null},{"url":"/paper/how-well-can-text-to-image-generative-models","slug":"how-well-can-text-to-image-generative-models","title":"How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?","date":"2022-10-27","arxiv_id":"2210.15230","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"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, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/how-well-can-text-to-image-generative-models#ran","syntology_url":"https://syntology.ai/paper/2210.15230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15230"}},"official":{"repos":["hritikbansal/entigen_emnlp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-single-cell-gene-expression-language-model","slug":"a-single-cell-gene-expression-language-model","title":"A single-cell gene expression language model","date":"2022-10-25","arxiv_id":"2210.14330","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"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, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-single-cell-gene-expression-language-model#ran","syntology_url":"https://syntology.ai/paper/2210.14330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.14330"}},"official":{"repos":["keiserlab/exceiver"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/preference-learning-emitters-for-mixed","slug":"preference-learning-emitters-for-mixed","title":"Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms","date":"2022-10-25","arxiv_id":"2210.13839","repositories_listed":1,"syntology":null},{"url":"/paper/towards-standardizing-korean-grammatical","slug":"towards-standardizing-korean-grammatical","title":"Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation","date":"2022-10-25","arxiv_id":"2210.14389","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":2,"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/towards-standardizing-korean-grammatical#ran","syntology_url":"https://syntology.ai/paper/2210.14389","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.14389"}},"official":{"repos":["soyoung97/standard_korean_gec"],"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/empirical-analysis-of-pga-map-elites-for","slug":"empirical-analysis-of-pga-map-elites-for","title":"Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains","date":"2022-10-24","arxiv_id":"2210.13156","repositories_listed":1,"syntology":null},{"url":"/paper/multi-objective-gflownets","slug":"multi-objective-gflownets","title":"Multi-Objective GFlowNets","date":"2022-10-23","arxiv_id":"2210.12765","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 4 unverified","sample_list":"/paper/multi-objective-gflownets#ran","syntology_url":"https://syntology.ai/paper/2210.12765","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12765"}},"official":{"repos":["gfnorg/multi-objective-gfn"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/neurocounterfactuals-beyond-minimal-edit","slug":"neurocounterfactuals-beyond-minimal-edit","title":"NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation","date":"2022-10-22","arxiv_id":"2210.12365","repositories_listed":1,"syntology":null},{"url":"/paper/augmentation-with-projection-towards-an","slug":"augmentation-with-projection-towards-an","title":"Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation","date":"2022-10-21","arxiv_id":"2210.11768","repositories_listed":1,"syntology":null},{"url":"/paper/generative-range-imaging-for-learning-scene","slug":"generative-range-imaging-for-learning-scene","title":"Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data","date":"2022-10-21","arxiv_id":"2210.11750","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"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) · 4 unverified","sample_list":"/paper/generative-range-imaging-for-learning-scene#ran","syntology_url":"https://syntology.ai/paper/2210.11750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11750"}},"official":{"repos":["kazuto1011/dusty-gan-v2"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/discovering-many-diverse-solutions-with","slug":"discovering-many-diverse-solutions-with","title":"Discovering Many Diverse Solutions with Bayesian Optimization","date":"2022-10-20","arxiv_id":"2210.10953","repositories_listed":1,"syntology":null},{"url":"/paper/local-intraspecific-aggregation-in","slug":"local-intraspecific-aggregation-in","title":"Local intraspecific aggregation in phytoplankton model communities: spatial scales of occurrence and implications for coexistence","date":"2022-10-20","arxiv_id":"2210.11364","repositories_listed":1,"syntology":null},{"url":"/paper/diversely-regularized-matrix-factorization","slug":"diversely-regularized-matrix-factorization","title":"Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation","date":"2022-10-19","arxiv_id":"2211.01328","repositories_listed":1,"syntology":null},{"url":"/paper/lamar-benchmarking-localization-and-mapping","slug":"lamar-benchmarking-localization-and-mapping","title":"LaMAR: Benchmarking Localization and Mapping for Augmented Reality","date":"2022-10-19","arxiv_id":"2210.10770","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-channel-attention-blocks-for-cross","slug":"spatio-channel-attention-blocks-for-cross","title":"Spatio-channel Attention Blocks for Cross-modal Crowd Counting","date":"2022-10-19","arxiv_id":"2210.10392","repositories_listed":1,"syntology":null},{"url":"/paper/arithmetic-sampling-parallel-diverse-decoding","slug":"arithmetic-sampling-parallel-diverse-decoding","title":"Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models","date":"2022-10-18","arxiv_id":"2210.15458","repositories_listed":1,"syntology":{"n":7,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 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; 0 where Syntology's instrument failed) · 6 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/arithmetic-sampling-parallel-diverse-decoding#ran","syntology_url":"https://syntology.ai/paper/2210.15458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.15458"}},"official":{"repos":["google-research/google-research"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/discup-discriminator-cooperative-unlikelihood","slug":"discup-discriminator-cooperative-unlikelihood","title":"DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation","date":"2022-10-18","arxiv_id":"2210.09551","repositories_listed":1,"syntology":null},{"url":"/paper/intra-source-style-augmentation-for-improved","slug":"intra-source-style-augmentation-for-improved","title":"Intra-Source Style Augmentation for Improved Domain Generalization","date":"2022-10-18","arxiv_id":"2210.10175","repositories_listed":1,"syntology":null},{"url":"/paper/online-damage-recovery-for-physical-robots","slug":"online-damage-recovery-for-physical-robots","title":"Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity","date":"2022-10-18","arxiv_id":"2210.09918","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-hierarchical-image-vaes-for-sample","slug":"optimizing-hierarchical-image-vaes-for-sample","title":"Optimizing Hierarchical Image VAEs for Sample Quality","date":"2022-10-18","arxiv_id":"2210.10205","repositories_listed":1,"syntology":null},{"url":"/paper/diffuseq-sequence-to-sequence-text-generation","slug":"diffuseq-sequence-to-sequence-text-generation","title":"DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models","date":"2022-10-17","arxiv_id":"2210.08933","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 1 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; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/diffuseq-sequence-to-sequence-text-generation#ran","syntology_url":"https://syntology.ai/paper/2210.08933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08933"}},"official":{"repos":["Shark-NLP/DiffuSeq"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-diversified-feature-representations","slug":"learning-diversified-feature-representations","title":"Learning Diversified Feature Representations for Facial Expression Recognition in the Wild","date":"2022-10-17","arxiv_id":"2210.09381","repositories_listed":1,"syntology":null},{"url":"/paper/packed-ensembles-for-efficient-uncertainty","slug":"packed-ensembles-for-efficient-uncertainty","title":"Packed-Ensembles for Efficient Uncertainty Estimation","date":"2022-10-17","arxiv_id":"2210.09184","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/packed-ensembles-for-efficient-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2210.09184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09184"}},"official":{"repos":["ENSTA-U2IS-AI/torch-uncertainty"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/watch-the-neighbors-a-unified-k-nearest","slug":"watch-the-neighbors-a-unified-k-nearest","title":"Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery","date":"2022-10-17","arxiv_id":"2210.08909","repositories_listed":1,"syntology":null},{"url":"/paper/a-patch-based-algorithm-for-diverse-and-high","slug":"a-patch-based-algorithm-for-diverse-and-high","title":"A Patch-Based Algorithm for Diverse and High Fidelity Single Image Generation","date":"2022-10-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/protovae-a-trustworthy-self-explainable","slug":"protovae-a-trustworthy-self-explainable","title":"ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model","date":"2022-10-15","arxiv_id":"2210.08151","repositories_listed":1,"syntology":null},{"url":"/paper/dart-articulated-hand-model-with-diverse","slug":"dart-articulated-hand-model-with-diverse","title":"DART: Articulated Hand Model with Diverse Accessories and Rich Textures","date":"2022-10-14","arxiv_id":"2210.07650","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/dart-articulated-hand-model-with-diverse#ran","syntology_url":"https://syntology.ai/paper/2210.07650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07650"}},"official":{"repos":["DART2022/DART"],"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/style-transfer-as-data-augmentation-a-case","slug":"style-transfer-as-data-augmentation-a-case","title":"Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition","date":"2022-10-14","arxiv_id":"2210.07916","repositories_listed":1,"syntology":null},{"url":"/paper/can-we-use-common-voice-to-train-a-multi","slug":"can-we-use-common-voice-to-train-a-multi","title":"Can we use Common Voice to train a Multi-Speaker TTS system?","date":"2022-10-12","arxiv_id":"2210.06370","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"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) · 3 unverified","sample_list":"/paper/can-we-use-common-voice-to-train-a-multi#ran","syntology_url":"https://syntology.ai/paper/2210.06370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06370"}},"official":null}},{"url":"/paper/near-optimal-multi-agent-learning-for-safe","slug":"near-optimal-multi-agent-learning-for-safe","title":"Near-Optimal Multi-Agent Learning for Safe Coverage Control","date":"2022-10-12","arxiv_id":"2210.06380","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/near-optimal-multi-agent-learning-for-safe#ran","syntology_url":"https://syntology.ai/paper/2210.06380","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06380"}},"official":{"repos":["manish-pra/safemac"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/what-makes-graph-neural-networks","slug":"what-makes-graph-neural-networks","title":"What Makes Graph Neural Networks Miscalibrated?","date":"2022-10-12","arxiv_id":"2210.06391","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/what-makes-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2210.06391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06391"}},"official":{"repos":["hans66hsu/gats"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/banglaparaphrase-a-high-quality-bangla","slug":"banglaparaphrase-a-high-quality-bangla","title":"BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset","date":"2022-10-11","arxiv_id":"2210.05109","repositories_listed":1,"syntology":null},{"url":"/paper/measuring-and-improving-semantic-diversity-of-1","slug":"measuring-and-improving-semantic-diversity-of-1","title":"Measuring and Improving Semantic Diversity of Dialogue Generation","date":"2022-10-11","arxiv_id":"2210.05725","repositories_listed":1,"syntology":null},{"url":"/paper/core-a-retrieve-then-edit-framework-for","slug":"core-a-retrieve-then-edit-framework-for","title":"CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation","date":"2022-10-10","arxiv_id":"2210.04873","repositories_listed":1,"syntology":null},{"url":"/paper/eva3d-compositional-3d-human-generation-from","slug":"eva3d-compositional-3d-human-generation-from","title":"EVA3D: Compositional 3D Human Generation from 2D Image Collections","date":"2022-10-10","arxiv_id":"2210.04888","repositories_listed":1,"syntology":null},{"url":"/paper/scam-transferring-humans-between-images-with","slug":"scam-transferring-humans-between-images-with","title":"SCAM! Transferring humans between images with Semantic Cross Attention Modulation","date":"2022-10-10","arxiv_id":"2210.04883","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-semantic-segmentation-with-6","slug":"semi-supervised-semantic-segmentation-with-6","title":"Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization","date":"2022-10-10","arxiv_id":"2210.04388","repositories_listed":1,"syntology":null},{"url":"/paper/sda-simple-discrete-augmentation-for","slug":"sda-simple-discrete-augmentation-for","title":"SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning","date":"2022-10-08","arxiv_id":"2210.03963","repositories_listed":1,"syntology":null},{"url":"/paper/stasy-score-based-tabular-data-synthesis","slug":"stasy-score-based-tabular-data-synthesis","title":"STaSy: Score-based Tabular data Synthesis","date":"2022-10-08","arxiv_id":"2210.04018","repositories_listed":1,"syntology":null},{"url":"/paper/training-deep-learning-algorithms-on","slug":"training-deep-learning-algorithms-on","title":"Training Deep Learning Algorithms on Synthetic Forest Images for Tree Detection","date":"2022-10-08","arxiv_id":"2210.04104","repositories_listed":1,"syntology":null},{"url":"/paper/automated-segmentation-and-morphological","slug":"automated-segmentation-and-morphological","title":"Automated segmentation and morphological characterization of placental histology images based on a single labeled image","date":"2022-10-07","arxiv_id":"2210.03566","repositories_listed":1,"syntology":null},{"url":"/paper/winner-takes-it-all-training-performant-rl-1","slug":"winner-takes-it-all-training-performant-rl-1","title":"Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization","date":"2022-10-07","arxiv_id":"2210.03475","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"5 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/winner-takes-it-all-training-performant-rl-1#ran","syntology_url":"https://syntology.ai/paper/2210.03475","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03475"}},"official":null}},{"url":"/paper/neuroevolution-is-a-competitive-alternative","slug":"neuroevolution-is-a-competitive-alternative","title":"Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery","date":"2022-10-06","arxiv_id":"2210.03516","repositories_listed":1,"syntology":null},{"url":"/paper/training-diverse-high-dimensional-controllers","slug":"training-diverse-high-dimensional-controllers","title":"Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing","date":"2022-10-06","arxiv_id":"2210.02622","repositories_listed":1,"syntology":null},{"url":"/paper/why-should-i-choose-you-autoxai-a-framework","slug":"why-should-i-choose-you-autoxai-a-framework","title":"Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions","date":"2022-10-06","arxiv_id":"2210.02795","repositories_listed":1,"syntology":null},{"url":"/paper/alphafold-distillation-for-improved-inverse","slug":"alphafold-distillation-for-improved-inverse","title":"AlphaFold Distillation for Protein Design","date":"2022-10-05","arxiv_id":"2210.03488","repositories_listed":1,"syntology":null},{"url":"/paper/isfl-trustworthy-federated-learning-for-non-i","slug":"isfl-trustworthy-federated-learning-for-non-i","title":"ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling","date":"2022-10-05","arxiv_id":"2210.02119","repositories_listed":1,"syntology":null},{"url":"/paper/making-your-first-choice-to-address-cold","slug":"making-your-first-choice-to-address-cold","title":"Making Your First Choice: To Address Cold Start Problem in Vision Active Learning","date":"2022-10-05","arxiv_id":"2210.02442","repositories_listed":1,"syntology":null},{"url":"/paper/reprogramming-large-pretrained-language","slug":"reprogramming-large-pretrained-language","title":"Reprogramming Pretrained Language Models for Antibody Sequence Infilling","date":"2022-10-05","arxiv_id":"2210.07144","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":4,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/reprogramming-large-pretrained-language#ran","syntology_url":"https://syntology.ai/paper/2210.07144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07144"}},"official":{"repos":["ibm/reprogbert"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/the-vendi-score-a-diversity-evaluation-metric","slug":"the-vendi-score-a-diversity-evaluation-metric","title":"The Vendi Score: A Diversity Evaluation Metric for Machine Learning","date":"2022-10-05","arxiv_id":"2210.02410","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-vendi-score-a-diversity-evaluation-metric#ran","syntology_url":"https://syntology.ai/paper/2210.02410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02410"}},"official":{"repos":["vertaix/vendi-score"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/concise-and-interpretable-multi-label-rule","slug":"concise-and-interpretable-multi-label-rule","title":"Concise and interpretable multi-label rule sets","date":"2022-10-04","arxiv_id":"2210.01533","repositories_listed":1,"syntology":null},{"url":"/paper/triplee-easy-domain-generalization-via","slug":"triplee-easy-domain-generalization-via","title":"TripleE: Easy Domain Generalization via Episodic Replay","date":"2022-10-04","arxiv_id":"2210.01807","repositories_listed":1,"syntology":null},{"url":"/paper/gendexgrasp-generalizable-dexterous-grasping","slug":"gendexgrasp-generalizable-dexterous-grasping","title":"GenDexGrasp: Generalizable Dexterous Grasping","date":"2022-10-03","arxiv_id":"2210.00722","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gendexgrasp-generalizable-dexterous-grasping#ran","syntology_url":"https://syntology.ai/paper/2210.00722","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00722"}},"official":{"repos":["tengyu-liu/GenDexGrasp"],"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/generative-category-level-shape-and-pose","slug":"generative-category-level-shape-and-pose","title":"Generative Category-Level Shape and Pose Estimation with Semantic Primitives","date":"2022-10-03","arxiv_id":"2210.01112","repositories_listed":1,"syntology":null},{"url":"/paper/gflownets-and-variational-inference","slug":"gflownets-and-variational-inference","title":"GFlowNets and variational inference","date":"2022-10-02","arxiv_id":"2210.00580","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/gflownets-and-variational-inference#ran","syntology_url":"https://syntology.ai/paper/2210.00580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00580"}},"official":{"repos":["gfnorg/gfn_vs_hvi"],"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/analyzing-the-dialect-diversity-in-multi","slug":"analyzing-the-dialect-diversity-in-multi","title":"Analyzing the Dialect Diversity in Multi-document Summaries","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/investigating-metric-diversity-for-evaluating","slug":"investigating-metric-diversity-for-evaluating","title":"Investigating Metric Diversity for Evaluating Long Document Summarisation","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/social-and-environmental-impact-of-recent","slug":"social-and-environmental-impact-of-recent","title":"Social and environmental impact of recent developments in machine learning on biology and chemistry research","date":"2022-10-01","arxiv_id":"2210.00356","repositories_listed":1,"syntology":null},{"url":"/paper/parea-multi-view-ensemble-clustering-for","slug":"parea-multi-view-ensemble-clustering-for","title":"Parea: multi-view ensemble clustering for cancer subtype discovery","date":"2022-09-30","arxiv_id":"2209.15399","repositories_listed":1,"syntology":null},{"url":"/paper/the-minority-matters-a-diversity-promoting","slug":"the-minority-matters-a-diversity-promoting","title":"The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm","date":"2022-09-30","arxiv_id":"2209.15292","repositories_listed":1,"syntology":null},{"url":"/paper/domain-unified-prompt-representations-for","slug":"domain-unified-prompt-representations-for","title":"Domain-Unified Prompt Representations for Source-Free Domain Generalization","date":"2022-09-29","arxiv_id":"2209.14926","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":0,"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/domain-unified-prompt-representations-for#ran","syntology_url":"https://syntology.ai/paper/2209.14926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.14926"}},"official":{"repos":["muse1998/source-free-domain-generalization"],"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/federated-stain-normalization-for","slug":"federated-stain-normalization-for","title":"Federated Stain Normalization for Computational Pathology","date":"2022-09-29","arxiv_id":"2209.14849","repositories_listed":1,"syntology":null},{"url":"/paper/start-small-training-game-level-generators","slug":"start-small-training-game-level-generators","title":"Start Small: Training Controllable Game Level Generators without Training Data by Learning at Multiple Sizes","date":"2022-09-29","arxiv_id":"2209.15052","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-few-shot-learning-from-a-causal","slug":"revisiting-few-shot-learning-from-a-causal","title":"Revisiting Few-Shot Learning from a Causal Perspective","date":"2022-09-28","arxiv_id":"2209.13816","repositories_listed":1,"syntology":null},{"url":"/paper/ucepic-unifying-aspect-planning-and-lexical","slug":"ucepic-unifying-aspect-planning-and-lexical","title":"UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation","date":"2022-09-28","arxiv_id":"2209.13885","repositories_listed":1,"syntology":null},{"url":"/paper/draw-your-art-dream-diverse-digital-art","slug":"draw-your-art-dream-diverse-digital-art","title":"Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion","date":"2022-09-27","arxiv_id":"2209.13360","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-counter-stochastic-feature-based","slug":"learning-to-counter-stochastic-feature-based","title":"Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations","date":"2022-09-27","arxiv_id":"2209.13446","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-clustering-based-pseudo-labeling","slug":"rethinking-clustering-based-pseudo-labeling","title":"Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning","date":"2022-09-27","arxiv_id":"2209.13635","repositories_listed":1,"syntology":null},{"url":"/paper/acrofod-an-adaptive-method-for-cross-domain","slug":"acrofod-an-adaptive-method-for-cross-domain","title":"AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection","date":"2022-09-22","arxiv_id":"2209.10904","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 1 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/acrofod-an-adaptive-method-for-cross-domain#ran","syntology_url":"https://syntology.ai/paper/2209.10904","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.10904"}},"official":{"repos":["hlings/acrofod"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/counterfactual-explanations-using","slug":"counterfactual-explanations-using","title":"Counterfactual Explanations Using Optimization With Constraint Learning","date":"2022-09-22","arxiv_id":"2209.10997","repositories_listed":1,"syntology":null},{"url":"/paper/semantically-consistent-data-augmentation-for","slug":"semantically-consistent-data-augmentation-for","title":"Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model","date":"2022-09-22","arxiv_id":"2209.10875","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-vr-sketching-dataset-and","slug":"fine-grained-vr-sketching-dataset-and","title":"Fine-Grained VR Sketching: Dataset and Insights","date":"2022-09-20","arxiv_id":"2209.10008","repositories_listed":1,"syntology":null},{"url":"/paper/metadata-archaeology-unearthing-data-subsets","slug":"metadata-archaeology-unearthing-data-subsets","title":"Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics","date":"2022-09-20","arxiv_id":"2209.10015","repositories_listed":1,"syntology":null},{"url":"/paper/keypoint-graspnet-keypoint-based-6-dof-grasp","slug":"keypoint-graspnet-keypoint-based-6-dof-grasp","title":"Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input","date":"2022-09-19","arxiv_id":"2209.08752","repositories_listed":1,"syntology":null},{"url":"/paper/learning-distinct-and-representative-modes","slug":"learning-distinct-and-representative-modes","title":"Learning Distinct and Representative Styles for Image Captioning","date":"2022-09-17","arxiv_id":"2209.08231","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/learning-distinct-and-representative-modes#ran","syntology_url":"https://syntology.ai/paper/2209.08231","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.08231"}},"official":{"repos":["bladewaltz1/modecap"],"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/cold-start-data-selection-for-few-shot","slug":"cold-start-data-selection-for-few-shot","title":"Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach","date":"2022-09-15","arxiv_id":"2209.06995","repositories_listed":1,"syntology":null},{"url":"/paper/style-variable-and-irrelevant-learning-for","slug":"style-variable-and-irrelevant-learning-for","title":"Style Variable and Irrelevant Learning for Generalizable Person Re-identification","date":"2022-09-12","arxiv_id":"2209.05235","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-recommendation-reasoning","slug":"reinforcement-recommendation-reasoning","title":"Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality","date":"2022-09-11","arxiv_id":"2209.04954","repositories_listed":1,"syntology":null},{"url":"/paper/towards-diversity-tolerant-rdf-stores","slug":"towards-diversity-tolerant-rdf-stores","title":"Towards Diversity-tolerant RDF-stores","date":"2022-09-10","arxiv_id":"2209.04593","repositories_listed":1,"syntology":null},{"url":"/paper/improved-masked-image-generation-with-token","slug":"improved-masked-image-generation-with-token","title":"Improved Masked Image Generation with Token-Critic","date":"2022-09-09","arxiv_id":"2209.04439","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":2,"n_ran_checked":3,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"6 ran (of which 2 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) · 4 unverified","sample_list":"/paper/improved-masked-image-generation-with-token#ran","syntology_url":"https://syntology.ai/paper/2209.04439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.04439"}},"official":null}},{"url":"/paper/aargh-end-to-end-retrieval-generation-for","slug":"aargh-end-to-end-retrieval-generation-for","title":"AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog","date":"2022-09-08","arxiv_id":"2209.03632","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-combination-of-a-genetic-algorithm","slug":"adaptive-combination-of-a-genetic-algorithm","title":"Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution","date":"2022-09-08","arxiv_id":"2209.03618","repositories_listed":1,"syntology":null},{"url":"/paper/text-free-learning-of-a-natural-language","slug":"text-free-learning-of-a-natural-language","title":"Text-Free Learning of a Natural Language Interface for Pretrained Face Generators","date":"2022-09-08","arxiv_id":"2209.03953","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-complementarity-between-pre-training-1","slug":"on-the-complementarity-between-pre-training-1","title":"On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation","date":"2022-09-07","arxiv_id":"2209.03316","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":11,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-the-complementarity-between-pre-training-1#ran","syntology_url":"https://syntology.ai/paper/2209.03316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.03316"}},"official":{"repos":["zanchangtong/ptvsri"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mlphon-a-multifunctional-grapheme-phoneme","slug":"mlphon-a-multifunctional-grapheme-phoneme","title":"Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers","date":"2022-09-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-auto-regressive-modeling-of","slug":"large-scale-auto-regressive-modeling-of","title":"Large-Scale Auto-Regressive Modeling Of Street Networks","date":"2022-09-01","arxiv_id":"2209.00281","repositories_listed":1,"syntology":null},{"url":"/paper/table-detection-in-the-wild-a-novel-diverse-1","slug":"table-detection-in-the-wild-a-novel-diverse-1","title":"Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method","date":"2022-08-31","arxiv_id":"2209.09207","repositories_listed":1,"syntology":null}],"record_sha256":"486eba80bae1017ff25e1af1df6617710a2be9dc6657d9322f420236092d4790","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}